Reviewerly/exHarmony huggingface dataset:
RETRACTED: Social networks to improve the creative thinking of students: How does it works?

The purpose of the research is to study the capabilities of social networks in the context of improving students' creative thinking within the framework of educational activities. Thus, an innovative concept of the online course Foreign Economic Activity in the Service Sector: Innovative Trends was developed; it was tested on the Facebook platform among the students of the [BLINDED] University and [BLINDED] University. The educational activity was divided into an individual activity and a group activity that involved the introduction of the Six Thinking Hats technology, which made it possible to organize and systematize students' creative thinking and focus on the main objectives of the course. According to students, the activation of creative thinking in the learning process was greatly affected by such elements of the concept of the educational course as daily educational practice (98%); joint projects (88%); thematic mentoring and supervision (92%); well-organized and structured individual (95%) and group discussion (89%); introduction of the Six Thinking Hats technology into the system of group and individual communications with the course participants, experts and moderators (87%). Learning in the Facebook environment had a dramatic impact on the development of communication and collaboration skills, creativity, critical thinking, digital literacy, and self-directedness while forming an intellectual basis for the generation of innovative solutions and creative improvisations within the framework of certain topics. The research can be useful for educators searching for innovative approaches to teaching.
EncDBDB: Searchable Encrypted, Fast, Compressed, In-Memory Database Using Enclaves

Data confidentiality is an important requirement for clients when outsourcing databases to the cloud. Trusted execution environments, such as Intel SGX, offer an efficient solution to this confidentiality problem. However, existing TEE-based solutions are not optimized for column-oriented, in-memory databases and pose impractical memory requirements on the enclave. We present EncDBDB, a novel approach for client-controlled encryption of a column-oriented, in-memory databases allowing range searches using an enclave. EncDBDB offers nine encrypted dictionaries, which provide different security, performance, and storage efficiency tradeoffs for the data. It is especially suited for complex, read-oriented, analytic queries as present, e.g., in data warehouses. The computational overhead compared to plaintext processing is within a millisecond even for databases with millions of entries and the leakage is limited. Compressed encrypted data requires less space than a corresponding plaintext column. Furthermore, EncDBDB's enclave is very small reducing the potential for security-relevant implementation errors and side-channel leakages.
Cooperative Distributed GPU Power Capping for Deep Learning Clusters

The recent GPU-based clusters that handle deep learning (DL) tasks have the features of GPU device heterogeneity, a variety of deep neural network (DNN) models, and high computational complexity. Thus, the traditional power capping methods for CPU-based clusters or small-scale GPU devices cannot be applied to the GPU-based clusters handling DL tasks. This article develops a cooperative distributed GPU power capping (CD-GPC) system for GPU-based clusters, aiming to minimize the training completion time of invoked DL tasks without exceeding the limited power budget. Specifically, we first design the frequency scaling approach using the online model estimation based on the recursive least square method. This approach achieves the accurate tuning for DL task training time and power usage of GPU devices without needing offline profiling. Then, we formulate the proposed FS problem as a Lagrangian dual decomposition-based economic model predictive control problem for large-scale heterogeneous GPU clusters. We conduct both the NVIDIA GPU-based lab-scale real experiments and real job trace-based simulation experiments for performance evaluation. Experimental results validate that the proposed system improves the power capping accuracy to have a mean absolute error of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$&lt; \!1\%$</tex-math></inline-formula> , and reduces the deadline violation ratio of invoked DL tasks by 21.5% compared with other recent counterparts.
Learning With Memory For Few-Shot Semantic Segmentation

Despite great progress made in the few-shot semantic segmentation task, the existing works still suffer from problems of incompleteness and inconsistency of segmentation. In this paper, a novel attention-aided LSTM optimization network called LONet is proposed, which optimizes predictions without forgetting useful inner cues. Particularly, we calculate an attention map to align and match possible locations with query features to deal with incomplete segmentation. Then, an LSTM-based module is designed to overcome the segmentation inconsistency by memorizing and updating useful cues iteratively. Extensive experiments are conducted on two popular few-shot segmentation datasets including PASCAL-5 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sup> and FSS-1000. The experimental results on the FSS-1000 dataset demonstrate that our LONet exceeds the state-of-the-art results by 2.1% and 2.3%, respectively.
Weisfeiler-Leman in the <scp>Bamboo</scp>: Novel AMR Graph Metrics and a Benchmark for AMR Graph Similarity

