Reviewerly/exHarmony huggingface dataset:
Learning cognitive embedding using signed knowledge interaction graph

Measuring learner cognition based on their problem-solving performance is a joint discipline of cognitive psychology and machine learning. In the case of learner problem-solving, the interaction between learner and knowledge forms a typical type of signed interaction graph. Interaction graphs are a widely used and effective solution to model the relationships between interacting entities. However, most of previous interaction graph methods are inclined to the observed interactions as positive links but they often fail to consider unobserved and negative links, which leads to an insufficiency in capturing the complete cognition/mis-cognition proximity information. To address this limitation, we propose a knowledge graph representation learning method that is based on signed knowledge interaction network (SKIN). We explicitly model the correct/incorrect cognitive performance as the positively +/negatively − signed links in the graph. The model simultaneously measures the nodes’ local and global proximity , and then preserves them in the learned knowledge embedding. We architect a pairwise neural network that is based on a tri-sampling strategy and a sign-driven distance measuring objective function. The network generates knowledge representations by maximizing the knowledge distance between oppositely-signed pairs and minimizing the distance between identically-signed pairs. Our experimental results show the learned knowledge embedding demonstrates a desired Euclidean property and can be visualized with clear classification boundaries. We also show it can power downstream tasks such as learner-performance-prediction. The learned embeddings generate promising prediction scores on this task when compared to several methods in network sign prediction and learner-performance-prediction.
Manifold Matching via Deep Metric Learning for Generative Modeling

We propose a manifold matching approach to generative models which includes a distribution generator (or data generator) and a metric generator. In our framework, we view the real data set as some manifold embedded in a high-dimensional Euclidean space. The distribution generator aims at generating samples that follow some distribution condensed around the real data manifold. It is achieved by matching two sets of points using their geometric shape descriptors, such as centroid and p-diameter, with learned distance metric; the metric generator utilizes both real data and generated samples to learn a distance metric which is close to some intrinsic geodesic distance on the real data manifold. The produced distance metric is further used for manifold matching. The two networks learn simultaneously during the training process. We apply the approach on both unsupervised and supervised learning tasks: in unconditional image generation task, the proposed method obtains competitive results compared with existing generative models; in super-resolution task, we incorporate the framework in perception-based models and improve visual qualities by producing samples with more natural textures. Experiments and analysis demonstrate the feasibility and effectiveness of the proposed framework.
Research on Collision Avoidance Between UAV Flocks Using Behavior-Based Approach

Safety is a prerequisite for the realization of UAV flocks, this article analyzes the collision problem between flocks of quadrotor UAVs. Based on the of "Boid" flock model, we use behavior rules to model flock aggregation, separation, velocity consistency, and collision avoidance behavior. We propose the collision avoidance behavior coordination mechanism between flocks. The PF (Potential Force) which is improved artificial potential field method and the DVO (Detect Velocity Obstacles) method are innovatively introduced into the collision avoidance behavior. We analyzed and compared the application of the two algorithms in the simulation experiment. The simulation results show that both algorithms can achieve collision avoidance when the flock meets together. In the results, the DVO algorithm has shorter trajectory and faster speed, but the calculations are more complicated. Then, we analyze the influence the force threshold parameters, the number of flocks and the safety radius parameters in the DVO function. Finally, the operation time of the two algorithms is analyzed and compared. Based on the analysis, the optimization directions of flight safety and computation amount are summarized as follows: 1. Create more collision avoidance space inside or outside the flock; 2. Reasonably set the number of interactions with obstacles and the size of the algorithm's search area.
A novel class restriction loss for unsupervised domain adaptation

Domain adaptation has demonstrated promising performance to learn models in new environments. It aims to transfer the knowledge learned in labeled source domain to a new target domain, avoiding expensive efforts of label annotation. Many popular methods attempt to assign pseudo labels to unlabeled target samples and train the models as if they are true labels. However, the pseudo labels contain inevitable noises while their training strategies would enlarge and accumulate the errors, so that the model easily overfits to noisy data. Instead of focusing on assigning correct pseudo labels, in this paper, an unsupervised domain adaptation approach is proposed by preventing the model from overfitting false samples. Two different types of restrictions are considered on the training data to leverage the intra-class centralization and inter-class normalization. Each training sample and individual category are equally treated, where each point has the same opportunity to contribute to the model learning. A novel class restriction loss is proposed, which is further integrated into a teacher-student architecture, where the outputs from the teacher are treated as the pseudo labels for the student. The whole framework is trained in an end-to-end manner. Extensive experiments conducted on several image classification benchmarks demonstrate that the proposed method can significantly improve the performance, which outperforms the state-of-the-art methods.
Automated refactoring of legacy JavaScript code to ES6 modules

