Reviewerly/exHarmony huggingface dataset:
Covariance-free Partial Least Squares: An Incremental Dimensionality Reduction Method

Dimensionality reduction plays an important role in computer vision problems since it reduces computational cost and is often capable of yielding more discriminative data representation. In this context, Partial Least Squares (PLS) has presented notable results in tasks such as image classification and neural network optimization. However, PLS is infeasible on large datasets, such as ImageNet, because it requires all the data to be in memory in advance, which is often impractical due to hardware limitations. Additionally, this requirement prevents us from employing PLS on streaming applications where the data are being continuously generated. Motivated by this, we propose a novel incremental PLS, named Covariance-free Incremental Partial Least Squares (CIPLS), which learns a low-dimensional representation of the data using a single sample at a time. In contrast to other state-of-the-art approaches, instead of adopting a partially-discriminative or SGD-based model, we extend Nonlinear Iterative Partial Least Squares (NI-PALS) - the standard algorithm used to compute PLS - for incremental processing. Among the advantages of this approach are the preservation of discriminative information across all components, the possibility of employing its score matrices for feature selection, and its computational efficiency. We validate CIPLS on face verification and image classification tasks, where it outperforms several other incremental dimensionality reduction techniques. In the context of feature selection, CIPLS achieves comparable results when compared to state-of-the-art techniques.
Joint Position Registration between OpenPose and Motion Analysis for Rehabilitation

We developed a registration system between OpenPose and Motion Analysis, that is a gold standard of human motion analysis, to evaluate the accuracy of human joint position. We took the data of the both for walking ten healthy persons: five males and five females. The registration result was evaluated by comparing with their obtained data. As a result, OpenPose had an error of 63.4mm on horizontal axis, 30.2mm on vertical axis in videos taken from the side view, and 29.2mm on horizontal axis, 34.1mm on vertical axis in videos taken from the front view with comparison to Motion Analysis.
An Improved Dictionary Cracking Scheme Based on Multiple GPUs for Wi-Fi Network

The Internet has penetrated all aspects of human society and has promoted social progress.Cyber-crimes in many forms are commonplace and are dangerous to society and national security.Cybersecurity has become a major concern for citizens and governments.The Internet functions and software applications play a vital role in cybersecurity research and practice.Most of the cyber-attacks are based on exploits in system or application software.It is of utmost urgency to investigate software security problems.The demand for Wi-Fi applications is proliferating but the security problem is growing, requiring an optimal solution from researchers.To overcome the shortcomings of the wired equivalent privacy (WEP) algorithm, the existing literature proposed security schemes for Wi-Fi protected access (WPA)/WPA2.However, in practical applications, the WPA/WPA2 scheme still has some weaknesses that attackers exploit.To destroy a WPA/WPA2 security, it is necessary to get a PSK pre-shared key in pre-shared key mode, or an MSK master session key in the authentication mode.Brute-force cracking attacks can get a phase-shift keying (PSK) or a minimum shift keying (MSK).In real-world applications, many wireless local area networks (LANs) use the pre-shared key mode.Therefore, brute-force cracking of WPA/WPA2-PSK is important in that context.This article proposes a new mechanism to crack the Wi-Fi password using a graphical processing unit (GPU) and enhances the efficiency through parallel computing of multiple GPU chips.Experimental results show that the proposed algorithm is effective and provides a procedure to enhance the security of Wi-Fi networks.
Gaze tracking dataset for comparison of smooth and saccadic eye tracking

Pupil tracking data are collected through the use of an infrared camera, and a head-mounted system [1]. The head-mounted system detects the relative pupil position and adjusts the mouse cursor position accordingly. The data are available for comparison of eye tracking with saccadic movements (with the head fixed in space) versus those from smooth movements (with the head moving in space). The analysis comprises two experiments for both types of eye tracking, which are performed with ten trials each for two participants. In the first experiment, the participant attempts to place the cursor into a target boundary of varying sizes. In the second experiment, the participant attempts to move the cursor to a target location within the shortest time.
Compression Techniques for Handwritten Digit Recognition

