Reviewerly/exHarmony huggingface dataset:
University Recommendation System for Higher Studies in USA

With the increasing number of graduates seeking to pursue higher education, getting a student’s admission into the university becomes more challenging. New graduate students are typically unaware of the postgraduate admission criteria and procedures and may spend a substantial amount of time obtaining guidance from consulting firms to help them define their admission opportunities. But this methodology can be biased and misleading considering the small number of universities that a human consultant may consider. Thus, a machine learning approach was built in this paper to automatically predict the prospects for postgraduate admissions, enabling graduates to recognise and target the most appropriate universities for their profile. The University Recommendation System for Higher Studies is a system framework that prescribes universities for students or users of the system who recommend universities to students looking for education for their higher studies. The main parameters used to recommend university are CGPA percentages, GRE Score, TOFEL Score, university rank, etc. This system uses machine learning algorithms and compares the accuracy of the algorithms.
A Robot Obstacle Avoidance Approach with LiDAR and RGB Camera Data Combined

A robot obstacle avoidance approach is proposed in this paper, which combines 2D LiDAR data with images from the RGB camera attached to the mobile robot. With accurate information of distance to obstacles provided by the LiDAR, conventional obstacle avoidance methods have good performance navigating in simple environments, which means that the ground is level and no low reflectivity object is blocking the robot's way. However, lacking global awareness of its surrounding environment, the robot may not perform well in complex environments. In our approach to obstacle avoidance, the robot uses the camera as a second data source to get more information about its surroundings. Based on the end-to-end deep learning algorithm proposed in this paper, images from the camera are classified into two categories, telling the possibility of whether there are obstacles ahead. A method integrating the possibility to the navigation process is also proposed, making the robot more reliable and less likely to run into an obstacle while navigating in a complex environment. Finally, The Timed Elastic Band (TEB) local path planning method is used to produce the local path and generate the velocity command for the robot. An experiment is conducted to verify the overall performance by deploying the approach to an embedded system on a mobile robot. By evaluating the experimental result, we found the method effective and reliable for obstacle avoidance.
On Implementation of Parallel Map and Shuffle Phases for Coded Distributed Computing

In this letter, we consider the MapReduce-type distributed system and jointly design the Map and Shuffle phases of the coded distributed computing (CDC) scheme proposed by Li, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> By delicately setting the order of file mapping and data shuffling, we implement the Map and Shuffle phases in parallel, and thus significantly reduce the overall task latency (time cost at the Map, Shuffle, Reduce operations and codes generation). Furthermore, our scheme enables all nodes to remove their auxiliary intermediate values once these values are no longer useful in the Shuffle phase, leading to great reduction in the storage cost of the system.
Gaze Analysis of Modification Policy in Debugging an Embedded System

In embedded system development, the debugging is difficult for novice because developers must consider the state of the hardware and the software. Therefore, this study analyzed the gaze transition in the debugging of the embedded system by experts and novices. The gaze data reveals the difficult points of novices and the hidden technique of experts in the debugging process. The analysis segmented the time-series data of gaze object using GP-HSMM, which is an unsupervised learning and time-series data division method with great accuracy. The results showed that experts tend to debug in three phases, namely, circuit debugging in the early stage, source code debugging in the middle stage, and confirmation of both the circuit and source code in the final stage. Based on the temporal trend of gazing at an object, we proposed the teaching contents of modification policy for the novices in order to increase debugging efficiency.
A Survey on Building Recommendation Systems Using Data Mining Techniques

Classification is a data mining technique or approach used to estimate the grouped membership of items on a basis of a common feature. This technique is virtuous for future planning and discovering new knowledge about a specific dataset. An in-depth study of previous pieces of literature implementing data mining techniques in the design of recommender systems was performed. This chapter provides a broad study of the way of designing recommender systems using various data mining classification techniques of machine learning and also exploiting their methodological decisions in four aspects, the recommendation approaches, data mining techniques, recommendation types, and performance measures. This study focused on some selected classification methods and can be so supportive for both the researchers and the students in the field of computer science and machine learning in strengthening their knowledge about the machine learning hypothesis and data mining.
Lego®-like Bricks to Go from the Real to the Virtual World

