Reviewerly/exHarmony huggingface dataset:
Data Mining Approach to Effort Modeling On Agile Software Projects

Software production is a complex process. Accurate estimation of the effort required to build the product, regardless of its type and applied methodology, is one of the key problems in the field of software engineering. This study presents the approach to effort estimation on agile software project using local data and data mining techniques, in particular k-nearest neighbor clustering algorithm. The applied process is iterative, meaning that in order to build predictive models, sets of data from previously executed project cycles are used. These models are then utilized to generate estimate for the next development cycle. Used data enrichment process, proved to be useful as results of effort prediction indicate decrease in estimation error compared to the estimates produced solely by the estimators. The proposed approach suggests that similar models can be built by other organizations as well, using the local data at hand and this way optimizing the management of the software product development.
Cement Texture Synthesis Based on Feedforward Neural Network

Texture is of great significance to the study of cement field. It can reflect various information, such as cement strength and hydration age. However, the texture of cement hydration image is complex and diverse, and most of the methods are relatively inefficient at present. Therefore, we propose a fast way to synthesize texture through neural network. It uses the information of the causal neighborhood to extract their implicit features. This method is more perfect than the simple expression method, and can extract more implicit features and get a better neural network model. Through this model we can quickly and easily synthesize cement texture images. This algorithm is faster than the current popular methods and more diverse than the methods of gene expression programming.
Image Smear Removal via Improved Conditional GAN and Semantic Network

Image inpainting is one of the most important problems in the field of image algorithm, and it is also an effective preprocessing method for many other image applications. In this paper, we suggest an image decontamination method which is mainly used to remove mesh stains and also provide a data set for this task. To our knowledge, this work is the first attempt to solve this kind of problem. Specifically, the proposed method is composed of two phases: we first remove the mesh stains with an Improved Conditional Generative Adversarial Network, and then utilize a Semantic Network to fine tune the details. Experiments demonstrated that this two-stage model can remove the mesh stains. Results show that our method significantly out-performs existing methods and achieves superior inpainting results on challenging cases.
CYBERSPACE HAS GREATLY HELPED ENTREPRENEURS TO FLOURISH

The invasion of technology is to such an extent that any brand new digital technologies, including social media, enterprise analytics, the Cyberspace of factors, large facts, advanced manufacturing, dont spare any organization and pervade every non-public and public agency. Even though the concept of technology has been analyzed in detail with reference to entrepreneurship but its impact on entrepreneurship still remains a question that is unanswered. With the intent of filling this hole, this paper proposes a novel contribution concerning the rising concept of virtual instructional Entrepreneurship. Based on a qualitative literature assessment, an interpretative framework for digital academic Entrepreneurship is deductively proposed this is composed of the following additives: the cause for the adoption of virtual technology for instructional entrepreneurship (why), the emerging forms of virtual instructional entrepreneurship (what), the stakeholders concerned through the virtual technology to gain the instructional entrepreneurship purpose (who), and the techniques of academic entrepreneurship supported by means of digital technology (how). This paper presents a based literature review of digital entrepreneurship to generate insights into current trends in the discipline, critique the studies thus far, and identify opportunities for future studies. We have implemented the three components of critical research  insight, critique, and transformative redefinition  to analyze and synthesize the literature. We distil the definitions of the key constructs and perceive three studies development levels similar to exercise development.
Integration and use of an augmented reality display in a maritime helicopter simulator

Operating a helicopter in offshore wind parks with degraded visual environments from clouds or fog can endanger crew and material due to the presence of unseen obstacles. Utilizing on-board sensors such as LIDAR or radar, one can sense and record obstacles that could be potentially dangerous. One major challenge is to display the resulting raw sensor data in a way that the crew, especially the pilot, can make use of it without distracting them from their actual task. Augmented reality and mixed reality applications play an important role here. By displaying the data in a see-through helmet-mounted display (HMD), the pilot can be made aware of obstacles that are currently obscured by degraded visual conditions or even parts of the helicopter. This can be accomplished in one HMD. No attention sharing between the outside view and a head-down instrument is necessary. The German Aerospace Center (DLR) is continuously aiming at testing and evaluating both flight-proof and consumer grade HMDs. One particular widely known system is the Microsoft HoloLens. DLR will integrate this low-cost HMD into their experimental helicopter. For this, as a first step, a Microsoft HoloLens was integrated into DLR’s Air Vehicle Simulator (AVES). The integration process is detailed. The simulation capabilities are described, especially for conformal, open-loop LIDAR sensor data. Furthermore, first concepts of the display format are shown, and strengths and drawbacks of the HoloLens in a cockpit environment are discussed.
Empirical Remarks on the Translational Equivariance of Convolutional Layers

