Reviewerly/exHarmony huggingface dataset:
The Case for Validating ADDIE Model as a Digital Forensic Model for Peer-to-Peer Network Investigation

Abstract Rapid technological advancement can substantially impact the processes of digital forensic investigation and present a myriad of challenges to the investigator. With these challenges, it is necessary to have a standard digital forensic framework as the foundation of any digital investigation. State-of-the-art digital forensic models assume that it is safe to move from one investigation stage to the next. It guides the investigators with the required steps and procedures. This brings a great stride to validate a non-specific framework to be used in most digital investigation procedures. This paper considers a new technique for detecting active peers that participate in a peer-to-peer (P2P) network. As part of our study, we crawled the µTorrent P2P client over ten days in different instances while logging all participating peers. We then employed digital forensic techniques to analyse the popular users and generate evidence within them with high accuracy. We evaluated our approach against the standard Analysis, Design, Development, Implementation, and Evaluation (ADDIE) model for the digital investigation to achieve the credible digital evidence presented in this paper. Finally, we presented a validation case for the ADDIE model using the United States Daubert Test and the United Kingdom’s Forensic Science Regulator Guidance – 218 (FSR-G-218) and Forensic Science Regulator Guidance – 201 (FSR-G-201) to formulate it as a standard digital forensic model.
Dual-Server Boolean Data Retrieval for Highly-Scalable Secure File Sharing Services

Searchable encryption (SE) is a promising strategy for cloud-based file retrieval services, via structuring correspondences between files and keywords. Public key encryption with keyword search (PEKS) has been generally employed in file-sharing services, as compared to searchable symmetric encryption (SSE). However, PEKS is inherently vulnerable to keyword guessing attacks (KGA) launched by a malicious server. To resist such attacks, classic solutions are dual-server PEKS (DS-PEKS) [TIFS’2015] and server-aided PEKS (SA-PEKS) [TIFS’2016]. However, the query model in these two solutions only support single keyword search pattern, which inevitably limits their wide deployments in practice due to efficiency concern. In this work, we present DSB-SE, a new cloud-based file sharing & retrieval system that supports boolean queries while retaining KGA-resistance. Compared to DS-PEKS and SA-PEKS, the cost of documents searching in DSB-SE is 25, 000 times (resp. 6, 600 times) faster when <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\#\text {keyword}=10$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$s\text {-term}=1$ </tex-math></inline-formula> , where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$s$ </tex-math></inline-formula> -term is the least frequent keyword in the query pattern. Technically, the performance gain derives from revisiting traditional boolean SSE by: (i) introducing a pairing-free DDH-based transformation key modular that allows a data reader’s query pattern to be treated as a data writer’s; (ii) employing the dual-server methodology to support boolean query with efficient validity checks. In particular, the client-to-cloud communication cost for retrieving index of a single document is bounded to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$10^{-2}s$ </tex-math></inline-formula> , and the cost of sending a token ranges from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$8\times 10^{-2}s\sim 13\times 10^{-2}s$ </tex-math></inline-formula> . Nevertheless, DSB-SE is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.5\times 10^{-2}s$ </tex-math></inline-formula> slightly slower than DS-PEKS (but <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.35\times 10^{-2}s$ </tex-math></inline-formula> faster than SA-PEKS) for key generation cost. Overall, the experiments show that the DSB-SE is practical and sufficient for real cloud applications, which is conducted over Enron dataset under a real-world cloud platform.
Evaluating Websites Audit Tools: A Case Study of the Amazon Website

Modern businesses and organizations cannot operate without an official website. A well-designed website should be informative, visually appealing, and easily navigable to encourage return visits. The website gives many users the first impression and insight into the company's nature, organizational structure and culture, product or service offerings, and past successes. Usability testing, therefore, enables firms to evaluate how easy their websites are to understand and use. It evaluates user-friendliness, user satisfaction, efficiency, and error detection. A website's usability is also affected by its suitability for people of all ages and genders. Usability, or how easy it is to use, is essential to a website's success, especially as websites become more interactive, complex, and full of features. Various testing tools are available offline/online and legacy/general-purpose. This paper focuses on three online tools - Qualidator, SEOptimer, and Website Grader – used to evaluate the usability or ease of use of the Amazon website.
A Data Mining Approach to Determine Prospective Debtor of Unsecured Credit (Case Study: Bank XYZ in Indonesia)

