Reviewerly/exHarmony huggingface dataset:
Class Specific Dictionary Learning - Local Kernel Collaborative Representation Classification for Face Recognition

Illumination, expression and occlusion are inherent problems in face recognition. Thus, this paper proposes a new method based on kernel function and segmentation. A face image is firstly blocked, and each block is mapped to a higher dimension space by Gaussian kernel. Then, combining with class specific dictionary learning, the reconstruction error corresponding different class of each block based on local kernel collaborative representation is computed. Finally, according to the reciprocal of the reconstruction error, the process from local discrimination to the global classification is completed by the form of voting. Experimental results on three face databases (Extend Yale B, AR and CMU PIE) and the mixed face database (including AR, Extend Yale B and CMU PIE) show that the proposed method has high recognition accuracy of 99.8%, 98.8%, 93.9%, 87.1 %, respectively, compared with the recent CSDL-CRC method, increased by 10.4%, 7.5%, 4.6%, 8.2%.
Analyzing a Learner Corpus with a Concordancer

One of the benefits of using (learner) corpora and applying corpus-linguistic techniques is the possibility of analyzing large amounts of learner data automatically. While some learner corpora have built-in concordancers that can be used online with that specific corpus, e.g. the latest edition of the International Corpus of Learner English, others consist of the learner data alone. The most ‘basic’ format of learner texts is ‘raw’ text, where each learner file contains nothing but the learner’s actual output (which may be written or spoken transcriptions) in the file. Concordancers offer a selection of tools that may be used to extract data from learner corpora. The two major concordance programs that have been used in (learner) corpus research in the past two decades are AntConc and WordSmith. In a standard search on lexical items, the concordance program ‘ignores’ everything that is put between angle brackets, so that the tags themselves will not be counted as words.
Development and Evaluation of M-Learning Application for Learning the Foundation of Islamic Knowledge

Prophet Muhammad (blessings of God be upon him and his progeny and grant him peace) said “Seeking knowledge is an obligation upon every Muslim” [Ibn Majah] and “Whoever travels a path in search of knowledge, Allah will make easy for him a path to Paradise” [Shahih Muslim]. From this hadith, stated that one of the most important pray on Islam is about to learn about the religion itself. It is obligatory for a Muslim to study the science of religion to know Allah, His Prophet, and Islam along with the proofs. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[21]</sup> However, Islamic knowledge students who do not attend formal Islamic Institution class generally will need information of available live Islamic Studies around the cities (sometimes countries) and time adjustment to attend it. intense activities can be an obstacle to attend live Islamic lectures. On the other hand, current generation has been providing solutions for flexible learning. One of them is m-Learning. Based on this issue, this research discusses the development and evaluation of m-learning application for learning the foundation of Islamic knowledge design by integrating online learning theory with Islamic learning methods to help learn Islam easier. This research was conducted using a mixed method by combining qualitative and quantitative approaches. The design development in this study uses the User-Centred Design (UCD) method. Research data were obtained through interview with Islamic education experts, online questionnaires, and contextual interviews. After doing requirements gathering, we obtained 22 design insights of the Islamic foundation m-Learning application. Design evaluation provides the results of SUS score with the score of 76, and we have proposed 56 suggestions related to interaction design; three suggestions are related to learning systems.
Software-Behavior-Aware Prefetching Method in Embedded RISC Systems

An efficient instruction-based software behavior model is proposed to fully utilize the benefits of high-performance cache. This new model, which is established using process algebra, is hardware-aware (e.g., instruction set). To examine the benefits of our proposed software behavior model, an embedded RISC system is chosen in a case study, where our proposed software behavior model achieves an accuracy of more than 96% and helps to improve the cache hit rate by over 5%.
PEMBUATAN APLIKASI INFORMASI LAPANGAN OLAHRAGA BERBASIS ANDROID

Exercise is something everyone must do. Not apart from sports that can be done at home or sports that require a field. Currently there are many sports rental courts that are spread in every city. But there is no system that can connect userswith field owners. This app connects between field owners in particular basketball, badminton, soccer, volleyball and futsal with users. This app uses Phonegap that integrates with Framework7. Phonegap is a framework that allows web developers to turn it into a mobile app. Phonegap can be combined with framework7 which is an open source framework created for mobile application makers. This app also has a location feature with the help of googlemaps that serves to show the location of the sports field. With the creation of this application as well as with the results of the questionnaire can be concluded, help tenants field to find out the latest sports field information and order the field directly using thesmartphone.
Quantum Firmware: Ingredients and Applications

