The most valuable asset of IEEE business that attracts members and paying customers to the IEEE is its Electronic Library (IEL). It contains the paper repository known as IEEE Xplore Digital Library with over 4.5 million conference and journal papers. IEEE Xplore includes some 3rd party content, but it mostly consists of papers from IEEE’s owned 186 Transactions, Journals, Letter and Magazines and IEEE conferences, both financially or technically sponsored by IEEE. Included in this most impressive body of scientific and engineering knowledge are legacy issues going back several decades. Moreover, around a quarter million manuscripts are added each year. Much to read and follow!
Although IEEE’s strength is in creating the intellectual property (IP), we format the IP in a classic not to say, traditional, way. The format is raw and unprocessed much like 200 years ago when scientific publishing was incubated. IP nugget is presented in IEEE Xplore as a traditional paper which does not easily help answering questions that a reader might have. It’s disconnected from other papers and the valuable content is buried in text, mixed with formulas, figures, graphs, tables, illustrations.
If we provide our IP users/members with the knowledge and process the IP to get answers to users’ questions, or if a design or algorithm is recommended, this will be of much higher value than purely traditional papers. Papers contain lots of buried, heavily wrapped technical/scientific information as an overhead. Concepts are disconnected or spread between papers and evaluation tools beyond keywords unavailable at IEEE Xplore. As a result, papers are searched (and retrieved) today for isolated keywords much like a bag of unrelated objects that have no associations.
Today’s search techniques used in IEEE Xplore to discover the ‘knowledge’ rely on Author’s, IEEE’s and INSPEC defined keywords. If the keyword of interest is included in the paper Metadata (Title and Abstracts), this paper is returned after the search. Fig. below illustrates the full set of keywords for one of the 949 papers retrieved in IEEE Xplore with the ‘autoencoder’ keyword (2013 T-PAMI, doi: 10.1109/TPAMI.2013.50).
To distinguish between information and knowledge retrieval, let’s look at this example further: Autoencoders (AE) are deep learning/neural network tools proven to be very efficient and accurate in pattern recognition. They come in many variations and each can be used for various tasks. If a researcher is interested in inventing a better AE for handwritten digit recognition or a data engineer wants to program an existing AE that is best for this job, an advanced Boolean search of ((AE) AND (MNIST database of handwritten digits)) on the Metadata would now yield 41 papers in IEEE Xplore. These are papers that comprehensively discuss AEs in context of the popular database MNIST.
Now assume that in addition to this information, a quality criterion is of interest, such as the classification accuracy of the AEs. The knowledge of the specific AEs accuracy is essential in order to select the best AE. With the accuracy as an added quality measure, a refined Boolean search to ((AE) AND (MNIST database of handwritten digits) AND (most accurate)) on Full Text and Metadata can be done. However, this search leads to trouble since as much as 100 papers are returned. An inspection of 100 papers is needed since no search mechanisms exist in IEEE Xplore that would find the most accurate AE model worthy of further consideration. Information is there for us as a pile of papers to scroll unless we have tools to extract the information about accuracy. It’s a missing piece of evaluative knowledge that we need to acquire. Intelligent data analysis could be used at this stage to help with the process of knowledge extraction.
The knowledge about accuracy is hidden in the tables or graphs of each paper, and both are typically reported as statistics of simulations. However, looking for the accuracy digest visually would require combing through hundreds of pages. This could be easily automated by extracting and analyzing the data from papers’ tables and graphs. If the results reported by various papers are ordered by the accuracy results, this would complete the task of knowledge retrieval.
I believe the IEEE can do better on comprehensive knowledge retrieval beyond a simple keyword search or Boolean query. The key question is how to access and reach the most essential knowledge from the information that the users wants but it’s hidden in the mass of papers. The query could be using probabilistic, fuzzy or association rules and analysis of semantic meaning of sentences and analysis of objects such as tables and graphs. For practicing engineers, quantitative measures could be addressed in similar ways to locate the designs or products specifications.
