Reviewerly/exHarmony huggingface dataset:
Differentiated Learning for Multi-Modal Domain Adaptation

Directly deploying a trained multi-modal classifier to a new environment usually leads to poor performance due to the well-known domain shift problem. Existing multi-modal domain adaptation methods treated each modality equally and optimize the sub-models of different modalities synchronously. However, as observed in this paper, the degrees of domain shift in different modalities are usually diverse. We propose a novel Differentiated Learning framework to make use of the diversity between multiple modalities for more effective domain adaptation. Specifically, we model the classifiers of different modalities as a group of teacher/student sub-models, and a novel Prototype based Reliability Measurement is presented to estimate the reliability of the recognition results made by each sub-model on the target domain. More reliable results are then picked up as teaching materials for all sub-models in the group. Considering the diversity of different modalities, each sub-model performs the Asynchronous Curriculum Learning by choosing the teaching materials from easy to hard measured by itself. Furthermore, a reliability-aware fusion scheme is proposed to combine all optimized sub-models to support final decision. Comprehensive experiments based on three multi-modal datasets with different learning tasks have been conducted, which show the superior performance of our model while comparing with state-of-the-art multi-modal domain adaptation models.
Smartphone Handwritten Circuits Solver Using Augmented Reality and Capsule Deep Networks for Engineering Education

Resolving circuit diagrams is a regular part of learning for school and university students from engineering backgrounds. Simulating circuits is usually done manually by creating circuit diagrams on circuit tools, which is a time-consuming and tedious process. We propose an innovative method of simulating circuits from hand-drawn diagrams using smartphones through an image recognition system. This method allows students to use their smartphones to capture images instead of creating circuit diagrams before simulation. Our contribution lies in building a circuit recognition system using a deep learning capsule networks algorithm. The developed system receives an image captured by a smartphone that undergoes preprocessing, region proposal, classification, and node detection to get a Netlist and exports it to a circuit simulator program for simulation. We aim to improve engineering education using smartphones by (1) achieving higher accuracy using less training data with capsule networks and (2) developing a comprehensive system that captures hand-drawn circuit diagrams and produces circuit simulation results. We use 400 samples per class and report an accuracy of 96% for stratified 5-fold cross-validation. Through testing, we identify the optimum distance for taking circuit images to be 10 to 20 cm. Our proposed model can identify components of different scales and rotations.
Data Mining Method of Enterprise Human Resource Management Based on Simulated Annealing Algorithm

The human resources department of an enterprise relies on the “mining” of big data when carrying out human resource management and proposes a data mining method for enterprise human resource management based on the simulated annealing algorithm. Applying the simulated annealing algorithm, using the Metropolis algorithm to generate the sequence of solutions to the combinatorial optimization problem, finding the overall optimal solution of the combinatorial optimization problem, using big data directional mining and analysis to help companies establish and find a “radar” system suitable for talents, the maximum tree method is adopted; that is, a special graph is constructed to realize the effective application of data mining technology in enterprise human resource management. The optimization of nurse scheduling in a hospital was used for case analysis. The results show that the target value of the nurse scheduling model is 43.43% lower than the actual manual scheduling target value, the salary cost is reduced by 10.8%, and the nurse’s satisfaction with the shift is increased by 35.24%. After several iterations based on the simulated annealing algorithm, the optimal value of the solution of the simulated annealing algorithm remains unchanged at the 60th generation. Then, the search process is stopped when the 100th generation is reached, and the solution at this time is the optimal optimization value found by the algorithm.
On the transferability of adversarial perturbation attacks against fingerprint based authentication systems

The growing availability of cheap and reliable fingerprint acquisition scanners is resulting in an increasing spread of Fingerprint-based Authentication Systems (FAS) in consumer electronics. This has giving rise to a new wave in research on both smarter spoofing attacks, aimed to bypass a FAS by using a counterfeit fingerprint, and on more effective Liveness Detectors (LD), aimed to discern authentic (live) fingerprints from fake ones. As in many other computer vision tasks, deep Convolutional Neural Networks (CNN) demonstrated to be very effective also for fingerprint liveness detection. However, we showed that it is possible to adapt adversarial perturbation approaches to mislead CNN-based LD. In this paper, we want to make a step further toward the design of a black-box attack by investigating whether it is possible to transfer a perturbation across different CNN liveness detectors in the case of a target LD very different from the one used to compute the perturbations. To this aim, we designed an attack scenario where a shadow LD (i.e. an adaptation of the substitute technique for the liveness detection application) is used to generate an adversarial fingerprint in a white-box setting before submitting it to the real target LD, invoked in a total back-box manner. Finally, we analysed the impact that such attack has on the authentication system, also analysing if and to what extent the scanner and the spoofing material combinations affect the success of the attack.
Anti-Quantum Lattice-Based Ring Signature Scheme and Applications in VANETs

