Reviewerly/exHarmony huggingface dataset:
Multi-Factor based Face Validation Attendance System with Contactless Design in Training Event

Various methods for face validation-based authentication systems have been applied in a number of access control applications. However, using only one biometric factor such as facial data may limit accuracy and use, and is not practical in a real environment. This paper presents the implementation of a face time attendance system with an additional factor, a QR code to improve accuracy. This two- factor authentication system was developed in the form of a kiosk with a contactless process, which emerged due to the COVID-19 pandemic. The experiment was conducted at a well- known training event in Thailand. The proposed two-factor system was evaluated in terms of accuracy and satisfaction. Additionally, it was compared to a traditional single-factor system using only face recognition. The results confirm that the proposed two-factor scheme is more effective and did not incorrectly identify any users.
Automated Authentication of Large-Scale IoT Devices with Hybrid Feature Selection

Nowadays, large-scale IoT (Internet of Things) devices are connected to the network. However, due to the simplicity, limited memory and poor computing power of most IoT devices, it is difficult to apply complex security authentication protocols to these devices. Many attackers use this loophole, impersonating legitimate devices to launch malicious attacks on the network, threatening the security of other legitimate devices. Therefore, in order to strengthen the identification and management of devices, the existing device fingerprint identification technology based on machine learning is to extract device traffic at the access gateway and generate device fingerprinting to realize the identification of IoT devices. However, to achieve high recognition accuracy, existing device fingerprint algorithms need to extract a large number of fingerprint features from network traffic for machine learning, which increases the complexity of implementation and delay of device identification. Also, it is not suitable for the current large-scale access environment. Therefore, this paper proposes a hybrid feature selection method to preprocess features for the optimal feature subset, which can not only reduce the feature dimension, but also improve the accuracy of device recognition. Firstly, the method filters the irrelevant features according to the characteristic fluctuation and the entropy of correlation information between the feature and the target. Then, based on the classification accuracy of the learning algorithm, a random search method—forest optimization algorithm is used to further optimize the filtered feature subsets under the evaluation criteria. Experiments show that this method can reduce feature dimension by 81.0% and improve classification accuracy from 86.4% to 93.1%.
Quantization of Deep Neural Networks for Accurate Edge Computing

Deep neural networks have demonstrated their great potential in recent years, exceeding the performance of human experts in a wide range of applications. Due to their large sizes, however, compression techniques such as weight quantization and pruning are usually applied before they can be accommodated on the edge. It is generally believed that quantization leads to performance degradation, and plenty of existing works have explored quantization strategies aiming at minimum accuracy loss. In this paper, we argue that quantization, which essentially imposes regularization on weight representations, can sometimes help to improve accuracy. We conduct comprehensive experiments on three widely used applications: fully connected network for biomedical image segmentation, convolutional neural network for image classification on ImageNet, and recurrent neural network for automatic speech recognition, and experimental results show that quantization can improve the accuracy by 1%, 1.95%, 4.23% on the three applications respectively with 3.5x-6.4x memory reduction.
Particle Filtering: A Priori Estimation of Observational Errors of a State-Space Model with Linear Observation Equation

Observational errors of Particle Filtering are studied over the case of a state-space model with a linear observation equation. In this study, the observational errors are estimated prior to the upcoming observations. This action is added to the basic algorithm of the filter as a new step for the acquisition of the state estimations. This intervention is useful in the presence of missing data problems mainly, as well as sample tracking for impoverishment issues. It applies theory of Homogeneous and Non-Homogeneous closed Markov Systems to the study of particle distribution over the state domain and, thus, lays the foundations for the employment of stochastic control against impoverishment. A simulating example is quoted to demonstrate the effectiveness of the proposed method in comparison with existing ones, showing that the proposed method is able to combine satisfactory precision of results with a low computational cost and provide an example to achieve impoverishment prediction and tracking.
Adaptive Mechanism For Enhanced Performance Of Shark Smell Optimization

Various meta-heuristic approaches have been developed to find the optimal solution to optimization problems.However, different approach takes different amount of time and efficiency to achieve the optimal solution.Determination of high performance and lower computing time with simple algorithm is therefore continuously established.Shark Smell Optimization (SSO) algorithm has been proven to have high efficiency in many optimization applications.However, like the other swarm intelligence, SSO algorithm also has possibility to get trapped in local maxima or premature convergence.Thus, a new adaptive shark smell optimization (ASSO) is proposed to improve the convergence efficiency of standard SSO algorithm.An overview and performance comparison of six well-known meta-heuristic optimization algorithm is also presented in this paper.In order to verify the effectiveness of this newly developed method, the algorithm was tested on common benchmark functions used in the literature.Numerical results indicate that the ASSO algorithm strategy outperforms the basic SSO algorithm, Genertic Algorithm (GA), Particle Swarm Intelligence (PSO), Firefly Algorithm (FA), Artificial Bee Colony (ABC) and Teaching Learning Based Optimization (TBLO) in term of reaching for global solution.
BERTAC: Enhancing Transformer-based Language Models with Adversarially Pretrained Convolutional Neural Networks

