Reviewerly/exHarmony huggingface dataset:
Forensic Analyses Based on Predictive Coding

Cybercrime is growing rampantly around the world, which has caused huge monetary damages in recent years. One of the major difficulties in cybercrime forensic analysis is to identify relevant digital evidence from a large amount of electronic documents. Traditional methods, such as manual inspection and keyword searching, are no longer effective both in terms of time and accuracy. In order to reduce the cost, save time and improve the accuracy of forensic investigation, the paper proposed a predictive coding scheme to study and identify relevant digital evidence. The experimental results show that the predictive coding based on semantic searching is feasible, and more efficient and accurate than the keyword searching.
Generalized Net Model of the Network for Automatic Turning and Setting the Lighting in the Room

Turning the lights on automatically helps people who enter a current room to turn on the lighting depending on the settings made by a person. In this paper we will take a look how by scanning the iris the system recognizes the person who is entering the room depending on if the the outside light exceeds the set in advance threshold value, the lighting turns on with the settings for lighting of a specific person and it also lets corrections to be made in the set in advance parameters.
Representaciones docentes sobre las modalidades educativas sustentadas en las Tecnologías de la Información y la Comunicación (TIC), en el Centro Universitario del Norte

The integration of Information and Communication Technologies (ICT) in the learning processes, have radically transformed the content approach in educational programs, as well as a change of role in the actors; Students become more self-managing and teachers become guides or advisors in the generation of knowledge and skills. The objective of the work is to know in depth the representations of teachers on educational modalities supported by ICTs, what are the possible advantages and disadvantages, as well as some of their reflections on their implementation in the CUNorte. The approach to the object of study is carried out through a qualitative investigation of descriptive type, in which four units of analysis based on the theory of social representations are considered. The paper rescues the experience of how the role of the actors in the performance of their academic activities has been transformed with the implementation of educational modalities based on ICT.
Postmodernity, technology and human communication

The link postmodern technology/human communication has in mass media, TV, cinema, the internet and its derivatives, uncensored publications and advertising tools of educational distortion and community health. Postmodernity influences social organization and culture with its dichotomy rationalism vs. irrationalism.The Kantian vision of science, morality and art for a fairer, freerand happier society crumble in the attempt to «overcome modernity». Cybernetic society bureaucratizes and impedes man’s freedom. Mass media and the communication society have not generated a more transparent or self-aware society, but more complex and chaotic. Subject-object and public-private dichotomies make the individual oblivious to his life, an element of science fiction, immersed in a virtual process that displaces him to electronic worlds. In the ecstasy of communication, the medium predominates with its dispersion over the message, scientific knowledge is oriented to technological development and power,highlighting its linguistic aspect that does not prevent more and more from being talked about and written worse. The impact of mass media drives antisocial behaviours and «the search for intense sensations» to placate emotional states stemming from the existential vacuum, frustration and vital dissatisfaction of certain populations. The persistence of alienating, self-aware and reality-distorting disclosure does not encourage the development of a healthy personal, linking and social function.
Test Case Generation of Composite Web Services Based on Semantic Matching and Condition Recognition

With the rapid development of Internet technology, the application of Web service and their combinations is spreading widely as a vital role. However, due to the black box feature of Web services, test cases of Web service can be obtained only from the perspective of users, which is more challenging than traditional tests. We propose a method based on semantic matching and condition recognition, combined with document parsing to generate test cases of composite Web services. In this method, the first step is to parse the relevant XML documents, then to obtain the parameter types, keywords, as well as conditions and orders in the control flow. The following step is to match the parameter instances according to the conditions in the process or the semantic knowledge base. The last is to encapsulate them into test cases. Experimental results show that our method can generate test cases with low redundancy and high coverage, which can cover more than 85% nodes and paths in BPEL.
Preliminary findings on FOSS dependencies and security

Developers are known to keep third-party dependencies of their projects outdated even if some of them are affected by known vulnerabilities. In this study we aim to understand why they do so. For this, we conducted 25 semi-structured interviews with developers of both large and small-medium enterprises located in nine countries. All interviews were transcribed, coded, and analyzed according to applied thematic analysis. The results of the study reveal important aspects of developers' practices that should be considered by security researchers and dependency tool developers to improve the security of the dependency management process.
Creative methods for sustainable design for happiness and wellbeing

