Reviewerly/exHarmony huggingface dataset:
Light-field spatial super-resolution via enhanced spatial-angular separable convolutional network

Light-field images captured by light-field cameras usually suffer from low spatial resolution due to the inherent limited sensor resolution. Light-field spatial super-resolution thus becomes increasingly desirable for subsequent applications. Although continuous progress has been achieved, the existing methods still failed to thoroughly explore the coherence among light-field views. To address this issue, we propose an efficient neural network for light-field spatial super-resolution, in which the spatial and angular information can be fully exploited by repeatedly alternating spatial and angular domain. Specifically, an enhanced spatial-angular separable convolution block is proposed to efficiently exploit the correlation information between sub-aperture images. Moreover, a multi-scale feature extraction block is introduced to extract feature representations at different scales and capture rich texture and semantic information. Experimental results on both synthetic and real-world light-field datasets demonstrate that the proposed method outperforms other state-of-the-art methods with higher peak signal-to-noise ratio (PSNR)/structural similarity (SSIM) values and fewer parameters.
Dynamics of multiphoton scattering in a two-level mixer

Mixing of coherent pulses with different carrier frequencies by a two-level atom results in a finite number of sidebands in the emitted spectrum. The authors studied the dynamics of mixing side components in response to characteristics of the coherent pulses such as their number, amplitudes, durations, and overlapping.
Analysis of Computer Network Security Storage System Based on Cloud Computing Environment

A fundamental component of cloud computers from a business perspective is that users are allowed to use any desire and pay with a product that desire. Its cloud services were accessible anytime and anywhere consumers needed them. As a result, consumers are free to purchase whatever IT services they want, and they don't have to worry about how easy things can be managed. The remote server is used in a new information storage computing architecture that is considered an Internet generation. Ensuring security, material at resource providers' sites is a challenge that must be addressed in cloud technology. Thus, rather than reliance on a single provider for knowledge storing, this research implies developing construction for protection of knowledge stockpiling with a variation of operations, in which knowledge is scrambled and divided into numerous cipher frames and distributed across a large number of provider places. This support was applied to provide greater security, scalability, or reliability that was suggested according to the new structure. This paper, presented an encoded model for the cloud environment to improve security. The proposed model comprises the parity metadata for the database management provision to the provider. In the developed encoder chunks parity is not stored within the single resources with the provision of the available information chunks. The constructed security architecture in the RAID layer increases the dependability of the data with the deployment of the RAID 10 deployment. The developed RAID-based encoder chunks exhibit improved efficiency for the higher uptime at a minimal cost.
Peripersonal space (PPS)

The experience of our embodied self is not limited to the physical constraints of our body, but it extends into the space where the body interacts with the environment, i.e. peripersonal space (PPS). PPS is represented via the integration of multisensory-motor signals related to the body and to external stimuli in space. Normally, the boundaries of PPS coincide with the limits where our physical body can potentially interact with external objects. However, PPS boundaries shape as a function of experiences, such as acting upon further locations of space via tool-use or technology, or social interactions with other people. Multisensory bodily illusions (such as the rubber hand, the enfacement or the full body illusion) introducing spatio-temporal conflicts in the processing of multisensory bodily cues to study and manipulate bodily experience and self-consciousness, are probably induced via the PPS system and in turn affect PPS representation. This chapter will review insights from neurophysiology, psychophysics, neuropsychology, neuroimaging and neural network models to propose a general framework of the neural mechanisms of PPS, its plasticity and its role in bodily awareness.
DEVELOPING REGISTRATION MODULE IN HEALTH INFORMATION SYSTEMS USING THE DESIGN THINKING METHOD

In applications, usability is an essential factor that shows the user experience whether the application is easy to use and fits its function or not. For this reason, this study aims to determine the usability of the registration module on the Khanza Hospital Management Information System (SIMRS) used by RSUD Banyuasin employees and to find out the difficulties faced, needs, and desires of RSUD Banyuasin employees as users of SIMRS Khanza. This research is helpful for the agency as a subject of application evaluation and helps discover what opportunities can be applied to overcome user difficulties. The approach used is Design Thinking. Meanwhile, the usability measurement uses the System Usability Scale (SUS) involving five respondents. The initial results of measuring the usability of SIMRS Khanza obtained were 26 with a poor rating. In addition, the interview results show that users have difficulty learning the application, the features and fields are too many, and there is a redundancy of patient data. Based on these findings, the researcher recommends a solution design with a High Fidelity Prototype (Hi-fi) with a final value of SUS 87. The implementation of this research recommendation can help the performance of RSUD Banyuasin with an application design that is easy to understand and supports the creation of accurate and consistent data.
Signals and incentives in blockchain applications

Since Bitcoin's invention in 2009, permissionless blockchain technology has gone through several waves of interest and development. While applications related to payments have advanced at breakneck speed, progress in financial and nonmonetary applications have largely failed to live up to initial excitement. This chapter considers the incentives facing network participants in light of the fundamental problem of signal verification. Doing so can account for both the successes and the failures: first, why are payments a particularly suitable problem to be solved by blockchain technology? And second, what additional problems do financial and nonmonetary applications pose?
“Are working memory models WEIRD? Testing models of working memory in a non- WEIRD sample": Correction to Cockcroft (2022).