Message authentication is crucial because it encourages participants to accept countermeasures and further transmit messages to legitimate users in a network while maintaining the legitimacy of the identity of network members. An unauthorized user cannot transmit false messages to a given network. Although traditional public key cryptography is suitable for message authentication, it is also easy to manage and generate keys, and, with the expansion of an entire network, the system needs a lot of computing power, which creates additional risks to network security. A more effective method, such as ring signature, can realize this function and guarantee more security. In this paper, we propose an anti-quantum ring signature scheme based on lattice, functionality analysis, and performance evaluation to demonstrate that this scheme supports unconditional anonymity and unforgeability. After efficiency analysis, our scheme proved more effective than the existing ring signature schemes in processing signature generation and verification. The proposed scheme was applied to VANETs that support strong security and unconditional anonymity to vehicles.
Software Defect-Based Prediction Using Logistic Regression: Review and Challenges

Software analysis and prediction system development is the significant and much-needed field of software testing in software engineering. The automatic software predictors analyze, predict, and classify a variety of errors, faults, and defects using different learning-based methods. Many research contributions have evolved in this direction. In recent years, however, they have faced the challenges of software validation, non-balanced and unequal data, classifier selection, code size, code dependence, resources, accuracy, and performance. There is, therefore, a great need for an effective and automated software defect-based prediction system that uses machine learning techniques, with great efficiency. In this paper, a variety of such studies and systems are discussed and compared. Their measurement methods such as metrics, features, parameters, classifiers, accuracy, and data sets are found and discriminated. In addition to this, their challenges, threats, and limitations are also stated to demonstrate their system's effectiveness. Therefore, it was discovered that such systems accounted for 44% use of the NASA's PROMISE data samples, 68.18% metrics use of software, and also 16% use of the Logistic Regression method.
Recommendation System Based on Machine Learning and Deep Learning in Varied Perspectives: A Systematic Review

Being in the digital world wherein limitless information is in the reach of everyone, people spend enormous amount of time in various search engines to find information of their interest. Simple search engines are no more an option as many a times needs are not explicitly understood. This needs search engines that are smart enough to gather user needs based on history and ability to find relevant information from infinite information. Machine Learning and Deep Learning are some of the techniques solving the need on personalized recommendations. In this state-of-the-art comprehension, majorly based on the published research paper results from around 98 journals, books, and conference proceedings obtained during the past decade to the latest one. This will expose the various types of recommendation system domains and their machine learning techniques that have been implemented in the prior works. Then will discuss the problems, trends, and solution that were presented not only on the research papers but also on the developments that are practically happening on real-world.
Modeling Cameras for Autonomous Vehicle and Robot Simulation: An Overview

Simulation is increasingly important in the development and testing of robots and autonomous vehicles as it opens the door for candidate navigation, perception, and sensor fusion algorithms to be expeditiously probed in complex and safety-critical scenarios. As most robots and autonomous vehicles make heavy use of cameras to perceive their surroundings, camera modeling becomes a prerequisite for the successful simulation of these autonomous agents. This contribution outlines the context in which camera models are used; provides a component-by-component analysis of the image acquisition pipeline along with algorithms used for its modeling; and closes with a discussion of data-driven approaches that embrace a different perspective on camera modeling.
Regulation of virtual currencies in the United Arab Emirates: accounting for the emerging public/private distinction

This article maps the changing global conversation on virtual currencies (VCs) internationally And brings that to bear on the development of a suitable regulatory framework for VCs in the United Arab Emirates (UAE). Through a review of recent international developments, the article notes a reversal in regulatory attitude towards VCs. This review suggests two emerging horizons of regulatory intervention. The first concerns the public issue of VCs to develop financial traceability and economic transparency – an arrangement that would be particularly relevant for developing countries with large informal economies. In particular, public VC issue raises follow-up questions of technological adequacy and the legal status of a means of payment guaranteed by the State. The second concerns privately issued VCs traded on independent platforms and focuses on the trade off between economic development, through a private market for VCs, and the risks connected to money laundering and the financing of terrorism. Here, a two-pronged system of ex ante licensing to private venture capitalists, coupled with ex post-sanctions, appears to be an emerging international standard for supporting economic development in countries marked by the preponderant weight of an underground economy.
Lexical attention and aspect-oriented graph convolutional networks for aspect-based sentiment analysis