The JavaScript language did not specify, until ECMAScript 6 (ES6), native features for streamlining encapsulation and modularity. Developer community filled the gap with a proliferation of design patterns and module formats, with impact on code reusability, portability and complexity of build configurations. This work studies the automated refactoring of legacy ES5 code to ES6 modules with fine-grained reuse of module contents through the named import/export language constructs. The focus is on reducing the coupling of refactored modules through destructuring exported module objects to fine-grained module features and enhancing module dependencies by leveraging the ES6 syntax. We employ static analysis to construct a model of a JavaScript project, the Module Dependence Graph (MDG), that represents modules and their dependencies. On the basis of MDG we specify the refactoring procedure for module migration to ES6. A prototype implementation has been empirically evaluated on 19 open source projects. Results highlight the relevance of the refactoring with a developer intent for fine-grained reuse. The analysis of refactored code shows an increase in the number of reusable elements per project and reduction in the coupling of refactored modules. The soundness of the refactoring is empirically validated through code inspection and execution of projects' test suites.
CAP: Communication-Aware Automated Parallelization for Deep Learning Inference on CMP Architectures

Real-time inference of deep learning models on embedded and energy-efficient devices becomes increasingly desirable with the rapid growth of artificial intelligence on edge. Specifically, to achieve superb energy-efficiency and scalability, efficient parallelization of single-pass deep neural network (DNN) inference on chip multiprocessor (CMP) architectures is urgently required by many time-sensitive applications. However, as the number of processing cores scales up and the performance of cores has grown much fast, the on-chip inter-core data movement is prone to be a performance bottleneck for computation. To remedy this problem and further improve the performance of network inference, in this work, we introduce a communication-aware DNN parallelization technique called CAP, by exploiting the elasticity and noise-tolerance of deep learning algorithms on CMP. Moreover, in the hope that the conducted studies can provide new design values for real-time neural network inference on embedded chips, we also have evaluated the proposed approach on both multi-core Neural Network Accelerators (NNA) chips and general-purpose chip-multiprocessors. Our experimental results show that the proposed CAP can achieve 1.12×-1.65× system speedups and 1.14×-2.70× energy efficiency for different neural networks while maintaining the inference accuracy, compared to baseline approaches.
Fake reviews on online platforms: perspectives from the US, UK and EU legislations

User-generated online reviews have become essential sources of information for potential consumers. Given the increasing importance of online reviews on consumer purchases, some traders try to publish or promote fake reviews on online platforms to improve the reputation of their goods or services, or to damage the reputation of their competitors. This paper aims (1) to analyse whether and how fake reviews violate the main legal and ethical principles of advertising, according to the legislations of the US, UK and EU as well as the International Advertising and Marketing Communications Code; (2) to assess how the EU, the UK and the US are dealing with them, and find out which approaches and actions are working best; and (3) to offer normative recommendations for effective prosecution of fake reviews. The paper uses a combination of theoretical and sociological approaches (section 1), black letter analysis of law (sections 2 and 3) and a comparative approach (sections 2 and 3). Among others, the article observes (1) that fake reviews are a form of illegal advertising and (2) that the European regulatory instruments provide more protection against fake reviews than the US and argues (3) that an effective fight against fake reviews requires strong administrative bodies, endowed with sufficient resources.
Clones in deep learning code: what, where, and why?

