Reviewerly/exHarmony huggingface dataset:
Stochastic Pairing for Contrastive Anomaly Detection on Time Series

Anomaly detection for predictive maintenance is a significant concern for industry. Unanticipated failures cause high costs for experts involved in maintenance policy. Traditional reconstruction-based anomaly detection methods perform well on multivariate time series but they do not consider the diversity of samples in the training dataset. An abrupt change of operating conditions, which is labeled as anomaly by experts, is often not detected due to the lack of sample diversity. Besides, obtaining large volumes of labeled training data is cumbersome and sometimes impossible in practice, whereas large amounts of unlabelled data are available and could be used by unsupervised learning techniques. In this paper, we apply the principles of contrastive learning and augmentation in a self supervised way to improve feature representation of multivariate time series. We model a large variety of operating conditions with an innovative distance based stochastic method to prepare an anomaly detection downstream task. Our approach is tested on NASA SMAP/MSL public dataset and shows good performance close to the state-of-the-art anomaly detection methods.
“Wall Hack AR”: AR See-through system with LiDAR and VPS

We propose a method to realize the experience of seeing through an occluding wall by using a 3D scanner and an AR (Augmented Reality) device. This system uses the LiDAR (Light Detection And Ranging) scanner of the Apple iPad Pro as a 3D scanner, sending scanned spatial mesh data to the AR device of the experiencer in real-time. By using a VPS (Visual Positioning System) to also share the actual spatial position of the mesh, a receiving AR device can also draw the scan data registered at the correct position in the actual space. Placing the LiDAR scanner behind an occlusion allows an experience as if the user were seeing through a wall.
Data Security Awareness in Online Learning

The Covid-19 pandemic has intensified the online learning activities, which have becoming the most crucial platforms for learning sessions.With these online learning activities, a major concern arises particularly on the security of educators and students' data.Every element in an online learning system can be a potential target of hacking or attack.Therefore, this paper examines students' awareness of data security in online learning.Google forms have been used to gather the students' feedback.Forty-two (42) students, involving the secondary school, pre-university and university students, responded to the survey questions.The results show that most of the respondents are well aware of how to keep their data safe in online learning.It is discovered that the level of awareness about data security in online learning amongst the students is commendable.The findings of this study indicate there are various ways to secure students' data especially during online learning.
An Efficient Quantum Private Comparison Protocol Based on Cluster State and Bell State

Quantum private comparison (QPC) is the quantum method that can compare the equality of the distrustful participants’ private information without leaking them. The 4-particle cluster state has a strong violation of local reality and shows to be robust against decoherence than any other 4-particle state. In this paper, a new efficient QPC protocol based on the entanglement swapping between the 4-particle cluster state and Bell state has been proposed. With the pre-shared secret key sequence between the two participants, the semi-honest third party (TP) can only execute the protocol’s processes without obtaining information about the participants’ secrets and the final comparison results. Then, we show that the proposed QPC protocol is secure against outside or participant attacks and more efficient than similar literature.
Automatically Identifying &amp; Counting Animals in Camera Trap Image Using DL

It is vital to make a remember of animals as they're being extinct now a days, to be able to store them we should hold a word well in order that we are able to take vital measures to store them. In current approach there may be simplest guide checking in which a individual want to provide to hold a remember of animals. Which take a lot of time to perceive the animals. To conquer the ones troubles we're introducing an automated figuring out and counting the animals through the use of deep studying methods.This manner offers an correct effects to detects and hold right remember of animals.
Deep Learning Channel Estimation for OFDM 5G Systems with Different Channel Models

This paper presents our initial results in assessing the efficiency of deep learning-based channel estimation compared to the conventional Pilot-Assisted Channel Estimation (PACE) techniques, such as least-square (LS) and minimum mean-square error (MMSE) estimators. A simulation environment to evaluate OFDM performance at different channel models has been used. A DL process that estimates the channel from training data is also employed to get estimated channel impulse response. Two channel models have been used in the comparison: Clustered Delay Line (CDL) and Nakagami-m fading channel models. The performance is evaluated under different parameters including number of pilots, number of subcarriers, the length of cyclic prefix and carrier frequency through computer simulation using MATLAB. From the simulation results, the trained DL estimator provides better results in estimating the channel and detecting the transmitted symbols compared to LS and MMSE estimators at a remarkably less complexity. Furthermore, the DL estimator also demonstrates its effectiveness with various pilot densities and with different cyclic prefix periods.
Forward Prediction and Inverse Design of Nanophotonic Devices Based on Capsule Network

