Reviewerly/exHarmony huggingface dataset:
Personality Detection on Social Media Twitter Using Long Short-Term Memory with Word2Vec

Personality is one of the things that exist in every human being. From this personality, it will be seen that humans are unique and diverse individuals. One of the causes is because each human being has a personality. This personality will create humans to have a way of thinking and behave. Personality can also be a benchmark or valuation. For example, in employee recruitment, some companies have standards for candidate workers to be recruited. One tool for doing this is a psychological test. But the drawback of this psychological test method is that it takes a long time. So we created a system that can predict personality based on the user’s tweet data on Twitter because it’s contain many opinions or thoughts from the user that can describe the user’s personality. The methods we use are Word2Vec for word embeddings and LSTM (Long Short-Term Memory) for predicting. The system created focuses on seeing how a collection of tweets can predict a user’s personality. The first scenario it’s to combine every two rows of tweet and have a scoring accuracy 45%. The second scenario it’s to combine every ten rows of a tweet and have the best accuracy of 48%. The system uses five labels of personality that we call the Big Five Personality.
Initial Experiments on Question Answering from the Intrinsic Structure of Oral History Archives

Large audio archives with spoken content are natural candidates for question answering systems. Oral history archives generally contain many facts and stories that would be otherwise hard to obtain without listening to hours of recordings. We strive for making the archive more accessible by allowing natural language question answering. In this paper, we present challenges our dataset poses. We propose our initial approach that uses questions and answers mined from the archive itself and evaluate the performance in experiments with pretrained language representation and question answering models.
Opinion Classification via Word and Emoji Embedding Models with LSTM

As social networks are rapidly growing, the content created in them is also growing. Mining the emotional tendency of comments on this content through opinion classification technologies is very useful for the timely understanding of public opinion on social media, monitoring of brands, and customer support. Deep learning methods have shown good results in opinion classification. In this paper, we analyze the opinion classification in Uzbek movie reviews taken from YouTube using various pre-trained word embedding models and a classification model based on long short-term memory. Users often use emojis along with text to express their opinions and feelings. Therefore, we also investigated the importance of emojis in opinion classification of Uzbek texts.
Exploring Patch Similarity in an Image

This article describes an experimental procedure to analyze (and verify) the self-similarity concept in natural images and to explore the Gaussianity of groups of similar patches extracted from a single image.The self-similarity assumption means that most image patches of a sufficient size are repeated, of course not identically, but with small variations.The procedure proposed in this paper, and implemented in the accompanying online demo, permits to explore and visualize these clusters of similar patches in a given image.Thanks to it, a user can select a patch in an image, group all patches similar to it up to a translation, or to an isometry, apply PCA to the group, make visual tests about the Gaussianity of the set of patches, and finally apply EM to the set to see if it is a mixture of Gaussians.
Distilling blockchain requirements for digital investigation platforms

When handling a security incident, there is a lot of information that needs to be stored, processed, and analyzed. As a result of the volume of information and the necessity to deal with a security incident investigation promptly, different forensic tools have been developed to provide cyber threat intelligence and security incident response management platforms and solutions. These platforms enable responders to effectively collaborate in identifying and investigating incidents, manage their work on a case from creation until resolution or completion, and automate incident response tasks with the external threat information. Since incident response services are a growing priority at organizations, there is a pressing need for a trustworthy and transparent way to maintain the authenticity and integrity of investigative actions that is independently verifiable. Generally, security incident case management allows a security analyst to add related logs. Asides from the possibility of a log being deleted, it is difficult to audit the log for traceability and provenance if a user decides to be malicious. To address this problem, we propose utilizing a blockchain ledger for security investigative actions and associated metadata by extracting requirements for cybersecurity incident response from the models gathered through the analysis of an open-source incident management platform. We demonstrate the applicability of the proposed techniques and methods by investigating a case scenario of evidence actions within TheHive security incident response platform (SIRP).
Smart Web Investigation Framework

In this paper, we have developed a framework for web scraping and text analysis. Most of the real-world applications require text analysis and web scraping; however, due to their disjoint behavior, there is no unified frame work that will take care of both needs. In the proposed scalable, portable framework, a centralized toolkit is designed for scraping and text analysis. Further, it allows the analysis of large data in one go and provides inferences about changes in web-based text over time. The framework will give inferences about web-based topics as well as summarize the data in a crisp format which will make it easy for the user to analyze the text-based data of their choosing from the internet. The proposed framework is very much useful when analyzing web-based reviews of various products, entertainment, and the news about a specific topic in www.
A Variability-Enabling and Model-Driven Approach to Adaptive Microservice-based Systems

