Reviewerly/exHarmony huggingface dataset:
Research on Performance Optimization for Power Big Data Storage based on HBase

With the development of smart grid, the online monitoring of power equipment has become the focus. Due to the increasing of power data, data storage in power grid is faced with great challenge. HBase is widely used for storing and managing power data due to its excellent performance in distributed access to data. However, HBase is prone to write a hot spot problem when storing data. To address this problem, this paper proposes an optimization method for data storage performance based on a dynamic load balancing strategy, which is implemented by pre-partitioning processing, RowKey substitution, and WLC (Weighted Least Connection) algorithm adapted to HBase storage mechanism. Through the experimental analysis of insulator current data storage, the results show that the method improves the performance of HBase cluster parallel data storage.
Latency Comparison of MMT and ROUTE/DASH for the Transport Layer of the TV 3.0 Project

The need for internet compatibility in modern digital television systems led to the development of new standards for their transport layer. The Moving Picture Experts Group (MPEG) took part by working on a standard based on Hypertext Transfer Protocol (HTTP), called Dynamic Adaptive Streaming over HTTP (DASH) for broadband services, and by developing MPEG Media Transport (MMT) for broadcast and hybrid services. A protocol called Real-time Object delivery over Unidirectional Transport (ROUTE) allows for the delivery of DASH-formatted content on a broadcast channel, making ROUTE/DASH another alternative for television systems. The Brazilian Digital Terrestrial Television System (SBTVD) Forum released a Call for Proposals named TV 3.0 Project, with requirements and evaluation methods to be considered by technology proponents. This paper compares the latency of MMT and ROUTE/DASH on an Advanced Television Systems Committee 3.0 (ATSC 3.0) setup considering the specifications of the TV 3.0 Project.
A Formal Engineering Approach to Product Family Modeling

Software Product Line deals with the development of product families for diverse market needs and includes feature model to describe the structure of the included products. Since feature model is lack of detailed specification of individual features, some behavior-oriented methods have been proposed to analyze the inner functionalities of features. But how these functions relate to the feature model remains a problem and a systematic approach is still needed to support the whole process of product family modeling. This paper provides a formal engineering approach to modeling product family where feature model evolves as individual features are formalized through informal, semi-formal and formal stages. For each stage, a set of evolvement rules are given to guide the refactoring of the feature model which will then serve as a basis for formal specifications of individual features. Such an iterative process repeats until achieving a feature model with consistent feature specifications. A case study is described to illustrate the effectiveness of our approach.
Data privacy protection based on homomorphic encryption

Data security and privacy protection are increasingly becoming a bottleneck in the development of cloud computing. To improve the existing homomorphic encryption technology, ECC homomorphic encryption algorithm is proposed, and ECC homomorphic encryption algorithm is designed and implemented. Based on this, the aggregation model of data privacy protection is constructed, and the effect of ECC homomorphic encryption data security operation is analyzed. Compared with the traditional RSA encryption algorithm, ECC homomorphic encryption algorithm shows a higher level of security. When the key length increases gradually, the security of ECC with more than 300 bits is much greater than that of RSA with more than 2000 bits. At the same time, ECC homomorphic encryption algorithm is better than RSA in terms of encryption time and cipher size. ECC homomorphic encryption algorithm has the advantages of low computational complexity and good security performance.
Mini-Cave Automatic Virtual Environment Application in Security, Safety, and Defence Studies

The aim of the article is to analyse the use of mini-Cave Automatic Virtual Reality Environment (mini-CAVE) in academic studies on security, safety, and defence. The literature review based on Web of Science database and the case study of the War Studies University research activities indicated the mini-CAVE can be used in two areas as a research infrastructure and as a didactic tool. It can be concluded that due to the development of virtual reality worlds, the application of the mini-CAVE supports research and didactics activities, specifically in creating 3D scenarios for teaching concepts and processes, and simulating situations related to saving life and health.
Chapter 5: The Low-Grade, Neoproterozoic, Vein-Style, Carbonaceous Phyllite-Hosted Paracatu Gold Deposit, Minas Gerais, Brazil

