Reviewerly/exHarmony huggingface dataset:
Extracting Contextual Semantic from a Concordance Containing Mathematical Definition

With the increased penetration of screen reading software, effective audio rendering of equations can significantly assist in making many electronic mathematics documents accessible. However, linear syntactic rendering of equations not only creates a considerable cognitive load, even for relatively simple equations, but also becomes crucial in more advanced mathematical subjects where the precise and correct interpretation of symbols is essential. To overcome this challenge, we are working on a procedure to extract contextual semantics for mathematical expressions from the surrounding text. In this paper, we will present one of its modules: the Semantic Extractor. This module aids in extracting semantics from the concordance that contains valid mathematical definitions. This approach enables contextually aware audio rendering of complex mathematical expressions, rather than relying solely on syntactic rendering.
Explanations for Answer Set Programming

The paper presents an enhancement of xASP, a system that generates explanation graphs for Answer Set Programming (ASP). Different from xASP, the new system, xASP2, supports different clingo constructs like the choice rules, the constraints, and the aggregates such as #sum, #min. This work formalizes and presents an explainable artificial intelligence system for a broad fragment of ASP, capable of shrinking as much as possible the set of assumptions and presenting explanations in terms of directed acyclic graphs.
Research Collaborator Recommendation System based on citations and Influential citations

Recommendation systems are tools that use various factors to suggest items or ideas to users. These systems often rely on networking and filtering, a technique that utilizes algorithms to analyze user reviews and provide personalized recommendations to users with similar preferences. In this work, a recommendation system to assist with the identification of research collaborators based on their areas of interest and published papers has been explored. With the use of the DBLP database, important attributes such as domain, and citation scores are considered to build a research collaboration system. Three different formulae are used to compute research scores (R_Scores) and the scholars are ranked based on these scores and recommendations are made on the basis of the ranks.
Image Stitching via Augmentation Selection and Comprehensive Optimization of Homography Models

The naturalness of prominent objects is important information in image stitching research, which directly affects the satisfaction of electronic consumers when acquiring large scenes. Most current image stitching algorithms use the best homography model for registration features, which usually cannot guarantee the integrity of static and dynamic objects. Aiming to alleviate the problem, we propose an image stitching method in this paper. Specifically, it consists of two parts, augmentation selection and comprehensive optimization of homography models. We first obtain multiple homography models and further refine the model results by cluster analysis to form a reasonable homography candidate set, next the most reasonable homography model and best stitching result can be obtained with the maximal evaluation value. Then, mesh optimization and multiple matching are performed on each model to eliminate artifacts, while optimal seams are optimized to reduce distortion. Extensive experimental results show that our method improves the eval scores by 10.15%, and reaches superior performance over integrity, primitiveness, and uniqueness on both static and dynamic markers.
Extractive Negative Opinion Summarization of Consumer Electronics Reviews

Online consumers create enormous reviews of electronic devices or services daily. Extracting negative opinions from such an amount of data is a crucial task for improving products and developing new features. Opinion summarization can help public consumers and businesses understand and extract the proper amount of negative information from large-scale data. However, automatically and concisely summarizing opinions with negative emotions and sentiments has yet to be explored. This paper proposes an extractive summarization framework that automatically detects fine-grained negative opinions. While the conventional opinion summarization only considers a general full affective coverage, our proposed method exploits submodular diversity, relevance, and opinion functions focusing on summarizing reviews with negative emotional variations. At the same time, an algorithm with 1-1/e--approximation is applied to optimize the proposed functions. Most of the existing datasets cannot provide golden summaries with negative opinions. Our experiment explores reference-free metrics for evaluation, which requires neither reference nor human-created golden summaries. According to the metric scores, the proposed framework outperforms all baselines at summarizing negative opinions of consumer electronics on eight popular online shopping platforms. We analyze the generated summaries in detail and provide a possible application example in electronic product development.
Message-Product Matching for Advertising Systems: Hierarchical Category Vectorization and Deep Learning Approach

