Reviewerly/exHarmony huggingface dataset:
Age-Puzzle FaceNet for Cross-Age Face Recognition

Cross-Age Face Recognition (CAFR) has drawn increasing attention in recent years. Technically however, due to the nonlinear variation of face aging and insufficient datasets covering a wide range of ages, it remains a major challenge in the field of face recognition. To address this problem, we propose a novel model called Age-Puzzle FaceNet (APFN) based on adversarial training mechanism. The model we propose can be subdivided into two networks consisting of three elementary parts: (a) a Generator G: our core part for generating age-invariant identity features; (b) an Identity Classifier which forms the first identity recognition network (namely IRN) with the generator G to enhance identity recognition performance; (c) an Age Discriminator which attempts to retrieve age information from the generated features and forms the second network (namely Age Verification network AVN) with the same generator. Our extracted features achieve improvement on age invariance via adversarial training in AVN while remaining identity discriminative utilizing joint training in IRN. Apart from achieving state-of-the-art performance, APFN has demonstrated two other distinct characteristics as follows. First, identity-labeled dataset and age-labeled dataset are used respectively for above two networks such that no more effort to search for training data labeled by both age and identity. As a consequence, more training data is available to give a better recognition performance. Second, the strategy we adopt to handle age and identity attributes can provide a new insight on other robust recognition domain with respect to multi-attributes or attributes separation. We conducted comprehensive experiments on two publicly available datasets called Cross-Age Celebrity and Cross-Age LFW. The results of our proposed architecture demonstrate a better performance and effectiveness.
An Efficient Multi-View Panoramic Imaging and Extra Compression of Surveillance Cameras’ Footage Using Stitching

Saving data stored and transferred is a critical topic in modern era, especially after the evolution in the numbers of surveillance cameras in the world. So an extra level of compression is needed to contribute in solving the problem of the huge data produced by those cameras. The proposed system: “An Efficient multi-view panoramic imaging and Extra Compression of surveillance cameras' footage using Stitching” (ECS) aims to contribute in the solution of this problem by merging feature extraction stitching techniques with geometric relational matrix calculations. ECS creates a new real-time efficient stitching algorithm to minimize the sending and storing of the redundant pixels between the footage of the surveillance cameras. The limitations of using stitching on video footage and not on still images is due to the high complexities of stitching algorithms; the proposed system, in this paper, uses the projective affine transformation matrix between images of camera footage to reduce this limitations. The work presented in this paper introduces, for the first time, an algorithm to reconstruct images of each camera from the panoramic view. The experimental work of this research tests multiple feature extraction algorithms for stitching; Scale Invariant Feature Transform (SIFT), Speed Up Robust Feature (SURF) and Oriented FAST and Rotated BRIEF (ORB) with different order of stitching, and the amount of compression per image after reconstruction is analyzed. Moreover, the panoramic view is mostly a better monitoring option rather than multiple monitors in complex surveillance cameras' control rooms. Experimental results show that the proposed method could be faster than other state-of-the-art image stitching methods and accurate panoramic image. The system also gives a better visual effect in a large view panorama due to the high speed real time stitching introduced in this paper.
Whole Image and Modular Image Face Classification - What is Really Classified?

Our aim is to explore the importance of chosen parts of frontal face images for person recognition. We have used logistic regression as the method of face image classification based on rough image classification, and on selected parts of an image divided into rectangular image blocks. Rough image means that no image processing transformation is performed before classification. Experiments on the images of 40 persons taken from the ORL face database show that a person classification based on collections of rough face images are effective, high accuracy rates are easy to obtain, but deeper analysis based on image partitioning suggests that the most important factor for correct classification are border parts of the face image. Furthermore, the experiments confirm the thesis that randomly generated projections do not degrade, or only slightly reduce the accuracy of classification, reducing the size of the vector of features in a significant way.
Video quality assessment using generative adversarial network