A microservice-based system is composed of numerous independently deployed and executed microservices, among which normally exist the complex dependencies. Traditional service composition approaches usually expect the business process predefined at design time. As a result, it is difficult for the microservice-based system to quickly adapt to the frequently changing operation environments and business requirements. To address the above limitations, we propose a variability-enabling and model-driven approach to developing adaptive microservice-based systems. Our approach first models the business process with variability using VxBPMN4MS, an extension of Business Process Model and Notation (BPMN) with support for variability, then transforms the business process model to variability supported microservice composition frameworks, and finally derives business process instances at run-time according to the predefined process configuration. We have developed a platform to automate the proposed approach as much as possible, and conducted a case study to evaluate the effectiveness of the proposed approach and platform.
Skill Extraction for Domain-Specific Text Retrieval in a Job-Matching Platform

We discuss a domain-specific retrieval application for matching job seekers with open positions that uses a novel syntactic method of extracting skill-terms from the text of natural language job advertisements. Our new method is contrasted with two word embeddings methods, using word2vec. We define the notion of a skill headword, and present an algorithm that learns syntactic dependency patterns to recognize skill-terms. In all metrics, our syntactic method outperforms both word embeddings methods. Moreover, the word embeddings approaches were unable to model a meaningful distinction between skill-terms and non-skill-terms, while our syntactic approach was able to perform this successfully. We also show how these extracted skills can be used to automatically construct a semantic job-skills ontology, and facilitate a job-to-candidate matching system.
Attention LSTM for Scene Graph Generation

A scene graph is a structured representation of an image that consists of objects detected within the image and the relationships between them. Scene graphs can play a significant role in understanding an image. However, to date, research regarding scene graph generation has met with limited success, and the performance of the existing methods is unsatisfactory. Based on the fused form of the features of detected objects and the way a message passes between them, we have developed a novel deep learning network, dubbed Attention Long Short-term Memory Network (ALNet), that can generate scene graphs more effectively. We have designed a simple feature fusion module that combines the spatial features of the detected objects with their visual features and semantics. To represent connections between all related objects, we introduced a message passing module that can transfer the features of detected objects through the bidirectional ALNet. By means of various ablation experiments, we evaluated the ALNet and confirmed that the scene graph generation indicators were significantly improved.
SinMorphy: A Morphological Analyzer for the Sinhala Language

A morphological analyzer analyses a word into its lemma and a set of morphosyntactic tags. It is a crucial tool for natural language processing-related tasks especially in morphologically rich languages such as Sinhala. We present SinMorphy, the first comprehensive morphological analyser and synthesizer for the Sinhala language. SinMorphy is a rule-based system with a comprehensive vocabulary of Sinhala words. Therefore, it accurately handles a great majority of contemporary Sinhala text. It also synthesizes the lexical form of a word given a lemma and a set of tags. The system is based on a finite-state transducer and is written in the Foma and Lexc languages. It handles all types of words including nouns, verbs (including compound nouns and verbs), adjectives, adverbs, and particles. It also includes a guesser to analyze out-of-vocabulary words. It correctly analyses 81.3% of the most common 20,000 Sinhala words and 85.2% of a random test set of 1000 words.
Audio Surveillance of Road Traffic: An Approach Based on Anomaly Detection and Interval Type-2 Fuzzy Sets

Surveillance systems are increasingly exploiting multimodal information for improved effectiveness.This paper presents an audio event detection method for road traffic surveillance, combining generative deep autoencoders and fuzzy modelling to perform anomaly detection.Baseline deep autoencoders are used to compute the reconstruction error of each audio segment, which provides a primary estimation of outlierness.To account for the uncertainty associated to this decision-making step, an interval type-2 fuzzy membership function composed of an optimistic/upper component and a pessimistic/lower component is used.The final class attribution employs a probabilistic method for interval comparison.Evaluation results obtained after defuzzification show that, with a careful parameter setting, the proposed membership function effectively improves the performance of the baseline autoencoder, and performs better than the stateof-the-art one-class SVM in anomaly detection.
SSRNAS: Search Space Reduced One-shot NAS by a Recursive Attention-based Predictor with Cell Tensor-flow Diagram

