Reviewerly/exHarmony huggingface dataset:
Efficient Neural Network Analysis with Sum-of-Infeasibilities

Abstract Inspired by sum-of-infeasibilities methods in convex optimization, we propose a novel procedure for analyzing verification queries on neural networks with piecewise-linear activation functions. Given a convex relaxation which over-approximates the non-convex activation functions, we encode the violations of activation functions as a cost function and optimize it with respect to the convex relaxation. The cost function, referred to as the Sum-of-Infeasibilities (SoI), is designed so that its minimum is zero and achieved only if all the activation functions are satisfied. We propose a stochastic procedure, , to efficiently minimize the SoI. An extension to a canonical case-analysis-based complete search procedure can be achieved by replacing the convex procedure executed at each search state with . Extending the complete search with achieves multiple simultaneous goals: 1) it guides the search towards a counter-example; 2) it enables more informed branching decisions; and 3) it creates additional opportunities for bound derivation. An extensive evaluation across different benchmarks and solvers demonstrates the benefit of the proposed techniques. In particular, we demonstrate that SoI significantly improves the performance of an existing complete search procedure. Moreover, the SoI-based implementation outperforms other state-of-the-art complete verifiers. We also show that our technique can efficiently improve upon the perturbation bound derived by a recent adversarial attack algorithm.
Security risks of porting C programs to webassembly

WebAssembly is a compilation target for cross-platform applications that is increasingly being used. In this paper, we investigate whether one can transparently cross-compile C programs to WebAssembly, and if not, what impact porting can have on their security. We compile 17 802 programs that exhibit common vulnerabilities to 64-bit x86 and to WebAssembly binaries, and we observe that the execution of 4 911 binaries produces different results across these platforms. Through manual inspection, we identify three classes of root causes for such differences: the use of a different standard library implementation, the lack of security measures in WebAssembly, and the different semantics of the execution environments. We describe our observations and discuss the ones that are critical from a security point of view and need most attention from developers. We conclude that compiling an existing C program to WebAssembly for cross-platform distribution may require source code adaptations; otherwise, the security of the WebAssembly application may be at risk.
Modeling of Fuzzy Logic-Based Classification System Using the Gravitational Search Algorithm

Data clustering with the idea obtained from the statistics is an intricate data mining crisis. In this article, a new optimization algorithm depending on the gravity law along with mass connections is introduced called algorithm of gravitational search (GSA) and proposed for clustering law detection and syncing of membership process for fuzzy logic-induced clustering form. In the proposed gravitational search algorithm, the identifier parameters are a compilation of masses which relate with other parameters depending on the Newton’s gravitational law and the laws of acceleration. The gravitational force is directly related to the multiplication of the masses and inversely related to the square of the detachment between the two particles. GSA states that a pile of parameters known as masses projected to get the optimal result using recreation of Newton’s gravity law and law of motion. The working of the projected GSA is verified using ten publicly available benchmark datasets.
Pre-training to Match for Unified Low-shot Relation Extraction

Low-shot relation extraction (RE) aims to recognize novel relations with very few or even no samples, which is critical in real scenario application.Few-shot and zero-shot RE are two representative low-shot RE tasks, which seem to be with similar target but require totally different underlying abilities.In this paper, we propose Multi-Choice Matching Networks to unify low-shot relation extraction.To fill in the gap between zero-shot and few-shot RE, we propose the triplet-paraphrase meta-training, which leverages triplet paraphrase to pre-train zero-shot label matching ability and uses metalearning paradigm to learn few-shot instance summarizing ability.Experimental results on three different low-shot RE tasks show that the proposed method outperforms strong baselines by a large margin, and achieve the best performance on few-shot RE leaderboard 1 .
Feasibility of Simultaneous Application of Fuzzy Neural Network and TOPSIS Integrated Method in Potential Mapping of Lead and Zinc Mineralization in Isfahan-Khomein Metallogeny Zone

