Reviewerly/exHarmony huggingface dataset:
An Investigation of Atomic Synchronization for Sort-Based Group-By Aggregation on GPUs

Using heterogeneous processing devices, like GPUs, to accelerate relational database operations is a well-known strategy. In this context, the group byoperation is highly interesting for two reasons. Firstly, it incurs large processing costs. Secondly, its results (i.e., aggregates) are usually small reducing data movement costs whose compensation is a major challenge for heterogeneous computing. Generally for group by computation on GPUs, one relies either on sorting or hashing. Today, empirical results suggest that hash-based approaches are superior. However by concept, hashing induces an unpredictable memory access pattern being in conflict with the architecture of GPUs. This motivates studying why current sort-based approaches are generally inferior. Our results indicate that current sorting solutions cannot exploit the full parallel power of modern GPUs. Experimentally, we show that the issue arises from the need to synchronize parallel threads that access the shared memory location containing the aggregates via atomics. Our quantification of the optimal performance motivates us to investigate how to minimize the overhead of atomics. This results in different variants using atomics, where the best variants almost mitigate the atomics overhead entirely. The results of a large-scale evaluation reveal that our approach achieves a 3x speed-up over existing sort-based approaches and up to 2x speed-up over hash-based approaches.
Cloth Consultant Robot With Temperature &amp; Weather Report Using Uipath – Rpa

This paper utilizes the Cloth Recommendation with weather report Robot using UiPath. The main aim of this project is to create an automatic background robot, which will type, search and script data from internet instead of manual search by humans. RPA (Robot Process Automation) is a technology that is similar to AI (Artificial Intelligence) and RPA will replace AI in future. RPA [Robot Process Automation] is considered as top 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> fastly growing technology. Here, in this project a sequential flow is created on how a robot should script data from internet and store that particular data in the variable, which is created for data storage. Finally, the robot will display the result on the present weather condition and how the Cloud (Either Rainy, Partially Cloud etc) and also will recommend the cloth to wear according to the climatic conditions. This project mainly focuses on the aim to minimize the time of mankind in planning to go anywhere across the globe.
Design and Development of Fuzzy Logic Application Mamdani Method in Predicting The Number of Covid-19 Positive Cases in West Java

Abstract The increase in covid-19 positive patients is unpredictable which results in being unprepared in dealing with Covid-19 cases. People in monitoring and patients under supervision are the category that is breastfeed to be covid-19 positive patients after passing the incubation period for 14 days. Artificial intelligence used to predict the increase in cases of covid-19 positive patients is fuzzy logic. The fuzzy logic method used in this study is the mamdani method. The enter variables used are the increase in ODP, PDP, and Covid-19 positive with 3 sets of low, medium, and high respectively. While the output variable is a positive prediction 14 days later. The curve used for fuzzification needs is a linear curve. The method of composition of the rules used is the MAX function and defuzzification using Centroid.
Translating Natural Language Instructions for Behavioral Robot Indoor Navigation with Attention-History Based Attention

In typical indoor robot navigation scene, it is reasonable to use natural language to navigation due to GPS signal lacking. Translating natural navigation instructions to an executable behavior plan can be implemented by a traditional encoder decoder model with attention mechanism. In traditional attention mechanism, the attention distribution is generated based only on the current decoder state, which may ignore previous useful patterns and introduce translation error. In this work, we propose an attention-history reader network that captures the patterns in the attention history. The results test on the Stanford Navigation Datasets show that our method achieves higher scores than the traditional model.
Study on the classroom attention mechanism of deaf students based on three-in-one education model

The deaf students' classroom attention in higher education is an important factor affecting the acquisition of deaf students' classroom knowledge. This paper studies the deaf students' attention, combines with the measures taken by institutions of higher learning at home and abroad to improve the deaf students' attention, analyzes the main factors affecting the deaf students' classroom attention, builds the mechanism of improving the deaf students' classroom attention by unintentional attention, intentional attention and external environment, forms a three-in-one education mode, improves the deaf students' classroom attention mechanism, and optimizes the deaf students' teaching quality.
Use the Genetic Algorithm to Optimize the Alpha Network of the RETE Algorithm

