Reviewerly/exHarmony huggingface dataset:
Deep Neural Network Accelerator Design for the Multi-task, High performance, and Energy-efficient Edge Computing

In recent years, Edge computing for AI applications has been the focus of research by various companies and academic units in order to achieve high speed real-time computations. Therefore, the submitted work separates the convolution operations so that the proposed accelerator can not only operate in the convolutional networks, but also operate in the lightweight neural networks. The main design focus is to utilize a tiled structure to optimize the suitable size resided in the block of input data. The proposed design also implements the data flow and parallelism analysis to minimize the access of the memory for the DNN hardware circuit and to achieve the high efficiency of the operation. The PE hardware utilization rate of 100% for different kernel sizes can be realized through the PE design and the reduced kernel size without accounting for the data loading time. The proposed accelerator is constituted with 32 MACs and internal logic of 450k gates by implemented in TSMC 40nm process. The simulated energy efficiency can reach to 213.3 GOPS/W while operating at 500MHz.
Who owns your DNA?

With the recent news that George Church is selling his genome as a nonfungible token and more unresolved murder cases being solved through familial DNA identification, BioTechniques is asking: who owns your DNA?
PIR Codes from Combinatorial Structures

A k-server Private Information Retrieval (PIR) code is a binary linear [m, s]-code admitting a generator matrix such that for every integer i with $$1\le i\le s$$ there exist k disjoint subsets of columns (called recovery sets) that add up to the vector of weight one, with the single 1 in position i. As shown in [8], a k-server PIR code is useful to reduce the storage overhead of a traditional k-server PIR protocol. Finding k-server PIR codes with a small blocklength for a given dimension has recently become an important research challenge. In this work, we propose new constructions of PIR codes from combinatorial structures, introducing the notion of k-partial packing. Several bounds over the existing literature are improved.
Breast Cancer Classification with Machine Learning Classifier Techniques

Cancer is a well-known disease that leads to death of human beings and breast cancer (BC) is one of the types of cancer diagnosed in women. About one of eight women is diagnosed with BC during her life time. Treatment for BC can be easy if it is diagnosed early. The approach of this study is to identify a patient having BC or not by different Machine Learning (ML) Techniques. In this study Wisconsin Diagnostic Breast Cancer (WDBC) dataset is going to classify with Support Vector Machine (SVM), k-Nearest Neighbours (k-NN), Na√Øve Bayes (NB), Decision-Tree (DT) and Logistic Regression (LR). There is pre-processing stage prior to classification in which five different classifiers applied with 5-fold cross-validation method. Classification performance is measured by performance measuring parameters i.e.accuracy, sensitivity, and specificity with the use of confusion metrics. The best performance found by SVM with an accuracy of 99.12% after normalization process in this study.
Design and Application of Tibetan Long Chess Using Monte Carlo Algorithm and Artificial Intelligence

Abstract In order to study the algorithm of Tibetan Jiuqi, a new kind of chess in Chinese computer game, there are too many possibilities to be considered in solving the problems of falling pieces, and the influence of falling pieces on Xingzi and Tiezi. On the basis of forming different chess patterns as the main source of raising pieces, this paper puts forward a design scheme which takes the Monte Carlo algorithm randomly as the core and the final evaluation algorithm as the secondary one, and takes a variety of possibilities in Tibetan chess as a breakthrough. Get different benefits from different chess types to cooperate with the final evaluation algorithm to get the assignment of each replicated chessboard. It is proved by the program computer game that the program is complete and the three stages of Tibetan chess can be completed successfully. The experiment shows that the Monte Carlo algorithm is very suitable for the AI development of Tibetan chess.
Research on visual object tracking algorithm based on improved twin network

Abstract The tracking accuracy of traditional algorithm decreases when the target is in a complex environment or the target itself changes, and an improved correlation filtering twin network algorithm combining the attention mechanism is proposed. Fast wavelet is used for filtering and multi-scale feature signal extraction, and loss function is used to minimize the squared error between sample and label. Then correlation filtering algorithm is used to calculate the similarity of image between target template and candidate region. Attention mechanism is introduced to improve correlation filtering twin network structure, and the GloU’s online learning is advanced as an aid to determine template updating. The experimental results show that the algorithm have certain improvement effect for target shade, quick movement and deformation problems, so performance and accuracy of target tracking, the algorithm has better competitiveness.
Power Consumption Data Privacy-preserving Scheme Based on Improved Multi-key Fully Homomorphic Encryption

