Reviewerly/exHarmony huggingface dataset:
Syntaxis-based extraction method with type and function of word detection approach for machine translation of Indonesian-Tolaki and English sentences

To build an Indonesian Machine Translation (MT), it is not only needed a related syntactic analysis to the correct spelling of words but also needed related contextual analysis, consist type and function of word, morphology, and semantic. The dictionaries usage is needed to translates Indonesian basic words and to captures good word translations through the semantic and context of words in a sentence or document. This study purposes to extracts Indonesian and Tolaki words for building a good MT by comparing the development of Indonesian MT which focuses on deep cases of morphology and syntactic. We developed morphtool to captures the morphological elements of Indonesian and Tolaki words. For working in deep syntactic case, we build a rule to captures the function and type of word that can affect the word itself translation in the sentence. We combine supervised and unsupervised techniques to work on the text extraction in the words, sentences, and documents through the morphonemic rules of Indonesian- Tolaki syntaxis manner. Then, we use hybrid MT, combining Statistical MT (SMT) and Rule Based MT (RBMT), for sentence translation process. The hybrid MT evaluation process from the Indonesian-Tolaki to English translation performance test shows the accuracy result is 0.74. Meanwhile, the performance test of the English to Indonesian-Tolaki translation shows the accuracy result is 0.71. These results indicate that the proposed MT method can work better than the SMT and RBMT methods with an average accuracy of around 70%.
Proctor net: An AI framework for suspicious activity detection in online proctored examinations

Due to recent pandemic and other factors, global Education System transiting from traditional paradigm to online paradigm in learning as well as assessment. Online proctored examinations have ensured continuity in assessing learners in award of various global certifications and university degrees. Traditional human proctored examination requires huge infrastructure, human resources, effort, and physical presence of examinees. To overcome these limitations, an automated AI-based proctoring system “Proctor Net” proposed in this work for detecting suspicious behavior of the examinee. The proposed system captures live video of the examinee and generate alerts based on three aspects, 1. Examinee Recognition, 2. Eye-gaze Tracking, and 3. Mouth Opening Detection. In first phase, Proctor Net recognizes examinee faces using inception-Resnet v1 blocks. In next phase, the Proctor Net calculates the pitch and yaw of the authenticated examinee face from the spatial landmarks extracted by hour-glass model. Further, Mouth Aspect Ratio was verified to check if examinee is speaking to others. The proposed model generate alters to proctor if found any deviation in examinee’s behavior. The proposed Proctor Net is evaluated using standard data sets such as Labelled Faces in the wild Dataset (LFW), Unity Eyes Dataset and real time data - “Proctor Dataset” with various types of malpractices. Extensive experimentation is carried out and results demonstrate that proposed work with the combination of Inception-Resnet-v1 blocks with Hourglass modules is more accurate with an accuracy rate of 91%, making it reliable and robust.
Multiscale Global-Aware Channel Attention for Person Re-identification

Most person re-identification methods are researched under various assumptions. However, viewpoint variations or occlusions are often encountered in practical scenarios. These are prone to intra-class variance. In this paper, we propose a multiscale global-aware channel attention (MGCA) model to solve this problem. It imitates the process of human visual perception, which tends to observe things from coarse to fine. The core of our approach is a multiscale structure containing two key elements: the global-aware channel attention (GCA) module for capturing the global structural information and the adaptive selection feature fusion (ASFF) module for highlighting discriminative features. Moreover, we introduce a bidirectional guided pairwise metric triplet (BPM) loss to reduce the effect of outliers. Extensive experiments on Market-1501, DukeMTMC-reID, and MSMT17, and achieve the state-of-the-art results on mAP. Especially, our approach exceeds the current best method by 2.0% on the most challenging MSMT17 dataset.
DRYDet: decoupling YOLOv5 detector and refining the tasks in remote sensing scenario

You only look once version 5 (YOLOv5) algorithm uses one convolutional layer for both classification and localization tasks. They share the same set of weights, leading to interference between the two tasks. Thus, the application of this method in remote sensing scenario is very limited. To solve the problem, we decouple the detection head of the YOLOv5 algorithm so that these two tasks can be separated. Furthermore, we achieve high quality detection in remote sensing scene with task refinement. Specifically, we propose a weight allocation method based on Huffman coding theory to ease imbalance of the number of categories and improve the classification accuracy. If the number of a category is large, a small weight is set. Otherwise, a large weight is given. To improve the localization accuracy, we use the efficient intersection over union (IOU) loss to alleviate the problem of penalty failure caused by central IOU loss. In this way, the predicted boxes are closer to the ground truth. A large number of experiments were carried out on DOTA and UCAS-AOD datasets to verify the effectiveness of our proposed method. Compared with baseline, the mean average precision totally increased by 2.29% in DOTAv1.5 dataset. Simultaneously, our method also showed superior performance when comparing with start of the art algorithms.
Few-shot Learning for Multi-modal Social Media Event Filtering

