Reviewerly/exHarmony huggingface dataset:
A compiler architecture for domain-specific type error diagnosis

Abstract Domain-specific languages (DSLs) permeate current programming practices. An important kind of DSLs includes those developed and integrated within a host language, which we call embedded or internal DSLs. Unfortunately, embedded DSLs usually fall short on domain-specific error diagnosis, that is, they do not give control to DSL authors over how errors are reported to the programmer. As a consequence, implementation details of the DSL leak through in error messages, and programmers need to understand the internals of the DSL implementation to fix their code in a productive way. This paper addresses the challenge of building a compiler with integrated support for domain-specific error diagnosis. We assume that the type system is described using a constraint-based approach, and constraint solving is specified using rewrite rules. Domain information can then be injected at constraint gathering time via type rules, during constraint solving via specialized rules and axioms, and finally at blaming and reparation time via transformations. Furthermore, we define error contexts as a way to control the order in which solving and blaming proceeds. We engineer domain-specific error diagnosis in such a way that the compiler can also reuse the techniques for improving general error diagnosis.
Rolling Bearing Fault Diagnosis Method based on EEMD and GBDBN

Aiming at the complexity, nonlinearity, and non-stationarity of the rolling bearing vibration signal, a fault diagnosis method based on Ensemble Empirical Mode Decomposition (EEMD) and Gauss Bernoulli Deep Belief Network (GBDBN) model is proposed.The method first carries out EEMD on the vibration signal; second, the eigenvalues of each intrinsic mode function (IMF) are statistically analyzed; then, the feature vectors are constructed by selecting less change features; finally, the normalized feature vectors are input into the GBDBN to identify the fault types.The experimental results show that the proposed method achieves more than 90% recognition rate of fault types and has better fault diagnosis ability, which can provide convenience for maintenance.
Prediction of Length of Study of Student Applicants Using Case Based Reasoning

Graduation is important matter in college. Length of study can be used to evaluate curriculum. It affect accreditation score of the sutdy program. Based on Akreditasi Program Studi Magister Buku V Pedoman Penilaian Instrumen Akreditasi 3rd standard there is rule about students and graduation, such as profile of the graduates including average length of study time and gpa (grade point average) of graduates.In this study, system built to predict Gadjah Mada University Master of Computer Science student applicant’s length of study. It used new case with 13 features from applicant that will be predict as new case, then calculate local similarity using euclidean distance and hamming distance while global similarity using nearest neighbor. Maximum value of global similarity taken as solution while revised will be done if it’s value below threshold.Result of this study show that system can help study program to manage educational process. It show 76% accuracy of 50 data.
Context Graph Based Video Frame Prediction Using Locally Guided Objective

This paper proposes a feature reconstruction based approach using pixel-graph and Generative Adversarial Networks (GAN) for solving the problem of synthesizing future frames from video scenes. Recent methods of frame synthesis often generate blurry outcomes in case of long-range prediction and scenes involving multiple objects moving at different velocities due to their holistic approach. Our proposed method introduces a novel pixel-graph based context aggregation layer (PixGraph) which efficiently captures long range dependencies. PixGraph incorporates a weighting scheme through which the internal features of each pixel (or a group of neighboring pixels) can be modeled independently of the others, thus handling the issue of separate objects moving in different directions and with very dissimilar speed. We also introduce a novel objective function, the Locally Guided Gram Loss (LGGL), which aides the GAN based model to maximize the similarity between the intermediate features of the ground-truth and the network output by constructing Gram matrices from locally extracted patches over several levels of the generator. Our proposed model is end-to-end trainable and exhibits superior performance compared to the state-of-the-art on four real-world benchmark video datasets.
Classification using semiparametric mixtures

A new density-based classification method that uses semiparametric mixtures is proposed. Like other density-based classifiers, it first estimates the probability density function for the observations in each class, with a semiparametric mixture, and then classifies a new observation by the highest posterior probability. By making a proper use of a multivariate nonparametric density estimator that has been developed recently, it is able to produce adaptively smooth and complicated decision boundaries in a high-dimensional space and can thus work well in such cases. Issues specific to classification are studied and discussed. Numerical studies using simulated and real-world data show that the new classifier performs very well as compared with other commonly used classification methods.
Automatic concept identification of software requirements in Turkish

