Reviewerly/exHarmony huggingface dataset:
How to practically deploy deep neural networks to distributed network environments for scene perception

Recently, intelligent machine agents, such as a deep neural network (DNN), have been showing unparalleled capabilities in recognizing visual patterns, objects, semantic activities/events embedded in real-world images and videos. Hence, there has been an increasing need to deploy DNNs, to a battlefield to provide the Solider with realtime situational understanding by capturing a holistic view of battlespace. Soldiers engaged in tactical operations can greatly benefit from leveraging advanced at-the-point-of-need data analytics running on multimodal and heterogeneous platforms in distributed and constrained network environments. The proposed work aims to decompose DNNs and then distribute over edge nodes in such a way that a trade-off between resources available in the constrained network and recognition performance can be optimized. In this work, we decompose DNNs into two stages: an initial stage on an edge device and the remaining portion running on an edge cloud. To effectively and efficiently divide DNNs into two separate stages, we will rigorously analyze multiple widely used DNN architectures with respect to its memory size and FLOPs (Floating Point Operations) per each layer. Based on these analyses, we will develop advanced splitting strategies for DNNs to handle various network constraints.
Principles of Information Security of a Child on the Internet

The article is devoted to the definition and justification of the principles of information security of the child on the Internet. In determining the principles considered and taken into account a set of international and domestic regulations and strategic policy documents, reflecting the main approaches to ensure the rights of the child to information and regulating the safety of the child when handling information. The necessity of taking into account the age, psychological characteristics of the child as a participant of information relations is noted. The authors substantiate the conclusion that the status of the child involves the performance of parents (persons replacing them) their responsibilities for the education of children, which necessarily includes the formation of children’s ability to navigate in the modern information environment. The authors proposed and justified the following principles: the principle of recognition of children as equal participants in the information society, the principle of responsible parenthood, the principle of state and legal regulation; the principle of improving information literacy. Methodology. The paper uses the dialectical method of cognition, which allowed to take into account the successive principles of various normative legal acts, strategic documents, their relationship and the rationale for the need for a unified state policy in the field of protection of children from negative information. The application of General scientific methods of cognition—analysis and synthesis, which allowed establishing internal links between the individual principles of information security of the child and their conceptual expression in strategic documents and normative legal acts. The comparative legal private-scientific method contributed to the comparison of the approaches reflected in the documents fixing information security and the rights of the child in the Russian Federation for the formulation and justification of the principles of information security of the child on the Internet.
Ensemble-Initialized k-Means Clustering

As one of the most classical clustering techniques, the k-means clustering has been widely used in various areas over the past few decades. Despite its significant success, there are still several challenging issues in the k-means clustering research, one of which lies in its high sensitivity to the selection of the initial cluster centers. In this paper, we propose a new cluster center initialization method for k-means based on ensemble learning. Specifically, an ensemble of base clusterings are first constructed by using multiple k-means clusterers with random initializations. Then, a co-association matrix is computed for the base clusterings, upon which the agglomerative clustering algorithm can thereby be performed to build a pre-clustering result. From the pre-clustering, the set of initial cluster centers are obtained and then used for the final k-means clustering process. Experiments on multiple real-world datasets have demonstrated the superiority of the proposed method.
Integrating computer vision and non-linear optimization for automated deformable registration of 3D medical images

Deformable image registration (DIR), the process of estimating and applying non-linear transforms to spatially align sets of two or more images, is a challenging task with many important clinical applications including kinetic analysis, cancer treatment targeting, and evaluation of treatment response. Current techniques use non-linear optimizations only to reach a local minimum and not a globally optimal solution, limiting application to cases with small spatial displacements. Various semi-dense feature-based methods drawing inspiration from mammalian systems as the basis for 2D visual processing have been implemented for automated wide baseline registration and object detection applications with great success. Extension to the 3D case, which has been shown to enable highly efficient coarse global image search, in theory could be adapted also to allow precise semi-dense global optimization. The algorithm described in this paper, dubbed constrained robust affine feature transform (CRAFT), incorporates paradigms from various computer-vision techniques to combine aspects of the human visual pathway with proven non-linear optimization methods to automate general deformable registration with unprecedented robustness. This hybrid technique is able to estimate registration confidence and can serve as the basis of machine perception of medical images for machine learning.
Quadratic Reformulation of Nonlinear Pseudo-Boolean Functions via the Constraint Composite Graph

