Reviewerly/exHarmony huggingface dataset:
Relationship Between Personnel Management Skill And Happiness Index Among Lecturers At Higher Education Institutions In Henan Province, China

The teaching profession has become one of the most stressful jobs, which has a negative impact on the performance of lecturers and their sense of well-being. The main purpose of this study is to investigate the influence of factors related to personnel management skills on the happiness index of teachers in colleges and universities. Teachers’ well-being can promote efficiency, create a positive work climate, and help achieve organizational goals and objectives. By analysing teachers’ happiness index, organizations can guarantee the sustainability of their operations.&#x0D; The method of this study focuses on the main quantitative methods and studies the relationship between personnel management, personnel training, personnel welfare, personnel performance evaluation, and the teacher happiness index. The survey was conducted among 300 participants to obtain sufficient data for analysis. These data were subjected to descriptive analysis, correlation analysis, and regression analysis with SPSS.&#x0D; It was observed that there is a strong positive correlation between personnel management and the happiness index. In order to improve the lecturer’s happiness, there is need for personnel performance appraisal. The result indicates that there is positive relation between personnel training and lecturers' happiness index. The interconnectedness among the factors influencing each other has been verified through these statistical tests. The Pearson correlation values for each of the predictors were positive with happiness index. Thus, it is evident that to improve happiness index among the lecturers, it is important to have personnel management skills.
Helmet wearing detection algorithm for e-bike riders based on improved YOLOv7-tiny

In order to more accurately and quickly determine whether e-bike riders in the road are wearing helmets or not, a holistic behavioral research method focusing on riding behavior is proposed, and a helmet wearing detection algorithm for e-bike riders is proposed based on the improved YOLOv7-tiny(You Only Look Once version7-tiny). The improved algorithm first uses a smoother Mish activation function to enhance the nonlinear representation in the network. Secondly, a SIoU(SCYLLA-IoU) loss function that takes into account the regression angle of the prediction frames is introduced to solve the phenomenon of the prediction frames wandering around during training. Finally, in conjunction with the parameter-free attention mechanism SimAM(A Simple, Parameter-Free Attention Module), the parameter-free attention feature aggregation module and CBMS module are designed to enhance the network’s attention to key feature information and information transfer between deep and shallow networks. The experimental results show that the improved algorithm improves Precision by2.4%, Recall by 1.8%, and mAP (Mean Average Precision) by 1.3% in the homemade e-bike rider dataset compared to the YOLOv7-tiny algorithm. Effectively solves the phenomenon of missed detections and false detections, and improves the detection effect under different traffic conditions.
Instrumental Meta-Orchestration: studies and outspreads

This paper discusses the trajectory of the instrumental meta-orchestration model (IMO) development, studies and results, and extensions. Instrumental meta-orchestration, developed as a solution for teacher training on instrumental orchestration (IO), relied on its development of concepts such as meta-situation, didactic meta-configuration, and meta-exploration model. An experimental study with its analysis led to the development of data visualisation instruments in an internal analysis context at the IO. These concepts and analysis instruments were used in research that followed that of the IMO, such as the extension of the IMO to interdisciplinary teachers’ education. The concepts of ad hoc reaction, artefacts such as didactic webdoc, and analysis of events between orchestrations show their potential and subsidise new research for teacher training in integrating digital technologies.
Normal Line Invariance-Based Lens Distortion Calibration Applied in a Precision-Sensitive Self-Calibrated Planar Metrology System

Although the decoupled straight line-based method is more flexible than full metric calibration, precision is the main burden for its practical use. Therefore, this paper introduces a novel method to estimate an optimal lens distortion model using local features of straight lines from the image, which are easily found or established patterns. The proposed method exploits the geometric invariance of the normal line passing the center of distortion (COD) for accurate normal vector estimation. The grid search technique within a narrowed search range determines an optimal COD position at a reasonable calculation cost. The metric of line straightness after undistortion is used for performance evaluation. This quantitively proves that the proposed method has superior undistortion performance than the state-of-the-art straight line-based lens and metric camera calibration methods. Combined with a simple homography estimation, the method is verified in a precision-sensitive self-calibrated planar metrology system. The system is calibrated with a few target objects, one of which has known geometry. Experiments show that the calibration process for the system achieves competitive measurement accuracy compared with state-of-the-art full metric calibration while being more flexible.
Towards Online Review Verification: A Literature Review of Deceptive Online-Reviews Detection

