Miracle-dz/UniFAR-FTUs huggingface dataset:
Applying network analysis to measure organizational behaviors using R software

The amount of research investigating psychological networks has grown substantially over the last decade but to our knowledge this is the first study applying network analysismethodology to the fields of OB/HRM. As such, this study aims to provide researchers and practitioners with an easy-to-use syntax to conduct network analysis for the exploration of relationships among organizational behaviors. Unlike the mainstream techniques used in psychometrics (e.g., principal component analysis and structural equation modelling), which are constrained by the number of associations among variables or assumptions regarding dimensionality, network analysis is able to analyze the whole set of items at once in order to find the most representative associations among them. A step-by-step guide is provided with an example showing how to test potential relationships between engagement and authentic leadership using the R package bootnet. Besides information on edge-weights and centrality measures, this paper covers a bootstrapping procedure to test their accuracy and stability when small sample sizes are used. The possibilities of applications of psychological networks to organizational behavior and HRM practices are endless and can help overcome some of the limitations of the traditional statistical techniques applied to these fields.
Computational Geometry in C: Arrangements

The first edition of this book is recognised as one of the definitive sources on the subject of Computational Geometry. In fact, O'Rourke has a long history in the field, has published many papers on the subject and is responsible for the computer graphics algorithms newsgroup which is where all computer geometers meet to discuss their ideas and problems. Typical problems discussed include how a polygon can be represented, how to calculate its area, how to detect if two polygons intersect and how to calculate the convex hull of a polygon. This leads onto more complex issues such as motion planning and seeing if a robot is able navigate from point x to point y without bumping into objects. The algorithms for these (and other) problems are discussed and many are implemented. In addition, many of the ideas are also discussed from the point of view of three and more dimensions. The only disappointment is that many problems are posed as questions at the end of the chapters and, as far as I could see, you cannot get the answers in the forms of a lecturer's supplement. This is fine in academia but not a lot of use for the commercial world. Due to the range of problems that incorporate computational geometry this book cannot be expected to answer every problem you might have. You will undoubtedly need access to other textbooks but I have been using the first edition of this book for many years and the second edition is a welcome addition to my bookshelf. If I was only allowed one computational geometry book then it would undoubtedly be this one.
Iterative Compensation for Clipping Noise in Spatial Modulation OFDM Systems

Clipping is an effective technique to adjust the peak-to-average power ratio (PAPR) of spatial modulation (SM) orthogonal frequency-division multiplexing (OFDM) systems. In this paper, an iterative soft compensation method is proposed for mitigating the effect of the clipping noise. In the proposed method, the sparsity and relativity of all transmit antennas are exploited, and the mean and power of the clipping noise are estimated based on the a priori information. Furthermore, the attenuation factor in each iteration is updated to yield a more accurate system model. Simulation results show that the proposed method is capable of providing considerable bit error rate (BER) performance gains over conventional compensation methods for both balanced and unbalanced antenna configurations.
Classification of Sugarcane Variety using Image Processing and Multivariate Analysis

Sugarcane variety classification is essential for data collecting and learning for the breeder. It is difficult for a farmer to identify a sugarcane variety without specialist help. In this research, three Japanese sugarcane varieties (Ni15, Ni22, and Ni27) from six areas in the south of Japan were classified according to full pixel and color features of the sugarcane bud. The 54 images of sugarcane bud were acquired from the sugarcane field using a mobile phone’s digital camera, equipped with a fixed distance accessory. To develop classification models, two types of data; The full pixel data and color feature data from images were investigated for input to the model. The full pixel and color features were subjected to Principal component analysis (PCA) to describe the sugarcane bud samples. Then, the samples were classified into varieties by performing partial least squares discriminant analysis (PLS-DA) and support vector machine classification (SVM-C). The results of the full pixel show that the pooled classification rates (averaged three classification rate) by PLS-DA and SVM-C were 79.6% and 84.5%, respectively, while the pooled classification rates by PLS-DA and SVM-C of the color features were 75.9% and 74.1%, respectively. Therefore, these results show that the size and color
ArgueBot: Enabling debates through a hybrid retrieval-generation-based chatbot

The goal of this study is to develop a debate platform, the ArgueBot, that is able to maintain a meaningful debate with the user for various topics. The goal of the chatbot is to carry out human-like debates with the users. The Arguebot uses a hybrid model, combining retrieval- and generative-based models. The retrieval model uses cosine similarity to compare the user input with the argument candidates for a specific debate. The generative model is used to compensate for the limitations of the retrieval model that is restricted to the arguments stored in the database. The Arguebot utilizes Dialogflow, Flask, spaCy, and Machine Learning technologies within its architecture. The user tests and the survey are used to evaluate the chatbot’s performance. The user tests showed that there is a potential in the Arguebot, but it needs a better context understanding, a more accurate stance classifier, and a better generative model to become a more reliable debate partner.
Security Log Analysis in Critical Industrial Systems Exploiting Game Theoretic Feature Selection and Evidence Combination