Background: In the power Internet of Things (IoT), power consumption data faces the risk of privacy leakage. Traditional privacy-preserving schemes cannot ensure data privacy on the system, as the secret key pairs shall be shared between all the interior nodes once leaked. In addition, the general schemes only support summation algorithms, resulting in a lack of extensibility. Objective: To preserve the privacy of power consumption data, ensure the privacy of secret keys, and support multiple data processing methods we propose an improved power consumption data privacypreserving scheme. Method: Firstly, we have established a power IoT architecture based on edge computing. Then the data is encrypted with the multi-key fully homomorphic algorithm to realize the operation of ciphertext, without the restrictions of calculation type. Through the improved decryption algorithm, ciphertext that can be separately decrypted in cloud nodes is generated, which contributes to reducing communication costs and preventing data leakage. Results: The experimental results show that our scheme is more efficient than traditional schemes in privacy preservation. According to the variance calculation result, the proposed scheme has reached the application standard in terms of computational cost and is feasible for practical operation. Discussion: In the future, we plan to adopt a secure multi-party computation based scheme so that data can be managed locally with homomorphic encryption, so as to ensure data privacy. Conclusion: A privacy-preserving scheme based on improved multi-key fully homomorphic encryption is proposed for the power consumption data, and the experimental results demonstrate the effectiveness and advantage of the proposed scheme.
Time-space topological dataset for fuzzy logic mineral prospectivity modelling in mesoarchean terranes of Reguibat shield (Mauritania)

Summary This research is devoted to the estimation of gold and uranium mineralization in MesoArchean Tasiast-Tijirit terrane. Study area located in Northern-west part of Reguibat shield (Western African craton). The first stage of the prospectivity model has composed a dataset with space-time topological relationships between the geological, geophysical and geochemical features. We used a fuzzy logic mapping approach to assess different hypothesis about time and type of ore process.
A new twofold Cornacchia-type algorithm and its applications

We focus on exploring more potential of Longa and Sica's algorithm (ASIACRYPT 2012), which is an elaborate iterated Cornacchia algorithm that can compute short bases for 4-GLV decompositions. The algorithm consists of two sub-algorithms, the first one in the ring of integers $ \mathbb{Z} $ and the second one in the Gaussian integer ring $ \mathbb{Z}[i] $. We observe that $ \mathbb{Z}[i] $ in the second sub-algorithm can be replaced by another Euclidean domain $ \mathbb{Z}[\omega] $ $ (\omega = \frac{-1+\sqrt{-3}}{2}) $. As a consequence, we design a new twofold Cornacchia-type algorithm with a theoretic upper bound of output $ C\cdot n^{1/4} $, where $ C = \frac{3+\sqrt{3}}{2}\sqrt{1+|r|+|s|} $ with small values $ r, s $ given by the curves.The new twofold algorithm can be used to compute $ 4 $-GLV decompositions on two classes of curves. First it gives a new and unified method to compute all $ 4 $-GLV decompositions on $ j $-invariant $ 0 $ elliptic curves over $ \mathbb{F}_{p^2} $. Second it can be used to compute the $ 4 $-GLV decomposition on the Jacobian of the hyperelliptic curve defined as $ \mathcal{C}/\mathbb{F}_{p}:y^{2} = x^{6}+ax^{3}+b $, which has an endomorphism $ \phi $ with the characteristic equation $ \phi^2+\phi+1 = 0 $ (hence $ \mathbb{Z}[\phi] = \mathbb{Z}[\omega] $). As far as we know, none of the previous algorithms can be used to compute the $ 4 $-GLV decomposition on the latter class of curves.
A Step-by-Step Time-Saving Approach to Coding Office Visits.

Follow these four steps to code quickly and accurately, while reducing the need to count up data points.
Fake Product Detection Using Blockchain Technology

In recent years, Counterfeit products play an important role in product manufacturing industries. This affects the companies name, sales, and profit of the companies. Blockchain technology is used to identification of real products and detects fake products. Blockchain technology is the distributed, decentralized, and digital ledger that stores transactional information in the form of blocks in many databases which is connected with the chains. Blockchain technology is secure technology therefore any block cannot be changed or hacked. By using Blockchain technology, customers or users do not need to rely on third-party users for confirmation of product safety. In this project, with emerging trends in mobile and wireless technology, Quick Response (QR) codes provide a robust technique to fight the practice of counterfeiting the products. counterfeit products are detected using a QR code scanner, where a QR code of the product is linked to a Blockchain. So this system may be used to store product details and generated unique code of that product as blocks in the database. It collects the unique code from the user and compares the code against entries in the Blockchain database. If the code matches, it will give a notification to the customer, otherwise it will give the notification to the customer that the product is fake.
Generative Machine Learning for Resource-Aware 5G and IoT Systems