One-shot neural architecture search(NAS) has attracted more attention since it can provide a reasonable performance ranking on all paths in the super-net. The ranking ability of the one-shot NAS model relies heavily on the training process of the super-net. However, the competition among architectures for shared weights during the training process and the Matthew effect caused by reward-based methods make it difficult to train the super-net fairly and effectively. In this paper, a novel method named Search Space Reduced One-shot NAS(SSRNAS), which continuously decreases the search space and train fairly to improve performance, is proposed. Specifically, a predictor based on graph recursive attention is trained to evaluate the performance of the architectures during the training process. In addition, a dynamic divided threshold is used as the basis for discarding architectures, and the architectures with poor performance in the search space are discarded continuously. The remaining architectures in the search space are uniformly sampled for fair training to avoid the Matthew effect. Experimental results show that our proposed method performs well on three datasets of NAS-Bench-201 search space and cifar-10 dataset of DARTS space.
A value‐driven, integrated approach to Model‐Based Product Line Engineering

Abstract Pushed to the edge of their capabilities in a highly competitive world market, organizations everywhere look for efficient means to innovate and develop their products and services. This paper proposes and illustrates a holistic integration of Product Line Engineering (PLE) and Model‐Based Systems Engineering (MBSE) to connect and align market and business analysis, architecting, design, and engineering. This value‐driven, integrated approach capitalizes on MBSE best practices to tackle the concrete challenges of product line engineering.
The soccer game, bit by bit: An information-theoretic analysis

In this article, we present an original method to measure the rate of positional change observed during a soccer match based on the relative spatial distribution of players on the pitch. This is justified as players use their relative position as a key tactical tool to contribute to their team’s objectives. A temporal network representation of the game was used, where nodes are players discretely clustered by physical proximity into disjoint clusters. This study, observational and descriptive in nature, was applied to a set of matches from a major European national football league, with players’ coordinates sampled at 10Hz, resulting in ≈ 60,000 network samples per match. We took an information theoretic approach to measuring the distance between successive samples. Significant correlations were found between measurements and key match events that are empirically known to result in players jostling for position, such as when striving to get unmarked or to mark. These events increase the information distance between samples, while breaks in game play have the opposite effect. Having a measurement of dynamic, structural change in soccer is an original contribution that can complement full match statistical analysis. Hierarchical decomposition of the measurements is possible at multiple levels, building an overall multi-layer map that provides insights into the game dynamics, from the individual player, to the clusters of interacting players, up to the teams and their matches. This comprehensive view of the players’ interacting behavior can be useful for training, tactics and strategy development.
Information-based Teaching Ability training of PE Teachers and Information-based Teaching Practice Paths

With the contemporary swift development of informatization, more and more schools have applied information technologies to PE teaching and significantly improved their PE teaching quality. PE teachers are organizers, directors, executors, and supervisors of PE teaching activities, and their information-based teaching abilities have a direct bearing on the final quality of information-based PE teaching. The information-based teaching ability training strategies of PE teachers and the concrete information-based teaching practice paths were discussed, aiming to cultivate outstanding PE teacher teams conforming to the contemporary information-based teaching needs, and further realize the information-based, modernized and intelligent development of PE teaching.
Visual Explanation by Unifying Adversarial Generation and Feature Importance Attributions

Explaining the decisions of deep learning models is critical for their adoption in medical practice. In this work, we propose to unify existing adversarial explanation methods and path-based feature importance attribution approaches. We consider a path between the input image and a generated adversary and associate a weight depending on the model output variations along this path. We validate our attribution methods on two medical classification tasks. We demonstrate significant improvement compared to state-of-the-art methods in both feature importance attribution and localization performance.
Systematic Literature Review on Service Oriented Architecture Modeling

