Reviewerly/exHarmony huggingface dataset:
Experience With Managing Technical Debt in Scientific Software Development Using the EXA2PRO Framework

Technical Debt (TD) is a software engineering metaphor that resembles the production of poor-quality code to going into debt. In particular, a development team that “saves” effort while developing by not removing inefficiencies, has to “pay-back” with interest, in the form of additional maintenance costs (i.e., fixing bugs, adding features, etc.) due to the poor maintainability of the developed code. Although maintainability assurance is an established practice in traditional software development (lately known as TD management), it has still not attracted the attention of scientific software developers; i.e., researchers writing code and developing tools for purely research purposes. Nevertheless, based on the literature and practice, maintainability seems to be ranked as an important key-driver for the development of such applications; since the effort needed to update the code before the experimentation (e.g., executing a simulation) is common and should not receive low priority. In this paper, we present the outcome of a 3-year research project on Technical Debt Management (TDM) for scientific software development. The outcome of the project is a framework (termed: EXA2PRO TDM framework) and an accompanying platform for assisting scientific software developers in managing the TD of their applications. The framework is a collection of methods tailored for the mainstream programming languages of scientific software development, which have been empirically validated through five pilot applications. The majority of the EXA2PRO framework suggestions have been applied by scientific software developers and eased future maintenance activities.
Memorization Techniques for Peter Chew Rule

We normally use sine rule to find the opposite side angle given when we are given two angles and one side. We also can use sine rules to find non-included angle when we are given two sides and a non-included angle. However, sine rule is not suitable to find the included angle when we are given three sides or to find the third side when we are given two sides and the included angle. This is because if we use the sine rule, the equation will have 2 unknowns. As we know, an equation with 2 unknowns cannot be solved. Therefore, we need to learn Cosine rule to solve the above problem. When giving 2 sides and an included angle, the sine rule and the cosine rule also are not suitable for finding the non-included angle directly. This is because if you use a sine rule or a cosine rule, the equation will have 2 unknowns. As mentioned earlier, equations with 2 unknowns cannot be solved. Therefore, we need to learn Peter Chew rule to solve the above problem. Peter Chew Rule allows us to find non included angles directly, more easier and more accurate. Symmetry, we can get 3 similar sine rules, 3 similar cosine rules and 6 similar Peter Chew rules. A Memorization Techniques must be created to make us happily remember 6 similar Peter Chew rules. For sine rule, a/sin⁡A = b/sin⁡B = c/sin⁡C . Techniques to remember rules is sides versus angles. If the angle we are looking for is A, then the side is also a. As same, If the angle we are looking for is B, then the side is also b. For cosine rule, a^2 = b^2 〖+ c〗^2 -2 bc cos⁡A. If the side we are looking for is a ,then the angle given normally is also A .As with the sine and cosine rules, the memorization techniques to remember Peter Chew rules, A = (a sin B)/(c - a cos B) , is also sides versus angles. If the angle we are looking for is A, then for the right hand side, the side is also a. If the angle is known as B, then for the denominator, the other side is c. the denominator will be c - a cos B. If the angle is known as C, the other side of the denominator is b, the denominator will be b - a cos C. Therefore, Peter Chew rule is tan A = (a sin C)/(b - a cos C) . So the technique to remember is very simple, the denominator will involve a, b, c. Therefore, remembering Peter Chew's rules is as easy as remembering a, b, and c. A game will be developed to test the efficiency of Peter Chew Rule's memorization techniques.
3M: Multi-style image caption generation using Multi-modality features under Multi-UPDOWN model

In this paper, we build a multi-style generative model for stylish image captioning which uses multi-modality image features, ResNeXt features, and text features generated by DenseCap. We propose the 3M model, a Multi-UPDOWN caption model that encodes multi-modality features and decodes them into captions. We demonstrate the effectiveness of our model on generating human-like captions by examining its performance on two datasets, the PERSONALITY-CAPTIONS dataset, and the FlickrStyle10K dataset. We compare against a variety of state-of-the-art baselines on various automatic NLP metrics such as BLEU, ROUGE-L, CIDEr, SPICE, etc \footnote{code will be available at https://github.com/cici-ai-club/3M}. A qualitative study has also been done to verify our 3M model can be used for generating different stylized captions.
A General Framework for Domain-Specialization of Stance Detection

