Reviewerly/exHarmony huggingface dataset:
COMPILING: A Benchmark Dataset for Chinese Complexity Controllable Definition Generation

The definition generation task aims to generate a word’s definition within a specific context automatically. However, owing to the lack of datasets for different complexities, the definitions produced by models tend to keep the same complexity level. This paper proposes a novel task of generating definitions for a word with controllable complexity levels. Correspondingly, we introduce COMPILING, a dataset given detailed information about Chinese definitions, and each definition is labeled with its complexity levels. The COMPILING dataset includes 74,303 words and 106,882 definitions. To the best of our knowledge, it is the largest dataset of the Chinese definition generation task. We select various representative generation methods as baselines for this task and conduct evaluations, which illustrates that our dataset plays an outstanding role in assisting models in generating different complexity-level definitions. We believe that the COMPILING dataset will benefit further research in complexity controllable definition generation.
Effects of Multiple Avatar Images Presented Consecutively with Temporal Delays on Self-Body Recognition

Self-body awareness refers to the recognition of one's body as one's own and consists of two senses: “sense of body ownership” and “sense of agency.” In telexistence/telepresence robot operation, time delays in the robot's motion degrade self-body awareness of the robot body. We investigated how self-body recognition can be affected in a telexistence robot operation in a VR space when the robot is presented with a real robot arm that simulates a real robot with a delay and a virtual robot arm, or several virtual robot arms, with a delay less than that of the real robot. These experimental conditions include a ‘Predictive Display,’ which is well known as a time delay countermeasure. The results suggest that virtual robot arms presented consecutively with less delay than a real robot arm do not induce a sense of body ownership to the real robot arm, but they enhance the sense of agency to the real robot arm, and that sense of agency is stronger when the task requires precision.
Exploration and Practice of Joint Cultivation Model of University-Enterprise Laboratory for Information Applied Talents under the Background of Deep Integration of Industry‐University‐Research

Along with the high-speed development of information technology, more and more applied talents are required in the area of information engineering.The joint Cultivation Model of the Universityenterprise laboratory provides an effective way to promote reform in higher education.Herein, the characteristics of information engineering are analyzed based on the actual situation of the university, and construction problems of information university enterprise cooperation are discussed in two aspects of cultivation mode and laboratory construction.An innovative solution for joint cultivation of information majors in the background of industry-academia-research is proposed to cultivate more highquality information application talents for the fast-developing society.
INTEGRATION OF MULTIPLATFORM DESKTOP, WEB AND MOBILE APPLICATIONS AT PT. PRIMA ARTHA KREASINDO USING WEB SERVICE

The research entitled “Integration of Multi-Platform Desktop, Web and Mobile Applications at PT. Prima Artha Kreasindo Using Web Service” aims to integrate several desktop, web and mobile-based application systems into one application system using web services to make it more practical, effective and efficient. The developed application system can be installed on computers, laptops and cellular phones. The research was conducted by coding an integrated application program with a web service link facility and simulating the operation of the application. The use of the right device makes the system run more effectively and efficiently. The results showed that the multi-platform application program can be applied at PT. Prima Artha Kreasindo on the sales application system, with a point of sale system (POS, point of sales) and inventory control in the warehouse. The sales application program at the company became more practical, effective and efficient.
The Realistic Dilemma and Countermeasures of Talent Revitalization under the Background of Rural Revitalization——Taking the Construction of Rural Talent Team in Youxian District, Mianyang City as an Example

Talent is the key factor for rural revitalization.Since the implementation of the rural revitalization strategy, General Secretary Xi Jinping has repeatedly emphasized the important role of talents in the rural revitalization strategy.The development of rural agriculture requires not only financial policies, but also a large number of rural talents.But the reality is that the brain drain in rural areas is serious, which greatly affects the further development of agriculture and rural areas.Youxian District of Mianyang City firmly establishes the idea that talents are the first resource, focuses on solving the problem of talent shortage in rural development, adheres to the guidance of talent revitalization policies, and strengthens talent support through measures such as improving talent incentives, talent training, talent referrals, and policy guarantees.Comprehensively promote rural revitalization to gather talents and wisdom.
On-Device Major Indian Language Identification Classifiers to Run on Low Resource Devices

