Reviewerly/exHarmony huggingface dataset:
A Computational Study of the DC Minimization Global Optimality Conditions Applied to K-Means Clustering

Clustering is traditionally one of the basic tools of data analysis widely applied in diverse fields. By now, one of the most common clustering models is the Euclidean minimum-sum-of-squares clustering problem (MSSC). Consequently, Lloyd’s algorithm, often referred to as k-means, is probably the most popular clustering algorithm. Despite its popularity, Lloyd’s algorithm is a local search heuristic for MSSC that in general converges to local optima only. In this paper, we aim at enhancing k-means by employing the global optimality conditions for MSSC represented as a problem with DC (difference of convex) functions. We then embed the k-means algorithm into the so-called global search framework for DC minimization problems where it is employed to find local optimal solutions. We tested such an improved implementation of k-means in a series of computation experiments on well-known test library of medium-size datasets and compared it with the conventional k-means and k-means++ algorithms.
How Digital Technologies Are Changing Business Education

Digital technologies are changing business education. Over the past few years, the topic of digital transformation has been one of the most relevant in the global business community. A powerful driver of digitalization in 2020 was the pandemic. New economic realities, changing expectations and people’s behavior, new technologies that make it possible to realize the previously impossible-these are the factors that force all industries to rethink their business and use new opportunities for its growth. How should business schools position themselves in this volatile environment? This article is devoted to the main trends that are calling for a revolution in business education. The author concludes that as a result of forced distancing due to the Covid-19 pandemic, there was an irrevocable large-scale transition to mainly online learning and suggests that the model consisting of three phases best reflects the evolution of online learning in business schools.
A Case Study of Deep Learning-Based Multi-Modal Methods for Labeling the Presence of Questionable Content in Movie Trailers

In this work, we explore different approaches to combine modalities for the problem of automated age-suitability rating of movie trailers.First, we introduce a new dataset containing videos of movie trailers in English downloaded from IMDB and YouTube, along with their corresponding age-suitability rating labels.Secondly, we propose a multi-modal deep learning pipeline addressing the movie trailer age suitability rating problem.This is the first attempt to combine video, audio, and speech information for this problem, and our experimental results show that multi-modal approaches significantly outperform the best mono and bimodal models in this task.
Sports Competition Assistant System Based on Fuzzy Big Data and Health Exercise Recognition Algorithm

When material desires are satisfied, people begin to pursue more and more spiritual levels. Health exercises have an excellent auxiliary effect on people’s flexibility and physical fitness, so more and more people choose health exercises. However, the movement of health exercises returns to Chengdu and affects the efficiency of physical training. Therefore, we have designed a sports competition assistance system based on vague big data and a health exercise recognition algorithm. First of all, in this article, the standard score comparison database is created by extending the standard action data. In addition, the system architecture is further given, and the key 3D data-based acquisition module design is given. In addition, the system architecture is further given, and the basic 3D data acquisition unit design is given. In this document, the depth characteristics filtered by the Fourier Pyramid are fused to the bone characteristics, and the merged data is sorted based on the support engine, thus designing the action recognition unit. A hidden Markov model (HMM) human action recognition algorithm based on pose selection is proposed. This method uses two affine propagation (AP) clustering algorithms to cluster the features, automatically select the key posture of each action, and correspond to the hidden state of the HMM. These hidden state labels are used to initialize the parameters of the HMM to train the model, and the trained model is used to implement action classification. The result shows that the design in the article has a more accurate recognition result, which provides a powerful tool for the referee to score. Using the Fourier Pyramid filtering method, through a large number of health exercises for comparison, the ability to judge the degree of standard health exercises is significantly improved, the efficiency is increased by 25%, and the accuracy rate is increased by 15%.
Modulation Classification in a Multipath Fading Channel Using Deep Learning: 16QAM, 32QAM and 64QAM

A method based on a constellation diagram is proposed to identify QAM modulation of different orders in static, slow, and frequency selective fading channels. Although constellation diagrams have been studied and classified in literature, most of the work focused on noise. Little has been done to study the effect of multipath fading channels. We develop a highly accurate modulation classification method by exploiting deep learning with the constellation diagram. Based on the experimental results, our CNN model achieves a classification accuracy of 100% at −10 dB signal-to-noise ratio (SNR) under a multipath Rayleigh fading channel.
Magnetic tracking using a modular C++ environment for image-guided interventions

