Reviewerly/exHarmony huggingface dataset:
A dependently typed calculus with pattern matching and erasure inference

Some parts of dependently typed programs constitute evidence of their type-correctness and, once checked, are unnecessary for execution. These parts can easily become asymptotically larger than the remaining runtime-useful computation, which can cause normally linear-time programs run in exponential time, or worse. We should not make programs run slower by just describing them more precisely. Current dependently typed systems do not erase such computation satisfactorily. By modelling erasure indirectly through type universes or irrelevance, they impose the limitations of these means to erasure. Some useless computation then cannot be erased and idiomatic programs remain asymptotically sub-optimal. In this paper, we explain why we need erasure, that it is different from other concepts like irrelevance, and propose a dependently typed calculus with pattern matching with erasure annotations to model it. We show that erasure in well-typed programs is sound in that it commutes with reduction. Assuming the Church-Rosser property, erasure furthermore preserves convertibility in general. We also give an erasure inference algorithm for erasure-unannotated or partially annotated programs and prove it sound, complete, and optimal with respect to the typing rules of the calculus. Finally, we show that this erasure method is effective in that it can not only recover the expected asymptotic complexity in compiled programs at run time, but it can also shorten compilation times.
Martial Arts Routine Difficulty Action Technology VR Image Target Real-Time Extraction Simulation

With the gradual increase in the difficulty of competitive martial arts, athletes must complete fine, stable, high-quality and difficult movements in order to achieve excellent performance. The real-time extraction of martial arts movements is a topic that many martial arts enthusiasts care about. This study mainly discusses the real-time extraction and simulation of VR image target in the difficult movement technology of martial arts routine. Considering the complex characteristics of martial arts movements, this article will analyze the preprocessing content of existing images. This includes image enhancement and image filtering, and uses median filtering methods to enhance the characteristics of the collected images. In this way, the visual effect of the original image can be improved, and the processed image will contribute to the subsequent segmentation. A new image segmentation method is proposed for the color model of the image. According to the H component of the HSV model representing the characteristics of chromaticity, the color image is transformed into the HSV model, and the H component is extracted. The histogram concept applies to H components. Based on the histogram of the H component, the segmentation threshold is determined, and the cropping target in the image is detected. Because the model space is very sensitive to color, VR technology is used to automatically determine the segmentation target. Combined with the above division methods, the automatic extraction of objects in the image is completed. The method of using VR technology for image extraction processing has high precision, and the error value is 3.92%<; 5%. The research results show that the method has good segmentation results, and is suitable for image segmentation under complex background and automatic image extraction under complex background.
Cat got your tongue? Free speech, democracy and Australia’s ‘ag-gag’ laws

In recent years Australian governments have proposed, and enacted, 'ag-gag' laws which extend the criminalisation of peaceful, non-violent activities such as trespass to deter animal protection advocates from obtaining information about animal welfare, information that is not obtainable in other ways and that contributes to public deliberation around an important policy issue. This article asks two questions. First, if a speaker engages in non-violent, peaceful, but illegal activities, such as trespass in order to obtain information that is important to democratic deliberation, can this be justified from the perspective of free speech theory? Answering this question in the affirmative, we then analyse the contours of proposed, and new provisions in Australian law designed to extend the criminalisation of such activities. We conclude that since the impact on freedom of speech of such provisions is excessive, governments should be mindful of their free speech obligations when considering, and enacting, such laws.
Hardware Penetration Testing Knocks Your SoCs Off

This article highlights how software simulations can, on the one hand, help uncover bugs, but on the other hand, help exploit them. As a case study, this article details how to exploit bugs in cryptographic hardware.
Difficulty Prediction for Proof-of-Work Based Blockchains

The fast growing of blockchain technologies has inspired an exceptionally broad set of new social applications due to its special features. The most two popular blockchains (Bitcoin and Ethereum) are Proof-of-Work (PoW) based. However, the randomness of the block produce time (BPT) greatly affects the performance, user experience, and even the security of a blockchain. Mining difficulty controls the stability of the BPT. However, the current difficulty control algorithm in Ethereum has two problems. First, the difficulty control algorithm cannot track the network hashrate fast enough. Thus, the BPT has a large variance and cannot converge to the target BPT on time. The second problem is that the target BPT cannot be flexibly set to any given value by adjusting the difficulty. To mitigate these two problems, we propose a linear predictor based difficulty control algorithm. Based on the relationship among the hashrate, the difficulty, and the BPT, the prediction based difficulty control algorithm has much better stability and flexibility on BPT. It is also shown that the proposed prediction based algorithm outperforms the existing Ethereum one in the real situation.
Social and Human Factor Classification of Influence in Productivity in Software Development Teams