Abstract The Paracatu deposit in Brazil is a shallowly dipping, bulk-tonnage, low-grade, vein-style orogenic Au orebody hosted in very strongly deformed Neoproterozoic carbonaceous phyllite of the southern Brasília fold belt. At regional to district scales, the gold orebody lies along the eastern, hanging-wall edge of a major thrust of the ~630 Ma Brasiliano orogeny. This thrust cuts through a facies transition between clastic-dominated rocks of the Canastra Group and carbonate-dominant rocks of the Vazante Group, deposited at ~1000 Ma in a rift to passive-margin environment on the flank of the São Francisco craton. At the same scales, the footwall of this major thrust system hosts numerous structurally controlled zinc deposits including Vazante and Morro Agudo. At Paracatu, ore genesis occurred primarily by the formation of early tectonic quartz sulfide-carbonate veins, prior to substantial ductile deformation (boudinage), local physico-chemical reworking of these veins, and redistribution of some gold. Structural, geochemical, and isotopic data indicate a strong influence of the local rocks (cm to 100-m scales) on many ore ingredients, and the quartz and carbonate in ore veins were most likely derived locally (cm to m scales). However, the coassociation of gold and arsenic with the boudinaged veins and a major thrust, and the absence of metal enrichments normally associated with syngenetic metalliferous black shales, supports a model of far-field derivation of gold within this metasedimentary package (km to 10-km scales). Transport of metal-bearing fluids toward a favorable structural and chemical site during thrusting and orogenesis was possibly focused, during precipitation to ore grades, by the position of transverse structures in the basement, which also influenced deposition of the adjacent zinc deposits. Successful mining of the low-grade resource was initially favored by the subhorizontal orebody geometry and weathering characteristics, and subsequently by high production rates from the 100-m-thick mineralized zone.
The Quantum Supremacy Tsirelson Inequality

A leading proposal for verifying near-term quantum supremacy experiments on noisy random quantum circuits is linear cross-entropy benchmarking. For a quantum circuit $C$ on $n$ qubits and a sample $z \in \{0,1\}^n$, the benchmark involves computing $|\langle z|C|0^n \rangle|^2$, i.e. the probability of measuring $z$ from the output distribution of $C$ on the all zeros input. Under a strong conjecture about the classical hardness of estimating output probabilities of quantum circuits, no polynomial-time classical algorithm given $C$ can output a string $z$ such that $|\langle z|C|0^n\rangle|^2$ is substantially larger than $\frac{1}{2^n}$ (Aaronson and Gunn, 2019). On the other hand, for a random quantum circuit $C$, sampling $z$ from the output distribution of $C$ achieves $|\langle z|C|0^n\rangle|^2 \approx \frac{2}{2^n}$ on average (Arute et al., 2019). In analogy with the Tsirelson inequality from quantum nonlocal correlations, we ask: can a polynomial-time quantum algorithm do substantially better than $\frac{2}{2^n}$? We study this question in the query (or black box) model, where the quantum algorithm is given oracle access to $C$. We show that, for any $\varepsilon \ge \frac{1}{\mathrm{poly}(n)}$, outputting a sample $z$ such that $|\langle z|C|0^n\rangle|^2 \ge \frac{2 + \varepsilon}{2^n}$ on average requires at least $\Omega\left(\frac{2^{n/4}}{\mathrm{poly}(n)}\right)$ queries to $C$, but not more than $O\left(2^{n/3}\right)$ queries to $C$, if $C$ is either a Haar-random $n$-qubit unitary, or a canonical state preparation oracle for a Haar-random $n$-qubit state. We also show that when $C$ samples from the Fourier distribution of a random Boolean function, the naive algorithm that samples from $C$ is the optimal 1-query algorithm for maximizing $|\langle z|C|0^n\rangle|^2$ on average.
Improving User Attention to Chatbots through a Controlled Intensity of Changes within the Interface

Designing user interfaces requires the efficient management of elements that interact with the user and attract their attention. In the development of technologies supporting the development of chatbots, few studies have focused on the visual aspects of the interface. Changes in the chatbot area can be of different natures, such as increasing or decreasing the number of messages. Due to habituation (change blindness), messages delivered by chatbots may not be noticed. The main goal of the present study was to examine the impact of changes within the chatbot on user behaviour and the possibility of directing user attention to the chatbot area. This study showed that the intensity and types of changes within the chatbot can improve performance and successful message delivery to users.
Detecting School Violence Using Artificial Intelligence to Interpret Surveillance Video Sequences

In this study, we present a skeleton-based approach for detecting aggressive activity. The approach does not require much powerful hardware but is very fast in realization. There are two stages in our method: feature extraction from video frames to evaluate a person's posture, and then action classification using a neural network to determine if the frames contain bullying scenes. We also selected 13 classes for identifying aggressor's and victim's behavior, created a dataset of 400 min of video data that contains actions of one person and 20 h of video data containing actions of physical bullying and aggression. The approach was tested on the assembled dataset. Results show more than 97% accuracy in determining aggressive behavior in video sequences.
Energy-Based Supervised Hashing for Multimorbidity Image Retrieval