Social media has become an important data source for event analysis. When collecting this type of data, most contain no useful information to a target event. Thus, it is essential to filter out those noisy data at the earliest opportunity for a human expert to perform further inspection. Most existing solutions for event filtering rely on fully supervised methods for training. However, in many real-world scenarios, having access to large number of labeled samples is not possible. To deal with a few labeled sample training problem for event filtering, we propose a graph-based few-shot learning pipeline. We also release the Brazilian Protest Dataset to test our method. To the best of our knowledge, this dataset is the first of its kind in event filtering that focuses on protests in multi-modal social media data, with most of the text in Portuguese. Our experimental results show that our proposed pipeline has comparable performance with only a few labeled samples (60) compared with a fully labeled dataset (3100). To facilitate the research community, we make our dataset and code available at https://github.com/jdnascim/7Set-AL.
Impact of organizational characteristics on employees’ entrepreneurial orientation with mediating role of knowledge process capabilities and moderating role of psychological factors in the era of COVID-19

The study aims to investigate the impact of organizational characteristics and knowledge process capabilities on the entrepreneurial orientation among the manufacturing industry employees in the Punjab province of Pakistan. Additionally, this study has examined the mediating role of knowledge process capabilities in the relationship between organizational characteristics and entrepreneurial orientation among those employees and the moderating effect of psychological factors on the relationship between organizational characteristics and entrepreneurial orientation.The study has employed the survey-based methodology and data are collected with the aid of self-administered questionnaires. This study utilized the partial least squares structural equation modeling (PLS-SEM) to establish the validity and reliability of the measurement model and test the relationships. The response rate of the current study is 64.66%.The study findings have shown mixed results as one of the organizational characteristics, namely, resource and time availability is an insignificant determinate of entrepreneurial orientation among the manufacturing industry employees in Punjab province of Pakistan. Whereas management support, rewards, work discretion, and knowledge process capabilities appear as significant determinates of employees' entrepreneurial orientation. The results indicated that knowledge process capabilities have a mediating role in the relationship between organizational characteristics and employees' entrepreneurial orientation. Moreover, psychological factors, namely, propensity to take risk and locus of control have a significant moderating role on the relationship of management support, rewards, and work discretion with employees' entrepreneurial orientation.The empirical insights on the study are valuable for policymakers and managers in manufacturing sectors of developing countries, such as Pakistan, to enrich their work performance through the understanding impact of organizational characteristics and knowledge process capabilities on the entrepreneurial orientation with moderating role of psychological factors.Studies on the mediating impact of knowledge process capabilities on the linkage between organizational characteristics and entrepreneurial orientation with the moderating role of psychological factors remain limited. This study is one of the earliest studies that investigate these inter-relationships.
Changing the Perceived Views of Student Nurses Concerning Healthcare Robots

Zrínyi, Miklós PhD, RN; Lampek, Kinga PhD; Pakai, Annamária PhD, RN; Vass, Dezső BSc; Oláh, András PhD, RNEditor(s): Alexander, Susan DNP, ANP-BC, ADM-BC Author Information
Immutability and Encapsulation for Sound OO Information Flow Control

Security-critical software applications contain confidential information which has to be protected from leaking to unauthorized systems. With language-based techniques, the confidentiality of applications can be enforced. Such techniques are for example type systems that enforce an information flow policy through typing rules. The precision of such type systems, especially in object-oriented languages, is an area of active research: an appropriate system should not reject too many secure programs while soundly preserving noninterference. In this work, we introduce the language SIFO which supports information flow control for an object-oriented language with type modifiers. Type modifiers increase the precision of the type system by utilizing immutability and uniqueness properties of objects for the detection of information leaks. We present SIFO informally by using examples to demonstrate the applicability of the language, formalize the type system, prove noninterference, implement SIFO as a pluggable type system in the programming language L42, and evaluate it with a feasibility study and a benchmark.
A complete characterization of normal cones and extreme points for p-boxes

Probability boxes, also called p-boxes, correspond to sets of probability distributions bounded by a pair of distribution functions. They belong to the class of models known as imprecise probabilities. One of the central issues related to imprecise probabilities is the intervals of values corresponding to the expectations of random variables, and in particular the interval bounds. In general, these are reached at the extreme points of credal sets, which denote convex sets of compatible probabilistic models. The goal of this paper is to characterize and identify extreme points corresponding to p-boxes on finite domains. To achieve this, we use the concept of normal cones. In the context of imprecise probabilities, these correspond to sets of random variables whose extreme expectations are reached at a common extreme point. Our main results include a characterization of all possible normal cones of p-boxes, their relation to extreme points, and the identification of an adjacency structure on the collection of normal cones that is closely related to the adjacency structure in the set of extreme points.
The Class Algorithm: Evolution Based on Division of Labor and Specialization

