Reviewerly/exHarmony huggingface dataset:
PROBLEMS AND DIFFICULTIES OF SPEAKING AT MUHAMMADIYAH LAMPUNG UNIVERSITY STUDENTS IN COVID PANDEMIC

The purpose of this research was to describe the problem and difficulties of speaking at Muhammadiyah Lampung University students in Covid pandemic. This research used descriptive qualitative research. The subject of this research were Muhammadiyah Lampung University students at first semester in English Education Department. The data collecting technique of this research used questionnaire. The result of this research indicated that the students in first semester of English Department have some problems and difficulties in speaking subject during Covid pandemic. There some problems and difficulties in practicing speaking in Covid pandemic namely 1) understanding the conversation; (2) less of vocabularies; (3) pronunciation; (4) shy, (5) Worrying for making grammatical errors, (6) lack of Technology, and (7) lack of motivation. Based on the analysis of questionnaire indicated that that 5 % in meaning, 22 % in vocabularies, 8 % in pronunciation, 32 % in shy, 8% Worrying for making grammatical errors, 10% lack of Technology, and 15% lack of motivation. Based on the data the majority students indicated that confidence is the most problem in speaking skill.
Utilizing Steganography and Cryptography to Conceal Information Within BMP Images

Every day, more sophisticated methods will be needed to safeguard confidential data from intrusion and hacking attempts.Both cryptography and steganography are well-respected for their ability to protect confidential information during transmission via potentially unsafe channels.However, when these two approaches are combined, data transmission security is much improved.For that reason, the purpose of this study is to present a new steganography method that, when combined with a novel encryption method, offers an additional layer of protection against assaults.The new solution is based on the Least Significant Bits (LSB) methodology and works with BMP files to conceal sensitive information.The experimental outcomes in this study is done using VB6 language.Peak Signal-to-Noise Ratio and Mean Squared Error are two common metrics used to evaluate picture quality (PSNR).Varied host images have been tried with different sizes of files to be concealed.From what we can see, the new method provides much improved PSNR and lower MSE values across the board.For this reason, the suggested method provides an effective means of guarding sensitive data while transmission across unsafe networks.
Novel View Synthesis for Surgical Recording

Recording surgery in operating rooms is one of the essential tasks for education and evaluation of medical treatment. However, recording the fields which depict the surgery is difficult because the targets are heavily occluded during surgery by the heads or hands of doctors or nurses. We use a recording system which multiple cameras embedded in the surgical lamp, assuming that at least one camera is recording the target without occlusion. In this paper, we propose Conditional-BARF (C-BARF) to generate occlusion-free images by synthesizing novel view images from the camera, aiming to generate videos with smooth camera pose transitions. To the best of our knowledge, this is the first work to tackle the problem of synthesizing a novel view image from multiple images for the surgery scene. We conduct experiments using an original dataset of three different types of surgeries. Our experiments show that we can successfully synthesize novel views from the images recorded by the multiple cameras embedded in the surgical lamp.
A hybrid approach using Bidirectional Neural Networks for Human Activity Recognition

Human Activity Recognition has a vital role for its immense applications in human life. With the advances in sensor technologies and utilizing the IoT, HAR has a wide area of research with the help of deep learning algorithms. The advanced deep learning paradigm provides end-to-end learning models from unstructured, complex data. IoT wearables and smartphones are now widely used embedded with mobile apps for telemedicine, e-health monitoring, AAL, sports monitoring, smart homes etc. This paper presents a novel approach using hybrid neural networks implemented with Bidirectional GRU, Bidirectional LSTM and CNN. The algorithm was tested using two activity recognition datasets WISDM and MHEALTH. It provides improved accuracy over the other activity recognition techniques.
BNNAS++: Towards Unbiased Neural Architecture Search With Batch Normalization

Neural Architecture Search (NAS) achieves significant progress in many computer vision tasks, yet training and searching high-performance architectures over large search space are time-consuming. The NAS with BatchNorm (BNNAS) is an efficient NAS algorithm to speed up the training of supernet ten times over conventional schemes by fixing the convolutional layers and training the BatchNorm only. In this paper, we systematically examine the limitation of the BNNAS and present improved techniques. We observe that the BNNAS is prone to select convolution and fails to find plausible architectures with small FLOPs, especially on the search space with operations other than convolutions. This is because the number of learnable parameters on BatchNorm is imbalanced for different operations, i.e., BatchNorm is only attached to the convolutional layer. To fix the issue,We present the unbiased BNNAS for training. However, our empirical evidence shows that the performance indicator in BNNAS is ineffective on the unbiased BNNAS. To this end, we propose a novel performance indicator, which decomposes the model performance into model <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">expressivity, trainability</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">uncertainty</i> , to preserve the rank consistency on the unbiased BNNAS. Our proposed NAS method, named as BNNAS++, is robust on various search spaces and can efficiently search high-performance architectures with small FLOPs. We validate the effectiveness of BNNAS++ on NAS-Bench-101, NAS-Bench-201, DARTS search space, and MobileNet search space, showing superior performance over existing methods.
Crypto-Litigation: An Empirical Overview for 2020–Present