Extrapolations predict that the sheer number of Internet-of-Things (IoT) devices will exceed 40 billion in the next five years. Hand-crafting specialized energy models and monitoring sub-systems for each type of device is error prone, costly, and sometimes infeasible. In order to detect abnormal or faulty behavior as well as inefficient resource usage autonomously, it is of tremendous importance to endow upcoming IoT and 5G devices with sufficient intelligence to deduce an energy model from their own resource usage data. Such models can in-turn be applied to predict upcoming resource consumption and to detect system behavior that deviates from normal states. To this end, we investigate a special class of undirected probabilistic graphical model, the so-called integer Markov random fields (IntMRF). On the one hand, this model learns a full generative probability distribution over all possible states of the system-allowing us to predict system states and to measure the probability of observed states. On the other hand, IntMRFs are themselves designed to consume as less resources as possible-e.g., faithful modelling of systems with an exponentially large number of states, by using only 8-bit unsigned integer arithmetic and less than 16KB memory. We explain how IntMRFs can be applied to model the resource consumption and the system behavior of an IoT device and a 5G core network component, both under various workloads. Our results suggest, that the machine learning model can represent important characteristics of our two test systems and deliver reasonable predictions of the power consumption.
Role And Effect of A Character On Scenario Of Multimedia Learning

Learning that uses information technology tools will be able to help students to more easily understand the subject of lesson content. Today multimedia technology is the most commonly used. Multimedia technology with a scenario that refers to lesson content is easier to understand than without scenarios. The effect of a scenario causes a learning media must have a character to make the learning media more quality.In this research, the role and influence of a character in the learning scenario will be investigated. Characters that match the content of the lesson and according to age Students must be properly considered so that students have an interest in the material that is in a learning media. From the results of the research obtained several important variables that must be considered by developers who want to create a character in the multimedia-based learning media.
Tracing of the Blackmailers in Sextortion Case and Tactics to Defend It - An Experimental Cybercrime Case Study

Sextortion is a new form of cybercrime that is spreading very fast in India. Sextortion is such a type of incident where victims are very ashamed of sharing and talking. And that is why it becomes a very easy and suitable target for blackmailers. Innocent social media users are being trapped in Sextortion. Victims not only lose their money but also pride and respect in society especially family members and friends. The research paper has explored to understand the modus operandi of the blackmailers. The present study has attempted to trace blackmailers in a sextortion incident. The research paper has described the process of tracing the blackmailers online. The study analyzed the incident from a cybercrime detection and identification perspective. The research paper is also attempting to spread awareness by describing tactics to defend against cybercrimes such as sextortion.
Application of Deep Learning in Cartography Using UNet and Generative Adversarial Network

The history of the term “machine learning” can be traced back to as far as the year 1959. During this period, an American named Arthur Lee Samuel, also regarded as the pioneer of artificial intelligence and computer gaming, came up and disseminated this term. However, the breakthroughs in the field of Deep Learning didn’t come until the year 1967 when Alexey Ivakhnenko developed the first feedforward, multilayer perceptron network and revolutionized the field. The field of cartography has been progressing from the first world map being introduced by Anaximander up to today when every direction can be accessed at a click of a button. The dataset that the authors use to solve this problem has been used from Pappas’ dataset, who created it by taking a random crop of the global map of the earth. To understand how a residual neural network works, the authors start by explaining a basic convolution network.
Guia norteador para condução de grupo focal na identificação de competências gerenciais: Relato de experiência

Introduction: The use of the Focus Groups technique in nursing is present in the literature, however, gaps are highlighted in relation to guides or scripts that can guide their conduct in the area of ??identifying managerial competences. Objectives: To present the researchers' experience with the elaboration of a guide to guide the conduct of FG related to the identification of management skills that are priority to the development of nursing students. Methods: Experience report carried out in a Brazilian Public Higher Education Institution. The guide was built based on articles available in the literature, reference proposed and the experience of the authors in a course offered by Spain. This guide has been validated and tested by four PhD specialists. After construction and validation, three focus groups were held with professors from the management area of ??the selected institution, who had worked for more than two years in the function. The collection took place from March to April 2020. The groups were had an average duration of 55 minutes. An inductive thematic analysis was performed. Results: The construction of a guiding guide for driving must follow rigorous steps evidenced by the referential, and proved to be a strategy capable of identifying relevant aspects for the training of nurses with regard to the identification of managerial skills. Also can serve as a basis for conducting other groups. In addition, from the guide built and its implementation, priority managerial competencies were identified: leadership, communication, interpersonal relationships and managerial decision making. Conclusions: The strategy of focus groups with nursing professors accompanied by a guiding guide proved to be positive in identifying managerial competencies that are a priority in nursing education, enabling the formation of a better prepared professional to face the job market.
Point spread function for the wide-field-of-view plenoptic cameras

