Reviewerly/exHarmony huggingface dataset:
Adequacy, fluency and cognitive processes: evidence from translating English news articles into Arabic

ABSTRACTABSTRACTThis article reports on an empirical study on cognitive processes [i.e. (dis) fluency, temporal management of the translation process, and revision] and their relationship with fluency and adequacy. Based on Jakobsen and Schou's (1999 Jakobsen, A., & Schou, L. (1999). Translog documentation: Version 1.0. In G. Hansen (Ed.), Probing the process in translation: Methods and results (pp. 151–186). Copenhagen: Samfundslitteratur. [Google Scholar]) computational model of human translation, the author utilizes keylogging data retrieved from a Translog-II-based task to investigate the cognitive processes behind Arab translator trainees' translations of an English news article into Arabic and unfold whether these processes predict fluency and adequacy (N = 32). Results demonstrate trainees' segmentation of a news article into frequent short lexical segments manifested in the mean translation units (M = 141.34, SD = 44.37), reliance on online revision over end revision, and temporal delay in the drafting stage. Multiple-line regression analysis shows that translation quality is predicted by revision time, orientation time, and translation speed in addition to a positive association between translation units and translation adequacy. According to the findings, translation trainees should be equipped with ST comprehension, and TT production and revision skills.KEYWORDS: Adequacyfluencytranslation unittranslationrevisionorientationdrafting AcknowledgmentsThe author would like to thank the Deanship of Scientific Research, Najran University for funding this study under National Research Priorities (Grant Code NU/NRP/SEHRC/11/10). The author thanks the editor and anonymous reviewers for their constructive comments and suggestions.Disclosure statementNo potential conflict of interest was reported by the author.Additional informationFundingThis work was supported by Najran University: [Grant Number NU/NRP/SEHRC/11/10].Correction StatementThis article has been corrected with minor changes. These changes do not impact the academic content of the article.
EADTC: An Approach to Interpretable and Accurate Crime Prediction

Machine learning applications related to high-stakes decisions are often surrounded by significant amounts of controversy. This has led to increasing interest in interpretable machine learning models. A well-known class of interpretable models is that of decision trees (DTs), which mirror a common strategy used by humans to arrive at solutions through a series of well-defined decisions. However, much of previous research on DTs for criminal justice predictions has focused primarily on collections (ensembles) of DTs whose results are aggregated together. Such DT ensembles are used to help improve accuracy; however, their increased complexity and deviation from human decision-making processes makes them much less interpretable compared to single-DT approaches. In this paper, we present a new DT model for criminal recidivism prediction that is designed with high interpretability, accuracy, and fairness as core objectives. The interpretability of the model stems from its formulation in terms of a single DT structure, while accuracy is achieved through an intensive optimization process of DT parameters that is carried out using a novel evolutionary algorithm. Through extensive experiments, we analyze the performance of our proposed EADTC (Evolutionary Algorithm Decision Tree for Crime prediction) method on relevant datasets. Our experiments show that the EADTC approach achieves competitive accuracy and fairness with respect to state-of-the-art ensemble DT models, while achieving higher interpretability due to the simpler, single-DT structure.
Novel Consensus Algorithm for Blockchain Using Proof-of-Majority (PoM)

The popularity of Blockchain is rising on account of its far-reaching applications in diverse industries. However, recently, blockchain has seen a rise of energy extensive mining pools which is leading to centralization, contradicting the basic blockchain tenet of decentralization. This letter proposes a novel consensus algorithm using Proof of Majority (PoM), to increase decentralization and to eliminate resource-intensive tasks leading to a reduced carbon footprint. We have evaluated the proposed algorithm in terms of latency and throughput. The proposed consensus algorithm outperforms popular existing consensus algorithms.
Fear of Crime and Victimization: An Explorative Study

The fear of crime is a new dimension of crime. It is not only a burning issue in Bangladesh but also all over the world. Fear of crime was theorized to be related to experiences of victimization, but this assumption was soon to be disputed. This study is based on empirical data which are from quantitative and qualitative research methods and the research is exploratory in nature. This study finds that 38-47 years of age group 36% of them are highest victimized by fear of crime. And 71% of males and 29% of females are victimized by fear of crime. Fear of crime affects far more people in Bangladesh than the crime itself, and there are sound reasons for treating crime and fear of crime as distinct social problems. Every year a huge number of people are suffered from fear of crime. Fear of crime has a great impact on our education, economy, and infrastructure. Important links exist between fear of crime and causation of victimization, location of occurrences, time of victimization, presence of law enforcement agencies, and levels of knowledge of the victims about the victim support services. The study findings play an important role in policy making and also for the upcoming researcher in the criminological arena.
Review of Generative Adversarial Networks in mono- and cross-modal biomedical image registration

