Reviewerly/exHarmony huggingface dataset:
Optimization and improvement of bidirectional connections with Web Socket on Synapse framework using Web Workers technology

Every year, the demand of Smart Tv increases constantly, same as Android Devices. These devices are used by a lot of population worldwide, but there is not focus on developing apps that uses both platforms. Therefore, is a great opportunity to take advantage of both technologies creating a framework that allows bidirectional connection and interaction between both devices using Web Sockets. This project compares the optimization of search and interconnection of Android devices and LG Smart TVs by reducing the waiting Using Web Workers will allow multi-threaded execution search in Web Socket servers on Android devices much faster. On the other hand, it allows to add multiplayer support to use more than one device at the same time. The result of our project is reduce by 60% the time with parallel search respect to the linear search, and allow the connection to the tv up to 4 players simultaneously. To validate the framework, a video game is developed to test the optimized functionalities in the Web Socket connection in the source code to show the improvements for all devices connected and compare without web workers implementation.
A functional approach to cyber risk assessment

Information security has become a crucial issue not only from the technical standpoint, but also from the managerial standpoint. The necessity for organizations to understand and manage cyber risk has led to the rise of a plethora of risk assessment methods and tools. These approaches are often difficult to interpret and complex to manage for organizations. In this paper, we propose a simple and quantitative method for the estimation of the likelihood of occurrence of a cyber incident. Our approach uses a generalized logistic function and a cumulative geometric distribution to combine the maturity and the complexity of the technical infrastructure of an organization with its attractiveness towards cyber criminals.
Application of Artificial Intelligence Technology in College English Teaching Under the Background of Big Data

China enters a new era of deep integration of information technology and education, which leads the development of education. Big data and artificial intelligence are the emerging information technologies which have supported and promoted the reform and development of education. Based on the realistic requirement of college English teaching, how to improve the teaching effectiveness of the teaching mode with the aid of artificial intelligence technology; implement personalized, wisdom and interactive learning; cultivate students critical thinking ability, cooperative ability, thus realize the artificial intelligence in the effective use of English classroom become a key problem to be solved. This paper expounds the background of big data in the artificial intelligence application value in language learning, and then explores the application of AI in college English teaching from five aspects—listening, speaking, writing, translation and intelligent management respectively and finally expounds the big data under the background of AI technology innovation strategy in college English teaching.
Multi-word term variation

Abstract Complex nominals (CNs) are frequently found in specialized discourse in all languages, since they are a productive method of creating terms by combining existing lexical units. In Spanish, a conceptual combination may often be rendered with a prepositional CN (PCN) or an equivalent adjectival CN (ACN), e.g., demanda de electricidad vs. demanda eléctrica [electricity demand]. Adjectives in ACNs – usually derived from nouns – are known as ‘relational adjectives’ because they encode semantic relations with other concepts. With recent exceptions, research has focused on the underlying semantic relations in CNs. In natural language processing, several works have dealt with the automatic detection of relation adjectives in Romance and Germanic languages. However, there is no discourse studies of these CNs, to our knowledge, for the goal of establishing writer recommendations. This study analyzed the co-text of equivalent PCNs and ACNs to identify factors governing the use of a certain form. EcoLexicon ES, a corpus of Spanish environmental specialized texts, was used to extract 6 relational adjectives and, subsequently, a set of 12 pairs of equivalent CNs. Their behavior in co-text was analyzed by querying EcoLexicon ES and a general language corpus with 20 expressions in CQP-syntax. Our results showed that immediate linguistic co-text determined the preference for a particular structure. Based on these findings, we provide writing guidelines to assist in the production of CNs.
LECC: Location, energy, carbon and cost-aware VM placement model in geo-distributed DCs

The wide adoption of the cloud computing model in the business environment has led to a rapid increase in the development of geo-distributed data centers (DCs) to support customers’ needs. On the other hand, cloud DCs that contain thousands of computing and storage nodes consumes a large amount of energy that leads to a high carbon footprint. Therefore, minimizing the geo-distributed DCs’ energy consumption is a must which will decrease the cloud providers’ operational cost and minimize the high non-environment carbon emission. In addition, minimizing the cloud users’ network latency, in such a distributed environment, is one of the important challenges faced by cloud providers. Thus, to address these challenges, this paper proposed a novel location, energy, carbon, and cost-aware (LECC) virtual machine (VM) placement model for geo-distributed cloud DCs. Both online and offline placement problems are tackled. The migration technology is employed in consolidating the VMs to less number of active servers for significant energy reductions. The VM placement problem is formulated as a multi-objective optimization problem and solved by greedy policies. Also, an intelligent machine-learning model is constructed and implemented to leverage the performance of the LECC model. To validate the usefulness of the LECC model, extensive simulations using synthetic and real data are conducted on CloudSim toolkit. The experimental results showed the merits of our proposed LECC model in solving the important VM placement problem.
Stroke medical ontology QA system for processing medical queries in natural language form

