Reviewerly/exHarmony huggingface dataset:
Unsupervised Person Re-Identification with Attention-Guided Fine-Grained Features and Symmetric Contrast Learning

Unsupervised person re-identification has attracted a lot of attention due to its strong potential to adapt to new environments without manual annotation, but learning to recognise features in disjoint camera views without annotation is still challenging. Existing studies tend to ignore the optimisation of feature extractors in the feature-extraction stage of this task, while the use of traditional losses in the unsupervised learning stage severely affects the performance of the model. Additionally the use of a contrast learning framework in the latest methods uses only a single cluster centre or all instance features, without considering the correctness and diversity of the samples in the class, which affects the training of the model. Therefore, in this paper, we design an unsupervised person-re-identification framework called attention-guided fine-grained feature network and symmetric contrast learning (AFF_SCL) to improve the two stages in the unsupervised person-re-identification task. AFF_SCL focuses on learning recognition features through two key modules, namely the Attention-guided Fine-grained Feature network (AFF) and the Symmetric Contrast Learning module (SCL). Specifically, the attention-guided fine-grained feature network enhances the network's ability to discriminate pedestrians by performing further attention operations on fine-grained features to obtain detailed features of pedestrians. The symmetric contrast learning module replaces the traditional loss function to exploit the information potential given by the multiple samples and maintains the stability and generalisation capability of the model. The performance of the USL and UDA methods is tested on the Market-1501 and DukeMTMC-reID datasets by means of the results, which demonstrate that the method outperforms some existing methods, indicating the superiority of the framework.
Ketersediaan Sumber Belajar Bahasa Indonesia di SMP Negeri 2 Gondangrejo

This study aims to determine the availability of Indonesian language learning resources at SMP Negeri 2 Gondangrejo. This research is a qualitative descriptive study with a qualitative approach. The research was conducted through interviews with Indonesian teachers in grades 7, 8, and 9. The research location was SMP Negeri 2 Gondangrejpo. There are two kinds of Indonesian language learning resources available at SMP Negeri 2 Gondangrejo, namely books and the internet. Indonesian language learning resources sourced from books are books from the government in 2018. The availability of Indonesian language learning resources at SMP Negeri 2 Gondangrejo in addition to books, namely the internet. Karanganyar Regency created a website called Indoprima, this website is used for grades 7, 8, and 9. Indonesian language teachers for grade 7 have a learning innovation during this pandemic by creating a radio web. This web radio is called Esperojo.fm. The results of this study are the availability of Indonesian language learning resources at SMP Negeri 2 Gondangrejo already using learning resources well. So that from the source of learning Indonesian at SMP Negeri 2 Gondangrejo achieve the goals of learning Indonesian.
Construction of Edge Computing Platform Using 3D LiDAR and Camera Heterogeneous Sensing Fusion for Front Obstacle Recognition and Distance Measurement System

This research aims to utilise heterogeneous sensor fusion using 3D Light Detection and Ranging (LiDAR) and cameras, combined with an object recognition system and a ranging system, to construct an edge computing platform such that a vehicle equipped with the platform can perform computations offline in real time. This work comprises two main sections: the first is heterogeneous fusion, and the second is obstacle recognition and ranging detection. To achieve heterogeneous sensor fusion, 3D–3D point matching was used to find rigid body transformation between two sensors and finally project the LiDAR 3D point cloud image onto the 2D image. For object recognition, YOLOv4-Tiny was used as the detection network. A lightweight network architecture and high computational speed could be effectively used on edge computing hardware with limited performance. Further, by drawing the bounding box, we could detect the point cloud within the bounding box to estimate the distance to the obstacle. For detecting distance, we conducted experiments in two ways: ‘minimum point in box’ and ‘median point in box’ and compared the results. With heterogeneous sensor fusion, object recognition and the ranging system, detecting the category and distance of obstacles ahead of the vehicle was possible in real time. Furthermore, integrating the edge computing platform architecture enabled moving the entire system offline, making it an independent system that returns results in real time. Finally, a dynamic test was conducted on a road. The experiment showed that the detection speed of YOLOv4-Tiny in the dynamic test was higher than 60 FPS, and the accuracy rate surpassed 70%. Furthermore, the distance detection error of the 3D LiDAR was less than 3 cm, which is sufficiently accurate to be applied to complex environments on roads.
What motivates the most? Money or Empowerment: Mediating Role of Employee Commitment to Organizational Performance