Deep Learning applications are becoming increasingly popular worldwide. Developers of deep learning systems like in every other context of software development strive to write more efficient code in terms of performance, complexity, and maintenance. The continuous evolution of deep learning systems imposing tighter development timelines and their increasing complexity may result in bad design decisions by the developers. Besides, due to the use of common frameworks and repetitive implementation of similar tasks, deep learning developers are likely to use the copy-paste practice leading to clones in deep learning code. Code clone is considered to be a bad software development practice since developers can inadvertently fail to properly propagate changes to all clones fragments during a maintenance activity. However, to the best of our knowledge, no study has investigated code cloning practices in deep learning development. The majority of research on deep learning systems mostly focusing on improving the dependability of the models. Given the negative impacts of clones on software quality reported in the studies on traditional systems and the inherent complexity of maintaining deep learning systems (e.g., bug fixing), it is very important to understand the characteristics and potential impacts of code clones on deep learning systems. This paper examines the frequency, distribution, and impacts of code clones and the code cloning practices in deep learning systems. To accomplish this, we use the NiCad clone detection tool to detect clones from 59 Python, 14 C#, and 6 Java based deep learning systems and an equal number of traditional software systems. We then analyze the comparative frequency and distribution of code clones in deep learning systems and the traditional ones. Further, we study the distribution of the detected code clones by applying a location based taxonomy. In addition, we study the correlation between bugs and code clones to assess the impacts of clones on the quality of the studied systems. Finally, we introduce a code clone taxonomy related to deep learning programs based on 6 DL software systems (from 59 DL systems) and identify the deep learning system development phases in which cloning has the highest risk of faults. Our results show that code cloning is a frequent practice in deep learning systems and that deep learning developers often clone code from files contain in distant repositories in the system. In addition, we found that code cloning occurs more frequently during DL model construction, model training, and data pre-processing. And that hyperparameters setting is the phase of deep learning model construction during which cloning is the riskiest, since it often leads to faults.
An Intelligent Prototype for Requirements Validation Process Using Machine Learning Algorithms

with the advancement of technology, the world is growing and developing rapidly, and the demand for software has become greater and more, and one of the most important steps in building any program is the requirements of the programs, and checking the requirements when applying them manually, requires great effort, time, cost and accuracy. Automated requirements were not sufficient. In this study, we will propose a technique for automatically checking software requirements by using machine learning to represent textual data from software requirements specifications, an overview of prototyping-based models in machine learning is presented in this paper. The framework, notes, i.e. data, are stored in terms of typical reps. the system can be used in conjunction with an appropriate similarity scale in the context of the unsupervised analysis of high-dimensional complex datasets. Supervised learning is represented in prototyping systems in terms of vector quantization learning. In most cases, The familiar Euclidean distance serves as a measure of difference. We present framework extensions to non-standard measures give an introduction to use adaptive distances in related learning, Briefly, the prototype is less costly from any other technical for validating the requirement because it make in the first and the prototype too can reuse and customer involvement
Application of Structural Entropy and Spatial Filling Factor in Colonoscopy Image Classification

For finding colorectal polyps the standard method relies on the techniques and devices of colonoscopy and the medical expertise of the gastroenterologist. In case of images acquired through colonoscopes the automatic segmentation of the polyps from their environment (i.e., from the bowel wall) is an essential task within computer aided diagnosis system development. As the number of the publicly available polyp images in various databases is still rather limited, it is important to develop metaheuristic methods, such as fuzzy inference methods, along with the deep learning algorithms to improve and validate detection and classification techniques. In the present manuscript firstly a fuzzy rule set is generated and validated. The former process is based on a statistical approach and makes use of histograms of the antecedents. Secondly, a method for selecting relevant antecedent variables is presented. The selection is based on the comparision of the histograms computed from the measured values for the training set. Then the inclusion of the Rényi-entropy-based structural entropy and the spatial filling factor into the set of input variables is proposed and assessed. The beneficial effect of including the mentioned structural entropy of the entropies from the hue and saturation (H and S) colour channels resulted in 65% true positive and 60% true negative rate of the classification for an advantageously selected set of antecedents when working with HSV images.
Model-Checking Support for File System Development

Developing and maintaining a file system is time-consuming, typically requiring years of effort. Developers often test compliance with APIs such as POSIX with hand-written regression suites that, alas, examine only a fraction of a file system's state space. Conversely, formal model checking can explore vast state spaces efficiently, increasing confidence in the file system's implementation. Yet model checking is not currently part of file system development. Our position is that file systems should be designed a priori to facilitate model checking. To this end, we introduce MCFS, an architecture for efficient and comprehensive file-system model checking. MCFS relies on two new APIs that save and restore a file system's in-memory and on-disk state. We describe our earlier attempts at model-checking file systems, including unsuccessful or inefficient ones. Those attempts led us to develop VeriFS, which implements the new APIs. We illustrate MCFS's model-checking principles with VeriFS, a FUSE-based file system we were able to quickly develop with MCFS's help.
Attentive Convolution Network-Based Video Summarization