Content-based image retrieval (CBIR) has attracted increasing attention in the field of computer-aided diagnosis, for which learning-based hashing approaches represent the most prominent techniques for large-scale image retrieval. In this work, we propose a Supervised Hashing method with Energy-Based Modeling (SH-EBM) for scalable multi-label image retrieval, where concurrence of multiple symptoms with subtle differences in visual feature makes the search problem quite challenging, even for sophisticated hashing models built upon modern deep architectures. In addition to similarity-preserving ranking loss, multi-label classification loss is often employed in existing supervised hashing to further improve the expressiveness of hash codes, by optimizing a normalized probabilistic objective with tractable likelihood (e.g., multi-label cross-entropy). On the contrary, we present a multi-label EBM loss without restriction on the tractability of the log-likelihood, which is more flexible to parameterize and can model a more expressive probability distribution over multimorbidity image data. We further develop a multi-label Noise Contrastive Estimation (ml-NCE) algorithm for discriminative training of the proposed hashing network. On a multimorbidity dataset constructed by the NIH Chest X-ray, our SH-EBM outperforms most supervised hashing methods by a significant margin, implying its effectiveness in facilitating multilevel similarity preservation for scalable image retrieval.
Nurse Care Activity Recognition from Accelerometer Sensor Data Using Fourier- and Wavelet-based Features

Nurse care activity recognition is an emerging segment in healthcare automation systems based on physical movement recognition applying machine learning techniques using various sensor-based datasets. In this paper, different machine learning models have been used to recognize the activities. However, before that, our user dataset has been preprocessed using data cleaning, resampling, data labeling, windowing, and filtering techniques in order to handle the ununiform data. Various analytical features have been extracted using Fast Fourier Transformation, Power Spectral Density, and Discrete Wavelet Transformation. After that, the best combinational features have been selected from the extracted features, and class imbalance has been mitigated before applying the conventional machine learning models. After applying all methodology, 87.00% accuracy has been obtained using the Light Gradient Boosting Machine Classifier.
Liability for Early Safety Obsolescence of IoT Devices due to Information Security Reasons

The manufacturer or dealer is obliged to ensure that the device is repaired during the warranty period. In some countries, there is also an obligation to provide maintenance and spare parts for a certain number of years after the warranty period expires. However, the regulations do not specify any obligation to provide security upgrades to IoT and/or other devices connected to the Internet (e.g., household routers that connect most citizens to the Internet). For example, what if a serious security flaw is discovered a few years after purchase in the communication protocol that the expensive IoT device uses? The manufacturer has no obligation to provide safety upgrades after the warranty period has expired. Such practices lead to serious security breaches. Therefore, we believe that such a practice is not appropriate, neither technologically, nor ecologically, and it violates the fundamental principle of conscientiousness and honesty in obligatory relations, known since Roman law, according to which in dealing with the other party one should act with confidence and respect to the person and interests of the other party. In this paper, we would like to explain the existence of the problem of the early safety obsolescence of devices from the view of information security and make proposals for improving legal regulation that would reduce the rapid obsolescence of devices in terms of safety.
Game Theory on the Blockchain: A Model for Games with Smart Contracts

We propose a model for games in which the players have shared access to a blockchain that allows them to deploy smart contracts to act on their behalf. This changes fundamental game-theoretic assumptions about rationality since a contract can commit a player to act irrationally in specific subgames, making credible otherwise non-credible threats. This is further complicated by considering the interaction between multiple contracts which can reason about each other. This changes the nature of the game in a nontrivial way as choosing which contract to play can itself be considered a move in the game. Our model generalizes known notions of equilibria, with a single contract being equivalent to a Stackelberg equilibrium, and two contracts being equivalent to a reverse Stackelberg equilibrium. We prove a number of bounds on the complexity of computing SPE in such games with smart contracts. We show that computing an SPE is $\textsf{PSPACE}$-hard in the general case. Specifically, in games with $k$ contracts, we show that computing an SPE is $\Sigma_k^\textsf{P}$-hard for games of imperfect information. We show that computing an SPE remains $\textsf{PSPACE}$-hard in games of perfect information if we allow for an unbounded number of contracts. We give an algorithm for computing an SPE in two-contract games of perfect information that runs in time $O(m\ell)$ where $m$ is the size of the game tree and $\ell$ is the number of terminal nodes. Finally, we conjecture the problem to be $\textsf{NP}$-complete for three contracts.
Design of UAV Single Object Tracking Algorithm Based on Feature Fusion

Considering the real-time performance and robustness of the object tracking algorithm, this paper proposes an improved tracking algorithm for single object tracking based on UAV. The algorithm uses Tiny-YOLOv3 for preliminary detection, and the detection results combine histogram of orientation gradient(HOG) and RGB histogram to extract the features. We use histogram matching to find the highest similarity between the detected candidate object and the object to be tracked to achieve the purpose of tracking. Different tracking strategies are designed when the object is stationary and moving. The experimental results show that the algorithm improves tracking accuracy and robustness while ensuring real-time tracking.
A Current and Future Challenge to Contemporary Policing: The Changing Profile of Crime and the Police Response