Transformer-based language models (TLMs), such as BERT, ALBERT and GPT-3, have shown strong performance in a wide range of NLP tasks and currently dominate the field of NLP.However, many researchers wonder whether these models can maintain their dominance forever.Of course, we do not have answers now, but, as an attempt to find better neural architectures and training schemes, we pretrain a simple CNN using a GAN-style learning scheme and Wikipedia data, and then integrate it with standard TLMs.We show that on the GLUE tasks, the combination of our pretrained CNN with ALBERT outperforms the original ALBERT and achieves a similar performance to that of SOTA.Furthermore, on open-domain QA (Quasar-T and SearchQA), the combination of the CNN with ALBERT or RoBERTa achieved stronger performance than SOTA and the original TLMs.We hope that this work provides a hint for developing a novel strong network architecture along with its training scheme.Our source code and models are available at https://github.com/nict-wisdom/bertac.
Nonverbal Communication Analysis as Mixed Analysis

Inspired by Onwuegbuzie and Combs's three-dimensional model for categorizing and organizing orientations for mixed analysis, the nonverbal communication analysis model calls attention to the complexity of meaning making. However, for the purpose of the chapter, the focus on NVC enables the reader to concentrate on the features of mixed methods analysis, also known as mixed analysis. NVC analysis can involve quantitative analysis, qualitative analysis, and mixed analysis. Of all the available mixed analysis techniques, integrated data displays appear to have the most potential for incorporating NVC analysis. The use of digital and non-digital tools to collect NVC data has been demonstrated as being a common approach for researchers of online or digital meaning making. Research of learning in online spaces, however, can hinge on data collected solely in an online space. NVC analysis can and should be methodical and attend to multiple meanings, including those that challenge, clarify, and/or contradict verbal communication.
MR Slice Profile Estimation by Learning to Match Internal Patch Distributions

To super-resolve the through-plane direction of a multi-slice 2D magnetic resonance (MR) image, its slice selection profile can be used as the degeneration model from high resolution (HR) to low resolution (LR) to create paired data when training a supervised algorithm. Existing super-resolution algorithms make assumptions about the slice selection profile since it is not readily known for a given image. In this work, we estimate a slice selection profile given a specific image by learning to match its internal patch distributions. Specifically, we assume that after applying the correct slice selection profile, the image patch distribution along HR in-plane directions should match the distribution along the LR through-plane direction. Therefore, we incorporate the estimation of a slice selection profile as part of learning a generator in a generative adversarial network (GAN). In this way, the slice selection profile can be learned without any external data. Our algorithm was tested using simulations from isotropic MR images, incorporated in a through-plane super-resolution algorithm to demonstrate its benefits, and also used as a tool to measure image resolution. Our code is at https://github.com/shuohan/espreso2 .
PROTEUS: Predictive Explanation of Anomalies

Numerous algorithms have been proposed for detecting anomalies (outliers, novelties) in an unsupervised manner. Unfortunately, it is not trivial, in general, to understand why a given sample (record) is labelled as an anomaly and thus diagnose its root causes. We propose the following reduced-dimensionality, surrogate model approach to explain detector decisions: approximate the detection model with another one that employs only a small subset of features. Subsequently, samples can be visualized in this low-dimensionality space for human understanding. To this end, we develop PROTEUS, an AutoML pipeline to produce the surrogate model, specifically designed for feature selection on imbalanced datasets. The PROTEUS surrogate model can not only explain the training data, but also the out-of-sample (unseen) data. In other words, PROTEUS produces predictive explanations by approximating the decision surface of an unsupervised detector. PROTEUS is designed to return an accurate estimate of out-of-sample predictive performance to serve as a metric of the quality of the approximation. Computational experiments confirm the efficacy of PROTEUS to produce predictive explanations for different families of detectors and to reliably estimate their predictive performance in unseen data. Unlike several ad-hoc feature importance methods, PROTEUS is robust to high-dimensional data.
Æternum: A Decentralized Voting System with Unconditional Privacy