The motivation of this research is to provide alternative way for determining prospective debtor of unsecured credit using a data mining approach. In credit provision, especially for unsecured credit, bank needs to analysis the need of prospective debtor, since the high level of credit in Bank and the large amount of prospective customer applying for loan make the credit analyst find it difficult to determine the creditworthiness. By using appropriate credit assessment model, bank may evaluate whether prospective debtor is worthy to be approved or not. So far, the credit assessment model utilized in banking mostly based on human expertise as the credit analyst. Not often the evaluation of determining the prospective debtor turn out incorrect result and lead to bad credit. A solution to solve the existing problem is to use data mining approach that create a classification model C4.5 algorithm. This model supports the decision making based on classification of customer’s creditworthiness. The classification model generated 9 rules in classifying the data. Utilizing the k-fold cross validation, the accuracy level reach 94.78% and it is considered as excellent findings. We can understand that the C4.5 classification model can be used to assist in determining prospective debtor.
Integration of Face-to-Face Instruction and CALL in Computer-Assisted Cooperative Learning

This study aimed to investigate how face-to-face cooperative learning and CALL are integrated when teaching through face-to-face Computer Assisted Cooperative Learning (CACL). Although CACL is not a new term and the integration of technology with face-to-face instruction has been discussed in previous literature, there is no clear evidence for a model that can be used as a guide in the process of integrating the two methods, namely face-to-face cooperative learning and CALL, in teaching through CACL. This study employed the qualitative research design, and semi-structured interviews and observation were carried out to investigate the process of integration in face-to-face CACL during teaching reading skills. The study used Neumeier (2005) model as a theoretical framework; however, the findings showed that this model needs to be adapted to suit teaching through face-to-face CACL. The main adaptation is in the first two parameters because face-to-face CACL is considered one mode. It is the leading mode, mainly because face-to-face cooperative learning and CALL are used simultaneously during teaching, making it difficult to count the time of each mode separately. The study concluded with a model for integration in face-to-face CACL, which can be adapted for teaching through synchronous or asynchronous CACL. Also, the paper provided implications for teachers and educators interested in the design and implementation of CACL.
Ontology-Assisted and Autonomic Testing Verified Model for Automated and Reliable Web Development

This paper proposed a web engineering architecture based on three main phenomena called Structured Development, high reliability, and multi-level flexibility. The ontology is formed and mapped to generate a structured and user-assisted design. The user requirements are captured in this model based on an ontology map. Web engineering is applied to capture the various elements of web development including the functional features. In the development phase, the captured inputs and features are processed for designing the static and active web pages. The functional interconnectivity, validations, and security are integrated for all pages. The second important aspect of high reliability is achieved by managing the control level validations, BlackBox testing, cookies control, and session tracking. Multi-level flexibility is achieved at each level of this model including requirement gathering, ontology formation, webpage development, control validations, and testing. The XML files are generated at different levels to manage and control user responses. The proposed model is verified on seven case studies and the analysis is done in terms of design time and the number of cycles required for finalizing the website. The qualitative evaluation of the proposed model is done against 11 existing web designing tools. The project development is accomplished from 15 to 70 s based on the project size. The number of change cycles of different projects lies between 4 and 12. The analytical study identified that the proposed model is effective and reliable enough against template support, requirement gathering, customization, control validation, testing, and security.
Digital Literacy in Ukraine and the United States

The authors present evidence-based digital literacy practices and resources for teaching and learning English as an additional language in two different educational contexts where these practices have been applied successfully with adolescent learners.
Natural-Language Predicates as Relations of the Relational Model of Data

Abstract In this paper I review the Neo-Davidsonian semantics of prepositional phrases and secondary predication. I argue that certain types of examples pose challenge to this semantics. I present an alternative to the Neo-Davidsonian analysis which successfully deals with the problematic examples. The core idea lies in representing theta-roles not as functions from events to their participants, but rather as argument-labels encoding the role of each argument in a given verb. As a result, natural-language predicates can now be treated in the manner in which relations are treated in the relational model of data, that is, as naming sets of tuples in which every object is given together with its role named by a corresponding attribute (a theta-role). Such a representation allows the employment of relational algebra operators to calculate the extensions of complex predicates (predicates built out of atomic predicates and/or atomic predicates and prepositions). I lay out the foundations of a relational FOL appropriate for the representation of natural-language predicates and present solutions to the problematic examples. From a practical perspective, the expressions of a relational FOL can be translated to a relational algebra or SQL, which makes it possible to operate with these three languages on the same relational model of data. From a philosophical perspective, a relational FOL permits a return to ‘property-based’ semantics, one in which properties named by predicates are those of individuals, and not of events.
Comparative Analysis of Generative Adversarial Network-Based Single-Image Super-Resolution Approaches