Biomedical image registration refers to aligning corresponding anatomical structures among different images, which is critical to many tasks, such as brain atlas building, tumor growth monitoring, and image fusion-based medical diagnosis. However, high-throughput biomedical image registration remains challenging due to inherent variations in the intensity, texture, and anatomy resulting from different imaging modalities, different sample preparation methods, or different developmental stages of the imaged subject. Recently, Generative Adversarial Networks (GAN) have attracted increasing interest in both mono- and cross-modal biomedical image registrations due to their special ability to eliminate the modal variance and their adversarial training strategy. This paper provides a comprehensive survey of the GAN-based mono- and cross-modal biomedical image registration methods. According to the different implementation strategies, we organize the GAN-based mono- and cross-modal biomedical image registration methods into four categories: modality translation, symmetric learning, adversarial strategies, and joint training. The key concepts, the main contributions, and the advantages and disadvantages of the different strategies are summarized and discussed. Finally, we analyze the statistics of all the cited works from different points of view and reveal future trends for GAN-based biomedical image registration studies.
A Hierarchical Searchable Encryption Scheme Using Blockchain-Based Indexing

Focusing on the fine-grained access control challenge of multi-user searchable encryption, we propose a hierarchical searchable encryption scheme using blockchain-based indexing (HSE-BI). First, we propose a hierarchical search index structure based on a DAG-type access policy and a stepwise hierarchical key derivation mechanism; which we outsourced to the blockchain network to achieve reliable hierarchical search. We design a dynamic append-only update protocol for the blockchain-based index to deal with adding and deleting files. Secondly, we propose a hierarchical authorization mechanism based on broadcast encryption to achieve fine-grained search permission granting and revoking, which can prevent a malicious server from colluding with corrupted users. The security and complexity analysis shows that HSE-BI achieves optimal search time while satisfying adaptive secure and revocation secure. Our experimental results are encouraging, e.g., compared with the traditional multi-user searchable encryption schemes, HSE-BI’s hierarchical search policy does not impact the search performance visually. The growth rate of the search latency decreases with the increasing number of hierarchical users, which can act as an efficient crypto tool to open up venues for other applications. We demonstrate that HSE-BI is more suitable for actual applications with fine-grained access requirements and can act as an efficient crypto tool to open up venues for other applications.
An analysis of fake social media engagement services

Fake engagement services allow users of online social media and other web platforms to illegitimately increase their online reach and boost their perceived popularity. Driven by socio-economic and even political motivations, the demand for fake engagement services has increased in the last years, which has incentivized the rise of a vast underground market and support infrastructure. Prior research in this area has been limited to the study of the infrastructure used to provide these services (e.g., botnets) and to the development of algorithms to detect and remove fake activity in online targeted platforms. Yet, the platforms in which these services are sold (known as panels) and the underground markets offering these services have not received much research attention. To fill this knowledge gap, this paper studies Social Media Management (SMM) panels, i.e., reselling platforms—often found in underground forums—in which a large variety of fake engagement services are offered. By daily crawling 86 representative SMM panels for 4 months, we harvest a dataset with 2.8 M forum entries grouped into 61k different services. This dataset allows us to build a detailed catalog of the services for sale, the platforms they target, and to derive new insights on fake social engagement services and its market. We then perform an economic analysis of fake engagement services and their trading activities by automatically analyzing 7k threads in underground forums. Our analysis reveals a broad range of offered services and levels of customization, where buyers can acquire fake engagement services by selecting features such as the quality of the service, the speed of delivery, the country of origin, and even personal attributes of the fake account (e.g., gender). The price analysis also yields interesting empirical results, showing significant disparities between prices of the same product across different markets. These observations suggest that the market is still undeveloped and sellers do not know the real market value of the services that they offer, leading them to underprice or overprice their services.
Medical Image Segmentation of Bio-medical Images with Deep Convolution Neural Networks using Ensemble Approach

