Reviewerly/exHarmony huggingface dataset:
Role of Explainable Edge AI to Resolve Real Time Problem

The growth of information technology (IT) has resulted in physical de-vices being connected to the internet and having the ability to recognize other devices. Artificial Intelligence algorithms are processed on edge or the devices of users. Edge Computing based on the same premise, stores, processes, and manages data directly at Internet of Things (IoT) endpoints. Edge artificial intelligence uses the device's hardware to process data and performs machine learning and deep learning procedures. In the model, you can troubleshoot and improve model performance while also assisting others in understanding the behavior of your models. To make the area more real, explainable edge devices come in a wide range of costs and capabilities. A decade ago, we couldn’t imagine that explainable edge artificial intelligence would be at today’s level. Now it is a part of industries and even devices for customer service. The best example of explainable edge AI is virtual assistants such as Alexa, google assistant. They learn from the user’s world and phrases and can store them directly on the device. These are just a few examples later, and we have possible applications in future works on the explainable edge artificial intelligence. Edge Computing Platform facilitates the development and elastic operation of apps and services. Its benefits the AI assisting in overcoming the technical obstacles that AI-enabled apps experience. Combination of edge and AI is buzzwords within the industry to deliver the performance and reduce the cost compared to state of arts XAI applications. Moreover edge artificial intelligence are reducing the latency, improving user experience, and reducing the necessary bandwidth, consequently reducing the costs of internet services. It surfs this movement since the need for data processing on the device themselves also represents the increasing use of artificial intelligence. Artificial intelligence edge processing focused on model which trained them in central data center using historical datasets. Compression techniques of data that enables squeezing large artificial intelligence models into small hardware form factors could push some training to the edge over time.
Time Series Predictive Models for Opponent Behavior Modeling in Bilateral Negotiations

In agent-based negotiations, it is crucial to understand the opponent’s behavior and predict its bidding pattern to act strategically. Foreseeing the utility of the opponent’s coming offer provides valuable insight to the agent so that it can decide its next move wisely. Accordingly, this paper addresses predicting the opponent’s coming offers by employing two deep learning-based approaches: Long Short-Term Memory Networks and Transformers. The learning process has three different targets: estimating the agent’s utility of the opponent’s coming offer, estimating the agent’s utility of that without using opponent-related variables, and estimating the opponent’s utility of that by using opponent-related variables. This work reports the performances of these models that are evaluated in various negotiation scenarios. Our evaluation showed promising results regarding the prediction performance of the proposed methods.
Cybersecurity and the Internet

Cybercrime as an activity and as an industry has been a part of the digital electronic landscape from the beginning of the computer era. This paper discusses aspects of the growth, evolution and current state of cybercrime and our response to it. Special considerations are discussed that relate to cybersecurity in industrial control environments.
Anomaly Detection in Industrial Control System Based on the Hierarchical Hidden Markov Model

An approach based on a hierarchical hidden Markov model for anomaly detection in industrial control systems is proposed. The signals of the system components are fed to the input of the proposed model. The hidden state is an independent probabilistic model, so each state is also a hidden Markov model. In the proposed model, the detection of anomalies according to the readings of the industrial control system sensors is combined with modeling at the event level. The model has several levels, and the event is modeled at the highest level. The approach is evaluated on a secure water treatment dataset and compared with the results of the previous work, which showed that the proposed model is better in terms of recall and F-measure metrics and amounted to 0.9164 and 0.9563, respectively.
Capturing Manufacturing Knowledge Using Augmented Reality Technologies

Manufacturing is a knowledge-intensive business in which companies are distinguished for their ability to deliver products and services using numerous forms of information and knowledge. Various information and communication technologies and systems have been created throughout the years to effectively manage information and knowledge in industry. However, capturing knowledge, particularly tacit knowledge, remains a challenge. Virtual Reality (VR) and Augmented Reality (AR) technologies for industrial tasks have been developed by the research and practitioner communities. For example, in manufacturing, VR/AR have been used to aid in different machining and assembly tasks. This paper presents a proposed an AR equipment ‘Head Mounted Device (HMD)’ under development, as a tool for capturing manufacturing expertise’s knowledge. The technology intends to capture tacit knowledge using a 3D camera installed on the operator’s HMD. The proposed approach also supplies the operator with information on production and ongoing processes.
Challenges and Issues of Load Balancing Algorithms in Cloud System