Quantum firmware captures the layer between physical gate operations and their logical equivalent at a higher level. Our experiments on trapped-ion qubits demonstrate its use to suppress errors, error correlations and increase gate robustness.
Multi-strategy Evolutionary Computation for Automated Jigsaw Puzzles

Solving jigsaw-puzzles has been of increasing importance in many real-world applications. The existing methods endure the problem of local or premature convergence, which perform inefficiently on some challenging images. For an efficient optimizer of jigsaw puzzles, this paper utilizes the powerfulness of the global optimization technique and develops a multi-strategy evolution algorithm. The algorithm constantly generates jigsaw puzzle solutions by mimicking the process of natural evolution, while adopting a new objective function to evaluate the solutions. An elite-based crossover operator is designed to exploit the historically good patterns for generating competitive solutions. Then, a new mutation operator consisting of four perturbation strategies is developed to handle different puzzle situations. Experimental results verify the promising performance of the proposed algorithm that it outperforms the state-of-the-art methods on various image datasets.
Poster: Prototype of Configurable Redfish Query Proxy Module

Redfish is a next-generation API standard for the management of data center infrastructures. This rich API can flexibly obtain data using a query string from the client side. However, this feature is optional and not fully supported by many services. We implemented a prototype Redfish query processing module on Nginx, a well-known open source web server. The Redfish query processing module can run with a proxy module and work with any server-side or client-side applications. Additionally, our prototype implementation can be configured to properly utilize queries, which are supported on a backend server, and improve performance. Our implementation was evaluated on an OpenBMC server and a mockup server and showed potential for performance improvement.
High Efficient LSTM-based Network for Human Interaction Understanding

Human interaction recognition has been widely studied in recent years and is still a very meaningful topic due to broad real-world applications. Recent research based on dominating human joints provides a new method for human posture recognition. The visual computer judges human's action behavior according to the position relationship of key joints. Traditional methods based on deep neural network focus more on the fusion of semantic information from different convolutional layers, but ignores the features from different time nodes. In addition, large scale network architecture also increases computational load and therefore limits its applications. In this paper, we propose a lightweight architecture based on LSTM (long-short time memory) module, in order to integrate the temporal information from series of skeleton frames.
Informational Analysis of Go, Part 2: Zuozi and Huanqitou

This paper explores the second nature of the Go game (Go rules), based on its long history that played out in 9 × 9 board in BC 2356, which later developed into the 19 × 19 with several variants of its rules. An open-source implementation of AlphaZero, namely LeelaZero, as the artificial intelligence (AI) player, is adopted for data collection through self-play experiments. A particular emphasis on two ancient rules of Go, namely as the Huanqitao and Zaozi were conducted. The results are then analyzed and assessed via game refinement theory and motions in mind concepts. An informational analysis indicates that the Go game has been searching for fair and exciting game playing mechanisms that had evolved from the educational needs to competitive settings. Also, the change in the ancient rules to the komi rule mainly emphasized the dominance of such rule change in term of competitive pairing and fairness, while preserving the challenge and excitement of the 19 × 19 Go board game.
String instrument acoustic transfer processing

A real-time method of string instrument acoustic transfer which includes damping is proposed. Acoustic transfer of string instruments is relevant when trying to make a non-resonant instrument, such as an electric guitar, sound more similar to an acoustic guitar. Unlike previous acoustic transfer methods which only perform equalization, this method includes damping changes by using a time-varying filter which adds frequency-dependent exponential damping. Efficient digital filters are fit to bridge admittance measurements of an acoustic guitar and used to create equalization filters as well as damping correction filters. The damping correction filters work in real-time as they are triggered by onset and pitch detection of the signal measured through an under saddle pickup to determine the intensity of the damping.
Generating High‐quality Superpixels in Textured Images

Abstract Superpixel segmentation is important for promoting various image processing tasks. However, existing methods still have difficulties in generating high‐quality superpixels in textured images, because they cannot separate textures from structures well. Though texture filtering can be adopted for smoothing textures before superpixel segmentation, the filtering would also smooth the object boundaries, and thus weaken the quality of generated superpixels. In this paper, we propose to use the adaptive scale box smoothing instead of the texture filtering to obtain more high‐quality texture and boundary information. Based on this, we design a novel distance metric to measure the distance between different pixels, which considers boundary, color and Euclidean distance simultaneously. As a result, our method can achieve high‐quality superpixel segmentation in textured images without texture filtering. The experimental results demonstrate the superiority of our method over existing methods, even the learning‐based methods. Benefited from using boundaries to guide superpixel segmentation, our method can also suppress noise to generate high‐quality superpixels in non‐textured images.
Modeling of embolism and malformation phenomena in the brain