The RETE algorithm greatly improves the efficiency of the production reasoning system by sharing rule conditions and saving temporary matching results, making it one of the most efficient production reasoning algorithms. However, with the increasing size of data, frequent changes of business information, and the emergence of incomplete data and fuzzy logic, the reasoning time and the storage consumption of the RETE algorithm in the reasoning process are increasing. Many studies are devoted to improving the RETE algorithm to reduce the inference time and the occupation of storage resources. In this article, we propose a method that using the genetic algorithm (GA) to optimize the Alpha network of the RETE algorithm. Experimental results show that genetic algorithm has an optimization effect on the rule matching system in reasoning time and memory consumption.
A Tool to Support the Investigation and Visualization of Cyber and/or Physical Incidents

Investigating efficiently the data collected from a system’s activity can help to detect malicious attempts and better understand the context behind past incident occurrences. Nowadays, several solutions can be used to monitor system activities to detect probable abnormalities and malfunctions. However, most of these systems overwhelm their users with vast amounts of information, making it harder for them to perceive incident occurrences and their context. Our approach combines a dynamic and intuitive user interface with Machine Learning forecasts to provide an intelligent investigation tool that facilitates the security operator’s work. Our system can also act as an enhanced and fully automated decision support mechanism that provides suggestions about possible incident occurrences.
Interpretable Attention Guided Network for Fine-Grained Visual Classification

Fine-grained visual classification (FGVC) is challenging but more critical than traditional classification tasks. It requires distinguishing different subcategories with the inherently subtle intra-class object variations. Previous works focus on enhancing the feature representation ability using multiple granularities and discriminative regions based on the attention strategy or bounding boxes. However, these methods highly rely on deep neural networks which lack interpretability. We propose an Interpretable Attention Guided Network (IAGN) for fine-grained visual classification. The contributions of our method include: i) an attention guided framework which can guide the network to extract discriminitive regions in an interpretable way; ii) a progressive training mechanism obtained to distill knowledge stage by stage to fuse features of various granularities; iii) the first interpretable FGVC method with a competitive performance on several standard FGVC benchmark datasets.
Mutative ABC Based Load Balancing in Cloud Environment

Cloud computing provides its users with adjustable, on-demand virtual resources over the Internet with pay-per-use models. Due to the cost-benefits, a large number of individuals and organizations have moved their business on the cloud. The services offered by service providers range from software to infrastructure. To accommodate such a large number of users, it becomes vital for the service providers to use the resources efficiently. Thus, load balancing becomes one of the major concerns for service providers. Load balancing in the cloud environment is an NP-hard problem and where researchers have proposed different meta-heuristic solutions for it. To utilize resources efficiently and reducing makespan time of the submitted jobs, the paper proposes an algorithm based on the foraging behavior of honey bees, a mutative artificial bee colony algorithm. The proposed algorithm reduces makespan time as well as improves the fitness function. Through experiments implemented in CloudSim, the results verify that the proposed algorithm reduces makespan when compared with algorithms in literature.
News Monitor: A Framework for Querying News in Real Time

News articles generated by online media are a major source of information. In this work, we present News Monitor, a framework that automatically collects news articles from a variety of web pages and performs various analysis tasks. The framework initially identifies fresh news and clusters articles about the same incidents. For every story, it extracts a Knowledge Base (KB) using open information extraction techniques and utilizes this KB in order to build a summary for the user. News Monitor allows the users to query the article in natural language using the state-of-the-art framework BERT. Nevertheless, it allows the user to perform queries also in the KB in order to identify relevant articles. Finally, News Monitor crawls Twitter using a dynamic set of keywords in order to retrieve relevant messages. The framework is distributed, online and performs analysis in real-time.
Data Security Mechanism for Green Cloud

Data and veracious information are an important feature of any organization; it takes special care as a like asset of the organization. Cloud computing system main target to provide service to the user like high-speed access user data for storage and retrieval. Now, big concern is data protection in cloud computing technology as because data leaking and various malicious attacks happened in cloud computing technology. This study provides user data protection in the cloud storage device. The article presents the architecture of a data security hybrid infrastructure that protects and stores the user data from the unauthenticated user. In this hybrid model, we use a different type of security model.
Research of the effectiveness of the research team: sociological analysis on the example of the Ural Federal University