Critical industrial systems have become profitable targets for cyber-attackers. Practitioners and administrators rely on a variety of data sources to develop security situation awareness at runtime. In spite of the advances in security information and event management products and services for handling heterogeneous data sources, analysis of proprietary logs generated by industrial systems keeps posing many challenges due to the lack of standard practices, formats, and threat models. This article addresses log analysis to detect anomalies, such as failures and misuse, in a critical industrial system. We conduct our study with a real-life system by a top leading industry provider in the air traffic control domain. The system emits massive volumes of highly-unstructured proprietary textual logs at runtime. We propose to extract quantitative metrics from logs and to detect anomalies by means of game theoretic feature selection and evidence combination. Experiments indicate that the proposed approach achieves high precision and recall at small tuning efforts.
Five deep learning recipes for the mask-making industry

Deep learning has an increasing impact on our personal and professional lives. Deep learning has the potential to transform mask, semiconductor and electronics manufacturing. This paper reviews key results from the Center for Deep Learning in Electronics Manufacturing’s (CDLe’s) first year of operation. We consider results from adapting five common types of deep learning recipes to solve key challenges in the manufacture of photomasks, printed circuit boards (PCBs), and flat panel displays (FPDs). These deep learning applications include 1) grouping similar items to automatically categorize mask rule errors; 2) using U-Net architecture to construct fast mask designs; 3) using vision-based object classification to find and classify pick-and-place (PnP) errors on PCB assembly lines; 4) using anomaly detection to improve the quality of FPDs; and 5) using digital twins to create SEM images and optimize Inverse Lithography Technology (ILT). While we compare the relative benefits of these techniques, all show the importance of data to improve the success of deep learning networks and of electronics manufacturing. These applications rely on varying neural network architectures such as autoencoders, segmentation networks, deep convolutional networks, anomaly detection, and generative adversarial networks (GANs).
Base station traffic prediction using XGBoost-LSTM with feature enhancement

With the development of information technology, base station traffic prediction is becoming more and more vital in allocating resource, and finally improving terminal users' quality of experience. Temporal and periodic characteristics are important for handling the issue of efficient and accurate traffic prediction. Considering these characteristics, this study proposes base station traffic prediction using extreme gradient boosting-long-short-term memory (XGBoost-LSTM) with feature enhancement. First, the collected dataset is preprocessed, especially realising missing values filling. Then, to mine the tidal property, feature engineering is performed, which contains feature creation and feature selection. More importantly, the variance contribution of the indicators is calculated based on the factor analysis. The variance contribution of the indicators is used to determine the weights of each selected features. Finally, the XGBoost-LSTM model is adopted to predict the traffic of base stations. By observing the predicted values, the authors find that the simple combination of XGBoost and LSTM can achieve great improvement. Experimental results show that the proposed scheme can get much better performance when compared with competing algorithms.
The Intensity of Human Resources Information Systems usage and Organizational Performance

Information technologies have become pervasive in various organizational functions. However, their usage impacts organizational performance in various manner, and it depends on the intensity of information technology usage. Human resources information systems (HRIS) are widely used in various organizations. Previous research investigated impact of HRIS to organizational performance, but on the country level and from the adoption perspective. In this work, we focus on the intensity of HRIS usage in organizations and their impact on organizational performance. In order to investigate this relationship, we develop several regression models, using the global dataset with large number of companies worldwide. Research results revealed that the strongest impact to organizational performance is attained trough the intensity of HRIS usage, measured by the number of different functionalities available in the software systems. However, this could also be the result of overall development of organizational management and its impact on organizational performance.
Research on the Prediction of Popular Opinion Trend of Web News based on BP neural Network and LSTM

Current prediction methods on public opinion trend of network news are often carried out according to human experience or traditional time series prediction, but they sometimes lack scientific principle and immediacy. This paper researches on the prediction of public opinion trend of network news based on deep learning. Firstly, we use the BP neural network and LSTM to predict public sentiment trend, then combine word2vec with LSTM to classify the text sentiment and finally use doc2vec algorithm and k-means to cluster the text. Experiment result shows that the mean square error with good performance, which reveals the beneficial effect of our prediction method.
Machine Learning Models for Abnormality Detection in Musculoskeletal Radiographs