The study leads to understanding the relationship between human resources (HR) practices and outcomes to maintain organizational performance (OP) in Saudi Arabia. It concentrates on whether the HR practices quality overcome the quantity or not in the educational sectors. Convenience sampling was used to collect data using google forms based on A structured questionnaire. A total of 759 responses were successfully received and completed. The questionnaire consists of items measuring different aspects of HR practices, employee commitment, and organizational performance. The structural equation modelling (SEM) method used path modelling analysis using Smart Partial Least Squares (PLS) analytical software. The results show a reasonable fit between the data analysed and the proposed research model: SRMR (0.072), CR &gt; 0.7, AVE &gt; 0.5, Cronbach Alpha &gt; 0.7, and VIF &lt; 5. The study reveals that empowerment is a strong predictor of organizational performance than compensation. Moreover, employee commitment (mediate) partially contributes to organizational performance. HR practitioners may collect the employees’ data more regularly to learn if HR practices produce the desired commitment level to produce organizational performance. This study is limited to the Saudi universities and responders who participated in the questionnaire. Future studies should focus on other economic sectors to help generalize the research outcomes and validate the study’s hypothetical model.
Using two-dimensional fast Fourier transform for estimating spectral correlation function

The paper presents the algorithm for estimating spectral correlation function (SCF) of a wide-sense cyclostationary random process. SCF provides the quantitative representation of the correlation in frequency domain and relates to cyclic autocorrelation function via Fourier transform. The algorithm is based on two-dimensional Fourier transform, which is being applied to the discrete diadic correlation function weighted by a two-dimensional windowing function, chosen rectangular in the direction orthogonal to the current-time axis. This transform can be implemented by means of the fast Fourier transform (FFT) algorithm, which is built-in in a variety of modern mathematical platforms. A pulse-amplitude modulated process masked by the additive stationary Gaussian noise was considered as an example of a random process exhibiting strong cyclostationarity. The numerical simulation where the estimation of spectral correlation function of such process is conducted, and it proved the effectiveness of the proposed algorithm.
Doing More with a DM: A Survey on Library Social Media Engagement

Objectives – This study sought to determine the role social media plays in shaping library services and spaces, and how queries are received, responded to, and tracked differently by different types of libraries. Methods – In April and May of 2021, researchers conducted a nine-question survey (Appendix A) targeted to social media managers across various types of libraries in the United States, soliciting a mix of quantitative and qualitative results on prevalence of social media interactions, perceived changes to services and spaces as a result of those interactions, and how social media messaging fits within the library’s question reporting or tracking workflow. The researchers then extracted a set of thematic codes from the qualitative data to perform further statistical analysis. Results – The survey received 805 responses in total, with response rates varying from question to question. Of these, 362reported receiving a question or suggestion via social media at least once per month, with 247 reporting a frequency of less than once per month. Respondents expressed a wide range of changes to their library services or spaces as a result, including themes of clarification, marketing, reach, restriction, collections, access, service, policy, and collaboration. Responses were garnered from all types of libraries, with public and academic libraries representing the majority. Conclusion – While there remains a disparity in how different types of libraries utilize social media for soliciting questions and suggestions on library services and spaces, those libraries that participate in the social media conversation are using it as a resource to learn more from their patrons and communities and ultimately are better situated to serve their population.
Computer-Based Blood Type Identification Using Image Processing and Machine Learning Algorithm

Blood type identification is a method used for determining the specific blood type of a person. It is a requirement before blood transfusions or blood donations is undertaken especially during emergency situations. Presently, the tests are performed manually by medical technologists in the laboratories. Sometimes, manual blood typing is prone to human error, resulting to incorrect blood grouping and wrong typing in the report, leading to fatal transfusion reactions. The study was focused on the development of a device that is capable of identifying the blood type of an individual using an image processing and machine learning algorithms. The study covered the identification of eight blood types, specifically rhesus positive and negative, A, B, O, and AB, by developing a capturing box integrated with a web camera system that could effectively capture blood sample images. In this study, the methodologies utilized were image processing through segmentation, feature extraction by color and texture properties, and different machine learning algorithms. After training, the results showed that coarse tree DT has the best performance accuracy score of 97.77% using 70:30 holdout validation. The testing results showed that the system is 100% accurate as validated by a registered medical technologist.
Mask-Aware Semi-Supervised Object Detection in Floor Plans

Research has been growing on object detection using semi-supervised methods in past few years. We examine the intersection of these two areas for floor-plan objects to promote the research objective of detecting more accurate objects with less labeled data. The floor-plan objects include different furniture items with multiple types of the same class, and this high inter-class similarity impacts the performance of prior methods. In this paper, we present Mask R-CNN-based semi-supervised approach that provides pixel-to-pixel alignment to generate individual annotation masks for each class to mine the inter-class similarity. The semi-supervised approach has a student–teacher network that pulls information from the teacher network and feeds it to the student network. The teacher network uses unlabeled data to form pseudo-boxes, and the student network uses both label data with the pseudo boxes and labeled data as the ground truth for training. It learns representations of furniture items by combining labeled and label data. On the Mask R-CNN detector with ResNet-101 backbone network, the proposed approach achieves a mAP of 98.8%, 99.7%, and 99.8% with only 1%, 5% and 10% labeled data, respectively. Our experiment affirms the efficiency of the proposed approach, as it outperforms the previous semi-supervised approaches using only 1% of the labels.
A Secure and Efficient Identity based Proxy Signcryption Scheme for Smart Grid Network

