Reviewerly/exHarmony huggingface dataset:
Securing Big Data Integrity for Industrial IoT in Smart Manufacturing Based on the Trusted Consortium Blockchain (TCB)

The smart manufacturing ecosystem enhances the end-to-end efficiency of the mine-to-market lifecycle to create the value chain using the big data generated rapidly by edge computing devices, third-party technologies, and various stakeholders connected via the industrial Internet of things. In this context, smart manufacturing faces two serious challenges to its industrial IoT big data integrity: real-time transaction monitoring and peer validation due to the volume and velocity dimensions of big data in industrial IoT infrastructures. Modern blockchain technologies as an embedded layer substantially address these challenges to empower the capabilities of the IIoT layer to meet the integrity requirements of the big data layer. This paper presents the trusted consortium blockchain (TCB) framework to provide an optimal solution for big data integrity through a secure and verifiable hyperledger fabric modular (HFM). The TCB leverages trustworthiness in heterogeneous IIoT networks of governing end-point peers to achieve strong integrity for big data and support high transaction throughput and low latency of HFM contents. Our proposed framework drives the fault-tolerant properties and consensus protocols to monitor malicious activities of tunable peers if compromised and validates the signed evidence of big data recorded in real-time HFM operated over different smart manufacturing environments. Experimentally, the TCB has been evaluated and reached tradeoff results of throughput and latency better than the comparative consortium blockchain frameworks.
Soft Wireless Headband Bioelectronics and Electrooculography for Persistent Human–Machine Interfaces

Recent advances in wearable technologies have enabled ways for people to interact with external devices, known as human-machine interfaces (HMIs). Among them, electrooculography (EOG), measured by wearable devices, is used for eye movement-enabled HMI. Most prior studies have utilized conventional gel electrodes for EOG recording. However, the gel is problematic due to skin irritation, while separate bulky electronics cause motion artifacts. Here, we introduce a low-profile, headband-type, soft wearable electronic system with embedded stretchable electrodes, and a flexible wireless circuit to detect EOG signals for persistent HMIs. The headband with dry electrodes is printed with flexible thermoplastic polyurethane. Nanomembrane electrodes are prepared by thin-film deposition and laser cutting techniques. A set of signal processing data from dry electrodes demonstrate successful real-time classification of eye motions, including blink, up, down, left, and right. Our study shows that the convolutional neural network performs exceptionally well compared to other machine learning methods, showing 98.3% accuracy with six classes: the highest performance till date in EOG classification with only four electrodes. Collectively, the real-time demonstration of continuous wireless control of a two-wheeled radio-controlled car captures the potential of the bioelectronic system and the algorithm for targeting various HMI and virtual reality applications.
Multi-Sensor Data Fusion for Real-Time Multi-Object Tracking

Sensor data fusion is essential for environmental perception within smart traffic applications. By using multiple sensors cooperatively, the accuracy and probability of the perception are increased, which is crucial for critical traffic scenarios or under bad weather conditions. In this paper, a modular real-time capable multi-sensor fusion framework is presented and tested to fuse data on the object list level from distributed automotive sensors (cameras, radar, and LiDAR). The modular multi-sensor fusion architecture receives an object list (untracked objects) from each sensor. The fusion framework combines classical data fusion algorithms, as it contains a coordinate transformation module, an object association module (Hungarian algorithm), an object tracking module (unscented Kalman filter), and a movement compensation module. Due to the modular design, the fusion framework is adaptable and does not rely on the number of sensors or their types. Moreover, the method continues to operate because of this adaptable design in case of an individual sensor failure. This is an essential feature for safety-critical applications. The architecture targets environmental perception in challenging time-critical applications. The developed fusion framework is tested using simulation and public domain experimental data. Using the developed framework, sensor fusion is obtained well below 10 milliseconds of computing time using an AMD Ryzen 7 5800H mobile processor and the Python programming language. Furthermore, the object-level multi-sensor approach enables the detection of changes in the extrinsic calibration of the sensors and potential sensor failures. A concept was developed to use the multi-sensor framework to identify sensor malfunctions. This feature will become extremely important in ensuring the functional safety of the sensors for autonomous driving.
Common statistical concepts in the supervised Machine Learning arena

One of the core elements of Machine Learning (ML) is statistics and its embedded foundational rules and without its appropriate integration, ML as we know would not exist. Various aspects of ML platforms are based on statistical rules and most notably the end results of the ML model performance cannot be objectively assessed without appropriate statistical measurements. The scope of statistics within the ML realm is rather broad and cannot be adequately covered in a single review article. Therefore, here we will mainly focus on the common statistical concepts that pertain to supervised ML (i.e. classification and regression) along with their interdependencies and certain limitations.
Impact of Strategic Vigilance on Competitive Capabilities in Jordanian Insurance Companies