Due to the increasing use of the Internet, the development of the information society, and public awareness about health, many patients are using the Internet to find health information. In addition, medical field information retrieval is exploding to retrieve specific medical knowledge about diseases due to the current corona pandemic and the prevalence of mobile handsets such as smartphones. Currently, much of the knowledge information in the medical field is provided in ontology, a method of expressing knowledge information. However, medical knowledge built in this ontology form requires the general user to know basic logic-based representations of ontology, such as the web ontology language OWL and semantic web technologies, for searching. Furthermore, the usage and understanding of the SPARQL protocol and RDF query language (SPARQL), a formalized query language in the form of ontology, is essential. To overcome the limitations of this ontology form of knowledge retrieval, this paper proposes the stroke medical ontology question and answering (QA) system that can analyze user medical knowledge in natural language form for medical knowledge curation services and automatically convert it to the structured query language, SPARQL. The proposed system analyzes questions and answers through query analysis, abstracts each syntax word through top-level medical ontology, and deduces the structured query template for abstracted questions and answers based on SWRL to complete the structured query template.
Entropy and Autoencoder-Based Outlier Detection in Mixed-Type Network Traffic Data

Mixed-type data containing categorical and numerical features are pervasive in real life, but very limited outlier detection methods are available for these data. Some existing methods handle mixed-type data by feature converting, whereas their performance is downgraded by information loss and noise caused by the transformation. Meanwhile, the existing general algorithms cannot combine the characteristics of outliers in specific fields, leading to an unsatisfying performance in actual scenarios, such as the field of network security. This paper proposes a novel Entropy and Autoencoder-based Outlier Detection in mixed-type network traffic data, termed EAOD, which combines characteristics of outliers in specific fields and machine learning models to detect outliers. EAOD utilizes the expert rules made by domain knowledge summarized based on characteristics of existing outliers to label known outlier data. It performs holoentropy and deep autoencoder for the category and numerical feature spaces, respectively, in unlabeled data to obtain outlier scores integrated to get the final outlier scores via a dynamic integration strategy. Especially in the numerical feature space, to fully mine known outlier behavior patterns, deep autoencoders of outlier and normal types are constructed separately to capture unknown outliers jointly. Experiments show that EAOD significantly outperforms eight state-of-the-art outlier detectors on seven real network traffic datasets.
Motion Strategy Using Opponent Player’s Serial Learning for Air-Hockey Robots

In recent years, there have been many studies on sports robots that can play against humans, including studies on the strategies that sports robots use by taking into account the physical conditions of their opponents. However, there have been few studies on strategies that take into account psychological conditions of the opponents, such as carelessness and habituation. This paper proposes a motion strategy for an air-hockey robot that intentionally coaxes the opponent to learn the robot’s attack sequence and then uses a different attack sequence to catch the opponent off guard. We explicitly model the change in reaction time during serial learning and represent the opponent’s reaction as an evaluation function. By applying this evaluation function to a game tree and selecting the optimal motion, the robot could catch the opponent by surprise. Actual experiments with several subjects confirmed the effectiveness of the proposed method.
Detection of Malicious URLs in Twitter

With spam filtering techniques have been improved in social websites like G mail., spammers find their place in other famous social platforms like Twitter, Facebook. Therefore, an effective spam filtering technology is essential for platforms like Twitter, Facebook, etc. We have developed a web application that will be able to find out whether a particular tweet from Twitter is malicious or non- malicious based on the Url that the tweet possesses by considering both text-based and Url-based features. We have employed machine learning techniques to classify the tweet content after preprocessing the data that we have fetched from Twitter with the help of tokens that we obtain after creating the Twitter developer account. We are classifying a tweet based on five different features, these features can be most commonly found in malicious tweets as per our research. The results that are obtained from our experiment show that our approach could efficiently identify malicioustweets.
A novel user review-based contextual recommender system