In this paper study for users have unquestionably profited from the rapidly changing paradigm of cloud computing. It employs virtual machines instead of actual equipment to host, store, and network multiple components, and it charges per use. As the amount of data being stored grows, so does the importance of load balancing as a research area. One of the most difficult problems in cloud computing is distributing the ever-changing burden among the nodes in an efficient manner known as load balancing. There are numerous load balancing methods in use today that considerably improve the efficiency and management of resources. We use cloud analyst as a simulator in this article to compare techniques with a single broker policy that helped boost cloud and related application performance.
On the Beckhoff PLC Security and Forensic Analysis Using Digital Forensic Evidence-Based Threat (DFET) Modeling

With the increasing advent of smart buildings and smart cities, the use of Operational Technology (OT) and Industrial control systems (ICSs) has been rising. Recent trends of cyber attacks on OT demand more attention for forensic and security analysis of such environments. As such in this paper, we examine a widely used PLC, the Beckhoff CX9020 PLC from a digital forensic perspective. First, we configure the PLC to log as much activity as possible using the available options. Next, we test a set of basic cyber attacks on the PLC. Finally, we devise forensic acquisition and analysis of the system.
Design of deaf instruction during the COVID-19 pandemic

The value of this study is to draw teaching presence aspect analysis of the Community of Inquiry for the design of deaf instructions during COVID-19. This study applied a narrative qualitative research approach. Data were collected through deep interviews and document studies by two teachers of a special school in Surakarta, one of these was an experienced teacher engaged in teaching profession since 1991 and a novice teacher who practices teaching since 2004. The data was analyzed using the design and organization instruction under the teaching presence concept of the COI framework, which consists of creating categories of coding the data, reducing the data, presenting the data, and withdrawing conclusions. Based on the design and organization of deaf instruction, teaching presence aspect of the COI framework gives a design of asynchronous learning. Findings revealed that teachers have different ways to provide instructional materials and conduct assessments. The experienced teacher made a module and deaf students were expected to go to school for the module collection every week, meanwhile, the novice teacher prepared the module and sent it via WhatsApp. Indirectly, the design and organization of deaf instruction under the teaching presence aspect of the COI framework gives deaf students new experiences using technology in learning and provides emotional understanding and communication practices through discussion. The implication is that the government provides professional development to the teachers providing special education.
A Support Vector Machine–Based Deep Learning Approach for Object Detection

The object detection and tracking is widely processed and researched topics in the computer vision area. There are a lot of different approaches used for the detection of objects still there is a gap in finding the small objects and its tracking. In our proposed methodology, we have presented a novel technique inspired from the convolutional neural network (CNN). The proposed work is based on the fast-CNN where the second polling layer is fed by the features extracted by the support vector machine (SVM). We have projected a very simple deep learning approach for the detection of small objects despite any weather conditions. The SVM is used to extract the desired parts from the noise and in parallel, the features maps are obtained by applying the single stride on to the convolutional matrix. The experimental work is performed using MATLAB 2021 R software and the results are obtained in terms of accuracy and precious. The results obtained from the proposed methodology are compared with the previous approach and the present work outperforms the existing work.
Task-dependent Optimal Weight Combinations for Static Embeddings

A variety of NLP applications use word2vec skip-gram, GloVe, and fastText word embeddings. These models learn two sets of embedding vectors, but most practitioners use only one of them, or alternately an unweighted sum of both. This is the first study to systematically explore a range of linear combinations between the first and second embedding sets. We evaluate these combinations on a set of six NLP benchmarks including IR, POS-tagging, and sentence similarity. We show that the default embedding combinations are often suboptimal and demonstrate 1.0-8.0% improvements. Notably, GloVe’s default unweighted sum is its least effective combination across tasks. We provide a theoretical basis for weighting one set of embeddings more than the other according to the algorithm and task. We apply our findings to improve accuracy in applications of cross-lingual alignment and navigational knowledge by up to 15.2%.
Semi-automated Analysis of Collaborative Interaction: Are We There Yet?

In recent years, research on collaborative interaction has relied on manual coding of rich audio/video recordings. The fine-grained analysis of such material is extremely time-consuming and labor-intensive. This is not only difficult to scale, but, as a result, might also limit the quality and completeness of coding due to fatigue, inherent human biases, (accidental or intentional), and inter-rater inconsistencies. In this paper, we explore how recent advances in machine learning may reduce manual effort and loss of information while retaining the value of human intelligence in the coding process. We present ACACIA (AI Chain for Augmented Collaborative Interaction Analysis), an AI video data analysis application which combines a range of advances in machine perception of video material for the analysis of collaborative interaction. We evaluate ACACIA's abilities, show how far we can already get, and which challenges remain. Our contribution lies in establishing a combined machine and human analysis pipeline that may be generalized to different collaborative settings and guide future research.
Türkçe Radyoloji Raporlarının Doğal Dil İşlenmesi