The purpose of aspect-based sentiment analysis is to predict the sentiment polarity of different aspects in a text. In previous work, while attention has been paid to the use of Graph Convolutional Networks (GCN) to encode syntactic dependencies in order to exploit syntactic information, previous models have tended to confuse opinion words from different aspects due to the complexity of language and the diversity of aspects. On the other hand, the effect of word lexicality on aspects’ sentiment polarity judgments has not been considered in previous studies. In this paper, we propose lexical attention and aspect-oriented GCN to solve the above problems. First, we construct an aspect-oriented dependency-parsed tree by analyzing and pruning the dependency-parsed tree of the sentence, then use the lexical attention mechanism to focus on the features of the lexical properties that play a key role in determining the sentiment polarity, and finally extract the aspect-oriented lexical weighted features by a GCN.Extensive experimental results on three benchmark datasets demonstrate the effectiveness of our approach.
Multi-Level Visual Representation with Semantic-Reinforced Learning for Video Captioning

This paper describes our bronze-medal solution for the video captioning task of the ACMMM2021 Pre-Training for Video Understanding Challenge. We depart from the Bottom-Up-Top-Down model, with technical improvements on both video content encoding and caption decoding. For encoding, we propose to extract multi-level video features that describe holistic scenes and fine-grained key objects, respectively. The scene-level and object-level features are enhanced separately by multi-head self-attention mechanisms before feeding them into the decoding module. Towards generating content-relevant and human-like captions, we train our network end-to-end by semantic-reinforced learning. Finally, in order to select the best caption from captions produced by distinct models, we perform caption reranking by cross-modal matching between a given video and each candidate caption. Both internal experiments on the MSR-VTT test set and external evaluations by the challenge organizers justify the viability of the proposed solution.
DeepFlow: Abnormal Traffic Flow Detection Using Siamese Networks

Nowadays, many cities are equipped with surveillance systems and traffic control centers to monitor vehicular traffic for road safety and efficiency. The monitoring process is mostly done manually which is inefficient and expensive. In recent years, several data-driven solutions have been proposed in the literature to automatically analyze traffic flow data using machine learning techniques. However, existing solutions require large and comprehensive datasets for training which are not readily available, thus limiting their application. In this paper, we develop a traffic anomaly detection system, referred to as DeepFlow, based on Siamese neural networks, which are suitable in scenarios where only small datasets are available for training. Our model can detect abnormal traffic flows by analyzing the trajectory data collected from the vehicles in a fleet. To evaluate DeepFlow, we use realistic vehicular traffic simulations in SUMO. Our results show that DeepFlow detects abnormal traffic patterns with an F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> score of 78%, while outperforming other existing approaches including: Dynamic Time Warping (DTW), Global Alignment Kernels (GAK), and iForest.
Auxiliary Loss of Transformer with Residual Connection for End-to-End Speaker Diarization

End-to-end neural diarization (EEND) with self-attention directly predicts speaker labels from inputs and enables the handling of overlapped speech. Although the EEND outperforms clustering-based speaker diarization (SD), it cannot be further improved by simply increasing the number of encoder blocks because the last encoder block is dominantly supervised compared with lower blocks. This paper proposes a new residual auxiliary EEND (RX-EEND) learning architecture for transformers to enforce the lower encoder blocks to learn more accurately. The auxiliary loss is applied to the output of each encoder block, including the last encoder block. The effect of auxiliary loss on the learning of the encoder blocks can be further increased by adding a residual connection between the encoder blocks of the EEND. Performance evaluation and ablation study reveal that the auxiliary loss in the proposed RX-EEND provides relative reductions in the diarization error rate (DER) by 50.3% and 21.0% on the simulated and CALLHOME (CH) datasets, respectively, compared with self-attentive EEND (SA-EEND). Furthermore, the residual connection used in RX-EEND further relatively reduces the DER by 8.1% for CH dataset.
Low-Complexity Resource-Shareable Parallel Generalized Integrated Interleaved Encoder