In general, convolutional neural networks (CNNs) maintain some level of translational invariance. However, the convolutional layer itself is translational-equivariant. The pooling layers provide some level of invariance. In object recognition, invariance is more important than equivariance. In this paper, we investigate how vulnerable CNNs without pooling or augmentation are to translation in object recognition. For CNNs that are specialized in learning local textures but vulnerable to learning global geometric information, we propose a method to explicitly transform an image into a global feature image and then provide it as an input to neural networks. In our experiments on a modified MNIST dataset, we demonstrate that the recognition accuracy of a conventional baseline network significantly decreases from 98% to less than 60% even in the case of 2-pixel translation. We also demonstrate that the proposed method is far superior to the baseline network in terms of performance improvement.
DIGITAL STEGANOGRAPHY FOR PREVENTING CYBERCRIME USING ARTIFICIAL INTELLIGENCE TECHNOLOGY

Steganography is a process of hiding data into an image, audio, video or text file. The word Steganography is obtained from Greek: stegos means to cover and grayfia, means writing. i.e. covered writing. The main objective is to protect the sensitive data against the malicious attack. The present paper discuss the steganography used to embedded data in an image file. The text can be encoded in an image and the modified image is to be sent to the authorized person. This prevents the secret data from outside attacks tried by cybercriminals. After receiving the image, the person will decode the image and obtain the confidential information. The proposed embedded algorithm is based on Artificial Intelligence (AI) technology. The encryption and decryption are done using MATLAB.
Looking for Energy Efficient Genetic Algorithms

When Evolutionary Algorithms (EAs) are applied to optimization problems, two main measures are taken into account to understand their performance: fitness quality and computing time. These two values are used to compare the performance of different versions of an algorithm, different parameter settings of a single algorithm or even compare a particular EA with other available heuristics. Nevertheless, a new trend in computer science tries to contextualize these features under a new perspective: power consumption. This paper presents a preliminary analysis of the standard genetic algorithm, using two well known benchmark problems, considering their fitness quality, the computing time and also the power consumption when battery-powered devices are used to run them. Results show that some of the main parameters of the algorithm have an impact on instantaneous energy consumption -that departs from the expected behavior, and therefore affects the amount of energy required to run the algorithm. Although we are still far from finding a way to design energy-efficient EAs, we think the results open up a new perspective that will enable us to achieve this goal in the future.
Towards a full body narrative: a communicational approach to techno-interactions in virtual reality

The essay approaches the sociocultural and technological issues linked to the communicational process that exists in the user’s relationship with immersive digital environments, specifically: Virtual Reality (VR). A critical look at VR as a communication interface is launched, precisely because it indicates assumptions of enhancing the creation of meanings on media surfaces by leveraging multisensory stimuli that cause the immersion of individual’s body in a 360° framed experience. To instigate the problematization about transformations resulting from these techno-experience, empirical explorations are conducted to Brazilian immersive environments. In addition, data are qualitatively analyzed based on Kerckhove, Gumbrecht and Flusser and others ideas to illuminate the debate of technical-images, digital narratives and appropriations on technological devices, media languages and coding in the digital age.
Enhancing the Accuracy of the Use of Verb Tenses by Iranian Intermediate EFL Students through Mall: The Case of English Grammar Ultimate Software App

Teaching grammar to EFL students has always been a challenge for EFL teachers. Many studies have acknowledged the role of using mobile apps to remove this problem. Therefore, the present study was conducted to see if the use of English Grammar Ultimate Software app can enhance the use of verb tenses by Iranian intermediate students. To this end, thirty-two students participated in this study based on their performance in a Quick Oxford Placement Test. They were randomly assigned to experimental and control groups. Each group consisted of 16 learners. Then a test of grammar was administered to two groups as the pre-test to assess their knowledge of simple present, simple past, present continuous, past continuous, and simple future tenses and ensure their homogeneity. Learners in the control group were taught these tenses through traditional language classrooms and received no extra treatment at all. However, learners in the experimental group were taught those tenses through English Grammar Ultimate Software app. Finally, in order to measure the effect of the treatment, a posttest was given to both groups. The results of the comparison between and within groups showed that the participants who had benefited from mobile-assisted learning had a significantly better performance on the post-test than the participants in the control group. The findings of the present study can have implications for material developers and EFL language teachers teaching grammar.
Efficient Proposals: Scale Estimation for Object Proposals in Pedestrian Detection Tasks