This chapter presents the Design for Happiness Framework (DfH) and the Designing for Home Happiness Framework as applicable specific creative methods for this purpose; they provide relevant processes and tools to design for happiness and wellbeing. It also presents a short summary of prevalent creative methods available to designers, such as probes, toolkits and prototypes. The most common creative methods used by designers can be consolidated under the headings: probes, toolkits and prototypes. The chapter also presents a comprehensive review of relevant aspects for designing for happiness and wellbeing to demonstrate a need to specialise methods in this area. The unique design process of DfH combines for the first time elements of holistic sustainable design plus happiness characteristics. However, for the purposes of explaining DfH in a clear manner, the following sections split it into three separate foundations: design approach; design process; and the toolkit.
De-Background Generative Adversarial Networks for Object Detection

Region proposal algorithms tend to become the mainstream among most object detection tasks. As a prominent representative, Faster R-CNN introduces region proposal networks (RPN), a deep neural network, to tell the unified network where to put more attention on. Based on the huge success of deep neural network, we are convinced that Generative Adversarial Networks (GAN) can also be competent for this task. For this purpose, we introduce a novel use of GAN, called De-background Generative Adversarial Networks (DBGAN), to generate the bounding boxes in one image under the simple background, namely detect object locations. We use intersection-overlap-union (IoU) to measure the quality of the generated boxes and we get good results on PASCAL VOC 2007, 2012 datasets.
Real-Time Machine-Translated Instant Messaging: A Brief Overview with Implications for Translator Training

Over the decades, machine translation (MT) systems have contributed to improving the quality of MT output. Consequently, MT has become increasingly popular and is currently applied in more domains and fields of studies than ever before. The integration of MT into mobile communication technology, especially into real-time instant messaging (IM), a global fast-growing, and highly researched communication medium, has partially led to the elimination of linguistic barriers. Users of IM translation, who speak various languages, can now converse with each other. This chapter examines the usefulness of MT, briefly outlines current IM translation clients, and examines the impact of IM translation particularly on foreign language (FL) learning, translation theory, and translation practice. The article lays the groundwork for future research that seeks to better comprehend the crucial role of machine-translated IM in our society.
Energy-efficient and Robust Cumulative Training with Net2Net Transformation

Deep learning has achieved state-of-the-art accuracies on several computer vision tasks. However, the computational and energy requirements associated with training such deep neural networks can be quite high. In this paper, we propose a cumulative training strategy with Net2Net transformation that achieves training computational efficiency without incurring large accuracy loss, in comparison to a model trained from scratch. We achieve this by first training a small network (with lesser parameters) on a small subset of the original dataset, and then gradually expanding the network using Net2Net transformation to train incrementally on larger subsets of the dataset. This incremental training strategy with Net2Net utilizes function-preserving transformations that transfers knowledge from each previous small network to the next larger network, thereby, reducing the overall training complexity. Our experiments demonstrate that compared with training from scratch, cumulative training yields ~2x reduction in computational complexity for training TinyImageNet using VGG19 at iso-accuracy. Besides training efficiency, a key advantage of our cumulative training strategy is that we can perform pruning during Net2Net expansion to obtain a final network with optimal configuration (~0.4x lower inference compute complexity) compared to conventional training from scratch. We also demonstrate that the final network obtained from cumulative training yields better generalization performance and noise robustness. Further, we show that mutual inference from all the networks created with cumulative Net2Net expansion enables improved adversarial input detection.
Single Image Super Resolution using a Hybrid Feature Dictionary for Remotely Sensed Images

In this paper, a sparse representation based single image super-resolution reconstruction (SRR) method using a self-learned hybrid feature dictionary is proposed. Efficient feature extraction plays a very important role in image SRR and most of the models use gradient-based feature extraction. These models, being an intensity-based, can be easily influenced by the intensity gradient to ignore essential edge profile and also fails to address the edge profiles like delta, roof and ramp edge which are very crucial for efficient reconstruction. Inspired by this, a hybrid feature based over-complete dictionary is formed by extracting various features of the LR input image. Features are extracted using the Fast Fourier Transform (FFT) procedure along with the first-and-second-order gradients. This dictionary is jointly trained with the HR image patch features using Orthogonal Matching Pursuit (OMP) and K-singular value decomposition (K-SVD) algorithm. By considering the similarity between the LR and HR image patch pairs, the output HR image is reconstructed using the sparse recovery model. Experimental analysis and results for remotely sensed data demonstrate that the proposed method reduces the computational complexity as well as outperforms other state-of-the-art methods in terms of qualitative and quantitative parameters. The average signal to noise ratio (PSNR) has improved significantly by +0.6 dB along with a notable boost in computational time and perceptual quality.
Leveraging Multisource Information in Matrix Factorization for Social Collaborative Filtering