Language Identification acts a first and necessary step in building intelligent Natural Language Processing (NLP) systems that handle code mixed data. There is a lot of work around this problem, but there is still scope for improvement, especially for local Indian languages. Also, earlier works mostly concentrates on just accuracy of the model and neglects the information like, whether they can be used on low resource devices like mobiles and wearable devices like smart watches with considerable latency. Here, this paper discusses about both binary classification and multiclass classification using character grams as the features. Considering total nine languages in this classification which includes, eight code mixed Indian languages with English (Hindi, Bengali, Kannada, Tamil, Telugu, Gujarati, Marathi, Malayalam) and standard English. Binary classifier discussed in this paper will classify Hinglish (Hindi when written using English script is commonly known as Hinglish) from seven other code-mixed Indian Languages with English and standard English. Multiclass classifier will classify the previously mentioned languages. Binary classifier gave an accuracy of 96% on the test data and the size of the model was 1.4 MB and achieved an accuracy of 87% with multiclass classifier on same test set with model size of 3.6 MB.
Comparative analysis of the efficiency of classical and neural network approaches for text vectorization in solving classification problems

Data analysis models tend to work with numeric features, so you must convert the string representation to numeric before applying existing models to text data. This representation is called vector representation or vector model, and the transformation process is called vectorization. In connection with the appearance in recent years of a variety of developed text vectorization methods based on neural network methods for forming words embeddings, there is a need for a comparative analysis of vectorization approaches in order to determine promising development directions. The paper contains the results of an experimental study of various approaches to vectorization of text, as well as the results of the operation of classification algorithms with different approaches of vectorization. It is shown that the use of pre-trained text vectorization models in a number of cases provides the maximum classification accuracy, as well as the fact that, as a machine learning method among the tested, logistic regression is best suited to the task.
IMPLEMENTASI ELECTRONIC GOVERNMENT MELALUI PROGRAM PENERIMAAN PESERTA DIDIK BARU (PPDB) ONLINE SEBAGAI UPAYA PENINGKATAN PELAYANAN PENDIDIKAN KOTA BEKASI

The development of information technology has entered various fields, one of which is the use of information technology and telecommunications in public services in the field of education. Based on the Ministry of Education and Culture No. 51 of 2018 concerning the improvement of online New Student Admission which makes it easier to register, and minimizes queues, effective time without queuing which can improve the quality of New Student registration. This study aims to analyze the Implementation of New Student Admission and the factors that influence New Student Admission. The research paradigm uses constructivism paradigm, case study research design, qualitative research methods with data acquisition techniques by observation and interviews. The results of the study concluded that the implementation of Online New Student Admission greatly facilitates the registration of prospective students in terms of time, data that is accurate, effective and efficient. Even so, there are several shortcomings in the Implementation of New Student Admissions, namely an inadequate internet network, the high cost of the internet network, the number of parents who do not understand Information Technology Science, the lack of maximum socialization regarding New Student Admission such as coordinate points or zoning systems.
Hierarchical reinforcement learning with advantage function for entity relation extraction

Unlike the traditional pipeline methods, the joint extraction approaches use a single model to distill the entities and semantic relations between entities from the unstructured texts and achieve better performances.A pioneering work, HRL-RE, uses a hierarchical reinforcement learning model to distill entities and relations that decompose the entire extraction process into a high-level relationship extraction and a low-level entity identification.HRL-RE makes the extraction of entities and relations more accurate while solving overlapped entities and relations to a certain extent.However, this method has not achieved satisfactory results in dealing with overlapped entities and relations in sentences.One reason is that learning a policy is usually inefficient, and the other one is the high variance of gradient estimators.In this paper, we propose a new method, Advantage Hierarchical Reinforcement Learning for Entity Relation Extraction (AHRL-ERE), which combines the HRL-RE model with a new advantage function to distill entities and relations from the structureless text.Specifically, based on the reference value of the policy function in the high-level subtask, we construct a new advantage function.Then, we combine this advantage function with the value function of the strategy in the low-level subtask to form a new value function.This new value function can immediately evaluate the current policy, so our AHR-ERE method can correct the direction of the policy gradient update in time, thereby making policy learning efficient.Moreover, our advantage function subtracts the reference value of the high-level policy value function from the low-level policy value function so that AHRL-ERE can decrease the variance of the gradient estimator.Thus our AHRL-ERE method is more effective for extracting overlapped entities and relations from the unstructured text.Experiments on the diffusely used datasets demonstrate that our proposed algorithm has better manifestation than the existing approaches do.
Enhancing Data spillage in Multi-Cloud Storage Services

