Reviewerly/exHarmony huggingface dataset:
Quantization Analysis and Robust Design for Distributed Graph Filters

Distributed graph filters have recently found applications in wireless sensor networks (WSNs) to solve distributed tasks such as reaching consensus, signal denoising, and reconstruction. However, when implemented over WSNs, the graph filters should deal with network limited energy constraints as well as processing and communication capabilities. Quantization plays a fundamental role to improve the latter but its effects on distributed graph filtering are little understood. WSNs are also prone to random link losses due to noise and interference. In this instance, the filter output is affected by both the quantization error and the topological randomness error, which, if it is not properly accounted in the filter design phase, may lead to an accumulated error through the filtering iterations and significantly degrade the performance. In this paper, we analyze how quantization affects distributed graph filtering over both time-invariant and time-varying graphs. We bring insights on the quantization effects for the two most common graph filters: the finite impulse response (FIR) and autoregressive moving average (ARMA) graph filter. Besides providing a comprehensive analysis, we devise theoretical performance guarantees on the filter performance when the quantization stepsize is fixed or changes dynamically over the filtering iterations. For FIR filters, we show that a dynamic quantization stepsize leads to more reduction of the quantization noise than in the fixed-stepsize quantization. For ARMA graph filters, we show that decreasing the quantization stepsize over the iterations reduces the quantization noise to zero at the steady-state. In addition, we propose robust filter design strategies that minimize the quantization noise for both time-invariant and time-varying networks. Numerical experiments on synthetic and two real data sets corroborate our findings and show the different trade-offs between quantization bits, filter order, and robustness to topological randomness.
Image Aesthetic Assessment Based on Latent Semantic Features

Image aesthetic evaluation refers to the subjective aesthetic evaluation of images. Computational aesthetics has been widely concerned due to the limitations of subjective evaluation. Aiming at the problem that the existing evaluation methods of image aesthetic quality only extract the low-level features of images and they have a low correlation with human subjective perception, this paper proposes an aesthetic evaluation model based on latent semantic features. The aesthetic features of images are extracted by superpixel segmentation that is based on weighted density POI (Point of Interest), which includes semantic features, texture features, and color features. These features are mapped to feature words by LLC (Locality-constrained Linear Coding) and, furthermore, latent semantic features are extracted using the LDA (Latent Dirichlet Allocation). Finally, the SVM classifier is used to establish the classification prediction model of image aesthetics. The experimental results on the AVA dataset show that the feature coding based on latent semantics proposed in this paper improves the adaptability of the image aesthetic prediction model, and the correlation with human subjective perception reaches 83.75%.
SHEMB:A secure approach for healthcare management system using blockchain

Medical records need to be private. At the same time, it must be accessible for regular interaction authorized users. Ethereum-based blockchain allows privacy preserving sharing of decentralized databases with cryptographic data obfuscation and access control. In this work, we analyze the limitations of Ethereum-based blockchain with respect to electronic health record (EHR) sharing through a third party. A Ethereum framework for decentralized and transactional privacy preserving data sharing is proposed to address the needs of different stakeholders like patients, providers and other third involved in the generation and access of patient data records. A secure approach for healthcare management system using blockchain (SHEMB) obviates the need for a trusted third party for storing data. SHEMB uses symmetric searchable encryption technique to speedup the access to the records using the search query provided by the patient. The experimental results indicates the practical and secure nature of SHEMB.
VIDEOGAMES AS MEANS OF INCREASING STUDENTS’ MOTIVATION

The article is devoted to the use of videogames in order to increase students’ motivation and influence on educational process. Motivation is of particular interest to re-searchers because of its importance to learning and work with students. Several types of motivation apart from motivation for achieving: the motivation for power, the need for affiliation, the need to know (curiosity), the need for approval. Main components of the learning motivation are described. It is stated that videogames are complex phenomenon the main characteristics of which are apparatus for playing, interactivity and rules of playing game. Two ways of use of videogames in educational process are described: gamification and game-based learning. Typical elements of gamification which increase students’ motivation are defined. It is concluded that videogames have significant impact on emotional state of students, encourage them to participate in learning, improve social interaction and decrease anxiety and fear of failure. The use of videogames in educational process can solve the problems with lack of motivation during the work in class.
Region-based Detection of Essential Differences in Image-based Visual Regression Testing