In the past decade, a convolutional neural network that can work on image became most popular since there were many powerful GPUs and datasets available to work. Convolutional neural network advanced the performance in various image processing tasks. In single-image-based super-resolution, convolutional neural network predicated methods have obtained remarkable performance in terms of error quantification (i.e., PSNR and SSIM) than earlier traditional machine learning predicated methods. Even though achieving better error measurements, the super-resolution results look blurry in appearance because of the classical $$L_1$$ or $$L_2$$ loss functions that have been used in the training process. Recently, generative adversarial network has been used to obtain a super-resolve image with better perceptual quality as compared to a convolutional neural network. In this manuscript, the technique and performance of those generative adversarial networks for single-image super-resolution approaches are compared for upscaling factor $$\times \, 4$$ .
BP-DEBUG: A Fault Debugging and Resolution Tool for Business Processes

Cloud computing and Internet-ware software paradigm have enabled rapid development of distributed business process (BP) applications. Several tools are available to facilitate automated/ semi-automated development and deployment of such distributed BPs by orchestrating relevant service components in a plug-and-play fashion. However, the BPs developed using such tools are not guaranteed to be fault-free. In this demonstration, we present a tool called BP-DEBUG for debugging and automated repair of faulty BPs. BP-DEBUG implements our Collaborative Fault Resolution (CFR) approach that utilizes the knowledge of existing BPs with a similar set of web services fault detection and resolution in a given user BP. Essentially, CFR attempts to determine any semantic and structural differences between a faulty BP and related BPs and computes a minimum set of transformations which can be used to repair the faulty BP. Demo url: https://youtu.be/mf49oSekLOA.
An ensemble clustering method based on consistency cluster consensus approach and <scp>MapReduce</scp> model

Abstract Ensemble clustering is an efficient unsupervised learning technique that has attracted a lot of attention. The purpose of this technique is to aggregate the results of several basic clustering algorithms in order to create a better clustering. This is not only possible, but has been developed with many techniques in recent years. However, there are still challenges such as similarity measure, agreement function and how to implement clustering algorithms. To address these challenges, this article proposes an ensemble clustering algorithm based on the consistency cluster consensus approach and the MapReduce model. In addition, the proposed algorithm is equipped with a new membership similarity measure. The consistency cluster consensus is defined as a new agreement function for the consensus of the results of the basic clustering methods. Besides, the proposed similarity measure consists of two factors: one is cluster similarity and the another is membership similarity. The process of the proposed ensemble clustering method is summarized in four steps. In the first step, partitions are generated by applying a number of individual clustering algorithms to the data. The second step is to convert the primary clusters from the partitions to a binary representation. The third step is to identify the candidate primary clusters for the consensus, where a subset with highest similarity between the clusters is emphasized. In the fourth step, the consistency cluster consensus is performed through a new agreement function. In addition, we use the MapReduce model to implement basic clustering algorithms to reduce computational complexity. Our method is evaluated on several real‐world datasets and their performance was compared with other clustering ensemble methods, such as TWCE and RDPC‐DSS. Experiments show that our method is more efficient and accurate and is 7% to 15% better than the best method compared.
Calibrating an unfocused plenoptic camera based on parameters grouping and the light field structure point

Accurately calibrating an unfocused plenoptic camera is essential to its applications. Rapid progress has been made in this area in the past decades. In this paper, detailed analysis is first performed toward the state-of-the-art projection model. Based on the analysis, parameters in the projection model are divided into two groups. Then, based on the parameter analysis, a new, to the best of our knowledge, form of the projection model, together with a new image feature light field structure point (LF-structure-point), is proposed. The LF-structure-point provides a completely non-redundant representation of the signal structure of the recorded light field raw data and induces a virtual space, “light field structure space,” which is related to the real physical space by a 3D-to-3D projective transformation. The extracting algorithm of the LF-structure-point is also presented. Finally, based on the 3D-to-3D projective transformation and parameter grouping, a simple two-step calibration method using the LF-structure-point as the input data is then proposed and achieves satisfactory experimental results.
EVALUASI DAN PEMODELAN SISTEM DRANASE MENGGUNAKAN PROGRAM EPA SWMM 5.1 DI JALAN AH. NASUTION KOTA TASIKMALAYA