Reports an error in "Are working memory models WEIRD? Testing models of working memory in a non-WEIRD sample" by Kate Cockcroft (Neuropsychology, 2022[Jul], Vol 36[5], 456-467). In the article, in Table 2, for Verbal STM and Verbal WM, the means and standard deviations should have been set in bold but were not, and the median, 25th p, and 75th p values were set in bold but should not have been. In this table, bold values were those most appropriate for the data distribution. In Table 3, the correlations between the LR subtest and the OOO, MRX, and SR subtests were incorrectly listed as .13, .05, and .18*, respectively. They should have been .26***, .30****, and .27***, respectively. In Table 4, the df for the four-factor model was listed as 54 but should have been 48. The online version of this article has been corrected. (The following abstract of the original article appeared in record 2022-50702-001.) Objective: There are many competing working memory models, generally formulated from young adult, typically student, samples from Western, educated, industrialized, rich, and developed (WEIRD) contexts. Whether such models are adequate conceptualizations of working memory (WM) in young adults from non-WEIRD contexts is unclear.This study tested five possible models of the structure of working memory in young South African adults (n = 162; Mage = 20.41, SDage = 1.82, 38% female) who are multilingual, from predominantly low socioeconomic, largely rural, contexts.Confirmatory factor analyses showed that a four-component structure composed of domain-specific (verbal, visuospatial) storage and processing provided the best fit for the data.Neuropsychological tests and rehabilitation approaches are based on leading models of working memory, and so their cross-cultural appropriateness is important. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Native Type Theory

Native type systems are those in which type constructors are derived from term constructors, as well as the constructors of predicate logic and intuitionistic type theory. We present a method to construct native type systems for a broad class of languages, lambda-theories with equality, by embedding such a theory into the internal language of its topos of presheaves. Native types provide total specification of the structure of terms; and by internalizing transition systems, native type systems serve to reason about structure and behavior simultaneously. The construction is functorial, thereby providing a shared framework of higher-order reasoning for many languages, including programming languages.
Grounding Game Semantics in Categorical Algebra

I present a formal connection between algebraic effects and game semantics, two important lines of work in programming language semantics with applications in compositional software verification.Specifically, the algebraic signature enumerating the possible side-effects of a computation can be read as a game, and strategies for this game constitute the free algebra for the signature in a category of complete partial orders (cpos).Hence, strategies provide a convenient model of computations with uninterpreted side-effects.In particular, the operational flavor of game semantics carries over to the algebraic context, in the form of the coincidence between the initial algebras and the terminal coalgebras of cpo endofunctors.Conversely, the algebraic point of view sheds new light on the strategy constructions underlying game semantics.Strategy models can be reformulated as ideal completions of partial strategy trees (free dcpos on the term algebra).Extending the framework to multi-sorted signatures would make this construction available for a large class of games.
3D CNN-Residual Neural Network Based Multimodal Medical Image Classification

Multimodal medical imaging has become incredibly common in the area of biomedical imaging. Medical image classification has been used to extract useful data from multimodality medical image data. Magnetic resonance imaging (MRI) and Computed tomography (CT) are some of the imaging methods. Different imaging technologies provide different imaging information for the same part. Traditional ways of illness classification are effective, but in today's environment, 3D images are used to identify diseases. In comparison to 1D and 2D images, 3D images have a very clear vision. The proposed method uses 3D Residual Convolutional Neural Network (CNN ResNet) for the 3D image classification. Various methods are available for classifying the disease, like cluster, KNN, and ANN. Traditional techniques are not trained to classify 3D images, so an advanced approach is introduced in the proposed method to predict the 3D images. Initially, the multimodal 2D medical image data is taken. This 2D input image is turned into 3D image data because 3D images give more information than the 2D image data. Then the 3D CT and MRI images are fused and using the Guided filtering, and the combined image is filtered for the further process. The fused image is then augmented. Finally, this fused image is fed to 3DCNN ResNet for classification purposes. The 3DCNN ResNet classifies the image data and produces the output as five different stages of the disease. The proposed method achieves 98% of accuracy. Thus the designed modal has predicted the stage of the disease in an effective manner.
Improved Graph-Based Arabic Hotel Review Summarization Using Polarity Classification