Deep neural networks have been successfully applied to forward predicting optical response and inverse designing topological structure of nanophotonic devices.However, the existing deep learning based methods need sufficient simulated data to train the model effectively.For those devices with complex structures that containing many design variables, obtaining enough training data through numerical simulations will become extremely time-consuming.In order to reduce the requirement of large amounts of training data, we present a new deep learning approach based on the Capsule Network in this paper.By employing the proposed model, we have designed and verified a series of silicon-based wavelength demultiplexer with more than one thousand design variables.The numerical simulations validate that the trained model can both effectively predict the optical response with a fixed topological structure, and inverse design the approximate topological structure for a needed given optical response.Comparison with the classical convolutional neural networks show that our model can obtain nearly the same performance when using only 60% of the training data.
A Game Theoretic Group Coordination Strategy for Multi Robot Navigation

Navigating multiple robots across a terrain to achieve their individual goals is a challenging research problem and has been addressed by incorporating a myriad of soft computing techniques. This article discusses this problem from the perspective of enacting a group coordination strategy among a group of robots. Such a scheme can go a long way in paving the path for quick and automatic disaster recovery through sharing critical information. Experiments have been carried out with four groups of robots with their respective sources and destinations and varying the number of robots between 8 to 20 with a novel game-theoretic group coordination strategy. Results obtained using the proposed approach have been compared with those obtained by traditional Potential Field Method (PFM) and standard soft computing based strategy such as Genetic Algorithm-Fuzzy (GA-Fuzzy) for performance metrics, both individual as well as for group, traveling time and the obtained results demonstrate the efficacy of the proposed method.
Implementation of Four-Pass Protocol Scheme Using Mathematical Series Cipher Encryption and Decryption in a Communication Network

Security features come into play when it is compulsory or enviable to defend the information communication from an opponent who attempts to break the confidential information. Security of the information is the encryption of the message, which messes up the message so that the message is indecipherable by the opponent and the addition of the code based on the contents of the message which can be used to verify the uniqueness of the sender. Some information shared by two persons should be unidentified to the malicious intruders. An example is an encryption key used in concurrence with the transformation to mess up the message before transmission and unscramble it on reception. In this connect, a new algorithm has to be designed for performing and ensuring the security-related information transmission between two parties. The algorithm should be designed in such a way that an opponent cannot overcome its purpose. The secret message needs to be generated for implementing security in the algorithm. In designing the consent security service, the method has to be developed for the sharing and allocation of secret information. This research paper proposes a new technique for implementing four-pass protocol schemes which will use mathematical series perceptions for the encryption and decryption of information in a communication network.
Crowd Anomaly Detection in Surveillance Video

With the advancement of technology, the analysis of crowd abnormal behavior has become a hot topic in the field of computer vision. The research not only includes the analysis of the abnormal behavior of a single pedestrian in a simple scene, but also includes the analysis of the overall abnormal behavior of the crowd in a complex scene. This paper research on the detection and alarm of abnormal crowd behavior in surveillance video. First, the moving target is detected by the background subtraction method. Secondly, the fall behavior in the video is detected through two-level SVM and human feature action recognition. At this stage, the human body features obtained in the target detection stage are analyzed, and the key point features of the human body are analyzed to determine whether the detection target has fallen behavior. Finally, a counting module is introduced to count the pedestrians on the basis of target detection in the current scene. We compare the changes in the number of people in the video frames at a set time interval in the same scene to determine whether a sudden crowd gathering occurred abnormal behavior.
An adaptive neuro-fuzzy inference for blockchain-based smart job recommendation system

Blockchain is a technology that supports secured transaction in a public distributed database.It maintains a peer-to-peer network where a transaction cannot be modified or tampered by unauthenticated users.It provides a safe message transfer from a sender to a receiver.Job recommendation is an online system that provides a mapping between the job seeker and the employer.This paper proposes a public blockchain of job recommendations based on incremental hashing.The examinations show that this blockchain job recommendation provides process integrity, traceability, security, high levels of transparency, drastic reduction in operational cost and high standard and systematic.The system has two stages.Firstly, using blockchain technology, the authenticated data is fetched.Secondly, a classification model using adaptive neuro-fuzzy inference system is built for mapping the job seeker to the recruiter.This approach proves to be authenticated as well as a smart job recommendation system.
A consortium blockchain-based information management system for unmanned vehicle logistics