Research activity is an integral function of modern higher education, since withoutthe development of scientific research, the training of graduates cannot be carried out at asufficient level of modern preparation of scientific research. Despite the fact that scientificactivity is carried out in two interrelated forms - individual and collective, in recent years, mostscientific research is carried out by scientific teams, and not by individual scientists. On thebasis of this important issue, research the effectiveness and efficiency of research teams,through the methods of their formation and functioning. In addition, the issue of motivation for scientific work of the research and teaching staff of the university is important. To analyzethe above aspects, we carried out a sociological study that was implemented in two stages - amass survey (N = 211) and a series of in-depth interviews (N = 15) among scientists of the UralFederal University named after the first President of Russia B.N.Yeltsin. The article presentssome of the results of the analysis, in particular, the author comes to the conclusion that establishingties between scientists and joint work in a research team is an important conditionfor increasing the productivity and efficiency of scientific activity, and the desire for scientificcooperation indicates a desire to achieve a higher level of performance scientific activity.
Common machine reading comprehension models

Many outstanding machine reading comprehension (MRC) models have emerged in recent years. These models contain various innovations in network architecture, modular design, and training methods. They achieve remarkable results in many MRC datasets and competitions. Thus it is important to analyze the design philosophy and implementation techniques behind these models to create better architectures. This chapter selects four outstanding MRC models and provides a detailed analysis of the features, architecture, and innovations of each model.
Optimization of K-Nearest Neighbors for Classification

KNN is one of the simplest algorithm used for classification of new data point. In this paper, another strategy of classification method is proposed by combining Class Confidence Weighted (CCW) KNN and Weighted KNN (WKNN) for enhancing the performance of KNN. Inspired from the classic KNN, the main idea being classifying test sample using the most frequent neighbors tag. The optimized KNN uses weights to classify test sample. The new technique is tested on four standard datasets. Results show the significant increment in accuracy in comparison with conventional KNN strategy.
Rocket Sensor Data Prediction Based on LSTM with Attention Mechanism

Accurately predicting the sensor data of the rocket in the future for a period of time from historical rocket sensor data is of great significance to make corresponding control adjustments in time and prevent major danger. Traditional linear models are difficult to solve multi-variable or multi-input problems, while neural networks such as LSTM are skilled at handling multivariable problems. This feature makes it helpful to solve time series prediction problems. This paper proposes a method for rocket sensor data prediction, which uses the attention mechanism and recurrent neural network to predict. It is verified that the average absolute error of this method for rocket sensor data prediction is only 1.38%, and it has strong applicability and accuracy in predicting the rocket attitude.
Recommendation Systems in Healthcare

Recommender systems are a subclass of information filtering systems which is broadly used to find the right products or services for the right consumers. In the era of ‘Big Data’, recommender systems are of great importance as they are used to solve the information overload in many areas of people’s lives (e-commerce/e-shopping, e-library, movies, TV programs, prescriptions, health recommendations, news, cooking, and more). In their simplest form, recommender systems create a personalized list of predictions that are ranked based on the user’s history, preference, solution/domain constraints, and other factors.
A Critical Review on Security Issues in Cloud Computing

Cloud computing has grown significantly in computing services. It is defined as a bunch of computing resources accessed by the Internet. Security in cloud computing, a vicinity filled with challenges and of overriding importance, is in its initial stage of growth currently, and lots of analysis issues are yet to be known. Cloud computing has become the boon of the IT industry. It is like the next step of Internet evolution with a managed and efficient platform for data storage with enormous capabilities and applications. It assists the authorities to access one’s file or data without installing the particulars on the user’s machine using Internet services. Cloud computing enables on-demand network access or shared access to configured computing resources. The use of multiple operating systems on a machine maximizes the use of that particular resource. In this context, it specifies the creation of a virtual version of resources such as storage devices, virtual operating system, deploying an application or software on cloud (SaaS). A system has both pros and cons; cloud computing is no exception. Some of its consequences are cloud security, manageability, integrity, theft, data loss, and so on. In the presented paperwork, we would be reviewing different challenges and issues faced in the data security over cloud.
Implementation of Pre-Subject Oriented Instruction and Industry-Specific Training As a Condition of Learners’ Vocational Self-Determination

The changes in the economy and social sphere raise the problem of revising approaches to the vocational self-determination of young people. Special attention should be paid to the system of general secondary education which requires significant modifications.&#x0D; The article formulates real challenges posed to the education system that affect the formation of vocational self-determination. The paper presents some benchmarks for developing career guidance aimed at schoolchildren and adults that are enshrined in federal, regional and municipal regulations.&#x0D; The author also describes some approaches to the formation of schoolchildren vocational self-determination implemented through pre-subject oriented instruction and industry-specific training in the context of cluster-network forms of interaction between the school and institutions of secondary vocational, higher and additional education, as well as with enterprises and business.&#x0D; This paper also presents the innovative project developed by Municipal Budgetary General Education Institution called "Secondary School No. 60 in Novokuznetsk" of the Kemerovo Region and describes career guidance activities within the framework of its implementation.
An iterative MPD‐CNN structure for massive MIMO detection under correlated noise channels