Due to projective transformation, a great variety of pedestrian sizes appears on the image depending on their depths in the real world. In this letter, we analyze the object proposal generation strategies in existing object detection methods that underperform in some real-world applications owing to ignorance of spatial factors. To this end, we propose a neural network predictor to estimate the sizes of object proposals based on their locations on the image. Furthermore, our model can efficiently generate high-quality proposals using very few training samples with the help of a data augmentation strategy. The proposed size estimation method is compared against several state-of-art object proposal estimation methods by two metrics on a driving dataset and a train station surveillance dataset which shows significant performance advantages.
Exploring Contextual word representation for Arabic question classification

Identifying the category of a question is an essential task in Question Answering Systems. Contextual continuous word representation proved to be effective in various natural language processing tasks. However, these models have not been considered in the field of Arabic question classification. In this paper, we investigate the use of a contextual word representation named Embeddings from Language Models (ELMo) on Arabic question classification. We study the behaviour of this representation by building numerous neural network architectures trained to classify questions. The dataset contains 3173 questions annotated with two taxonomies including Arabic taxonomy and an updated Li & Roth taxonomy. By comparing to enriched word2vec with subword information technique, which is a context-free representation, we show that ELMo representation achieves better performance at the cost of reduced sized word vector. The best classifier achieves up to 94% in terms of accuracy, macro F 1 score, and weighted F 1 score.
Eigenlogic in the Spirit of George Boole

This work presents an operational and geometric approach to logic. It starts from the multilinear elective decomposition of binary logical functions in the original form introduced by George Boole. A justification on historical grounds is presented bridging Boole’s theory and the use of his arithmetical logical functions with the axioms of Boolean algebra using sets and quantum logic. It is shown that this algebraic polynomial formulation can be naturally extended to operators in finite vector spaces. Logical operators will appear as commuting projection operators and the truth values, which take the binary values $$\{0,1\}$$, are the respective eigenvalues. In this view the solution of a logical proposition resulting from the operation on a combination of arguments will appear as a selection where the outcome can only be one of the eigenvalues. In this way propositional logic can be formalized in linear algebra by using elective developments which correspond here to combinations of tensored elementary projection operators. The original and principal motivation of this work is for applications in the new field of quantum information, differences are outlined with more traditional quantum logic approaches.
THE INTERFERENCE OF INDONESIAN ON ENGLISH SECOND LANGUAGE WRITING

Language transfer means the application of knowledge and understanding of a source language on a target language. Dissimilarities between the source language and the target language could result in a negative transfer. This essay aims to investigate the interference of Indonesian language on English second language writing regarding the order of noun phrase constituents, number marking, and verb tenses. The results presented in this essay were based on three elicitation tasks that were answered by 20 Indonesian students who studied English at a senior high school in Sumbawa Regency, Indonesia. Their ages were between 15 to 18 years old. The participants were given a test that consisted of three elicitation tasks focusing on sentence writing. The results showed that the negative transfer was still found in the students’ writing. However, the students may make mistakes since they had a lack of knowledge, or because they were unable to apply what they knew in their writing.
uFETCH: A Unified Searchable Encryption Scheme and Its Saas-Native to Make DBMS Privacy-Preserving

As encrypted-search techniques such as Searchable Encryption (SE) were devised for homogeneous data type, i.e. textual or numerical, it is a nature presumption that multiple techniques have to be intertwined to make database management system (DBMS) privacy-preserving. In effect, such a presumption has led to popular designs such as CryptDB, putting efforts on heterogeneous integration. In this paper, an easier option is made available when a unified SE scheme named uFETCH is proposed to accommodate both. Namely, uFETCH is able to build unified SE indexes for both the types while enabling encrypted search even if the SE indexes built for texts and numbers are mingled. To demonstrate how uFETCH can bring up simpler designs, a security agent is exemplified to work with off-the-shelf DBMS while making it privacy-preserving. Since uFETCH transforms the problem of encrypted search into a simple problem of subsequence matching for SaaS-native, it requires only sub-linear search time w.r.t. the volume of indexed items and is secure in the widely-adopted 3-tier cloud structure to help cloud service providers ease regulation compliance with out-sourced repository.
Personalizing Fast-Forward Videos Based on Visual and Textual Features from Social Network

The growth of Social Networks has fueled the habit of people logging their day-to-day activities, and long First-Person Videos (FPVs) are one of the main tools in this new habit. Semantic-aware fast-forward methods are able to decrease the watch time and select meaningful moments, which is key to increase the chances of these videos being watched. However, these methods can not handle semantics in terms of personalization. In this paper, we present a new approach to automatically creating personalized fast-forward videos for FPVs. Our approach explores the availability of text-centric data from the user's social networks such as status updates to infer her/his topics of interest and assigns scores to the input frames according to her/his preferences. Extensive experiments are conducted on three different datasets with simulated and real-world users as input. Our method achieved an average F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> score of up to 12.8 percentage points higher than the best competitors. We also present a user study to demonstrate the effectiveness of our method.
Living in the Future: New Actors in the Field of Criminal Law – Artificial Intelligence