Compressing images before recognition leads to many benefits including efficient computation, compact models, and optimal memory utilization. Several techniques for compression of handwritten digits have been investigated and implemented. This paper presents three compression techniques used in signal processing for compressing handwritten digit images, which are Discrete Cosine Transform (DCT), Discrete Sine Transform (DST) and Wavelet Transform (WT). These techniques are evaluated for their ability to compress the digit images while retaining useful information needed for classifying them, subsequently. Experiments conducted on the publicly available MINST dataset show the effectiveness of the techniques. With the presented techniques, we were able to compress the original images by 48.98%, 71.30%, and 87.24% while leading to reduction in accuracy by only 1.413%, 3.187%, and 7.238%, respectively, on an independent test set.
Babel: A Generic Benchmarking Platform for Big Data Architectures

In this era of big and fast data, software architects tend to find it really hard to make consistent decisions about which architecture and technologies are ideal for a certain business need. It is even harder to make them while dealing with the scarcity of clear methodologies, best practices and reference architectures. In this prospect, architecture evaluation through benchmarking can be of great interest, as it enables the detection of performance anomalies or bottlenecks as you go. The problem when talking about Big Data benchmarking, is that existing solutions remain technology-related, and do not deal with the heterogeneous aspect of complex architectures. In addition to that, businesses are in general dealing with multi-layered complex systems, involving various technologies, paradigms and micro-architectures. This means that the benchmarking solution must be able to give fine-grained insights about each of the layers. A successful benchmarking system must also be seamless, easy to use, scalable, and preferably cloud native. To satisfy these requirements, we designed and implemented Babel, a generic Big Data benchmarking platform, that insures an end-to-end performance evaluation and monitoring. We present in this paper the principles, architecture, integration and deployment steps of Babel.
Teleportation Criteria Based on Maximum Eigenvalue of the Shared d ⊗ d Dimensional Mixed State: Beyond Singlet Fraction

We derive a criteria for the detection of d ⊗ d dimensional negative partial transpose (NPT) entangled state useful for teleportation. The newly derived criteria are based on the maximum eigenvalue of the NPT entangled state, which is easier to determine experimentally than to completely reconstruct the state via tomography. We then illustrate our criteria by considering a class of qubit-qubit system and qutrit-qutrit system.
New Graph Embedding Approach for 3D Protein Shape Classification

We address the problem of 3D protein deformable shape classification. Proteins are macromolecules characterized by deformable and complex shapes which are related to their function making their classification an important task. Their molecular surface is represented by graphs such as triangular tessellations or meshes. In this paper, we propose a new graph embedding based approach for the classification of these 3D deformable objects. Our technique is based on graphs decomposition into a set of substructures, using triangle-stars, which are subsequently matched with the Hungarian algorithm. The proposed approach is based on an approximation of the Graph Edit Distance which is characterized by its robustness against both noise and distortion. Our algorithm defines a metric space using graph embedding techniques, where each object is represented by a set of selected 3D prototypes. We propose new approaches for prototypes selection and features reduction. The classification is performed with supervised machine learning techniques. The proposed method is evaluated against 3D protein benchmark repositories and state-of-the-art algorithms. Our experimental results consistently demonstrate the effectiveness of our approach.
AI-Vision Towards an Improved Social Inclusion

The promise of Artificial Intelligence (AI)-based technologies is immense, and benefits range from efficiency profits to unprecedented improvements of quality of life in a social exclusive world, where discrimination and marginalization pose significant difficulties for building inclusive cities and providing equal access to opportunities for disabled persons. The challenges faced by visually impaired individuals in today's world are too many to count. While many tools have been introduced to help address these problems, these tools remain very costly to afford. The proposed smart glasses AI-Vision, which aim to provide the needed support in daily routine tasks utilizes machine learning algorithms in two applications of great interest. A facial expression/age/gender recognition for meetings, and a color recognition for outfit sorting are integrated in the device's framework. The generated feedback sound can be heard by the user through the bone conduction technology while avoiding disturbance of surrounding noise. The proposed AI-Vision reduces substantially the cost of a similar product while keeping an efficient recognition accuracy.
Open Named Entity Modeling From Embedding Distribution

In this paper, we report our discovery on named entity distribution in a general word embedding space, which helps an open definition on multilingual named entity definition rather than previous closed and constraint definition on named entities through a named entity dictionary, which is usually derived from human labor and replies on schedule update. Our initial visualization of monolingual word embeddings indicates named entities tend to gather together despite of named entity types and language difference, which enable us to model all named entities using a specific geometric structure inside embedding space, namely, the named entity hypersphere. For monolingual cases, the proposed named entity model gives an open description of diverse named entity types and different languages. For cross-lingual cases, mapping the proposed named entity model provides a novel way to build a named entity dataset for resource-poor languages. At last, the proposed named entity model may be shown as a handy clue to enhance state-of-the-art named entity recognition systems generally.
Aspect-based sentiment analysis on hair care product reviews