Iran is located on a silver, lead, and zinc belt and according to the latest studies holds 11 million tons of lead, zinc, and silver stones which constitute 4 percent of global resources.Considering that mineral materials are explored in an uncertain space, exploration investment risk is an inseparable part of these activities.The important fact is to minimize the effect of this undesired factor in exploration.To achieve this, it is required that exploration activities and withdrawals are performed in a certain framework in which risk minimization is considered.Using mineral potential modelling for determining promising zones which should be taken into consideration in more detailed stages could make achieving the purpose possibly.This work is aimed at applying fuzzy neural network and TOPSIS methods simultaneously in order to explore zinc and lead resources.In this article, geological, telemetry, geophysics, and geochemistry data is integrated using fuzzy-neural network (neuro fuzzy) and using TOPSIS method rating for lead and zinc ore deposit potential mapping in Isfahan-Khomein strip which has been introduced as one of zinc and leads mineral scopes in Iran.This area which is composed of several zinc and lead ore deposits has been considered as the target area.Fuzzy integration results of zinc and lead mineralization witness layers confirm the relatively high potential of lead and zinc mineralization in this region having a northwest-southeast trend and involving more than 90 percent of the known indices and ore deposits of the region.In this research, it was shown that the results of TOPSIS-Neuro-Fuzzy integrated model (a combina-How to cite this paper:
Fairness evaluation in deepfake detection models using metamorphic testing

Fairness of deepfake detectors in the presence of anomalies are not well investigated, especially if those anomalies are more prominent in either male or female subjects. The primary motivation for this work is to evaluate how deepfake detection model behaves under such anomalies. However, due to the black-box nature of deep learning (DL) and artificial intelligence (AI) systems, it is hard to predict the performance of a model when the input data is modified. Crucially, if this defect is not addressed properly, it will adversely affect the fairness of the model and result in discrimination of certain sub-population unintentionally. Therefore, the objective of this work is to adopt metamorphic testing to examine the reliability of the selected deepfake detection model, and how the transformation of input variation places influence on the output. We have chosen MesoInception-4, a state-of-the-art deepfake detection model, as the target model and makeup as the anomalies. Makeups are applied through utilizing the Dlib library to obtain the 68 facial landmarks prior to filling in the RGB values. Metamorphic relations are derived based on the notion that realistic perturbations of the input images, such as makeup, involving eyeliners, eyeshadows, blushes, and lipsticks (which are common cosmetic appearance) applied to male and female images, should not alter the output of the model by a huge margin. Furthermore, we narrow down the scope to focus on revealing potential gender biases in DL and AI systems. Specifically, we are interested to examine whether MesoInception-4 model produces unfair decisions, which should be considered as a consequence of robustness issues. The findings from our work have the potential to pave the way for new research directions in the quality assurance and fairness in DL and AI systems.
Implementing Epic Beaker Laboratory Information System for Diagnostics in Anatomic Pathology

Medicine is expeditiously evolving, and the number of diagnostic opportunities has increased exponentially in the last decade. Electronic medical records (EMRs) have been welcomed in most institutions worldwide following an early period of suspicious behavior. Unfortunately, several cracks dictated the initial approach to hospital systems and leadership incompetency. However, the pathway for a successful decade of EMRs is paved. This narrative review illustrates some principles implementing Epic Beaker software for anatomic pathology in academic medical institutions. Implementing such software improves the diagnostic approach in the division of anatomic pathology because the pathologists can directly access an enormous amount of clinical and radiological information now at their front desk using extremely versatile windows.
Lecturer’s digital literacy ability in the pandemic

This study aimed to determine the adaptation of lecturers and students to using technology as a learning medium. The research method used is descriptive qualitative research. The instrument used is a questionnaire. The population in this study were all Madrasah Ibtidaiyah Teacher Education (PGMI) students in Indonesia, and the sample was more than 40 Madrasah Ibtidaiyah Teacher Education (PGMI) Study Programs at public university (PTN) and private university (PTS) in Indonesia involving 1294 students. The study results found that the effectiveness of the application of distance learning methods in the pandemic era was usually responded to by most students; some even responded less and very poorly. Mastery of lecturers in distance learning media is good; some dominant lecturers use google classroom, zoom, and WhatsApp.&#x0D; &#x0D; Keywords: digital literacy, media technology, online learning, pandemic, lecturers PGMI
Development of the spiritual and creative potential of future teachers in the school-university system

The purpose of this research is to seek the opinions of teacher candidates in order to develop the spiritual and creative potential of future teachers in the ‘school-university’ system. This research was carried out using the qualitative research method. The participant group of the research consisted of 40 pre-service teachers who were studying at the faculty of education in the 2021–2022 academic year and agreed to participate in the study voluntarily. Research data were collected through the semi-structured interview form developed by the researchers. As a result of the research, it has been determined that the spiritual well-being and creative potential of the teacher candidates are at a moderate level. In addition, pre-service teachers suggested that courses should be given in universities in order to develop their spiritual and creative potentials. Some of the teachers offered suggestions to make practices to develop their spiritual and creative potential in the school-university environment and to benefit from experienced teachers in this system. In addition, teacher candidates stated giving importance to personal development and participating in personal development seminars as suggestions for the development of their spiritual and creative potentials. In line with the results of the research, the necessity of carrying out applications for the development of the spiritual well-being and creative potential of teacher candidates in the school-university system has arisen.&#x0D; &#x0D; Keywords: School-university system, spiritual and creative potential, teachers of the future
Optimisation of multi-body fishbot undulatory swimming speed based on SOLEIL and BhT simulators