In addition to quantitative knowledge extraction from numbers, modern text search techniques can apply semantic analysis. Methods that are intensely researched in the marketplace include analytics of metadata with proximal searches, clustering using nearest neighbor analysis, visualization of concepts and graph analysis, to name a few of most known techniques. In the long run, scripted searches for the bit of knowledge can not only be automated, but could be initiated using natural language.
I believe we should plan to deliver to our members and subscribers more knowledge as opposed to providing them with the classic information. The AI-aligned IEEE Xplore would reduce the learning curve, discover highly relevant results that people are looking for and are spending considerable amount of time to find.
Venturing into the future of AI tools, we can imagine that conversational systems will eventually help humans interface with computers. So, a query for knowledge can be initiated and an engineer or scientist can be engaged in a back-and-forth dialogue with the machine. After a conversational system receives a message, it could generate alternative responses and rank them.
IEEE exists and prospers thanks to its legions of volunteers who are passionate about the technology and help in fulfilling The Institute’s mission. They serve as committee members for Societies, Councils, Chapter and Sections chairs, reviewers for journals and our conferences, conference committee members and chairs, associate editors and Editors-in-Chief for its 186 premier journals and many other roles. Their numbers are certainly in tens of thousands at any given time.
This novel idea of cumulative activity reward points is embraces the concept that active, long-term volunteers can achieve higher status and/or honors, like Premier Gold members, distinguished reviewers, lifetime Premier members and alike. I believe this idea is worthwhile, especially as IEEE is both governed and driven by an extraordinary community of volunteers who certainly not only deserve respect but also recognition. They are The Institute’s most precious resource and since the technology advances so fast, this scarce resource is in high demand. Our volunteers, however, derive no other benefits than their personal satisfaction from championing technologies and serving the IEEE mission they are passionate about.
Our present reward system is largely based on Certificates of Appreciation that are awarded for volunteering in specific roles and for a specific time period. In addition, we have a number of service awards. But in general, the IEEE recognition system tends to reward current or recent activities. In contrast, the Cumulative Rewards System would be more cumulative rather than rewarding a volunteer role for a specific period. I would favor such new reward system. However, I’d prefer that no financial benefits be attached to the higher grade status.
In fulfilling its mission of advancing technology, IEEE technical activities are primarily pursued within 46 technical Societies and Councils (S/C) with additional support from Standards Association and geographical units grouped in 336 Sections. The vertical organization of IEEE Technical Activities follows the dividing lines defined by the technical fields of interest of Societies and Councils. The divides are natural and reflect the breadth of IEEE’s technology portfolio.
While each of the S/Cs advances different technology defined in its field of interest, such as communications, computers, power and energy, robotics and automation to name a few, the S/Cs’ tasks typically overlap. These include actual technical activities, membership and community development, events and conferences, standards development, educational/career services, and development of resources for industry professionals. Despite overlapping activities, however, joint S/C ventures are typically limited only to co-sponsored journals and conferences.
If elected President, I will encourage cross-S/C and Standards collaborations since they’re economically more efficient and consume less volunteer resources. They also promote mutual sharing of best practices. Whether they’re one-year or multiyear projects, such partnerships would require joint funding of inter-S/C projects. Pooling volunteer talent from across the units will follow. S/C volunteer leaders could develop such joint ventures through more frequent consultations, and cross-pollination of their boards, especially if their fields of interest are closely related.
My experience with AI and Machine Learning: Being one of the pioneers of this technology that I first have exploited in my MSc Thesis, I have been actively contributing to it for about four decades. In fact, my classic text “Introduction to Neural Networks” was the first comprehensive engineering text of the field and I have received my IEEE Fellow Grade in 1996 for this particular contribution.