Software development organizations continuously seek to increase the productivity of their teams. However, productivity in the development of a software product is characterized by a high dependence on social and human factors. Therefore, studying these factors is essential to meet the performance needs of software development organizations. The above supports the motivation to consider the social and human factors that may influence the productivity of software development teams. This research has been conducted to classify such factors associated with the productivity of software development teams. For this purpose, a methodology was designed based on systematic literature review processes and evaluation processes with a psychology expert. The classification of social and human factors and their respective definitions are the partial results of this investigation. The proposed classification is an essential input for the establishment of improvement actions.
Design of Effective Robotic Gaze-Based Social Cueing for Users in Task-Oriented Situations: How to Overcome In-Attentional Blindness?

Robotic eye-gaze-based cueing has been studied and proved to be effective, in controlled environments, in achieving social functions as humans gaze. However, its dynamic adaptability in various real interactions has not been explored in-depth. This paper addresses a case where a simplistic robotic gaze fails to achieve effective social cueing in human–robot communication, primarily due to in-attentional blindness (IB), and presents a method that enables the robot to deliver gaze-based social signals adequately. To understand the implications of IB and figure out ways to overcome any limitations from IB, which frequently arise in task-oriented situations, we designed a set of 1-on-1 communication experiments consisting of a robotic tutor and human learners participating in multiple-choice quiz sessions (task-oriented situation). Here, multimedia contents were utilized alongside the robot as visual stimuli competing for the human’s attention. We hypothesized that quiz scores would increase when humans successfully recognize the robot’s gaze-based cue signals hinting answers. We compared the impacts of two different cueing methods: oblique cueing (OC), where cues were straightforwardly provided regardless of whether participants were potentially experiencing IB or not, and leading cueing (LC), where cueing procedures were led through achieving eye contact and securing the participants’ attention before signaling the cue. By comparing participants’ test scores achieved by the control group with no cueing (NC) and two experimental groups of OC and LC, respectively, we found that there was a significant increase in test scores only when the LC method was utilized. This experiment illustrates the importance of proactively guiding a user’s attention through sophisticated interaction design in effectively attaining a user’s attention and successfully delivering cues. In future studies, we aim to evaluate different methods by which a social robot can intentionally shift a human’s attention, such as incorporating stimuli from various multi-modal human communication channels.
Comparative Analysis of DoS and DDoS Attacks in Internet of Things Environment

With the high rate of accepting Internet of Things (IoT) from individual nowadays. The IoT devices comprises television sets (TV), voice controller, smart lock, smart light, speakers, nest smoke alarm, air conditioning, ring doorbell and smart plug etc. Thus, the impact of IoT provides many industries with new ways of handling business operations such as procurement, manufacturing, distribution of goods and service, etc. With the aid of intelligent sensors, actuators, etc., the industries have revolutionized to an era known as Industry 4.0 (IR4.0), where business operations can be done and potentially optimized, by controlling interconnecting "things" via data communication channels. Similar to other electronic communication platforms in IoT ensuring security is paramount important especially in detecting the menace of the security challenges in the IoT environment. This paper aims to perform a comparative analysis between Denial of service (DoS) and Distributed denial of service (DDoS) attacks in the IoT environment. In essence, the comparative analysis reveals that the DDoS attack is the most significant attack to address in the IoT environment with about 68% Gbps requests compared to the DoS attack with 58% Gbps request.
A modified graph cuts image segmentation algorithm with adaptive shape constraints and its application to computed tomography images

The low contrast, blurred edges, and irregular contours of organs, such as the liver, kidney, and spleen, in abdominal computed tomography (CT) images hamper the machine-aided extraction of specific image regions pertaining to individual target organs. This problem is addressed herein by proposing an improved graph cuts segmentation algorithm with adaptive shape constraints. First, the original image is segmented based on multi-atlas registration. Then, the segmentation result is employed as a shape prior to constrain the shape of the final graph cuts segmentation result by adding a shape constraint energy term to the graph cuts energy function. The levels of shape constraint applied to the graph cuts energy function are adjusted according to differences in the probability of adjacent pixels residing within the target region, which are obtained in the initial segmentation process. Finally, the images of individual target organs in abdominal CT images are extracted by minimizing the energy function using the maximum-flow minimum-cut algorithm. Experimental results demonstrate that the proposed method can segment target organs well and can effectively reduce the occurrences of over-segmentation and under-segmentation caused by the conventional graph cuts algorithm.
Comparison of Online Learning Versus Face to Face Learning in English Grammar Learning