Video related technology has grown rapidly due to the progress of digital devices such as virtual reality, 3D cameras, 3D films, and 3D display and Internet. During video acquisition and processing (compression, transmission, and reproduction) they may suffer some types of distortions which lead degradation and has a direct effect on the subjective sensation about human eyes. Moreover, the subjective evaluation is boring, time-consuming, and we do not have a specialist to do this kind of work. So, it is necessary to evaluate the quality of the videos by computers. Which means that the video quality evaluation/assessment has become vital. The goal of video quality assessment is to predict the perceptual quality for improving the performance of practical video application systems. In other words, the users’ experience is worse, so we need a metric to measure the distortions. The commonly used videos’ quality assessment methods: a) consider the video as a sequence of two-dimensional images and the videos’ quality assessment (scores) computing by weighted averaging of per frames (2D images) score, which conflicts with the fact that a video signal is a 3D volume and which ignores the movement features, b) are designed for specific distortions (for example blockiness and blurriness). In this paper, we present a novel deep learning architecture for no-reference video quality assessment. It based on a 3D convolutional neural network and generative adversarial network (GAN). We evaluate the proposed approach on the LIVE, ECVQ, TID2013 and EVVQ databases. Computer simulations show that the proposed video quality assessment: a) get convergence on a small amount of data, b) more “universal”- it can be used for different video quality degradation, including denoising, deblocking, deconvolution, and c) outperforms existing no-reference video quality assessment/methods. In addition, we demonstrate how our predicted no-reference quality metric correlates with qualitative opinion in a human observer study.
Simultaneous computation of discrete Radon transform quadrants for its efficient implementation on real time systems

Discrete Radon transform is a technique that allows to detect lines in images. It is much lighter to compute than Radon transforms based on Fourier slice theorem that use FFT as basis computing block. Even then, it is not that prone to optimal fine grain parallelization due to the need of running 4 passes to mirrored and flipped versions of the input in order to compute the 4 quadrants comprising 45 degrees each that arises of the decomposition of discrete lines in slope-intercept form. A new method is proposed that can solve the 4 quadrants simultaneously allowing for a more efficient parallelization. In higher dimensions Radon transform needs even more ‘runs’ of the basic algorithm, v.g., in 3 dimensions instead of 4 quadrants there are 12 dodecants to be solved. The proposed method can be extended to alleviate also the problem in those higher dimensions achieving an even greater gain.
Extracting and Evaluating Morphological Features from Microcalcifications in Breasts Mammograms

Breast cancer is the one that most affects women worldwide. Mammographic examinations are considered the best way of detecting non-palpable breast lesions, although there are several factors that make difficult the diagnostic accuracy in breast cancer screening. Computer-aided diagnosis (CADx) systems have been developed aiming to help breast cancer diagnosis. They are based on microcalcifications (MCs) characteristics and can be treated as a three-step system: (i) MCs segmentation; (ii) extraction and selection of features from the segmented MCs; (iii) lesions classification. In this work, a set of 25 morphological features were extracted from lesions containing MCs presented on 190 mammographic images. The MCs of these images were segmented and binarized in a previous work, using Mathematical Morphology techniques. After extracting the morphological features, a selection method based on mutual information ranked the features, and a linear discriminant analysis used a forward procedure to search, among the ranked features, the best subset to classify the lesions. The best classification performance was achieved with 4 morphological features: Fourier Factor, Zero crossing, Long-axis to short axis ratio and Mean value of the Normalized Radial Length.
Leveraging Explicit Products Relationships for Improved Collaborative Filtering Recommendation Algorithm

Collaborative filtering (CF) is widely applied in recommender systems. This study aims to incorporate product relationships into traditional CF based recommendation algorithms to address data sparsity and cold start problems. We propose a novel matrix factorization model, where each user has a category-specific user latent factor vector to more accurately describe the user latent factors. In the meanwhile, the explicit product relationships have been leveraged as the regular term to constrain the learning of product latent feature vector. Lastly, the empirical results demonstrate the superiority of our model against other counterparts on recommendation accuracy. Besides, our model has a good theoretical and practical significance.
Improving speed and realism of an evolutionary minibus network design process

This paper deals with improving an existing evolutionary minibus network design process. Computation time was reduced by using a more suitable public transit routing algorithm. Furthermore, a minibus stop creator was developed which attempts at identifying road junctions and putting stops only on approach links and between junctions. Lastly, the circuity of the minibus routes returned was reduced by applying penalty scores for certain route circuity and complexity measures.
3D Coordinate Calculation and Pose Estimation of Power Meter based on Binocular Stereo Vision

To establish an intelligent power meter verification automatic flow operation platform, it is necessary to transport different types of power meters from the area to be verified to the conveyor belt of the instrument verification line. Aiming at the background demand of this practical application, this paper studies the power meter positioning based on binocular stereo vision, including instrument 3D coordinate calculation and pose estimation. The instrument 3D coordinate calculation mainly calculates the coordinates of the instrument center point through the stereo matching algorithm. The instrument pose estimation mainly expresses the space pose of the instrument by increasing the inclination angle of the instrument in the horizontal plane. Through the 3D coordinate calculation and pose estimation of the power meter, the spatial position of the instrument relative to the binocular stereo vision system can be obtained, which lays a foundation for automatic grabing of power meter by mechanical arm.
Exam Keeper: Detecting Questions with Easy-to-Find Answers