Collaborative Filtering is a technique that automatically predicts the interest of an active user by collecting rating information from other similar users or items. However, existing works either leave out the user reliability or cannot capture the implicit correlation between two users who are similar but not socially connected. Besides, they also take the trust value between users is either 0 or 1, thus will degenerate the prediction accuracy. In this work, we propose a social collaborative filtering algorithm via leveraging multisource information in matrix factorization, which aims to solve the above problems via employing both rating records and users' social network information. To this end, we design a novel recommendation framework, which consists of two fundamental components: computation of user reliability and implicit interaction, designing of user-shared feature space. In particular, the latter performs matrix factorization based on user reliability. Finally, a reasonable objective function is designed to synthetically consider the multisource information of users, such that high-quality recommendation can be produced. Experimental results demonstrate that the proposed model achieves better accuracy than other counterpart recommendation techniques.
NatVi - A Framework for Agile Software Development, Service-Oriented Architecture and Quality Assurance

This research presents the NatVi Framework for Agile Software Development, Service-Oriented Architecture (SOA), and Quality Assurance (QA). The research took place in a context of profound and rapid changes in business environments that affect the software development environment. Our previous work did a Systematic Literature Review trying to find articles dealing with SOA and Agile and the challenges inherent in this combination of solutions. In order to build the Framework, this work took the shortcomings found in the solutions presented in the papers and further incorporated the necessary QA concept. In this context, the Framework attempts to provide an answer to how to develop software with quality and rapid deliveries in an ever-changing environment, where the traditional forms of software development may not handle it. Background research identified trade-offs among SOA, QA, and Agile, e.g., formality, documentation, and planning. The background research also identified strengths, weaknesses, and gaps in papers that addressed solutions to problems that arise in software development in the presented context. The results of the background research were assessed and exploited in Framework construction. In a single life-cycle, the NatVi Framework combines Agile, SOA, and QA and addresses the values and principles that guide them in 13 phases distributed in four layers. Each phase is carried out by people who perform specific roles, expressed in terms of inputs, tasks, and outputs. As a Framework, it is not exhaustive, and its main concepts may change and adapt to each environment. The Framework validation is underway, and a forthcoming paper will present the results.
Light Field Stitching Based On Concentric Spherical Modeling

VR image in form of the spherical panoramic image is already widely available while enhancing its immersive experience with six degrees of freedom (6-DoF) is fundamentally required. Spherical panoramic light field (LF) becomes a potential solution because of recording the spatial and angular information of the light rays in the 360° spherical space. In this paper, a novel method is proposed to generate spherical panoramic LF by stitching LFs captured at different rotational angles. First, concentric spherical modeling is proposed to parameterize the recorded rays to eliminate the projection biases in registration. Then, the concentric spherical model-based LF registration which is insensitive to the ordering is introduced to transform each 4D LFs mesh accurately. Finally, the stitching result is projected to Two-parallel-plane (TPP) coordinates for viewing. Experimental results show that the proposed method outperforms the existing methods in terms of subjective quality and continuity in the stitched LF.
Analysis of Differentially-Private Microdata Using SIMEX

We are concerned here with the publication of microdata which preserve the confidentiality of the respondents, as measured by differential privacy. We borrow the SIMEX methodology from the measurement error literature to analyze microdata perturbed with the Laplace mechanism, hoping to recover unbiased estimates of the parameters of interest. Our simulations show that the strategy works in theory, but only for values of \(\epsilon \) too large of be of used in practice. It might however be of use in the case of normal noise addition, when one does not require differential privacy.
M-Sosanet: An Efficient Convolution Network Backbone For Embedding Devices