Now, two decades later, with a recently added paradigm of Deep Learning (DL), neural networks can do amazing and innovative pattern recognition on a much larger scale. All of it is based on extending the powers and architectures of the classic perceptron by equipping it with true multi-layer learning capabilities and, simultaneously, by harnessing the new powers of unsupervised learning in the existing architectures. These networks with thousands of units per layer can do great recognition tasks, such as tagging your picture with your name among millions of other pictures. They can identify road signs, cars and traffic patterns which will soon lead to a disruptive technology of self-driving cars.
Ethical Concerns: Progress in AI is bringing increasing societal benefits in human-computer interaction, transportation, and robotics and intelligent systems. As AI becomes entwined in the fabric of life with applications in smart homes, health care, social services, and the environment, the public expectation is that these technologies will be secure, safe, and transparent. Ethical concerns in AI are quickly gaining importance due to its so rapid growth and disruptive nature.
And here lies a great difficulty. We can guarantee these machines to have no worse performance than us, humans on comparable tasks on one side. However, their understandability and their ability to explain for their decision is one of the greatest technical AI challenge. For the time being I believe we will have to take the AI outputs at their face value without asking questions, as there would be no answers. The answers from neural networks are so intertwined that they become practically worthless. For more details how to get the most information out of the AI machines, please see my 2015 and 2016 papers in IEEE Transactions on Neural Networks.
AI in Ethics and IEEE: As an important contributor to AI-based technologies, IEEE must be a key player in this area. Further, we need to work with policy makers to support regulations that protect the public. We need to support understanding and discourse about AI. One important aspect is to upgrade the intellectual property rights laws to account for new developments in AI as the characteristics of the AI are very novel.
In 2016 IEEE has launched the Ethically Aligned Design Initiative and I’m very supportive of its initial findings. If elected 2020 IEEE President I will embrace and champion its recommendations expected to be published later this year.
What is the focus of your research?
My specialty is machine learning and artificial intelligence. Both deal with theory and development of computer systems that are able to perform tasks which normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages. These are emerging technologies that have an amazing range of applications and serve humanity by making our lives easier and more connected, and also promote social good. I’m sure you own a smartphone, the flagship achievement of AI of our generation.
What does it mean to have such a high citation score? To what do you attribute this distinction?
Traditionally, researchers had difficulties with quantifying their research contributions. The number of publications that people author or co-author is not a good metric, because publications can both be in quality journals and conferences, but also can be submitted to outlets that require no reviews prior to publication and would accept papers with little to no vetting by peers. While the former approach requires a lot of effort, the latter is a more opportunistic and less risky path that I’d compare to posting on blogs, usually done without the rigors of peer review. To sum it up – having published 50 papers in your career doesn’t tell the full story. It’s because the research impact of a paper is initially reflected by the journal title where it has been published, but it’s ultimately and accurately evidenced only several years later by how many times other researchers have cited your work. And they tend to reference papers that have been reviewed and appeared in high impact journals.
There is a general consensus in the research community that the number of citations to your body of work is the ultimate metric to evaluate the impact of your research. It’s a first-hand measure of how many times people read, notice and refer to your work in order to extend or improve it. To help answer these questions from researchers Google Scholar has equipped us with a fantastic public tool for searching through scientific literature. It also helps authors to keep track of citations to their articles and thus monitor the impact of their work.
Was this gradual? Did your research evolve to fit more into the zeitgeist, so to speak, or vice versa?
My first cited publications related to my PhD work. More recent citations have been to a very well received 2009 paper that collected over 400 citations and was published while I was on sabbatical. In the meantime, citations by other authors to some 380 papers of mine keep growing at a rate exceeding 600/year for the last 6 years.
How does this influence your work? Do you think that you may focus subconsciously with citation in mind? For example, maybe you might focus on trends in your field?