Magnetic tracking enables instrument tracking for image-guided interventions when no line of sight is available. This paper describes the first steps towards a more cost-effective, modular, and adaptable approach that builds upon prior work in open hardware architectures for magnetic tracking in image-guided interventions. An exemplary C++ framework is implemented and demonstrated with the open-hardware Anser EMT system. System performance in speed, accuracy, and precision of the C++ implementation is analysed. Static positioning accuracy and precision are calculated within the Region of Interest (ROI) and an average position error of 1.0 ± 0.1 mm is demonstrated. Results show an indicative increase in the update rate using the C++ framework and substantially lower memory requirements, compared to the previously optimised Python and Matlab solvers. These preliminary results provide the basis for future development which will integrate the C++ framework in a 3D Slicer module, greatly extending the adaptability of the platform for customisation in advanced image-guided procedures.
An improved image steganalysis method based on convolutional neural network

With the continuous development of Steganalysis based on convolutional neural network, in order to obtain higher steganalysis accuracy, softpooling method is used to replace the original conventional pooling, which not only retains the advantages of conventional pooling in reducing the amount of calculation and compressing the size of feature map, but also prevents the loss of steganographic feature information. The Relu activation function is replaced by the Mish activation function. The accuracy of steganalysis is improved. The experimental results show that the accuracy of Xu and SRNET is improved by 0.53% and 1.27% respectively.
Implementation of AI based Safety and Security System Integration for Smart City

Our Indian government has set a goal of creating 100 smart cities that will use smart technology such as smart grids, smart phones, and various monitoring devices to generate large amount of data. Traditionally, data centres have been in charge of these files. One of the most pressing issues in data centres is resource management. One efficient strategy to address this issue is to use the best method for handling data, and when we're talking about Smart Cities, which will create a big quantity of data, it's becoming increasingly important to manage this massive amount of data. It is also necessary to provide the better living standard for every citizen in the smart cities by providing good safety and security to them as well. As we know that every citizen is not smart enough to protect himself against the physical as well as cyber-crimes. In this paper, we have designed an advanced Artificial Intelligence (AI) based safety and security system for the human beings and their personal data in a smart city. The system architecture is designed with AI module with machine learning algorithms, IoT technology and sensors, smart drones, intelligent video surveillances, data analytics and cyber security modules. This system can efficiently protect the citizens and their personal data against the criminals with high speed and accuracy.
A graph database of scholastic relationships in the Babylonian Talmud

Abstract Envisioning the dense scholastic interactions of the Babylonian Talmud as a multi-generational social network, we built a social network graph of the Talmud. We performed Named Entity Recognition on statement-aligned Hebrew and English texts to produce graphs of rabbis (nodes) and their interactions (edges). The new graph database, currently available online, delivers visual and color-coded information about scholastic generation, teacher–student relationships, and both local (page-level) and global (Talmud-level) interactions. By making a wealth of knowledge accessible and highlighting relevant relationships, this tool can provide valuable insights into the complex dynamics of Talmudic discourse.
Gossiping for Communication-Efficient Broadcast

Byzantine Tsimos, Georgios Loss, Julian Papamanthou, CharalamposBroadcast is crucial for many cryptographic protocols such as secret sharing, multiparty computation and blockchain consensus. In this paper we apply gossiping (propagating a message by sending to a few random parties who in turn do the same, until the message is delivered) and propose new communication-efficient protocols, under dishonest majority, for Single-Sender Broadcast (BC) and Parallel Broadcast (PBC), improving the state-of-the-art in several ways. As our warm-up result, we present a randomized protocol for BC which achieves $$O(n^2\kappa ^2)$$ communication complexity from plain public key setup assumptions. This is the first protocol with subcubic communication in this setting, but operates only against static adversaries. Using ideas from our BC protocol, we move to our central contribution and present two protocols for PBC that are secure against adaptive adversaries. To the best of our knowledge we are the first to study PBC specifically: All previous approaches for Parallel Broadcast naively run n instances of single-sender Broadcast, increasing the communication complexity by an undesirable factor of n. Our insight of avoiding black-box invocations of BC is particularly crucial for achieving our asymptotic improvements. In particular: Both PBC protocols yield an almost linear improvement over the best known solutions involving n parallel invocations of the respective BC protocols such as those of Dolev and Strong (SIAM Journal on Computing, 1983) and Chan et al. (Public Key Cryptography, 2020). Central to our PBC protocols is a new problem that we define and solve, which we name "Converge". In Converge, parties must run an adaptively-secure and efficient protocol such that by the end of the protocol, all honest parties that remain possess a superset of the union of the initial honest parties' inputs.
The Interaction Design of AR Game Based on Hook Model for Children's Environmental Habit Formation

Human beings are facing the crisis of deteriorating ecological environment. To strengthen the cultivation of children's environmental awareness and make them develop the habit of environmental protection life are important parts of ecological environment improvement and protection.
Classification for Avionics Capabilities Enabled by Artificial Intelligence