This article is an empirical analysis of the past two years of litigation around cryptocurrencies and other crypto-assets. We collected data points, from nearly 300 cases, over the past two years and then classified them by the various litigated issues. This article provides a breakdown of these issues as well as the jurisdictions from where these cases come from. The discussion reviews a few notable cases to illustrate what kinds of disputes have been brought to the courts. As we move into a new round of litigation due to a recent drop in the prices of cryptocurrencies, we hope that past experiences will guide lawyers and the courts in navigating the next wave of cryptolitigation.
Semantic Segmentation of Remote Sensing Images With Self-Supervised Semantic-Aware Inpainting

Semantic segmentation of remote sensing imageries plays a crucial role in resource exploration, urban planning, weather forecasting, etc. For this task, deep learning-based methods have shown significant achievement, typically trained with large-scale labeled data. However, these methods often suffer the performance deterioration facing limited labeled data in real-world applications. To address this problem, a novel self-supervised semantic segmentation framework is proposed for remote sensing imageries with limited labeled data. Specifically, image inpainting is acted as pixel-level pretext task for learning dense feature representations suitable for semantic segmentation. Further, rather than trivially leveraging the conventional random inpainting strategy, a novel adversarial training scheme is proposed to drive the pretext task to adaptively mask and restore salient local regions. The adversarial training scheme consists of instructor network and inpainting network, the instructor network increasingly predicts meaningful salient regions as erased regions, and meanwhile the inpainting network seeks for restoring the corrupted image as pretext task to learn its intrinsic representation. Moreover, the structural similarity (SSIM) is applied as a patch-level loss function for semantic segmentation considering that remote sensing images are highly structured. The experimental results on the ISPRS Potsdam dataset demonstrate that our method outperforms state-of-the-art self-supervised methods and the ImageNet pre-training methods. The source code is available at https://github.com/JasmineBJTU/self-supervised_RSSS.
UX- for Smart-PSS: Towards a Context-aware Framework

Smart-product service systems are a business strategy that combines product and service into one value proposition.The user experience of digital services and the smart product can be a clear differentiator among competitors to achieve economically sustainable solutions.Hence, offering a more personalized experience is an important aspect of S-PSS.This paper aims to provide a theoretical framework for a context-aware user experience in S-PSS by providing adaptive and personalized services to the users according to their needs in a given context, by exploiting the digital capabilities of smart products and referring to the use of recommendation systems.The paper presents an application scenario using a smart-wearable as an example of a product-oriented PSS to better describe the framework and each component while stating the future challenges.
Unlocking Urban Simulation Data with a Semantic City Planning System - Ontologically representing and integrating MATSim output data in a knowledge graph

Simulation models generate an abundance of rich raw data that remains difficult to access for non-experts.However, such data could be unlocked and utilised with a Semantic City Planning System that improves data accessibility and transparency.This paper describes a process of ontologically representing mobility simulation output data using Semantic Web technologies and storing it in a dynamic geospatial knowledge graph.Our work presents two benefits: 1) formally representing simulation output data increases the accessibility and transparency of urban simulation models, and 2) access to under-utilised rich data unlocks novel cross-domain knowledge explorations and research possibilities.We demonstrate these benefits by means of cross-domain queries related to typical city planning questions.
Formalization and verification of Kafka messaging mechanism using CSP

Apache Kafka is an open source distributed messaging system based on the publish-subscribe model, which achieves low latency, high throughput and good load balancing. As a popular messaging system, the transmission of messages between applications is one of the core functions of Kafka. Therefore, the reliability and security of data in the process of message transmission in Kafka have become the focus of attention. The formal methods can analyze whether a model is highly credible. Therefore, it is significant to analyze Kafka messaging mechanism which describes the communication process and rules between each module entity in Kafka from the perspective of formal methods. In this paper, we apply the process algebra CSP (Communicating Sequential Processes) and the model checking tool PAT (Process Analysis Toolkit) to analyze Kafka messaging mechanism. The results of verification show that the model caters for its specification and guarantees the reliability of messages in the normal communication process. Moreover, in order to further analyze the security of Kafka messaging mechanism, we add the intruder model and the authentication protocol Kerberos model and compare the verification results of Kafka messaging mechanism with or without the secure protocol Kerberos. The results show that the Kerberos protocol has improved the security of Kafka messaging mechanism in some aspects, but there are still some security loopholes.
Heart Disease Classification for Early Diagnosis based on Adaptive Hoeffding Tree Algorithm in IoMT Data