The citations can be compared to the number of records sold by a band, and perhaps less so to the number of ‘likes’ on social media. But a citation is much more than a ‘like’, because someone must have read your article and built on it. You can click 20 “likes” per minute, but can only cite several papers per your day’s work when writing a paper. Reading a paper that you want to reference requires considerably more effort than clicking on your favorite picture.
Obviously, citations offer an author a great deal of encouragement, if not a public approval for the idea. They also validate the work that your team is doing. When you publish something, you initially don’t know its future impact. So, when people carry on what you have started, that’s a big deal. It indicates that you’ve done something seminal or inspiring, or of high impact that others want to continue or expand. However, when writing a research article, I pay no attention to its future citations. Frankly, I know of no ways to engineer your citations. I focus, however, on how to contribute an original method, outline a new theory, describe a new application, or offer insights into data that I and my students have studied. Novelty, usefulness, and a lucid presentation in ahigh impact journal are all “must have” of a good, citable publication and are true gatekeepers for your future citations.
How do citations fit into the overall research landscape?
When you reflect on research, it turns out that it is a continuum in time, space and focus. It never stops, it never dies. Within this context, we don’t write papers for ourselves, so when they resonate with research community, that’s the goal they have achieved. These communities have no borders, and at any given time we know of hundreds of thousands of researchers busy with new cancer cures, thousands of researchers working on new humanoid robots or speech recognition, and perhaps only hundreds of researchers who look at how the human eye differentiates a cat from a dog. We need to keep in mind that any research serves humanity and should benefit others rather than ourselves.
How do you balance your research and other (teaching, administrative, professional) responsibilities?
University teaching, especially graduate teaching, reinforces our research and all academics agree on this. While administrative duties on campus can be viewed by some as distraction from research, we also need to realize that a collective effort is needed for the university to smoothly function as an institution of higher learning. Therefore, input from faculty with considerable research experience to the university governance is necessary. I therefore had to find the time to serve in such roles as a Speed School Senator and as a Parliamentarian for the Faculty Senate, member of the SpeedFaculty Activity Committee, Graduate Council member and as a Chair of the ECE Curriculum Committee.
This still leaves me the time to be actively engaged in the Institute of Electrical and Electronics Engineers, which, with over 400,000 members, is the world’s largest professional organization. It happens to be an organization created for and by electrical and electronic engineers. It is well known and highly regarded for advancing technology for humanity and for its vast and prestigious intellectual property. It publishes 180 top periodicals of the field and sponsors over 1800 conferences per year. I have served as IEEE Vice President in 2013-15, and was Chair of its Periodicals Committee and Periodicals Review Committees in 2010-13. I am also an IEEE Life Fellow.
What advice do you have for burgeoning researchers in academia?
My first and lofty message for aspiring researchers-authors is that you don’t write a paper for yourself, neither for your chair or dean or a promotion committee, but for the readers and in a broader sense you target the humanity. The wider is your target audience, the more success can be scored. My other message is of more practical nature: authors need to pay careful attention to the reproducibility of results presented in their papers so that other researchers with a similar specialized knowledge could replicate the papers’ outcomes. Dead-ended publications with no follow up aren’t useful because when our published disclosures can’t be applied, improved on or otherwise continued, our efforts have been wasted.
The President of IEEE has to play a triple role: of a volunteer, of a leader, and of someone who understands the mission of the organization that is quite complex, unique and operates on the forefront of the technological progress – because it leads the very progress by pushing the technology’s growth.
About volunteering: I think volunteering for a profession to be a meaningful and gratifying experience. When I am donating my professional skills to a good cause, I demonstrate a commitment to a community that has shaped me as I am. Volunteering also gives me an opportunity to return to the profession that has given me so much. This way I can share my skills and enthusiasm in order to have an impact, and at the same time I can make a difference.
My motivation here is fully altruistic except for one aspect. I derive a personal satisfaction from serving goals that I am passionate about. When volunteering for IEEE, I always feel the trust, collegiality, and professionalism of the organization, and of the people who I work with. It’s probably because we are all motivated by the goals that we all believe in. It feels as though IEEE volunteers share a connected mind as we are all dedicated to working for the common good.