This paper proposes the class algorithm, a new type of evolutionary algorithm. The methodology is inspired by the concepts of division of labor and specialization. Individuals form subpopulations of different classes, where each class has its own characteristics. The entire population evolves through influences among individuals within and between the different subpopulations. The proposed approach can be applied in both continuous and discrete problem domains. The performance of the class algorithm surpasses other evolutionary algorithms for many test functions of single-objective continuous optimization benchmark problems. The class algorithm also shows a competent ability to solve the large-scale discrete optimization problems.
Toward Asynchronously Weight Updating Federated Learning for AI-on-Edge IoT Systems

Recently, Internet of Things (IoT) systems in the network edge with embedded intelligence emerged as a trending research topic. Edge computing offers a significant advantage over the traditional form of sharing personal data with a centralized entity since the latter paradigm may affect the user’s privacy, e.g., due to explicit exchange of sensitive biomedical data. To address this inherent data privacy issue, in this paper, we focus on designing an asynchronously weight updating federated learning algorithm toward the much anticipated AI-on-Edge IoT systems. Among numerous use-cases, we consider the face mask detection problem, which is traditionally considered as a centralized computer vision task. We take a different approach to distribute the learning tasks to the users in a federated learning framework, and then investigate the performance trade-off between synchronous and asynchronously weight updating methods. In our proposed system, the models are penalized by their performance metrics to limit a model’s participation in the aggregation stage. By developing the asynchronously weight updating method for deep learning (e.g., Convolutional Neural Network (CNN)) models, we also investigate its impact on model parameters exchange with the centralized aggregator. Experimental results demonstrate that our proposed asynchronously weight updating method achieves results comparable to those attained with the centralized training and the synchronously weight updating strategy. Also, we provide numerical analysis to demonstrate a significant transmission time overhead with our proposal.
Audio source identification based on residual network

Large number of audio recordings are used in law enforcement and litigation procedures, and it also brings security issues such as the identification of the audio source. This paper mainly studies the problem of source identification (device detection). We proposed an audio source identification framework based on an improved residual network model that introduces a character category output, which will help to improve the identification accuracy for the special case of cross speaker. Experiments show that this audio source identification framework based on residual network has achieved good results under the condition of non-target recognition task, with the highest accuracy rate reaching above 98%, which outperforms the current audio source identification algorithm.
BPPIR: Blockchain-assisted privacy-preserving similarity image retrieval over multiple clouds

With the progress of the times and the development of electronic products, more and more images are captured and stored on multiple cloud servers, which are more likely to exhibit dishonesty because of interdependence rather than semi-honesty and thus how to achieve search over multiple clouds becomes intractable. Moreover, most of the existing research works on content-based image retrieval so far do not consider dishonest clouds and thereby do not support multi-cloud retrieval. On the one hand, a central search server is generally introduced for the convenience of retrieval in the system with multiple cloud servers. However, the central search server may return false or incomplete search results for possible commercial profit, which reduces the retrieval accuracy. On the other hand, cloud servers may destroy the image integrity by deleting or tampering images to save computing and bandwidth resources, resulting in the loss of important information for the image owner and incorrect decryption of the image for the user. In order to address these two problems, we propose a blockchain-assisted privacy-protected similarity image retrieval in the multiple cloud environment, which can not only ensure the accuracy of the retrieval results, but also can verify the integrity of images. The performance of this scheme is evaluated on a real-world dataset, and the results show the high retrieval precision and efficiency of this scheme.
Bottleneck Analysis of Dynamic Graph Neural Network Inference on CPU and GPU

Dynamic graph neural network (DGNN) is becoming increasingly popular because of its widespread use in capturing the dynamic features in the real world. A variety of dynamic graph neural networks designed from algorithmic perspectives have succeeded in incorporating temporal information into graph processing. Despite the promising algorithmic performance, deploying DGNNs on hardware presents additional challenges due to the model complexity, diversity, and the nature of the time-dependency. Meanwhile, the differences between DGNNs and static graph neural networks make hardware-related optimizations for static graph neural networks unsuitable for DGNNs. In this paper, We select eight prevailing DGNNs with different characteristics and profile them on both CPU and GPU. The profiling results are summarized and analyzed, providing in-depth insights into the bottlenecks of DGNNs on hardware and identifying potential optimization opportunities for future DGNN acceleration. Followed by a comprehensive survey, we provide a detailed analysis of DGNN performance bottlenecks on hardware, including temporal data dependency, workload imbalance, data movement, and GPU warm-up. We suggest several optimizations from both software and hardware perspectives. This paper is the first to provide an in-depth analysis of the hardware performance of DGNN* Code is available at https://github.com/sharc-lab/DGNN_analysis.
Device Interoperability for Industrial IoT using Model-Driven Architecture

