Reviewerly/exHarmony huggingface dataset:
Representation learning for software engineering and programming languages

Recently, deep learning (DL) and machine learning (ML) methods have been massively and successfully applied in various software engineering (SE) and programming languages (PL) tasks. The results are promising and exciting, and lead to further opportunities of exploring the amenability of DL and ML to different SE and PL tasks. Notably, the choice of the representations on which DL and ML methods are applied critically impacts the performance of the DL and ML methods. The rapidly developing field of representation learning (RL) in artificial intelligence is concerned with questions surrounding how we can best learn meaningful and useful representations of data. A broad view of the RL in SE and PL can include the topics, e.g., deep learning, feature learning, compositional modeling, structured prediction, and reinforcement learning. This workshop will advance the pace of research in the unique intersection of representation learning and SE and PL, which will, in the long term, lead to more effective solutions to common software engineering tasks such as coding, maintenance, testing, and porting. In addition to attracting the community of researchers who usually attend FSE, we have made intensive efforts to attract researchers from the RL (broadly AI) community to the workshop, specially from local, very strong groups in local universities, and research labs in the nation.
Multi-granularity Attribute Sentiment Analysis Based on Neural Network

Aiming at the problem that traditional neural network cannot accurately capture the emotional characteristics of multiple target attributes, A multi-granularity attribute sentiment analysis model based on neural network is proposed. First, the relationship between objects and target attributes is mined through a bidirectional attention mechanism. Then, capture the important information inside the target attribute through a strong attention mechanism. Combine bidirectional attention and strong attention mechanisms for sentiment analysis. Experiment on SimEval 2014 and Twitter. Through experiments, the accuracy of the model on laptops, restaurants, and Twitter is 72.88%, 80.53%, and 73.49%, respectively. Accuracy is improved compared to previous neural networks.
Phishing Attack Simulation: Measuring Susceptibility among Undergraduate Students

The Internet Users Survey 2018 statistics compiled by Malaysian Communications and Multimedia Commission (MCMC) has shown significant increase in the total of internet users. Out of 87.4 % internet users, 30.0% of them are in their 20' s, with the average of 8.0 hours daily internet usage. Thus, the study focuses on undergraduate students and aims to identify and measure their susceptibility to falsely trust a bogus source through phishing attack simulation, as there is a high susceptibility of them being drawn to such attacks due to high rate of internet usage.
Offline Handwriting Recognition Pipeline Testing Tool

Recognition of handwritten text from historical documents is a very difficult problem that has not been fully solved. Many approaches have been used to recognize text and many new machine-learning-based approaches are currently being developed. To develop new methods of handwriting recognition, many ancillary steps that are not directly related to the new method must be put in place. These additional steps include loading images, cleaning documents, measuring performance, and displaying outputs. These extra actions take time that could instead be focused on the new method. This paper describes an application that was designed and created to provide all the supplementary steps needed to develop new approaches to offline handwriting recognition. It explains a pipeline-based approach that fits the sequence of steps most often used in the recognition process. The resulting application provides the framework that allows researchers to focus on their primary area of experimentation in the handwriting recognition process.
Chain-of-Evidence in Secured Surveillance Videos using Steganography and Hashing

Video sharing from closed-circuit television video recording or in social media interaction requires self-authentication for responsible and reliable data sharing. Similarly, surveillance video recording is a powerful method of deterring unlawful activities. A Solution-by-Design can be helpful in terms of making a captured video immutable, as such recordings cannot become a piece of evidence until proven to be unaltered. This paper presents a computationally inexpensive method of preserving a chain-of-evidence in surveillance videos using steganography and hashing. The method conforms to the data protection regulations which are increasingly adopted by governments, and is applicable to network edge storage. Security credentials are stored in a hardware wallet independently of the video capture device itself, while evidential information is stored within video frames themselves, independently of the content. The proposed method has turned out to not only preserve the integrity of the stored video data but also results in very limited degradation of the video data due to steganography. Despite the presence of steganographic information, video frames are still available for common image processing tasks such as tracking and classification.
A Novel Risk Assessment Model in Tunnel Leakage by Using Cloud Model and Dempster-Shafer Evidence Theory