Remote Electronic Voting (REV) systems allow voters to cast their votes in an uncontrolled, distributed environment. At the same time, the REV system must provide ballot privacy and verifiability of the final tally. Research has proposed REV schemes offering ballot privacy based on computational intractability assumptions, but only a few provide Unconditional Privacy (UP). Therefore, this work proposes Æternum, a REV system with a voting scheme providing UP. Æternum does not require trust in a central authority, nor does it assume computational intractability of an underlying mathematical problem to provide UP. To satisfy UP's minimal trust assumptions, Æternum uses a permissioned Distributed Ledger (DL), that forms a decentralized network of permissioned nodes, which serve as a transparent, tamper-proof Decentralized Public Bulletin Board (DPBB).
Strong and Light Baseline Models for Fact-Checking Joint Inference

How to combine several pieces of evidence to verify a claim is an interesting semantic task.Very complex methods have been proposed, combining different evidence vectors using an evidence interaction graph.In this paper, we show that in case of inference based on transformer models, two effective approaches use either (i) a simple application of max pooling over the Transformer evidence vectors; or (ii) computing a weighted sum of the evidence vectors.Our experiments on the FEVER claim verification task show that the methods above achieve the state of the art, constituting strong baseline for much more computationally complex methods.
Optical XOR-XNOR logic circuits using mechanical movable mirrors

Using movement of mirrors mechanically one can easily create the different path for light. In this paper, we have performed three-variable XOR and XNOR logical operations using mechanical movable mirrors. It is utilized in basic computerized expansion circuits which figure the entirety and convey of two bit or three bit numbers. XOR entryways are additionally used to decide the equality of a parity of binary number, i.e., if the all out number of 1's in the number is odd or even. The XNOR rationale gates are utilized in mistake identifying circuits which are to recognize odd parity or even parity bits in advanced information transmission circuits. XNOR entryway is basically utilized in arithmetic and encryption circuits. This simple design can be easily fabricated into chip level.
Moving beyond the tools: Pre-service teachers’ views on what they value in a digital literacy short course

Background: A digital literacies short course, rooted in a pedagogical model of authentic learning and mapped against the TPACK model, was conceptualised and implemented to enhance the existing digital literacies and technological pedagocial content knowledge of student teachers and to promote an awareness of technology-enhanced curriculum practices. Aim: In aiming to inform course improvement, our study interrogates student teachers’ perceptions of the aspects that they valued in the short course. Methods: Guided by social constructivism and situated within a qualitative paradigm, twenty-four 2nd and 3rd year Bachelor of Education (BEd) students who completed the course participated in three semi-structured focus group interviews whereby data was analysed by means of constant comparison analysis. Results: Findings suggest participants found value in authentic tasks and assignments as well as the process of knowledge creation. They did, however, differ in their views of the purpose and aim of such a course. Conclusion: This study contributes to the gap in South African research and the growing South African interest in preparing teachers in adopting technology-enhanced practices and curriculum changes in schools, and argues that standardised theoretical training courses ignoring psycho-socio-cultural factors and individual differences should be reconsidered.
Two Parents, One Child: Dual Transfer for Low-Resource Neural Machine Translation

Neural machine translation suffers when parallel data for training is scarce.Previous works have explored transfer learning to assist training in low-resource scenarios.However, they transfer either from high-resource parallel data, or from monolingual data.In this work, we propose a framework to transfer multiple sources of auxiliary data, including both high-resource parallel data and monolingual data of involved languages.Knowledge in those sources is respectively encoded in a high-resource translation model and pretrained language models, and dually transferred to the low-resource translation model by our approach.Extensive experiments show that our approach yields consistent improvements over strong competitors for multiple translation directions.Furthermore, our approach still exhibits benefit on top of back-translation, making it a useful addition to practitioners' toolbox.
Research of the impact on the ecology of the state of cybersecurity of the critical infrastructure objects

The analysis of the impact on the ecology of the state of cybersecurity of critical infrastructure objects and the factors influencing the state of cybersecurity of the information system of the critical infrastructure object is performed. An explanation is given of why cybersecurity violations in automated process control systems can lead to consequences in the industrial sector and environmental impact. The need to develop effective and adequate proposals and measures for cybersecurity of information systems of the critical infrastructure objects is shown. The classification of assets that are the objects of cyberattacks by attackers and the categories of impact on the critical infrastructure objects are given. Approaches to determining the cyber threat risk factor of the critical infrastructure object and the relevance of threats are presented. The method of assessing the degree of possible damage from the implementation of information security threats is considered. The results of this analysis can be used to develop proposals and measures to avoid the effects of cyberattacks on the critical infrastructure objects. The prospect of further research is to develop a methodology for determining the relationship between specific cyberattacks and possible quantitative damage.
Teaching Efficient Computer Science and Cybersecurity Courses Amidst the COVID-19 Pandemic

