Reviewerly/exHarmony huggingface dataset:
An Approach for Evolving Transformation Sequences Using Hybrid Genetic Algorithms

© 2020 The Authors. The digital transformation revolution has been crawling toward almost all aspects of our lives. One form of the digital transformation revolution appears in the transformation of our routine everyday tasks into computer executable programs in the form of web, desktop and mobile applications. The vast field of software engineering that has witnessed a significant progress in the past years is responsible for this form of digital transformation. Software development as well as other branches of software engineering has been affected by this progress. Developing applications that run on top of mobile devices requires the software developer to consider the limited resources of these devices, which on one side give them their mobile advantages, however, on the other side, if an application is developed without the consideration of these limited resources then the mobile application will neither work properly nor allow the device to run smoothly. In this paper, we introduce a hybrid approach for program optimization. It succeeded in optimizing the search process for the optimal program transformation sequence that targets a specific optimization goal. In this research we targeted the program size, to reach the lowest possible decline rate of the number of Lines of Code (LoC) of a targeted program. The experimental results from applying the hybrid approach on synthetic program transformation problems show a significant improve in the optimized output on which the hybrid approach achieved an LoC decline rate of 50.51% over the application of basic genetic algorithm only where 17.34% LoC decline rate was reached.
What Agile Processes Should We Use in Software Engineering Course Projects?

While project-based software engineering courses aim to provide learning opportunities grounded in professional processes, it is not always possible to replicate every process in classrooms due to course constraints. Previous studies observed how students react to various processes and gave retroactive recommendations. In this study, we instead combine a field study on professional Agile (eXtreme Programming, XP) teams and an established team process taxonomy to proactively select team processes to incorporate in a project-based software engineering course. With collected knowledge from the field study, we choose three XP processes to augment the design of a mature software engineering project course. We choose processes that are 1) considered important by professionals, and 2) complete with respect to coverage of the taxonomy's main categories. We then compare the augmented course design with the original design in a case study. Our results suggest that 1) even without extra resources, adding these new processes does not interfere with learning opportunities for XP processes previously existing in the course design; 2) student teams experience similar benefits from these new processes as professional teams do, and students appreciate the usefulness and value of the processes. In other words, our approach allows instructors to make conscious choices of XP processes that improve student learning outcomes while exposing students to a more complete set of processes and thus preparing them better for professional careers. Course designers with limited resources are encouraged to use our methodology to evaluate and improve the designs of their own project-based courses.
Continuous-Variable Quantum Key Distribution Over Air Quantum Channel With Phase Shift

Continuous-variable quantum key distribution (CV-QKD) over air quantum channel enables to provide unconditional information security, which is the one of most promising techniques for information wireless transfer. Since the air channel is of fluctuation in nature due to the presence of air turbulence, the random variations have to be taken into account when the security of CV-QKD is analyzed. We study the secret key rate of CV-QKD over the air quantum channel in this paper. The fluctuation of air channel is characterized by beam wandering and phase distortion under weak air turbulence conditions. The impact of fluctuation on the CV quantum signals is reflected by transmittance perturbation and phase shift. Our numerical results show that the secret key rate may keep relatively high level as the air quantum link increases at the outset. However, when the air link distance further increases beyond certain range, the secret key rate decreases strikingly since the both transmittance perturbation and the phase shift simultaneously deteriorate the performance of CV-QKD. Nevertheless, operating the CV-QKD under weak air turbulence conditions is available.
An Overview of Blockchain Technology Adoption

Governments of emerging economies need to stimulate awareness and adoption of emerging technologies to facilitate effective and efficient citizen service delivery.The need for an integrated Information Communication Technology ecosystem in developing countries, nurture the call for adoption of emerging technologies.Similar to every emerging technology, the adoption of blockchain is dependent on the extent that government and relevant stakeholders take the lead in supporting and unveiling market-creating innovation on blockchain platforms.This paper provides an insight into the need for blockchain technology in developing economy; with Nigeria as a case study.Possible challenges and limitation of blockchain implementation are highlighted with a recommendation on the role of ICT regulatory agency towards achieving a sustainable blockchain technology adoption.
Performance comparison of 3DES, AES, Blowfish and RSA for Dataset Classification and Encryption in Cloud Data Storage

