Reviewerly/exHarmony huggingface dataset:
DESIGN AND DEVELOPMENT OF WEB-BASED INFORMATION SYSTEM FOR OFFICE STATIONERY PROCUREMENT MANAGEMENT

Office Stationery (ATK) is an item used to do written work such as paper, books, ink, pencils, pens, paper clamps, and others. ATK is a supporting tool that has an important role in implementing the administrative function of a company or agency. The management of ATK procurement in the Bogor Agricultural Institute (IPB) is under the coordination of the Directorate of Infrastructure, Facilities, and Campus Environmental Security (DPSPLK) as a coordination unit at the central level. So far, the procurement of ATK in every work unit in the IPB environment uses a direct purchase method. Direct purchasing methods cause problems and weaknesses, including: (1) price differences between providers for the same goods, (2) the quality of providers varies because each work unit directly selects providers, (3) vulnerability to price manipulation, (4) needed storage space for goods in each unit, (5) management and organization of ATK data are carried out partially in each work unit. Atk data management and organization have not been systemized, making it difficult for DPSPLK to reconcile data. An umbrella contract procurement method (framework contract) is applied to deal with problems in the direct purchase method. Management and organization of unsystematic ATK data, an integrated information system is built to facilitate DPSPLK reconciling data for reporting needs. The information system development method uses the waterfall method and testing using the User Acceptance Test (UAT) black-box type. This research resulted in the procurement and management application of ATK used by DPSPLK
Controlling media player using hand gestures with VLC media player

In today's international, anyone opts for instant interaction with complicated structures that ensure a brief response. Thus, with increasing improvement in technology, reaction time and ease of operations are the issues. Here is where human-computer interaction comes into play. This interplay is unrestricted and challenges the used gadgets consisting of the keyboard and mouse for input. Gesture recognition has been gaining tons of attention. Gestures are instinctive and are often utilized in everyday interactions. Therefore, communicating using gestures with computer systems creates an entire new trend of interaction. In this assignment, with the help of laptop vision and deep studying techniques, person hand movements (gestures) are used in real-time to manipulate the media player. In this project, seven gestures are defined to control the media gamers' usage of hand gestures. The proposed internet application permits the person to use their neighborhood device digicam to become aware of their gesture and execute the control over the media participant and comparable packages (with no extra hardware). It will increase performance and make interaction convenient through letting the user manage his/her pc/laptop from a distance.
Sign Language Recognition System Using Neural Networks

Abstract: Sign language is that the only tool of communication for the one who isn't ready to speak and listen to anything. Language could be a boon for the physically challenged people to precise their thoughts and emotion. During this work, a unique scheme of signing recognition has been proposed for identifying the alphabets and gestures in linguistic communication. With the assistance of computer vision and neural networks we will detect the signs and provides the respective text output. Keywords: Sign language, Neural networks, Recognition, Detect signs
A Model for Sign Language Recognition System using Deep Learning

Abstract: Conversing to someone with listening disability is usually the main challenge. Sign language has indelibly ended up the final panacea and is a completely effective device for people with listening and speech inability to speak their emotions and critiques to the world. It makes the combination technique among them and others easy and much less complex. However, the discovery of signal language alone, isn't always enough . There are many strings connected to this boon.The signal gestures regularly get blended and stressed for a person who has by no means learned or is aware of it in a exclusive language. However, this communique gap which has existed for years can now be narrowed with the advent of diverse strategies to automate the detection of signal gestures . In this paper, we introduce a Sign Language reputation the use of Sign Language. In this study, the consumer have to be capable of seize snap shots of the hand gesture the use of internet digital digicam and the device shall expect and display the call of the captured image. We use the HSV shade set of rules to come across the hand gesture and set the historical past to black. The snap shots go through a chain of processing steps which consist of diverse Computer imaginative and prescient strategies including the conversion to grayscale, dilation and masks operation. And the location of hobby which, in our case is the hand gesture is segmented. The capabilities extracted are the binary pixels of the snap shots. We employ Convolutional Neural Network(CNN) for schooling and to categorise the snap shots. We are capable of realising 10 Sign gesture alphabets with excessive accuracy. Our version has carried out a wonderful accuracy of above 90%.
THE CONTENT OF PROFESSIONAL INFORMATION COMPETENCE OF FUTURE TEACHERS OF MUSIC ART