Nonlinear pseudo-Boolean optimization (nonlinear PBO) is the minimization problem on nonlinear pseudo-Boolean functions (nonlinear PBFs). One promising approach to nonlinear PBO is to first use a quadratization algorithm to reduce the PBF to a quadratic PBF by introducing intelligently chosen auxiliary variables and then solve it using a quadratic PBO solver. In this paper, we develop a new quadratization algorithm based on the idea of the constraint composite graph (CCG). We demonstrate its theoretical advantages over state-of-the-art quadratization algorithms. We experimentally demonstrate that our CCG-based quadratization algorithm outperforms the state-of-the-art algorithms in terms of both effectiveness and efficiency on randomly generated instances and a novel reformulation of the uncapacitated facility location problem.
An Energy Efficient In-Memory Computing Machine Learning Classifier Scheme

Large-scale machine learning (ML) algorithms require extensive memory interactions. Managing or preventing data movement can significantly increase the speed and efficiency of many ML tasks. Towards this end, we devise an energy efficient in-memory computing kernel for a ML linear classifier and a prototype is designed. Compared with another in-memory computing kernel for ML applications [1], we achieve a power savings of over 6.4 times than a conventional discrete system while improving reliability by 54.67%. We employ a split-data-aware technique to manage process, voltage and temperature variations. We utilize a trimodal architecture with hierarchical tree structure to further decrease power consumption. Our scheme provides a fast, energy efficient, and competitively accurate binary classification kernel.
Research on Computer Aided Design Software Innovation Based on VR Technology

Focusing on the features of current computer-aided design software and the pain points faced by users in the use process, this paper analyzes the technical features of VR technology and discusses the path of computer-aided design software innovation. It clarifies the direction and content of the innovative development of computer-aided design software, which should be based on VR virtual reality technology’s sensory expression system and virtual control system, create more realistic operating forms, strengthen man-machine interactive feedback, and improve users’ immersive experience.
Dynamic Neural Network Model of Speech Perception

Research results in neurobiology showed that the spatial organization of the somatosensory cortex, with linear or planar topology, seems to be the underlying support for the internal representation of the environment. This paper examines the feasibility of constructing self-organizing feature maps (SOFMs) suitable to model speech perception. The objective was to construct a class of dynamic SOFMs that can extract the time–amplitude and time–frequency features of the phonemes that appear in the formation of words. Two approaches are presented. One is based on constructing time-based embedding maps. The second method involved the construction of a dynamic SOFM having the Gabor transform as a transfer function. The time–frequency features of the speech sounds are revealed in the second approach. The results may be useful in applications of speech recognition.
Trusted Friends’ Computation Method Considering Social Network Interactions’ Time

Based on the assumption that users generally tend to use entities proposed by friends rather than strangers and that trust among friends significantly correlates with user’s trends, we decided to refer to research conducted on the evolving field of social trust computation. Although many models were proposed to analyze computational trust for various applications of social web, little importance is given to the time factor even by models that represent trust as a source of recommendation measurement. In this paper, we propose to integrate the temporal factor in assessing trust between social network friends. We define a time-aware method for an implicit trust determination between Facebook friends. Then, we integrate it in a mobile app we have developed for identifying trusted friends in social networks. Finally, we assess our method using an objective statistical method and compare it with others methods in the literature to show the importance of the time of interactions between users for a better calculation of social trust and consequently for an enhanced identification of trusted friends in social networks.
Hand Pose Estimation Based on 3D Residual Network with Data Padding and Skeleton Steadying

In this paper, we propose a deep-learning-based approach for real-time hand pose estimation from a single depth image by using 3D convolutional neural network which takes a 3D voxelized grid generated by a depth image as input. Most of the previous works for hand pose estimation only take a single 2D depth image as input and estimate coordinates of the key points of a hand with 2D convolutional neural network. The disadvantage of those methods is that 2D depth image can not represent the spatial information of 3D data, while the 3D voxelized grid can represent the point cloud of the surface of the hand in a spatial way. Hence, we design a 3D convolutional neural network which takes a 3D voxelized grid with data padding as input and steadying the hand skeleton with an additional loss function for regression. Experiments show that our approach outperforms previous methods on two public datasets and can run in real time with a single GPU.
Transfer Learning to Timed Text Based Video Classification Using CNN