Cloud data storage are logical pools containing physical storages on multiple servers in different locations.All over the world, organizations and individuals are leveraging the storage of their confidential data to cloud data storages to reduce cost and enjoy the numerous benefits the cloud has to offer.Nonetheless, there are several security risks associated with outsourcing data to cloud data storages and the most concerning among these risks is the breach of data confidentiality.Data confidentiality is crucial in cloud data storages as the impact risk of a data breach can be catastrophic.Encryption is the best way to ensure data confidentiality and that, data is protected from unauthorized disclosure.The two most important features of encryption algorithms are the guarantee of security without any vulnerabilities (strength of encryption) and the efficiency (performance) of the algorithm.This paper presents a survey of popular encryption algorithms (Triple DES, AES, Blowfish and RSA) and their performance comparison report by simulating the selected encryption algorithms with a classification framework.
An enhanced media ciphertext-policy attribute-based encryption algorithm on media cloud

With the development of cloud computing, more and more video services are moving to the cloud. How to realize fine-grained access control of those data on cloud becomes an urgent problem. Attribute-based encryption provides a solution. However, heavy computation is still a bottleneck restricting the wider application of attribute-based encryption in cloud computing. In addition, we find that expression of the access control structure on media cloud can be further improved. To solve these problems, we propose an enhanced media ciphertext-policy attribute-based encryption algorithm and introduce its two key components, the multiple access tree and the key chain. To increase the scalability of the proposed algorithm, we discussed the issues of multi-authorization, user revocation, and 1 − n multiple access tree. Security analysis shows that enhanced media ciphertext-policy attribute-based encryption can successfully resist chosen-plaintext attacks under the decisional bilinear Diffie–Hellman assumption. Performance analysis proves both theoretically and practically that the proposed algorithm incurs less computational cost than the traditional ciphertext-policy attribute-based encryption, multi-message ciphertext-policy attribute-based encryption, and scalable ciphertext-policy attribute-based encryption by optimizing the access control structure. Our proposed algorithm has strong practical significance in media cloud.
A Genetic Programming-based Wrapper Imputation Method for Symbolic Regression with Incomplete Data

Dealing with missing values is one of the challenges in symbolic regression on many real-world data sets. One of the popular approaches to address this challenge is to use imputation. Traditional imputation methods are usually performed based on the predictive features without considering the original target variable. In this work, a genetic programming-based wrapper imputation method is proposed, which wrappers a regression method to consider the target variable when constructing imputation models for the incomplete features. In addition to the imputation performance, the regression performance is considered for evaluating the imputation models. Genetic programming (GP) is used for building the imputation models and decision tree (DT) is used for evaluating the regression performance during the GP evolutionary process. The experimental results show that the proposed method has a significant advance in enhancing the symbolic regression performance compared with some state-of- the-art imputation methods.
A MODIFIED ON TWOFISH ALGORITHM BASED ON CYCLIC GROUP AND IRREDUCIBLE POLYNOMIAL IN GF (28)

In this “article”, a “new “adjustment” is made on Two-fishalgorithm based on using a new operation called cyclic group extended # (CGE#) operation for increasing the randomness of algorithm. This isa new operation works on 8-bits and using 30 tables constructed with cyclic group and multiplication in Galois Field (GF) (28). A new (CGE#) operation is used instead of (X-OR)“operation in “each “round of Fe-istel of Two-fish. This is done by using dual keys: one key is used for selecting one table among 30 tables, and the other” key” is used for: encryption: and “decryption. The proposed algorithms are evaluated by using many security metrics such as complexity, NIST, histogram and correlation coefficients. The modification has givengood results in these metrics, and this leads to make the proposed algorithmmuch more robust against many the attacks.
Mobile Learning Applications for Android und iOS for German Language Acquisition Based on Learning Analytics Measurements

The use of digital media is increasingly being promoted in school teaching. Since this aspect changes the interaction between teachers and pupils, this research is concerned with the development of a prototype of a mobile application for Android and iOS, in which different learning applications for language acquisition are offered on the basis of learning analytical measurements provided by experts in the field. By logging and collecting interactions of the user, it is possible to create a variety of statistical evaluations and thus respond to the needs and weaknesses of students. For the evaluation of the application, a user experience test was carried out, whereby the child-friendly operation of the application was tested. Due to the very positive feedback, the design was found to be good and can therefore be further developed.
Who is Influencer and How to Choose the Right One to Improve Brand Reputation?

