Reviewerly/exHarmony huggingface dataset:
The Acceptance of E-learning as a Tool for Teaching Entrepreneurship During the COVID-19 Pandemic The Case of HITS of Sidi Bouzid and Ksar Hellal -Tunisia

A relentless mutation characterized universities around the world by the advent of the infamous virus that gave rise to the pandemic called COVID-19. This study aims at exploring e-learning readiness and its acceptance as a tool for teaching entrepreneurship in two higher institutes of technological studies in Tunisia, and it has become imminent to resort to e-learning. The desired results are more far-sighted in these new special circumstances, which have made this mode of learning the gateway to the entire educational system be called into question. The question is: How did students perceive this new mode of distance education? An online questionnaire was distributed to extrapolate a descriptive analysis. We have explored certain facets in both institutes that highlight many disadvantages between both students and areas: mastery of technology, poor internet connectivity, technological accessibility, and unkind study environments. Besides this, research highlights the role of new approaches to distance learning in entrepreneurship. It will end with the proposal of recommendations that are relevant for different stakeholders in the phenomenon of distance education in general and the entrepreneurial discipline in particular.
Cross-Camera Deep Colorization

In this paper, we consider the color-plus-mono dual-camera system and propose an end-to-end convolutional neural network to align and fuse images from it in an efficient and cost-effective way. Our method takes cross-domain and cross-scale images as input, and consequently synthesizes HR colorization results to facilitate the trade-off between spatial-temporal resolution and color depth in the single-camera imaging system. In contrast to the previous colorization methods, ours can adapt to color and monochrome cameras with distinctive spatial-temporal resolutions, rendering the flexibility and robustness in practical applications. The key ingredient of our method is a cross-camera alignment module that generates multi-scale correspondences for cross-domain image alignment. Through extensive experiments on various datasets and multiple settings, we validate the flexibility and effectiveness of our approach. Remarkably, our method consistently achieves substantial improvements, i.e., around 10dB PSNR gain, upon the state-of-the-art methods. Code is at: https://github.com/THU-luvision .
The Era of Artificial Intelligence Reforming Tourism Industry in Society 5.0

The main intention of this paper is to elucidate the tourism sector of India and how the emerging technologies like Artificial Intelligence is impacting the tourism industry for good. It will also determine that how the tours and travels will be more secure and could be increased in future. While talking about the traveling and tourism the very first thing that comes in our mind is how convenient the technologies have made everything for us. Form booking to check-out everything is technological based. Coming to the scenario of artificial intelligence in traveling sector, it is already creating a huge significance. With no time we are going to experience the best of part of traveling and tourism. The whole report will analyze the impact of Artificial Intellect on the traveling industry of India. India, as a developing country has a much scope of being the revolutionary hub of the travel and tourism. The advanced presentations of AI and Automation will definitely provide a valuable skill to the clients of the touring industry. Nearly few of the well-established claims are linguistic translator, chat-bots, simulated reality, besides several more are certainly displaying a huge influence and their efficacy when applied in the tourism and travel industry. This paper will discuss the current viewpoint regarding the Artificial Intellect and Robotics relevant to the travel industry of India, by understanding the current trends and keeping note on the recent data and thus will be predicting the future scopes and opportunities of AI in this field.
Coalitional Formation-Based Group-Buying for UAV-Enabled Data Collection: An Auction Game Approach

Unmanned aerial vehicles (UAVs) enable promising solutions in assisting data collection in wide-area distributed sensor networks, leveraging their advanced properties of high mobility and line-of-sight communication links.However, existing UAV-assisted data collection methods mainly focus on unilaterally maximizing the utility of UAVs or sensors.Unfortunately, the problem driven by the market economy is ignored, namely the game between buyer and seller, in the process of sensors competing for UAV services.To address this problem, we propose a group-buying coalition auction method that encourages sensors to form coalitions to bid for UAV data collection services.Then, a parallel variable neighborhood ascent search algorithm is designed to quickly search the approximately optimal group-buying coalition structure.We further propose a novel group-buying coalition auction method, named TRUST, which can ensure the economical properties, i.e., truthfulness, individual rationality, and maximization of social welfare.Numerical results show that the sensors' average age of information (AoI) under the proposed method is reduced by 16.7% and 44.5% compared with the coalition formation game (CFG) and joint trajectory design-task scheduling (TDTS) UAV-to-community methods.To our best knowledge, this is the first effort on truthful coalition formation-based group-buying auction.
Software Defect Prediction via GCN based on Structural and Context Information

