Reviewerly/exHarmony huggingface dataset:
Flowchart-Based Cross-Language Source Code Similarity Detection

Source code similarity detection has various applications in code plagiarism detection and software intellectual property protection. In computer programming teaching, students may convert the source code written in one programming language into another language for their code assignment submission. Existing similarity measures of source code written in the same language are not applicable for the cross-language code similarity detection because of syntactic differences among different programming languages. Meanwhile, existing cross-language source similarity detection approaches are susceptible to complex code obfuscation techniques, such as replacing equivalent control structure and adding redundant statements. To solve this problem, we propose a cross-language code similarity detection (CLCSD) approach based on code flowcharts. In general, two source code fragments written in different programming languages are transformed into standardized code flowcharts (SCFC), and their similarity is obtained by measuring their corresponding SCFC. More specifically, we first introduce the standardized code flowchart (SCFC) model to be the uniform flowcharts representation of source code written in different languages. SCFC is language-independent, and therefore, it can be used as the intermediate structure for source code similarity detection. Meanwhile, transformation techniques are given to transform source code written in a specific programming language into an SCFC. Second, we propose the SCFC-SPGK algorithm based on the shortest path graph kernel to measure the similarity between two SCFCs. Thus, the similarity between two pieces of source code in different programming languages is given by the similarity between SCFCs. Experimental results show that compared with existing approaches, CLCSD has higher accuracy in cross-language source code similarity detection. Furthermore, CLCSD cannot only handle common source code obfuscation techniques used by students in computer programming teaching but also obtain nearly 90% accuracy in dealing with some complex obfuscation techniques.
On the condition number of the Vandermonde matrix of the <i>n</i>th cyclotomic polynomial

Abstract Recently, Blanco-Chacón proved the equivalence between the Ring Learning With Errors and Polynomial Learning With Errors problems for some families of cyclotomic number fields by giving some upper bounds for the condition number Cond( V n ) of the Vandermonde matrix V n associated to the n th cyclotomic polynomial. We prove some results on the singular values of V n and, in particular, we determine Cond( V n ) for n = 2 k p ℓ , where k , ℓ ≥ 0 are integers and p is an odd prime number.
Automated Speech Recognition System For Speaker Emotion Classification

Over this decade the voice recognition plays a vital role for speaker identification and identification of the various characteristics of the person involved in a particular section of the speech. The information obtained from those systems are used to interaction between the user and the machine. The emotion detection from the face recognition requires the face of that person need to be captured in front of the sensor to detect emotion from those people involved in that session. But speech recognition can be done without the knowledge of the that person. The application can be used in the client service, education and health care. The high accuracy system of this type can be used in the intelligence service to know the truthfulness in investigation. For this task various parameters of the audio is obtained as feature MFCC. The feature MFCC is successfully modelled by the GMM modelling. In addition to that UBM it gives better result in emotion of the speaker.
RETRACTED: Design and development of mobile internet control system for embedded fitness training cycling device

As the world financial development, a gym is becoming quickly around the world. In the interim, the wellness business is blasting, particularly for white populace in metropolitan territories. For the situation, individuals need more logical and pragmatic direction to fabricate their bodies. In this article, we intended to screen the development of an Internet of Things (IOT) fit framework dependent on Internet wellbeing. The framework gives practice direction. At the point when the activity, the activity band information gathered by the sensor and wellness. At that point communicated to the framework for examination of these information. With the assistance of man-made consciousness innovation, the framework can separate the client's working out helpful direction. In this article, we will depict the subtleties of the framework, and connect with introduce the innovation. This kind of framework configuration is the pattern for the fate of wellness applications.
On the Cost-Effectiveness of TrustZone Defense on ARM Platform

In recent years, research efforts have been made to develop safe and secure environments for ARM platform. The ARMv8 architecture brought in security features by design. However, there are still some security problems with ARM. For example, on ARM platform, there are risks that the system is vulnerable to cache-based attacks like side-channel attacks. The success of such attacks highly depends on accurate information about the victim’s cache accesses. Cortex-M series, on the other hand, have some design so that the side-channel attack can be prevented, but it also needs a security design to ensure the security of the users’ privacy data. In this paper, we focus on TrustZone based approach to defend against cache-based attack on Cortex-A and Cortex-M series chips. Our experimental evaluation and theoretical analysis show the effectiveness and efficiency of FLUSH operations when entering and leaving TrustZone, which helps in design defense framework based on our research.
Exploring the Pragmatics of Computer-Mediated English as a Lingua Franca Communication