In massive multiple-input multiple-output (MIMO) systems, most of the existing detection work mainly assumes that the channel is the additive white Gaussian noise (AWGN). However, this assumption is difficult to apply to practical communication scenarios. To this end, this paper proposes a message passing detection (MPD) algorithm with a convolutional neural network (CNN) (denoted as iterative MPD-CNN structure) under correlated noise channels, which is helpful to solve the issue of detection performance degradation in non-ideal AWGN channels. Firstly, the MPD algorithm based on the channel hardening phenomenon is used to initially estimate the transmitted signal, and then the CNN is concatenated to remove the estimation error for obtaining more accurate channel noise, which provides a beneficial noise distribution for the MPD algorithm. Finally, the theoretical analysis and simulation results show that the proposed iterative MPD-CNN structure can improve the detection performance in conditions of correlated noise channels and fewer antennas. Compared with the traditional MPD algorithm, its detection performance is more superior.
Training a Robot to Attend a Person at Specific Locations using Soft Actor-Critic under Simulated Environment

We present the application of soft actor-critic (SAC) learning algorithm to train a mobile robot to attend a target person at specific locations inside a Gazebo simulator. Since our previous study confirmed that the appropriate attending position while the target person is standing or walking is at his left or his right side, we design a novel U-shaped reward function behind the target person's position with respect to the robot's position. To make the robot can better portray the surroundings, we also propose a novel SAC architecture which employs 1D convolutional neural networks to extract features from laser scans automatically during the training process. Our preliminary experiment result shows that the robot is able to attend the target person at the designed location using our proposed reward function and SAC architecture.
Open-Domain Conversational Search Assistant with Transformers

Open-domain conversational search assistants aim at answering user questions about open topics in a conversational manner. In this paper we show how the Transformer architecture [30] achieves state-of-the-art results in key IR tasks, leveraging the creation of conversational assistants that engage in open-domain conversational search with single, yet informative, answers. In particular, we propose an open-domain abstractive conversational search agent pipeline to address two major challenges: first, conversation context-aware search and second, abstractive search-answers generation. To address the first challenge, the conversation context is modeled with a query rewriting method that unfolds the context of the conversation up to a specific moment to search for the correct answers. These answers are then passed to a Transformer-based re-ranker to further improve retrieval performance. The second challenge, is tackled with recent Abstractive Transformer architectures to generate a digest of the top most relevant passages. Experiments show that Transformers deliver a solid performance across all tasks in conversational search, outperforming the best TREC CAsT 2019 baseline.
Reed: An Approach Towards Quickly Bootstrapping Multilingual Acoustic Models

Multilingual automatic speech recognition (ASR) system is a single entity capable of transcribing multiple languages sharing a common phone space. Performance of such a system is highly dependent on the compatibility of the languages. State of the art speech recognition systems are built using sequential architectures based on recurrent neural networks (RNN) limiting the computational parallelization in training. This poses a significant challenge in terms of time taken to bootstrap and validate the compatibility of multiple languages for building a robust multilingual system. Complex architectural choices based on self-attention networks are made to improve the parallelization thereby reducing the training time. In this work, we propose Reed, a simple system based on 1D convolutions which uses very short context to improve the training time. To improve the performance of our system, we use raw time-domain speech signals directly as input. This enables the convolutional layers to learn feature representations rather than relying on handcrafted features such as MFCC. We report improvement on training and inference times by atleast a factor of 4× and 7.4× respectively with comparable WERs against standard RNN based baseline systems on SpeechOcean's multilingual low resource dataset.
FPGA based Structural Radial Basis Function Neural Network with Hybrid Optimization for Neural Activity

Emotion Recognition is one of the prerequisites for Human to Machine (H2M) cognitive communication. H2M communication using Electro-Encephalogram (EEG) based cognitive interfaces lowers overall reaction latency and improves decision-making throughput of cyber-physical systems. This work presents an emotion classification technique that is suitable for deployment in edge based IoT devices with sufficiently high accuracy and specificity compared to state-of-the-art architectures. Proposed technique has been verified on a real time PYNQ Z2 based hardware accelerator platform.