Recommendation systems are shrewd applications for knowledge mining that profoundly handle the problem of data overload. Various literature explores different philosophies to create ideas and recommends different strategies according to the needs of customers. Most of the work in the suggested structure space focuses on extending the accuracy of the recommendation by using a few possible methods where the principle purpose remains to improve the accuracy of suggestions while avoiding other plan objectives, such as the particular situation of a client. By using appropriate customer rating data, the biggest test for a suggested system is to generate substantial proposals. A setting is an enormous concept that can think of numerous points of view: for example, the community of friends of a client, time, mindset, environment, organization, type of day, classification of an item, description of the object, place, and language. The rating behavior of customers typically varies in different environments. We have proposed a new review-based contextual recommender (RBCR) system application from this line of analysis, in particular a novel recommender system, which is an adaptable, quick, and accurate piece planning framework that perceives the significance of setting and fuses the logical data using piece stunt while making expectations. We have contrasted our suggested calculation with pre- and post-sifting methods as they have been the most common methodologies in writing to illuminate the issue of setting conscious suggestion. Our studies show that considering the logical data, the display of a system will increase and provide better, appropriate and important results on various evaluation measurements.
A fast algorithm for mining temporal association rules in a multi-attributed graph sequence

In real life, there exist a lot of attributed graphs each of which contains attribute information as well as structural information. As time goes on, a group of attributed graphs form an attributed graph sequence. Being the generalization of single-attributed graph sequences, multi-attributed graph sequences are arising vastly and quickly. Mining the temporal associations hidden in a multi-attributed graph sequence is in urgent need from data owners. To meet the need and fill the gap of research on mining such kind of temporal associations, we first give a definition of temporal association rules for describing temporal associations in a multi-attributed graph sequence, and then propose a fast algorithm for mining temporal association rules in a multi-attributed graph sequence which is based on the anti-monotonicity of support. The proposed algorithm is designed in two steps, namely finding frequent temporal association rules and verifying the credibility of these rules. Equipped with two novel joining and pruning strategies, the proposed algorithm exhibits much higher efficiency which is specially pursued in the process of rule mining. Experiments performed on synthetic datasets and real datasets show that the proposed algorithm is effective and more efficient than other existing algorithms.
Path Planning Algorithm for Mobile Robot Based on Improved Ant Colony Algorithm

Abstract Due to the influence of full traversal environment, the path length obtained by existing methods is too long. In order to improve the performance of path planning and obtain the optimal path, a full traversal path planning method for omnidirectional mobile robots based on ant colony algorithm is proposed. On the basis of the topology modeling schematic diagram, according to the position information of the mobile robot in the original coordinate system, a new environment model is established by using the Angle transformation. Considering the existing problems of ant colony algorithm, the decline coefficient is introduced into the heuristic function to update the local pheromone, and the volatility coefficient of the pheromone is adjusted by setting the iteration threshold. Finally, through the design of path planning process, the planning of omnidirectional mobile robot’s full traversal path is realized. Experimental results show that the proposed method can not only shorten the full traversal path length, but also shorten the time of path planning to obtain the optimal path, thus improving the performance of full traversal path planning of omnidirectional mobile robot.
Determination of the refractive index profile and surface topography of optically smooth objects using interference of optical vortices

Abstract In this paper, we present the results of the propagational dynamics of vortex beams in the scope of their possible applications for interferometric non-contact robust and precision optical surface profilometry with nanoscale longitudinal resolution. The result of coaxial superposition of the reference plane wave with singly charged vortex beams represents a dynamically changing intensity distribution. The nature of this changes, namely, rotational effects of intensity zeros, allows to determine directly the optical path difference which is introduced by the surfaces and internal structure of test object. We have proposed the experimental setup for examination of reflecting and transmitting objects.
Genetic Algorithms: A Practical Approach to Generate Textual Patterns for Requirements Authoring

The writing of accurate requirements is a critical factor in assuring the success of a project. Text patterns are knowledge artifacts that are used as templates to guide engineers in the requirements authoring process. However, generating a text pattern set for a particular domain is a time-consuming and costly activity that must be carried out by specialists. This research proposes a method of automatically generating text patterns from an initial corpus of high-quality requirements, using genetic algorithms and a separate-and-conquer strategy to create a complete set of patterns. Our results show this method can generate a valid pattern set suitable for requirements authoring, outperforming existing methods by 233%, with requirements ratio values of 2.87 matched per pattern found; as opposed to 1.23 using alternative methods.
Data Augmentation Guidelines for Cross-Dataset Transfer Learning and Pseudo Labeling