Online learning has shifted from being merely marginal trend to become popular in English language learning. Where, the number of higher education institutions has dramatically increased to offer English language learning using the online learning. Online learning has led to extremely changes in education generally and language learning in particular. At the same time, it is demanded to improve the learning outcomes of English by using technology effectively. The lack of attention to grammar was causing falling standards in language. So, the studies no longer address whether you need to learn English grammar or not, but how to choose a good way to learn English grammar to produce better learning outcomes. Consequently, research is necessary to examine whether the advantages of online learning apply to learning English grammar, especially in improving satisfactory learning outcomes. Therefore this study will conduct research on the effects of online learning on learning outcomes of English grammar. This study concluded that the learning outcomes of students who received English grammar online learning showed very good learning outcomes. Furthermore, students who are treated with online learning in English grammar learning provides better cognitive outcomes than students who attend face to face learning.
Evaluation of glass evidence at activity level: A new distribution for the background population

For evidence evaluation of the physicochemical properties of glass at activity level a well-known formula introduced by Evett & Buckleton [1,2] is commonly used. Parameters in this formula are, amongst others, the probability in a background population to find on somebody's clothing the observed number of glass sources and the probability in a background population to find on somebody's clothing a group of fragments with the same size as the observed matching group. Recently, for efficiency reasons, the Netherlands Forensic Institute changed its methodology to measure not all the glass fragments but a subset of glass fragments found on clothing. Due to the measurement of subsets, it is difficult to get accurate estimates for these parameters in this formula. We offer a solution to this problem. The heart of the solution consists of relaxing the assumption of conditional independence of group sizes of background fragments, and modelling the probability of an allocation of background fragments into groups given a total number of background fragments by a two-parameter Chinese restaurant process (CRP) [3]. Under the assumption of random sampling of fragments to be measured from recovered fragments in the laboratory, parameter values for the Chinese restaurant process may be estimated from a relatively small dataset of glass in other relevant cases. We demonstrate this for a dataset of glass fragments collected from upper garments in casework, show model fit and provide a prototypical calculation of an LR at activity level accompanied with a parameter sensitivity analysis for reasonable ranges of the CRP parameter values. Considering that other laboratories may want to measure subsets as well, we believe this is an important alternative approach to the evaluation of numerical LRs for glass analyses at activity level.
Estimating Densities with Non-Linear Support by Using Fisher–Gaussian Kernels

Current tools for multivariate density estimation struggle when the density is concentrated near a nonlinear subspace or manifold. Most approaches require choice of a kernel, with the multivariate Gaussian by far the most commonly used. Although heavy-tailed and skewed extensions have been proposed, such kernels cannot capture curvature in the support of the data. This leads to poor performance unless the sample size is very large relative to the dimension of the data. This article proposes a novel generalization of the Gaussian distribution, which includes an additional curvature parameter. We refer to the proposed class as Fisher-Gaussian (FG) kernels, since they arise by sampling from a von Mises-Fisher density on the sphere and adding Gaussian noise. The FG density has an analytic form, and is amenable to straightforward implementation within Bayesian mixture models using Markov chain Monte Carlo. We provide theory on large support, and illustrate gains relative to competitors in simulated and real data applications.
Prediction of N-Gram Language Models Using Sentiment Analysis on E-Learning Reviews

Sentiment Analysis describes the branch of the study of Natural Language Processing that seeks to identify and learn insights from the text or sentences considered to be reviews or opinions about a product or service. These opinions are collected from any platforms like social media, online surveys, online product selling applications, and blogs, etc. The process of sentiment analysis roughly starts by collecting the reviews or opinions, pre-processing of text or sentences, classifying the text to find the polarity whether it is found to be as positive, negative, or neutral. The main objective of this research work is to apply sentiment analysis to the e-learning review dataset. To attain the above-said objective, we predict which n-gram model best suits in feature extraction with machine learning algorithms.
RUMBA-Mouse: Rapid User Mouse-Behavior Authentication Using a CNN-RNN Approach

