Reviewerly/exHarmony huggingface dataset:
A Survey on RealSense: in context of Research and Application

RealSense technology is such a technology which is implemented for machines and device for measuring the capabilities of those apparatus. The capabilities are related to the perceptions of those machines and devices. This technology mainly learns a machine to understand the world with cogitation. RealSense technology introduces cameras to explore the environment with ins and out as it is parallel working with high definition cameras. This technology is used in many categories. Object detection, facial expression detection, shape measurement, image processing, and so on. Among all of the types, RealSense technology is used in huge amount for object detection. In this category real sense mainly focus on the object which is vitally important for understanding the full scene, and understanding the other aspect of the scene with which the object can be classify for better understanding. This paper mainly provides an overview of RealSense technique to the modern technology with its 3D visual functionalities adding the developed application which is published based on Microsoft Kinet and also with SR300 technology. Also this work can give a clear overview to the researchers about the fact that which domain of research is easier with this RealSense technology and in which domain they get more accuracy.
A Framework to Figure Out Breast Cancer Cells Using Ultrasound Images

Cancer shows the unregulated existence of a cluster of cells in a specific area of the body parts. Research says that among all cancers, breast cancer is the most happening cancer in this world that brings a dead percentage high every year. It is a general issue not only for women but also for men, so, early detection of breast cancer cells is highly appreciated work in both computer science and medical science. To diagnose breast cancers, electronic modalities are incorporated. Ultrasound scanning is regarded among the most generally used digital methods for breast cancer identification which is combined with less cost and protection. In this research paper, we have made a proposal of a computational framework to detect cancer cells using gray-scale ultrasound images of the breast. We have used some image processing techniques for feature extraction from potential cancerous regions and classified them using SVM classifiers. The enhancement process was initially used to optimize gradients on a gray-scale. The median filter has been used to promote better segmentation to prevent unwanted peak noise. Segmentation based on thresholds has used since the sample images used in this research are gray. We have implemented our strategy in MATLAB and tested with the SVM classifier to verify, this proposed strategy achieved 95.37% accuracy that outperforms the satisfactory level by comparing it with existing works and this accuracy is accepted by pathology experts.
Integrating Blockchain With AI

The advancement in AI and blockchain can fundamentally reshape our way of working, learning, and almost our way of living. Though the research in both areas helps us in advancing our knowledge, it is even more advantageous by converging concepts of blockchain and AI (i.e., developing the thinking through blockchain). Blockchain is decentralized, distributed, and often a public system. Thinking, fundamentally defined as rationally concluding something, with a few known premises, is the fundamental concept behind AI and implementing the AI to become as close to human intelligence as possible; therefore, improvements in thinking are always welcomed. Both being incomplete, AI suffers from issues with trustworthiness, explainability, and privacy, while blockchain suffers in case of security, scalability, and efficiency. Bringing these two together can complement each other and make the system efficient. The chapter deals with various uses of integrating blockchain with AI. It also gives a brief on the uses of blockchain for improving both human and machine thinking.
Generative Enhancement of 3D Image Classifiers

In this paper, we propose a methodology for generative enhancement of existing 3D image classifiers. This methodology is based on combining the advantages of both non-generative classifiers and generative modeling. Its purpose is to streamline the synthesis of novel deep neural networks by embedding existing compatible classifiers into a generative network architecture. A demonstration of this process and evaluation of its effectiveness is performed using a 3D convolutional classifier and its generative equivalent—a 3D conditional generative adversarial network classifier. The results of the experiments show that the generative classifier delivers higher performance, gaining a relative classification accuracy improvement of 7.43%. An increase of accuracy is also observed when comparing it to a plain convolutional classifier that was trained on a dataset augmented with samples created by the trained generator. This suggests a desirable knowledge sharing mechanism exists within the hybrid discriminator-classifier network.
Estimating Topic Difficulty Using Normalized Discounted Cumulated Gain

Information retrieval evaluation has to consider the varying "difficulty" between topics. Topic difficulty is often defined in terms of the aggregated effectiveness of a set of retrieval systems to satisfy a respective information need. Current approaches to estimate topic difficulty come with drawbacks such as being incomparable across different experimental settings. We introduce a new approach to estimate topic difficulty, which is based on the ratio of systems that achieve an NDCG score that is better than a baseline formed as random ranking of the pool of judged documents. We modify the NDCG measure to explicitly reflect a system's divergence from this hypothetical random ranker. In this way we achieve relative comparability of topic difficulty scores across experimental settings as well as stability to outlier systems?features lacking in previous difficulty estimations. We reevaluate the TREC 2012 Web Track's ad hoc task to demonstrate the feasibility of our approach in practice.
Learning from Textual Data in Database Systems