Today one of the main directions of art and pedagogical education in our country is the introduction of the concept of competence approach in the educational process of higher education institutions for the preparation of professional information competence of future teachers of music. In the course of training future music teachers in higher education institutions, they gain key competencies for their future professional activities, but quite often they are not enough for the entire period of work, and therefore there is a problem in retraining and advanced training of these respondents. Over the past few years in music and pedagogical education there has been a reorientation of the concepts of "education", "readiness" for "professionalism", "competence" of the future music teacher. In modern conditions, the information competence of the future teacher of music is a radically new field of activity, which is formed through the introduction into the educational process of higher education institutions of electronic and information technologies.The formation of professional information competence in future music teachers in higher education institutions is closely related to the use of motivational, operational and content-oriented activities provided by the formation and functioning of professional and pedagogical training of future teachers. It is thanks to this statement that we can consider professional information musical competence as an integral characteristic of the individual, which determines its ability to solve problems and tasks that arise in various areas of musical and artistic activities based on the use of knowledge, skills, personal experience and personal orientation. Thus, the professional information competence of the future music teacher combines the ability to work with information, the ability to solve educational problems, the ability to use interactive software on the basis of which is the implementation of applied methods combined with teaching methods that are based on individual and collective work with respondents. Nowadays, the professional use of information media is the main criterion for the formation of professional information competence of the future music teacher. This type of activity of the future teacher aims at active and creative use of knowledge and skills in the direction of information and communication technologies, which is a criterion of professional suitability of the future teacher of music art.
Offline RL for Natural Language Generation with Implicit Language Q Learning

Large language models distill broad knowledge from text corpora. However, they can be inconsistent when it comes to completing user specified tasks. This issue can be addressed by finetuning such models via supervised learning on curated datasets, or via reinforcement learning. In this work, we propose a novel offline RL method, implicit language Q-learning (ILQL), designed for use on language models, that combines both the flexible utility maximization framework of RL algorithms with the ability of supervised learning to leverage previously collected data, as well as its simplicity and stability. Our method employs a combination of value conservatism alongside an implicit dataset support constraint in learning value functions, which are then used to guide language model generations towards maximizing user-specified utility functions. In addition to empirically validating ILQL, we present a detailed empirical analysis of situations where offline RL can be useful in natural language generation settings, demonstrating how it can be a more effective utility optimizer than prior approaches for end-to-end dialogue, and how it can effectively optimize high variance reward functions based on subjective judgement, such as whether to label a comment as toxic or not.
Universal Learned Image Compression With Low Computational Cost

Recently, learned image compression methods have developed rapidly and exhibited excellent rate-distortion performance when compared to traditional standards, such as JPEG, JPEG2000 and BPG. However, the learning-based methods suffer from high computational costs, which is not beneficial for deployment on devices with limited resources. To this end, we propose shift-addition parallel modules (SAPMs), including SAPM-E for the encoder and SAPM-D for the decoder, to largely reduce the energy consumption. To be specific, they can be taken as plug-and-play components to upgrade existing CNN-based architectures, where the shift branch is used to extract large-grained features as compared to small-grained features learned by the addition branch. Furthermore, we thoroughly analyze the probability distribution of latent representations and propose to use Laplace Mixture Likelihoods for more accurate entropy estimation. Experimental results demonstrate that the proposed methods can achieve comparable or even better performance on both PSNR and MS-SSIM metrics to that of the convolutional counterpart with an about 2x energy reduction.
Formation of legal culture of college students

The legal culture of students (hereinafter LCS) can be defined as a spiritual and material system which ensures the effective usage of legislative and other regulatory documents in professional activities to solve issues. The LCS in professional training is a unity of gnoseological, axiological, activity-based and technological components which can be considered as criteria in forming this culture. The selected structural components of the LCS in professional training are refracted into functional ones. The most important functions are: cognitive-transformative, legal-regulatory, value-normative, communicative, prognostic, legal socialization, and humanistic functions. The pedagogical model of the LCS formation, represented by the target, structural-content, organizational-procedural and control-evaluation components, which reveals the theoretical essence, internal structure and logic of the complex process under consideration, the unity of all its elements that are interrelated and interdependent, allows creating a legal educational space and provides for the process of forming the LCS in the conditions of professional and pedagogical education. The basic principles in the formation of LCS in professional training are revealed: consistency, conceptuality, legal conditionality, and prognostic-based principles.
Adaptation of the Four Levels of Test Maturity Model Integration with Agile and Risk-Based Test Techniques

