Reviewerly/exHarmony huggingface dataset:
Efficient NoSQL Graph Database for Storage and Access of Health Data

Mondal, Safikureshi Mukherjee, NandiniGraph data models are mostly used for storage and access of large, unstructured real-life data generated from real-life applications. The graph data model can be implemented by NoSQL graph databases. In this paper, eight well-known NoSQL graph databases are compared to study their properties. After rigorous review of different research works which are focused on different parametric measures of storage and access, only the best three NoSQL graph databases, Neo4j, OrientDB and ArangoDB, are chosen. The efficiency of these three graph databases is compared based on searching or traversing and querying operations on the databases for storage and access. A particular type of graph, i.e., disease-symptom graph database has been used for this purpose.
Deliberating Data-Driven Societies Through Live Action Role Play

People’s data practices and their supporting personal informatics are imbued with a wide range of concepts of value that are central to their data imaginaries. They inform how individuals adopt particular products and services. This elusiveness of value creates a challenge for designers to gauge users’ value preferences, which are personal, contextual and abstract. We propose deliberation as a process to operationalise design for elicitation, reflection and value formation, using Live Action Role Play (LARP). The paper demonstrates how our deliberative LARP precipitates the otherwise ethereal data values to unfold in a social setting. We argue that engaging people in reflecting on and playing out their own value formation offers designers more powerful insights to address complex challenges of data-driven societies. We report on our findings that agency and negotiability are key to transcend the politics of preferences in data practices and thus enhance acceptability.
Research on Expression Recognition Algorithm Based on Attention Mechanism

Abstract Facial expressions carry the emotional state of human beings and are one of the most powerful signals for humans to express themselves. Therefore, the research of facial expression recognition has very important significance and broad application prospects. However, in complex scenes, the blurring of facial images, the interference of redundant information, and the similarity of expressions will cause unsatisfactory expression recognition. In response to these shortcomings, this article adds a convolutional attention module to the basic Resnet network structure, infers the attention map in turn along two independent dimensions (channel and space), performs adaptive feature optimization, captures expression feature information, and reduces human faces. The interference of redundant information; and the introduction of island loss ISlandloss classification to optimize the distance between classes, so that the network can learn more discriminative features, and improve the discrimination of expressions. Compared with the existing mainstream facial expression classification algorithms, this method has certain advantages in objective evaluation indicators. The experimental results on FER2013 show that the accuracy of this algorithm is 73.85%, which is higher than 71.23% of the Resnet network.
Scheduling Optimization of Agricultural Vehicles Operation Path in Field of Rectangular Boundary

In this paper, the optimal path sequence strategies about direction parallel path for the agricultural vehicles are introduced. In the light of direction parallel paths under the rectangular boundary, considering various possible turning strategies on the ground, the agronomic requirements of different operation modes and the turning characteristics of vehicles, the computer technology is used to seek the optimal scheduling strategy of line operation tasks, so as to reduce the turning distance and time of the vehicles as much as possible, and reduce the total consumption of the operation. Then, the scheduling problem is transformed into traveling salesman problem (TSP) and solved by greedy algorithm. The simulation results show that the greedy algorithm is efficient and stable. Compared with the conventional scheduling strategy, the turning efficiency of the vehicle is significantly improved.
Rates of asymptotically statistical equivalents of measurable functions

The primary goal of this article is to present the concepts of asymptotically equivalent function and asymptotic regular function transformations. Moreover, by using these definitions, we examine the bivariate function transformation of asymptotically statistical equivalent measurable functions.
Skull stripping and tumor detection using 3D U-Net

Skull stripping from magnetic resonance imaging (MRI) is an important field of research. Its applications and requirements in the field of medical image processing have led to a need for more research in this area, and advancements in the field have resulted in the generation of many different techniques. The model demonstrated here is based on the 3D U-Net architecture, consisting of a layered approach to segment the brain, for extraction of lower-grade gliomas from the stripped tissues. This method is capable of performing automatic skull stripping and extracting the lower-grade gliomas from an MRI dataset of the human brain. When trained appropriately this model has shown effective results via the Dice coefficient and IoU metrics, and has outperformed other existing methods. This model can be useful in estimating tumor stages because of its well-defined architecture.
한국어 학습자의 어순 오류 양상 연구 - 숙달도와 제1 언어를 중심으로 -