Visual regression testing (VRT) is a useful method for confirming that application screens are correctly displayed. VRT systems detect differences between the screens of an old version and a new version of an application to support the tester in detecting failures on the screen of the new version. One approach to VRT is image-based; i.e., before and after screenshot images are compared. It is particularly promising because screenshots are independent of the application's environment (operating system, web browser, etc.). Existing image-based VRT systems simply compare two images in pixel units and highlight pixels with differences, so if there are changes that affect the entire screen (e.g., parallel movements of screen elements), a large number of unessential differences are detected, and the essential differences are buried within them. An image-based VRT method named ReBDiff is presented that solves this problem. Before and after screen images are each divided into multiple regions, and appropriate matchings are made between corresponding regions in the two images. For each matching, differences such as shift, alteration, and addition, if any, are detected. In addition, suitable views are provided on the basis of the detected differences. By observing these views, the tester can efficiently identify the essential differences even when there are changes that affect the entire screen, e.g., parallel movements of screen elements. Experiments on a prototype system using websites for PCs and smartphones and an application screen of an Electron application demonstrated the effectiveness of the proposed method.
DMAPS: An Effective and Efficient Way for the Air Purification of the Outdoors

The air pollution these days is a serious environmental concern and it is not just a mere fact but a harsh reality which is creating problems for the mankind such as some serious health issues. In some parts of the world the air quality index have reached to an irrefutable level which demands for a solution now. Hence, the purpose of this paper is to tackle this problem at a large scale by providing with an artificially intelligent mobile air purifier for "outdoors". The main problem of DMAPS is to provide with purified air in the outdoors, such as a residential colony, in apartments, office complex, etc. since most of the times people travel outdoors and stay away from their home for a long time. This ability of providing with such a smart-sensing, self-driving machine is the advantage of the paper because other air purifiers exist but they are made for indoors, they do not have mobility and also do not possess AI capabilities. The method used in this air purifier is formulated by connecting three major functions. The first one being, 'the smart mobility' of the machine using the approach of deep reinforcement learning, the second is the air 'purification using the Arduino system' and the last is, 'the detection of humans' so that DMAPS can be around them and provide them with purified air, based on the YOLO algorithm. The whole system, works in a way that each and every function is very much connected and works simultaneously. In a nutshell, this air purifier, moves from one place (where there is low pollution content) to another place (where there is higher pollution content), purifies the environment and simultaneously have an objective to smartly detect humans to give out fresh air to them. Hence, in this way the whole methodology tries to fix the problem of air pollution by purifying the air outdoors to make it safe to inhale.
High-Speed Optimization Method for Resource Allocation in Wireless Communication Systems by Coherent Ising Machine

Coherent Ising Machines (CIM) can solve NP-hard combinatorial optimization problems in very high-speed. The CIM obtains a solution of a NP hard problem in several milliseconds. The CIM can run the optimization algorithm based on the Hopfield Tank neural networks and is applicable to various combinatorial optimization problems. In this paper, we proposed the CIM-based optimization method to radio resource allocation in wireless communication systems. As examples of the applications, we apply the proposed scheme to channel assignment in IEEE 802.11 wireless LANs, channel assignment and power control in Device-to-Device communications. We derive coupling strength and external magnetic fields for the CIM and enable that the CIM can solve these optimization problems in millisecond order.
Skew constacyclic codes over a non-chain ring $${\mathbb {F}}_{q}[u,v]/\langle f(u),g(v), uv-vu\rangle $$

Let f(u) and g(v) be two polynomials, not both linear, which split into distinct linear factors over $${\mathbb {F}}_{q}$$ . Let $${\mathcal {R}}={\mathbb {F}}_{q}[u,v]/\langle f(u),g(v),uv-vu\rangle $$ be a finite commutative non-chain ring. In this paper, we study general skew cyclic codes and $$\theta _t$$ -skew constacyclic codes over the ring $${\mathcal {R}}$$ where $$\theta _t$$ is an automorphism of $${\mathcal {R}}$$ .
Neural architecture search under black-box objectives with deep reinforcement learning and increasingly-sparse rewards

In this paper, we address the problem of neural architecture search (NAS) in a context where the optimality policy is driven by a black-box Oracle <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{O}$</tex> with unknown form and derivatives. In this scenario, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{O}(A_{C})$</tex> typically provides readings from a set of sensors on how a neural network architecture <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$A_{C}$</tex> fares in a target hardware, including its: power consumption, working temperature, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{cpu}/\mathbf{gpu}$</tex> usage, central bus occupancy, and more. Current differentiable NAS approaches fail in this problem context due to lack of access to derivatives, whereas traditional reinforcement learning NAS approaches remain too expensive computationally. As solution, we propose a reinforcement learning NAS strategy based on policy gradient with increasingly sparse rewards. We rely on the fact [1] that one does not need to fully train the weights of two neural networks to compare them. Our solution starts by comparing architecture candidates with almost fixed weights and no training, and progressively shifts toward comparisons under full weights training. Experimental results confirmed both the accuracy and training efficiency of our solution, as well as its compliance with soft/hard constraints imposed on the sensors feedback. Our strategy allows finding near-optimal architectures significantly faster, in approximately 1/3 of the time it would take otherwise.
Critical power prediction using SVM algorithms

