Reviewerly/exHarmony huggingface dataset:
Evaluation and Analysis of Traditional Customary Law Based on the Perspective of Big Data

Today’s rule of law construction in China is walking between the conflict and coordination of factors such as reality and ideals, tradition and modernity, local and foreign, and local knowledge and universal principles, all while continuing to strengthen the unification of the legal system and advance the modernization of the rule of law. Traditional customary law, which is the most representative local resource culture, is unquestionably one of the most important themes in the formation of the rule of law. It has far-reaching significance for the development of ethnic jurisprudence, the reunderstanding of traditional culture, and the construction of ethnic unity and harmonious society. Based on this background, this paper uses big data technology to collect relevant experimental data and proposes a traditional customary law value assessment based on BPNN. The completed work is as follows: (1) this paper clarifies the concept of customary law and the difference between it and related concepts and introduces the domestic relevant research on traditional customary law and the interactive relationship between customary law and national law in dynamic legal practice and puts forward the status and influence of customary law in contemporary legal practice. (2) The related technologies of neural network are introduced, and a traditional customary value evaluation system that can be used for experiments is constructed. (3) Experiment with the designed data set to see if the BP model is feasible. The experimental results suggest that the model proposed in this study has a low error rate and performs well while evaluating traditional common law values.
Evaluation of the Mendeley Utilization Training for Academic Writing through Usability Approach

Mendeley is one of the best reference managers that has been used by many academic writers around the world. In Indonesia, Mendeley became the first place application for references management. Several training programs had been implemented in order to improve the capability of users in using Mendeley for academic writing. However, the evaluation is still limited to the normative approaches. This community engagement used an online method to convey the Mendeley course to participants from various academic background. Mendeley onlinine training was only given in one session of training. This method was chosen due to the COVID-19 pandemic situation. Evaluation of training used the USE questionnaire to evaluate the usability score of this application. Our findings showed, however, the online training could give a positive impact but there was an ease of learning variable that was not equal to the others perceived by participants. Future training programs need to address this challenge to achieve the highest possible outcome.
Game in Philosophy of Jean Baudrillard: Limits of Understanding

This paper analyzes the essence of the phenomenon of the game in the philosophy of J. Baudrillard. The conceptual core of the study consists of the following aspects: game as a sign of reality, game as a game, in reality, game with reality through the media, and game as a postmodern carnival. The study led to the following conclusions: the game is an integral part of the leading concepts and themes in the philosophy of Baudrillard, such as implosion, apotropia, singularity, hyperreality, simulacra, etc.; the interpretation of the essence of the game in the philosophy of the French philosopher changes depending on the plane of the research; the game escapes definition, fitting into the various contexts of the topics raised, being a phenomenon with an ambiguous status; the game has conceptual boundaries within various themes, but acts as a transgressive phenomenon.
Design of intelligent IoT for smart healthcare monitoring system using optimal neural network (IONN) model

One of the most important aspects of human life is health. Every society is paying more attention to and implementing technology in the areas of health and healthcare. In the healthcare sector, Artificial Intelligence (AI) is frequently employed to deliver quick and reliable results. Early disease predictions assist doctors in making timely decisions to save patients' lives. The Internet of Things (IoT) is assisting AI applications in healthcare by acting as a catalyst. The data of the patients is gathered by IoT sensor, and the data is analysed using machine learning algorithms. The IONN model, an innovative and intelligent healthcare monitoring system based on modern technologies such as the IoT, optimization techniques, and machine learning, is introduced in this study to detect various diseases early and precisely. For people who reside in rural places, this system gives a low-cost solution. The accuracy of the proposed IONN model has enhanced by 4% to 15% when compared to existing approaches. Furthermore, as compared to ANNs that use the BPN algorithm, the IONN model reduces the overall training time by 15% to 52%.
Cognitive Analysis of Pragmatic Functions of Discourse Markers in Spoken English in the Context of Computational Intelligence

To improve the level of oral English teaching, improve students' oral communicative competence (OCA), and promote successful communication with native English speakers, this study studies the pragmatic function of dialogue markers and constructs the cognitive evaluation system of artificial intelligence (AI) by comparing the two cognitive evaluation systems of human subjectivity and knowledge base; in this study, the credibility of human subjective evaluation and the coupling degree of machine objective evaluation are discussed. The key coefficient R2 is obtained by the linear regression method, and the correlation is obtained by the Spearman correlation algorithm. The cognitive effects of knowledge base and AI are verified; the results show that the cognitive analysis system of pragmatic function of discourse markers in oral English based on AI has good teaching value. In the context of AI computing, we can put forward targeted learning methods and learning methods for students according to the amount and accuracy of markers in oral English that students have mastered, so that students can quickly improve the learning quality and the learning effect of oral English markers, which is more conducive to improving students' oral English level and realizing students' effective communication.
Privacy Protection Algorithm Model of We-Media Network from the Perspective of Big Data