Personalized ad display is an important topic for sales and marketing strategies of ad platforms. In this study, hierarchical category vectorization and deep learning approaches are combined to perform message-product matching for personalized ad display. Poincaré model is trained for hierarchical category vectorization while BERT model is utilized for text vectorization. The message-product matching obtained is reranked through the siamese neural network architecture, resulting in offering users product recommendations that they may be interested in at higher ranks. The evaluation of the developed ad display system showed that it presents users with product recommendations that they are more likely to be interested in at higher ranks.
Detection of Hate Speech in Turkish Social Media Posts with BERT-Base Model

With the development of the Internet, the amount of data expressed by users worldwide has increased. Detecting hate speech in social media, preventing harmful language and promoting safe online communities is an important task. Bidirectional Encoder Representations from Transformers (BERT) is a popular language model that performs well in natural language processing tasks. In this study, the BERT-Base model is used to detect hate speech in the comments of Twitter users in Turkey. It was trained with a dataset of examples labeled as harmful and harmless and achieved 92.53% test accuracy. The developed model was presented to users in a live environment.
Improving Experience Replay Architecture with K-Means Clustering

Replay memory highly affects the performance of deep reinforcement learning algorithms in terms of data efficiency and training time. How the experiences will be stored in the memory and sampling will be realized are subjects of ongoing research in the field. In this paper, a new replay memory module, called K-Means Replay Memory is designed. The module consists of two submodules called Recent Memory and Global Memory. New experiences are inserted only into recent memory and when the number of experiences in recent memory exceeds a certain limit, experience share occurs from recent memory to global memory. After the experience share, similarity sets are constituted via K-Means clustering algorithm within the stored experiences. While sampling, the distribution of experiences sampled from recent memory with respect to similarity sets and average losses obtained from neural networks are taken into account in order to compute set probabilities. Experiences are sampled from global memory by using these probabilities. Experiments are performed by using Prioritized Experience Replay, Uniform Experience Replay and K-Means Replay Memory, and obtained results are given in this paper.
DoG-Loss for Semantic Segmentation

Semantic segmentation is an important machine vision problem with many applications. It aims to classify images based on pixels and label each pixel. One of the main challenges of this problem is to ensure that the contours of the objects are accurate and the areas they cover are detected in a holistic manner. In addition, the successful learning of low-frequency classes in the datasets by the model and the preservation of object integrity also significantly affect the success. In this study, a difference of Gaussian (DoG) based loss function is proposed to improve segmentation accuracy and class estimation. In this way, the segmentation model focuses on the contours of the objects to better preserve their shape integrity. Experiments show that the proposed DoG loss function achieves up to %3.9 better results than the commonly used segmentation loss functions.
A deep learning method for classifying tumors of the central nervous system

The development of deep learning has significantly altered computer vision and given rise to a wide range of pre-made training models. It is currently utilized often in medical pathology imaging. Histopathology image analysis with computer assistance can speed up diagnosis and increase precision. This study aims to address the issue of classifying images of central nervous system tumor pathology using a new deep learning network architecture (SPACH). Ultimately, our model had a classification accuracy of 92.5%.
Sentimental Analysis of Movie Reviews Using NLP Techniques

The goal of natural language processing is to build machines that can comprehend text or speech input and respond to it in a way that is similar to how people do. To accomplish this, various Natural Language Processing (NLP) tasks are employed to analyze human language data, aiding computers in understanding the information it consumes. These tasks include sentiment analysis, voice recognition, part-of-speech tagging, and natural language generation. Sentiment analysis that detects emotions such as happiness, frustration, rage, and sadness goes beyond polarity. To do this, advanced machine learning techniques or lexicon collections of words and the emotions arouse are used. In this study, the Internet Movie Database (IMDB) Reviews Dataset is subjected to sentiment analysis to determine if a user's review is favourable or unfavourable based on the supplied details. Utilizing data from the real world, the proposed approach's correctness is assessed.
Research on the Distributed Secure Storage of Embedded Terminals Based on the Blockchain