Robotic fish design is an upcoming and interesting research area with lot of challenging tasks due to the impulsive dynamics of water space.In this paper an evolutionary computational approach is performed to design caudal fins under carangi form and sub-carangi form swimming modes.Size and Shape with SOLEIL and multi-body evolutionary experiments were carried out using Euler-Lagrangian equation-based BhT tool to experiment and validate the hydrodynamic effects of caudal fin by avoiding complex and time consuming CFD simulations to achieve realistic motion.To improve average velocity of robotic fish two approaches have been suggested, one is a hill climbing algorithm to find optimal shape with standard stiffness whereas the second approach considers both shape and stiffness together in a genetic algorithm.Finally simulated fin models are compared against physical models to identify the correlation and performances of both to accurately approximate real world performances in a simulated environment leading to design optimised caudal fins for robotic fish.
Something Phish-y is Going On Here: A Teaching Case on Business Email Compromise

SUMMARY This case utilizes a real-world example of a U.S. public company that fell victim to a Business Email Compromise (BEC) scheme in which an employee inadvertently wired millions of dollars to fraudulent accounts based upon email instructions purportedly sent by a company executive and external legal counsel. This is a timely issue to examine given its rising prevalence and magnitude in the corporate world. The case allows students to examine a topic (phishing techniques and email scams) that they are likely to be familiar with on a conceptual level, through the lens of internal controls and external auditing. Examining the case information, SEC filings, and auditing guidance, students will gain an understanding of internal control issues related to BEC and critically think of ways to remediate or implement controls to reduce cybersecurity risk, as well as consider the external auditor's growing responsibilities related to technology and its associated risks.
Eye Gaze Controlled Virtual Keyboard

Human Computer Interaction is an emerging technology which refers to a vast number of algorithms and different types of techniques to enhance the interaction process. Eye gaze technology is one of the most significant techniques of modern science and can be used in many areas like security, typing, information tracking etc. The need of the system for physically disable people motivated many researchers to develop systems which can be used only using the eye gaze and blinking. In this paper, we are going to represent the development of a virtual keyboard which work by detecting eye gaze and eye blinking. It involves building a system that capture video directly from PC camera and detects human face, eyes. To detect face accurately we will follow a simple rule as the eyes and lips are always in the sample place as image, which will make the eye detection process much easier. To do this we are going to use an approach that involves the 68 points of face which is specific and must exist in every face as- the eye area, top of the chin, eyebrow, nose, outside edge of face etc. It also detect eye gaze as left, right to select keyboard portion and eye blinking to select the desired key from the virtual keyboard on the board. The goal of this system is to type without using finger or hands. Such types of application are really important and blessings for those people who completely lost the control of their limbs. The methodology is described including flow-charts for each stage of the system and then implementation results has described.
A Framework for Dark Web Threat Intelligence Analysis

This article describes how the Dark Web is usually considered the dark side of the World Wide Web. Cyber criminals usually use specialized tools, e.g. TOR, to access the hidden services inside the Dark Web anonymously. Law enforcement officers have difficulty tracing the identity of these cyber criminals using traditional network investigation techniques that are based on IP addresses. The information available in the Dark Web, which includes BitCoin wallets, email addresses, hyperlinks, images and user behavior profiles, can be used for further analysis, such as a correlation analysis. Present within this artcile is a threat intelligence analysis framework to help analyze the crimes and criminals in the Dark Web and the framework is realized by the implementation of the Dark Web Threat Intelligence Analysis (DWTIA) Platform.
On the Improvement of Multiple Circles Detection from Images Using Hough Transform

The automatic detection of lines and curves from color images is a very important task in many applications, such as object recognition and scene reconstruction. Although there are closed formulation for curve fitting to a set of points, if the point set describes more than one instance of the object, as two circles for example, there is no closed formulation for obtaining the individual set of parameters without a priori information of which points belong to each object. However, it is usual the presence of multiple instances of objects such as lines and circles on an image. The well known Hough Transform is an efficient tool for recovering multiple objects from images using a voting process where the usual presence of false positives is an issue. In our work, we present an improvement on the voting process to detect multiple circles using Hough Transform in order to avoid false positives. Our experiments show that our voting process leads to a more robust detection, reducing the number of false positive and providing a more accurate detection even with large number of circles.
Entanglement polygon inequality in qudit systems