Open educational video resources are gaining popularity with a growing number of massive open online courses (MOOCs). This has created a niche for content providers to adopt effective solutions in automatically organizing and structuring of educational resources for maximum visibility. Recent advances in deep learning techniques are proving useful in managing and classifying resources into appropriate categories. This paper proposes one such convolutional neural network (CNN) model for classifying video lectures in a MOOC setting using a transfer learning approach. The model uses a time-aligned text transcripts corresponding to video lectures from six broader subject categories. Video lectures and their corresponding transcript dataset is gathered from the Coursera MOOC platform. Two different CNN models are proposed: i) CNN based classification using embeddings learned from our MOOC dataset, ii) CNN based classification using transfer learning. Word embeddings generated from two well known state-of-the-art pre-trained models Word2Vec and GloVe, are used in the transfer learning approach for the second case.
Optimizing Clustering with Cuttlefish Algorithm

The Cuttlefish Algorithm, a modern metaheuristic procedure, is a very recent solution to a broad-range of optimization tasks. The aim of the article is to outline the Cuttlefish Algorithm and to demonstrate its usability in data mining problems. In this paper, we apply this metaheuristic procedure for a clustering problem, with the Calinski-Harabasz index used as a measure of solution quality. To examine the algorithm performance, selected datasets from the UCI Machine Learning Repository were used. Furthermore, the well-known and commonly utilized k-means procedure was applied to the same data sets - to obtain a broader, independent comparison. The quality of generated results were assessed via the use of the Rand Index.
A Joint Local and Global Deep Metric Learning Method for Caricature Recognition

Caricature recognition is a novel, interesting, yet challenging problem. Due to the exaggeration and distortion, there is a large cross-modal gap between photographs and caricatures, making it nontrivial to match the features of photographs and caricatures. To address the problem, a joint local and global metric learning method (LGDML) is proposed. First, joint local and global feature representation is learnt with convolutional neural networks to find both discriminant features of local facial parts and global distinctive features of the whole face. Next, in order to fuse the local and global similarities of features, a unified feature representation and similarity measure learning framework is proposed. Various methods are evaluated on the caricature recognition task. We have verified that both local and global features are crucial for caricature recognition. Moreover, experimental results show that, compared with the state-of-the-art methods, LGDML can obtain superior performance in terms of Rank-1 and Rank-10.
Integrating User Opinion in Decision Support Systems

We propose an approach to decision support systems (DSS) that starts with the user first making their own unassisted decision αU and providing this as an input to the algorithm. Then, if the algorithm disagrees with the user’s initial decision, it iteratively works with the user to converge on a common decision or at least make the user reconsider input values that are inconsistent with αU. We provide a detailed description of this approach along with examples, and then discuss potential benefits and limitations.
Towards language-agnostic alignment of product titles and descriptions: a neural approach

The quality of e-Commerce services largely depends on the accessibility of product content as well as its completeness and correctness. Nowadays, many sellers target cross-country and cross-lingual markets via active or passive cross-border trade, fostering the desire for seamless user experiences. While machine translation (MT) is very helpful for crossing language barriers, automatically matching existing items for sale (e.g. the smartphone in front of me) to the same product (all smartphones of the same brand/type/colour/condition) can be challenging, especially because the seller’s description can often be erroneous or incomplete. This task we refer to as item alignment in multilingual e-commerce catalogues. To facilitate this task, we develop a pipeline of tools for item classification based on cross-lingual text similarity, exploiting recurrent neural networks (RNNs) with and without pre-trained word-embeddings. Furthermore, we combine our language agnostic RNN classifiers with an in-domain MT system to further reduce the linguistic and stylistic differences between the investigated data, aiming to boost our performance. The quality of the methods as well as their training speed is compared on an in-domain data set for English–German products.
ISUTD: Intelligent System for Urdu Text De-Summarization

Text De-Summarization is a method of increasing the document and explains the substantial point of the text. It is very rough assignment for humans to manually explain the central subject from the large article. De- Summarization can be separating into two branches as Abstractive and Extractive approaches. Extractive accumulates the imperative paragraph or sentence from the original document and presents them as an explanation. Urdu inherits a lot of vocabulary from Arabic, Persian and the native languages of South Asia. Due to this effect, Urdu has a complex morphology. In terms of syntax, it has a relatively free word order (Subject, Object, and Verb). Despite spoken by millions of people, Urdu is an under-resourced language in terms of available computational resources. We extent the single document extractive de-summarization methodology for Urdu based on the sentence weight algorithm especially for the news, sports, and health etc. topics. We encapsulate the manuscript by preprocessing (sentence segmentation, tokenization, stop words and lemmatization) and apply sentence weight algorithm.
Truncated-statistics-based bilateral filter for speckle reduction in synthetic aperture radar imagery