Software requirements include description of the features for the target system and express the expectations of users. In the analysis phase, requirements are transformed into easy-to-understand conceptual models that facilitate communication between stakeholders. Although creating conceptual models using requirements is mostly implemented manually by analysts, the number of models that automate this process has increased recently. Most of the models and tools are developed to analyze requirements in English, and there is no study for agglutinative languages such as Turkish or Finnish. In this study, we propose an automatic concept identification model that transforms Turkish requirements into Unified Modeling Language class diagrams to ease the work of individuals on the software team and reduce the cost of software projects. The proposed work is based on natural language processing techniques and a new rule-set containing twenty-six rules is created to find object-oriented design elements from requirements. Since there is no publicly available dataset on the online repositories, we have created a well-defined dataset containing twenty software requirements in Turkish and have made it publicly available on GitHub to be used by other researchers. We also propose a novel evaluation model based on an analytical hierarchy process that considers the experts' views and calculate the performance of the overall system as 89 %. We can state that this result is promising for future works in this domain.
Identifying criminal organizations from their social network structures

Identification of criminal structures within very large social networks is an essential security feat. By identifying such structures, it may be possible to track, neutralize, and terminate the corresponding criminal organizations before they act. We evaluate the effectiveness of three different methods for classifying an unknown network as terrorist, cocaine, or noncriminal. We consider three methods for the identification of network types: evaluating common social network analysis metrics, modeling with a decision tree, and network motif frequency analysis. The empirical results show that these three methods can provide significant improvements in distinguishing all three network types. We show that these methods are viable enough to be used as supporting evidence by security forces in their fight against criminal organizations operating on social networks.
Why can't we make research grant allocation systems more consistent? A personal opinion

Abstract Uncertainty is expected to enter into our grant allocation processes at many points, not limited to those directly involving assessment by peers. The selection of grants for funding is thus prodigiously low in statistical power and will remain so. The replacement of current systems with some form of lottery, as has been proposed, seriously risks weakening the quality of applications. Opportunities exist for agencies to encourage and reward greater clarity and innovation in research outcomes.
Neural Network Controller Application on a Visual based Object Tracking and Following Robot

Navigation is the main issue for autonomous mobile robot due to its mobility in an unstructured environment. The autonomous object tracking and following robot has been applied in many places such as transport robot in industry and hospital, and as an entertainment robot. This kind of image processing based navigation requires more resources for computational time, however microcontroller currently applied to a robot has limited memory. Therefore, effective image processing from a vision sensor and obstacle avoidances from distance sensors need to be processed efficiently. The application of neural network can be an alternative to get a faster trajectory generation. This paper proposes a simple image processing and combines image processing result with distance information to the obstacles from distance sensors. The combination is conducted by the neural network to get the effective control input for robot motion in navigating through its assigned environment. The robot is deployed in three different environmental setting to show the effectiveness of the proposed method. The experimental results show that the robot can navigate itself effectively within reasonable time periods.
Spectrum-Centric Differential Privacy for Hypergraph Spectral Clustering

In real world, most of complex networks can be represented by hypergraphs. Hypergraph spectral clustering has drawn wide attention in recent years due to it is more suitable to describe high-order information between objects. In this paper, we focus on the spectrum of hypergraph-based complex networks, propose a spectrum-centric differential privacy scheme by using stochastic matrix perturbation. The main idea is to project the hypergraph Laplacian matrix into a low dimensional space and perturb the eigenvector with random noise. We present a differential privacy mechanism for hypergraph clustering based on the exponential mechanism. We evaluated the computational efficiency and data utility of the existing methods on both synthetic datasets and real datasets, and the experimental results reveal that our proposed mechanism achieves a better performance in both data utility and efficiency.
The World Wants Mangoes and Kangaroos: A Study of New Emoji Requests Based on Thirty Million Tweets

As emojis become prevalent in personal communications, people are always looking for new, interesting emojis to express emotions, show attitudes, or simply visualize texts. In this study, we collected more than thirty million tweets mentioning the word "emoji" in a one-year period to study emoji requests on Twitter. First, we filtered out bot-generated tweets and extracted emoji requests from the raw tweets using a comprehensive list of linguistic patterns. Then, we examined patterns of new emoji requests by exploring their time, locations, and context. Finally, we summarized users' advocacy behaviors and identified expressions of equity, diversity, and fairness issues due to unreleased but expected emojis, and concluded the significance of new emojis on society. To the best of our knowledge, this paper is the first to conduct a systematic, large-scale study on new emoji requests.
WEB PHISING ATTACK ANALYSIS ON E-COMMERCE SERVICE USING NETWORK FORENSIC PROCESS METHOD