On the basis of an interdisciplinary approach informed by ethnographic methods, and computer-mediated discourse analysis, this paper sets out to explore the relationship between multimodal and multilingual practices in Computer-Mediated English as a Lingua Franca (CMELF) communication on Facebook. As the analysis of some naturally occurring interactions shows, CMELF users exploit multimodal resources in novel ways to achieve mutual understanding in intercultural communication. For instance, when a target term in English is unknown to the subject utterer, a corresponding picture can be searched for on the Internet in their own L1, to be then embedded in the CMELF utterance and fill the lexical gap. Multimodal resources, however, can also be employed as part of pragmatic strategies to proactively avoid misunderstanding: emojis, for example, can be used to flag words which are infrequent and therefore potentially problematic for the intended multicultural audience, while pictures can gloss these visually, thus enhancing utterance clarity.
A Predictive-Trend-Aware and Critical-Path-Estimation-Based Method for Workflow Scheduling Upon Cloud Services

The cloud computing paradigm is featured by its ability to offer elastic computational resource provisioning patterns and deliver on-demand and versatile services. It's thus getting increasingly popular to build business process and workflow-based applications upon cloud computing platforms. However, it remains a difficulty to guarantee cost-effectiveness and quality of service of cloud-based workflows because real-world cloud services are usually subject to real-time performance variations or fluctuations. Existing researches mainly consider that cloud are with constant performance and formulate the scheduling decision-making as a static optimization problem. In this work, instead, we consider that scientific computing processes to be supported by decentralized cloud infrastructures are with fluctuating QoS and aim at managing the monetary cost of workflows with the completion-time constraint to be satisfied. We address the performance-trend-aware workflow scheduling problem by leveraging a time-series-based prediction model and a Critical-Path-Duration-Estimation-based (CPDE for short) scheduling strategy. The proposed method is capable of exploiting real-time trends of performance changes of cloud infrastructures and generating dynamic workflow scheduling plans. To prove the effectiveness of our proposed method, we build a large-prime-number-generation workflow supported by real-world third-party commercial clouds and show that our method clearly beats existing approaches in terms of cost, workflow completion time, and Service-Level-Agreement (SLA) violation rate.
Free-Head Pose Estimation under Low-Resolution Scenarios

Head pose offers vital cues to infer one's social attention in wide applications. Most existing head pose estimation algorithms have demonstrated competitive results taking high resolution images of frontal view as input. However, these approaches still work poorly if they are fed with low-resolution images. In a more common realistic scene such as computer vision assisted autism screening, images of unconstrained patients that are taken from distant cameras often have low-resolution and non-frontal faces. To this end, we present a multi-view scheme based CNN for free-head pose classification. Residual Networks are taken as the backbone model to generate effective feature representations of the low-resolution images. A novel multi-view feature fusion layer is proposed to address facial appearance variation over multi perspectives owing to free movement. Also, a multi loss function by combining binned classification and regression losses of different pose angles is employed to obtain a more precise pose estimation. The proposed method is evaluated on two scenarios: (1) a publicly available dataset and (2) a practical application to explore the social attention of a group with social deficits: autistic children. Experimental results suggest that our method significantly outperforms state-of-the-art multi-view pose classification methods and achieves comparable pose estimation results. Moreover, the proposed method can be extended to applications of quantitative analysis of social deficits.
Multiscale Representation Learning of Graph Data With Node Affinity

Graph neural networks have emerged as a popular and powerful tool for learning hierarchical representation of graph data. In complement to graph convolution operators, graph pooling is crucial for extracting hierarchical representation of data in graph neural networks. However, most recent graph pooling methods still fail to efficiently exploit the geometry of graph data. In this paper, we propose a novel graph pooling strategy that leverages node affinity to improve the hierarchical representation learning of graph data. Node affinity is computed by harmonizing the kernel representation of topology information and node features. In particular, a structure-aware kernel representation is introduced to explicitly exploit advanced topological information for efficient graph pooling without eigendecomposition of the graph Laplacian. Similarities of node signals are evaluated using the Gaussian radial basis function (RBF) in an adaptive way. Experimental results demonstrate that the proposed graph pooling strategy is able to achieve state-of-the-art performance on a collection of public graph classification benchmark datasets.
Using phidelta diagrams to discover relevant patterns in multilayer perceptrons