The aim of the paper is to provide insight on the emerging community of influencers, which is largely driven by the high impact of social media, has significant power over the perception of brands and companies.These new capabilities require communications professionals who strive for continuous collaboration with target customers through the various social web channels.The paper processed influencers in a well-arranged way, describes its factors and common features for their classification.It describes the main attributes in influencer selection and identification.
Weighted Mutual Information for Aggregated Kernel Clustering

Background: A common task in machine learning is clustering data into different groups based on similarities. Clustering methods can be divided in two groups: linear and nonlinear. A commonly used linear clustering method is K-means. Its extension, kernel K-means, is a non-linear technique that utilizes a kernel function to project the data to a higher dimensional space. The projected data will then be clustered in different groups. Different kernels do not perform similarly when they are applied to different datasets. Methods: A kernel function might be relevant for one application but perform poorly to project data for another application. In turn choosing the right kernel for an arbitrary dataset is a challenging task. To address this challenge, a potential approach is aggregating the clustering results to obtain an impartial clustering result regardless of the selected kernel function. To this end, the main challenge is how to aggregate the clustering results. A potential solution is to combine the clustering results using a weight function. In this work, we introduce Weighted Mutual Information (WMI) for calculating the weights for different clustering methods based on their performance to combine the results. The performance of each method is evaluated using a training set with known labels. Results: We applied the proposed Weighted Mutual Information to four data sets that cannot be linearly separated. We also tested the method in different noise conditions. Conclusions: Our results show that the proposed Weighted Mutual Information method is impartial, does not rely on a single kernel, and performs better than each individual kernel specially in high noise.
Aspect-based Opinion Mining for Code-Mixed Restaurant Reviews in Indonesia

The goal of opinion mining is to extract the sentiment, emotions, or judgement of reviews and classified it. These reviews are very important because they can affect the decision-making from a person. In this paper, we conducted an aspect-based opinion mining research using customer reviews of restaurants in Indonesia and we focused into analyzing the code-mixed dataset. The evaluation conducted by making four scenarios namely removing stopwords without stemming, without removing stopwords but with stemming, without removing stopwords and stemming, and preprocessing with removing stopwords and stemming. We compared five algorithms which are Random Forest (RF), Multinomial Naive Bayes (NB), Logistic Regression (LR), Decision Tree (DT), and Extra Tree classifier (ET). The models were evaluated by using 10 folds cross validation, and the results show that all aspects achieved highest scores with different algorithms. LR achieved highest score for food (81.76%) and ambience (77.29%) aspects while the highest score for price (78.71%) and service (85.07%) aspects were obtained by DT.
Hybrid Tiled Convolutional Neural Networks (HTCNN) Text Sentiment Classification

The tiled convolutional neural network (TCNN) has been applied only to computer vision for learning invariances.We adjust its architecture to NLP to improve the extraction of the most salient features for sentiment analysis.Knowing that the major drawback of the TCNN in the NLP field is its inflexible filter structure, we propose a novel architecture called hybrid tiled convolutional neural network (HTCNN) that applies a filter only on the words that appear in similar contexts and on their neighbouring words (a necessary step for preventing the loss of some n-grams).The experiments on the IMDB movie reviews dataset demonstrate the effectiveness of the HTCNN that has a higher level of performance of more than 3% and 1% respectively than both the convolutional neural network (CNN) and the TCNN.These results are confirmed by the SemEval-2017 dataset where the recall of the HTCNN model exceeds by more than six percentage points the recall of its simple variant, CNN.
Recycling weak labels for multiclass classification

This paper explores the mechanisms to efficiently combine annotations of different quality for multiclass classification datasets, as we argue that it is easier to obtain large collections of weak labels as opposed to true labels. Since labels come from different sources, their annotations may have different degrees of reliability (e.g., noisy labels, supersets of labels, complementary labels or annotations performed by domain experts), and we must make sure that the addition of potentially inaccurate labels does not degrade the performance achieved when using only true labels. For this reason, we consider each group of annotations as being weakly supervised and pose the problem as finding the optimal combination of such collections. We propose an efficient algorithm based on expectation-maximization and show its performance in both synthetic and real-world classification tasks in a variety of weak label scenarios.
A Novel Approach to Data Storage Using Blockchain Technology

Unlike long ago information storage in recent times heavily relies on technology. Physical representations like paper-based documents of data have minimized to a negligible amount. But relying on technology has also its drawbacks. These drawbacks can essentially be in terms of security and security breaches can result in compromising data worth a lot of money. In order to tackle this problem, this research looks into the state-of-the-art cryptographic algorithms and data storing paradigms, more specifically focus on the form of data storage known as Blockchains. Currently no such system is available where the user has the capability to create dynamic schemas to store their data in Blockchains along with the its integration with their existing system. Leveraging the concepts of Smart Contracts and Blockchain, on this paper a novel data storing engine by which users can create dynamic data schemas is proposed and also the data query abstractions to manage the data, integration of this system with the users existing system will also be an unchallenging problem. Finally, the results are presented by building an application which is built using the proposed engine and thus evaluate the solution.
Detection of ship targets in photoelectric images based on an improved recurrent attention convolutional neural network