A smart grid is a modern network system that configures variety of equipment based on Internet of Things and such equipment while working with smart grid systems produces huge amounts of data.As a result the data requires confidentiality and access control.To secure the data access, the proxy signcryption scheme is the most suitable option.In this work we proposed an identity based proxy signcryption scheme for smart grid (IDPSC-SG).The proposed scheme can provide the advantages of both, the proxy signature and proxy signcryption using single algorithm.The comprehensive security study of the proposed scheme shows that, IDPSC-SG can achieve the security requirements of IND-IDPSC-SG-CCA2.Furthermore it also achieve the existential unforgeability against adaptive chosen messages attack (EUF-CMA).The formal verification using AVISPA tool shows that IDPSC-SG is safe under OFMC and CL-Atse.Moreover, extensive performance evaluation indicates its efficiency with respect to computation time.
Applied AI teaches handwriting

In an increasingly digital world, how do you teach students cursive handwriting?
Exploration with Model Uncertainty at Extreme Scale in Real-Time Bidding

In this work, we present a scalable and efficient system for exploring the supply landscape in real-time bidding. The system directs exploration based on the predictive uncertainty of models used for click-through rate prediction and works in a high-throughput, low-latency environment. Through online A/B testing, we demonstrate that exploration with model uncertainty has a positive impact on model performance and business KPIs.
human activity recognition using CNN and long term short term memory

Human activity recognition aims to work out the activities performed by someone in a picture or video. Examples of actions are running, sitting, sleeping, and standing. Complex movement patterns and harmful occurrences like falling may be a part of these activities. The suggested ConvLSTM network can be created by successively combining fully connected layers, long immediate memory (LSTM) networks, and convolutional neural networks (CNN). The acquisition system will pre calculate skeleton coordinates using human detection and pose estimation from the image/video sequence. The ConvLSTM model builds new controlled features from the raw skeleton coordinates and their distinctive geometric and kinematic properties. Raw skeleton coordinates are utilized to generate geometric and kinematic properties supported by relative joint position values, joint differences, and their angular velocities. By utilizing a multi-player trained CNN-LSTM combination, novel spatiotemporal directed features can be obtained. The classification head with completely connected layers is then utilized. The suggested model was tested using the KinectHAR dataset, which consists of 130,000 samples with 81 attribute variables and was compiled using the Kinect (v2) sensor Experimental data is used to compare the performance of independent CNN and LSTM networks.
Implementation of Highly Reliable and Energy‐Efficient Nonvolatile In‐Memory Computing using Multistate Domain Wall Spin–Orbit Torque Device

Spintronic In-Memory Computing In article number 2200028 by Guozhong Xing and co-workers, in-memory computing technology that promises to tackle the memory wall bottleneck in modern systems is presented. The developed magnetic domain wall devices demonstrate reliable multistates driven by synergistic spin-orbit torque and Dzyaloshinskii–Moriya interaction, facilitating new applications of time and energy-efficient spintronic in-memory computing with a full adder implementation.
Private Savings and COVID-19 in Sub-Saharan Africa

The paper reexamines the main private savings determinants in Sub-Saharan Africa (SSA), followed by an analysis of the COVID-19 pandemic impact on private savings in SSA and other country groupings.Using an unbalanced panel data from 1983-2021 for 31 SSA economies, the paper finds that real per capita economic growth remains a key historical determinant of private savings in the region.In contrast with other regions, private saving rates have not increased during COVID-19 in SSA.Instead, COVID-19 deaths in our estimations are significantly associated with a decline in private savings in SSA.Robustness checks and a descriptive analysis of household surveys during the pandemic corroborate those results.
Initial Stage of the COVID-19 Pandemic: A Perspective on Health Risk Communications in the Restaurant Industry

Restaurant online review websites have made changes to adapt to customers’ shifting needs during the COVID-19 crisis. Based on information behavior theory and social penetration theory, the present study investigated the changes in customers’ emotions and how the volume of online reviews as an indication of sales is impacted by the instructional (i.e., with quantitative variables) and emotional (i.e., with qualitative variables) information on review websites. By comparing the same month (January–April) during 2017–2020, positive sentiment experienced a plunge, while negative sentiment showed an upsurge in April 2020. The volume of reviews was impacted by five quantitative variables (i.e., confirmed COVID-19 case number, food delivery option, takeout option, delivery fee, and delivery time) and seven qualitative variables (i.e., anticipation, fear, trust, anger, disgust, joy, and sadness). This study provides new insight into understanding information content on review websites during the crisis (e.g., pandemic) from the perspective of health risk communication.
Application of an improved Discrete Salp Swarm Algorithm to the wireless rechargeable sensor network problem