About leadership: Effective leadership is about establishing a shared vision by a leader who can encourage others to follow. This can be done in many ways such as by energizing others to follow great goals and by lowering the barriers to do things while sharing the vision. An important attribute of a leader is effective communication. Communication to be credible, however, must rely on trust and mutual respect and, in my opinion, has to be followed by the leader’s passion and a degree of charisma. Effective communication skills in addition to speaking, writing and presenting includes also effective and active listening.
About leading IEEE: The aspect of volunteering and leading is different from general volunteering because it requires a special set of skills and experience. When I think about how to be a leader of a large organization such as the IEEE, I often think of the special attributes that I feel a leader must have. In my order of priority, a leader must be:
- passionate and knowledgeable about the organization
- forward-thinking and ready to take a reasonable risk
- able to identify and understand critical directions that are vital for the organization’s success
- ready to propose new initiatives that the organization should pursue
- able to understand the process and timeline of how to get financial and grass-root support for these initiatives
An important job for a leader of a membership-based organization such as the IEEE is to keep the a balance between the services that our members are expecting to receive from the IEEE and the investment expenditures that the organization requires to succeed in the future. This is akin to balancing the current consumption levels with future investments, a dilemma that most families or businesses face and IEEE is no different.
However, IEEE as an organization that leads the advancement of technology, must constantly operate on its cutting edge. And we can’t champion the technology effectively if our operations are not using the most modern infrastructures available today, at this information age. To use an example: since our strength is in building the intellectual property and advancement of technology, we should plan to deliver to our members and subscribers more knowledge as opposed to providing them with the classic information. The classic information is formatted as traditional papers and it does not answer questions that a reader might have. If we provide our IP users/members with the knowledge and answers to their questions, or if a design or algorithm is recommended, this will be of more value than purely traditional papers. This is akin to what search engines can do today for standard internet content and I believe the IEEE can do so as well through the use of a discovery platform and data analytics.
My flagship/signature project will be to embrace advances in AI to help transform IEEE from today’s traditional technical ‘paper provider’ to a ‘knowledge provider.’ The way the existing IEEE knowledge base (IEEE Explore) of over 4.5 million+ papers and standards is organized doesn’t fit well the needs of engineers, scientists and researchers. Today, instead of offering users the knowledge, we provide them very traditional, raw information formatted as ‘papers.’ Text analytics and knowledge discovery tools can turn this ever-growing collection of documents into a much more valuable, customized resource. This way we will deliver personalized bits of knowledge such as directly applicable algorithms, design solutions, data, codes, engineering methods that meet users’ expectations. This will meet expectation both of the academics and also of industry practitioners.
My second priority will be to better respond to the needs of all segments of members, especially industry practitioners and underserved/underrepresented communities of young professionals and women. Our products, services, and educational offerings have to be more relevant to their jobs and career aspirations. I will focus on providing members in the industry with information through topical industry resource centers. Such centers with a single point of entry will help them find quality technical information quickly and in addition they allow them to continue their education, aiding to their lifelong career growth.
With more than 4 million documents in the IEEE Xplore Digital Library, our members in academia need better, more intelligent tools to retrieve knowledge in addition to accessing traditional papers, titles and abstracts. Our members would benefit from productivity tools that use data analytics and are able to answer a technical question or recommend a design or algorithm to fit their specifications. While this goal may appear somewhat distant, it’s important to realize that five years ago we could not make inquiries with a smartphone and get instant answers, as we do today. I will lead IEEE in the direction of offering better search tools for research and design.
Our members in academia would also benefit from quick information exchange, especially in emerging technologies. As we nurture communities working in new technical areas, IEEE needs to continue to expand our support for sharing technical information and for networking. This includes facilitating inexpensive web-based workshops and conferences.