The blockchain is a distributed public ledger technology that originates from the digital crypto currency bitcoin. Blockchain's key advantages include decentralization, time-series data, collective maintenance, programmability and security. It breaks through the limitation of traditional center-based technology and has broad military development prospect. Nevertheless, with the distributed embedded devices in the battlefield in the future, the cooperative computing task needs to be performed effectively and rapidly among them. Furthermore, in order to achieve the aim of the cooperative computation on the multi-device, the urgent and primary problem is how to realize the effective share and secure storage about the critical data. Consequently, given the requirement of data sharing, the architecture design of data distributed secure storage based on the blockchain is proposed in this paper. Besides, the vivid descriptions of some key functional modules are given.
Optical Characterization of Low-loss Phase Change Material Sb<sub>2</sub>S<sub>3</sub> Based on Quantitative Phase Microscopy

Sb <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> S <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</inf> , a low-loss optical phase change material, has attracted increasing attention in the fields of neuromorphic photonics, metasurfaces, and optical memory. However, the non-uniform of the phase-transition region of Sb <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> S <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</inf> induced by the Gaussian intensity profile of the laser beam at the micron scale makes it difficult to measure the crystallized fraction distribution and optical properties using current measurement technology. In this work, we present the measurement approach that characterizes the distribution of crystallization degree of phase change materials at micron scale using quantitative phase microscopy. Moreover, we experimentally mapped the crystallized fraction distribution of multiple intermediate phase-transition regions of Sb <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> S <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</inf> . This work provides a non-destructive and contact-free measurement for measuring the crystallized fraction distribution with potential applications in optical metasurfaces, neuromorphic computing, and optical memory.
Refraction correction for deep-water three-dimensional visual measurement based on multi-objective optimization

Refraction-induced errors affect the accuracy of three-dimensional visual measurements in deepwater environments. In this study, a binocular camera refractive imaging model was established, and a calibration method for the refraction parameters was proposed for high-accuracy shape and deformation measurements in deep-water environments. First, an initial estimate of the refractive axis was obtained using a three-dimensional calibration target. Then, the errors in the distance between the spatial point pairs and the reprojection errors are taken as the dual optimization objectives, and the Non-dominated Sorting Genetic Algorithm II is applied to optimize the refraction parameters. To efficiently calculate the reprojection error, an improved numerical computation method is proposed to accelerate the calculation of the analytical forward projection. Underwater experiments were conducted to verify the method’s effectiveness. The results showed that the average error of the absolute position of the reconstructed points was less than 1.1 mm and the average error of the displacement was less than 0.04 mm. This study provides a sound solution for accurate three-dimensional visual measurement in deep-water environments.
HIGHER EDUCATION DIGITALIZATION - FRIEND OR FOE?

The direction in our university, and indeed in the whole of Finland, regarding the teaching methods in higher education (HE) was rather strongly towards utilization of the digital tools during the first two decades of the new millennia.This translates to creating flexibility for the students to use.Also, whilst working simultaneously with the studies.The digital proficiency of the staff members was constantly under development schemes.The aim of this study is to examine the digitalization in higher education.There are lucrative benefits to be harvested in using digitalization but simultaneously there are significant risks in their use.In this paper these benefits and risks will be studied through practical examples.The results show the need for different pedagogical and didactic approaches in learning events in higher education.The year 2020 however, with the pandemic, brought the need to alter all at once to the digital mode; lectures for the students were to be held in Teams or in Zoom, the assignments were to be made and presented there too.The change went rather well, we daresay.Seemed that the worst scenario was avoided.Maybe so, but suddenly the news grew ill with various messages proclaiming that the students were feeling ill.Similarly, the university community saw cracks in the traditional institution.The unity of the university was worse than before, the students did not work together as they did before, the staff was driven away from the campus to work from home, i. e. they could not meet with their colleagues and peers to exchange views and pleasantries.Now, after the ban has been lifted, the university management wishes the return to the campus.However, the staff has witnessed the virtual mode and working from home to be very adequate to say the least and the same applies to the students.An on-campus day was announced, with other similar measures in order to get the staff back on campus together with the students.In addition, the conundrum presented itself, whether to make the on-site learning events obligatory, to stream them, to record them, or a combination of these features.The students, when asked, reported their willingness to take part in the teaching if 'it was worth it'.I. e. the traditional lecturing, with little interaction bore benefits when watched from home.The answer is that there is no silver bullet to solve various issues with one shot, but rather to address the individual questions case-specifically.
A Business-Oriented Methodology to Evaluate the Security of Software Architecture Quantitatively