In recent years, with the in-depth research on the privacy protection methods of We-media network, various types of big data analysis technologies gradually protect the network privacy data, but there are still problems of low intelligence and poor protection in the existing research. Based on this, this paper first uses big data technology and artificial intelligence deep learning algorithm to complete the construction of different types of self-media control databases. Then, it analyzes the common privacy data types of We-media network, constructs an optimization protection model based on secondary identification and verification strategy, and forms a data query system. Finally, simulation experiments are conducted to verify whether the constructed network privacy protection model can realize the intelligent protection of network privacy algorithms from different dimensions. In the process of privacy protection of experimental data at different stages, the internal correlation differences of different types of protection algorithm strategies are obvious in the multidimensional analysis of specific databases. For different types of factor data in different types of We-media networks, the protection rate of the We-media network privacy protection model designed in this study has reached more than 95%. The research results show that the self-media network privacy protection model based on big data and artificial intelligence deep learning technology can realize protection from the aspects of gateway verification and data encryption and has high accuracy and reliability.
Application of Internet of Things Architecture in Intelligent Classroom Teaching Analysis in Colleges and Universities

Smart classroom is based on information technology, and its operation, upgrading, and development need technical support. At present, most of the smart classroom teaching adopts traditional architecture, highlighting the characteristics of disciplines, insufficient data processing, and narrow application fields. Based on this, this paper studies and analyzes the application of Internet of things architecture in college smart classroom teaching analysis. Based on the simple analysis of the development of intelligent algorithms and smart classrooms of the Internet of things, this paper constructs a smart classroom system based on the architecture of the Internet of things, transfers it to information-based teaching, introduces an intelligent service platform, and uses intelligent algorithms to mine and cluster data. The core algorithm is improved based on K-means algorithm and grid algorithm to improve the pertinence and effectiveness of the algorithm. Through the simulation results, the improved clustering analysis algorithm can shorten the running time and improve the clustering effect. At the same time, the application test results of the algorithm also show the superiority of the clustering results, which can provide more strategies for later teaching.
Identification and extraction of multiword expressions from Hindi &amp; Urdu language in natural language processing

A class of linguistic forms called "multiword expressions" (MWEs) includes expressions that span traditional word boundaries and are found in a wide variety of languages.We must rethink linguistic processing in order to accommodate MWEs, which relies on a clear distinction between words and phrases.For natural language processing (NLP) applications, MWE handling is critical, as it raises a number of issues.This paper's primary goal is to shed light on how NLP applications like machine translation (MT) deal with MWEs.MWE processing and downstream applications such as MWE-aware parsing or MT are discussed in greater detail here.MWE handling and MWE-aware NLP applications have an insufficiency of proposed approaches.In fact, this research is motivated by the emergence of new approaches in the absence of a set of defined rules [1].During the identification and extraction of MWE, we faced various challenges as follows [2].
Increased Leverage of Transprecision Computing for Machine Vision Applications at the Edge

Abstract The practical deployment of machine vision presents particular challenges for resource constrained edge devices. With a clear need to execute multiple tasks with variable workloads, there is a need for a robust approach that can dynamically adapt at runtime and which can maintain the maximum quality of service (QoS) within the available resource constraints. A lightweight approach that monitors the runtime workload constraints and leverages accuracy-throughput trade-offs on a graphics processing unit (GPU), is presented. It includes optimisation techniques that identify the configurations for each task in terms of optimal accuracy, energy and memory and management of the transparent switching between configurations. Using a neural network architecture search that statically generates a range of implementations that target a resource-precision trade-off, we explore the detection of the optimal parameters for the required QoS under specific memory and energy constraints. For an accuracy loss of 1%, we demonstrate that a $$1.6\times$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>1.6</mml:mn> <mml:mo>×</mml:mo> </mml:mrow> </mml:math> higher frame processing rate can be achieved on GPU with further improvements possible at further relaxed accuracy. In order to further improve the switching between configurations, we enhance the proposed mechanism by employing central processing units (CPUs) for offloading some of the executed frames, which helps to improve the frame rate by further 0.9%.
Predicting the Length of Stay in Neurosurgery with RuGPT-3 Language Model