Unmanned vehicle (UDV) delivery technology can meet the special needs of users and realize efficient and flexible distribution of logistics orders. However, there are risks of order data leakage and tampering in the intelligent logistics distribution environment. To solve this problem, this paper designs and implements a system based on the Hyperledger Fabric blockchain platform. Based on the blockchain technology, the system adopts a distributed architecture to establish a secure and trustworthy logistics data management platform to achieve the integrity and traceability of data in the logistics process. The data dual-chain storage strategy is used to ensure the efficiency of data queries. Furthermore, four smart contracts including order management contract (OMC), access control management contract (ACC), access control policy management contract (ACPC), and environmental data management contract (EDC) are designed in combination with the attribute-based access control strategy. By triggering the smart contract, the controllable access of order data can be realized. Finally, two groups of experiments are designed to test the performance of the system. Experimental results show that the proposed system can maintain high throughput in a large-scale request environment under the premise of ensuring data security.
Point-to-set distance functions for output-constrained neural networks

Training a neural network for semantic segmentation with many images and pixel-level segmentations is a well-established computer-vision technique.When pixel-level segmentations are unavailable, weak supervision or prior information like bounding boxes and the size/shape of objects still enables training a network.Directly including prior knowledge on the segmentations means constraining the network output.This complicates the possible optimization strategies because the regularization then acts on the non-linear neural-network function output and not on the optimization variables.We present a new algorithm to include prior information via constraints on the network output, implemented via projection-based point-to-set distance functions, that are differentiable and always have the same functional form for the derivative.Thus, there is no need to adapt penalty functions or algorithms to various constraints.The distance function's differentiability also avoids issues related to constraining properties typically associated with non-differentiable penalties.We show that by explicitly taking a general neural network structure into account, the Lagrangian for the problem 'naturally' decouples the constraints and neural network, which allows for a gradient computation via backpropagation/adjoint-state as is common in deep learning.We present a suite of constraint sets suitable for segmentation problems.The numerical experiments show that learning from constraint sets applies to the broader imaging sciences, including visual and non-visual imagery, even when training a network for a single example.
The time to lead: ACRL seeks nominees for section offices

The time to lead ACRL seeks nominees for section offices Standing for ACRL section office is an opportunity to become involved with important issues affecting academic and research librarians in the 21st century, gain leadership experience, and build ties with colleagues.It is also an opportunity to be of service to your ACRL section community,
Analytical Decision-Making System Based on the Analysis of Air Pollution in the City of Nur-Sultan

This study provides recommendations on urbanization, such as gasification, road construction, green park areas, residential facilities, air quality tips, etc. Using a data-driven approach based on the correlation between factors contributing to air pollution, the Nur-Sultan Air Quality Index. Correlation between factors contributes to air pollution. To collect data, the study uses obtaining such information as the temperature from PM2.5, CO2, and the temperature of Kazhydromet, Limited Liability Company (LLC) “KORKEM TELECOM” and Airkaz organizations. The analysis on Power BI for visualization of input data is carried out. The results of the analysis and monitoring have a wide range of applications, in particular, they can be used to build a decision-making system for forecasts on the urbanization of the city, namely, determining the territory/site for gasification and urbanization of the city.
Analysis and Design of the Health Management System for Medical Staffs

The concern about health of medical staffs is increasing recently with the outbreak of COVID-19.In this case, it is necessary to design a particular health management system for medical staff with the function to prevent and control both physical diseases and emotional issues.The system proposed in this paper is successful in combining the physical and psychological health of medical workers.It starts by evaluating the level of social support by big data mining and then calculates the possible stress felt by medical staff.Physiological indexes and stress sensing are recorded and then analysed through the knowledge base.Eventually, the medical advice generated by inference engine will be sent to the users if needed.
Analysis of ten stocks based on IM model

This paper collects historical daily total return data for the last 20 years for 10 stocks belonging to three or four different industries, one (S&P 500) stock index (11 risky assets in total) and A proxy for the risk-free rate (1-month federal funds rate) designed to aggregate daily data into monthly observations, and based on those monthly observations to calculate all proper optimization inputs alongside the Index Model ("IM") under five constrains.This paper also presents the results in sheet to make inferences comparisons, inferences and instructions between the sets of constraints for the sensitivity analysis.
A Comparative Study of Deep Learning Techniques for Farmer Query Text Classification