Understanding the inner behaviour of multilayer perceptrons during and after training is a goal of paramount importance for many researchers worldwide. This article experimentally shows that relevant patterns emerge upon training, which are typically related to the underlying problem difficulty. The occurrence of these patterns is highlighted by means of [Formula: see text] diagrams, a 2D graphical tool originally devised to support the work of researchers on classifier performance evaluation and on feature assessment. The underlying assumption being that multilayer perceptrons are powerful engines for feature encoding, hidden layers have been inspected as they were in fact hosting new input features. Interestingly, there are problems that appear difficult if dealt with using a single hidden layer, whereas they turn out to be easier upon the addition of further layers. The experimental findings reported in this article give further support to the standpoint according to which implementing neural architectures with multiple layers may help to boost their generalisation ability. A generic training strategy inspired by some relevant recommendations of deep learning has also been devised. A basic implementation of this strategy has been thoroughly used during the experiments aimed at identifying relevant patterns inside multilayer perceptrons. Further experiments performed in a comparative setting have shown that it could be adopted as viable alternative to the classical backpropagation algorithm.
Two-Stage Fusion of Local Binary Pattern and Discrete Cosine Transform for Infrared and Visible Face Recognition

Combination the corresponding advantage of infrared and visible images, the fusion of visible-light and near-infrared features is becoming one of the key research directions of uncontrolled face recognition. In this paper, a new face fusion recognition method based on near-infrared and visible-light imaging is proposed to make full use of the complementarity in two modalities. Firstly, for near infrared imaging, the low frequency part is retained for discrete cosine transform (DCT) coefficients and the block histogram of local binary pattern (LBP) is jointly applied to represent the discriminative features. Then, LBP features from visible faces are gotten to make up the incapable of local representation in near-infrared imaging. Finally, the three features are sent to the novel two-state fusion module to leverage the performance of face recognition. The experimental results show that the combination of DCT and LBP can extract the complementary features from infrared and visible images, and the two-stage fusion strategy lifts the robustness of the multi-modality face recognition. Especially, the recognition rate of the proposed fusion recognition algorithm has been improved significantly, with regard to the other methods based on statistical feature fusion under the circumstance of small samples.
Poly'19 Workshop Summary

Data privacy within the context of heterogenous data and data management systems continues to be an important issue. At the Poly?19 workshop, held in conjunction with VLDB 2019 in Los Angeles, CA, one of the major themes explored was the implication of data privacy regulations such as GDPR to systems composed of multiple heterogenous databases. This summary outlines some of the major approaches and directions presented by various presenters during the privacy portion of the Poly?19 workshop.
International criteria for assessment of accreditation of educational activities

The article gives a comparative and analytical review of the national systems of accreditation of educational programs according to European, Asia-Pacific and Russian standards of quality assurance in higher education. The emphasis is on the structuring of the main provisions and features of foreign and domestic accreditation systems in the field of education. The main universal blocks of educational activity assessed at accreditation are identified: management of educational process, resources and provision of educational activity, competence and qualification model of a graduate. In accordance with the principles of international and national standards, the requirements to the accreditation procedures of educational activity are set out in the main (universal) diagnostic blocks. For the evaluation of educational activity during the accreditation of higher education institutions the criteria of achievements are given. It is noted that the most important criterion of quality of education at accreditation is the evaluation of educational achievements of students. Difficulties of evaluation of educational results and ensuring their comparability at the international level for understanding the quality of training of specialists in different educational systems are noted. The lack of reliable evaluation tools creates certain difficulties in accreditation. The most problematic is the development and application of evaluation materials with planned activities to identify the levels of competence of students and graduates.
The dual function of explanations: Why it is useful to compute explanations

Whilst the legal debate concerning automated decision-making has been focused mainly on whether a ‘right to explanation’ exists in the GDPR, the emergence of ‘explainable Artificial Intelligence’ (XAI) has produced taxonomies for the explanation of Artificial Intelligence (AI) systems. However, various researchers have warned that transparency of the algorithmic processes in itself is not enough. Better and easier tools for the assessment and review of the socio-technical systems that incorporate automated decision-making are needed. The PLEAD project suggests that, aside from fulfilling the obligations set forth by Article 22 of the GDPR, explanations can also assist towards a holistic compliance strategy if used as detective controls. PLEAD aims to show that computable explanations can facilitate monitoring and auditing, and make compliance more systematic. Automated computable explanations can be key controls in fulfilling accountability and data-protection-by-design obligations, able to empower both controllers and data subjects. This opinion piece presents the work undertaken by the PLEAD project towards facilitating the generation of computable explanations. PLEAD leverages provenance-based technology to compute explanations as external detective controls to the benefit of data subjects and as internal detective controls to the benefit of the data controller.
Interdisciplinary Development of Geoscience OER: Formative Evaluation and Project Management for Instructional Design