This article discusses the application of the support vector method to predict the critical heat load.
A Semantic Collaborative Clustering Approach Based on Confusion Matrix

In this paper we discuss about a new images retrieval technique based on clustering. We argue that images don't have an intrinsic meaning, but they can receive different interpretation. These images can complicate documents retrieval. However, users need a quick and direct access to documents. To answer this requirement, we propose a retrieval approach which use a collaborative clustering technique based on Confusion matrix.
Knowledge Representation Models and Cognitive Search Support Tools

The purpose of the work is to develop a cognitive-process-oriented model of data search, as well as tools of human interaction in according to this model. Cognitive search is considered as process of formation of ontology of the subject area (of target object) as a system of three systems (functional, conceptual and terminological). Accordingly, the results and trajectory of information retrieval are a reflection and component of the process of cognition. In order to reduce the dimension of the graph (to perception possibilities) aspect projections operation based on the taxonomy of relationships and entities is used.
Hash-Based Tree Similarity and Simplification in Genetic Programming for Symbolic Regression

We introduce in this paper a runtime-efficient tree hashing algorithm for the identification of isomorphic subtrees, with two important applications in genetic programming for symbolic regression: fast, online calculation of population diversity and algebraic simplification of symbolic expression trees. Based on this hashing approach, we propose a simple diversity-preservation mechanism with promising results on a collection of symbolic regression benchmark problems.
A Coverless Image Information Hiding Algorithm Based on Fractal Theory

To improve the robustness and imperceptibility of the existing coverless image information hiding, a generative coverless image information hiding algorithm based on fractal theory is proposed in this paper. Firstly, four fractal image generation methods are analyzed, and the relationship between the coverless information hiding and these methods is discussed. Secondly, based on the fractal image generation algorithm, secret information is hidden by controlling pixel rendering during the generation process. The robustness, imperceptibility, and capability of resisting steganalysis are balanced by adjusting the rendering distance. As it directly generates stego images, this can resist the detection of most existing steganalysis methods. Meanwhile, different capacities can be achieved by adjusting the size of the generated image. Experimental results and analysis show that the proposed scheme can effectively resist steganalysis and has good robustness against various image attacks. Furthermore, it can achieve large capacity, and it has broad prospects for covert communication.
Entity identification and security solutions in IoT based on PKI and Blockchain technology

In networked information systems security basic problem is reliable entity identification and trustworthy system history creation. This is especially challenging task in the networks with computationally constrained devices like Internet of Things (IoT) is. In this paper we considered standard approaches to these problems, standard X.509 certificates and log files, showed that they are not appropriate for IoT and suggest solutions that better fit to IoT. Suggested solutions are applied in Institute VLATACOM current projects.
“An Iot Enabled Smart Health Care Kit for Expedite Living”

Day by day checking of wellbeing condition is imperative for a powerful plan for early treatment and counteractive action of way of life related ailments. It is also expected to perform checking of the wellbeing state of a patient with cluttered amid every day exercises. IoT empowered shrewd social insurance unit can be utilized for checking indispensable parameters of the patients, for example, temperature, pulse and fall recognition utilizing sensors which are associated with the board. The task includes telling the nearest hospital and furthermore to the guardian if the patient is observed to be in abnormal condition. The key advantages of such social insurance unit incorporates simple establishment, profoundly secure two way correspondence, long battery life and ease.
Kannada-Mnist Classification Using Skip CNN

In this paper, we tried out a new pipeline on the new handwritten digits dataset termed Kannada-MNIST for the Kannada script as well as a real-world handwritten dataset termed Dig-MNIST. The Kannada-MNIST was created to potentially serve as a direct drop-in replacement for the original MNIST dataset whiles Dig-MNIST serves as an out-ofdomain test dataset.We achieved a state-of-the-art result in challenging the baseline. Our model achieved 97. 53% accuracy on KannadaMNIST against 96. 8% and 85. 02% accuracy against 76. 1% on the Dig-MNIST dataset.
Image Resolution Enhancement Using Improved Edge Directed Interpolation Algorithm