In this study, we update the evaluation of the Russian GPT3 model presented in our previous paper in predicting the length of stay (LOS) in neurosurgery. We aimed to assess the performance the Russian GPT-3 (ruGPT-3) language model in LOS prediction using narrative medical records in neurosurgery compared to doctors’ and patients’ expectations. Doctors appeared to have the most realistic LOS expectations (MAE = 2.54), while the model’s predictions (MAE = 3.53) were closest to the patients’ (MAE = 3.47) but inferior to them (p = 0.011). A detailed analysis showed a solid quality of ruGPT-3 performance based on narrative clinical texts. Considering our previous findings obtained with recurrent neural networks and FastText vector representation, we estimate the new result as important but probably improveable.
Human Following Robot using Kinect in Embedded Platform

This paper presents an embedded robotic system using Kinect sensor technology that is utilized to detect an individual target and track its movement in the surrounding. The developed system integrates both the features of computer vision and embedded robotics simultaneously. In this paper, a skeleton-based tracking algorithm is used because tracking plays an important role in localization and mapping. This system uses a gesture-based recognition that relies on Microsoft Kinect XBox 360 instead of a standard touch-based control system that can be utilized as a stand-alone system or subsystem to integrate into a larger system. The Kinect sensor captures the 3-dimensional information of the surroundings and recognizes the human body by retrieving the depth information that does not require wearing any kind of intrusive sensors. Firstly, this robotic system follows an individual by detection of torso point that is required for steering and maintaining a fixed safe distance for localization and mapping and provides a robust and reliable system. The proposed system can be utilized in a wide variety of applications such as work assistants, luggage carrying carts, etc.
Application of Accelerometer to Monitor Students’ Exercise Load in 50 m Round Trip

With the further advancement of microelectronics innovation and sensors, sensors can be broadly implanted in cell phone gadgets, compact gadgets, and so forth. The utilization of speed increase sensors for human running checking has expansive application possibilities. From one perspective, the everyday development of the human body is firmly connected with the physical and emotional wellness of the person. Observing the day-to-day developments of the human body is of incredible importance in planning a logical running activity plan and working on actual wellbeing. On the other hand, it is also of practical value to monitor human abnormal movements. This kind of abnormal movement caused by accidental falls can bring certain harm to the human body. Real-time monitoring of the fall can provide timely assistance to the person and reduce the risk brought by the fall. This article analyzes and summarizes the research theories and common research methods in the field of 50 m round-trip movement monitoring based on the acceleration sensor. According to the process of 50 m round-trip movement pattern recognition, the data collection, preprocessing, feature extraction, and selection of 50 m round-trip movement are evaluated. The classification and recognition of each module were analyzed. This article proposes a human body motion recognition mechanism based on acceleration sensors by looking at the three trademark upsides, the wavefront edge, wavefront limit, and time stretch between the pinnacle and valley of the speed increase sensor vertical information waveform, and joining the rule of choice tree order to accomplish the activities of hunching down, taking off, and running. To get an accurate recognizable proof and recognize ways of behaving, a human fall identification calculation is proposed. This calculation removes human movement attributes throughout the fall and focuses on four sorts of falls: forward fall, reverse fall, left fall, and right fall by utilizing the connection of the three tomahawks of the speed increase sensor. The trial results show that the normal right acknowledgment pace of the human body’s 50 m full-circle running way of behaviour is more than 90%, which has specific useful application esteem.
An Innovative Thinking About the Management Mode in Colleges and Universities in the Context of “Da Zhi Yi Yun”

In recent years, “Da Zhi Yi Yun” (integration of big data, intelligentization, mobile internet, and cloud computing) has taken the wave of global management technology reform, especially in management decision-making and the education mode in colleges and universities. China boasts the world’s largest higher education; additionally, its internet education and information education are also developing at a rapid pace. Therefore, it offers a suitable environment for the application of “Da Zhi Yi Yun.” In this context, this paper aims at the problems in the management and application of current mobile cloud platforms in colleges and universities and proposes strategies for the effective application of “Da Zhi Yi Yun” from two levels: teaching management and student management.
On the Prediction Network Architecture in RNN-T for ASR