Numerous enterprises have been of late up to the nanosecond for putting away information on different clouds. Dispersing information over various distributed storage suppliers artificially gives a specific level of data spillage control, for no single place of assault can blunder all the information. All things considered, umplanned circulation of information pieces can prompt high data distributing indeed while utilizing multiple clouds. Then we have plant a significant data spillage issue brought about by spontaneous information appropriation in multi-cloud storage services. Also, we present StoreSim, a data spillage worried capacity framework in multi-cloud. StoreSim coordinates to store syntactically undifferentiated from information on the same cloud, therefore limiting the client’s data spillage across multiple clouds. We have arranged an approximate algorithm to prompt effectively induce safeguarding marks for information pieces grounded on MinHash and Bloom filter, and furthermore a capacity to encode the data spillage grounded on these marks. Additionally, we proposed a viable storage plan generation algorithm grounded on grouping for appropriating information pieces with least data spillage across various mists. Eventually, we look at our plan utilizing two genuine data sets. We show that our plan can reduce data spillage by more than to 60 percent contrasted with spontaneous situation. Similarly, our examination of framework attackability demonstrates that our plan makes assaults on data much more intricate.
Attention-based Deep Learning Method for Arm Image Deblurring

Individuals could be differentiated by biometric characteristics. Arm is an emerging biometric which contains rich skin and vein distribution information. However, when acquiring arm pictures, there may exist motion blur which will cause image degradation and further deteriorate recognition accuracy. Also, different from natural scene images, vein information is hard to recover from deblurring. To solve the problems above, we present an attention-based deep learning method for arm image motion deblurring. Firstly, in order to recover more vein details, we use the near-infrared (NIR) image of the corresponding RGB arm image as a priori of vein location. Secondly, since we found more information distributes in the R channel of RGB arm images, we use channel attention blocks to further improve the recovery of blurred arm images. Our method performs better than the SOTA algorithms in terms of vein detail recovery and the accuracy of authentic verification.
An Improved Recommendation System with Aspect-Based Sentiment Analysis

With the rise of Amazon, Netflix, and other e-commerce portals, many users widely depend on reviews by customers who used the product, before deciding to go ahead with a purchase. Users’ reviews are generally diverse. While some reviews can genuinely be relied upon, a few other reviews, at the same time, can be misleading. In this paper, an improved recommendation system with aspect-based sentiment analysis that replaces the attention sublayers with simple fast Fourier transform in the input embedding, to model heterogeneous semantic relationships in text is proposed. Developing a high-quality recommendation system, to recommend with excellent coverage over different aspects of a product review, is the need of the hour these days. Different deep learning techniques for aspect-based recommendation systems make use of attention mechanism to capture diverse syntactic and semantic relationships from the reviews. Experimental analysis on datasets such as SemEval 2014 Laptop Reviews, Restaurant Reviews, Twitter Data shows that the aspect-based sentiment analysis of the model outperforms the baseline models considerably, with an accuracy rate of 75.06 %, 79.93%, and 72.31% on Laptop Reviews, Restaurant Reviews, and Twitter data, respectively. Despite using attention-based model with many parameters, the model is able to be trained with less number of parameters with the proposed variant of the recommendation systems with aspect-based sentiment analysis model. The performance of the model was also evaluated on three fine-tuned environments showing promising results.
Experiencias en el desarrollo de videos didácticos en las enseñanzas remota e híbrida: el caso de las prácticas de laboratorio de Ciencia de los Materiales

The COVID-19 pandemics greatly affected teaching methods and tools. In the last two years the teaching method switched from face-to-face to online or hybrid and later on, to face-to-face again. This paper presents the results of the experience carried out in the lab-sessions of Materials Science subject within the Degree in Engineering in Industrial Design and Product Development, taught at the Higher Technical School of Design Engineering of the Universitat politècnica de València. The aim of this work is to develop an audio-visual teaching tool to be used during the course, to aid students understand the experimental procedures and the use of laboratory equipment in order to increase their academic performance. For this purpose, the obligatory didactic videos employed to undertake the laboratory session during the pandemics, when the students participated remotely, were processed and employed in a shorter version as an optional TIC tool during the last academic course. The student satisfaction towards the utility of didactic videos was measured by using an individual questionnaire. A comparison between the academic performance during pre- and post-pandemics was performed, as well.
Autonomy in Music-Generating Systems