This study aims to examine the interlanguage pattern of Korean word order by inductively analyzing errors of word order according to proficiency and L1 of Korean learners and confirming the distribution of word order error types. For this, the study identified the acquisition patterns of Korean word order based on 567,000 word Learners Corpus of 3,600 samples constructed by the National Institute of Korean Language. The analysis results are as follows: First, 157 word order errors were found in 149 Korean learners. Among them, Korean learners have the highest number of adverb word order errors which appeared 81 times (51.6%), followed by noun word order errors which appeared 50 times (31.8%). Discourse word order errors appeared 13 times (8.3%), sentence word order errors appeared 11 times (7.0%) and other word order errors appeared 2 times (1.3%) in this order. Second, the error frequency of adverb word order was the highest in common among beginner, intermediate, and advanced, and the error frequency of noun word order was the second highest. Though learners of beginner and intermediate level and learners of advanced and intermediated level each have differences in the error frequency ranking of sentence word order and discourse word order, it was not statistically significant. Third, the acquisition distribution of word order according to learner’s L1 was found to be statistically significant in the adverb order and the noun word order. In the case of adverb word order, Vietnamese learners had fewer errors in adverb word order than other learners, while Japanese learners had more adverb word order errors. Next, in the case of noun word order, Vietnamese learners had more noun word order errors than other learners of Korean, but English learners of Korean found fewer noun word order errors.
Integrating a Teaching Concept for the Use of Virtual Reality in University Teaching

As many of the common mistakes made by engineering students are based on weak spatial imagination, the use of virtual reality could help to enhance those skills in undergraduate teaching. As part of a university lecture in product development that covers methods of product development and technical design, the use of virtual reality was integrated into an engineering exercise. Supplementing the usual technical tools like CAD, half of the 14 student groups were allowed to use a dedicated VR-application to review their machine designs in virtual reality. While the objective performance of those groups was not better in comparison to the “non-VR” groups, most of the student groups were able to identify design issues or mistakes through the use of VR, which they couldn't find using only CAD. According to interviews, 69.7% of the VR-users reported that they were only able to experience and realize “the true spatial dimensions” through the use of VR.
Problems of Organizing the Process of Distance Learning at University in Modern Conditions

The article deals with technical and didactic-pedagogical problems that arose during the period of mass transition to distance learning technologies in the context of a pandemic. Models of distance learning are analyzed. The advantages and disadvantages of online services, means for building an educational space and organizing a communication system between its participants are noted. The main difficulties arising in the process of using distance learning technologies are shown.
Blockchanging Politics

There has been a fundamental change in the genesis of political-economic trust, with the arrival of a decentralized but structured way to reach consensuses and automatically implementing decisions through self-executable contracts. Blockchain technology (BT) is a distributed, consensus-based, and secure way for individuals to make enforceable censorship-resistant quantifiable agreements. Every vote is a transaction, and BT is paving the way for decentralizing politics, defending privacy, and streamlining voting procedures. It has the potential to provide much more granular governance that hopefully will preserve freedom and defend democracy. However, especially in an embarrassing post-COVID-19 world, BT's centralization can, instead, pave the way for citizens' control, turning cryptographic protocols into an authoritarian digital corset tightened by some to menace the privacy and freedom of many.
Multi-Wave Mixing Process can be Used to Generate Single Photon Source for Quantum Information Processing

In quantum cryptography, single photon sources are to be used, which must represent higher order antibunching. We have examined nonclassical effects, i.e., higher order antibunching and higher order sub-Poissonian photon statistics (HOSPS) in pump mode in eight wave mixing nonlinear optical process using short-time interaction techniques. We have found that nonclassical effects directly depend on the number of photons before their interaction with nonlinear medium, and higher the number of photons exist in the system before the interaction, higher will be the nonclassicality of the system.
A Parameter Selection Method Based on Reinforcement Learning: The QoS-Privacy Trade-off for Adversarial Training Framework

Deep learning is widely used in different fields. When service providers use deep learning models to provide image recognition services to clients, they need to collect data from them and also protect their privacy. An adversarial deep learning model is proposed to help service providers protect user's privacy when guaranteeing the quality of the service. However, how to decide on a threshold number to make an appropriate trade-off between Quality of Service (QoS) and privacy is worth researching. In this paper, we use deep reinforcement learning to automate the threshold searching process. In deep learning, the threshold number is also called hyperparameter. We first use a random hyperparameter to train an adversarial deep learning model. According to the performance of it, we train the hyperparameter optimizer to learn what kind of hyperparameter adjustment is good. Finally, we run the hyperparameter optimizer to get the best threshold. The best trade-off can be found automatically through our approach.
CyberSecurity Privacy Risks