Nowadays, with almost everything being shared online, people are more verbal about their consumer experiences with products via reviews. Reviews can be vital for manufacturers to get insights into consumer opinions and consumers in their purchase decisions. Sentiment analysis, referring to the extraction of subjective opinions on a particular subject within a text, is a field within Natural Language Processing, that can convert this unstructured information hidden within reviews into structured information expressing public opinion. In regards to a specific product group like hair care products, certain brands are rising in the market due to their positive public opinion on particular aspects. While ecommerce websites facilitate users to view the reviews, they do not display which reviews contain which type of opinion on which aspect at a glance. This research aims to introduce an automated process that focuses on determining the polarity of online consumer reviews on different aspects of hair care products by using Aspect-based Sentiment Analysis. The system consists of processes like data gathering, pre-processing, aspect extraction and polarity detection and follows a sequential approach to achieve the intended goal. Consequently, by deciphering the aspect-wise polarity of the reviews, the implemented system demonstrates an accuracy of 85% from the test data for overall aspects, enabling consumers to get an at a glance idea about the public opinion and manufacturers to identify their strong and weak points.
Rigorous code review by reverse engineering

Agile software development methods advocate the importance of producing working software without comprehensive documentation. While this approach seems to suit the evolutionary nature of realistic software development for many applications, even including safety-critical systems, it faces two major challenges. One is the lack of a comprehensible specification for code evolution and future maintenance, and the other is the potentially huge cost in code verification. To address this problem, we believe that supporting the efficient production of system specification by reversing the program constructed as the result of an agile development will be a useful solution. The reverse engineering of specifications from programs will not only help us produce the necessary specification for future program evolution, but more importantly can help us rigorously review the program to detect bugs for the enhancement of program quality. In this paper, we put forward a novel method for rigorously reviewing code by reversing it into a comprehensible, formal specification. We elaborate on the principle of translating code into a specification and discuss how the translation process helps detect bugs in programs. We demonstrate how the proposed method works in practice with examples. We also present an experiment to evaluate the performance of the method by comparing it with existing checklist-based inspection. How to utilize reverse engineering of formal specifications from programs as a means to review the program for bug detection is an almost unexplored topic in software engineering. In this paper, we have described a specific method called RCRRE to reverse engineering of SOFL formal specifications from code and discussed how the reverse engineering process can be taken as an effective means to review the program for bug detection. The principle of converting code to a SOFL specification is reflected by a set of translation patterns and a two-step approach to construct a SOFL specification is established. To evaluate the performance, we have carried out an experiment on the effectiveness of our RCRRE method by comparing it with the CBI approach. The result of the experiment indicates that using our RCRRE method can effectively help the reviewer scrutinize the code and therefore find more bugs than the CBI when the reviewer is rather familiar with the SOFL specification language and skills. In the meanwhile, it also shows that the effectiveness of our RCRRE method may be affected in the situation where the reviewer lacks sufficient understanding and experience of SOFL, and using our RCRRE method may in general take a little longer time than the CBI.
The Effects Of Non-linear Operators In Voxel-Based Deep Neural Networks For 3D Style Reconstruction

Neural Style Transfer has been successfully applied for generating plausible novel 2D images by transferring the style of a painting to an existing content image and has been extended to image, video, motion and animation manipulation. Besides first works on 2D/3D hybrid approaches relying on 2D rendering operations, a consistent end-to-end 3D Neural Style Transfer is missing. For realizing such an end-to-end 3D Neural Style Transfer, we suggested a first 3D voxel-based neural network architecture in previous works. Here, we extend this architecture to a resolution of 256 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> voxels to process vastly finer visual features, and we include our recently introduced standardized gram matrix approach, which reduces noise and visually improves style transfer results. We furthermore show that the type of activation function used in the network is crucial for the successful application in 3D Neural Style transfer. In particular, we demonstrate that the recently proposed bipolar exponential linear unit (belu) operator achieves a consistent style reconstruction independent of filter orientation relative to the shape surface. We compare the operator to the commonly used exponential linear units (elu) and provide insights for the shown behavior. In addition to a purely visual inspection, we evaluate the constructed shapes using local 3D shape descriptors and confirm the subjective visual improvements. We conclude that the utilization of belu operators in the deep neural network architecture is a crucial component for realizing high-resolution voxel-based 3D Neural Style Transfer.
A hierarchical neural model for target‐based sentiment analysis