Online reviews play a critical role in the success of any business. It is an important tool that affects the decisions of both customers and vendors. Reviews also tell an organization's decision-makers when and how to make changes to their quality plan. Product makers depend on reviews to uncover their products' faults and get competitive intelligence about their competitors. So, it is important to be sure that reviews are real and represent a real customer opinion. Fabricated reviews have the potential to mislead the general audience, leaving people unsure of the review. In literature, different approaches have been proposed to address the problem of deceptive reviews. In this work, we aim to explore the area's current state, its implications, limitations, and possible future paths. We focused on the recent technologies and theories that were implemented to detect deceptive reviews. This review can be useful for anyone interested in detecting deceptive online reviews. This paper is part of another research focusing on the reliability of deceptive online reviews, to develop a comprehensive evaluation framework to validate online reviews.
Comparison of Autoencoder Models for Unsupervised Representation Learning of Skeleton Sequences

This study comprehensively explores the performance of six distinct feature extraction models on two widely used skeleton datasets, UCLA and NTU 60, with a focus on establishing the superiority of hybrid transformer architectures. The models include autoencoder, convolutional autoencoder, temporal autoencoder (GRU autoencoder), transformer, convolutional transformer, and transformer with a convolutional encoder, representing diverse approaches in skeleton data analysis. The primary objective is to compare the models’ effectiveness in extracting meaningful features from the skeleton datasets. Rigorous evaluations and comparisons are conducted using quantitative measures and visualizations to assess each model’s discriminative power in capturing relevant information. The findings demonstrate that the transformer model consistently outperforms all other models on both datasets, showcasing its unique capability in extracting meaningful and discriminative features from skeleton data. Additionally, the study investigates hybrid models, combining transformer architectures with other feature extraction techniques, revealing their potential to surpass individual model capabilities. These findings can guide future studies in selecting appropriate models for similar tasks and promoting the development of more accurate and robust action recognition systems based on skeleton data.
Strategic Incorporation of Synthetic Data for Performance Enhancement in Deep Learning A Case Study on Object Tracking Tasks

Obtaining training data for machine learning models can be challenging. Capturing or gathering the data, followed by its manual labelling, is an expensive and time-consuming process. In cases where there are no publicly accessible datasets, this can significantly hinder progress. In this paper, we analyze the similarity between synthetic and real data. While focusing on an object tracking task, we investigate the quantitative improvement influenced by the concentration of the synthetic data and the variation in the distribution of training samples induced by it. Through examination of three well-known benchmarks, we reveal guidelines that lead to performance gain. We quantify the minimum variation required and demonstrate its efficacy on prominent object-tracking neural network architecture.
Hybrid Region and Pixel-Level Adaptive Loss for Mass Segmentation on Whole Mammography Images

Breast cancer continues to be one of the most lethal cancer types, mainly affecting women. However, thanks to the utilization of deep learning approaches for breast cancer detection, there has been a considerable boost in the performance in the field. The loss function is a core element of any deep learning architecture with a significant influence on its performance. The loss function is particularly important for tasks such as breast mass segmentation. For this task, challenging properties of input images, such as pixel class imbalance, may result in instability of training or poor detection results due to the bias of the loss function toward correctly segmenting the majority class. Inspired by the success of sample-level loss functions, we propose a hybrid loss function incorporating both pixel-level and region-level losses, where the breast tissue density is used as a sample-level weighting signal. We refer to the proposed loss as Density-based Adaptive Sample-Level Prioritizing (Density-ASP) loss. Our motivation stems from the observation that mass segmentation becomes more challenging as breast density increases. This observation makes density a viable option for controlling the effect of region-level losses. To demonstrate the effectiveness of the proposed Density-ASP, we have conducted mass segmentation experiments using two publicly available datasets: INbreast and CBIS-DDSM. Our experimental results demonstrate that Density-ASP improves segmentation performance over the commonly used hybrid losses across multiple metrics.
Investigating the Impact of Attention on Mammogram Classification

Attention, one of the most important features of modern CNNs, has been shown to improve the performance of mammogram classification, but our understanding of why attention offers improvements is rather limited. In this paper, we present the first comprehensive comparison of different combinations of baseline models and attention methods at multiple resolutions for whole mammogram image classification of masses and calcifications. Our findings indicate that attention generally helps to improve the baseline model scores, but the benefits are variable depending on the resolution and abnormality type. Furthermore, we find that pooling and overall model architecture (i.e., combination of baseline and attention) significantly impact mammogram classification scores. Specifically, scores are generally improved by architectural features that allow the model to retain as much information as possible while still focusing on relevant features. We also find that attention improves the correlation between model performance and LayerCAM activation in the region of interest. Our work provides insightful information to help guide the future construction of attention-based models for mammogram classification.
I Got Your Emotion: Emotion Preserving Face De-identification Using Injection-Based Generative Adversarial Networks