Many projects that progress with failure, processes managed erroneously, failure to deliver products and projects on time, excessive increases taking place in costs, and an inability to analyze customer requests correctly pave the way for the use of agile processes in software development methods and cause the importance of test processes to increase day by day. In particular, the inability to properly handle testing processes and risks with time and cost pressures, the differentiation of software development methods between projects, the failure to integrate risk management, and risk analysis studies, conducted within a company/institution, with software development methods also complicates this situation. It is recommended to use agile process methods and test maturity model integration (TMMI), with risk-based testing techniques and user scenario testing techniques, to eliminate such problems. In this study, agile process transformation of a company, operating in factory automation systems in the field of industry, was followed for two and a half years. This study has been prepared to close the gap in the literature on the integration of TMMI Level 2, TMMI Level 3, and TMMI Level 4 with SAFE methodology and agile processes. Our research has been conducted upon the use of all TMMI Level sub-steps with both agile process practices and some test practices (risk-based testing techniques, user scenario testing techniques). TMMI coverage percentages have been determined as 92.85% based on TMMI Level 2, 92.9% based on TMMI Level 3, and 100% based on TMMI Level 4. In addition, agile process adaptation metrics and their measurements between project versions will be shown, and their contribution to quality will be mentioned.
The ArtBench Dataset: Benchmarking Generative Models with Artworks

We introduce ArtBench-10, the first class-balanced, high-quality, cleanly annotated, and standardized dataset for benchmarking artwork generation. It comprises 60,000 images of artwork from 10 distinctive artistic styles, with 5,000 training images and 1,000 testing images per style. ArtBench-10 has several advantages over previous artwork datasets. Firstly, it is class-balanced while most previous artwork datasets suffer from the long tail class distributions. Secondly, the images are of high quality with clean annotations. Thirdly, ArtBench-10 is created with standardized data collection, annotation, filtering, and preprocessing procedures. We provide three versions of the dataset with different resolutions ($32\times32$, $256\times256$, and original image size), formatted in a way that is easy to be incorporated by popular machine learning frameworks. We also conduct extensive benchmarking experiments using representative image synthesis models with ArtBench-10 and present in-depth analysis. The dataset is available at https://github.com/liaopeiyuan/artbench under a Fair Use license.
Assessing the Quality of Car Racing Controllers in a Virtual Setting under Changed Conditions

This paper discusses several controllers based on fuzzy logic and evolutionary concepts applied to a car racing simulation and their robustness to changing physics of the cars. The challenge is to design a car controller that passes the next three arising waypoints faster than an opponent car controller. Two fuzzy controllers are compared to two evolutionary optimized controllers in solo races as well as in head-to-head competitions, where all controllers compete head-to-head against the other controllers. The influence of some parameter settings is investigated as well. The results emphasize the robustness of the fuzzy controllers, not differing much from each other. Overall, the fuzzy controllers perform better with different parameter settings of the driving physics except when reverse speed is equal to forward speed.
Explore Spatio-temporal Aggregation for Insubstantial Object Detection: Benchmark Dataset and Baseline

We endeavor on a rarely explored task named Insubstantial Object Detection (IOD), which aims to localize the object with following characteristics: (1) amorphous shape with indistinct boundary; (2) similarity to surroundings; (3) absence in color. Accordingly, it is far more challenging to distinguish insubstantial objects in a single static frame and the collaborative representation of spatial and temporal information is crucial. Thus, we construct an IOD-Video dataset comprised of 600 videos (141,017 frames) covering various distances, sizes, visibility, and scenes captured by different spectral ranges. In addition, we develop a spatio-temporal aggregation framework for IOD, in which different backbones are deployed and a spatio-temporal aggregation loss (STAloss) is elaborately designed to leverage the consistency along the time axis. Experiments conducted on IOD-Video dataset demonstrate that spatio-temporal aggregation can significantly improve the performance of IOD. We hope our work will attract further researches into this valuable yet challenging task. The code will be available at: \url{https://github.com/CalayZhou/IOD-Video}.
MaskViT: Masked Visual Pre-Training for Video Prediction

The ability to predict future visual observations conditioned on past observations and motor commands can enable embodied agents to plan solutions to a variety of tasks in complex environments. This work shows that we can create good video prediction models by pre-training transformers via masked visual modeling. Our approach, named MaskViT, is based on two simple design decisions. First, for memory and training efficiency, we use two types of window attention: spatial and spatiotemporal. Second, during training, we mask a variable percentage of tokens instead of a fixed mask ratio. For inference, MaskViT generates all tokens via iterative refinement where we incrementally decrease the masking ratio following a mask scheduling function. On several datasets we demonstrate that MaskViT outperforms prior works in video prediction, is parameter efficient, and can generate high-resolution videos (256x256). Further, we demonstrate the benefits of inference speedup (up to 512x) due to iterative decoding by using MaskViT for planning on a real robot. Our work suggests that we can endow embodied agents with powerful predictive models by leveraging the general framework of masked visual modeling with minimal domain knowledge.
Morse code Translator Using Eye Blinks