Increasing radiologist workloads and increasing primary care radiology services make it relevant to explore the use of artificial intelligence (AI) and particularly deep learning to provide diagnostic assistance to radiologists and primary care physicians in improving the quality of patient care. This study investigates new model architectures and deep transfer learning to improve the performance in detecting abnormalities of upper extremities while training with limited data. DenseNet-169, DenseNet-201, and InceptionResNetV2 deep learning models were implemented and evaluated on the humerus and finger radiographs from MURA, a large public dataset of musculoskeletal radiographs. These architectures were selected because of their high recognition accuracy in a benchmark study. The DenseNet-201 and InceptionResNetV2 models, employing deep transfer learning to optimize training on limited data, detected abnormalities in the humerus radiographs with 95% CI accuracies of 83–92% and high sensitivities greater than 0.9, allowing for these models to serve as useful initial screening tools to prioritize studies for expedited review. The performance in the case of finger radiographs was not as promising, possibly due to the limitations of large inter-radiologist variation. It is suggested that the causes of this variation be further explored using machine learning approaches, which may lead to appropriate remediation.
Putting Library Assessment Data to Work

Effective Library Assessment is crucial for a successful academic library. But what do we mean by library assessment and how can it be used to improve the library service? This new book provides a practical guide about how to make effective use of existing sources of information for assessment activities with the aim of improving academic library services. Putting Library Assessment Data to Work brings together key library assessment methodologies detailing how they can be used to improve an academic library. The book takes common sources of data that academic libraries will already be collecting, and presents simple qualitative and quantitative techniques that can be used to evaluate and assess their services, both in detail and overall. The different assessment methods are presented from a practical perspective with a theoretical grounding, and include case studies to illustrate how the methodologies have successfully been applied. The book includes coverage of: - the theoretical framework for assessment, its purpose and the tools and techniques used - institutional, national and international student surveys and how they can be used to improve library service - the history and development of standardised library surveys (e.g. LibQUAL+), how they have been used and their impact - the benefits of in-house library surveys and case studies of where they gave been used - library statistics, including standardised statistics sets and key performance indicators - qualitative feedback in the library - emerging techniques including UX - taking a holistic approach to library assessment through advocacy and strategic planning. This book will be essential reading for library and information service managers, administrators, assessment practitioners, educators and policy shapers. It will also be useful for students and researchers interested in library assessment.
Visual System for Tracking Specific Human Body Extraction of 3D Skeletal Points Based on Monocular

In this paper, we introduce the Extracting specific human 3D skeleton point system based on monocular tracking. The system mainly consists of two parts. The first part is the detection and tracking of specific human body. This article uses simple online and real time tracking with a deep association metric (DEEP SORT)[1] algorithm, which is simple but effective, and meets system requirements in terms of efficiency and real-time. The second part is to extract the 3D bone points for the specific target of the tracking. We refer to Xingyi Zhou’s research work[2] in this area. Utilizing the correlation between 2D pose and depth estimation subtasks, the training is end-to-end, and the algorithm introduces 3D geometric constraints to normalize 3D pose prediction, which is effective without ground truth value depth labels. In this paper, the two methods are combined by improvement, and the Extracting specific human 3D bone point system based on monocular tracking is designed. It can realize the tracking of 3D skeletal points of specific targets. The system has high practical value in human-computer interaction, virtual reality and motion recognition.
Misspelling Detection by using Multiple Bidirectional LSTM Networks

Companies in the RECRUIT Group provide matching business between clients and customers, and create lots of manuscripts every day in order to tell the attractiveness of our clients. In this paper, we propose a method for detecting misspelling in manuscripts with machine learning. That system mainly consists of two parts. One is the multiple Bidirectional LSTM networks to estimate the probabilities of correctness in each characters. The other is the random forests algorithm to decide what sentence is correct or not by using outputs of these networks. The efficacy of our approach is demonstrated on two datasets: artificial sentences and real manuscripts created in our services.
Grasping VR: Presence of Pseudo-Haptic Interface Based Portable Hand Grip System in Immersive Virtual Reality

ABSTRACT This study proposes a pseudo-haptic interface optimized for a novel portable hand grip haptic system, providing simple and realistic interfaces in various situations using the hand grips of users in immersive virtual reality. The proposed haptic system accurately determines the force of the user grip through an Arduino-based system design using portable low-cost electromyography (EMG) sensors. Using this system, a visual illusion-based pseudo-haptic interface is designed to maximize presence in virtual reality. This involves processes that use a grasping illusion to enable the user to quickly become familiar with the system when tactile sensation is excluded, which maximizes immersion. Based on this process, an immersive virtual reality application is produced to survey and analyze user presence for the provided hand grip. Finally, the positive experience and improved presence for hand grip in immersive virtual reality are statistically analyzed and verified through user surveys.
Achieving Ultrareliable and Low-Latency Communications in IoT by FD-SCMA