Traditional visual information concealing methods involve blurring or pixelating the face, which can obscure emotional expression and lead to misunderstandings and misinterpretations. This research proposes the very first end-to-end emotion-preserving de-identification method that employs an end-to-end Generative Adversarial Network (GAN) for producing natural-looking de-identified images. The proposed method is based on the StyleGAN architecture to generate a synthetic face image dataset for proxy face images, followed by using the DeepFace model to classify the gender of the target image and selecting a representative image with the same gender for face swapping. An enhanced SimSwap framework is proposed to improve the emotion preservation quality of the de-identification. A new loss function is introduced specifically to preserve emotional expressions. The deep face model is used to classify the original image’s emotional expression, and then the same model is used to recognize the emotional expression of the de-identified swapped image. Emotional expressions are explicitly preserved during face-swapping by minimizing attribute preservation loss. The proposed method outperformed the most recent face de-identification method in terms of accuracy and emotion-preserving capabilities.
Role of Organizational Trust as Mediation on the Effect of Knowledge Management on Innovative Behavior

This study aims to analyze the direct effect of knowledge management on innovative behavior and examine the mediating role of organizational trust in the relationship. The sample used was 94 employees of manufacturing companies in the Bekasi area. Data collection using a 5-point Likert scale questionnaire. The data were analyzed using Partial Least Square (PLS) to test the research model and its hypotheses. The results prove that knowledge management has a significant positive effect on increasing innovative behavior. In addition, knowledge management is also proven to increase organizational trust. On the other hand, organizational trust has a significant positive effect on increasing employee innovative behavior. The results of path analysis also prove that organizational trust significantly mediates the effect of knowledge management on increasing innovative behavior. Thus this study succeeded in proving the proposed research model along with the four hypotheses. The practical implication is that companies need to pay balanced attention to the implementation of knowledge management programs and the cultivation of organizational trust in order to create an environment conducive to the growth of employee innovative behavior.
Demystifying Web3 Centralization: The Case of Off-Chain NFT Hijacking

Despite the ambitious vision of re-decentralizing the Web as we know it, the Web3 movement is facing many hurdles of centralization which seem insurmountable in the near future, and the security implications of centralization remain largely unexplored. Using non-fungible tokens (NFTs) as a case study, we conduct a systematic analysis of the threats posed by centralized entities in the current Web3 ecosystem. Our findings are concerning: almost every interaction between a user and a centralized entity can be exploited to hijack NFTs or cryptocurrencies from the user, through network attacks practical today. We show that many big players in the ecosystem are vulnerable to such attacks, placing large financial investments at risk. Our study is a starting point to study the pervasive centralization issues in the shifting Web3 landscape.
Understanding Polkadot Through Graph Analysis: Transaction Model, Network Properties, and Insights

In recent years, considerable efforts have been directed toward investigating the large amount of public transaction data in prominent cryptocurrencies. Nevertheless, aside from Bitcoin and Ethereum, little efforts have been made to investigate other cryptocurrencies, even though the market now comprises thousands, with more than 50 exceeding one billion dollars of capitalization, and some of them sporting innovative technical solutions and governance. This is the case for Polkadot, a relatively new blockchain that promises to solve the shortcomings in scalability and interoperability that encumber many existing blockchain-based systems. In particular, Polkadot relies on a novel multi-chain construction that promises to enable interoperability among heterogeneous blockchains. This paper presents the first study to formally model and investigate user transactions in the Polkadot network. Our contributions are multifolds: After defining proper and pseudo-spam transactions, we built the transaction graph based on data collected from the launch of the network, in May 2020, until July 2022. The dataset consists of roughly 11 million blocks, including 2 million user accounts and 7.6 million transactions. We applied a selected set of graph metrics, such as degree distribution, strongly/weakly connected components, density, and several centrality measures, to the collected data. In addition, we also investigated a few interesting idiosyncratic indicators, such as the accounts’ balance over time and improper transactions. Our results shed light on the topology of the network, which resembles a heavy-tailed power-law distribution, demonstrate that Polkadot is affected by the rich get richer conundrum, and provide other insights into the financial ecosystem of the network. The approach, methodology, and metrics proposed in this work, while being applied to Polkadot, can also be applied to other cryptocurrencies, hence having a high potential impact and the possibility to further research in the cryptocurrency field.
On the Sustainability of Bitcoin Partitioning Attacks