Industrial Internet of Things (IIoT) is an emerging domain, converting common objects into connecting objects with ubiquitous internet access to automate industry. Due to different vendors, supporting different infrastructures, a set of communication protocols such as Zigbee, 6LowPAN, Wireless Fidelity (Wi-Fi), Hyper Text Transfer Protocol (HTTP), etc. are introduced for IIoT. Thus, a closed ecosystem for smart devices is created. Particularly, when two or more industrial IoT applications are developed using different application-layer protocols such as Constrained Application Protocol (CoAP), Advanced Message Queuing Protocol (AMQP), or MQ Telemetry Transport (MQTT), devices are called heterogeneous devices and interoperability becomes a major challenge. In the existing literature, device-level interoperability using different application-layer protocols is enhanced with the help of intermediators at the network layer which includes servers, brokers, or gateways/adapters to route communication. However, these intermediators lead to several other issues such as dependency on network layer components, load balancing, single point of failure, and scalability. Therefore, the interoperability issue needs to be addressed at the application layer using a device intermediator instead of utilizing network layer components. For this purpose, Model Driven Engineering (MDE) is selected because less attention is paid to IIoT interoperable solutions development using MDE. To bridge this gap, a Model Driven Architecture (MDA) based approach is proposed that reduces the processing time and effort to develop these IIoT interoperable systems. For this purpose, (i) a metamodel, (ii) a UML profile, and (iii) transformation rules are developed to make heterogenous application-layer protocols interoperable using devices as intermediator. The initial feasibility of the proposed solution is demonstrated through a real-world case study i.e., a smart city. Results show that a complete solution for interoperability at the application layer for industrial IoT is provided using MDA. It will help the practitioners to automate industry 4.0 using model-driven based system development.
Evaluation of training efficiency of table tennis players based on computer video processing technology

In this paper to improve the training efficiency of table tennis players, this paper applies computer video processing technology to the evaluation of training efficiency of table tennis players, and uses intelligent video processing to identify the training process of table tennis players. Moreover, this paper introduces the curve coordinates to mathematicalize the projection problem, establishes the spherical coordinate system and the plane coordinate system, obtains the spherical quadrilateral and the plane quadrilateral, obtains Euler's idea of establishing a projection suitable for table tennis, and obtains the road map of Euler's projection theory. In addition, this paper builds a training efficiency of table tennis players evaluation system based on projection algorithm and athlete feature recognition. Through the experimental research, it can be seen that the training efficiency evaluation system of table tennis based on computer video processing technology proposed in this paper can effectively improve the training efficiency of table tennis players.
Real-Time Detection of Knives and Firearms using Deep Learning

Daily gun and knife related incidents are increasing due to lack of security check. In most of the places CCTV cameras are being installed however they require surveillance all the time. It is difficult due to limitations of humans in vigilant monitoring of the surveillance videos. The need of automated weapon detection is evident to limit and reduce these types of incidents. The proposed approach is mainly focused on developing an automated weapon detection system to detect different types of firearms and knives. In order to detect these types of incidents, we used a YOLOv5 deep learning model on a self collected dataset. The evaluation of the proposed approach shows its ability in the accurate detection of these weapons with an F1 score of 0.95 in CCTV video.
NEFM: Neural embedding based factorization machines for user response prediction

As Factorization Machine (FM) models linearly describe feature interactions, they cannot accurately capture complex non-linear features of data. Furthermore, random initialization in these FM models seriously affects system convergence and performance. Therefore, the random embedding process of FM models may not be sufficient to capture the data information. Although deep neural networks (DNNs)-based FM models have been recently proposed for advanced feature interactions, it is difficult to train. To address these challenges, we propose a neural embedding factorization machine (NEFM) model, which effectively initializes the embedding layers based on an unsupervised pre-training framework of probabilistic auto-encoder. The NEFM smartly couples the good linearity of FM models in modeling second-order feature interactions and the advantage of DNNs in modeling non-linear feature interactions. Experimental results show the effectiveness of the proposed NEFM. For example, the performance of NEFM is enhanced by at least 6.99% than the non-pre-trained FM models. Compared with the pre-trained FM by DNNs-based models, the NEFM model reduces at least 1.02% in test errors.