To enable ultrareliable and low-latency communications (URLLCs) in the Internet of Things (IoT), a sparse-code multiple-access (SCMA)-enhanced full-duplex (FD) scheme (FD-SCMA) is proposed in this article. FD-SCMA can support short-packet transmissions of several SCMA users in the uplink (UL) and downlink (DL) simultaneously by an FD next generation node B (gNB). First, the gNB and UL users can generate and superpose signals according to the preconfigured SCMA codebooks, and simultaneously transmit the signals via occupied subcarriers in a joint SCMA pattern. The receivers at the gNB and DL users can demodulate and decode the signals with multiuser detection (MUD). With the imperfect self-interference suppression (SIS) of FD considered, the effective signal-to-noise ratio (SNR) of FD-SCMA at the gNB and DL users is formulated. The error probability of FD-SCMA in the UL and DL is also derived under a given transmission latency constraint of short-packet transmissions. In the stationary flat-fading channel, it is proved that FD-SCMA can achieve better reliability than the existing FD and SCMA schemes. In the time-invariant frequency-selective fading channel, the upper bounds for error probability of the UL and DL users in FD-SCMA are derived, respectively. Through the theoretical calculation and Monte Carlo simulation, it is verified that the superiority of FD-SCMA in supporting ultrareliable and low-latency short-packet transmissions in IoT.
Exploring NEURAghe: A Customizable Template for APSoC-Based CNN Inference at the Edge

The NEURAghe architecture has proved to be a powerful accelerator for deep convolutional neural networks running on heterogeneous architectures based on Xilinx Zynq-7000 all programmable system-on-chips. NEURAghe exploits the processing system and the programmable logic available in these devices to improve performance through parallelism, and to widen the scope of use-cases that can be supported. In this letter, we extend the NEURAghe template-based architecture to guarantee design-time scalability to multiprocessor SoCs with vastly different cost, size, and power envelope, such as Xilinx’s Z-7007s, Z-7020, and Z-7045. The proposed architecture achieves state-of-the-art performance and cost effectiveness in all the analyzed configurations, reaching up to 335 GOps/s on the Z-7045.
Machine Vision Assistance for FlexCable-Actuator CombManufacturing in HardDiskDrives

Theworkpresented inthispapersuggests an cable dynamics becomethemostanddifficult task toresolve automated manufacturing methodforassembling offlexiblewhendisk drive's capacity isbeyond aterabyte point. printed circuit cable assembly ontoactuator comb-like structure inhighcapacity harddiskdrives.Apartfromtraditional alignment methods, robotic vision guided methodandassembling(a) system aredeveloped anddiscussed inthis paper.Itscapabilities ofreaching micro-scale position andalignment accuracy arer demonstrated inmoderndiskdrivemanufacturing lines along withprovensignificant lowerdrive-to-drive flexcabledynamic performance variation.
Detecting long-range cause-and-effect relationships in game provenance graphs with graph-based representation learning

—Success in Game Analytics tasks demands developers to identify what is happening in a game (an effect) and track its causes. Thus, game provenance graph tools have been proposed to capture cause-and-effect relationships. However, as a downside gathering provenance still may demand huge coding efforts for long-range relationships. In this dissertation, we contributed with a framework named PingUMiL for automatic edge detection using graph representation learning, aiming at alleviating the coding effort. We evaluated the generalization capacity of PingUMiL when learning from similar games and compared its performance to classical machine learning methods. The experiments conducted on two racing games show that PingUMiL (1) outperforms classical machine learning, (2) can be used for inference in unobserved data and (3) enhances detection cross games
Reading Between the Dies: Cross-SLR Covert Channels on Multi-Tenant Cloud FPGAs

Field-Programmable Gate Arrays (FPGAs) are becoming increasingly available via commercial cloud providers, which currently allocate devices on a per-user basis. As the underlying hardware is often underutilized, several proposals for multi-tenant use of FPGA resources have been brought forth, along with some initial work on security attacks in this setting. Simultaneously, high-end FPGAs are being produced with 2.5D integration of multiple distinct dies, called Super Logic Regions (SLRs), onto the same chip. Although one might expect that physical separation of logic onto separate dies could prevent multi-tenant attacks, this paper demonstrates for the first time that cross-SLR information leaks based on sensing voltage changes within the FPGA chip are possible, without physical access to or modification of the boards. The cross-SLR covert channel is characterized analytically and experimentally on five Xilinx Virtex UltraScale+ FPGAs, both locally and on the Amazon and Huawei clouds. Several configurations of the source transmitters and the sink receivers are tested, including their locations, types, and sizes. The power-based channel is shown to have a bandwidth upwards of 4.6 Mbps and accuracy of over 97.6%. Consequently, as physical separation of tenants onto separate dies (SLRs) is an insufficient countermeasure against information leaks, hardware-level architectural improvements are necessary to make secure multi-tenant FPGAs on shared clouds a reality.