Generalized integrated interleaved (GII) codes nest a set of linear block codewords to generate codewords belonging to stronger codes. They are among the best error-correcting codes for next-generation hyper-speed digital communications and storage. Serial encoders for GII codes based on BCH codes have been previous investigated. They consist of BCH encoders whose inputs and outputs are multiplied by vectors decided by the nesting scheme. However, parallel GII encoders for high-speed systems cannot be designed by directly extending serial encoders due to the unique feature that BCH codes of different error-correcting capabilities are involved. Moreover, GII decoder complexity and latency can be greatly reduced by sharing the encoder to compute short remainders for syndrome computation. Although previous resource-shareable BCH encoders can be utilized to implement resource-shareable GII encoders, they are all serial. This paper first proposes a low-complexity scheme to handle the different error-correcting capabilities of the involved codes and align the input and parity symbols for parallel processing. Then two efficient parallel resource-shareable BCH encoder architectures to be used as GII encoder components are developed. The first design is achieved by deriving parallel register state update formulas for concatenated linear-feedback shift registers (LFSRs). Through reformulating the remainder polynomial divisions, the second design allows the inputs to be added to different LFSR taps, and accordingly reduces the complexity by a significant portion. For an example 160-parallel GII-BCH encoder considered for Flash memory applications, the second proposed design requires 14% smaller area compared to the first one. Besides both of them lead to around 50% latency reduction in the nested syndrome computation with small area overheads compared to the best possible alternative design.
Hey There! What Do You Look Like? User Voice Switching and Interface Mirroring in <scp>Voice‐Enabled</scp> Digital Assistants (<scp>VDAs</scp>)

Abstract We investigated user voice switching behavior (VSB) in voice‐enabled digital assistants (VDAs), focusing on the importance of and preference for the voice accents, genders, and age to match with those of the users. We incorporated images of ten people with diverse races, ethnicities, age, genders, and religions to embody the voice interfaces (EVIs). In an online survey, we collected demographic, background, and VDA usage data. The sample consisted of 214 participants recruited through Amazon Mechanical Turk ( http://mturk.com ). The participants were selected based on owning a VDA (e.g., Alexa Home) or owning a device (e.g., smartphone, tablet, or computer), and setting the device on English as the default language. The age of the participants ranged from 18–35 years. Findings revealed that, regardless of age, the majority of the participants switched the voice interface and for various reasons. Further, participants placed importance on voice matching with their gender, accent, and age. Participants ranked the young White female, Asian female, and Black female EVIs as the most preferred for voice switching and interactions . We coin the concept, Interface mirroring , which should help designers to create more diverse and inclusive EVIs, ensuring fairness and equality in the design of VDAs.
Automatic CVSS-based Vulnerability Prioritization and Response with Context Information

Vulnerability prioritisation is essential in the process of the vulnerability management within an IT network environment. Accurate prioritisation of the detected vulnerabilities is an important factor in corporate cybersecurity. This is because the most critical vulnerabilities should be given an immediate attention. The process of vulnerability prioritization is further exacerbated by amounts of data produced by various security systems. Thus timely detection and elimination of a vulnerability critical to the company, is very difficult. In order to improve the efficiency of the vulnerability prioritisation process, in this paper a study of selected metrics that aim to facilitate detection of critical vulnerabilities is performed.
A language-matching model to improve equity and efficiency of COVID-19 contact tracing

Contact tracing is a pillar of COVID-19 response, but language access and equity have posed major obstacles. COVID-19 has disproportionately affected minority communities with many non-English-speaking members. Language discordance can increase processing times and hamper the trust building necessary for effective contact tracing. We demonstrate how matching predicted patient language with contact tracer language can enhance contact tracing. First, we show how to use machine learning to combine information from sparse laboratory reports with richer census data to predict the language of an incoming case. Second, we embed this method in the highly demanding environment of actual contact tracing with high volumes of cases in Santa Clara County, CA. Third, we evaluate this language-matching intervention in a randomized controlled trial. We show that this low-touch intervention results in 1) significant time savings, shortening the time from opening of cases to completion of the initial interview by nearly 14 h and increasing same-day completion by 12%, and 2) improved engagement, reducing the refusal to interview by 4%. These findings have important implications for reducing social disparities in COVID-19; improving equity in healthcare access; and, more broadly, leveling language differences in public services.
Comparing PCG metrics with Human Evaluation in Minecraft Settlement Generation

There are a range of metrics that can be applied to the artifacts produced by procedural content generation, and several of them come with qualitative claims. In this paper, we adapt a range of existing PCG metrics to generated Minecraft settlements, develop a few new metrics inspired by PCG literature, and compare the resulting measurements to existing human evaluations. The aim is to analyze how those metrics capture human evaluation scores in different categories, how the metrics generalize to another game domain, and how metrics deal with more complex artifacts. We provide an exploratory look at a variety of metrics and provide an information gain and several correlation analyses. We found some relationships between human scores and metrics counting specific elements, measuring the diversity of blocks and measuring the presence of crafting materials for the present complex blocks.