Relational database systems hold massive amounts of text, valuable for many machine learning (ML) tasks. Since ML techniques depend on numerical input representations, pre-trained word embeddings are increasingly utilized to convert text values into meaningful numbers. However, a naïve one-to-one mapping of each word in a database to a word embedding vector misses incorporating rich context information given by the database schema. Thus, we propose a novel relational retrofitting framework Retro to learn numerical representations of text values in databases, capturing the rich information encoded by pre-trained word embedding models as well as context information provided by tabular and foreign key relations in the database. We defined relation retrofitting as an optimization problem, present an efficient algorithm solving it, and investigate the influence of various hyperparameters. Further, we develop simple feed-forward and complex graph convolutional neural network architectures to operate on those representations. Our evaluation shows that the proposed embeddings and models are ready-to-use for many ML tasks, such as text classification, imputation, and link prediction, and even outperform state-of-the-art techniques.
Public Announcement Logic in HOL

A shallow semantical embedding for public announcement logic with relativized common knowledge is presented. This embedding enables the first-time automation of this logic with off-the-shelf theorem provers for classical higher-order logic. It is demonstrated (i) how meta-theoretical studies can be automated this way, and (ii) how non-trivial reasoning in the target logic (public announcement logic), required e.g., to obtain a convincing encoding and automation of the wise men puzzle, can be realized. Key to the presented semantical embedding—in contrast, e.g., to related work on the semantical embedding of normal modal logics—is that evaluation domains are modeled explicitly and treated as additional parameter in the encodings of the constituents of the embedded target logic, while they were previously implicitly shared between meta logic and target logic.
Blurred Image Detection In Drone Embedded System

This paper deals with the detection of blurred images that may eventually be captured by a drone. The embedded system should be able to measure the amount of blur affecting the images in order to decide whether to acquire the scene again or not. For this purpose, we have developed a simple model based on Discrete Cosine Transform (DCT) associated to Support Vector Machine Classifier SVM, to classify images into three categories and thus detect strongly, moderately and slightly blurred images. The proposed system has been tested on 550 images captured by a drone. The obtained results are very conclusive.
Software Techniques for Making Cloud Data Centers Energy-efficient: A Systematic Mapping Study

Due to high demand, many cloud data centers have been developed across the world, consuming a large amount of energy. Making cloud datacenters energy efficient has become essential. Energy consumption of data centers can be minimized by designing energy-efficient hardware, software, and infrastructure. In this paper, we aim at giving an overview of software techniques, affected stakeholders, performance features, datasets, and tools used to make cloud data centers energy-efficient. To achieve this goal, we conducted a systematic mapping study using five online databases. After applying inclusion/exclusion and quality criteria, we selected 58 publications for further analysis. Our results indicate that all publications are solution and validation type of publications. We did not find publications containing evaluations in industry. We found that workload scheduling is the most frequently proposed technique used to improve cloud datacenters' energy efficiency. We found that not considering violations of service level agreements mostly affects end-users of cloud data centers. When analyzing how suggested solutions are validated, we identified the need to develop a standardized set of performance measures to benchmark software techniques proposed to make cloud data centers greener.
Etoram: A More Efficient ORAM for Secure Computation

When scaling a distributed ORAM to a two-party secure computation, the overhead is dominated by the number of pseudo-random generator (PRG) calls in generation and evaluation of a distributed point function (DPF), which are O(logn) and O(n) respectively, where n is the number of data blocks. We propose a distributed ORAM scheme, in which the PRG calls are reduced to O(log(zn/λ)) for generation and O(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">log(zn/λ)</sup> ) for evaluation, where λ is the secure parameter and z is the length of outputs in DPF. Technically, we first extend the optimization of Function Secret Sharing (FSS), early termination for functions with small output groups, to the context of ORAM for secure computation. Then, we design a scheme named etoram to exploit the high efficiency achieved by early termination. In etoram, we introduce an access counter for each data block. Then, instead of {0,1} <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">λ</sup> , the output of the point function becomes the maximum value of these counters, i.e.,{0,1} <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">z</sup> . Data blocks are privately updated by masking random strings generated by their counters. In practice, according to different access rates, z ranges from 1 to logn in sequential mode and from 3 to logn in random mode if the total number of accesses is n.
Research on the Method and Application of Intelligent Information Service Demand Identification of Inland Waterway