We present a generalized framework for domain-specialized stance detection, focusing on Covid-19 as a use case. We define a stance as a predicate-argument structure (combination of an action and its participants) in a simplified one-argument format, e.g., wear(a mask), coupled with a task-specific belief category representing the purpose (e.g., protection) of an argument (e.g., mask) in the context of its predicate (e.g., wear), as constrained by the domain (e.g., Covid-19). A belief category PROTECT captures a belief such as “masks provide protection,” whereas RESTRICT captures a belief such as “mask mandates limit freedom.” A stance combines a belief proposition, e.g., PROTECT(wear(a mask)), with a sentiment toward this proposition. From this, an overall positive attitude toward mask wearing is extracted. The notions purpose and function serve as natural constraints on the choice of belief categories during resource building which, in turn, constrains stance detection. We demonstrate that linguistic constraints (e.g., light verb processing) further refine the choice of predicate-argument pairings for belief and sentiment assignments, yielding significant increases in F1 score for stance detection over a strong baseline.
Parity Quantum Optimization: Encoding Constraints

Constraints make hard optimization problems even harder to solve on quantum devices because they are implemented with large energy penalties and additional qubit overhead. The parity mapping, which has been introduced as an alternative to the spin encoding, translates the problem to a representation using only parity variables that encodes products of spin variables. In combining exchange interaction and single spin flip terms in the parity representation, constraints on sums and products of arbitrary <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>k</mml:mi></mml:math>-body terms can be implemented without additional overhead in two-dimensional quantum systems.
Voice Controlled News Web Application with Speech Recognition using Alan Studio

In this paper, a very fascinating proposition is presented, a new web-based service that is a fusion of the revolutionary new Alan Studio, News API and React.The proposed idea of the Voice Controlled Web Application provides a simplistic approach and ease to the user.The service provides all the components required for a user to be able to use voice and speech as a medium to find and look for news about his/her choice and the option to go through the news in a very concise or in very detailed manner.A voice-controlled system embedded in a web application can enhance user experience and can provide voice as a means to control the functionality of e-commerce websites.The system also offers the ability to search news on the basis of preferred location, source, theme, and interest.The system provides a very user-friendly, easy to use, dynamic, and informative user experience.The system enables the user to have the ability to not only stay informed and updated but that too by using one of the most sought-after technologies in the world right now.The system will reduce the amount of human effort required by the user to perform previously and will offer a more exciting way of getting informed.Figure 1 Working Diagram of the System
Pathways to a Knowledge Society: A Proposal for a Hierarchical Model for Measuring Digital Literacy Among Israeli pre-service Teachers

This paper presents the results of an empirical study on validation of performance statements for an inventory of the digital literacy skills required for learning. These skills are used to determine digital readiness of pre-service teachers. The paper reports on validation of an adjusted set of 54 performance statements which were categorized into seven digital learning domains. The seven digital learning domains were validated based on structural equation modelling which was then confirmed by factor analysis using AMOS software. The results of the analysis indicate the existence of a multi-layered model where all digital learning domains are positively connected to each other. Our findings therefore suggest a strong statistical validity of the performance statement inventory. The findings also point to the fact that pre-service teachers are in a transit phase from digital immigrants to digital natives. That, in the context of Covid-19 pandemic, implies the need to invest in appropriate preparation and training of pre-service teachers to teach in an online environment. The inventory for measuring digital literacy can be used to assess teachers’ digital readiness on a regular basis, thereby enabling them to adjust the teaching materials and pedagogy to achieve the required level of digital readiness, as defined by the school.
Distributed data hiding in a single cloud storage environment

Abstract Distributed steganography is an approach to concealing the secret in several files, leaving fewer traces than the classical approach. Recent work proposed by Moyou and Ndoumdan have improved this approach by preserving the integrity of these files in a multi-cloud storage environment. However, the approach requires a large size of the stego-key and the management of several cloud storage environments. Our contribution consists is to improve this approach by using a single cloud storage environment and reducing the size of the stego-key. In this work, a single cloud storage environment is used to solve the problems of managing several credentials, monetary costs and data controls associated with multi-cloud storage environments. The comparisons showed interesting results with simpler operations to be performed by the participants during the process.
Mutual Implication as a Measure of Textual Equivalence