Privacy is one of the greatest cybersecurity risks due to the extensive reliance on the internet. With the increase in work, social interactions, financial and medical transactions accomplished almost totally online during the pandemic, businesses’ and users’ critical data is at increased risk of exposure or theft. This stolen data can be used to steal corporate secrets, commit espionage or fraud or to steal a person’s identity and financials. Cyber risks cannot be eliminated, they must be managed, there must be an identification of what vulnerabilities will allow threats access to privacy information so that mitigations can be put in place to reduce the probability of the loss of information and privacy.
CGA-ELM:A network security situation prediction model

The combination of intelligent optimization algorithm and neural network is an efficient method for network security situation prediction, but the simple combination method can not dynamically adjust the parameters according to the existing state, which reduces the prediction accuracy. In order to get the prediction results more accurately and quickly, combined with the randomness and stability of cloud model, the global search ability and implicit parallelism of genetic algorithm and the fast learning ability of extreme learning machine, an adaptive Cloud Improved Genetic Algorithm Optimization Extreme Learning Machine (CGA-ELM) prediction model is proposed. Firstly, the crossover rate and mutation rate of genetic algorithm are improved by using the normal distribution characteristics of normal cloud. Secondly, the initial weight and deviation of extreme learning machine are optimized by the improved genetic algorithm. The simulation results show that, compared with the traditional GA-ELM, the prediction accuracy of CGA-ELM is improved by 4.9%, and the convergence speed is accelerated by 64.28%. CGA-ELM has better prediction effect and robustness. (Abstract)
Scania Lepidas V8 Sound V12.0

Greece Lepidas variant of the V8 is here. Compatible 1.35, 1.36 I’ve remade it almost from scratch. it works for : The SCS R, Streamline, RJL (R and ...
Thematic website: Development methods, structure and essential services

The author discussed the technology of building the library’s website devoted to local heritage based on practical experience. The essential features and relevant presentation formats (longread, storytelling) are described. The detailed set of structural elements and key services to be introduced to the webmaster as a requirements specification are formulated. Specific Internetmarketing instruments promote the web content. Based on the proposed technology, libraries will be able to draw up relevant requirements specifications for any type of cultural institution (e. g. library, museum, etc.), to include every service or element of external and internal system design essential for the institution’s mission. The requirements to website coding and content (information relevance and verification, multimediality, new forms of text presentation) are included. The tabulated services and structural elements of thematic websites will enable to formulate requirements to webmaster intelligently.
Sarcasm Detection on Social Networks using Machine Learning Algorithms: A Systematic Review

The researchers have been working rigorously on sarcasm detection on social media over the last decade. Identifying sarcasm in text is very difficult for humans so researchers planned automatic sarcasm detection with computational models. Nowadays people widely use social media to express their views, opinions and comments on others post. The trend of present day is that every person directly or indirectly uses sarcastic words in their day-to-day conversation. Sarcasm is giving a negative meaning with positive words in the conversation. In other words, it is the use of irony to mock or convey contempt or opposite of what you really mean to say. People criticize with positive words. This paper has reviewed different approaches in detection of sarcasm on online social media. An analysis has been performed on each approach as well as noticed drawbacks in it and represented the performance of each approach in terms of precision, recall and accuracy.
Implementation Fuzzy Logic in System Design for Predicting the Production of Songkets in West Sumatera

Abstract Fuzzy logic is widely applied in various fields, such as industry, communications, etc. Fuzzy logic was developed to solve an obscure problem. The problem that arises in the production at UMKM Songket Silungkang and Songket Pandai Sikek at this time is that there is no system used as a reference in predict the amount of production that will come. Where this method can utilize demand and supply data in the past which is then processed with fuzzy stages so as to produce production figures. The government has prioritized the development of the Silungkang songket handicraft business, which is a regional specialty, in order to enter the export market. In the early stages, the priority of the regional government was to increase the production of craftsmen by facilitating coaching for micro, small and medium enterprises (MSMEs), especially those engaged in songket crafts, to continue to be developed by increasing quality and creativity. By applying the Fuzzy Inference System method in predicting the production of Songket Silungkang Kota Sawahlunto and Songket Pandai Sikek, Agam City can help several parties such as the government, micro, small and medium enterprises in making efforts to handle and make good decisions towards increasing the production of Songket UMKM in each region. and can provide a comparison of the predicted results of production for the coming period so that it can produce the optimal number of songket based on market demand.
Method for Eye-Controlled Interaction for Digital Interface Function Icons