The study aimed to identify the effect of strategic vigilance on competitive capabilities in Jordanian insurance companies. The study population included (288) individuals, who are the managers of the upper and middle level at the Jordanian insurance companies, which are (24) companies. The study sample is chosen from study population and this sample was chosen by equal stratified random method, which is estimated at (167) respondent according to the sample selection schedule. The questionnaire was used as a tool for the study, the final sample was consisted of (138) individual. Two statistical analysis programs (IBM SPSS 24) (SMARTPLS, v. 3.2.6) to analyze the study data and test its hypotheses. The study showed a statistically significant impact of the strategic vigilance on competitive capabilities in Jordan insurance companies. According to the results, the study recommended the need to enhance interest strategic vigilance information in Jordanian insurance companies through the establishment of units and departments for the strategic vigilance in them, as they are important as strategic information resource.
FLEURS: FEW-Shot Learning Evaluation of Universal Representations of Speech

We introduce FLEURS, the Few-shot Learning Evaluation of Universal Representations of Speech benchmark. FLEURS is an n-way parallel speech dataset in 102 languages built on top of the machine translation FLoRes-101 benchmark, with approximately 12 hours of speech supervision per language. FLEURS can be used for a variety of speech tasks, including Automatic Speech Recognition (ASR), Speech Language Identification (Speech LangID), Speech-Text Retrieval. In this paper, we provide baselines for the tasks based on multilingual pre-trained models like speech-only w2v-BERT [1] and speech-text multimodal mSLAM [2]. The goal of FLEURS is to enable speech technology in more languages and catalyze research in low-resource speech understanding. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Feature Alignment by Uncertainty and Self-Training for Source-Free Unsupervised Domain Adaptation

Most unsupervised domain adaptation (UDA) methods assume that labeled source images are available during model adaptation. However, this assumption is often infeasible owing to confidentiality issues or memory constraints on mobile devices. Some recently developed approaches do not require source images during adaptation, but they show limited performance on perturbed images. To address these problems, we propose a novel source-free UDA method that uses only a pre-trained source model and unlabeled target images. Our method captures the aleatoric uncertainty by incorporating data augmentation and trains the feature generator with two consistency objectives. The feature generator is encouraged to learn consistent visual features away from the decision boundaries of the head classifier. Thus, the adapted model becomes more robust to image perturbations. Inspired by self-supervised learning, our method promotes inter-space alignment between the prediction space and the feature space while incorporating intra-space consistency within the feature space to reduce the domain gap between the source and target domains. We also consider epistemic uncertainty to boost the model adaptation performance. Extensive experiments on popular UDA benchmark datasets demonstrate that the proposed source-free method is comparable or even superior to vanilla UDA methods. Moreover, the adapted models show more robust results when input images are perturbed.
The Impact of Immersion through Virtual Reality in the Learning Experiences of Art and Design Students: The Mediating Effect of the Flow Experience

This study provides a theoretical model on the effectiveness of learning through virtual reality technology in bachelor art and design students. Surveys were applied to 200 undergraduate art and design students, and the data obtained were analyzed using multivariate partial least squares (PLS) structural equation modeling. Our model results indicate that immersion VR has a positive impact on the flow experience. Moreover, the data demonstrated a mediating effect of the flow experience on the learning experience variable explained through motivation, curiosity, cognitive benefits, reflective thinking, and the perception of value. These results have possibilities for academic art and design institutions to increase learning in the classroom through virtual reality technology, leaving the possibility of replicating the model in other areas of study.
Sign language recognition system for communicating to people with disabilities

Sign language is one of the most reliable ways of communicating with special needs people, as it can be done anywhere. However, most people do not understand sign language. Therefore, we have devised an idea to make a desktop application that can recognize sign language and convert it to text in real time. This research uses American Sign Language (ASL) datasets and the Convolutional Neural Networks (CNN) classification system. In the classification, the hand image is first passed through a filter and after the filter is applied, the hand is passed through a classifier which predicts the class of the hand gestures. This research focuses on the accuracy of the recognition. Our Application resulted in 96,3% accuracy for the 26 letters of the alphabet.
Camera Contrast Learning for Unsupervised Person Re-Identification