&#x0D; &#x0D; &#x0D; Semantic Textual Similarity (STS) and paraphrase de- tection are two NLP tasks that have a high focus on the meaning of sentences, and current research in both re- lies heavily on comparing fragments of text. Little to no work has been done in studying inference-centric ap- proaches to solve these tasks. We study the relation be- tween existing work and what we call mutual implica- tion (MI), a binary relationship between two sentences that holds when they textually entail each other. MI thus shifts the focus of STS and paraphrase detection to un- derstanding the meaning of a sentence in terms of its in- ferential properties. We study the comparison between MI, paraphrasing, and STS work. We then argue that MI should be considered a complementary evaluation met- ric for advancing work in areas as diverse as machine translation, natural language inference, etc. Finally, we study the limitations of MI and discuss possibilities for overcoming them.&#x0D; &#x0D; &#x0D;
A New Modulation Recognition Method Based on Flying Fish Swarm Algorithm

The modulation recognition method based on deep learning plays a significant role in the intelligent communication system. To further improve the recognition rate, especially in the case of small samples with a low signal-to-noise ratio, this paper proposes a new modulation recognition method based on flying fish swarm algorithm. First, Short-Time Fourier Transform, Choi-Williams Distribution, and Cyclic Spectrum are combined to complete multi-channel signal processing. Second, AlexNet, VGGNet, GoogLeNet, and ResNet are transferred to realize feature extraction. Third, the support vector machine classifies the modulations after dimension reduction and feature fusion. Finally, the flying fish swarm is proposed to optimize the signal processing methods, the types of networks, the layers of networks, the dimensions of features, and the parameters of the support vector machine. The method can accurately recognize BPSK, QPSK, OQPSK, 8PSK, 4ASK, QAM16, QAM32, and QAM64. The simulation results show that the average recognition rate of modulation is 94.5% at SNR of 0 dB and 84.7% at SNR of −4 dB. Besides, the proposed modulation recognition method possesses good robustness under low SNR conditions.
Target Detection and Classification Performance Enhancement using Super-Resolution Infrared Videos

Long range infrared videos such as the Defense Systems Information Analysis Center (DSIAC) videos usually do not have high resolution. In recent years, there are significant advancement in video super-resolution algorithms. Here, we summarize our study on the use of super-resolution videos for target detection and classification. We observed that super-resolution videos can significantly improve the detection and classification performance. For example, for 3000 m range videos, we were able to improve the average precision of target detection from 11% (without super-resolution) to 44% (with 4x super-resolution) and the overall accuracy of target classification from 10% (without super-resolution) to 44% (with 2x superresolution).
The Impact of Augmented Reality on the Social Skills of Children with High Functioning Autism

The present study determined the effectiveness of augmented reality on the social skills of children with high Functioning autism. The present study was a quasi-experimental study with a posttest test with a control group. The statistical population of the present study included all children with autism with high performance in Andimeshk in 2019. Participants included 20 children with high-functioning autism who were selected by available sampling method and randomly selected in the experimental and control groups. They were evaluated using the Bellini social profile, and then augmented reality was performed on the experimental group in 10 45-minute sessions, while not on the control group. The findings were analyzed using covariance analysis and showed that the augmented reality intervention program had an effect on the social skills of children with high-functioning autism. Based on these results, augmented reality intervention program can be considered as a priority for rehabilitation of children with autism with high performance.
A Plaintext-related Image Encryption Algorithm Usable in Biometric Systems

This paper proposes an image encryption algorithm that uses a plaintext-related solution. Since it utilizes a diffusion stage that spreads the differences also during decryption, it has several interesting properties for securing biometric image data. If the encrypted images are modified, their decryption provides noisy images without any important information. These assumptions are evaluated by several experiments with fingerprint and eye iris images and the obtained numerical data are compared with those reported in related papers. It is shown that the proposed solution achieves comparable results but it is much more effective.
Predictive Job Scheduling under Uncertain Constraints in Cloud Computing

Capacity management has always been a great challenge for cloud platforms due to massive, heterogeneous on-demand instances running at different times. To better plan the capacity for the whole platform, a class of cloud computing instances have been released to collect computing demands beforehand. To use such instances, users are allowed to submit jobs to run for a pre-specified uninterrupted duration in a flexible range of time in the future with a discount compared to the normal on-demand instances. Proactively scheduling those pre-collected job requests considering the capacity status over the platform can greatly help balance the computing workloads along time. In this work, we formulate the scheduling problem for these pre-collected job requests under uncertain available capacity as a Prediction + Optimization problem with uncertainty in constraints, and propose an effective algorithm called Controlling under Uncertain Constraints (CUC), where the predicted capacity guides the optimization of job scheduling and job scheduling results are leveraged to improve the prediction of capacity through Bayesian optimization. The proposed formulation and solution are commonly applicable for proactively scheduling problems in cloud computing. Our extensive experiments on three public, industrial datasets shows that CUC has great potential for supporting high reliability in cloud platforms.
Development of Ground Special Vehicle PHM with Case-Based Reason Model