The internet provides around the world facilities to reach customers without market restrictions, including e-commerce. As a result, the number of customers who depend on the Internet for purchases has increased dramatically. Potential cybercrime attacks such as phishing are criminal activities that attempt to obtain illegally sensitive information. The sensitive information can be in the form of a username, password, and credit card details. Then it is necessary to analyze the non-volatile data obtained in real and sourced from a Facebook forum. Data acquisition techniques use forensic live network data processing using Network Protocol Analyzer tools, namely Wireshark. The results of the investigation and analysis are summarized in the form of the table. The success of the investigation can be found in the source of the attack in the form of IP Address, URL phishing, DNS protocol, IMAP, username, password, FTP, SMTP, and HTTP. The analysis provides recommendations on how to avoid web phishing attacks.
Corruption, Political Instability and Sustainable Development: The Interlinkages

The study empirically probed the interdependence among corruption, political instability and sustainable development for a panel of 28 developing economies and disaggregated sample of lower-middle and upper-middle income economies for the time period 2000-2014.The three stage least square (3SLS) estimation revealed that corruption negatively affects sustainable development and political instability.The political instability impedes sustainable development and corruption.The sustainable development reduces political instability and corruption.It explains that corruption enhances political stability and political stability increases corruption.The disaggregated estimates of developing economies are almost same as aggregate estimates of developing economies, however political instability has statistically insignificant effect on sustainable development in upper-middle-income economies.To go forward for sustainable development, the elimination of corruption is imperative.
Variance Reduction via Footprint Estimation in the Presence of Path Reuse

Multiple importance sampling is a tool to weight the results of different samplers with the goal of a minimal variance for the sampled function. If applied to light transport paths, this tool enables techniques such as bidirectional path tracing and vertex connection and merging. The latter generalizes the path probability measure to merges—also known as photon mapping. Unfortunately, the resulting heuristic can fail, resulting in a noticeable increase of noise. This chapter provides an insight into why things go wrong and proposes a simple-to-implement heuristic that is closer to an optimal solution and more reliable over different scenes. The trick is to use footprint estimates of sub-paths to predict the true variance reduction that is introduced by reusing all the photons.
Quality, Nontechnical Skills, Blind Programmers, and Deep Learning

Reports from the 34th International Conference on Software Maintenance and Evolution, the 44th Euromicro Conference on Software Engineering and Advanced Applications, and the 12th International Symposium on Empirical Software Engineering and Measurement (ESEM).
A Fast Coherent Integration Algorithm for Maneuvering Target Detection

This paper investigates the coherent integration problem in the maneuvering target detection. We propose a new coherent integration algorithm based on the short-time Fourier transform (STFT) and the cubic phase function (CPF), i.e., STFT-CPF. First, the STFT is employed to construct a special compensation function for the linear range migration. Then, the CPF is applied to estimate the target’s acceleration and jerk, thereby compensating the linear Doppler frequency migration and the quadratic Doppler frequency migration. After that, the coherent integration can be achieved by the FT. The proposed algorithm involves only 1-D search. It reaches a good balance between the computational cost and detection performance in a certain range of acceleration and jerk. Simulations and real data demonstrate the effectiveness of the proposed algorithm.
On Distributive Join Semilattices

Motivated by Gentzen’s disjunction elimination rule in his Natural Deduction calculus and reading inequalities with meet in a natural way, we conceive a notion of distributivity for join semilattices. We prove that it is equivalent to a notion present in the literature. In the way, we prove that all notions of distributivity for join semilattices we have found in the literature are linearly ordered. We finally consider the notion of distributivity in join semilattices with arrow, that is, the algebraic structure corresponding to the disjunction-conditional fragment of intuitionistic logic.
Continuous-Variable Quantum Network Coding Against Pollution Attacks