The increasing number of online product and service reviews has created a substantial information resource for individuals and businesses. Automatic review summarization helps overcome information overload. Research in automatic text summarization shows remarkable advancement. However, research on Arabic text summarization has not been sufficiently conducted. This study proposes an extractive Arabic review summarization approach that incorporates the reviews’ polarity and sentiment aspects and employs a graph-based ranking algorithm, TextRank. We demonstrate the advantages of the proposed methods through a set of experiments using hotel reviews from Booking.com. Reviews were grouped based on their polarity, and then TextRank was applied to produce the summary. Results were evaluated using two primary measures, BLEU and ROUGE. Further, two Arabic native speakers’ summaries were used for evaluation purposes. The results showed that this approach improved the summarization scores in most experiments, reaching an F1 score of 0.6294. Contributions of this work include applying a graph-based approach to a new domain, Arabic hotel reviews, adding sentiment dimension to summarization, analyzing the algorithms of the two primary summarization metrics showing the working of these measures and how they could be used to give accurate results, and finally, providing four human summaries for two hotels which could be utilized for another research.
Fuzzy Keyword Searchable Encryption Scheme Based on Blockchain

Searchable encryption is a keyword-based ciphertext retrieval scheme, which can selectively retrieve encrypted documents on encrypted cloud data. Most existing searchable encryption schemes focus only on exact keyword searches and cannot return data of interest in fuzzy search. In addition, during the searchable encryption, the cloud server may return invalid results to the data user to save computing costs or for other reasons. At the same time, the user may refuse to pay the service fee after receiving the correct result. To solve the above problems, this paper proposes a fuzzy keyword searchable encryption scheme based on blockchain, which uses edit distance to generate fuzzy keyword sets and generates a secure index with verification tags for each fuzzy keyword set to verify the authenticity of the returned results. The penalty mechanism is introduced through the blockchain to realize the fairness of service payment between users and cloud servers. Security analysis shows that the scheme achieves non-adaptive semantic security. Performance analysis and functional comparison show that the scheme is effective and can meet the requirements of searching applications in the cloud environment.
Research on Frames and Network Agenda Setting Regarding Creating Shared Value in News Articles and Blogs: Focusing on Topic Modeling and Semantic Network Analysis

This study attempted to examine through what content structure keywords related to CSV have been delivered to consumers for 10 years from 2011 to September 2021. As a result of topic modeling related to CSV, similar concept confusion frames, CSV development necessity frames, and discussion necessity frames were common in news articles and blogs. However, in the case of news articles, there was a difference in cooperation and support for small and medium-sized enterprises, solving problems for the socially under-privileged, a win-win frame for local communities, and a strategy frame for securing competitiveness for blogs. On the other hand, as a result of examining the four major centrality indicators of news articles and blogs related to CSV through semantic network analysis, it was possible to guess the similarity of semantic network between news articles and blogs. Finally, as a result of QAP correlation analysis between news articles and blogs, it was found that there was a statistically significant positive correlation between the news article agenda and the blog agenda. In addition, as a result of MR-QAP regression analysis, it was confirmed that the agenda shown in the news article also affects the agenda shown in the blogs.
Identify the Color and Shape of Eggplant Using Back Propagation Method

Currently, artificial neural networks are being developed as a tool that can help human tasks. The main purpose of this study is to identify the structure of an eggplant, in order to distinguish the type of eggplant. This study empirically tested the shape and color of several eggplants using the back propagation neural network learning method. The data is obtained from an image that will be entered into the program. The data used in the identification process are two photos containing two types of eggplant, the first eggplant is green and round and the next eggplant is purple and oval. The results of the identification process using this back propagation from the tests that have been carried out previously, the highest calculation results obtained with the best results using a learning rate of 0.7 and epoch iterations of 500 and producing an accuracy of 73.33%.
GENEVE@TEIN: A Sophisticated Tunneling Technique for Communication between OpenStack-based Multiple Clouds at TEIN