Artificial Intelligence extended the limits considerably of what is technically feasible. In avionics, stakeholders are also pushing AI. However, research results are usually confronted with restrictions in avionics: Safety and certification are often assumed as showstoppers. This is not quite precise, since stakeholders are approaching each other. We collect the demand for AI applications in avionics and develop a classification scheme for these. For each identified AI class the status quo is compiled. We conclude with an assessment of AI readiness and the identification of necessary research effort for AI in avionics.
Enhancing Potential Re-Finding in Personalized Search With Hierarchical Memory Networks

The goal of personalized search is to tailor the document ranking list to meet user's individual needs. Previous studies showed users usually look for the information that has been searched before. This is called re-finding behavior which is widely explored in existing personalized search approaches. However, most existing methods for identifying re-finding behavior focus on simple lexical similarities between queries. In this paper, we propose a personalized framework based on hierarchical memory networks (MN) to enhance the identification of the potential re-finding behavior. Specifically, we explore the potential re-finding behaviors of users from two dimensions. (1) Granularity dimension. The framework carries out re-finding identification with external memories from word, sentence, and session levels. (2) Query intent dimension. Query-based re-finding and document-based re-finding are taken into account to cover user's different query intents. To enhance the interaction between different memory slots, we optimize the <inline-formula><tex-math notation="LaTeX">$READ$</tex-math></inline-formula> operation of MN with two strategies that utilize the information in memory in a multi-hop way. Endowed with these memory networks, we can enhance user's potential re-finding behaviors and build a fine-grained user model dynamically. Experimental results on two datasets have a significant improvement over baselines, and the optimized <inline-formula><tex-math notation="LaTeX">$READ$</tex-math></inline-formula> operation shows better performance.
Optimized Decision Forest for Website Phishing Detection

The development of web and internet technology has resulted in its application in a wide range of services. This has resulted in an increase in the number of cybersecurity issues over the years, the most famous of which is the phishing attack, in which hostile websites impersonate genuine websites to acquire naïve users’ data required for illegal access. Current mitigation measures, including anti-phishing software and machine learning (ML) approach, have proven to be successful in identifying phishing operations. Hackers, on the other hand, are coming up with new techniques to get around these counter-measures. Nonetheless, given the dynamism of phishing efforts, there is a constant requirement for novel and efficient website phishing detection solutions. In this study, an optimized decision forest (ODF) method for detecting website phishing is proposed ODF involves the use of a genetic algorithm (GA) for the selection of optimal diverse individual trees in a forest to generate an efficient sub-forest. Specifically, accurate and diverse trees from a decision forest are passed into GA as an initial population to generate a more robust forest with high efficacy. The performance of the proposed ODF is evaluated using three phishing datasets from the UCI repository. Findings from the experimental results revealed that ODF performed better than selected baseline classifiers. Particularly, ODF recorded a high detection accuracy (98.37%), AUC (0.999), f-measure (0.98), MCC (0.967) values with a low false-positive rate (0.016). In addition, ODF outperformed some existing ML-based phishing attack models. Consequently, the proposed ODF method is recommended for dealing with sophisticated phishing attacks.
Utilizing Treewidth for Quantitative Reasoning on Epistemic Logic Programs

Abstract Extending the popular answer set programming paradigm by introspective reasoning capacities has received increasing interest within the last years. Particular attention is given to the formalism of epistemic logic programs (ELPs) where standard rules are equipped with modal operators which allow to express conditions on literals for being known or possible, that is, contained in all or some answer sets, respectively. ELPs thus deliver multiple collections of answer sets, known as world views. Employing ELPs for reasoning problems so far has mainly been restricted to standard decision problems (complexity analysis) and enumeration (development of systems) of world views. In this paper, we take a next step and contribute to epistemic logic programming in two ways: First, we establish quantitative reasoning for ELPs, where the acceptance of a certain set of literals depends on the number (proportion) of world views that are compatible with the set. Second, we present a novel system that is capable of efficiently solving the underlying counting problems required to answer such quantitative reasoning problems. Our system exploits the graph-based measure treewidth and works by iteratively finding and refining (graph) abstractions of an ELP program. On top of these abstractions, we apply dynamic programming that is combined with utilizing existing search-based solvers like (e)clingo for hard combinatorial subproblems that appear during solving. It turns out that our approach is competitive with existing systems that were introduced recently.
Design of Chinese Vocabulary Smart Learning System