Deep learning algorithms have been increasingly used in ship image detection and classification. To improve the ship detection and classification in photoelectric images, an improved recurrent attention convolutional neural network is proposed. The proposed network has a multi-scale architecture and consists of three cascading sub-networks, each with a VGG19 network for image feature extraction and an attention proposal network for locating feature area. A scale-dependent pooling algorithm is designed to select an appropriate convolution in the VGG19 network for classification, and a multi-feature mechanism is introduced in attention proposal network to describe the feature regions. The VGG19 and attention proposal network are cross-trained to accelerate convergence and to improve detection accuracy. The proposed method is trained and validated on a self-built ship database and effectively improve the detection accuracy to 86.7% outperforming the baseline VGG19 and recurrent attention convolutional neural network methods.
Convergence and Iteration Complexity of Policy Gradient Method for Infinite-horizon Reinforcement Learning

We focus on policy search in reinforcement learning problems over continuous spaces, where the value is defined by infinite-horizon discounted reward accumulation. This is the canonical setting proposed by Bellman [3]. Policy search, specifically, policy gradient (PG) method, scales gracefully to problems with continuous spaces and allows for deep network parametrizations; however, experimentally it is known to be volatile and its finite-time behavior is not well understood. A major source of this gap is that unbiased ascent directions are elusive, and hence only asymptotic convergence to stationarity can be shown via links to ordinary differential equations [4]. In this work, we propose a new variant of PG methods that uses a random rollout horizon for the Monte-Carlo estimation of the policy gradient, which we establish yields an unbiased policy search direction. Furthermore, we conduct global convergence analysis from a nonconvex optimization perspective: (i) we first recover the results of asymptotic convergence to the stationary-point policies in the literature through an alternative supermartingale argument; (ii) we provide iteration complexity, i.e., convergence rate, of policy gradient in the infinite-horizon setting, showing that it exhibits comparable rates to stochastic gradient method in the nonconvex regime for diminishing and constant stepsize rules. Numerical experiments on the inverted pendulum demonstrate the validity of our results.
Machine Learning Based Performance Analysis and Prediction of Jobs on a HPC Cluster

There are a lot of middle-class or small-class high-performance computing clusters at universities and research institutes, etc. Large volumes of job logs have been accumulated after many years of operation. In this paper, on the basis of accumulated job logs on a high-performance computing cluster, we examine and analyze the job logs. Then, we study machine learning based performance analysis and prediction methods for parallel jobs. Various machine learning methods such as multivariate linear fitting, artificial neural network are used to build performance prediction models. We compare the errors of each model, and select the optimal prediction model for different users. The experimental results show that we can obtain reasonable prediction accuracy using the selected machine learning algorithms.
Adaptive Dynamic Disturbance Strategy for Differential Evolution Algorithm

To overcome the problems of slow convergence speed, premature convergence leading to local optimization and parameter constraints when solving high-dimensional multi-modal optimization problems, an adaptive dynamic disturbance strategy for differential evolution algorithm (ADDSDE) is proposed. Firstly, this entails using the chaos mapping strategy to initialize the population to increase population diversity, and secondly, a new weighted mutation operator is designed to weigh and combinemutation strategies of the standard differential evolution (DE). The scaling factor and crossover probability are adaptively adjusted to dynamically balance the global search ability and local exploration ability. Finally, a Gauss perturbation operator is introduced to generate a random disturbance variation, and to accelerate premature individuals to jump out of local optimization. The algorithm runs independently on five benchmark functions 20 times, and the results show that the ADDSDE algorithm has better global optimization search ability, faster convergence speed and higher accuracy and stability compared with other optimization algorithms, which provide assistance insolving high-dimensionaland complex problems in engineering and information science.
A Timeline Metaphor for Analyzing the Relationships between Musical Instruments and Musical Pieces

Digitization projects make cultural heritage data sustainably available.However, while digital libraries may capture various aspects, relations across different sources often remain unobserved.In our project, musicologists aimed to relate musical instruments with historical performances of musical pieces, both contained in different sources.We defined a similarity measure taking instrumentation, temporal as well as geospatial metadata into account, with which we were able to hypothesize potential relations.We propose a novel timeline design that offers a specific semantic zoom metaphor enabling the collaborating musicologists to observe and evaluate the results of our similarity analysis.The value of our system for research in musicology is documented in three case studies.