It is an understatement to say that teaching efficient Computer Science and Cybersecurity courses amidst the COVID19 pandemic has placed extreme pressure on faculty. In general, this is true for any academic area which has a strong hands-on component tied to the theoretical counterpart. In this paper, we chronicle and provide our approach, from the lens of St. John's University Computer Science Chairperson and the Cybersecurity Program Director, on the suitability of utilizing WebEx HandsOn-Labs tool to close the instructional gap that exists between face-to-face and fully online teaching.
BasisNet: Two-stage Model Synthesis for Efficient Inference

In this work, we present BasisNet which combines recent advancements in efficient neural network architectures, conditional computation, and early termination in a simple new form. Our approach incorporates a lightweight model to preview the input and generate input-dependent combination coefficients, which later controls the synthesis of a more accurate specialist model to make final prediction. The two-stage model synthesis strategy can be applied to any network architectures and both stages are jointly trained. We also show that proper training recipes are critical for increasing generalizability for such high capacity neural networks. On ImageNet classification benchmark, our BasisNet with MobileNets as backbone demonstrated clear advantage on accuracy-efficiency trade-off over several strong baselines. Specifically, BasisNet-MobileNetV3 obtained 80.3% top-1 accuracy with only 290M Multiply-Add operations, halving the computational cost of previous state-of-the-art without sacrificing accuracy. With early termination, the average cost can be further reduced to 198M MAdds while maintaining accuracy of 80.0% on ImageNet.
Automatically Generated and Graded Program Tracing Quizzes with Feedback

Code comprehension is an important skill to develop when learning to program. However, it can be hard to emphasise this to students when they are assessed with problems that overtly challenge their knowledge of syntax or their problem solving skills. This poster presents a prototype for developing automatically graded quizzes on program tracing for basic programs in both C and Python. Questions can be generated for each individual line of code, focusing in higher detail, or about a whole file. Feedback explaining the execution is automatically generated including a diagram showing the code represented as a tree and accompanied with a text explanation.
Landmark Generation in HTN Planning

Landmarks (LMs) are state features that need to be made true or tasks that need to be contained in every solution of a planning problem. They are a valuable source of information in planning and can be exploited in various ways. LMs have been used both in classical and hierarchical planning, but while there is much work in classical planning, the techniques in hierarchical planning are less evolved. We introduce a novel LM generation method for Hierarchical Task Network (HTN) planning and show that it is sound and incomplete. We show that every complete approach is as hard as the co-class of the underlying HTN problem, i.e. coNP-hard for our setting (while our approach is in P). On a widely used benchmark set, our approach finds more than twice the number of landmarks than the approach from the literature. Though our focus is on LM generation, we show that the newly discovered landmarks bear information beneficial for solvers.
The Domestication of Henry David Thoreau

Henry David Thoreau’s essay on civil disobedience is said to have influenced Mohandas K. Gandhi’s nonviolent campaign for Indian rights in South Africa, and subsequent drive for Indian independence, which in turn is thought to have inspired Martin Luther King, Jr.’s direction of the American Civil Rights Movement. In this chapter I reverse that genealogy, showing that King construed elements of satyagraha in ways that served the political agenda of the Civil Rights Movement in the United States, while playing down aspects of satyagraha that did not fit the American case or even clashed with Judeo-Christian themes that were important within the American movement.
The Free Speech Blind Spot

The current system for monitoring and removal of foreign election interference on social media is a free speech blind spot. Social media platforms’ standards for what constitutes impermissible interference are vague, enforcement is seemingly ad hoc and inconsistent, and the role governments play in deciding what speech should be taken down is unclear. This extraordinary opacity—at odds with the ordinary requirements of respect for free speech—has been justified by a militarized discourse that paints such interference as highly effective, and “foreign” speech as uniquely pernicious. But, in fact, evidence of such campaigns’ effectiveness is limited, and the singling out and denigration of “foreign” speech is at odds with the traditional justifications for free expression. Hiding in the blind spot created by this foreign-threat, securitized framing are more pervasive and fundamental questions about online public discourse, such as how to define appropriate norms of online behavior more generally, who should decide them, and how they should be enforced. Without examining and answering these underlying questions, the goal that removing foreign election interference on social media is meant to achieve—re-establishing trust in the online public sphere—will remain unrealized.
A Requirements Engineering Framework for Software Startup Companies

Requirements engineering (RE) for startups has only recently become an area of intense exploration. This paper provides results of a qualitative study with 45 practitioners from four startup companies in four countries. This research was planned and executed using the design science research (DSR) methodology and yielded a descriptive framework that was subjected to a first evaluation in empirical settings. The authors found that practitioners in startups deploy rapid prototyping practices and user feedback but in a different way than the agile methods assume. This research concludes with discussion on validity threats and some implications for practice and research.
Incrementalizing Graph Algorithms

Incremental algorithms are important to dynamic graph analyses, but are hard to write and analyze. Few incremental graph algorithms are in place, and even fewer offer performance guarantees.