The word autonomy is often used in the discussion of software-based music-generating systems. Whilst the term conveys a very clear concept — the sense of self-determination of a system — attempts to formalise autonomy are at an early stage, and the term is subject to a range of interpretations when practically applied. We consider how the evaluation of music-generating systems will be enhanced by a clearer understanding of autonomy and its application to music. We discuss existing definitions and approaches to quantifying autonomy and consider, through a series of examples, the information that is required in order to make precise formal judgements about autonomy, and the identification of relevant levels at which the principle of autonomy applies in music. We conclude that automated measures can supplement human evaluation of autonomy, but that (a) automated measures must be supported by sound reasoning about the features and timescales used in the measurement, and (b) they are improved by a having knowledge of the internal working of the system, rather than taking a black box approach. We consider multi-dimensional representations of system behaviour that may capture a richer sense of the notion of autonomy. Finally, we propose an approach to automatically probing music systems as a means of determining an autonomy `portrait'.
Graduate studies in acoustics at Northwestern University

Northwestern University has a vibrant and interdisciplinary community of acousticians. Of the 13 ASA technical areas, 3 have strong representation at Northwestern: Speech Communication, Psychological and Physiological Acoustics, and Musical Acoustics. Sound-related work is conducted across a wide range of departments including Linguistics (in the Weinberg College of Arts and Sciences), Communication Sciences &amp; Disorders, and Radio/Television/Film (both in the School of Communication), Electrical Engineering &amp; Computer Science (in the McCormick School of Engineering), Music Theory &amp; Cognition (in the Bienen School of Music), and Otolaryngology (in the Feinberg School of Medicine). In addition, The Knowles Hearing Center involves researchers and labs across the university dedicated to the prevention, diagnosis and treatment of hearing disorders. Acoustics research topics across the university include speech perception and production across the lifespan and across languages, dialects and socio-indexical properties of speech; sound art and design; social and cultural history of the sonic world; machine processing of music; musical communication; auditory perceptual learning; auditory aspects of conditions such as concussion, HIV, and autism; neurophysiology of hearing; and the cellular, molecular, and genetic bases of hearing function. We invite you to visit our poster to learn more about the “sonic boom” at Northwestern University!
The Influence of Microsoft Power Point Based Video Learning Media on Social Studies Learning Outcomes of Elementary School Students

This study aims to determine whether or not there is an influence and the level of influence of using Microsoft Power Point-based video learning media on social studies learning outcomes for fourth grade students at SDN Keret Krembung. The research method used is quantitative with a pre-experimental design type one group pretest-posttest. The subjects of this study were elementary school students in the fourth grade at SDN Keret Krembung. Data collection techniques using tests. Data analysis used t test (paired) and eta squared formula. The results of this study indicate that there is a significant effect on social studies learning outcomes for fourth grade students at SDN Keret Krembung. The result is tcount = 18,131 which is compared to ttable = 1,711 for a significance level of 5%. It is known that the result of tcount is greater than ttable (18,131 &gt; 1,711). Meanwhile, the results of the eta squared formula obtained a value of 0.93. This shows that the video learning media based on Microsoft Power Point has a major level of influence on the social studies learning outcomes of fourth grade students at SDN Keret Krembung.
Serial Convolution-Based Optical Accelerator for ECG Arrhythmia Classifier

This study focuses on the design of photonic hardware for an ECG arrythmia classifier based on convolutional neural network using optical delay lines for the implementation of “multiply and accumulate” (MAC) operations.
Benchmarking the Nigerian private innovation incubators: analysis of the influence of firm age, firm size, and employee qualification on their innovation activities

There is no doubt that innovation is integral to the development and competitiveness of nations and firms. The significance of innovation is partly due to the influence of innovation incubators (IIs). IIs are significant interfacial firms that contribute to the transfer of technologies from inventors to the commercial sector. Existing literature on the contribution of IIs in Nigeria is focused on government-funded technology-based incubators, whereas no study has investigated the impact of privately owned IIs. Since this is a novel study in the Nigerian context, we employed a mixed methods research approach. Qualitative data were gathered from private IIs (PIIs), and cross-tabulation analysis was used to test the relationships between the variables. The study reveals that firm size influences the ability of PIIs to generate funds through self-funding and their ability to generate revenue from their innovation expenditure.
Pelatihan Pemanfaatan Google Classrom Sebagai Media E-Learning Sekolah

The use of information technology in the world of education looks better. With the ease of getting this information, cases of parents who do not know the status of their children's education are increasingly rare. The use of technology in the world of education in teaching and learning activities cannot be done by one party alone. It takes the involvement of all, especially teachers and students. If you look at the age of the teachers, of course, not a few of them can be considered old. This is what makes not a few of the teachers still rigid with technology. But it's a different story with today's school students. The year of birth of school students is currently in the generation Z category, where technology is their lifestyle. The method of transferring knowledge from teachers to students must be changed so that knowledge can be well absorbed by students. This community service aims to provide training on the use of Google Classroom as one of the school's e-learning at SMP N 2 Kasihan.