Software quality plays an important role in the software engineering. To improve software reliability, software defect prediction which predicts code region is buggy or not aims to assist developers find bugs and allocate their test resources reasonably. Traditional defect prediction studies focus on handcrafted features and train machine learning classifiers to identify the defective code area. Since the source code contains rich semantic and structural information, and defective code is closely related to its context, now many researchers try to utilize semantic information of programs and build more accurate defect prediction model. Existing methods mainly analyze Abstract Syntax Tree (AST) which is obtained from source code and transform it into token sequences. But these methods fail to capture rich information of structural and context information of code. At the same time, some traditional features(e.g. The number of methods in the class(WMC), Lines of code(LOC)) are also very valuable in defect prediction. Therefore in this paper, we propose to a method to build the graph by connecting parent and child nodes of the AST and connecting leaf nodes from left to right to preserve the context information that contained in source code. Then we use Graph Convolutional Network(GCN) to automatically generate semantic and structural features from source code. In addition, we combine GCN learned features with handcrafted features for more accurate prediction model. Based on the above model, our evaluation on 12 data sets shows that our proposed method is superior to the state-of-the-art software defect prediction method by 10.37 % in recall, 1.62 % in precision, 5.15% in F-measure, and 8.11 % in MCC.
Mediation Model of E-Learning Adoption

The purpose of this study focuses on examining the relationship of expected benefits, e-learning adoption, and psychological motivation of students to e-learning. The study was conducted on students who have experience with e-learning in Bandung (Indonesia), there were 151 students who were asked for data through a questionnaire. Data from respondents was processed using SmartPLS, and model testing was carried out with Inner and Outer evaluations. To confirm the results of the research, the research hypothesis was tested. The results of research on students for two months are known that the expected adoption benefit that is considered by students has a positive relationship with e-learning. Furthermore, it is known that there is a positive relationship between e-learning adoption on students' psychological motivation. It is immediately known that there is a positive relationship between expected benefits and an increase in student psychological motivation. Reviewing the results of the model test, it is known that e-learning adoption is able to mediate between expected benefits and psychological motivation. Research findings become information for universities, where in the implementation of e-learning it is necessary to improve the quality of e-learning assessed by students, in order to improve the performance of e-learning.
PoseScript: 3D Human Poses from Natural Language

Natural language is leveraged in many computer vision tasks such as image captioning, cross-modal retrieval or visual question answering, to provide fine-grained semantic information. While human pose is key to human understanding, current 3D human pose datasets lack detailed language descriptions. In this work, we introduce the PoseScript dataset, which pairs a few thousand 3D human poses from AMASS with rich human-annotated descriptions of the body parts and their spatial relationships. To increase the size of this dataset to a scale compatible with typical data hungry learning algorithms, we propose an elaborate captioning process that generates automatic synthetic descriptions in natural language from given 3D keypoints. This process extracts low-level pose information – the posecodes – using a set of simple but generic rules on the 3D keypoints. The posecodes are then combined into higher level textual descriptions using syntactic rules. Automatic annotations substantially increase the amount of available data, and make it possible to effectively pretrain deep models for finetuning on human captions. To demonstrate the potential of annotated poses, we show applications of the PoseScript dataset to retrieval of relevant poses from large-scale datasets and to synthetic pose generation, both based on a textual pose description. Code and dataset are available at https://europe.naverlabs.com/research/computer-vision/posescript/ .
Evaluation of Benchmarking Pre-Trained CNN Model for Autonomous Vehicles Object Detection in Mixed Traffic

In the next few years, the new generation of Autonomous Vehicles (AVs) promises an advanced level of self-driving experiences. One of the most challenging topics in AVs development is the readiness of object detection models in complex urban environments. Mixed traffic is a complex urban environment that contains much uncertainty and is composed of heterogeneous objects. Therefore, this paper evaluates benchmarking the pre-trained CNN model for object detection in a mixed traffic environment. The evaluation is conducted for five modern algorithms and architecture of neural networks, including Faster RCNN, SSD, YOLOv3, YOLOv4, and EfficientDet. Then, we provide a new dataset in the mixed traffic environment under night conditions for more accurate object detection. Moreover, we conduct the simulation by considering the performance parameters that are recall, precision, and F measure. The performance of our dataset is also compared to the MS-COCO dataset. The result shows that the average precision value of Faster RCNN, SSD, YOLOv3, YOLOv4, and EfficientDet is 16.70 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> , 8.90%, 19.67%, 43.90%, and 55.56% respectively. It shows that YOLOv4 and EfficientDet provide better object detection accuracy than other CNN models.
Incremental Recursive Ranking Grouping for Large-Scale Global Optimization