In view of the problems that inland waterway comprehensive information service faces with many kinds of users, difficult to capture due to diverse and variable service demands, this paper proposes an identification technology in the field of inland waterway intelligent information service. It carries out the user investigation of inland waterway information service demand and build an identification model to identify the most urgent information needs among crews, shipping companies, management departments/scientific research units and port enterprises, four types of user. On the basis of requirement identification, this research builds a cloud-based information service platform architecture for inland waterway. Technical solutions are proposed for IWaaS (Inland Waterway as a Service), PaaS (Platform as a Service), DaaS (Data as a Service) and IaaS (Infrastructure as a Service) layers. The method and application of intelligent information service demand identification of inland waterway is an effective approach to promote information service quality of inland waterway continuously.
Multilabel Toxic Comment Classification Using Supervised Machine Learning Algorithms

Web-based life has turned into an integral part of the regular day-to-day existence of a large number of individuals around the globe. Online commenting spaces generate a plethora of expressive content in the public domain, which contributes to a healthy environment for humans. However, it also has threats and dangers of cyberbullying, personal attacks, and the use of abusive language. This motivates industry researchers to model an automated process to curb this phenomenon. The aim of this paper is to perform multi-label text categorization, where each comment could belong to multiple toxic labels at the same time. We tested two models: RNN and LSTM. Their performance is significantly better than that of Logistic Regression and ExtraTrees, which are baseline models.
Feeder-Level Deep Learning-based Photovoltaic Penetration Estimation Scheme

The increasing penetration of renewable energy to the distribution grids, especially photovoltaic (PV), helps smooth out supply and demand, and reduces greenhouse gas emissions. However, the PV generation is behind-the-meter, and cannot be detected by the smart meter. To address this problem, a hybrid regression multi-layer perception (MLP) deep neural network (DNN) model is designed to separate the PV generation from the overall grid measurements. The model utilizes grid measurements, weather-related measurements, satellite-driven irradiance measurements, and temporal information as inputs to evaluate the PV generation in real-time. We also examine the performance of the model with different levels of PV penetration. We show the proposed model reduces the mean square error by 49% compares to single variable input models.
Generic wavelet‐based image decomposition and reconstruction framework for multi‐modal data analysis in smart camera applications

Effective acquisition, analysis and reconstruction of multi‐modal data such as colour and multi‐/hyper‐spectral imagery is crucial in smart camera applications, where wavelet‐based coding and compression of images are highly demanded. Many existing discrete wavelet filtering banks have fixed coefficients hence their performance is highly dependent on the signal/image being processed. To tackle this problem, a unified framework is proposed in this study, which can produce a series of discrete wavelet filtering banks, where many existing discrete wavelet filtering banks become special cases of the framework. For each generated filtering bank, it consists of two decomposition filters and two reconstruction filters through an optimisation process. The efficacy of the filtering banks produced by the framework has been validated in two case studies, including colour image decomposition and reconstruction, and hyperspectral image classification. Comprehensive experiments have demonstrated the superior performance of the proposed framework, which will benefit the efficacy of smart camera and camera network applications.
Case-Based Reasoning: Problems And Importance Of Similarity Measure

This article attempts to provide an overview of the basic concepts, structure and terms related to case-based reasoning, because we aim to work on an evolution of the CBR process which allows us to integrate the knowledge base, ensure the consistency of information, integrate the modes of reasoning: similarity, classification and hybrid. In addition, this article discusses the concept and issues related to similarity measure, which is very important in medical system and using it allows to reduce the risks of health error for patients. However, it seems very complex because the measure of similarity must take into account the diversity of the types of information (qualitative, textual, quantitative, etc.) for each patient. And at the end, we represent an evaluation of similarity measure which was applied in (GS.52).
Statistics and Machine Learning Experiments in English and Romanian Poetry

This paper presents a quantitative approach to poetry, based on the use of several statistical measures (entropy, informational energy, N-gram, etc.) applied to a few characteristic English writings. We found that English language changes its entropy as time passes, and that entropy depends on the language used and on the author. In order to compare two similar texts, we were able to introduce a statistical method to asses the information entropy between two texts. We also introduced a method of computing the average information conveyed by a group of letters about the next letter in the text. We found a formula for computing the Shannon language entropy and we introduced the concept of N-gram informational energy of a poetry. We also constructed a neural network, which is able to generate Byron-type poetry and to analyze the information proximity to the genuine Byron poetry.
Fast Training of Light Binary Convolutional Neural Networks using Chainer and Cupy