The mechanism of speckle noise in synthetic aperture radar (SAR) images and its characteristics are analyzed. Combining the advantages of the traditional bilateral filter (BF) and alpha-trimmed median filter, a truncated-statistics-based bilateral filter (TS-BF) in SAR imagery is proposed. The despeckling method is based on the BF methodology, where the similarities of gray levels and spatial location of the neighboring pixels are exploited. However, traditional BF is not effective to reduce the strong speckle, which is often presented as impulse noise. The proposed TS-BF filtering method designs an adaptive truncation method to properly select the samples in the local reference window, where the mean and standard deviation of all the samples are estimated, and the background types of the current pixel-for-filtering are categorized. Finally, the samples of the local reference window are truncated with different levels according to different background types, and BF is applied using the truncated samples. TS-BF can effectively preserve the edge and texture information of the image while smoothing the speckle noise; it has a great application value. The experimental results show the effectiveness of the proposed algorithm through subjective and objective analyses.
Multi-class Ensemble Learning of Imbalanced Bidding Fraud Data

E-auctions are vulnerable to Shill Bidding (SB), the toughest fraud to detect due to its resemblance to usual bidding behavior. To avoid financial losses for genuine buyers, we develop a SB detection model based on multi-class ensemble learning. For our study, we utilize a real SB dataset but since the data are unlabeled, we combine a robust data clustering technique and a labeling approach to categorize the training data into three classes. To solve the issue of imbalanced SB data, we use an advanced multi-class over-sampling method. Lastly, we compare the predictive performance of ensemble classifiers trained with balanced and imbalanced SB data. Combining data sampling with ensemble learning improved the classifier accuracy, which is significant in fraud detection problems.
CDAS

The rapid development of smartphones has greatly facilitated our lives. At the same time, securing the data stored and accessed from smartphones makes it important to authenticate the user. However, current smartphones perform one-time authentication at the entrance while they don't authenticate users continuously when in use, which brings serious privacy and security issues, such as collisions and social engineering to bypass the authentication.
Principal Sample Analysis for Data Ranking

Because of the ever growing amounts of data, challenges have appeared for storage and processing, making data reduction still an important field of study. Numerosity reduction or prototype selection is one of the primary methods of data reduction. In this paper, we propose some possible improvements for Principal Sample Analysis (PSA) which is a numerosity reduction algorithm. The improvements are PSA in Hilbert space, improving its time complexity using anchor points, sample size estimation using PAC learning, and PSA for regression and clustering tasks.
Multilevel Ontology Framework for Improving Requirements Change Management in Global Software Development

Requirements engineering is one of the most important pillars of software engineering.Its success contributes greatly to that of the software as a whole.In fact, the software development process is not devoid from changing requirements, which affects the cost, time, and quality of the final software.The change problem is unavoidable and also swells when the development of the software is made globally.Therefore, there is a need to improve the quality of requirements change management (RCM), especially in global software development (GSD) environments.Our research hypothesis is that the RCM is naturally a knowledge-intensive process that can benefit substantially from ontology.Indeed, we assume that using a multilevel ontology framework will greatly support RCM in GSD environments by ensuring the semantic correctness of the requirement change request and accordingly solving miscommunication and misunderstanding problems.The framework was successfully evaluated using a questionnaire and a case study.The results indicate that using the proposed framework can intensely improve the semantic correctness of requirement change requests.Accordingly, the entire RCM process is then improved by increasing the reliability of the change and reducing the time consumed for dealing with semantically wrong change requests.
Concept to Code: Deep Learning for Fashion Recommendation

Deep Learning has shown significant results in various domains. In this tutorial, we provide conceptual understanding of embedding methods, Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNNs). We present fashion use case and apply these techniques for modeling image, text as well as sequence data to figure out user profiles, give personalized recommendations tailored to changing user taste and interest. Given the image of a fashion item, recommending complementary matches is a challenge. Users’ taste evolves over time and depends on persona. Humans relate objects based on their appearance and non-visual factors of lifestyle merchandise which further complicate recommendation task. Composing outfits in addition necessitates constituent items to be compatible - similar in some but different in other aspects.