Artificial Intelligence is omnipresent in all sectors of social life, affecting, influencing, determining or facilitating our lives and decisions.Recently, a robot was "killed" by a selfdriving car.The incident reported is mentioned only for the purpose of emphasizing the need of legal regulation in this growing field.Several other incidents -autonomous vehicle crashes with human casualties -have also raised questions on the issue of criminal liability, precisely on the issue of recognizing a new actor in the field of criminal law: Artificial Intelligence.Several initiatives at the European level and other scientific legal debates have joined in recognition of the need to acknowledge this new subject of law, or, at least to create legal tools in order to sanction Artificial Intelligence for harmful acts.The current study aims to urge a debate on issues like electronic personality, legal and criminal liability of Artificial Intelligence, on present and future obstacles in creating an efficient legislative frame in a field in need to be properly regulated.Old debates, such as the peril of over-criminalization or ratio of the criminalization are also reintroduced from an entirely new perspective.The conclusions of the study point out to the necessity of outlining a set of principles and rules with specific application to Artificial Intelligence, of prompt recognition of a legal and criminal liability of Artificial Intelligence, of setting moral and legal boundaries for the human intervention and of changing the whole paradigm of legal liability by accepting this new actor: Artificial Intelligence.
Introduction to Opinion Mining

The prime component that can enhance the quality of services is the opinion of the users. Factual information and opinion information are two categories of textual information [1]. Facts are sentences which are true and can be verified, whereas opinions are sentences which hold an element of belief and cannot be verified for their truth.
[Chemical Characteristics of Groundwater and Material Sources Analysis in Buckwheat Field, Yunnan Province].

In order to study the hydrochemistry characteristics and material sources of groundwater in the Buckwheat basin, we collected 32 groundwater samples in the study area in July 2017. The chemical characteristics of groundwater were analyzed by cluster analysis, factor analysis, and path model analysis. Meanwhile, we qualitatively and quantitatively assessed the sources of groundwater hydrochemistry in the Buckwheat basin. The results showed that the groundwater in the study area was weakly alkaline, and the major ions such as Ca2+, Mg2+, SO42-, HCO3-, and NO3- had high spatial variability. According to the clustering analysis, the groundwater in the study area can be divided into two categories, namely, class A and class B, and these have the following two subsets:A1, A2, B1, and B2. From A to B, the chemical types of groundwater become more complex. The hydrochemistry types of class A were all of the HCO3-Ca type, while those of class B1 were HCO3-Ca, HCO3-Na·Ca, and HCO3-Na·Ca·Mg, and those of class B2 were HCO3-Ca, HCO3-Ca·Mg, HCO3-Na·Ca. In order to further explore the influence factors of groundwater hydrochemistry, we determined that carbonate rocks, human activities, evaporite rocks, and silicate rocks were the main material sources of groundwater through factor analysis and path model analysis. Among all of the above factors, the dissolution of magnesium sulfate evaporite and the actions of human activities were found to be important reasons for the formation of carbonate rocks, which served as the main sources of groundwater chemical components and diverse hydrochemistry characteristics.
STUDENTS’ PERCEPTIONS OF INSTAGRAM CAPTION AS MEDIA IN ENGLISH LANGUAGE TEACHING

Everybody has Instagram account in this digital era. The lecturer uses Instagram as the media in English Language Teaching. The lecturer asks the students to write their idea in English as Instagram caption. This study presents the students’ perceptions of using social media, Instagram. All the participants in this study are students of Mathematic Study Program who enroll in English class at Nahdlatul Ulama University Purwokerto. The data were analyzed by using both quantitative and qualitative method. The data taken from survey through questionnaire were analyzed quantitatively. Then, the researcher used qualitative analysis to present the data from interview. The finding showed that the students’ perceived the advantages and disadvantages of using Instagram caption. The advantages showed that the participants have a positive perception of Instagram caption as a media in English Language Teaching. First, it stimulated the students to develop positive attitude in creative writing. Second, it improved students’ grammatical competences. The last, it enhanced the students’ motivation in English writing. Three disadvantages of using instagram as a media in teaching English included internet connection problem, low engagement rate of students’ in Instagram, and language barrier. Some of the students still used Google Translate (GT) to ensure that their sentences were correct. Keywords: Students’ Perceptions, Instagram, English Language Teaching