A series of recent studies have shown that permissionless blockchain peer-to-peer networks can be partitioned at low cost (e.g., only a few thousand bots are needed), stealthily (e.g., no control plane detection is available), or at scale (e.g., the entire bitcoin network can be divided into two). In this paper, we focus on the sustainability of partitioning attacks in Bitcoin, which is barely discussed in the literature. Existing studies investigate new partitioning attack strategies extensively but not how long the partition they create lasts. Our findings show that, fortunately for Bitcoin, the permissionless peer-to-peer network can be partitioned but only for a short time. In particular, two recent partitioning attacks (i.e., Erebus [12], SyncAttack [10]) do not maintain partitions for more than 10 min in most cases. After analyzing Bitcoin’s peer eviction mechanism (which makes the two original attacks difficult to sustain), we propose optimization strategies for the two attacks and calculate the total cost of the optimized attacks for a 1-hour attack duration. Our results complement the original attack studies: (i) the optimized Erebus attack shows that it requires at least one adversary-controlled Bitcoin node close to a target and a few additional expensive attack steps for sustainable attacks, and (ii) the optimized SyncAttack can create sustainable partitions only with excessive cost.
Short Paper: Estimating Patch Propagation Times Across Blockchain Forks

The wide success of Bitcoin has led to a huge surge of alternative cryptocurrencies (altcoins). Most altcoins essentially fork Bitcoin’s code with minor modifications, such as the number of coins to be minted, the block size, and the block generation time. In this paper, we take a closer look at Bitcoin forks from the perspective of vulnerability patching. By mining data retrieved from the GitHub repositories of various altcoin projects, we estimate the time it took to propagate relevant patches from Bitcoin to the altcoins. We find that, while the Bitcoin development community is quite active in fixing security flaws of Bitcoin’s code base, forked cryptocurrencies are not as rigorous in patching the same vulnerabilities (inherited from Bitcoin). In some cases, we observe that even critical vulnerabilities, discovered and fixed within the Bitcoin community, have been addressed by the altcoins tens of months after disclosure.
State Machines Across Isomorphic Layer 2 Ledgers

With the ever greater adaptation of blockchain systems, smart contract based ecosystems have formed to provide financial services and other utility. This results in an ever increasing demand for transactions on blockchains, however, the amount of transactions per second on a given ledger is limited. Layer-2 systems attempt to improve scalability by taking transactions off-chain, with building blocks that are two party channels which are concatenated to form networks. Interaction between two parties requires (1) routing such a network, (2) interaction with and collateral from all intermediaries on the routed path and (3) interactions are often more limited compared to what can be done on the ledger. In contrast to that design, recent constructions such as Hydra Heads (FC’21) are both multi-party and isomorphic, allowing interactions to have the same expressiveness as on the ledger making it akin to a ledger located on Layer-2. The follow up Interhead Construction (MARBLE’22) further extends the protocol to connect Hydra Heads into networks by means of a “virtual” Hydra Head construction. This work puts forth an even greater generalization of the Interhead Protocol, allowing for interaction across different Layer-2 ledgers with a multitude of improvements. As concrete example, our design is modular and lightweight, which makes it viable for both full virtual ledger constructions as well as straightforward one-time interactions and payments systems.
The Effectiveness of Online Games on the Learning Mindset of Class 5 Primary Children MIS Nurul Hidayah

The variety and convenience offered in these games means they can stay in front of the computer for a long time and their level of use is increasing. This increase in intensive playing and use of online games has given rise to various problems which are known among psychologists as online game addiction. Online games can make players forget time. This happens because the average player is too addicted, and many online game players use the act of playing games as a way to overcome boredom. This research explains the influence of online games on the learning mindset of 5th grade elementary school students, at MIS Nurul Hidayah Jln, Bukit Ngalau, Batu Gadang sub-district, Lubuk Gillian sub-district, Padang city. This research uses survey research methods, where research can find out whether there is an influence online game activities on learning patterns or students. The data collection technique used is to randomly give surveys to people who meet the criteria as respondents.
Problematics in the Use of Learning Media in Grade 1 SDN 13 Lolong Belanti

This observation was carried out to find out the problems and use of learning media in Elementary School 13 Lolong Belanti. This observation uses a qualitative method. Elementary schools used in this observation include SDN 13 lolong belanti. From the data collected by observation techniques, interviews, and documentation using observation sheets and interview guides. The results of this observation indicate that the learning media problem in elementary school SDN 13 lolong belanti is that learning media are not yet available in the independent curriculum, there is no storage space so that the media is damaged, lost and not maintained, and the teacher's insights are limited in using the media. The use of media in the Lolong Belanti elementary school has not been maximized due to the availability of media in each school. In general, the use of media by teachers at SDN 13 lolong belanti is the use of media en masse or in groups in front of the class. Some teachers have used environmental media and used materials in learning.