Entanglement is one of important resources for quantum communication tasks. Most of results are focused on qubit entanglement. Our goal in this work is to characterize the multipartite high-dimensional entanglement. We firstly derive an entanglement polygon inequality for the $q$-concurrence, which manifests the relationship among all the "one-to-group" marginal entanglements in any multipartite qudit system. This implies lower and upper bounds for the marginal entanglement of any three-qudit system. We further extend to general entanglement distribution inequalities for high-dimensional entanglement in terms of the unified-$(r, s)$ entropy entanglement including Tsallis entropy, R\'{e}nyi entropy, and von Neumann entropy entanglement as special cases. These results provide new insights into characterizing bipartite high-dimensional entanglement in quantum information processing.
Obstacle Avoidance and Profile Ground Flight Test and Analysis for Plant Protection UAV

In recent years, with the further development of agricultural aviation technology, the plant protection UAV has been widely used, especially in some agricultural environments with limited operating conditions due to its advantages of high efficiency, environmental protection and safety guarantee. A plant protection UAV generally flies at low altitude during operation. However, the low altitude operation environment, such as farmland and mountainous areas, is relatively complex, and is faced with many types of obstacles, proposing higher requirements for obstacle avoidance and the profiling system of a plant protection UAV. In order to test the obstacle avoidance and profiling performance of the commercialized plant protection UAV at this stage and explore the performance boundary of obstacle avoidance and profiling of the UAV, EAVISION E-A2021 and XAG P80, the flagship models of the plant protection UAV manufacturer on the market, were hereby selected as the experimental test objects in the paper. Firstly, the obstacle avoidance and profiling test scheme of plant protection UAVs is designed; then, the above two UAVs are adopted for corresponding tests, and the test data are discussed based on the analysis of software and hardware technology; finally, the practical application status of different obstacle avoidance and profiling technologies of plant protection UAVs is clarified, and the shortcomings of obstacle avoidance and profiling technology of plant protection UAVs on the market are summarized, providing a reliable reference for the future development of plant protection UAVs.
A Meta Reinforcement Learning Approach for Predictive Autoscaling in the Cloud

Predictive autoscaling (autoscaling with workload forecasting) is an important mechanism that supports autonomous adjustment of computing resources in accordance with fluctuating workload demands in the Cloud. In recent works, Reinforcement Learning (RL) has been introduced as a promising approach to learn the resource management policies to guide the scaling actions under the dynamic and uncertain cloud environment. However, RL methods face the following challenges in steering predictive autoscaling, such as lack of accuracy in decision-making, inefficient sampling and significant variability in workload patterns that may cause policies to fail at test time. To this end, we propose an end-to-end predictive meta model-based RL algorithm, aiming to optimally allocate resource to maintain a stable CPU utilization level, which incorporates a specially-designed deep periodic workload prediction model as the input and embeds the Neural Process [11, 16] to guide the learning of the optimal scaling actions over numerous application services in the Cloud. Our algorithm not only ensures the predictability and accuracy of the scaling strategy, but also enables the scaling decisions to adapt to the changing workloads with high sample efficiency. Our method has achieved significant performance improvement compared to the existing algorithms and has been deployed online at Alipay, supporting the autoscaling of applications for the world-leading payment platform.
One Reference Is Not Enough: Diverse Distillation with Reference Selection for Non-Autoregressive Translation

Non-autoregressive neural machine translation (NAT) suffers from the multi-modality problem: the source sentence may have multiple correct translations, but the loss function is calculated only according to the reference sentence.Sequence-level knowledge distillation makes the target more deterministic by replacing the target with the output from an autoregressive model.However, the multi-modality problem in the distilled dataset is still nonnegligible.Furthermore, learning from a specific teacher limits the upper bound of the model capability, restricting the potential of NAT models.In this paper, we argue that one reference is not enough and propose diverse distillation with reference selection (DDRS) for NAT.Specifically, we first propose a method called SeedDiv for diverse machine translation, which enables us to generate a dataset containing multiple high-quality reference translations for each source sentence.During the training, we compare the NAT output with all references and select the one that best fits the NAT output to train the model.Experiments on widely-used machine translation benchmarks demonstrate the effectiveness of DDRS, which achieves 29.82 BLEU with only one decoding pass on WMT14 En-De, improving the state-of-the-art performance for NAT by over 1 BLEU.