The availability of smart phones with embedded video capturing mechanisms along with gigantic storage facilities has led to generation of a plethora of videos. This deluge of videos grasped the attention of the computer vision research community to deal with the problem of efficient browsing, indexing, and retrieving the intended video. Video summarization has come up as a solution to aforementioned issues where a short summary video is generated containing important information from the original video. This paper proposes a supervised attentive convolution network for summarization (ACN-SUM) framework for binary labeling of video frames. ACN-SUM is based on encoder–decoder architecture where the encoder is an attention-aware convolution network module, while the decoder comprises the deconvolution network module. In ACN-SUM, the self-attention module captures the long-range temporal dependencies among frames and concatenation of convolution network and attention module feature map result in more informative encoded frame descriptors. These encoded features are passed to the deconvolution module to generate frames labeling for keyframe selection. Experimental results demonstrate the efficiency of the proposed model against state-of-the-art methods. The performance of the proposed network has been evaluated on two benchmark datasets.
Impact of Behavioural Biometrics on Mobile Banking System

Abstract The rise of online mobile banking systems has given customers a convenient and better way to interact with the bank as and when they want. Smartphones have revolutionized the world of payment. People now use mobile phones more often to make payments. But with increasing frauds and cybercrimes, customer experience and security become the major concern for any bank. In such situations, biometrics, especially behavioural biometrics, can be used. In this paper, we are going to discuss how behavioural biometrics can be widely adopted to reduce fraud. To increase the adoption of mobile payment acceptance, solutions need to be prioritized and reduce unnecessary obstacles. Customers demand mobile payments that are easy and quick. This is also used for fraud detection and prevention due to increased amounts of identity fraud attempts. Currently used methods for authentication such as passwords and email verification are quite problematic for security and user experience. This gives rise to behavioral biometrics. It is very important nowadays to authenticate the user throughout the session, and this is exactly what behavioral biometric do. Behavioral biometrics achieves this by invisibly running in the background and not interfering with the user experience. Fraud is significantly reduced by removing the setup authentication and risk-based authentication completely.
Protecting Cloud Data Privacy Against Attacks

Data security and privacy protection are the main factors of user’s concerns about the cloud technology. Various methods are used to protect users’ privacy, including anonymity, encryption, data distortion, and generalization. Another method is machine learning and its varied algorithms to forecast the likelihood of a violation of users’ privacy according to data. One of the most important issues in machine learning and data mining is information and data security. Placing data in separate and similar categories increases the efficiency and improves the results of data mining algorithms. One of the most important and widely used machine learning algorithms is the artificial neural network algorithm, which tries to increase the accuracy according to the model made and the hidden layers of the model. This algorithm is very simple and fast and has many applications in different sciences. In this study, to improve the results and forecast privacy in cloud computing, we have provided a model to predict the extent of privacy to improve user security and privacy by using the neural networks and used an optimization algorithm called World Competitive Contests (WCC). According to the results, the proposed algorithm is more accurate than the WCC, and better clustering can be performed using this algorithm, and therefore we perform better privacy detection.
Fuzzy PID Based Autonomous UAV Design for Railway Tracking

Unmanned aerial vehicles (UAV) are frequently used in many areas today. Examination of defects in railways using UAV is a relatively new application for UAVs. By enabling the UAV to follow the railroad autonomously, the investigation reduces the cost and speeds up the monitoring process. However, in such an application, it is an important issue that the path detection and tracking algorithm works well. This study focuses on autonomous tracking of the railway using UAV. In the proposed rail tracking algorithm, firstly, a ray detection algorithm based on Gabor filter is proposed to detect the rails from the image taken by the UAV. The vanishing point was estimated by applying the least squares method to the obtained rail lines. Finally, the vanishing point was kept in the center of the image taken using the PID controller. A fuzzy system is designed to tune the PID control parameters. While the vanishing point was kept in the center of the image, the UAV was moved forward at a constant speed. In this way, the forward movement of the UAV was provided by centering the railway. The experimental results show that the algorithm works satisfactorily for the movement of the UAV by centering the railway.