Convolutional Neural Networks require large amounts of labeled data in order to be trained. To improve such performances, a practical approach widely used is to augment the training set data, generating compatible data. Standard data augmentation for images includes conventional techniques, such as rotation, shift, and flip. In this paper, we go beyond such methods by studying alternative augmentation procedures for cross-dataset scenarios, in which a source dataset is used for training and a target dataset is used for testing. Through an extensive analysis considering different paradigms, saturation, and combination procedures, we provide guidelines for using augmentation methods in favor of transfer learning scenarios. As a novel approach for self-supervised learning, we also propose data augmentation techniques as pseudo labels during training. Our techniques demonstrate themselves as robust alternatives for different domains of transfer learning, including benefiting scenarios for self-supervised learning.
A Big Data-Driven Model for Secured Systems: A Blockchain-Based Architecture for Cloud Computing and IoT Security

With the advent of new networked elements such as smart cities, smart homes, the remarkable development of the Internet of things (IoT) has been driven, which has enabled the use of small devices to manage and perform complex tasks. Around these devices has come the incessant collection of a mass of unstructured data that sometimes needs to be analyzed and stored in the cloud computing, which has enabled the development of multiple applications for their proper functioning. The biggest challenge of information systems, especially when using IoT and cloud computing, remains the security of the data and the permanent availability of the information. Blockchain is one of the promising solutions applied to the various security problems today. In this paper, we propose a new architecture based on blockchain technology to ensure the security of IoT and cloud computing. A literature review on the use of blockchain for IoT security has also been presented.
RLConfig: Run-time Configuration of Cluster Schedulers via Deep Reinforcement Learning

Cluster schedulers provide a flexible resource sharing mechanism for short-running jobs, which are time-sensitive and occupy a majority of cloud jobs. The configuration of schedulers decides the resource allocation among jobs so that it is important to the jobs&#x2019; performance. The static and manual setting of the configuration is difficult to optimize the performance of different changing jobs in the cloud. In this paper, we propose a Deep Reinforcement Learning (DRL)-based run-time configuration tuning framework for cluster schedulers to automatically configure the schedulers according to the changing workloads and resource status, called "RLConfig". It includes two parts&#x2014;&#x2014;an estimator to evaluate the static configurations via building a relationship between the configuration and job performance, and a DRL-based optimizer to select configuration considering the performance-influencing factors such as work-load status and available resources. We implemented RLConfig on YARN Capacity scheduler and validated its effectiveness with workloads derived from real jobs. The experiment results show that our framework reduces the average job latency by one times compared with the static configuration and 27.8% comparing to the Queue model, but the time-cost is significantly lower than the existing run-time configuration tuning framework.
Evaluating the Fault-Focused Clustering Performance of Distance Metrics in Parallel Fault Localization: From an Omniscient Perspective

To locate multiple bugs in parallel, one common practice is to generate fault-focused clusters where failed test cases that are likely caused by the same bug are grouped together. With respect to the fault-focused clustering performance, a critical impact factor is the distance metric used to measure the similarity between two rankings. This paper proposes a method to evaluate the fault-focused clustering performance of distance metrics from an omniscient perspective where the fault-focused information for each failed test case is already given. Case studies are conducted using the proposed method to evaluate Jaccard and Kendall tau distance on three programs with multiple bugs. The findings seem to challenge previous perceptions regarding the performance of these two distance metrics in generating fault-focused clusters.
Design of Combinational Digital Circuits Optimized with Ising Model and PSO Algorithm

Evolutionary circuit is an important part of electronic design automation. It is a kind of automatic circuit design system by intelligent algorithm, widely used in robot controller design, circuit design and other fields. Compared with other intelligent algorithms, Particle Swarm Optimization (PSO) has better performance in the process of circuit evolution, but it has the disadvantage of falling into local optimum easily during evolution, resulting in meaningless increase of computing resources. This paper proposes a hybrid algorithm based on Ising model and PSO algorithm for optimizing combinational logic circuits. The Ising model is a kind of stochastic process model describing the material phase transition and has the property that it can accept a worse solution than the current one with a certain probability, which can increase the diversity of particles and avoid particles trapped in local optima point during the evolutionary process. Experimental results show that the hybrid algorithm has better performance in terms of computational complexity and circuit area.