We present Exam Keeper, a tool to measure the availability of answers to exam questions for ESL students. Exam Keeper targets two major sources of answers: the web, and apps. ESL teachers can use it to estimate which questions are easily answered by information on the web or by using automatic question answering systems, which should help teachers avoid such questions on their exams or homework to prevent students from misusing technology. The demo video is available at https://youtu.be/rgq0UXOkb8o 1
What Security Can We Achieve Within 4 Rounds?

Katz and Ostrovsky (Crypto 2004) proved that five rounds are necessary for stand-alone general black-box constructions of secure two-party protocols and at least four rounds are necessary if only one party needs to receive the output. Recently, Ostrovsky, Richelson and Scafuro (Crypto 2015) proved optimality of this result by showing how to realize stand-alone, secure two-party computation under general assumptions (with black-box proof of security) in four rounds where only one party receives the output, and an extension to five rounds where both parties receive the output. In this paper, we study the question of what security is achievable for stand-alone two-party protocols within four rounds and show the following results:
Privacy and Transparency within the 4IR: Two faces of the same coin

The Fourth Industrial Revolution (4IR) is characterized by a fusion of technologies, which is blurring the lines between the physical, digital, and biological spheres. In this context, two fundamental characteristics emerge: transparency and privacy. From one side, transparency can be seen as the quality that allows participants of a community to know which particular processes are being applied, by which agents, and on which data items. It is generally regarded as a means to enable checks and balances within this community, so as to provide a basis for trust among its participants. Privacy, on the other side, essentially refers to the right of an individual to control how information about her/him is used by others. The issue of public transparency versus individual privacy has long been discussed, and within already existing 4IR scenarios, it became clear that the free flow of information fostered by transparency efforts poses serious conflicting issues to privacy assurance. In order to deal with the myriad of often conflicting cross-cutting concerns, Internet applications and systems must incorporate adequate mechanisms to ensure compliance of both ethical and legal principles. In this paper, we use the OurPrivacy Framework as a conceptual framework to precisely characterize where in the design process the decisions must be made to handle both transparency and privacy concerns.
Dynamic multi-axis interferometric vibrometer for simultaneous observation of 5 degrees of freedom using a single beam

A multi-axis heterodyne interferometer concept is under development for observations of five degrees of dynamic freedom using a single illumination source. This paper presents a laboratory system that combines elements of heterodyne Doppler vibrometry, holography, and digital image correlation to simultaneously quantify in-plane translation, out-of-plane rotation, and out-of-plane displacement at the nanometer scale. The sensor concept observes a dynamic object by mixing a single optical field with heterodyne reference beams and collecting these combined fields at the image and Fourier planes, simultaneously. Polarization and frequency multiplexing are applied to separate two segments of a receive Mach-Zehnder interferometer. Different optical configurations are utilized; one segment produces a focused image of the optical field scattered off the object while the other segment produces an optical Fourier transform of the optical field scattered off the object. Utilizing the amplitude and phase from each plane allows quantification of multiple components of transient motion using a single, orthogonal beam.
Design and Functional Verification of Reversible Logic Based FFT Using OHRNS

This work shows importance of reversible logic circuits using One-Hot Residue Number System (OHRNS) for arithmetic circuit designing and signal processing (digital) for less delay and less power dissipation applications. OHRNS technique highlight is delay of one active (high) transistor which is equal to the implementation delay which shows better results in comparison to reversible logic design circuits and conventional methods. Another benefit of using OHR is lucidity of implementation when compared to reversible logic circuits and conventional methods. The advantage of using reversible logic design circuits is having minimal power dissipation. Design of arithmetic blocks adder and subtractor using one-hot encoding technique is focused mainly. Frequently these subsystems are used in the design of filters. In this work, the above subsystems implemented using OHR are utilized in designing of FFT computational blocks. The essential blocks required in designing a fast Fourier transform computation block using conventional, OHRNS, reversible logic circuits methods, are implemented in Tanner EDA tool with standard CMOS technology (250 nm), and comparative results are provided. Experimental results show the FFT block implementation using OHRNS technique which dissipates less power, consumes less transistors, and is faster and feasible option in design of efficient circuits when compared to similar implementation techniques such as reversible logic circuits and conventional techniques.
Optimizing Probability of Intercept Using XCS Algorithm