In this work, we prototype a system that allows for Lego®-like bricks to be used physically to create a model in the real world that is then mapped and recreated in an existing Lego CAD software. This allows users to build their designs tactically in the real world and have these designs translated into the virtual-world automatically. The key benefit is that bricks are, naturally, a tactile building tool, but there is a desire to have virtual representations of brick-based models for all sorts of reasons such as designing and creating instruction booklets, creating models that can be incorporated in virtual worlds (such as games), and simply sharing and archiving a design beyond the limits of a physical model. Our system shows that an embedded system that includes per brick computing intelligence is a viable method to achieve the simple goal of translating a real brick model into a virtual model.
Experiment and Performance Analysis for Streaming Data on Multicore Platform

Streaming data processing is one of the most important workloads that are handled by cloud gateways in supporting modern IoT applications to provide multimedia, browsing, management, monitoring and control functions. Diversified services place higher and higher requirements on the performance of the gateway computing platform, and the factors that affect the overall actual load and the performance of each part of the system are not direct. They depend on multiple dimensions and elements that restrict each other and need to be detailed according to the situation. In this work, the characteristics of streaming data workload on multi-core platform is analyzed based on benchmark experiments. The resource usage patterns of software and hardware is summarized and discussed in detailed through top-down performance factors. It helps to check pressure points, supports microbenchmarks, and creates performance models for further optimization.
Multi-Source Navigation Data Fusion Based on Factor Graph for Unmanned Helicopter Under Atmospheric Disturbance

Unmanned helicopter has unique flight performance and plays an important role in the field of military and civil. Strong atmospheric disturbance environment is the comprehensive wind which has the effect on the body of the unmanned helicopter. Relying on the key project of NSFC (61533008), the research of the navigation interference and the state estimation method of the unmanned helicopter caused by strong airflow are focused in this paper. In view of the fact that only the inertial navigation system is difficult to meet the reliability and accuracy requirements of the unmanned helicopter navigation system in the strong airflow environment, a multi-sensor integrated navigation method based on the frame of factor graph is proposed in this paper. The performance of the integrated navigation system of the unmanned helicopter in the strong airflow environment can be effectively improved by using the factor graph filtering method. At the end of the paper, the simulation platform is built, and the algorithm proposed in the article is experimented by the simulation system. Then we analyzed the result and got the conclusion. The results show that the algorithm is helpful to solve the problem of navigation system fault caused by error increasing of navigation sensor of the unmanned aerial helicopter in the strong airflow interference environment.
Impact Of Cyber Crime On Youth In Lockdown

Whenever the GDP goes down of any nation and unemployment increases and offences also increases the main of this paper is to analyze the cyber crime by teenager and youth and how they are affecting the society through cyber crime. The cyber crime bucks to extracts money from the people whether they belong do to nation or not and it affects the common people and I want to draw your attention towards the misuse of computer and laptop and internet and other social media networks so we have to use in a proper manner with security over computers and electronic gadgets and we have to aware about the crime and how it can affects people.
Chapter 8. Online text mapping

This is a corpus driven study on verbless constructions (Cxs[ØVP]) in spoken Italian and French that have a textual naming function, namely verbless constructions that speakers use to name what they are going to talk about. This paper suggests that these constructions are tools that speakers use as structure’s mapping in the discourse to orient the listener and themselves and that build meta-discursive and meta-textual ad hoc categories. These structures are frequent in very high informative texts (i.e. meetings and conferences) and their occurrence correlates with the information’s density in texts.
Efficient Processing of Streaming Data using Multiple Abstractions

Large websites and distributed systems employ sophisticated analytics to evaluate successes to celebrate and problems to be addressed. As analytics grow, different teams often require different frameworks, with dozens of packages supporting with streaming and batch processing, SQL and no-SQL. Bringing multiple frameworks to bear on a large, changing dataset often create challenges where data transitions-these impedance mismatches can create brittle glue logic and performance problems that consume developer time. We propose Plumb, a meta-framework that can bridge three different abstractions to meet the needs of a large class of applications in a common workflow. Large-block streaming (Block-Streaming) is suitable for single-pass applications that care about the temporal and spatial locality. Windowed-Streaming allows applications to process a group of data and many reductions. Stateful-Streaming enables applications to keep a long-term state and always-on behavior. We show that it is possible to bridge abstractions, with a common, high-level workflow specification, while the system transitions data batch processing and block- and record-level streaming as required. The challenge in bridging abstractions is to minimize latency while allowing applications to select between sequential and parallel operation, while handling out-of-order data delivery, component failures, and providing clear semantics in the face of missing data. We demonstrate these abstractions evaluating a 10-stage workflow of DNS analytics that has been in production use with Plumb for 2 years, comparing to a brittle hand-built system that has run for more than 3 years.
Real-Time Estimation of Eye Movement Condition Using a Deep Learning Model