Mouse behavior analysis has become an increasingly attractive area to biometric researchers in recent years. Many mouse behavior based user authentication schemes have been proposed in the past decades. However, most of them rely on statistical analysis or heuristic feature extraction of the mouse behavior. In this paper, we present a CNN-RNN combined neural network model for mouse behavior based user authentication, which takes raw sequential mouse data as input rather than relies on heuristic feature extraction. Additionally, we integrate the model with a practical framework of static user authentication and evaluate it on a real dataset. The results show our approach yields a 3.16% EER and a 99.39% AUC, with a short authentication delay of 6.11 seconds on average, which demonstrates the effectiveness and practicality of applying deep learning techniques for static mouse behavior based user authentication. Furthermore, by modifying the activation maximization method, we study and visualize the features learned by different layers of our neural network.
Darknet and Bitcoin De-anonymization: Emerging Development

Darknet websites, the warm beds for money laundry, child pornography, and illicit drug trafficking, are built on hidden services and anonymous communication protocols. Cryptocurrencies, such as Bitcoin, is the major payment method used on Darknet. In this paper, we summarize and introduce the latest development on de-anonymization techniques used to reveal the hidden information that are helpful for crime investigation, which is a key step for the future research work.
Personality Prediction System Based on Signatures Using Machine Learning

Abstract Many methods are used to assess a person’s character and personality. Some are through the face, body movements, body language, handwriting and signatures. Assessing a person’s character and personality by looking at the type of handwriting and signature can be learned with the science of graphology. Detecting a person’s personality system based on a signature pattern automatically is still difficult. This study aims to build a system of predicting personality based on signature patterns using machine learning. A person’s personality based on a signature has many features. In this study the analysed features consist of four features, namely curve start, end streak, middle stroke, and underline. The steps taken in developing a prediction system are model training and model testing. The method used to extract features is Principle Component Analysis (PCA) and the method for classifying is Support Vector Machine (SVM). Based on the test results using confusion matrix produces an accuracy value of 71%. It can be concluded that machine learning can be implemented to predict personality based on signatures with good accuracy.
A New Hybrid Deep Learning Model based-Recommender System using Artificial Neural Network and Hidden Markov Model

Recommendation systems based on Deep Learning have recently led to significant progress in different application domains. Most models are influenced by hyperparameter optimization or tuning, the stability of training, and architecture configuration. Hence, in the present study, we introduced a Deep Learning model, which is named RHMM, for recommender systems, by using the Hidden Markov Model and Artificial Neural Networks. The model selection technique is applied to optimize the bias-variance tradeoff of the expected prediction. The model aggregation technique is used to improve the robustness and accuracy of training. Experiment results showed that our Deep Leaning model led to significant improvement over benchmarking models.
First Time Miss : Low Overhead Mitigation for Shared Memory Cache Side Channels

Cache hit or miss is an important source of information leakage in cache side channel attacks. An attacker observes a much faster cache access time if the cache line has previously been filled in by the victim, and a much slower memory access time if the victim has not accessed this cache line, thus revealing to the attacker whether the victim has accessed the cache line or not.
PALOR: Poisoning Attacks Against Logistic Regression

With Google, Amazon, Microsoft, and other entities establishing “Machine Learning as a Service” (MLaaS), ensuring the security of the resulting machine learning models has become an increasingly important topic. The security community has demonstrated that MLaaS contains many potential security risks, with new risks constantly being discovered. In this paper, we focus on one of these security risks – data poisoning attacks. Specifically, we analyze how attackers interfere with the results of logistic regression by poisoning the training datasets. To this end, we analyze and propose an alternative formulation for the optimization of poisoning training points capable of poisoning the logistic regression classifier, a model that has previously not been susceptible to poisoning attacks. We evaluate the performance of our proposed attack algorithm on the three real-world datasets of wine cultivars, adult census information, and breast cancer diagnostics. The success of our proposed formulation is evident in decreasing testing accuracy of logistic regression models exposed to an increasing number of poisoned training samples.
Generative adversarial framework‐based one‐day‐ahead forecasting method of photovoltaic power output

Accurately forecasting the energy yields of photovoltaic (PV) systems is of great benefit to the power optimization of smart grid. However, most existing data-driven paradigm-based forecasting methods often suffer from the diversity of power fluctuation patterns, which results in poor performance while dealing with distributed PV systems that are more sensitive to external factors. To overcome this problem, this study first formulates an input data scheme based on historical PV power data, in which a novel decomposition algorithm is proposed to separate the structural information and fluctuation components in the power data. Then, a forecasting model based on the generative adversarial framework is developed, whose generative module can obtain the ability to learn the desired feature distribution with the discriminator, rather than selecting features via a certain trained preference. Moreover, the proposed method takes into account some meteorological elements to further characterise the power fluctuations. Extensive self-evaluation and comparison with several state-of-the-art methods highlight the superiority of the proposed method.