Real-world optimization problems may have a different underlying structure. In black-box optimization, the dependencies between decision variables remain unknown. However, some techniques can discover such interactions accurately. In Large Scale Global Optimization (LSGO), problems are high-dimensional. It was shown effective to decompose LSGO problems into subproblems and optimize them separately. The effectiveness of such approaches may be highly dependent on the accuracy of problem decomposition. Many state-of-the-art decomposition strategies are derived from Differential Grouping (DG). However, if a given problem consists of non-additively separable subproblems, DG-based strategies may discover many non-existing interactions. On the other hand, monotonicity checking strategies proposed so far do not report non-existing interactions for any separable subproblems but may miss discovering many of the existing ones. Therefore, we propose Incremental Recursive Ranking Grouping (IRRG) that suffers from none of these flaws. IRRG consumes more fitness function evaluations than the recent DG-based propositions, e.g., Recursive DG 3 (RDG3). Nevertheless, the effectiveness of the considered Cooperative Co-evolution frameworks after embedding IRRG or RDG3 was similar for problems with additively separable subproblems that are suitable for RDG3. After replacing the additive separability with non-additive, embedding IRRG leads to results of significantly higher quality.
PlaneFormers: From Sparse View Planes to 3D Reconstruction

We present an approach for the planar surface reconstruction of a scene from images with limited overlap. This reconstruction task is challenging since it requires jointly reasoning about single image 3D reconstruction, correspondence between images, and the relative camera pose between images. Past work has proposed optimization-based approaches. We introduce a simpler approach, the PlaneFormer, that uses a transformer applied to 3D-aware plane tokens to perform 3D reasoning. Our experiments show that our approach is substantially more effective than prior work, and that several 3D-specific design decisions are crucial for its success. Code is available at https://github.com/samiragarwala/PlaneFormers .
Securing Communication with Quantum Key Distribution: Implications and Impact on Network Performance

With a fully functional point-to-point quantum key distribution link, we demonstrate secret key retrieval by a pair of encryptors and investigate how their addition impacts key network performance indicators on a 10 Gbit/s data channel.
SALISA: Saliency-Based Input Sampling for Efficient Video Object Detection

High-resolution images are widely adopted for high-performance object detection in videos. However, processing high-resolution inputs comes with high computation costs, and naive down-sampling of the input to reduce the computation costs quickly degrades the detection performance. In this paper, we propose SALISA, a novel non-uniform SALiency-based Input SAmpling technique for video object detection that allows for heavy down-sampling of unimportant background regions while preserving the fine-grained details of a high-resolution image. The resulting image is spatially smaller, leading to reduced computational costs while enabling a performance comparable to a high-resolution input. To achieve this, we propose a differentiable resampling module based on a thin plate spline spatial transformer network (TPS-STN). This module is regularized by a novel loss to provide an explicit supervision signal to learn to “magnify” salient regions. We report state-of-the-art results in the low compute regime on the ImageNet-VID and UA-DETRAC video object detection datasets. We demonstrate that on both datasets, the mAP of an EfficientDet-D1 (EfficientDet-D2) gets on par with EfficientDet-D2 (EfficientDet-D3) at a much lower computational cost. We also show that SALISA significantly improves the detection of small objects. In particular, SALISA with an EfficientDet-D1 detector improves the detection of small objects by 77%, and remarkably also outperforms EfficientDet-D3 baseline.
Quantitative feature classification for breast ultrasound images using improved naive bayes

Abstract The quantitative feature classification of breast ultrasound images can explore the intrinsic connection between image features and lesions, and become an important basis for the diagnosis of tumor nature in clinical medicine. However, the tumor features extracted from breast ultrasound images are generally based on the overall situation, which has an impact on the classification of features in ultrasound images. Therefore, a quantitative feature classification algorithm for breast ultrasound images using improved Naive Bayes (NB) is proposed. First, histogram features, grayscale co‐generation matrix and other texture features are extracted from the ultrasound image. Second, NB is combined with decision tree calculation to improve the traditional NB algorithm. Finally, feature classification is optimized using improved NB algorithm to achieve high accuracy in the classification of ultrasound images. The results show that the contour of the lesion recognition results obtained by the proposed algorithm is the most complete, and the other regions can maximize the retention of the texture features of the original image. The classification accuracy is high, and the classification time of quantitative features of breast ultrasound images is short. It has application value in the field of breast ultrasound diagnosis.
Retracted: Multi‐vehicle group‐aware data protection model based on differential privacy for autonomous sensor networks