Image analysis relies heavily on segmentation, which encompasses detecting the features, extraction of features, classification, and treatment. For treatment planning, segmentation assists physicians in quantifying amount of tissue. The segmentation methods are divided into three groups: supervised, unsupervised, and DL segmentation, as well as traditional and computational modelling segmentation. Ensemble techniques in combine many learning models to achieve greater prediction results than each of the individual deep learning algorithms separately. In most scenarios, utilising a very simple ensemble resulted in a lower error rate than using a single model. This demonstrates the efficacy of ensemble approach. This research presents an ensemble deep learning model that was evaluated on ultrasound breast images of women between the ages of 25 and 75 using CNN, MaskR CNN, U Net, and Res Net. Since all four models operate with similar data shape, it makes sensible to design a single input layer that all models will use. The proposed model achieves the highest accuracy of 98.6% on the training, 94.5% validation accuracy, 94.5% test accuracy and a F1 score of 94.32% which outperforms all the individual neural networks.
Traceability of Tea powder Supply Chain based on Hyperledger Fabric

Trustworthy of the product from logistics stands as a big question in today’s life. Effective innovation management is essential for managing products in the tea powder supply chain. Any indifference in product traceability can lead to product fraud which can have negative impact in source product industry as well as consumer health. Monitoring and tracking tea powder supply chain activities will assist system stakeholders in finding origins and cause of product fraud and irregularities. Most of the systems and technologies used to manage tea powder supply chain processes fall short in providing traceability, accountability, transparency, reliability, trust, privacy, and security features. This article offers a Hyperledger fabric-based system to effectively manage the tea powder supply chain in a way which is decentralized, secure, trustworthy, reliable, transparent, and accountable. A decentralized governance for smart contracts, new chain code applications for collaboration and consensus, private data enhancements and external chain code launcher have been proposed. This proposed system is compared with the existing blockchain based and non-blockchain based systems to show the improvement.
Edge-Cloud Synergy: Unleashing the Potential of Parallel Processing for Big Data Analytics

If an edge-node orchestrator can partition Big Data tasks of variable computational complexity between the edge and cloud resources, major reductions in total task completion times can be achieved even at low Wide Area Network (WAN) speeds. The percentage time savings are greater with increasing task computational complexity and higher WAN speeds are required for low-complexity tasks. We demonstrate from numerical simulations that low-complexity tasks can benefit either by task partitioning between an edge node and multiple cloud servers. The orchestrator can also achieve greater time benefits by rerouting Big Data tasks directly to a single cloud resource if the balance of parameters (WAN speed and the ratio between edge and cloud processing speeds) is favourable.
Deep Auto Encoder based Chatbot for Discrete Math Course

Chatbots are used for the facilitation of human agents by decreasing their involvement in manually done tasks. This Chatbot is developed for students of discrete mathematics. The dataset for this experiment is accumulated from 3 books of discrete mathematics. Our model is a variant of transformer known as Bert-base-NLI-mean-tokens. Bidirectional Encoder Representations from Transformers (BERT). SBERT library is used for the generation of queryresponse embeddings. This model is fine-tuned according to our dataset and provided this model to students with android based mobile App. This model results in increase in the knowledge of students. The same approach will be implemented for other subjects as well.
Augmented reality for chemistry education to promote the use of chemical terminology in teacher trainings

Chemistry as a whole is divided into three levels. The macroscopic level describes real, observable phenomena of the material world. The submicroscopic level focuses on particles. The representative level includes pictorial and symbolic representations to visualize substance in its nature. Students often have problems separating these levels and conceptually transfer each of the three levels to the other. Therefore, teachers need to use chemical terminology correctly when teaching the substance-particle concept. Augmented reality (AR) connects the real and virtual worlds. The observer physically moves in a real environment that integrates virtual elements. This can be effective for learning when chemical processes that are invisible are made visible. The simultaneous presentation should avoid split attention and offers new possibilities to interactively deal with multiple external representations ((M)ER). The question arises whether AR has a positive effect on the use of technical language. With an AR app on the tablet and on the hololens, chemical processes of a real experiment are represented by AR visualizations. In this study, the chemistry terminology of chemistry teachers (N = 30) was captured using a pre-post survey. Each test includes five tasks elaborated by thinking aloud. Therefore, the AR app was piloted. The thinking-aloud protocols to acquire the use of the chemical terminology are evaluated in MAXQDA.
Extended reality as a means to enhance public health education

Technology has transformed the classroom and learning environments. From electronic whiteboards to tablet computers, educators now have access to a multitude of tools that enhance the learning experience. Educational technologies that rely on extended reality (XR) such as augmented and virtual reality are being used, or suggested for use, in various settings and often focus on technical fields such as medicine, dentistry, and aviation. Here, we propose that XR can be used in public health education to better prepare both undergraduate and graduate trainees for real world, complex public health scenarios that require public engagement, investigative skills, and critical decision making. Several opportunities for XR use are outlined that provide perspective on how XR can supplement traditional classroom instruction methods by providing an immersive, participatory training environment. XR offers an opportunity for public health students to gain confidence, have repeated simulated exposures in a safe and equitable environment, and build competency in critical functions they will likely perform as future public health professionals.
Depth guided feature selection for RGBD salient object detection