Heart disease is a rapidly increasing disease that causes death worldwide. Therefore, scientists around the globe start studying this issue from a different perspective to assure early prediction of diagnosis to save patients' life from bad consequences that cause death. In this regard, Internet of Medical Things (IoMT) applications and algorithms should be utilized effectively to overcome this problem. Hoeffding Tree Algorithm (HTA) is a standard decision tree algorithm to handle large sizes of data sets. In this paper, an Adaptive Hoeffding Tree (AHT) algorithm is suggested to carry out classifications of data sets for early diagnosis of heart disease-related factors, and the obtained results by this algorithm are compared with other suggested Machine Learning (ML) algorithms in the literature. Therefore, a total of 3000 records of data sets are used in the classification, 33% of the data are utilized for female patient information, and the rest of the data are utilized for male patient information. In the original data set, each patient record includes 76 attributes, however only the most important 16 patient attributes are used for the classification. Data are retrieved from the University of California Irvine (UCI) Machine Learning Repository, which is collected from the Hungarian Institute of Cardiology, University Hospital at Zurich, University Hospital at Basel, and V.A. Medical Center. The obtained results from this study and the provided comparative results show the effectiveness of the AHT algorithm over other ML algorithms. Compared to other ML algorithms, AHT outperforms other algorithms with 95.67% accuracy for early estimation of diagnosis of heart disease.
PARTIALLY LINEAR ADDITIVE HAZARDS REGRESSION FOR CLUSTERED AND RIGHT CENSORED DATA

For analyzing clustered survival data, a flexible partially linear additive hazards model is proposed.To accommodate the nonlinear effects, the unknown regression function is approximated by B-splines.All regression coefficients are estimated through a system of pseudo-score functions.Under certain conditions, the proposed estimators are shown to be asymptotically normal, where a consistent estimator of the covariance matrix is given.Simulation studies are also conducted to evaluate the finite sample performance of the proposed method, which is illustrated using a real data set from an AIDS clinical trial.
CapsLSTM-Based Human Activity Recognition for Smart Healthcare With Scarce Labeled Data

Scarcity of labeled data in sensitive research areas such as healthcare limits the performance of artificially intelligent (AI) models. The effort required in labeling the acquired healthcare data for carrying out a classification task is a concern faced by healthcare researchers. In this work, we design a capsule-long short-term memory (LSTM) model, abbreviated as CapsLSTM, capable of classifying human activities with scarce labeled activity data. The proposed CapsLSTM model is used to recognize multiple human activities sensed by the accelerometer and the gyroscope sensors embedded in smartphones leveraging the spatio-temporal information. We validate the proposed framework based on two human activity recognition (HAR) databases, namely, UCI-HAR and MotionSense. The CapsLSTM model yields close test accuracies for different fractions of the training data unlike the other models such as LSTM, 1-D convolutional neural network (1D-CNN), convolutional LSTM (ConvLSTM), or CNN-LSTM. For the state-of-the-art models, the test accuracies decrease significantly with the decrease in training data. Our proposed model classifies the human activities using a minimum of 20% labeled training data from each database. There is less decrease in accuracies from that obtained using 70% of training data in comparison to the existing models. The classification performance of the proposed CapsLSTM model for a small fraction of training data proves the effectiveness and reliability of the same in a data-scarce situation.
A Comprehensive Study On Medical Image Security Algorithms

With the technological developments, remote consultation and diagnosis have become a significant part of smart medical services. Medical data is considered the input for a smart health service environment. Medical records are frequently transferred and exchanged between hospitals and research com-munities worldwide through the internet and other communication mechanisms. Medical images form a substantial share of medical data. Thus, a tremendous amount of medical images are available in a structured and unstructured manner. For easy and timely access, medical images prefer to store in cloud-based environments. Many image outbreaks have been reported in the cloud-based structure in the last decade. Hence, the above outbreaks brought the attention of the research community to developing new cloud-based security frameworks and adopting improved security algorithms for clinical images. Therefore, in this paper, the authors classify available medical image security algorithms based on adopted methodologies.
Compositional Human-Scene Interaction Synthesis with Semantic Control