In this study, we conducted a basic investigation involving the discrimination of eye movement condition (peripheral and central vision) using deep learning techniques. The subjects were 6 males aged 21–23 years. They watched two three-minute videos for central vision and peripheral vision in a random order for a total of eight sessions (four sessions each). The subjects wore an eye movement measurement device, and their eye movements (viewing angles) during the viewing of each video were continuously. From the time series data for eye movement, with four different lengths (0.5 s, 1 s, 2 s, 3 s) and shift length of 0.5 s, short time series data for each 3 min was obtained in sets of 350, and the data were utilized for deep learning and its evaluation. For the deep learning model, input nodes according to data length were placed in the input layer. For the middle layer, seven to eight units were put in place that brought together the one-dimensional convolution layer, the batch-normalization layer, normalized linear function, and the max-pooling layer. The output layer consisted of the fully-connected layer, sigmoid function, and multi-class cross-entropy. As a result, the accuracy of the discrimination was improved as the data length increased, and it was possible to determine the condition with an accuracy of over 90% if the eye movement data was at least one second.
Constacyclic codes over the ring Fq[u,v,w]/〈u2 − 1, v2 − 1,w3 − w, uv − vu,vw − wv,wu − uw〉

Let [Formula: see text] be the ring [Formula: see text], where [Formula: see text] for any odd prime [Formula: see text] and positive integer [Formula: see text]. In this paper, we study constacyclic codes over the ring [Formula: see text]. We define a Gray map by a matrix and decompose a constacyclic code over the ring [Formula: see text] as the direct sum of constacyclic codes over [Formula: see text], we also characterize self-dual constacyclic codes over the ring [Formula: see text] and give necessary and sufficient conditions for constacyclic codes to be dual-containing. As an application, we give a method to construct quantum codes from dual-containing constacyclic codes over the ring [Formula: see text].
Blockchain Use Case in Multi-sensor Satellite Architecture

With the advent of the multi-sensor satellite system architecture, earth and space exploration such as swarms and constellations are feasible. This transition requires novel technologies to achieve future capabilities. In this context, the establishment of prioritized, secure, and efficient command and data communication is essential for space and ground systems. The use of blockchain assures a secure automated network infrastructure. The blockchain has the potential for usage in swarm satellite architectures and constellations. This paper presents the use of blockchain in multi-sensor satellite architecture with its distributed ledger and smart contract capabilities to provide prioritized and secure collaborative satellite systems. The paper further discusses the use of blockchain technology in securing tracking and logging of control events and commands in satellite architecture. The use of blockchain technology addresses the issues of constellation flight architecture, management of computational operation, autonomous observation, time-critical event planning of ground and space autonomous operation of satellite systems.
Web Series Recommendation System Using Machine Learning

Nowadays, the web series has turned out to be part and parcel of viewers entertainment world. At present when people can’t find out so much time from their busy schedule to take a correct decision over enormous options and happiness depends on others suggestions, they prefer to select or purchase a new product based on the feed-back from other users who have used a similar or similar type of products. Instead of doing these time-consuming tasks manually, a recommender system needs to be implemented to do all these tasks automatically. In this project, a Machine learning-based web Series recommendation system has been designed to suggest web series to the users which they have not watched yet. In this recommendation system at first, some parameters have been taken as features of tagging, and all the elements have been mapped with their respective id created separately for each of the selected para-meters and using tagging, we have got different classes for a list of web series. After tagging all the features of web series have been mapped and a recommendation system has been used to recommend unseen but preferable movies to the users. Different types of analysis on parameters which are used in our system and their results and different graphs prove the robustness of our recommendation system.
Study on the application of aerobic composting technology of organic solid waste in urban communities

With the rapid development of modern society and economy, the scale of urban communities is constantly expanding, people's living standards are also rapidly improving, resulting in an increasing number of household waste. This paper mainly discusses the status quo of municipal solid waste treatment, analyzes the prospect and application of organic solid waste aerobic composting technology, in order to provide new ideas for urban communities organic waste treatment, low carbon cycle, green operation has important significance.
Adaptive Light Spot Removal Algorithm for Endoscopic Scenes