Depth information can greatly benefit the saliency detection in RGBD images if they are utilized well. Prevalent methods generally directly fuse depth and RGB features in networks. However, due to the inherent inconsistent between RGB and depth information, the RGB features are easy to be interfered by the intrinsic noise existed in depth features, making the precise RGBD saliency detection still a challenge. In this paper, we propose a novel Depth Guided Feature Selection network (DGFSnet) that takes depth information as prior and dynamically selects the complementary RGB information for RGBD salient object detection. Specifically, DGFSnet first includes a Depth Weight Generation module (DWG) to learn a set of layer-specific weights from multi-scale depth features. Guiding by these learned weights, DGFSnet further devises a Weight-guided Feature Aggregation module (WFA) to assign them to their corresponding RGB layers for dynamically enhancing and selecting saliency-related RGB features. With two modules, DGFSnet is able to effectively integrate the multi-modality complementaries and further highlight salient regions. Experimental results over seven popular RGBD salient object detection benchmarks demonstrate that DGFSnet fairly locates salient regions and effectively segments the complete object.
SGD-Based Cascade Scheme for Higher Degrees Wiener Polynomial Approximation of Large Biomedical Datasets

The modern development of the biomedical engineering area is accompanied by the availability of large volumes of data with a non-linear response surface. The effective analysis of such data requires the development of new, more productive machine learning methods. This paper proposes a cascade ensemble that combines the advantages of using a high-order Wiener polynomial and Stochastic Gradient Descent algorithm while eliminating their disadvantages to ensure a high accuracy of the approximation of such data with a satisfactory training time. The work presents flow charts of the learning algorithms and the application of the developed ensemble scheme, and all the steps are described in detail. The simulation was carried out based on a real-world dataset. Procedures for the proposed model tuning have been performed. The high accuracy of the approximation based on the developed ensemble scheme was established experimentally. The possibility of an implicit approximation by high orders of the Wiener polynomial with a slight increase in the number of its members is shown. It ensures a low training time for the proposed method during the analysis of large datasets, which provides the possibility of its practical use in the biomedical engineering area.
A Miniaturized Device Coupled with Digital Image Correlation for Mechanical Testing

Miniaturized mechanical testing based on small sample testing technology is a powerful technique to characterize the mechanical properties of different materials, and it is being used in different application fields. However, the small size of the specimens poses several challenges because the results are highly sensitive to measurement accuracy and the corresponding mechanical properties can change substantially due to the so-called specimen size effect. In this work, a novel testing device based on miniaturized specimens is presented. The equipment is designed to test materials in tensile and compressive loadings, but it is also capable of performing reverse-loading tests. Buckling of the specimen is an inherent phenomenon in compression loadings, especially for thin materials. Therefore, specimen geometry is properly studied and optimized to mitigate this effect. To evaluate the deformation of the specimen, the digital image correlation (DIC) technique is used to capture the full-field strain in the central gauge section of the sample. A sensitivity analysis of the DIC setting parameters was performed for this application. To evaluate the performance of the developed system, experimental results of monotonic tests and tests with reverse loadings (tension-compression) are presented, considering two high-strength steels (DP500 and DP780).
Towards a Sustainable Spatial Planning Approach for PV Site Selection in Portugal

In the present paper, we introduce a decision-support framework to (a) classify and prioritize the municipalities of a country based on their suitability to host PV energy projects and (b) pinpoint and evaluate suitable technically and economically viable, as well as environmentally and socially sustainable, sites for PV installation in the most suitable municipalities of a country. The proposed framework is applied in Portugal. It consists of two distinctive stages: ‘Energy Roadmap for PV Deployment’ and ‘PV Site-Selection Analysis and Assessment’. In the first stage, the most and least suitable municipalities for PV deployment in Portugal are identified by analyzing important environmental and technoeconomic PV siting criteria in GIS and applying the TOPSIS method. In the second stage, an integrated PV site-selection assessment is conducted in the Portuguese municipality with the highest suitability index for PV installations. This is achieved by combining a proper GIS siting model with various multicriteria decision-making methods, such as ENTROPY, AHP and TOPSIS. The results illustrate the suitability of numerous municipalities in the country for PV deployment and verify the excellent suitability of the Municipality of Mértola for PV installations. In conclusion, a PV energy roadmap for Portugal is formulated, contributing to national energy autonomy.