Synthesizing natural interactions between virtual humans and their 3D environments is critical for numerous applications, such as computer games and AR/VR experiences. Recent methods mainly focus on modeling geometric relations between 3D environments and humans, where the high-level semantics of the human-scene interaction has frequently been ignored. Our goal is to synthesize humans interacting with a given 3D scene controlled by high-level semantic specifications as pairs of action categories and object instances, e.g., "sit on the chair". The key challenge of incorporating interaction semantics into the generation framework is to learn a joint representation that effectively captures heterogeneous information, including human body articulation, 3D object geometry, and the intent of the interaction. To address this challenge, we design a novel transformer-based generative model, in which the articulated 3D human body surface points and 3D objects are jointly encoded in a unified latent space, and the semantics of the interaction between the human and objects are embedded via positional encoding. Furthermore, inspired by the compositional nature of interactions that humans can simultaneously interact with multiple objects, we define interaction semantics as the composition of varying numbers of atomic action-object pairs. Our proposed generative model can naturally incorporate varying numbers of atomic interactions, which enables synthesizing compositional human-scene interactions without requiring composite interaction data. We extend the PROX dataset with interaction semantic labels and scene instance segmentation to evaluate our method and demonstrate that our method can generate realistic human-scene interactions with semantic control. Our perceptual study shows that our synthesized virtual humans can naturally interact with 3D scenes, considerably outperforming existing methods. We name our method COINS, for COmpositional INteraction Synthesis with Semantic Control. Code and data are available at https://github.com/zkf1997/COINS .
Some identities for derangement numbers

In this paper, we give some identities involving derangement numbers.In addition, we derive nonlinear differential equation from the generating function of r-derangement numbers and obtain some identities including generalized harmonic numbers and derangement numbers by using this differential equation.For example, for any positive integers N, n and r, N ∑ k=0 N-k
How COVID-19 Pandemic affects Software Developers’ Wellbeing, and the Necessity to strengthen Soft Skills

The coronavirus COVID-19 swept the world in early 2020, working from home was a necessity. In the software industry, thousands of software developers began working from home, many did so on short notice, under difficult and stressful conditions. The emotions of developers can be affected by this situation. On the other hand, some well-known soft skills have been emphasized as required for working remotely. Software engineering research lacks theory and methodologies for addressing human aspects in software development. In this paper we present an exploratory study with a survey applied to practitioners in the west border area of Mexico-USA. It is focused on the developers’ wellbeing during pandemic, expressed as emotions, and the perceptions of the level in which soft skills are practiced/required in the working from home mode. The results show that high percent expressed to experience positive emotions, however, a portion of respondents expressed to feel negative emotions. In general terms, 91.0% of respondents expressed to be satisfied working from home, but some conditions should be improved. In the case of soft skills, some of them are revealed as practiced in high level in working from home, but still there is not consensus.
A Characterization of Multiclass Learnability

A seminal result in learning theory characterizes the PAC learnability of binary classes through the Vapnik-Chervonenkis dimension. Extending this characterization to the general multiclass setting has been open since the pioneering works on multiclass PAC learning in the late 1980s. This work resolves this problem: we characterize multiclass PAC learnability through the DS dimension, a combinatorial dimension defined by Daniely and Shalev-Shwartz, (2014). The classical characterization of the binary case boils down to empirical risk minimization. In contrast, our characterization of the multiclass case involves a variety of algorithmic ideas; these include a natural setting we call list PAC learning. In the list learning setting, instead of predicting a single outcome for a given unseen input, the goal is to provide a short menu of predictions. Our second main result concerns the Natarajan dimension, which has been a central candidate for characterizing multiclass learnability. This dimension was introduced by Natarajan (1988) as a barrier for PAC learning. He furthered showed that it is the only barrier, provided that the number of labels is bounded. Whether the Natarajan dimension characterizes PAC learnability in general has been posed as an open question in several papers since. This work provides a negative answer: we construct a non-learnable class with Natarajan dimension 1. For the construction, we identify a fundamental connection between concept classes and topology (i.e., colorful simplicial complexes). We crucially rely on a deep and involved construction of hyperbolic pseudo-manifolds by Januszkiewicz and Światkowski. It is interesting that hyperbolicity is directly related to learning problems that are difficult to solve although no obvious barriers exist. This is another demonstration of the fruitful links machine learning has with different areas in mathematics.
The effect of anxiety due to coronavirus outbreak on job adjustment and change management in public libraries

The effect of anxiety due to coronavirus outbreak on job adjustment and change management in public libraries