This paper proposes a risk perception method combing the cloud model, Dempster-Shafer evidence theory, expecting to better perceive and assess the leakage risk of tunnel construction projects. This proposed method takes advantage of the cloud model theory that transforming qualitative to quantitative. The membership degree calculated from the cloud model is transformed into the accuracy of the indicator for each level of the leakage risk. Treating the membership degree as the evidence of the Dempster-Shafer (D-S) evidence theory and further fusing the evidence of risk status of the leakage in the tunnel construction with the Dempster's evidence fusion rules, the risk grades of the leakage disease can be confirmed. A section of the tunnel construction is utilized as a case study to test the applicability and effectiveness of the proposed approach. Through the case study, this proposed leakage assessing method is demonstrated capable of accurately perceiving the risk status of the leakage in tunnel construction projects, and thus preventive measurements can be adopted according to the corresponding risk status grades and the rank of different influencing factors.
Synthesizing correct code for machine learning programs

Success using machine learning (ML) in numerous fields has created a new class of users, who are not experts in the data science domain but want to use ML as a means to solve their inference problems. Various automatic machine learning (AutoML) approaches attempt to make ML solutions accessible to such users. In this work, we present a system that automatically synthesizes correct code within the context of the user’s data using sketching. In sketching, insight is determined through a partial program; a sketch expresses the high-level structure of implementation but leaves holes in place of the low-level details. We use meta-learning on meta-features to approximately solve holes. We observe that the sketch-based approach is more expressive, easier to implement, and easier to optimize than existing AutoML frameworks. Our initial results are very promising. Our approach uses fewer resources and still produces comparable results to existing techniques.
Decision Support System with Fuzzy Tahani Method for Selection of Travel Destinations in Garut

Indonesia has high tourism potential.This needs to be supported by providing complete information to prospective tourists.One of the provinces with the most tourists is West Java.Unfortunately, there are still many natural potentials and districts in West Java that have not been widely known, one of which is Garut City.This paper will explain the implementation of Fuzzy Tahani into the information system that provides recommendations on tourism places in the City of Garut.Based on the results of testing, this information system can provide recommendations with several criteria, namely price, number of visitors, facilities available at tourist attractions, and distance from tourist sites.The Black box testing results showed that the system was able to pass the test stage and provide satisfactory results.This signifies the information system that provides recommendations for tourism places with Fuzzy Tahani running well.
E-Learning vs. T-Learning

This chapter intends to explore distance learning today both in the television medium and in the online one, acknowledging that during these early decades of the 21st century multiple and diverse remote learning opportunities have arisen given the permanent technological evolution and of societies. In order to present this actual circumstance, focused analysis of both media is developed pointing out each main key characteristic, benefits, and disadvantages, examining the existence and reliability of each respective means of assessment.
UIC held online European Projects Workshop to design the 2021 work programme on 28 April 2020 – UIC Communications

On 28 April, UIC organised the European Projects Workshop online to present the European project proposals for 2021. Around 60 participants joined the meeting virtually from both UIC and its…
Two-particle indistinguishability and identification of boson and fermion species: a Fisher information approach

We present a study on two-particle indistinguishability and particle species identification by introducing a Fisher information (FI) approach—in which two particles pass through a two-wave mixing operation and the number of particles is counted in one of the output modes. In our study, we first show that FI can reproduce the Hong–Ou–Mandel (HOM) effect with two bosons or two fermions. In particular, it is found that even though bosons and fermions exhibit different physical behavior (i.e., “bunching” or “anti-bunching”) due to their indistinguishability, the aspects of HOM-like dip are quantitatively same. We then provide a simple method for estimating the degree of two-particle indistinguishability in a Mach–Zehnder interferometer-type setup. The presented method also enables us to identify whether the particles are bosons or fermions. Our study will provide useful primitives for various studies of boson and fermion characteristics.
Data Security in Cloud Environment Using Light Weight Secret Key

RSA is one of the popular technique used by cloud service provider to guarantee the security and privacy of user data. In this technique, large prime factors are required to generate public key and private key before transmission of data over the insecure channel. This has been analyze that private key is always equal or extremely large than the public key which increase the time complexity during the decryption process at receiver end. This paper propose a light weight secret key (LWSK) build with chinese remainder theorem (CRT) to compute the RSA encryption and decryption modular expressions for 2048 bit and 4096 bit. The proposed LWSK framework found the same computation time for encryption but during decryption process LWSK reduce the computation time. On the basis of computed time complexity, this has been observed that the proposed LWSK technique is faster approximately three times during decryption than RSA process.
Reconfigurable all-optical nonlinear activation functions

We experimentally demonstrate all-optical reconfigurable nonlinear activation functions in a cavity-loaded mach-zehnder interferometer device utilizing free-carrier dispersion effect in silicon. Our device can be flexibly programmed to generate the following functions: clamped-ReLU, sigmoid, radial-basis and softplus, which can be employed in various neuromorphic tasks. nonlinear optics, neuromorphic photonics.
Big Data Mining Algorithms