Unsupervised person re-identification (Re-ID) aims at finding the most informative features from unlabeled person datasets. Some recent approaches adopted camera-aware strategies for model training and have thereby achieved highly promising results. However, these methods simultaneously address intra-ID discrepancies of all cameras and require independent learning under each camera, which increases the complexity of algorithm. To resolve this issue, we present a camera contrast learning framework for unsupervised person Re-ID. Our method first proposes a time-based camera contrastive learning module to facilitate model learning. At each iteration, we follow the time contrast principle to select one camera centroid as proxy of each cluster. By enforcing the samples to converge to positive proxies, the correlation between features and cameras can gradually be reduced. Moreover, we design a 3-dimensional attention module to further reduce intra-ID discrepancies caused by background shifts. By re-weighting each feature map element in a spatial-channel order, our module can exactly find identity-invariant semantic cues from regions of interest in person images, no matter how the background change. Experimental results on several popular datasets prove that our work surpasses existing unsupervised person Re-ID approaches to a remarkable extent. The source codes can be found in <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/HongweiZhang97/CCL</uri> .
Topic Modelling and Opinion Analysis On Climate Change Twitter Data Using LDA And BERT Model.

Nowadays, Climate change is an important environmental factor that affects every living thing on the earth. It is very essential to study the public perceptions regarding the disaster events frequently happening due to climate change. In today's digital era individuals are using social network platforms namely Twitter, Facebook, and Weibo now and then to express their views about any events. In this paper, the climate change Twitter data set was considered for analyzing the topics and the opinions discussed by the public regarding climate change. The Latent Dirichlet Allocation(LDA) method was used to list out the various topics present in the data set and the Bidirectional Encoder Representation from Transformers(BERT uncased) is an efficient deep learning technique used to classify the sentiments present in the data set. Here the sentiments were labelled as pro news, support, neutral and anti. The performance of the proposed topic modelling and sentiment classification model was compared using the precision, recall, and accuracy measures. The BERT uncased model with has shown the best results such as precision of 91.35%, recall of 89.65%, and accuracy of 93.50% compared to other methods.
DCSNE: Density-based Clustering using Graph Shared Neighbors and Entropy

Density-based clustering techniques identify arbitrary shaped clusters in the presence of outliers by capturing the intrinsic distribution of data and separating high and low-density regions based on the neighborhood information. They use global parameters to compute the density distribution of data points, the optimization of which is quite a challenging and time intensive task. The similarity graphs constructed from the data points can easily capture the topology of the density regions without using any user-defined parameters. Moreover, the concept of entropy can be useful to capture the randomness and disorderliness present in highly complex data distributions. Our proposed algorithm makes use of entropy in conjunction with the graph local neighborhood information to compute density distribution of data points. Then, actual clusters are identified using the density distribution and the shared neighbor information between the regions. The experimental results show that the proposed technique outperforms other comparable methods in terms of cluster quality in the presence of noise on diversified gene expression and real datasets.
Integrating augmented reality into mathematics teaching and learning and examining its effectiveness

Innovative learning technologies and modernizing the education system can improve education. In the last decade, the use of Augmented Reality (AR) has increased rapidly, especially in the education sector. However, only limited articles have reviewed the stages of developing and applying AR material for learning mathematics. As AR is still considered a relatively new technology in educational fields, there is a natural need for research-based guidelines to design effective and applicable AR tools for school-based learning. The main purpose of this study is to design, develop and reveal the effect of an AR material to improve spatial ability in secondary school students using mobile devices. For this purpose, an AR material that focuses on the change of the surface area of the cube in case of removing cubes from different parts of the cube was developed within the framework of the Waterfall Model and the effect of this material was examined. In this research, in which case study methodology was adopted, data were collected through achievement tests, questionnaires, and interviews. The results showed that students were generally able to accept using AR to learn geometry and AR-supported teaching methods significantly improved students' learning outcomes.
SimpleTrack: Understanding and Rethinking 3D Multi-object Tracking

3D multi-object tracking (MOT) has witnessed numerous novel benchmarks and approaches in recent years, especially those under the “tracking-by-detection” paradigm. Despite their progress and usefulness, an in-depth analysis of their strengths and weaknesses is not yet available. In this paper, we summarize current 3D MOT methods into a unified framework by decomposing them into four constituent parts: pre-processing of detection, association, motion model, and life cycle management. We then ascribe the failure cases of existing algorithms to each component and investigate them in detail. Based on the analyses, we propose corresponding improvements which lead to a strong yet simple baseline: SimpleTrack. Comprehensive experimental results on Waymo Open Dataset and nuScenes demonstrate that our final method could achieve new state-of-the-art results with minor modifications. Furthermore, we take additional steps and rethink whether current benchmarks authentically reflect the ability of algorithms for real-world challenges. We delve into the details of existing benchmarks and find some intriguing facts. Finally, we analyze the distribution and causes of remaining failures in SimpleTrack and propose future directions for 3D MOT. Our code is at https://github.com/tusen-ai/SimpleTrack .
U-shape Transformer for Underwater Image Enhancement