Image resolution enhancement is a process to convert the low-resolution (LR) image into a high-resolution (HR) image. This method is applied in many image processing field. One of the commonly used techniques for image resolution enhancement is interpolation. The results of pixel interpolation can vary significantly depending on the interpolation algorithm. Moreover, the conventional interpolation methods are not efficient to assign accurate interpolation value to the HR edge pixels. Therefore, in this study, we propose an improved edge directed interpolation (EDI) algorithm, which is able to preserve the sharpness of edges. The proposed method is divided into three main steps: edge pixel filtering; bi-cubic interpolation, and EDI. The edge pixels and non-edge pixels are separated by the adaptive edge filtering method. After that bi-cubic interpolation is applied for non-edge pixels. The Lagrange interpolation polynomial is used for bi-cubic interpolation. Finally, an improved EDI is applied to the edge pixels. The proposed method is tested on the several standard grayscale images and compared with the existing methods. According to the evaluation results, the proposed method provides the higher performance of the subjective and objective quality than the standing EDI methods.
Concise Read-Only Specifications for Better Synthesis of Programs with Pointers

Abstract In program synthesis there is a well-known trade-off between concise and strong specifications: if a specification is too verbose, it might be harder to write than the program; if it is too weak, the synthesised program might not match the user’s intent. In this work we explore the use of annotations for restricting memory access permissions in program synthesis, and show that they can make specifications much stronger while remaining surprisingly concise. Specifically, we enhance Synthetic Separation Logic (SSL), a framework for synthesis of heap-manipulating programs, with the logical mechanism of read-only borrows . We observe that this minimalistic and conservative SSL extension benefits the synthesis in several ways, making it more (a) expressive (stronger correctness guarantees are achieved with a modest annotation overhead), (b) effective (it produces more concise and easier-to-read programs), (c) efficient (faster synthesis), and (d) robust (synthesis efficiency is less affected by the choice of the search heuristic). We explain the intuition and provide formal treatment for read-only borrows. We substantiate the claims (a)–(d) by describing our quantitative evaluation of the borrowing-aware synthesis implementation on a series of standard benchmark specifications for various heap-manipulating programs.
A Hybrid Solution To Abstractive Multi-Document Summarization Using Supervised and Unsupervised Learning

In this work, we aim to develop an abstractive summarization system in the multi-document setup. The main challenge in this kind of a system is the identification of redundant information. Our approach hybridizes three components, viz. Clustering, Word Graphs, Neural Networks. In clustering, all the information from multiple documents is divided amongst clusters based on context and importance analysis, such that each cluster possesses sentences of a similar context - Redundancy Identification. Further, Shortest Path Detection in Word Graphs reduces the text. Along with that, we use a sequence to sequence sentence compression and perform paraphrasing using Supervised Recurrent Neural Network to generate an almost completely abstractive summary. The dataset DUC 2004 that was used indicates that the proposed system outperforms other systems in terms of metrics like ROUGE <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</sup> and BLEU <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[2]</sup> .
Computational Offloading of Convolutional Neural Network on a Smart Watch

Smartwatches have improved significantly in terms of the processing resources and are a potential candidate for using deep learning for applications relying on a host of supported sensors. However, the tradeoff between running a deep neural network on the smartwatch verses sending the sensor data to the back-end server for processing is not well understood. This paper presents a case study of these tradeoffs for a Samsung S3 smartwatch in the context of a word recognition task using a Convolutional Neural Network (CNN) based on Google's Speech Command data set. Tensorflow.js was used to do edge inference on the watch and under various computational offloading conditions using 2G, Edge, 3G and WiFi profiles. The results are that running a CNN on the watch is superior in terms of memory, CPU, inference time, and battery depletion. In specific, the inference time was 3 times longer for 2G and 1.6 times longer for WiFi with computational offloading. In addition, the battery depletion rate was about the same when using 2G or Edge Lossy but was 60-70% worse when using 3G or WiFi for computational offloading.
Semantic Video Segmentation with Dense-and-Dual Warping Spatial Features

We propose a fast and accurate network for video semantic segmentation based on dual-warping of keyframe spatial features for capturing both sparse and dense motions between frames. The proposed dual-warping operation precisely propagates the keyframe features for subsequent frames, particularly improves the propagations of fast-paced spatial contents among frames. The spatial features from deep convolutional neural network are first warped based on the optical flow between current frame and previous frame. Then, we compute warped flow field between all frames from keyframe to current frame. The previously warped features are next warped with computed warped flow field to rectify the fast-moving features missed in initial warping operation. Our network is trained in end-to-end manner, and the accuracy is significantly increased by enhancing the propagation of keyframe features with high throughput. We evaluated our approach on Cityscapes benchmark video dataset.