Big data mining involves knowledge discovery from these large data sets. The purpose of this chapter is to provide an analysis of different machine learning algorithms available for performing big data analytics. The machine learning algorithms are categorized in three key categories, namely, supervised, unsupervised, and semi-supervised machine learning algorithm. The supervised learning algorithms are trained with a complete set of data, and thus, the supervised learning algorithms are used to predict/forecast. Example algorithms include logistic regression and the back propagation neural network. The unsupervised learning algorithms starts learning from scratch, and therefore, the unsupervised learning algorithms are used for clustering. Example algorithms include: the Apriori algorithm and K-Means. The semi-supervised learning combines both supervised and unsupervised learning algorithms. The semi-supervised algorithms are trained, and the algorithms also include non-trained learning.
New H.266/VVC Based Multiple Description Coding for Robust Video Transmission over Error-Prone Networks

In this paper, we propose a novel multiple description coding (MDC) method to operate at network edges for robust video transmission. The proposed MDC method, named VVC-MDC offers benefits of both the new H.266 Versatile video coding (H.266/VVC) and Distributed video coding (DVC) standards, which can provide not only higher performance compared to the traditional MDC methods but also effective scheme for the error resilience. At the encoder, the proposed VVC-MDC coder encode the source video sequence into two descriptions including odd and even subsequences and then transmit these descriptions to the receiver. At the receiver, our proposed MDC decoder is designed using a novel WynerZiv (WZ) coding introduced in the DVC to provide a high image quality for the video sequence. Unlike the conventional MDC methods, the redundant data in our proposed MDC can be effectively controlled based on the WZ coding scheme. Experimental results show that the proposed method can achieve a wide range of tradeoffs between coding efficiency and error resilience, and provide much better H.266/VVC peak signal-to-noise ratio (PSNR) performance than other conventional MDC methods.
Three-Layer Weighted Fuzzy Support Vector Regressions for Emotional Intention Understanding

A three-layer weighted fuzzy support vector regression (TLWFSVR)Three-Layer Weighted Fuzzy Support Vector Regression (TLWFSVR) model is proposed for understanding human intention, and it is based on the emotion-identification information in human-robot interaction. TLWFSVR model consists of three layers, including adjusted weighted kernel fuzzy c-means (AWKFCM)Adjusted Weighted Kernel Fuzzy c-means (AWKFCM) for data clustering, fuzzy support vector regressions (FSVR)Fuzzy Support Vector Regressions (FSVR) for information understanding, and weighted fusion for intention understanding.
Grammar Normalization to Support Automated Merging

Introduction: Current solutions to multilingual parsing, for programming languages, are flawed. Current implementations are either limited in scope or difficult to develop and maintain. The development of multilingual parsers requires the combination of multiple base grammars, leading to a maintenance headache as these grammars evolve. Such a repetitive process should be automated. Objective: Develop an approach to normalize a grammar in such a way that the grammar is equivalent to the original, but in a state which reduces the effort to merge grammars by reducing ambiguity in automated merge decision making. Methods: This normalization procedure transforms grammars such that each production is one of two forms. Additionally, the normalized grammars maintain a set of additional constraints we identified as useful. A pilot study demonstrating this approach was conducted on three existing grammars. Results: The normalization algorithm was shown to correctly normalize the three grammars. Conclusions: This work presents a normalization method towards easing the development of automatically merging programming language grammars.
Subpixel precision in registration of multimodal datasets

Abstract The motivation for our research is the huge demand for registration of multimodal datasets in restorers practice. With an increasing number of various screening modalities, each analysis built on the acquired dataset starts with the registration of images acquired from different scanners and with varying levels of mutual correspondence. There is currently no well-suited state of the art method for this task. There are many existing approaches, i.e. based on control points or mutual information, but they do not provide satisfying (subpixel) precision, thus the registration is very often realized manually in Adobe Photoshop or any similar tool. Another popular option is to use scanners able to produce registered datasets by design. During the last 10 years, datasets from these devices have extended available analytical techniques the most. In our research, we focus on solving the mentioned registration task. In [1] we concluded that the work with misregistered modalities is possible but limited. Now we present results of our experiments challenging these limits and conditions under which we can precisely register data from different modalities. The achieved results are promising and allow usage of more complex artificial neural networks (ANN) for dataset analysis e.g. [2]. We describe the construction of registration layers for estimation of shift, rotation and scale and a useful strategy and parametrization for ANN optimizer.
Image Edge Detection by Mean Difference Thresholding