This paper presents an improved Discrete Salp Swarm Algorithm based on the Ant Colony System (DSSACS). Firstly, we use the Ant Colony System (ACS) to optimize the initialization of the salp colony and discretize the algorithm, then use the crossover operator and mutation operator to simulate the foraging behavior of the followers in the salp colony. We tested DSSACS with several algorithms on the TSP dataset. For TSP files of different sizes, the error of DSSACS is generally between 0.78% and 2.95%, while other algorithms are generally higher than 2.03%, or even 6.43%. The experiments show that our algorithm has a faster convergence speed, better positive feedback mechanism, and higher accuracy. We also apply the new algorithm for the Wireless rechargeable sensor network (WRSN) problem. For the selection of the optimal path, the path selected by DSSACS is always about 20% shorter than the path selected by ACS. Results show that DSSACS has obvious advantages over other algorithms in MCV's multi-path planning and saves more time and economic cost than other swarm intelligence algorithms in the wireless rechargeable sensor network.
Blockchain-Based Privacy Securing G-Cloud Framework for E-Healthcare Service

Blockchain is an fascinate study place for a very long period as well as the benefits it provides are employed by a variety of various industrial application. Furthermore, the healthcare business helps to assist immensely from blockchain expertise due to reliability, privacy, confidentiality, and decentralization. Nevertheless, the electronic health record (EHR) systems deal with difficulties about info protection, integrity, and management. With this particular newspaper, how blockchain engineering might be employed to alter the EHR methods and also may be a formulation of these issues. Additionally, the structure which might be used with the setup of blockchain engineering contained healthcare sector for EHR. Medical devices provide fewer security measures to secure the health records. Blockchain will be essentially a dispersed and also decentralized ledger that rolls an important part inside preventing the info and deals. The objective of the suggested framework is first of most to stick hooked on motion blockchain engineering for EHR in addition to next to provide protected storage of electric papers by determining granular accessibility standards because of the proprietors of recommended framework. Furthermore, this particular framework in addition addresses the scalability issue that is happening to the blockchain technologies within typical through utilizing of from chain space on the info. This specific framework has the EHR procedure from the benefits of obtaining a scalable, integral, and protected blockchain-dependent remedy.
A New Hyperchaotic Two-Scroll System: Bifurcation Study, Multistability, Circuit Simulation, and FPGA Realization

With the swift advancement of chaos theory, the modeling, chaotic oscillations, and engineering applications of chaotic and hyperchaotic systems are important topics in research. In this research paper, we elucidate our findings of a new four-dimensional two-scroll hyperchaotic system having only two quadratic nonlinearities and carry out a detailed bifurcation study of the proposed dynamical model. Also, an electronic circuit has been constructed for the new system using MultiSim (Version 14). The implementation of the new 4-D hyperchaotic system in a field-programmable gate array (FPGA) is performed herein by applying two numerical methods, viz. Forward Euler Method and Trapezoidal Method. The experimental results show a good match with the simulated hyperchaotic attractors. We also provide details of the hardware resources used for an FPGA Basys 3 Xilinx Artix-7 XC7A35T-ICPG236C.
Dense Feature Learning and Compact Cost Aggregation for Deep Stereo Matching

Recently, Convolutional Neural Networks (CNN) based deep models have been successfully applied to the task of stereo matching.In this paper, we propose a novel deep stereo matching network based on the strategies of dense feature learning and compact cost aggregation, namely DFL-CCA-Net.It consists of three modules: Dense Feature Learning (DFL), Compact Cost Aggregation (CCA) and the disparity regression module.In DFL module, the CNN backbone with Dense Atrous Spatial Pyramid Pooling (DenseASPP) is employed to extract multi-scale deep feature maps of the given left and right images respectively.Then an initial 4D cost volume is obtained by concatenating left feature maps with their corresponding right feature maps across each disparity level.In the following CCA module, each initial 3D cost volume component (i.e., the component across the left or right image feature channel dimension) is aggregated into a more compact one by using the atrous convolution operation with different expansion rates.These updated 3D cost volume components are then fed into the disparity regression module, which consisting of a 3D CNN network with a stacked hourglass structure, to estimate the final disparity map.Comprehensive experimental results demonstrated on the Scene Flow, KITTI 2012 and KITTI 2015 datasets show that the 3D cost volume components obtained by the proposed DFL and CCA modules generally containing more multi-scale semantic information and thus can largely improve the final disparity regression accuracies.Compared with other deep stereo matching methods, DFL-CCA-Net achieves very competitive prediction accuracies especially in the reflective regions and regions containing detail information.