This chapter reports part of a multistage design-based research (DBR) project and focuses on the question: What are effective procedures for an interdisciplinary team to create low-cost, meaningful, and accessible open-access textbooks for laboratory methods in geosciences? Between August 2016 and May 2019, the researchers from multiple disciplinary areas studied the design and development of open educational resources (OER) for analytical methods in geoscience courses. The initial iteration of this DBR study consisted of analysis and exploration, followed by the OER design and development with formative evaluation. The analysis and exploration included identifying instructional and noninstructional problems based on a literature review, an analysis of context and resources, and formation of an interdisciplinary team. The formative evaluation included testing the developed OER usability by both faculty and students, reflecting on the design and development, and collecting peer review feedback. The findings provided recommendations for interdisciplinary team-based design and development of OER in a scientific laboratory environment. Suggestions included collaborative project management and data management for developing media-rich OER in STEM education.
AI-FML Agent with Patch Learning Mechanism for Robotic Game of Go Application

In this paper, we propose an AI-FML agent with a patch learning (PL) mechanism for the robotic game of Go applications. The proposed AI-FML agent contains three kinds of intelligence, including a perception intelligence, a cognition intelligence, and computational intelligence, for the robotic application. Additionally, we embed the PL mechanism into the AI-FML agent. The method for running PL involves three steps. It first trains an initial global model, then trains a patch model for each identified patch, and finally updates the global model using the training data that do not fall into any patch. This paper adopts the Google DeepMind Master 60 games to be the training data and testing data set. The experimental results show the AI-FML agent with the patch learning mechanism can improve the performance of regression for the robotic game of Go applications.
AHAC: Actor Hierarchical Attention Critic for Multi-Agent Reinforcement Learning

Deep reinforcement learning has made significant progress in multi-agent tasks in recent years. However, most previous studies focus on solving full cooperative tasks, which do not perform well in mixed tasks. In mixed tasks, the agent needs to comprehensively consider the information provided by friends and enemies to learn its strategy, and its strategy is sensitive to the received information. There is a great necessity to efficiently learn information representation for mixed tasks. To this end, we present an approach that conducts information representation learning for multiple agents using hierarchical attention mechanism. Our approach adopts the framework of centralized training and decentralized execution. It applies hierarchical attention to centrally computed critics, so critics process the received information more accurately and assist actors to choose better actions. The hierarchical attention critic uses two different attention levels, the agent-level and the group-level, to assign different weights to information of friends and enemies respectively and then summarize them at each time-step. It can achieve more effective and scalable learning in mixed tasks. In addition, our approach uses recurrent neural networks that process sequence input information more efficiently. Experimental results show that our approach is not only applicable to cooperative environments but also better in mixed environments. Especially in the predator-prey task, our approach receives twice as much reward as baselines.
SETGAN

We consider the task of photo-realistic unconditional image generation (generate high quality, diverse samples that carry the same visual content as the image) on mobile platforms using Generative Adversarial Networks (GANs). In this paper, we propose a novel approach to trade-off image generation accuracy of a GAN for the energy consumed (compute) at run-time called Scale-Energy Trade-off GAN (SETGAN). GANs usually take a long time to train and consume a huge memory hence making it difficult to run on edge devices. The key idea behind SETGAN for an image generation task is for a given input image, we train a GAN on a remote server and use the trained model on edge devices. We use SinGAN, a single image unconditional generative model, that contains a pyramid of fully convolutional GANs, each responsible for learning the patch distribution at a different scale of the image. During the training process, we determine the optimal number of scales for a given input image and the energy constraint from target edge device. Results show that with the SETGAN's unique client-server based architecture, we were able to achieve 56% gain in energy for a loss of 3% to 12% SSIM accuracy. Also, with the parallel multi-scale training, we obtain around 4x gain in training time on the server.