Continuous-variable quantum communication protocols have gained much attention for their ability to transmit more information with lower cost. To break through the bottleneck of quantum network coding schemes, continuous-variable quantum network coding (CVQNC) schemes were proposed. In spite of network throughput improvement, CVQNC also brings on security problems such as pollution attacks, in which case errors are accumulated and spread to all downstream nodes. In this paper, we propose a continuous-variable quantum network coding scheme with quantum homomorphic signature to resist pollution attacks. The scheme utilizes pre-shared quantum entanglement and classical communication to implement perfect crossing transmission of two quantum states. By combining two quantum signatures of classical messages generated by source nodes, the scheme will generate a homomorphic signature, which is used to verify the identities of different data sources in a quantum network. Security analysis shows the proposed scheme is secure against forgery and repudiation.
INTERNET FRAUD TO DECEIVE EMAIL BY USING DIFFERENT TECHNOLOGIES

: This research presents e-fraud types and clarifies the suggestions that have been reached to deal with the problems of electronic fraud so as not to be victims of this fraud, and explains how to act when detecting electronic fraud, especially with the spread of the Internet through institutions to make them more secure. Therefore, Arab countries must take a series of steps and strategies at all levels, particularly those organizational steps, which make many texts and provisions used effectively to prevent cybercrime. States should therefore take all legal measures to punish various types of attacks on information systems.
Student Perspective in Using Social Media As a Tool in English Language Learning

Abstract—Learning a foreign language by using social media at the University level is very important because social media has great potential to support student-centered learning as they are flexible, interactive, and resource–rich in nature. One of its drawbacks is the quality of online instruction that lead to unfavorable perception and perspective of the students. The objectives of this research are to analyze the students’ perceptions and perspectives in using Social Media as a tool in English Language learning. Qualitative and quantitative data were collected from the students of the English Study Program, Faculty of Education, the Islamic University of Riau. Research finding revealed that social media is very practical and useful for getting general information, knowledge and to increase their language competency.&#x0D; Keywords — social media, perspective, language
The Effect of Multisensory Teaching Method on The Students’ Reading Achievement

This study aims to investigate the effect of Multisensory Teaching Method ontheStudents’ Reading Achievement.Thisresearchappliedexperimentalresearch.The populationof thisresearchwastakenfromtheSevengradeandthesecondyearstudentsof juniorhigh school in SMPSwasta PGRI 3 Medanatacademic 2017/2018. There were 3 classes consisting 113 students. This research was applied cluster random sampling. This research used experimental researchdesignnamely Onegrouppre-testandpost-test,Therefore,theresearch tookoneclassastheexperimentalclass. The samplewastakenfromthe students in VII-1 who consisted of 38 students. Theresearcher were given treatment by Multisensory Teaching Method. The instrument of research is amultiplechoice test whichconsistof20items.Thedatawere analyzed by using t-test formula. The result of the data showed that tobserve 13.3 &gt; ttable 1.68 with degree of freedom (df)= n-k. The hypothesis was accepted. It proves that Multisensory Teaching Method significantly effect to the students’ reading achievement.
Expert System Development for Course Enrollment Process Using Ripple Down Rules in a University in Surabaya

The research aimed to develop expert system for helping lecturers to decide which courses to be taken by students in the next semester. The expert system contained specific pieces of knowledge to solve specific problems involved in the forms of system development and maintenance. This expert system was in the form of a website using the PHP programming language and MySQL database. The researcher used the Ripple Down Rules (RDR) method to identify the courses by putting forward the questions so that the system could decide which courses the students should take in next semester. The result shows that this web-based expert system can identify which courses that the students have to enroll after the students have answered questions generated by the system. The available data on the courses in the system adapt to the rules, so it is in line with the enrolled courses.
Planning of Personal Trajectors of Development: Systems of Automated Design

The problem of planning personal development trajectories in the implementation of computeraided design systems in conditions of uncertainty and risk is considered. The aim of the research is the network planning of personal development trajectories when implementing computer-aided design systems on the basis of a competency model that requires reasoning. To minimize risks, a level model of competencies has been developed, taking into account education, work experience, certificates and functional responsibilities. The process of building a competency model for the implementation of automated process design systems is described. The developed algorithm and the program allows to solve the problem of network planning of personal development trajectories by the criteria of minimization of time and cost costs. The results of the research are used in the educational process of the South Ural State University and the Azerbaijan Technical University when training personnel for work with the systems of computer-aided design of technological processes.