Abstract In a previously published mathematical model of blood circulation in the cerebral cortex, it was assumed that the circulatory system of the brain consists of a distribution network of relatively large arteries and a collector network of veins similar in structure [1]. The connection between the networks is carried out through a network of numerous small capillaries, in which arterial blood as a result of metabolic processes with the cellular tissue of the brain turns into venous. A system of equations of viscous fluid motion in a heterogeneous medium consisting of three interpenetrating, but not intersecting, continuums is written out. Intersections are pathology for a healthy human organism. Multiple calculations show that pathologies localized even in small areas of the brain significantly affect the nature of the distribution of blood pressure in the arterial and venous channels. The finite element method is used in the calculations. The network of elements is refined in the subdomains of pathologies in the form of malformation or embolism with a decrease in the conductive properties of a heterogeneous environment. The numerical solution of the planar problem was carried out with parameters close to the real conditions of blood circulation in the human brain.
A software library model for the internet of things

Heterogeneity, resource constraints, and scalability are obstacles to making the IoT approachable for non-specialist programmers. To be successful and appealing in these environments, library systems must be as space-efficient and flexible as possible without fundamentally changing the process of creating and maintaining shared software. Existing library frameworks emphasize some but not all of these attributes and rely on a monolithic model that preserves mutable state. We propose a finer-grained approach to software libraries that allows developers to use multiple components of a library concurrently at disjoint versions. This model defines a library as a set of independent functions with immutable global state to avoid maintaining distributed mutable state in dataflow environments. Library code is stored in a data dependency graph, which is traversed to produce a minimal copy of the library containing only what is necessary for a program. This design addresses the constraints of distributed systems and allows developers to quickly customize dependencies for their unique deployment situations.
Concept of a Regulatory Compliant Blockchain Based Instant Payment System. Limitations and Compromises

New technologies of communication along with global expansion of smart devices are connecting customers and services at high speed. But at the level of financial technologies this movement is comparable slow. Schema remains centralized by structure. Instant payments in traditional banking were applied just in the last five years. This paper presents a concept of a system based on peer-to-peer networking mechanisms like blockchain and smart contracts, and integration of regulatory services in order to be legal and compliant.
Research on Named Entity Recognition for Science and Technology Terms in Chinese Based on Dependent Entity Word Vector

Most of the researches on Chinese named entity recognition (NER) focus on the general field, and few on NER in the field of science and technology. On one hand, technical terms in the field of science and technology appear in general texts less frequently, with most of which being compound words, and the performance of word segmentation processing on texts in the field of science and technology is poor. On the other hand, texts in the field of science and technology are more accurate and standardized than those in general fields. By analyzing these characteristics of texts in the field of science and technology, this paper attempts to train word vectors by constructing terminology dictionaries and introducing dependency analysis. Referring to the latest NER research results in the current Chinese general field, i.e. the method of merging character vectors with word vectors, we will perform NER on texts in the field of science and technology. Through experiments, it is proved that the proposed method is more effective comparing to existing works. In addition, the effect of introducing attention mechanism on NER results is also studied.
Developing a Stress Prediction Tool for Arabic Speech Recognition Tasks

Developing natural language processing applications for Arabic must consider the different linguistic characteristics found in speech and translate those characteristics to script in order to reduce computational complexity and therefore reduce the word error rate (WER).Suprasegmental features are fundamental properties of speech that can enhance the performance of many natural speech processing applications.The present study considered stress as a prosodic feature comprising the prominence of syllables in speech by developing a tool that generated phonetic transcriptions and predicted the stress position.The generated transcription was used to create the phonetic dictionary necessary for developing an automatic speech recognition (ASR) system.This tool had to be accurate, linguistically motivated, and applicationally useful; therefore, the effectiveness of the generated stress-marked phonetic dictionary was tested by comparing the performance of a standard fixed dictionary-based system with that of one using the automatically generated dictionary.The research reported a 5.6% reduction in WER when using a dictionary with stress markers attached to each phone in the stressed syllable and a 3.5% reduction in WER when using a dictionary with stress markers assigned only to stressed vowels.These results encourage future studies to employ prosodic features of speech when developing different speech processing applications.( ‫الناجم.‬ ‫صالح‬ ‫و‬ ‫الشرهان‬ ‫إيمان‬ 2021 .)‫الكالم‬ ‫على‬ ّ ‫اآللي‬ ‫ف‬ ّ ‫التعر‬ ‫ألنظمة‬ ‫الكلمات‬ ‫في‬ ‫بر‬ ّ ‫الن‬ ‫مواضع‬ ‫لتمييز‬ ‫أداة‬ ‫تطوير‬ ‫العربي‬ .‫واإلدارية،‬ ‫اإلنسانية‬ ‫العلوم‬ ‫فيصل:‬ ‫امللك‬ ‫لجامعة‬ ‫العلمية‬ ‫املجلة‬ ( ‫املجلد‬ 22 ( ‫العدد‬ ،) 2 ) Eiman Alsharhan and Salah Alnajem.(2021).Developing a Stress Prediction Tool for Arabic Speech Recognition Tasks.The Scientific Journal of King Faisal University: Humanities and Mangement Sciences, Volume (22), Issue (2) ( ‫الناجم
Visualization Analysis of Top Papers in the Subject Category of Computer Science, Information Systems Based on ESI