Context: With the recent trend of shifting from traditional architectures towards Service Oriented Architectures (SOA), an enterprise can create, choreograph new business functions, deploy and integrate multiple services that communicate with each other using service interfaces to pass messages from one service to another. So, to build an SOA that accommodate business scalability, and flexibility and facilitate ongoing and changing needs of business, an SOA modeling language is required.Objective: The purpose of this work is to determine the current state of the art in the field of SOA modeling shedding light on techniques have been used to model a SOA, its importance and domains where it is applied.Method: In order to fulfill the objective of the research, the method which we choose was a Systematic Literature Review (SLR). This served in collecting and structuring the information that exists in the field of SOA modeling.Results: Service oriented applications have been modeled with different modeling languages. Choosing a suitable modeling language is depending on the criteria for what is being modeled; if is it structural view point or behavioral aspects or even both.Conclusion: Summing up the results, it can be concluded firstly that modeling mitigates the complexity of huge and complicated data of service oriented applications. Secondly, the research that has been performed has shown that the Unified Modeling Language (UML), Business Process Modeling Notation (BPMN), Service Component Architecture (SCA) and Event-B are the most modeling languages used on large scale.
Developing Business-Business Private Block-Chain Smart Contracts Using Hyper-Ledger Fabric for Security, Privacy and Transparency in Supply Chain

Inception of block-chain technology (BCT) has led to new dimensions to business, trade and technologies. Being a distributed technology block-chain (BC) has capability to enable secured, transparent transactions, implement immutable and shared ledger that enhances the trust among the stakeholders. In this research work, the investigation involves systematic review of the current state of the art of the smart-contract (SC), BCT and explore the case-study and apply the contemporary technology concepts and features for supply chain such as Transportation and Logistic problem and to implement trust in the business activities of the consortium. This work has adopted the industry widely used open platform namely Hyper-ledger which is expected to support the elements required to implement the trust in different sectors. The results show that Hyper-ledger framework and tools employed are effective in building the trust among the stakeholders by addressing the said key issues and further, advances the state of the art in transportation/logistics supply chain management (SCM) with unique and desired solutions.
Informer: Protecting Intel SGX from Cross-Core Side Channel Threats

As one of the major threats facing Intel SGX, side-channel attacks have been widely researched and disclosed as actual vulnerabilities in recent years, which can severely harm the integrity and confidentiality of programs protected by SGX. Most existing defense schemes are built based on the assumption that the adversary launches attacks from the same core as the victim, which however have been proved insufficient by newly-emerged cross-core side-channel attacks (e.g. CrossTalk). We present Informer, a defensive approach for SGX against side-channel attacks launched from any location, whether the adversary resides in the same physical CPU core as the victim or not. Informer achieves this goal by creating dummy threads that temporarily monopolize all CPU cores when security-critical codes are being executed, which breaks the essential concurrent execution condition of side-channel attacks. A key challenge is to ensure all those threads are scheduled exclusively to occupy all CPU cores even within an untrusted OS. Informer can defend against side-channel attacks from any core, and only incurs 22% performance overhead in OpenSSL. An additional mechanism is designed to reduce the impact on the operating system, as well as an optional extension to reduce the performance overhead brought to other programs.
Dynamic Transformer for Efficient Machine Translation on Embedded Devices

The Transformer architecture is widely used for machine translation tasks. However, its resource-intensive nature makes it challenging to implement on constrained embedded devices, particularly where available hardware resources can vary at run-time. We propose a dynamic machine translation model that scales the Transformer architecture based on the available resources at any particular time. The proposed approach, ‘Dynamic-HAT’, uses a HAT SuperTransformer as the backbone to search for SubTransformers with different accuracy-latency trade-offs at design time. The optimal SubTransformers are sampled from the SuperTransformer at run-time, depending on latency constraints. The Dynamic-HAT is tested on the Jetson Nano and the approach uses inherited SubTransformers sampled directly from the SuperTransformer with a switching time of <1s. Using inherited SubTransformers results in a BLEU score loss of 6 1.5% because the SubTransformer configuration is not retrained from scratch after sampling. However, to recover this loss in performance, the dimensions of the design space can be reduced to tailor it to a family of target hardware. The new reduced design space results in a BLEU score increase of approximately 1% for sub-optimal models from the original design space, with a wide range for performance scaling between 0.356s - 1.526s for the GPU and 2.9s - 7.31s for the CPU.
Image Arbitrary Style Transfer via Self-Attention Mechanism Based on Feature Fusion

Arbitrary style transfer is a technique for transferring an ordinary photograph to an image with another artistic style, which not only provides rich materials for film and game production, but also can be used for image restoration. To solve the problems that stylized images cannot maintain spatial consistency and content structure distortion, this paper proposes an arbitrary style transfer via self-attention network method. This algorithm can match the closest semantic style feature to the content feature, and adjust the local style efficiently and flexibly according to the semantic spatial distribution of the content image. Moreover, it can retain more original information about the structure. The experimental results show that the method can transfer the image into different styles with higher quality and better visual effects.