Abstract A convolutional neural network‐regional long Short‐Term memory (CNN‐RLSTM) is proposed, which is a convolutional neural network‐regional long short‐term memory (CNN‐RLSTM) that combines CNN and regional LSTM. The model can effectively distinguish the affective polarity of different targets through a regional LSTM while reducing the training time of the model. In addition, the model can retain the sentiment information of the whole sentence through a CNN network at the sentence level. Experimental results on different data sets show that the CNN‐RLSTM model is better than the traditional model and the deep network model.
A Bifocal Classification and Fusion Network for Multimodal Image Analysis in Histopathology

Recognition of key morphological features in histological slides is crucial for pathological diagnosis and monitoring therapeutic progress. However, the typical routine microscopic workflow is conducted by hand which is time-consuming and has unavoidable intra- and inter-observer variability like all human work. Therefore, we propose a bifocal classification and fusion network for the automated recognition and cross-modality analysis of diagnostic features in whole-slide multimodal images (WSIs). In brief, paired image tiles cropped from digitized tissue sections were fed into a modified dual-path CNN which accepts asymmetric inputs for classification, and then the inference results were converted to feature distribution heatmaps, which permit qualitative as well as quantitative morphological analyses of entire histological sections, even in combination with adjacent sections that have been stained differently. The multimodal heatmaps were aligned using image registration and fused for cross-modality analysis. Our experiments showed that the network achieved high recognition performance (AUCs of 0.985 and 0.988, and accuracies of 94.7% and 96.1% on two WSI modalities, respectively, against expert markings) and outperformed state-of-the-art methods without training on a large cohort or utilizing domain transfer. In addition, the new method involves a self-contained inference and fusion process and thus harbors significant potential for speeding up microscopic analysis workflows.
Real-Time Image Processing: Face Recognition based Automated Attendance System in-built with “Two-Tier Authentication” Method

With the advent of computer vision, there has been significant growth in the research and development of facial recognition based automated attendance systems. Although current systems have been successful in alleviating human interaction and manual efforts, there still exist several challenges such as severe misclassifications, undetectable face angles, and different lighting conditions which result in a drastic drop in the accuracy. The system introduced in this paper has achieved an overall accuracy of 93.33%. A concept termed the “two-tier authentication” method has been developed to improve the overall accuracy of the system and to integrate a mechanism of time allowance for students. This method facilitates granting attendance to students based on the number of recognized faces as well as the probability of each prediction allowing for a more robust method of marking attendance to students. The novelty of this approach is to introduce an accurate statistical sequence for the execution of a proxy-free automated attendance system that employs state-of-the-art algorithms. Composed of 3 distinct parts, every sub-system performs a specific task namely, face detection, generation of face embeddings (FaceNet), and face classification. A comparative study was carried out to select the most appropriate detection and classification algorithms where Faster R-CNN and Support Vector Classifier outperformed their corresponding competitors respectively.
Unsupervised Discovery of Sign Terms by K-Nearest Neighbours Approach

In order to utilize the large amount of unlabeled sign language resources, unsupervised learning methods are needed. Motivated by the successful results of unsupervised term discovery (UTD) in spoken languages, here we explore how to apply similar methods for sign terms discovery. Our goal is to find the repeating terms from continuous sign videos without any supervision. Using visual features extracted from RGB videos, we show that a k-nearest neighbours based discovery algorithm designed for speech can also discover sign terms. We also run experiments using a baseline UTD algorithm and comment on their differences.
Analysis of Work Efficiency and Quality of Software Maintenance Using Cross-Company Dataset

Software maintenance is an important activity in the software lifecycle. Software maintenance does not only mean removing faults found after software release. Software needs extensions or modifications of its functions owing to changes in the business environment and software maintenance also refers to them. To help users and service suppliers benchmark work efficiency for software maintenance, and to clarify the relationships between software quality, work efficiency, and unit cost of staff, we used a dataset that includes 134 data points collected by the Economic Research Association in 2012, and analyzed the factors that affected the work efficiency of software maintenance. In the analysis, using a multiple regression model, we clarified the relationships between work efficiency and programming language and productivity factors. To analyze the influence to the quality, relationships of fault ratio was analyzed using correlation coefficients. The programming language and productivity factors affect work efficiency. Higher work efficiency and higher unit cost of staff do not affect the quality of software maintenance.