Abstract: A Morse code Translator which translates Morse code into speech and text of any language chosen by the user, Morse code is given as input ,it can be either a recorded video or live feed of a person who is blinking eyes in a sequence .OpenCV is used to take Morse code input ,Mediapipe a google api detects face and maps facial landmarks, These landmarks are used to map various eye co-ordinates and then considered to formulate Eye Aspect ratios ,which in turn determines eye blink .Morse code which has been already loaded in the form of dictionary is going to map with the morse code given through video or cam ,and find the Alphabet assigned to it.
Constraints on Gaussian Error Channels and Measurements for Quantum Communication

Joint Gaussian measurements of two quantum systems can be used for quantum communication between remote parties, as in teleportation or entanglement swapping protocols. Many types of physical error sources throughout a protocol can be modeled by independent Gaussian error channels acting prior to measurement. In this work we study joint Gaussian measurements on two modes $\mathsf{A}$ and $\mathsf{B}$ that take place after independent single-mode Gaussian error channels, for example loss with parameters $l_\mathsf{A}$ and $l_\mathsf{B}$ followed by added noise with parameters $n_\mathsf{A}$ and $n_\mathsf{B}$. We show that, for any Gaussian measurement, if $l_\mathsf{A} + l_\mathsf{B} + n_\mathsf{A} + n_\mathsf{B} \geq 1$ then the effective total measurement is separable and unsuitable for teleportation or entanglement swapping of arbitrary input states. If this inequality is not satisfied then there exists a Gaussian measurement that remains inseparable. We extend the results and determine the set of pairs of single-mode Gaussian error channels that render all Gaussian measurements separable.
Enhancing geostatistical prediction of total hydrocarbon content (THC) using Bayesian maximum entropy (BME) in a region of Niger Delta, Nigeria

In this study, Bayesian maximum entropy (BME) was implemented to take advantage of the limited data available for reliable prediction of total hydrocarbon content (THC) in the soil because it allows assimilate of secondary and its uncertainty data. Thirteen (13) physico-chemical soil variables were obtained. Upon the application of multiple regression analysis to the soil variables, electrical conductivity, EC was found as a covariate to the eleven (11) data. Twenty-eight (28) data were then generated by regression model between and EC and their 28 uncertainty data were also derived using probability density function. Therefore, 11 , 28 and 28 error data in the computation of THC soft were integrated at BME technique to produce BME prediction and BME standard deviation maps of THC. The BME predicted THC value in the area ranges from 0 – 16 . The BME prediction map provides the mean variable of the estimation posterior pdf of THC. It shows the spatial variability of THC as the concentration decreases from the highest concentrated zone of THC.
Web based remote agricultural control and consultancy application: An early diagnostic warning system

In this study, the Web-Based Remote Agricultural Struggle and Counseling Application, which was developed within the Agriculture 4.0 approach for the spread of agricultural technologies and the use of technology in agriculture, has been discussed with the technical personnel who are experts in the field, and what improvements should be made regarding the application. In the study, literature review was made, agricultural consultancy, smart agriculture and the use of digital technologies in agriculture were discussed. The Web Based Remote Agricultural Control and Consultancy Application designed in this study was developed with the Python programming language. The development and operation of the application is presented in the form of screenshots. In the research, user opinions of the application were collected through a form consisting of 4 questions to measure demographic information and a qualitative question consisting of 7 main titles and 28 sub-titles to measure the opinions of technical personnel about the application, which was prepared by taking expert opinions. The data obtained from the research users were analyzed with the Nvivo 12 program. As a result of the analysis, it was concluded that the development of web-based agricultural control and consultancy application for technical personnel is appropriate, the interface design should be improved, and it will contribute positively to the dissemination of technology use in agriculture.
Review of Advanced Image Processing Techniques: Digital Elevation Model

Abstract: This paper gives an in depth insight into the current as well as the previous systems or techniques that have been used in order to perform advanced image processing. These systems or techniques have been implemented or tested in the areas of Germany, The United States of America, India, China, Indonesia and many more. The review aims to explore challenges and opportunities in the field computational and image processing areas of the industry.