RNN-T models have gained popularity in the literature and in commercial systems because of their competitiveness and capability of operating in online streaming mode. In this work, we conduct an extensive study comparing several prediction network architectures for both monotonic and original RNN-T models. We compare 4 types of prediction networks based on a common state-of-the-art Conformer encoder and report results obtained on Librispeech and an internal medical conversation data set. Our study covers both offline batch-mode and online streaming scenarios. In contrast to some previous works, our results show that Transformer does not always outperform LSTM when used as prediction network along with Conformer encoder. Inspired by our scoreboard, we propose a new simple prediction network architecture, N-Concat, that outperforms the others in our on-line streaming benchmark. Transformer and n-gram reduced architectures perform very similarly yet with some important distinct behaviour in terms of previous context. Overall we obtained up to 4.1 % relative WER improvement compared to our LSTM baseline, while reducing prediction network parameters by nearly an order of magnitude (8.4 times).
Hardware sales forecasting using clustering and machine learning approach

This research is a case study of an information technology (IT) solution company. There is a problem that is quite crucial in the hardware sales strategy which makes it difficult for the company to predict the number of various items that will be sold and also causes the excess or shortage in hardware stocking. This research focuses on clustering to group various of items and forecast the number of items in each cluster using a machine learning approach. The methods used in clustering are k-means clustering, agglomerative hierarchical clustering (AHC), and gaussian mixture models (GMM), and the methods used in forecasting are autoregressive integrated moving average (ARIMA) and recurrent neural network-long short-term memory (RNN-LSTM). For clustering, k-means uses two attributes, namely "Quantity and Stock" as the best feature in this case study. Using these features the k-means obtain silhouette results of 0.91 and davies bouldin index (DBI) values of 0.34 consisting of 3 clusters. While for forecasting, RNN-LSTM is the best method, where it produces more cost savings than the ARIMA method. The percentage of the difference in saving costs between ARIMA and RNN-LSTM to the actual cost is 83%.
Immersive Technologies in Indonesia Faces "New Normal" COVID-19

Indonesia is attempting to rebuild the entire country's condition to the "New Normal" era during the COVID-19 pandemic.One of the emerging technologies with significant potential for dealing with the crisis is Immersive Technology (ImTech).In Indonesia, there is a growing demand for immersive technologies such as Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR).The Indonesian government is actively prioritizing digitalization implementation in various sectors to stimulate a post-COVID-19 recovery.This study aims to map the current and potential future applications of ImTech in Indonesia about dealing with the "New Normal" following the COVID-19 pandemic.A systematic literature review (SLR) was used to analyze the publications for a thorough understanding of the existing ImTech applications in Indonesia.According to 50 papers, the ontology of current ImTech applications in Indonesia is produced, focusing on four sectors (medical, industry, tourism, and education).As indicated by the growth of "start-ups" relating to this technological application, Indonesia has enormous potential to adopt ImTech.The future application of this technology is discussed on the industrial sector.
A Scheme for Spatial Resolution Improvement of Thermal Images for Objects with Different Emissivity

We propose a method of acquiring spatial high-resolution thermal distribution from a low-resolution infrared camera while correcting the temperature by emissivity. Thermal images are dependent on the color and material of the measurement target. Therefore, the accurate temperature cannot be measured by infrared thermal camera as it is. This is a serious problem in the temperature measurement on electrical terminal with a large current for electrical equipment diagnosis. In this paper, we improve the temperature profile of targets by example-based super-resolution, that is, by a dictionary from a pair of low-resolution and high-resolution thermal images with corrected by emissivity. In some experimental results, we show that temperature profiles are improved, that is, the profile of temperature is close to the measurements by a thermocouple device, and those by a high-resolution infrared thermal camera.
Application of Wireless Sensor Network Technology in Multipoint Control in Music Performance Management System

Keeping in view the overwhelming characteristics of the wireless sensor networks, these networks are constantly being utilized in numerous domains such as industry, healthcare, and music. Aiming at the problem that there is a single-point and multipoint control in the music performance management system, this paper adopts the wireless sensor network multipoint control technology to realize the control of the music performance management system. The system uses TI’s CC2430 chip to design the hardware circuit, uses the TinyOS operating system as the software platform of the system, and designs the components of the base station and sensor nodes to achieve data acquisition and transceiver functions. The experimental test shows that the system can realize the multipoint control of the music performance system and has good performance.
Digitally Signed Document Chain (DSDC) Blockchain

This paper suggested the framework of digitally signed document chain (DSDC) blockchain which focuses on digitalization and decentralization of educational certificates storage, authentication, authorization, confidentiality, ownership, and privacy. Blockchain generation has lately emerged as a capacity suggests for authenticating the record verification and a sizeable device to fight record fraud and misuse. This research paper diagnosed the safety subject matters required for file verification with inside the blockchain. This paper also suggested the framework for modifications and deletions of data under circumstances by competent authority only which is against the principal of immutability of blockchain by utilizing chameleon hashing.