Abstract All of 1,647 top papers for Computer Science, Information Systems from 2009 to 2019, which included 1,642 highly cited papers and 32 hot papers. All papers were written in English, from 69 countries/territories and listed in 115 journals. Top 5 core journals were IEEE Communications Surveys and Tutorials, Information Sciences, IEEE Access, IEEE Internet of Things Journal, MIS Quarterly . The research was separated 9 clusters.
Bootstrapping Weakly Supervised Segmentation-free Word Spotting through HMM-based Alignment

Recent work in word spotting in handwritten documents has yielded impressive results, largely using supervised learning systems, which are dependent on manually annotated data, making deployment to new collections a significant effort. In this paper, we propose an approach that utilises transcripts without bounding box annotations to train segmentation-free query-by-string word spotting models, given a partially trained model. This is done through a training-free alignment procedure based on hidden Markov models. This procedure creates a tentative mapping between word region proposals and the transcriptions to automatically create additional weakly annotated training data, without choosing any single alignment possibility as the correct one. When only using between 1% and 10% of the fully annotated training sets for partial convergence, we automatically annotate the remaining training data and achieve successful training using it. In terms of mean average precision, our final trained model then comes within a few percent of the performance of a model trained with the full training set on all our datasets. We believe that this will be a significant advance towards a more general use of word spotting, since digital transcription data will already exist for parts of many collections of interest.
Fertilizer Value Updation to Farmers

Fertilizer demand updating has a good practical significance to direct agricultural yield and for notifying the growth in market price of fertilizer to the farmer. In this the data analysis technology is used to update the rate change via notification. When the fertilizer value changes market gets the notifications for different types of land states. Fertilizers quality is pick out by ranking process. By this process the rate of the low and high qualities fertilizer is informed. The usage of number of classifiers provides a way to make good decision on prophecy due to the usage of multiple classifiers. Additionally, a ranking process is adapted to take conclusion to select classifiers outcome. This forecast price of the fertilizers in future.
Residual Convolutional Neural Networks Model For Image Denoising On Real Time

This Nowadays, deep learning is one of the most used technique for image denoising until it outperforms so far, all other denoising methods. However, this method requires a lot of computing power, so it's quite difficult to achieve real-time deep learning denoisers especially on edge devices like embedded systems and mobile phones. In this paper, we proposed a deep learning denoiser that works in real-time on a Raspberry Pi 3B+, 1GB of ram, to increase in real-time the incoming noisy video from a Raspberry Pi Camera frame per frame, where each frame is an RGB image if size 256x256. We used a residual denoiser that extracts the noise and enhance the quality of obtained images. In fact, the proposed architecture has a very small size that can fit easily on any edge device. Furthermore, many optimization techniques were applied on the denoiser so it can run faster on a very limited computing resource. Each denoised frame where uploaded directly to a Microsoft storage service.
A Techno-Economic Assessment of Microservices

The microservices design paradigm enables applications, usually based on containers, exploiting the flexibility of cloud computing and bringing unique scalability, fault-tolerance and resource-allocation benefits. A number of orchestration facilities, including Kubernetes, target the efficient deployment and operation of containers and are mainly focusing on the maintenance of server resource allocation under predefined thresholds, i.e., through scaling up or down containers to mitigate dynamic changes in the workload. In this work, we highlight the technical capabilities and cost-saving impact of microservices in contrast to traditional monolithic applications, based on a techno-economic analysis. We also investigate the service performance vs resource allocation trade-off, uncovering interesting dynamics when elasticity is driven from service quality metrics. This approach allows the Service Providers (SPs) to balance their profit margins with the customer satisfaction, i.e., reducing the infrastructure cost while keeping the service performance at an acceptable level.