The case-based reasoning (CBR) method can effectively predict the future health condition of the system based on past and present operating data records, so it can be applied to the prognostic and health management (PHM) framework, which is a type of data-driven problem-solving. The establishment of a CBR model for practical application of the Ground Special Vehicle (GSV) PHM framework is in great demand. Since many CBR algorithms are too complicated in weight optimization methods, and are difficult to establish effective knowledge and reasoning models for engineering practice, an application development using a CBR model that includes case representation, case retrieval, case reuse, and simulated annealing algorithm is introduced in this paper. The purpose is to solve the problem of normal/abnormal determination and the degree of health performance prediction. Based on the proposed CBR model, optimization methods for attribute weights are described. State classification accuracy rate and root mean square error are adopted to setup objective functions. According to the reasoning steps, attribute weights are trained and put into case retrieval; after that, different rules of case reuse are established for these two kinds of problems. To validate the model performance of the application, a cross-validation test is carried on a historical data set. Comparative analysis of even weight allocation CBR (EW-CBR) method, correlation coefficient weight allocation CBR (CW-CBR) method, and SA weight allocation CBR (SA-CBR) method is carried out. Cross-validation results show that the proposed method can reach better results compared with the EW-CBR model and CW-CBR model. The developed PHM framework is applied to practical usage for over three years, and the proposed CBR model is an effective approach toward the best PHM framework solutions in practical applications.
Incorporating Syntactic and Phonetic Information into Multimodal Word Embeddings Using Graph Convolutional Networks

Multimodal models have been proven to outperform text-based models on learning semantic word representations. According to psycholinguistic theory, there is a graphical relationship among the modalities of language, and in recent years, the graph convolution network (GCN) has been proven to have substantial advantages in the extraction of non-European spatial features. This inspires us to propose a new multimodal word representation model, namely, GCNW, which uses the graph convolutional network to incorporate the phonetic and syntactic information into the word representation. We use a greedy strategy to update the modality-relation matrix in the GCN, and we train the model through unsupervised learning. We evaluated the proposed model on multiple downstream NLP tasks, and various experimental results demonstrate that the GCNW outperforms strong unimodal baselines and state-of-the-art multimodal models. We make the source code of both models available to encourage reproducible research.
Dynamic Multi-Branch Layers for On-Device Neural Machine Translation

With the rapid development of artificial intelligence (AI), there is a trend in moving AI applications, such as neural machine translation (NMT), from cloud to mobile devices. Constrained by limited hardware resources and battery, the performance of on-device NMT systems is far from satisfactory. Inspired by conditional computation, we propose to improve the performance of on-device NMT systems with dynamic multi-branch layers. Specifically, we design a layer-wise dynamic multi-branch network with only one branch activated during training and inference. As not all branches are activated during training, we propose shared-private reparameterization to ensure sufficient training for each branch. At almost the same computational cost, our method achieves improvements of up to 1.7 BLEU points on the WMT14 English-German translation task and 1.8 BLEU points on the WMT20 Chinese-English translation task over the Transformer model, respectively. Compared with a strong baseline that also uses multiple branches, the proposed method is up to 1.5 times faster with the same number of parameters.
Mapping the Alteration Zones for Uranium Exploration in Gabal Abu Garadi Area Central Eastern Desert, Egypt, Using Aster Data

The integration of the ASTER image processing and the airborne spectrometric Gamma-Ray interpretation helped in identifying the types and illustrating the distributions of the probable sites for radioactive mineralization at the Gabal Abu Garadi area.Appling various techniques of image processing for Landsat OLI and ASTER satellite to discrimination the differently exposed rock units and extract major and minor structural lineaments.This study resulted that the ASTER VNIR-SWIR bands are effectively used for clarifying the sericite, kaolinite, chlorite, illite and hematite bearing alteration zones, whereas the ASTER TIR bands are most effective in delineation of the silica-rich zones which could be pathfinders for radioactive mineralization within the study area.The resultant alteration maps show good coincidence with the explored uranium sites and suggest further similar unexplored sites.The predominance of low eTh/eU= 1.31 indicates U mobilization and reveals that magmatic differentiation was not the sole process responsible for the concentration of these radioactive elements in the Abu Gradi area.Where the presence of adequate structures such as shear zones and fault planes facilitates the circulation of hydrothermal fluids and meteoric waters leading to mobilization and a redistribution of uranium.This paper could assist the principals of uranium exploration techniques.