In battle scenarios, the amount of RF signals/energy intercepted by a sensor like Radar Warning Receiver is characterized by a metric called Probability of Intercept. Due to increase in density and complexity of radars, achieving optimal POI within given cost-constraints is challenging. The basic problem arises since radars scan in spatial (and hence time domain), whereas RWR scan in frequency domain. Synchronizing RWR’s reception frequency with same of Radar and, at the time when radar is illuminating Aircraft will require more than analytical techniques. Situation becomes more complicated with multiple radars and radars with Low Probability Intercept (LPI) signatures. In this paper, we explore how to exploit eXtended Classifier System (or XCS) to tackle this problem.
A Clicks-and-Mortar Information Exchange Mechanism Based on Blockchain Technology

Nowadays, e-commerce has made its official way into the clicks-and-mortar era. The advantage of the clicks-and-mortar strategy is that it enjoys logistics support from the "mortar" counterpart, which complements any operational inadequacy on the side of the virtual website -- the "clicks" counterpart. In the future, the clicks-and-mortar model can facilitate the merging of different businesses and strategic partnerships that will diversify sales channels as well as client sources. When an Internet brand attempts to establish a physical distribution channel, it must take into consideration three aspects in which physical channels are inferior to the virtual marketplace: (1) issues of lease costs, (2) issues of merchandise inventory, and (3) issues of product profit. Among all, the most concerning issue is the inability to keep real-time tabs of sales conditions, which can lead to slow-moving inventory sitting on shelves of physical stores, accumulating dead stocks. The price of a merchandise may differ at a physical store and at the virtual marketplace; moreover, the two channels have different schedules and paces in updating products and hosting sales events, creating an information discrepancy between the physical store and the virtual store, which leads to price competition and client source competition between the physical store and the virtual store.
Review

As a natural and intuitive way of interpersonal communication, gestures are widely valued in the application of human-computer interaction. Especially, gesture recognition based the vision has such advantages as no equipment, high freedom and high robustness, but it will still be influenced by the factors such as light or environment, so it is an important research side to deal with the process of gesture recognition with some optimization algorithms. This paper analyzes the shortcomings of traditional gesture recognition, the great potential of solving complex optimization problems from genetic algorithm and the successful application in the field of industrial engineering. At the same time, combining the characteristics of the performance of gesture recognition system, this paper summarizes the research status and application of hand gesture recognition based on GA.
The Impact of Quantum Teleportation on Business Ethics

The scandals associated with breaches in business ethics has had a negative impact on companies and governments. Numerous scandals have brought to the forefront the influence of power and profit as they relate to how business is conducted. This behavior in corporations could impact technological advancements. The advancements being recognized in quantum computing will have a profound impact on how business is conducted and business communications. The primary areas of concern are the teleportation of information and teleportation of matter. Given the abundance of ethical breaches within corporations and governments around the globe, it is necessary for business ethics to be revised as it relates to enforcement and accountability. The wealth possessed by companies and their immunity from portions of the law has placed business ethics at a crisis level. Quantum teleportation has the potential to resolve some of these issues, however, the scientific community must participate by building in safeguards such as cryptography that prevents third party access to information.
Robust/Recover Provable Data Possession Protocol

Provable data possession (PDP) allows a client that has stored data at remote server to verify that the server correctly possesses the original data. A long-standing problem is how to reduce I/O cost. Through the integration of Online-code and PDP, a challenge/check protocol that can verifies the possession is proposed. The protocol generates probabilistic proofs of possession by sampling tiny sets of data, which obviously reduces I/O cost. Meanwhile, the protocol can recover corrupted data. The authors formalize this notion in the Robust/Recover (RR) provable data possession guarantee. Briefly speaking, the client maintains a constant amount of metadata to verify the proof. The challenge/check protocol transmits a constant amount of data, which reduces communication complexity. The authors give a detailed analysis of this protocol and build a simulation to evaluate practicability in reliability, space overhead, computation complexity, and communication complexity.
Electronic Grading Workshop

As class sizes grow with large enrolments for Computer Science, providing quick and consistent feedback on assessments becomes more and more challenging. Automated approaches to support grading, and grading management, have been improving. There are now a range of options from scanning of hand graded exams to full featured grading management products like Crowdmark[1] and Gradescope[2]. This workshop will compare/highlight some of the advantages, disadvantages and challenges associated with using some of these tools based on the presenters' experiences.