Multiple clouds need to share resources as a service for various reasons, such as overcoming single points of failure or reducing latency. Tunneling serves as the mechanism for multiple clouds to share their resources. Generic Network Virtualization Encapsulation (GENEVE) tunnel has the advantage of a flexible header over other L2 tunnels such as GRE or VXLAN. This enables the GENEVE tunnel to transfer a larger payload in case of smaller header size. The service must be acquired from an optimal Cloud and Instance. An optimal Cloud has the best network performance forecasted. An optimal Instance is forecasted not to be overutilized during resource sharing. For this purpose, monitoring tools must be deployed for metering Instances and network links. Data obtained from monitoring tools can be utilized by Machine Learning for Instances and network performance forecasting. Initially, two clouds are deployed at Trans-Eurasia Internetworking (TEIN), one in Malaysia and the other in South Korea. Monitoring tools are deployed for network and Instance performance monitoring. GENEVE tunnel is then deployed on OVS bridges, after which Instances of a cloud can access Instances of the other Cloud for resource sharing.
DeepFake Videos Detection via Spatiotemporal Inconsistency Learning and Interactive Fusion

While the rapid expansion of DeepFake generation techniques has arisen a serious impact on human society, the detection of DeepFake videos is challenging because of their highly plausible contents on each frame, which are not visually apparent. To address that, this paper proposes a two-stream method to capture the spatial-temporal inconsistency cues, and then interactively fuse them to detect DeepFake videos. Since the traces of spatial inconsistency in DeepFake video frames mainly appear in their structural information, which reflects by the phase component in the frequency domain, the proposed frame-level stream learns the spatial inconsistency from the phase-based reconstructed frames to avoid fitting the content information. Aiming at the problem that the temporal inconsistency in DeepFake videos might be ignored, the temporality-level stream is proposed to extract the temporal correlation feature by the temporal difference networks and stacked ConvGRU module on consecutive multiple frames. When interacted with channel attention in the intermediate layer of two streams, and adaptively fused with the discriminative features of two streams from a global-local perspective, our proposed method performs better than the state-of-the-art detection methods.
Ontology Engineering Based on Spreadsheet Data Transformation

Spreadsheet data are an important source of information and can be used to develop domain ontologies. In this paper, we describe a novel approach for ontology engineering based on spreadsheet data transformation. A feature of the proposed approach is the automatic synthesis of a complete ontology in the OWL format both at the level of an ontological schema and at the level of instances (assertion). Our approach consists of five main stages: converting source spreadsheet to JSON representation and data cleaning; defining column types and a subject column; generating OWL ontology fragments; aggregating received OWL ontology fragments. The approach is implemented in the form a command-line tool. We also present an experimental evaluation of the implemented tool for generating ontology by transforming test spreadsheet data. The dataset of test spreadsheets called Tough Tables (2T) is used for our experiment. We obtained high scores of precision and recall for this dataset, which shows the applicability of our solution for automatic generation of domain ontologies.
Developing a Tool to Analyze the Use of Social Media to Identify New Types of Digital Financial Assets

This article examines the created method of identifying new digital financial assets in the Telegram social network. The importance and relevance of this topic is due to both a noticeable increase in the number of users of social networks and an increase in public interest in digital financial assets and digital currencies, especially cryptocurrencies. The object of the study is the popular Telegram messenger’s channels about the cryptocurrency. The main part of the article describes the developed algorithm consisting of four main steps, such as Data Collection, Pre-processing the received data, Analyzing the data obtained, Results interpretation. Python is chosen as the software for the implementation of the algorithm. In addition, the authors demonstrate the carrying out of the created algorithm in practice using the example Bitcoin and Ethereum. In the result, the developed tools allow an analyst to pay attention to a specific cryptocurrency in time, which is often mentioned in the Telegram messenger in order to prevent possible operations of money laundering and terrorist financing.
Optimal Load Balancing in Heterogeneous Server Systems

A fundamental problem in large-scale data centers is to reduce the average response time of jobs. The Join-the-Shortest-Queue (JSQ) load balancing scheme is known to minimise the average response time of jobs for homogeneous systems consisting of identical servers. However, heterogeneous systems consisting of servers with different speeds, JSQ performs poorly. Furthermore, JSQ suffers from high communication overhead as it requires knowledge of the queue length of all servers while assigning incoming jobs to one of the destination servers. Therefore, the Join-the-Idle-Queue (JIQ) scheme was introduced which not only reduces the communication overhead but also minimises the average response time of jobs under homogeneous systems. Despite these advantages, JIQ is still known to be inefficient in minimising the average response time of jobs under heterogeneous systems. In this paper, we consider a speed-aware version of JIQ for heterogeneous systems and show that it achieves delay optimality in the fluid limit. One of the technical challenges to establishing this optimality is to show the tightness of the sequence of steady-state distributions indexed by system size. We show this tightness result by evaluating the drift of appropriate Lyapunov functions. This approach to proving tightness is different from the usual coupling approach used for homogeneous systems. Another important challenge in proving the optimality result is to establish the fluid limit which is done using the time-scale separation technique. Finally, using the monotonicity of the fluid process we have shown that the fluid limit has a unique and globally attractive fixed point.