Tasikmalaya is a city that has problems with drainage channels. The location of area from this city that is often to a flood point is AH Nasution Street, Mangkubumi. The largest height of the flood is 50 cm with longest duration of flooding after the flood is started . The purpose of this research is to find out condition of the drainage channel of AH Nasution Street, Mangkubumi, length of drainage is 800 m. The study research using EPA SWMM 5.1 and to find the effective drainage to deal with frequent floods. The results of this research can be concluded that the condition of the channel on AH. Nasution street needs to be repaired, the drainage of this channels is overflow. After the simulation running there are 12 drainage channels that experience overflow and the the largest discharge is at the point of the outfalls 2 (SP.2) with 0.850 m3 / sec. On the channel of drainage AH Nasution Street was calculate to redesigned 2 types of drainage. The typical dimensions with calculation results formula is b = 1 m and H = 1.07 m and the outfalls drainage channel is b = 1 m and H = 1.33 m.&#x0D;
A systematic review of trends and gaps in the production of scientific knowledge on the sociopolitical impacts of emojis in computer-mediated communication

This systematic literature review analyses trends in original research on emoji use in computer-mediated communications (CMC) published between 2011 to 2021.In total, 823 articles were identified that met the search criteria.The mixedmethod approach included qualitative coding of articles and frequency analysis by year, impact quartile, research topic and multidisciplinarity, as well as a cluster analysis to examine trends in sociopolitical research.The results show that Computer Science, Communications and Social Sciences disciplines accounted for largest proportion of original research on emojis and CMC in the time period analysed and that the degree of scientific impact increased significantly across the time series.In recent years, sociopolitical research has had higher than average growth and can be clustered into various groups based on two broad objects of study: "culture-identity" and "social exclusion".The study also identified significant knowledge gaps, particularly in relation to emoji standardization and its sociopolitical implications.Overall, multidisciplinary approaches are epistemologically constrained, Spanish-language production is low, and there is an almost complete absence of context appropriate methodologies.The study concludes that there is a need to for more sociopolitical research on emoji use in CMC and multidisciplinary approaches, a shift away from the hegemony of Anglocentrism, and greater questioning of the structural influences of standardization process on questions of cultural, identity and social exclusion.
Revisiting Point Sampling in LiDAR-based 3D Object Detection

LiDAR-based 3D object detection has been rapidly developed in many fields, such as robotics and autonomous driving. Mainstream LiDAR-based 3D object detection includes various sampling methods that attempt to reduce computational costs by reaching sufficient accuracy via fewer points. To stimulate related researches, this paper reviews sampling methods in LiDAR-based 3D object detection. It covers both traditional sampling methods and deep learning sampling methods.
Research on User Experience Evaluation Method Based on Eye Movement Technology

With the development of automotive intelligence, networking and automation, the interaction between users and vehicles has changed. Automobile product development based on user experience is the only way for future OEMs (Original Equipment Manufacturers). Research on vehicle scenarios is the breakthrough point for the optimization and upgrading of intelligent cockpit products. User emotions under different scenarios are the core indicators to express users' satisfaction with vehicle functions. However, at present, the data of user emotions generally need to be obtained in the form of user interviews, which is subjective and not convincing. This paper aims to propose a quantitative method to identify users' emotions. Specifically, using eye tracker to record users' eye movement signals in different scenarios, and identifying users' emotions in different scenarios through data analysis, so as to judge the user's satisfaction.
Learning-based fast image enhancement for driving scenes

Visual sensors such as cameras can obtain rich image and video information, which is one of the most effective and lowest cost perception sensors for autonomous driving. However, when the environmental conditions change rapidly, the quality of acquired images decreases, resulting in various deep learning algorithms embedded in the camera may be difficult to complete object detection and other tasks. Although some existing image enhancement methods can mitigate the effects of environmental factors, these methods often involve complex calculations that ignore the importance of processing speed in the autonomous driving field. To solve these problems, double-branch image enhancement network is proposed in this paper, which includes low resolution branch and full resolution branch. With low resolution branch, local features of image can be obtained at a small computational cost. With full resolution branch, the details which are lost due to down-sampling are obtained. The enhanced image is obtained by fusing the feature graphs of two branches. The test result on BDD100K shows that our method has higher image processing speed on the premise of ensuring image quality.