As cyberspace becomes a major means of exchanging information and knowledge, and as the general public spends more time accessing and using technical gadgets, the possibility of unconsciously being associated with crime is increasing. In a similar vein, the incidence of violent crime is gradually decreasing in statistical indicators, but the number of fraud cases has increased more than two times between 2014 and 2019 (Korea—National Police Agency, 2020), which indicates that the proportion of cybercrime cases is growing compared with traditional crimes. Against this backdrop, the original paper delivers important discussions which should be paid attention to for academics and professionals. First, this paper well captured the significant changes in the profile of criminal offending as well as victimization patterns. To support their arguments, the authors compared the two countries and tried to elicit meaningful implications. Second, this paper explored various aspects of police responses to cyber...
LSMI-Sinkhorn: Semi-supervised Mutual Information Estimation with Optimal Transport

Estimating mutual information is an important statistics and machine learning problem. To estimate the mutual information from data, a common practice is preparing a set of paired samples \(\{({\boldsymbol{x}}_i,{\boldsymbol{y}}_i)\}_{i = 1}^n\) \({\mathop {\sim }\limits ^{\mathrm {i.i.d.}}}p({\boldsymbol{x}},{\boldsymbol{y}})\). However, in many situations, it is difficult to obtain a large number of data pairs. To address this problem, we propose the semi-supervised Squared-loss Mutual Information (SMI) estimation method using a small number of paired samples and the available unpaired ones. We first represent SMI through the density ratio function, where the expectation is approximated by the samples from marginals and its assignment parameters. The objective is formulated using the optimal transport problem and quadratic programming. Then, we introduce the Least-Squares Mutual Information with Sinkhorn (LSMI-Sinkhorn) algorithm for efficient optimization. Through experiments, we first demonstrate that the proposed method can estimate the SMI without a large number of paired samples. Then, we show the effectiveness of the proposed LSMI-Sinkhorn algorithm on various types of machine learning problems such as image matching and photo album summarization. Code can be found at https://github.com/csyanbin/LSMI-Sinkhorn.
A Unified Batch Selection Policy for Active Metric Learning

Active metric learning is the problem of incrementally selecting high-utility batches of training data (typically, ordered triplets) to annotate, in order to progressively improve a learned model of a metric over some input domain as rapidly as possible. Standard approaches, which independently assess the informativeness of each triplet in a batch, are susceptible to highly correlated batches with many redundant triplets and hence low overall utility. While a recent work [] proposes batch-decorrelation strategies for metric learning, they rely on ad hoc heuristics to estimate the correlation between two triplets at a time. We present a novel batch active metric learning method that leverages the Maximum Entropy Principle to learn the least biased estimate of triplet distribution for a given set of prior constraints. To avoid redundancy between triplets, our method collectively selects batches with maximum joint entropy, which simultaneously captures both informativeness and diversity. We take advantage of the submodularity of the joint entropy function to construct a tractable solution using an efficient greedy algorithm based on Gram-Schmidt orthogonalization that is provably \(\left( 1 - \frac{1}{e} \right) \)-optimal. Our approach is the first batch active metric learning method to define a unified score that balances informativeness and diversity for an entire batch of triplets. Experiments with several real-world datasets demonstrate that our algorithm is robust, generalizes well to different applications and input modalities, and consistently outperforms the state-of-the-art.
Research on Machine Understanding Math Word Problems: From the Perspective of Discourse Comprehension Models

With the rapid development of artificial intelligence (Al) technology, machine understanding math word problems (MWPs) has received growing attention. However, existing methods of automatic understanding MWPs are hardly integrated into cognitive intelligent systems used for individual learning. To address the integration problem, this paper firstly clarified the relationship between understanding MWPs and discourse comprehension. According to the trait of discourse comprehension models, the existing methods were divided into knowledge schema-based methods and mental processing-based methods. Then we shortly presented the construction-integration model and proposed a conceptual framework for machine understanding MWPs. The proposed conceptual framework was established from long and short-term memory, cognitive computing services, formal representation models, and human-computer interaction. Finally, we draw a conclusion that integrating cognitive models of human understanding discourse into the process of machine understanding MWPs is conducive to developing a humanized cognitive intelligence system for personalized learning.
Empirically Explaining SGD from a Line Search Perspective

Optimization in Deep Learning is mainly guided by vague intuitions and strong assumptions, with a limited understanding how and why these work in practice. To shed more light on this, our work provides some deeper understandings of how SGD behaves by empirically analyzing the trajectory taken by SGD from a line search perspective. Specifically, a costly quantitative analysis of the full-batch loss along SGD trajectories from common used models trained on a subset of CIFAR-10 is performed. Our core results include that the full-batch loss along lines in update step direction is highly parabolically. Further on, we show that there exists a learning rate with which SGD always performs almost exact line searches on the full-batch loss. Finally, we provide a different perspective why increasing the batch size has almost the same effect as decreasing the learning rate by the same factor.