Today, it is widely used on DDI. is an important problem. machine with Natural Language Processing understand human language and facilitate human life is targeted. Word preprocessing, information extraction, machine translation, text summarization and classification studies DDI are some of the application areas. Education, business and social Studies on DDI in almost every sector such as media available. Declaring a pandemic due to the Covid19 Virus in the world DDI became important in the health sector after the won. Studies on Turkish texts scarcity and the need for DDI applications in the field of health makes the work important. Turkish Radiology A dataset was created from the reports. Data NLTK, A set of rules specified by the mainspring and regular expressions. preprocessed and normalized. later Language modeling was carried out with Word2Vec.
A Quality-Driven Iterative Evolution Approach for Software Architecture

The quality attributes of software architecture (SA) determine whether SA can be easily understood, tested, modified and so on, so quality-driven architecture evolution is important for keeping the viability and competitiveness. SA evolution is a process, and it contains multiple steps, such as SA quality measurement, SA modification, code co-evolution and so on. In order to guarantee that the software can be continuously improved and iteratively evolved in the future, we need to focus on all steps. However, most existing approaches only focus on one aspect, so they did not pay attention to how to finish the evolution process. In this paper, we propose a quality-driven iterative evolution approach for SA. This approach focuses on the whole process. In the first step, we use a quantitative approach to measure the architecture quality. Then, we construct the conflict graph to detect conflicts between evolution requirements to generate the final evolution scheme. In the third step, we modify architecture based on the evolution scheme. Finally, we co-evolve file dependency graph (FDG) based on the modified architecture. By focusing on the above steps, our approach can support a complete quality-driven architecture evolution process and obtain the maximum benefit in terms of the combined SA quality. We conduct our experiments with four open source projects, the experimental results indicate that our approach can improve SA quality, and our approach can effectively co-evolve the FDG to lay the foundation for the next evolution.
Image Signal Decomposition Using Polynomial Representation with Hybrid Lossy and Non-Lossy Coding Scheme

This article presents a polynomial-based image compression scheme, which consists of using the color model (YUV) to represent color contents and using two-dimensional polynomial coding (first-order) with variable block size according to correlation between neighbor pixels. The residual part of the polynomial for all bands is analyzed into two parts, most important (big) part, and least important (small) parts. Due to the significant subjective importance of the big group; lossless compression (based on Run-Length spatial coding) is used to represent it. Furthermore, a lossy compression system scheme is utilized to approximately represent the small group; it is based on an error-limited adaptive coding system and using the transform coding scheme (discrete cosine transform or bi-orthogonal transform). Experimentally, the developed system has achieved high compression ratios with acceptable quality for color images. The performance results are comparable to those introduced in recent studies; the accomplishment of the introduced image compression system was analyzed and compared with the performance of the JPEG standard. The results of the developed system show better performance than that of the JPEG standard.
Semi-supervised Learning with In-domain Pre-training and Deep Co-training

Scarcity of labelled data places a significant limitation on the power of any supervised machine learning model. This challenge is further amplified by the domain-specific expertise and hours of manual labour involved in labelling. In the case, when even the available data is not enough, a strategy of transfer learning using in-domain pre-training can be of help. In this paper, we formulate a dual strategy of semi-supervised deep neural co-training technique along with transfer learning based on in-domain pre-training to vastly improve model efficiency relative to its supervised counterpart for the task of review polarity prediction. We additionally investigate and implement a co-teaching strategy as a possible use case of countering the problem of noisy labels in addition to scarce labels. This is part of an on-going investigation that we also document in this paper.
Study of Identification of Breast Tumor Through DL Technique

Breast cancer is becoming the most common type of cancer among women. According to the statistics released in December 2020 by the International Agency for Research on Cancer, Carcinoma has now surpassed lung issues as the most prevalent chronic cancer in the world. The earlier detection of breast cancer can lower the number of fatalities. For breast cancer diagnostics, pathologists are now utilizing the convolutional neural network [CNN] approach in order to reduce the workload and improve accuracy while diagnosing breast cancer. The proposed deep learning approach is designed, developed, and evaluated on the entire slide pathology image database. This approach yielded an accuracy of 96.54%. This research study observed that the proposed approach outperforms the existing deep learning approaches since it automatically identifies the best available features.
Dig and dust discrimination method for specular surfaces based on multi-view fusion