The concept of minimally invasive surgery was introduced in 1983, and the technique allows the surgeon to obtain a surgical field of view through the laparoscope and complete the procedure through it. The specular reflection of the illuminated light source due to the special scenario of the abdominal cavity often poses many problems. Especially in the field of computer-aided medicine, the specular reflections in the abdominal cavity cause significant misjudgment in feature point extraction in computer vision, which becomes an important factor resulting in final localization errors. In this paper, we propose an algorithm to achieve fast extraction and accurate elimination of light spots. The above algorithm quickly and efficiently achieves the elimination and repair of endoscopic image spot areas, achieving higher efficiency with guaranteed results compared to other spot removal algorithms.
DSTnet: Deformable Spatio-Temporal Convolutional Residual Network for Video Super-Resolution

Video super-resolution (VSR) aims at generating high-resolution (HR) video frames with plausible and temporally consistent details using their low-resolution (LR) counterparts, and neighboring frames. The key challenge for VSR lies in the effective exploitation of intra-frame spatial relation and temporal dependency between consecutive frames. Many existing techniques utilize spatial and temporal information separately and compensate motion via alignment. These methods cannot fully exploit the spatio-temporal information that significantly affects the quality of resultant HR videos. In this work, a novel deformable spatio-temporal convolutional residual network (DSTnet) is proposed to overcome the issues of separate motion estimation and compensation methods for VSR. The proposed framework consists of 3D convolutional residual blocks decomposed into spatial and temporal (2+1) D streams. This decomposition can simultaneously utilize input video’s spatial and temporal features without a separate motion estimation and compensation module. Furthermore, the deformable convolution layers have been used in the proposed model that enhances its motion-awareness capability. Our contribution is twofold; firstly, the proposed approach can overcome the challenges in modeling complex motions by efficiently using spatio-temporal information. Secondly, the proposed model has fewer parameters to learn than state-of-the-art methods, making it a computationally lean and efficient framework for VSR. Experiments are conducted on a benchmark Vid4 dataset to evaluate the efficacy of the proposed approach. The results demonstrate that the proposed approach achieves superior quantitative and qualitative performance compared to the state-of-the-art methods.
Modern trends in the development of communication technologies distance learning in the context of professionally oriented approach

The purpose of the study was to develop and implement a comprehensive project to improve communication technologies, in the context of a professionally oriented approach to distance education of academic English in technical faculties. The research methodology was related to the development and testing of the project, aimed at improving skills in the formation of students' communicative competence. To estimate the quality of the project, two stages were carried out in the application of the survey to the students. The first phase of the survey was carried out in March 2020 before the start of the project. The second stage of the student survey was carried out at the end of the first semester (in January 2021), being implemented remotely. The analysis carried out and the results of the survey indicate that the development of communication technologies and online learning are substantially related. The world of communication technologies should definitely be considered as a tool to improve communicative competence, self-organization, self-development and personal growth.
Spectrophotometric determination of folic acid using 1,10- phenanthroline materials with ninhydrin reagent

There have been 2 spectrophotometric techniques developed, validated to find out how much folic acid there is that is simple to use, sensitive to detect, and selective. One method reduces ferric chloride in a neutral medium while simultaneously adding folic acid, and the second method is to chelate iron (II) along with 1, 10-phenanthroline (method A) on one hand and Method B depends on the interaction of the primary amino group of folic acid with the amino group of Ninhydrin in an alkaline solution, On the other hand. In Method A, red chromogens with a wavelength of 505 nm are produced, whereas, in Method B, violet chromogens with a wavelength of 575 nm are produced, and so on. Beer's law holds under ideal conditions in molar absorptivity values of 2.331104 L.mol-1 cm-1 and 6.771104 L.mol-1 cm-1, respectively, for concentration ranges of 1.0–35.0 g ml-1 and 0.5–10.0 g ml-1. Sandell's sensitivity values of 1.944 × 10–6 µg cm-2 and 1.6728 × 10–6 µg cm-2, respectively, and Sandell Technically, technique A and method B had limits of detection (LODs) of 0.198 and 0.488, respectively, and limits of quantification (LOQs) of 0.600 and 1.479, respectively. The correlation coefficients (R2) for technique A and method B were both 0.9979 and 0.9988. A and B used stability constants of 5.6192 and 5.4633 kJ.mol-1, respectively, and Gibbs free energy change (ΔG) values of −25.6071 and −24.8963, respectively, in their calculations of the free energy change (ΔG). The proposed methodologies were successfully used to evaluate folic acid in both pure and tablet form, with no influence from common excipients found in pharmaceutical formulations, demonstrating their efficacy. According to the study, there were no statistically significant differences between the precision and accuracy of the suggested procedures and those produced using the reference approach.