Abstract Retraction: [Jiazheng Yuan, Zhuang Wang, Cheng Xu, Hongtian Li, Songyin Dai, Hongzhe Liu, Multi‐vehicle group‐aware data protection model based on differential privacy for autonomous sensor networks, IET Circuits, Devices &amp; Systems 2022 ( https://doi.org/10.1049/cds2.12140 )]. The above article from IET Circuits, Devices &amp; Systems , published online on 29 December 2022 in Wiley Online Library ( wileyonlinelibrary.com ), has been retracted by agreement between the Editor‐in‐Chief, Harry E. Ruda, the Institution of Engineering and Technology (the IET) and John Wiley and Sons Ltd. This article was published as part of a Guest Edited special issue. Following an investigation, the IET and the journal have determined that the article was not reviewed in line with the journal's peer review standards and there is evidence that the peer revie process of the special issue underwent systematic manipulation. Accordingly, we cannot vouch for the integrity or reliability of the content. As such we have taken the decision to retract the article. The authors have been informed of the decision to retract.
Patterning-Effect Calibration Algorithm for Secure Decoy-State Quantum Key Distribution

The decoy-state method for the quantum key distribution (QKD) is a great countermeasure against the fatal photon number splitting attack. At present, the decoy-state method has been widely employed in practical QKD systems. However, the patterning effect, namely the correlated intensity fluctuations in practical QKD systems, violates the important assumption of the decoy-state method and seriously threatens the protocol security. The previous works investigated the patterning effect between the adjacent pulses and proposed the countermeasures from the degree of post selection and modulators. In this work, we investigate the patterning effect with larger correlation range and present an algorithm to calibrate the driving voltages to mitigate the patterning effect. According to our methods, we experimentally observe the higher-order patterning effect and successfully mitigate it by our calibration algorithm. The experiment results indicate that our methods efficiently improve the protocol security and are expected to be widely employed in practical decoy-state QKD systems.
TxT: Real-Time Transaction Encapsulation for Ethereum Smart Contracts

Ethereum is a permissionless blockchain ecosystem that supports execution of smart contracts, the key enablers of decentralized finance (DeFi) and non-fungible tokens (NFT). However, the expressiveness of Ethereum smart contracts is a double-edged sword: while it enables blockchain programmability, it also introduces security vulnerabilities, i.e., the exploitable discrepancies between expected and actual behaviors of the contract code. To address these discrepancies and increase the vulnerability coverage, we propose a new smart contract security testing approach called transaction encapsulation. The core idea lies in the local execution of transactions on a fully-synchronized yet isolated Ethereum node, which creates a preview of outcomes of transaction sequences on the current state of blockchain. This approach poses a critical technical challenge — the well-known time-of-check/time-of-use (TOCTOU) problem, i.e., the assurance that the final transactions will exhibit the same execution paths as the encapsulated test transactions. In this work, we determine the exact conditions for guaranteed execution path replicability of the tested transactions. To demonstrate the transaction encapsulation, we implement a transaction testing tool, TxT, which reveals the actual outcomes (either benign or malicious) of Ethereum transactions. To ensure the correctness of testing, TxT deterministically verifies whether a given sequence of transactions ensues an identical execution path on the current state of blockchain. We analyze over 1.3 billion Ethereum transactions and determine that 96.5% of them can be verified by TxT. We further show that TxT successfully reveals the suspicious behaviors associated with 31 out of 37 vulnerabilities (83.8% coverage) in the smart contract weakness classification (SWC) registry. In comparison, the vulnerability coverage of all the existing defense approaches combined only reaches 40.5%.
Suppression of patterning effect using IQ modulator for high-speed quantum key distribution systems

Quantum key distribution (QKD) is an attractive technology for distributing secret encryption keys between distant users. The decoy-state technique has drastically improved its practicality and performance, and has been widely adopted in commercial systems. However, conventional intensity modulators can introduce security side channels in high speed QKD systems because of their non-stationary working points for decoy-state generation. Here, we analyze the transfer function of an in-phase/quadrature (IQ) modulator and reveal its superiority for stable decoy-state generation, followed by an experimental demonstration. Thanks to their convenient two-level modulation and inherent high speed, IQ modulators are ideal for use in high-speed decoy-state QKD systems.
A Privacy-Preserving Lightweight Energy Data Sharing Scheme Based on Blockchain for Smart Grid

As many users join the smart grid system, energy companies need user energy data to manage and improve energy delivery. However, while enjoying the services provided by energy companies, the energy data submitted by users may lead to the risk of privacy leakage. To solve this problem, this paper proposes a privacy-preserving scheme for smart grid based on blockchain and multi-receiver encryption (PPBME). The scheme utilizes lightweight multi-receiver encryption, blockchain, and smart contract technologies to solve the problem of privacy leakage when user data are shared. Also, our PPBME construction employs off-chain storage technology to improve the scalability of the blockchain, so that reducing the storage pressure of the blockchain. This paper also proposes a compensation mechanism for the loss of user privacy. According to the security analysis and computational cost analysis, our PPBME scheme is efficient and secure in supporting smart grid applications.