Automatic detection for specular surfaces is always the crucial problem in the manufacturing process. Dust particles and digs are too similar in shape to discriminate from each other by existing detection method. In this paper, a dig and dust discrimination method for specular surfaces based on multi-view fusion is systematically proposed. Structured light modulation analysis technique is adopted to obtain the distribution of defects and dusts. Then, a white light source and two polarizers whose transmission axis are perpendicular to each other are utilized to obtain dusts purely. Finally, dusts are removed from the modulation image with only defects left. The same process is repeated for several times at different observation angles θ<sub>s</sub>. Take the intersection of all the detection results to guarantee as much dusts is removed as possible. Simulation and experimental results verify the effectiveness and accuracy of proposed method.
The Convergence of Social Media, Technology, and Data

This chapter describes the interplay of social media, technology, and data, as well as its impact on risk management considerations. It explores the intersection of social media, technology, and big data as it relates to us as individuals, as well as in the broader cross-section of society and business, and accompanying risks, as all are inextricably intertwined with the other. The chapter takes the perspectives of risk management, legal, and responsive planning when something can (and will) go awry, with examples from the new language frontier, including use of hashtags and emojis, along with accompanying ethical considerations and legal requirements.
Protection of intellectual property rights during the Mobile World Congress

Abstract The Barcelona Mobile World Congress is one of the world’s largest events for the technology industry worldwide. This showcasing of creativity and innovation is also a prime opportunity for legal intellectual property violations to be committed with regard to copyright, trade marks, patents, designs, unfair competition law and advertising laws. In order to guarantee a fast, balanced and effective system of interim protection, the assemblies of commercial court judges of Barcelona and Alicante jointly adopt every year an on-call rapid response protocol. This unique protocol has proven to be a clear example of success in the protection of intellectual property rights on international exhibitions.
A Systematic Mapping Study on Quality Attribute Traceability

Quality attribute traceability (QAT), as a sub-discipline of software traceability, ensures that quality attributes are considered in all phases of software development. QAT is one of the most critical and challenging research areas in requirement engineering. In recent years, several studies have been conducted on quality attribute traceability. However, researchers in this field have problems to identifying the research status of this field due to the lack of a comprehensive secondary study that reveals research trends and gaps. Evidence-based software engineering (EBSE) proposes systematic mapping study (SMS) and systematic literature review (SLR) methods for secondary studies on software engineering. In this paper we use the semantically enhanced SMS method in the QAT research area. In order to improve SMS, we introduce a concept classification approach. In this approach by using a QAT domain ontology, the keywords classification process is improved. The most important innovation of this study is the presentation of the QAT research tree. This research tree has been created by gathering research topics identified in this field along with existing gaps. Identified research topics include “QAT Approach,” “QAT Development,” “QAT Model,” “QAT Framework,” and “QAT Algorithm”. The results also indicate that “QAT Frameworks” is a research trend, and most recent studies have focused on this topic. On the other hand, “Trace Maintenance” and “Trace Integrity” are the research gaps in this field. In this article, active researchers, journals, conferences and countries in the field of QAT are also introduced. We also introduce a set of tools that facilitate the SMS process.
Graph neural network‐based virtual network function deployment optimization

In Kim et al,1 the following errors were published on page 10 and Tables 6 to 10. On page 10, an equation label (13) was inadvertently added on an incorrect displayed equation. Hence, the succeeding equations and corresponding citations were renumbered accordingly. While on Tables 6 to 10, the word “Data center” was mistakenly changed to “Data left” throughout the article. The online version has been corrected. We apologize for this error.
How Ambonese Read: Challenges and Opportunities to Preserve Maluku Folklore and Culture

Preserving culture can be done through the folklore that are passed down from generation to generation. The main aim of this survey is to see the challenges and opportunity for Maluku Folklore preservation. This research provides an insight on the students’ reading habit and preference, the availability of Maluku Folklore, and students’ perception on Maluku folklore. The data collected 248 responses through an online survey. The result highlighted several interesting information; 1) Despite the result show respondents perceived reading as their hobby, their preference activity shows that in their free time books are not the first choice and the culture of bedtime stories and/or being read to by parents are an uncommon practice. 2) Students rarely read Maluku Folklore unless it is an assignment from school. 3) Students believed that Maluku Folklore is not as popular and available compared to folklore from other parts of Indonesia. 4) Maluku Folklore is not readily available for students to find and consume. 5) Students prefer to read on digital and online platforms compared to the printed ones. 6) Opportunities for preservation can be done by embracing the technology and internet to tapped in to the current young people reading preferences and habit.