This paper proposes an eye control interaction method for digital interface function icons. The method is divided into three steps: 1. Preprocessing of function icons and precoding of eye movement signals. Pairing function icons through the rectangular coordinate system of the two-dimensional image of the eye with the digital code of the eye movement signal, the eye movement signal array of the function icon is generated. 2. The collection of the eye movement signal. The subject performs eye movement according to the digital code of the function icon to complete the start and stop of signal recording. 3. Eye movement signal Output, calculation, recognition and confirmation. Calculate the quadrant position of the pupil center coordinate point, compile the function icon trigger command, and confirm by blinking. This method directly uses the subject’s eye movement signal on the function icon to realize the icon-to-digital interface eye machine Interactive.
Sentiment Analysis: Relationship Between Customer Sentiment and Online Customer Ratings for Price Comparison Engines. An Empirical Study

Sentiment analysis, in other words, opinion mining, is aiming at analyzing people’s sentiments, opinions, emotions, attitudes, etc. Customer sentiment refers to the emotions expressed by customers through their text reviews. These sentiments can be positive, negative or neutral. This study will explore customer sentiments and express them in terms of customer sentiment polarity. In the current days, Greece is facing one of the worst economic crisis in its history, so price comparison engine usage is more than needed, especially for the most competitive and pricy goods, such as athletic footwear and technology ones. In such circumstances, companies have to find more efficient ways to get the absolutely necessary information from their targeted audience by overcoming the problems that a researcher can face with the usage of an ordinary questionnaire, because the customer has written his own point of view; using his own words without being guided by a questionnaire or an interview. This study tries to identify this crucial information and help the contemporary e-shop to improve its ecommerce services and gain more income with less advertising, cpc campaigns, etc. Hence, in this case we gathered from «Skroutz» one of the most renowned PCE in Greece and extracted the sentiment from these core industries target groups, based on the user’s/buyer’s comments and their rating. We used WEKA for classifying the text and extracting knowledge.
Fast and Robust Image Matching Based on Depth-Wise Convolution Features and Unique Nearest Neighbour Similarity

Finding a template patch in a target image, including object detection and tracking, is a core component in computer vision applications. This paper introduces a fast and robust image matching based on depth-wise convolution features and unique nearest neighbour similarity. Experiments with its results show that our algorithm can effectively solve the problem of image mapping with complex deformation, and the matching performance is better than the existing optimal feature matching algorithms by improving the matching accuracy about 10.68%.
RVMA: Remote Virtual Memory Access

Remote Direct Memory Access (RDMA) capabilities have been provided by high-end networks for many years, but the network environments surrounding RDMA are evolving. RDMA performance has historically relied on using strict ordering guarantees to determine when data transfers complete, but modern adaptively-routed networks no longer provide those guarantees. RDMA also exposes low-level details about memory buffers: either all clients are required to coordinate access using a single shared buffer, or exclusive resources must be allocatable per-client for an unbounded amount of time. This makes RDMA unattractive for use in many-to-one communication models such as those found in public internet client-server situations. Remote Virtual Memory Access (RVMA) is a novel approach to data transfer which adapts and builds upon RDMA to provide better usability, resource management, and fault tolerance. RVMA provides a lightweight completion notification mechanism which addresses RDMA performance penalties imposed by adaptively-routed networks, enabling high-performance data transfer regardless of message ordering. RVMA also provides receiver-side resource management, abstracting away previously-exposed details from the sender-side and removing the RDMA requirement for exclusive/coordinated resources. RVMA requires only small hardware modifications from current designs, provides performance comparable or superior to traditional RDMA networks, and offers many new features. In this paper, we describe RVMA's receiver-managed resource approach and how it enables a variety of new data-transfer approaches on high-end networks. In particular, we demonstrate how an RVMA NIC could implement the first hardware-based fault tolerant RDMA-like solution. We present the design and validation of an RVMA simulation model in a popular simulation suite and use it to evaluate the advantages of RVMA at large scale. In addition to support for adaptive routing and easy programmability, RVMA can outperform RDMA on a 3D sweep application by 4.4X.