Light binary convolutional neural networks (LB-CNN) are particularly useful when implemented in hardware technologies, such as FPGA. In this paper, such a network and its implementation using the Chainer machine learning framework is presented. Up to three convolutional layers, each provided with binary convolution kernels, can be defined forming a nonlinear expander for the images to be categorized. Effective training is done in a single output linear layer (Adaline) using the very fast extreme learning machine training with GPU acceleration. It is shown that a significant speedup of more than 60 times can be achieved while employing the CHAINER environment instead the more traditional Keras/Tensorflow. Such fast training allows easy tuning of the randomly generated binary kernels in a loop. Results for widely used datasets including MNIST, GTRSB, ORL, VGG etc. are given, showing very good compromise between accuracy and complexity. Particularly, for face recognition problems the model provides up to 100% accuracies. Such LB-CNN models are an excellent solutions for low-power applications where bit-quantization is an important option to reduce implementation complexity and energy.
Scientific data management in the federal government: A case study of <scp>NOAA</scp> and responsibility for preserving digital data

Abstract In this paper, we examine the ways in which the evolution of federal and agency‐specific data management policies has affected and continues to affect the long‐term preservation of digital scientific data produced by the United States government. After reviewing the existing literature on the role of archival theory and practice in the preservation of scientific data, we present the case of the National Oceanic and Atmospheric Administration (NOAA) to analyze how data management activities at this agency are shaped by legislative mandates as well as both government‐wide and agency‐specific information‐management policies. Through the connected network of law, federal policy, agency policy, and the records schedules which govern recordkeeping practice in the federal government, we propose a number of further questions on how government agencies can effectively provide for the management of scientific data as federal records.
Activity Recognition and Fall Detection in Elderly People

With the advancement in technology, personalised monitoring is emerging. During the COVID-19 pandemic, the old age homes have the most susceptible population. We have used activity recognition to detect abnormal activities which also include symptoms of COVID-19 like sneezing, fatigue and headache. Elderly people are prone to falls which can be fatal or cause injuries. Activity recognition and fall detection can be used to keep a tab on them. Generally senior citizens live alone or in care homes. Hence if they are constantly monitored, adequate help can be provided. In this paper, we propose a system with activity recognition and fall detection. OpenCV is used for activity recognition and Markov model for the sequence of activities. The probability of that sequence of activities is predicted. If it is below a threshold, it is considered as an abnormal activity. A Single Shot Detector model is implemented for fall detection.
The Bisq DAO: On the Privacy Cost of Participation

The Bisq DAO is a core component of Bisq, a decentralized cryptocurrency exchange. The purpose of the Bisq DAO is to decentralize the governance and finance functions of the exchange. However, by interacting with the Bisq DAO, participants necessarily publish data to the Bitcoin blockchain and broadcast additional data to the Bisq peer-to-peer network. We examine the privacy cost to participants in sharing this data. Specifically, we use a novel address clustering heuristic to construct the one-to-many mappings from participants to addresses on the Bitcoin blockchain and augment the address clusters with data stored within the Bisq peer-to-peer network. We show that this technique aggregates activity performed by each participant: trading, voting, transfers, etc. We identify instances where participants are operating under multiple aliases, some of which are real-world names. We identify the dominant transactors and their role in a two-sided market. We conclude with suggestions to better protect the privacy of participants in the future.
Identification of Toga Plants Based on Leaf Image Using the Invariant Moment and Edge Detection Features

Currently, the price of drugs are rising time to time and still growing. The high cost for medical treatment is not compatible compared to the people's welfare, which is burdens to lives of some people. Indonesian people always look for medicine as the main shortcut, whereas in Indonesia, there are a lot of family medicinal garden (in bahasa - TOGA [Tanaman Obat Keluarga]) which are commonly use as herbs and natural medicine. Most people have a difficulty to identify the type of toga plants and the real efficacy of these plants. This research proposed an identification of toga plants using leaf images. the leaf images features will be extract using Invariant Moment and Canny edge will be used to recognize leaf textures. K-Nearest Neighbor is used for leaf type identification. According to the experiments, this system yields 80% classification accuracy.