This research proposes Long Short-Term Memory Network models with different stacked LSTM layers for farmer queries classification. The 23 different classes of 105,724 farmer queries were used to train the proposed LSTM models. Word to vector (Word2Vec), Global Vectors for Word Representation (GloVe), and FastText embedding techniques were compared on the query classification. The Word2vec embedding technique produced a better result than the GloVe and FastText embedding techniques in the farmer query classification. After an extensive simulation, the DLSTM network with three stacked LSTM layers achieved a testing accuracy of 90.35%. Classification performance of the proposed DLSTM network with three stacked LSTM layers in farmer queries was superior to Convolutional Neural Network (CNN), LSTM and other DLSTM models.
An Efficient Method for Three-Dimensional Precise Measurement Using Laser Spot Traversal and Target Point Interpolation

Discrete point interpolation has emerged as a prime candidate technology for dual-station accurate intersection in 3-D precise measurement. However, the operation is low-efficiency for capturing multiple reference points to measure a single target point. The quantitative model of linear small angle in the interpolation is proposed by analyzing the movement trajectories of laser beam and laser spot. An efficient method for 3-D precise measurement using laser spot traversal and target point interpolation is proposed to achieve accurate intersection without multiple approximations. The laser spot traverses the field of view of the camera to establish an interpolation grid. The target point inserts the traversal grid and can be aimed by laser beam in arbitrary position. The traversal step size is determined by the determining equation of linear small angle. The approach is time-efficient for capturing all measured points in the field of view. The experimental results show that the proposed method is suitable for multiple points and surface measurement.
Automated Support for Risk Management in Scrum Agile Projects

The importance of software in the modern world entails the need to develop technologies that make the software development process more agile. Agile software development approaches have been proposed to deal with constant changes in project requirements. In Scrum, the Product Owner manages such changes so that the developed software brings significant value to the customers. However, there are potential risks involved in Product Owner responsibilities that, if not properly managed, can lead to project failure and significant financial losses. In this paper, we introduce RIMPRO-AST, an automated tool support to manage risks involving the product owner. The automation of risk management processes is crucial since the lack of computational support imposes barriers to the success of risk management activities in agile projects. RIMPRO-AST supports the process defined in RIMPRO risk management framework to guide Scrum teams to manage risks involving Product Owner roles. The results obtained through the evaluation of RIMPRO-AST with potential users indicate its effectiveness in speeding up and controlling risk management activities. Therefore, our study demonstrates that RIMPRO-AST can be used to minimize the threats and their risks and maximize the opportunities that might arise throughout Scrum software development projects.
Using a Technique Based on Moment of Inertia About an Axis for the Recognition of Handwritten Digit in Devanagri Script

A neural network for handwritten digit recognition is dependent on the dataset used to train it. Most of the techniques available in the literature recognize the handwritten characters of any script based upon the moment of inertia of the character uses a central point to it and suffers from certain drawbacks. In this paper, we develop methods that can be applied to any neural network, to make the dataset insensitive to brush size and insensitive to rotation applied to the digit based on the moment of inertia that the digit produces about an axis. All images in the dataset undergo pre-processing. First, the image is made independent of brush size. Then, the image is rotated to negate the rotation that is applied to the image according to the axis of the least moment of inertia. This pre-processing makes the dataset independent of brush size and rotation. The results show that when this preprocessing technique is applied to neural a network, it does makes it insensitive to rotation and brush size.
An Image Encryption Method Based on Logistic Mapping and Phasmatodea Population Evolution Algorithm

Sun, Xiao-xue Song, Pei-Cheng Shieh, Chin-Shiuh Tsai, Pei-Wei Pan, Jeng-ShyangThe security of information is closely related to our daily life and occupies an important position. The data encryption algorithm is an indispensable element in modern life. Image encryption technology is one of the more commonly used technologies and methods in contemporary networks. Since the logistic transformation is sensitive to the initial value, the paper tries to employ the Phasmatodea population evolution (PPE) algorithm to train and select an optimal appropriate initial value to improve security. After the encryption simulation experiment test, the PPE algorithm in this paper has more outstanding encryption performance among several encryption methods, has better scrambling and diffusion effects, and has improved information security.
AI Based Chatbot for FAQs

A chatbot is a computer program that runs a conversation in the form of a text. Chat-bot usually provides user-based communication that allows input from the user and receives text from the outgoing. Chat-bot information is stored on a website provided by the owner and the user needs. This program will be given the answers to the user's question with great success. The user must place a requirement on the chat-bot used for the transport-related chat. This model is based on supervised learning strategies with pre-packaged information to focus and create productive models against a training set. The system will use an AI algorithm to provide relevant information about transit to the user.