Software architecture security design is a key stage in developing business-oriented system, such as business-critical system, ICT system and AI system. Many typical accidents also remind us that the security of software architecture even plays a more important role than the code security in most software systems. However, there are very few researches which focus on the security of software architecture. Especially, we don’t find a systematic and feasible quantitative method to evaluate the security of software architecture. To fill this gap, we launched a research project focusing on software architecture security since 2019 and try to provide a series of quantitative methods for evaluating the security of software architecture. In this paper, a business-oriented quantitative method was proposed to evaluate the security of software architecture from the view of business-critical security insurance. In our method, both business dependency graph (BDG) and multi-hierarchical dependency graph (MHDG) are defined and constructed first for describing businesses and their relationships, and system components and their relationships; then, attack points and business key-points are identified and labeled on BDG and MHDG; and further, all potential attack paths and effective attack paths in the system based on MHDG are detected; and finally, a set of security indicators is defined and used to evaluate the security of software architecture quantitatively. For more effective experiments, we use software architectures with different styles and different evolution versions. Experimental results show that our method can reflect effectively the security characteristics of software architecture with different styles, can find the security changes of different architecture evolution versions for the same system, and can perform quantitative evaluation of software architecture security efficiently.
A Description of Honeypot Systems

A honeypot framework is an online protection instrument intended to hoodwink and trap malignant entertainers looking for unapproved admittance to an organization or framework.It works as a charming fake, emulating authentic assets, applications, or administrations to draw in programmers.By observing and investigating the exercises of these interlopers, security experts gain significant bits of knowledge into their strategies and expectations.Honeypots are a proactive safeguard device, empowering associations to distinguish weaknesses, fortify their security stance, and improve episode reaction systems.This theoretical epitomizes the pith of a honeypot framework's job in shielding computerized resources by tricking and examining digital foes.
STUDENT GRADUATION PREDICTION SYSTEM BASED ON ACADEMIC AND NON-ACADEMIC (EQ) DATA USING C4.5 ALGORITHM

The graduation profile is an important element for higher education accreditation standards. It reflects the performance of the adopted education system within a certain period. The better the profile graduation, the better the value of the accreditation. Some students are unable to complete their studies on time or even fail to complete their studies because they exceed the specified time limit, which is seven years, and it negatively affects institutions' accreditation. To prevent this from happening, it is necessary to know what obstacles that cause these students could not complete their studies on time. by knowing this information, prevention can be done for students who are potentially unable to complete their studies on time. The purpose of this study was to make a system that can predict the graduation timeline and the factors that influence it. The data used was graduation data from undergraduate students majoring in psychology from 2015 to 2017 at a university in Cimahi. The data had a total record of 461 students, 44 subject value attributes, 13 psychotest attributes, and class attributes. We generated the result by using decission tree method with C4.5 algorithm, which produces 90.32% accuracy. The depth of the tree can also influence the accuracy of the algorithm. This study also found that academic and non-academic (EQ) scores can affect students’ graduation time.
Knowledge Management and its Role in the Development of a Smart University in Iraq

There is a lack of extensive research on the development of smart universities (SU). The aim of this paper is to investigate the impact of knowledge management (KM) on the development of SU (DSU) in Iraq. According to the knowledge-based view (KBV), resource-based view (RBV), and KM capability model, this study suggests that the KM process (KMP) and KM infrastructure (KMI) have a positive impact on SUs development. Furthermore, the study suggests that KMI functions as a mediator. The study was conducted using a sample of five public universities in Iraq. A purposive sampling method was utilised to collect data from a total of 209 academic staff members. The study's results demonstrate that KMP (knowledge acquisition, knowledge sharing, and knowledge application) and KMI (IT infrastructure and human KM) have a significant impact on the DSU. Additionally, it was discovered that KMI plays a role in mediating the influence of KMP on the DSU. The findings indicate that successful KM can support the progress of SU. Further cross-national and variable-based studies are required to enhance the validity of this conclusion.