The light absorption and scattering of underwater impurities lead to poor underwater imaging quality. The existing data-driven based underwater image enhancement (UIE) techniques suffer from the lack of a large-scale dataset containing various underwater scenes and high-fidelity reference images. Besides, the inconsistent attenuation in different color channels and space areas is not fully considered for boosted enhancement. In this work, we constructed a large-scale underwater image (LSUI) dataset including 4279 image pairs, and reported an U-shape Transformer network where the transformer model is for the first time introduced to the UIE task. The U-shape Transformer is integrated with a channel-wise multi-scale feature fusion transformer (CMSFFT) module and a spatial-wise global feature modeling transformer (SGFMT) module specially designed for UIE task, which reinforce the network's attention to the color channels and space areas with more serious attenuation. Meanwhile, in order to further improve the contrast and saturation, a novel loss function combining RGB, LAB and LCH color spaces is designed following the human vision principle. The extensive experiments on available datasets validate the state-of-the-art performance of the reported technique with more than 2dB superiority. The dataset and demo code are available on https://bianlab.github.io/codes.html .
Railway intrusion detection based on refined spatial and temporal features for UAV surveillance scene

Railway intrusion detection is important for the safety of the railway operation. However, how to detect intruding events in the scene of unmanned aerial vehicle (UAV) surveillance is a challenging task, especially when there are multiple railway regions or unknown types of intruding objects. To tackle the above problems, a railway intrusion detection method is proposed for the scene of UAV surveillance. It can detect intruding events accurately without pre-setting the intruding object type by detecting the normal railway region and the abnormal railway region with intruding objects rather than recognizing the intruding objects directly. The proposed network consists of a novel Fused-convolutional long short-term memory (Fused-ConvLSTM) module for efficiently propagating spatial and temporal information; several attention modules for adaptively enhancing the feature information, such as the improved feature fusion layer; several lightweight strategies for reducing the computation complexity, such as lightweight feature extraction and width multiplier. Experiments are performed on the real aerial videos obtained by a micro UAV. The experimental results demonstrate that the proposed method can accurately detect railway intrusion events in aerial video, and effectively deal with the case of multiple railway regions and unknown intruding object types. The experimental results also demonstrate that the proposed network can achieve better performance than other object detection networks.
Cloud-Native Workload Orchestration at the Edge: A Deployment Review and Future Directions

Cloud-native computing principles such as virtualization and orchestration are key to transferring to the promising paradigm of edge computing. Challenges of containerization, operative models and scarce availability of established tools make a thorough review indispensable. Therefore, the authors have described the practical methods and tools found in the literature as well as in current community-led development projects, and have thoroughly exposed the future directions of the field. Container virtualization and its orchestration through Kubernetes have dominated the cloud computing domain, while major efforts have been recently recorded focused on the adaptation of these technologies to the edge. Such initiatives have addressed either the reduction of container engines and the development of specific tailored operating systems or the development of smaller K8s distributions and edge-focused adaptations (such as KubeEdge). Finally, new workload virtualization approaches, such as WebAssembly modules together with the joint orchestration of these heterogeneous workloads, seem to be the topics to pay attention to in the short to medium term.
See Finer, See More: Implicit Modality Alignment for Text-Based Person Retrieval

Text-based person retrieval aims to find the query person based on a textual description. The key is to learn a common latent space mapping between visual-textual modalities. To achieve this goal, existing works employ segmentation to obtain explicitly cross-modal alignments or utilize attention to explore salient alignments. These methods have two shortcomings: 1) Labeling cross-modal alignments are time-consuming. 2) Attention methods can explore salient cross-modal alignments but may ignore some subtle and valuable pairs. To relieve these issues, we introduce an Implicit Visual-Textual (IVT) framework for text-based person retrieval. Different from previous models, IVT utilizes a single network to learn representation for both modalities, which contributes to the visual-textual interaction. To explore the fine-grained alignment, we further propose two implicit semantic alignment paradigms: multi-level alignment (MLA) and bidirectional mask modeling (BMM). The MLA module explores finer matching at sentence, phrase, and word levels, while the BMM module aims to mine more semantic alignments between visual and textual modalities. Extensive experiments are carried out to evaluate the proposed IVT on public datasets, i.e., CUHK-PEDES, RSTPReID, and ICFG-PEDES. Even without explicit body part alignment, our approach still achieves state-of-the-art performance. Code is available at: https://github.com/TencentYoutuResearch/PersonRetrieval-IVT .