Recently, single or multi-layer spherical lens (monocentric lens) coupled with a microlens array (MLA) and an imaging sensor are under investigation to expand the field of view (FOV) for handheld plenoptic cameras. However, there lacks modeling the point spread functions (PSFs) for them to improve the imaging quality and to reconstruct the light field in the object space. In this paper, a generic image formation model is proposed for wide-FOV plenoptic cameras that use a monocentric lens and an MLA. By analyzing the optical characteristics of the monocentric lens, we propose to approximate it by a superposition of a series of concentric lenses with variable apertures. Based on geometry simplification and wave propagation, the equivalent imaging process of each portion of a wide-FOV plenoptic camera is modeled, based on which the PSF is derived. By comparing PSFs captured by real wide-FOV plenoptic camera and those generated by the proposed model, the validity of this model is verified. Further, reconstruction process is applied by deconvolving captured images with the PSFs generated by the proposed model. Experimental results show that the quality of reconstructed images is better than that of subaperture images, which demonstrates that our proposed PSF model is beneficial for imaging quality improvement and light field reconstruction.
Analyzing Linguistic Features for Classifying Why-Type Non-Factoid Questions

Why-type non-factoid questions are complex and difficult to answer compared to factoid questions. A challenge in finding an accurate answer to a non-factoid question is to understand the intent of user as it differs with their knowledge and also the context of the question in which it is being asked. Predicting correct type of a question and its answer by a classification model is an important issue as it affects the subsequent processing of its answer. In this paper, a classification model is proposed which is trained by a combination of lexical, syntactic, and semantic features to classify open-domain why-type questions. Various supervised classifiers are trained on a featured dataset out of which support vector machine achieves the highest accuracy of 81% in determining question type and 76.8% in determining answer type which shows 14.6% improvement in predicting an answer type over a baseline why-type classifier with 62.2% accuracy.
A utility-based approach for business intelligence to discover beneficial itemsets with or without negative profit in retail business industry

Utility mining is defined as discovery of high utility itemsets from the large databases. It can be applied in business intelligence for business decision-making such as arranging products in shelf, catalogue design, customer segmentation, cross-selling etc. In this work a novel algorithm MAHUIM (matrix approach for high utility itemset mining) is proposed to reveal high utility itemsets from a transaction database. The proposed algorithm uses dynamic matrix structure. The algorithm scans the database only once and does not generate candidate itemsets. The algorithm calculates minimum threshold value automatically, without seeking from the user. The proposed algorithm is compared with the existing algorithms like HUI-Miner, D2HUP and EFIM. For handling negative utility values, MANHUIM algorithm is proposed and this is compared with HUINIV. For performance analysis, four benchmark datasets like Connect, Foodmart, Chess and Mushroom are used. The result shows that the proposed algorithms are efficient than the existing ones.
Predictive Analysis Using Regression Methods in Low Resource Language 'MISING'

In this paper, an attempt has been made to study and analyze sound data with a different attribute like time, pitch, formant frequencies, and speaker type etc. The dataset used here is a primary data set of sounds of Vowels of Mising language which is a tribal language of North-East India with a very few speakers and considered as one of the very few low resource languages. The current study shows that the pronunciation of a particular vowel by the male speaker has more variations than that of a female speaker as shown by the density plots. The regression model obtained using the regression analysis on the fundamental formant frequencies (F0) i.e. pitch, varies with the gender of the speaker for the vowels. This information can be utilized for speaker identification in Mising language.
Visual and data stationarity of texture images

The stationarity of a texture can be considered a fundamental property of images, although the property of stationarity is difficult to define precisely. We propose a stationarity test based on multiscale, locally stationary, 2D wavelets. Three separate experiments were performed to evaluate the capabilities and the limitations of this test. The experiments comprised a chessboard stationarity analysis, two classification tasks, and a psychophysical experiment. The classification tasks were performed on 110 texture images from a texture database. In one subtask, five texture feature vectors were extracted from each image and the classification accuracy of two classical methods compared, whereas in the second subtask, the classification accuracy of several methods was compared to the descriptors defined for each image within the database. In the psychophysical experiment, the correlation between the classification results and observer judgements of texture similarity were determined. It was found that a combination of wavelet shrinkage and rotation-invariant local binary pattern best predicted the observer response. The results show that the proposed stationarity test is able to provide relevant information for texture analysis.
Codec-Aware Video Delivery Over SDNs

To guarantee quality of delivery for video streaming over software defined networks, efficient predictors and adaptive routing frameworks are required. We demonstrate an agent that predicts video quality of delivery metrics in a scalable way using a bespoke codec-aware learning model. We also demonstrate the integration of this agent with an adaptive framework for centrally controlled software-defined networks that re-configures network operational paths in response to the learning agent, ensuring that good quality of delivery of video is maintained during periods of congestion. The demo scenario highlights the feasibility, scalability and accuracy of the framework.