In this paper, we build a lightweight convolution neural network M-SOSAnet that combines efficiency and accuracy for edge devices. Just as DenseNet connects each layer to every other layer in the neural network. Although dense connection can effectively keep information between the middle layers, the increasing input channels by dense connection leads to resource consumption, which greatly increases the amount of parameters and is inefficient. MobileNet series use depthwise separable convolutions, which reduces the amount of calculation and parameters, but will reduce the accuracy. ESPnetv2 uses group pointwise and depthwise dilated separable convolution to learn representation from a large receptive field with fewer flops and parameters but will get a little accuracy decrease. In order to solve these problems, we propose two module: 1. Mobile SOSA Module 2. Downsampling Block with scoring mechanism, and also introduce the attention mechanism. Compare to some past methods, our model M-SOSAnet has lower flops, higher accuracy, maintains the similar amount of parameters. We evaluated model in the areas of image classification, and semantic segmentation to prove that our methods have better performance.
Security of Patient’s Privacy in E-Health using Secret Sharing and Homomorphism Encryption Scheme

Cryptography schemes have been proposed in many applications to encrypt cloud storage health records (HRs) with affording benefits like low cost and flexibility. Among which is the secret-sharing protocol whereby, health care system split a secret into n shares and distribute each secret share to the shareholders, where only a threshold (t) or more shareholders can decode the secret. In this paper, we combine the property of homomorphism encryption and secret sharing to encrypt HRs so that complex computations of Lagrange are outsourced to the cloud service provider with the flexibility of adding and removing shareholders. We analyze the time, security level and communication complexities of our scheme, and confirm that it is much secure than the conventional methods.
Implementation of Cluster Analysis and Artificial Neural Networks as an Alternative for Klassen Typology and LQ: Case of Coconut

Abstract Due to the campaign supported by manufacturers of soy-oil in the United States which stated that consuming coconut oil might cause heart disease, coconuts’ popularity started to decline. As a result, the coconut was neglected by farmers and productivity was declining. Yet, recent study showed the opposite results. Coconut was good for health. As a result, the demand for coconut products increased. Indonesia, as the world’s largest coconut producers cannot maximize these opportunities due to aging coconut which have been neglected for many years. Coconut products may improve the income of farmers and encourage sustainable agriculture as well as diversify farmers’ income. Some simple methods, such as Klassen Typology or Location Quotient can be used to classify the potential area to be developed or rejuvenated. Nevertheless, these approaches are not able to generalize and not suitable to be implemented for cases with large data. This research tried to use cluster analysis such as dendrogram, Principle Component Analysis and Artificial Neural Network for classification. The results show that the dendrogram provided good results, whilst the Principle Component Analysis and Artificial Neural Networks required more data for better results.
Affine Transformation Based Hierarchical Extreme Learning Machine

Recently, the signal hidden layer feedforward network (SLFN) based extreme learning machine (ELM) has been extended to a hierarchical learning framework (HELM). Although the HELM shows better generalization performance with lower computational complexity than many deep neural networks (DNNs), it is found that as the layer increases, the input distribution of each layer may move to the saturated regime of the non-linear activation function, which affects the generalization performance. However, few attentions have been paid to the data distribution normalization to address this issue. Thus, in this paper, an affine transformation (AT) inputs based activation function layer is introduced to normalize the data distribution and a novel AT based HELM (AT-HELM) is developed. The proposed AT-HELM can adapt the activation function inputs to the distribution of each layer and obtains better generalization performance. Experiments on 29 benchmark datasets are carried out to demonstrate the superiority of AT-HELM.
An international perspective on design for wellbeing

Studies on design for wellbeing (DfW) from all over the globe focus on products, technology, services, built environment and sustainability. Such studies are vital to the development of the research field but are unique to any particular scientific community. In design research throughout the globe, wellbeing is explored under distinct perspectives pragmatism and phenomenology. Taken into account that pragmatism and phenomenology seem to be the most widely adopted approaches to DfW, in what follows, these approaches are discussed in more detail. Although the authors did offer a clear differentiation between health and wellbeing, the research focused on exploring ways to make the workers' tasks more comfortable, safer and easier to perform. Moving away from work focused on the effects of artefacts on wellbeing, but still dealing with the construct in a direct manner, some frameworks have been proposed for DfW.