In the existing vocabulary learning applications, there are several aspects needed to improve, such as the cold start problem, undifferentiated material presenting, inefficient learning sequence and low-quality exercises. With the purpose of making some useful attempts, we have built a Chinese Vocabulary Smart Learning System (CVSLS). Having conducted a survey on Chinese Second Language (CSL) learners and CSL teachers, we obtained the suitable number of initial test questions and the influence factors of learning resources. Based on this, we designed the initial test and the materials presenting strategy. Also, a vocabulary knowledge graph based on word frequency and word relations was constructed, so the system can obtain an optimal learning path for learners. Considering the difficulty degrees and training attributes of the exercises, we designed the practice strategies. CVSLS solves the problem of cold start and is provided with personalized material presenting, effective learning sequence and well-structured exercises.
Distribution Alignment and Discriminative Feature Learning for Domain Adaptation in Hyperspectral Image Classification

Domain adaptation (DA) aims to use a well-labeled source domain to predict the labels of the unlabeled or poor-labeled target domain. Most of the existing DA methods focus on the use of feature-level or sample-level information. Recent studies have shown that domain discriminative information is also important for classification. To jointly exploit feature-level information and discriminative information, a new DA method called distribution alignment and discriminative feature learning (DADFL) is proposed for hyperspectral image (HSI) classification in this letter. DADFL incorporates category-discriminative information preservation and structured prediction (SP)-based pseudolabeling into a unified framework to simultaneously reduce distribution and subspace differences between domains. Experimental results on three hyperspectral DA tasks show that the classification performance of the proposed DADFL is better than that of existing DA methods.
A Stereo Matching Method for 3D Image Measurement of Long-Distance Sea Surface

Tsunamis are some of the most destructive natural disasters. Some proposed tsunami measurement and arrival prediction systems use a limited number of instruments, then judge the occurrence of the tsunami, forecast its arrival time, location and scale. Since there are a limited number of measurement instruments, there is a possibility that large prediction errors will occur. In order to solve this problem, a long-distance tsunami measurement system based on the binocular stereo vision principle is proposed in this paper. The measuring range is 4–20 km away from the system deployment site. In this paper, we will focus on describing the stereo matching method for the proposed system. This paper proposes a two-step matching method. It first performs fast sparse matching, and then complete high precision dense matching based on the results of the sparse matching. A matching descriptor based on the physical features of sea waves is proposed to solve the matching difficulty caused by the similarity of sea surface image textures. The relationship between disparity and the y coordinate is built to reduce the matching search range. Experiments were conducted on sea surface images with different shooting times and distances; the results verify the effectiveness of the presented method.
Failure is (literally) an Option: Atomic Commitment vs Optionality in Decentralized Finance

Many aspects of blockchain-based decentralized finance can be understood as an extension of classical distributed computing. In this paper, we trace the evolution of two interrelated notions: failure and fault-tolerance. In classical distributed computing, a failure to complete a multi-party protocol is typically attributed to hardware malfunctions. A fault-tolerant protocol is one that responds to such failures by rolling the system back to an earlier consistent state. In the presence of Byzantine failures, a failure may be the result of an attack, and a fault-tolerant protocol is one that ensures that attackers will be punished and victims compensated. In modern decentralized finance however, failure to complete a protocol can be considered a legitimate option, not a transgression. A fault-tolerant protocol is one that ensures that the party offering the option cannot renege, and the party purchasing the option provides fair compensation (in the form of a fee) to the offering party. We sketch the evolution of such protocols, starting with two-phase commit, and finishing with timed hashlocked smart contracts.
An AR-Enabled See-Through System for Vision Blind Areas

The manual assembly has a high proportion in industry. However, in many industrial scenarios, manual assembly in the Vision Blind Areas (VBAs) is time-consuming and challenging due to the lack of necessary visual information. This study presented a see-through Augmented Reality (AR) system to solve the problems during manual assembly in the vision blind area. This system enabled users to see the inner components of the VBAs cross the surface of mechanical products. The human hand and the mechanical part in a VBA were tracked and rendered in an AR HMD. We developed a prototype system and conducted a user study to evaluate the system usability, users’ performance and workload. The results indicated that this system was well integrated and easy to use. Moreover, participants worked with this system had a lower workload with improved performance.
Chapter 5. TAligner 3.0

In 2010 the TRALIMA/ITZULIK research group began developing a tool to create parallel and multilingual corpora. This would serve (among other functions) to explain, describe, and predict translational behaviour in terms of translational norms and laws (Toury 2012). Instead of relying on already existing resources, it was decided to invest in the creation of a new tool. As a result, TAligner 3.0 enables users to create corpora simultaneously aligning any number of texts (as required by the research) as well as compile corpora consisting of different text types. Furthermore, unlike most tools the aligned corpora can be queried within the program. The aim of this paper is to present, firstly, TAligner 3.0 and secondly, two parallel corpora built using this tool.