Edge detection is very essential step for numerous image processing tasks. In this paper, the different techniques of edge detection, i.e., Roberts, Prewitt, Sobel, Isotropic, Laplacian of Gaussian (LoG), Difference of Gaussian (DoG) and Canny are discussed and also the diagrammatic difference between the some of these techniques are reviewed. This paper describes a simple edge detection technique based on thresholding, which is used to make fast process of edge detection and represents results in more efficient way. The validation of proposed algorithm is done by MSE and PSNR parameters. There are many edge detection techniques available, but any single technique is not suitable for all the applications due to the variation in quality of an image and the variability in shape of an image. So, the selection of the technique is dependent on the requirement of the applications.
Malware Detection via Machine Learning and Recognition of Non Stationary Tasks

Most research studies indicate that users are frequently targeted by malware. Mimicry attacks enable malware to operate in ways that are similar to the work of legitimate users. Furthermore, when malware land on a target machine, they carry out operations to explore the compromised machine, as well as to unearth resources that are of benefit to threat actors. In this paper, we describe a machine learning approach that we developed to distinguish non stationary user tasks by analyzing their resource utilization. In the process, our approach predicts the progressive steps in a user task. Our approach collects live performance counter data, which it then prepares and analyzes to recognize and define patterns of resource utilization for a non stationary task.
Secured distributed algorithms without hardness assumptions

We study algorithms in the LOCAL model that produce secured output. Specifically, each vertex computes its part in the output, the entire output is correct, but each vertex cannot discover the output of other vertices, with a certain probability. As the extensive research in the distributed algorithms field yielded efficient decentralized algorithms, the discussion about the security of distributed algorithms was somewhat neglected. Nevertheless, many protocols and algorithms were devised in the research area of secure multi-party computation problem. However, the focus in those protocols was to work for every function f at the expense of increasing the round complexity, or the necessity of several computational assumptions. We present a novel approach, which identifies and develops those algorithms that are inherently secure (which means they do not require any further constructions). This approach yields efficient secure algorithms for various labeling and decomposition problems without requiring any hardness assumption, but only a private randomness generator in each vertex.
Mone: Mutation Oriented Norm Evolution

A norm regulates the run-time behavior of the agent with the action and condition to trigger the action. Because of the incomplete understanding of the world, the result of the action may be different for the same agent with the `same' context, or unintended different context. To study this phenomenon, the classical norm definition is extended from the condition-action pair to cover the expectation, in order to verify the result of the action. The Norm evolution can be defined as a gradual process which makes a norm more complete and effective. In the terminology of evolution, a norm is called mutated if the result contradicts to the expectation, i.e. at least one of the expected conditions is invalid. At run-time, norms are executed in series. A mutation brings new knowledge to the following states and might affect the later execution of the norms. Such knowledge provides will help the norm designer to complete the definitions. A mutation based norm evolution (Mone) method is proposed in this paper to detect the mutations, to propagate the evidence and to crossover the norms for completeness. The method is formalized in the Description Logic, and implemented with the algorithms for mutation detection and norm crossover. The case study illustrates the Description Logic $\mathcal {ALCI}$ of the method and shows the potential to evolve the norms autonomously in the Blackboard system, GBBopen.
Reproducing the most general quantum channel in the lab: is it possible?

We model the presence of the most general combination of decoherence and mode filtering elements in a quantum channel. We assess the accuracy of using conventional fiber-optic emulators of polarization-mode dispersion and polarization-dependent loss in reproducing the general channel in the laboratory.
A novel security mechanism using AES cryptography approach in cloud computing

Summary For a smart cloud computing system, security and privacy are the required factors. In recent days, the evolvement of cloud technology is immense, according to which several cloud service providers (CSPs) evolved. Each CSP has its infrastructure with its security policies. Even though still the security in cloud computing is an existing challenge, it is more important to maintain the trustiness of cloud environment among the cloud users. On this motto, we propose a novel tailored—advanced encryption standard (AES) Cryptographic Algorithm (TCA). AES is one of the popular cryptography algorithms. We indulged in increasing the performance of this algorithm by minimizing the time taken for the cryptography process. Here, the cryptography is processed with AES algorithm. In order to improve the efficiency of AES, a concept of a chunk file system is introduced. The input file is chunked into multiple which facilitates quick and effective encryption. To prove the efficiency of our proposed system, a comparison work is carried out with the existing algorithms lightweight keyword searchable encryption (LFSE) and cloud key management system (CKMS). The comparison work is based on the aspect of time taken during key generation, encryption and decryption. The obtained result is plotted, and comparison output clearly states our TCA's excellence in the aspect of efficiency compared to others.