Abstract Several metrics have been proposed for assessing the similarity of (abstract) meaning representations (AMRs), but little is known about how they relate to human similarity ratings. Moreover, the current metrics have complementary strengths and weaknesses: Some emphasize speed, while others make the alignment of graph structures explicit, at the price of a costly alignment step. In this work we propose new Weisfeiler-Leman AMR similarity metrics that unify the strengths of previous metrics, while mitigating their weaknesses. Specifically, our new metrics are able to match contextualized substructures and induce n:m alignments between their nodes. Furthermore, we introduce a Benchmark for AMR Metrics based on Overt Objectives (Bamboo), the first benchmark to support empirical assessment of graph-based MR similarity metrics. Bamboo maximizes the interpretability of results by defining multiple overt objectives that range from sentence similarity objectives to stress tests that probe a metric’s robustness against meaning-altering and meaning- preserving graph transformations. We show the benefits of Bamboo by profiling previous metrics and our own metrics. Results indicate that our novel metrics may serve as a strong baseline for future work.
Exploring user historical semantic and sentiment preference for microblog sentiment classification

Microblog text is usually very short, thereby challenging existing sentiment classification methods by providing models with little context. Recently, historical user information has been widely used in many real-world applications, such as recommender systems. However, few research works consider user historical states in the loop of microblog sentiment analysis. In this work, we propose to involve historical user information for microblog sentiment analysis to alleviate the context sparsity problem. In particular, we propose a novel neural microblog sentiment classification method which learns informative representations of microblog posts by exploiting both a user’s contextual information and his/her historical state information. The proposed method consists of four components, i.e., a micropost encoder, a user historical sentiment encoder, a User Historical Semantic Encoder, and a micropost sentiment classification component. Extensive experiments are conducted on real-world data collected from Weibo, and experimental results show that the proposed approach achieves superior performance as compared to state-of-the-art baselines.
Overriding Autonomous Driving Systems Using Adaptive Adversarial Billboards

The success of deep neural networks (DNNs) has led to its increased deployment in various real-world applications, which provides strong incentives for motivated adversaries to manipulate the results and models generated by these algorithms. We present an automated, physically-realizable, dynamic adversarial attack to compromise an end-to-end trained DNN controlled autonomous vehicle. The attack is initiated by installing a billboard displaying videos on the roadside to incoming DNN controlled vehicles so that the vehicle tracks an adversary customized trajectory. The billboard contains an integrated camera to enable estimation of the pose of the approaching vehicle. The dynamic billboard images (i.e., a video) continuously adapt to the vehicle’s relative pose with respect to the billboard while being robust to variations in lighting, view angle, and weather. The attack’s effectiveness is shown on a recently developed off-the-shelf high-fidelity simulator, CARLA, for autonomous vehicles. CARLA utilizes an end-to-end learning-based autonomous navigation system. The proposed approach is applicable to other end-to-end trained autonomous cyber-physical systems.
RETRACTED: The role of mobile web platforms in the development of critical, strategic and lateral thinking skills of students in distance physical education courses

This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been retracted at the request of the Editors-in-Chief. After a thorough investigation, the Editors have concluded that the acceptance of this article was partly based upon the positive advice of one illegitimate reviewer report. The report was submitted from an email account which was provided to the journal as a suggested reviewer during the submission of the article. Although purportedly a real reviewer account, the Editors have concluded that this was not of an appropriate, independent reviewer. This manipulation of the peer-review process represents a clear violation of the fundamentals of peer review, our publishing policies, and publishing ethics standards. Apologies are offered to the reviewer whose identity was assumed and to the readers of the journal that this deception was not detected during the submission process.
Diversified text-to-image generation via deep mutual information estimation

Generating photo-realistic, text-matched, and diverse images simultaneously from given text descriptions is a challenging task in computer vision. Previous works mostly focus on visual realism and semantic relevance, but neglect variations of the generated results which is also an important target in text-to-image generation (T2I). In this paper, we design a new module that improves the training of generative adversarial nets (GANs) to generate diverse images from a text input. Our T2I method is based on conditional GANs and has three components: contextual text embedding module (CTEM), deep Mutual Information (MI) estimation module for stacked image generation (DMIEM), and text-image semantic relevance module (TISRM). CTEM attempts to learn text embeddings via leveraging the fine-tuning capabilities of BERT. DMIEM has a stack of attention embedded generators, integrating with global/local MI estimation and maximization between input and output of the generator to make the T2I mapping more relevant, and then generating diverse and photo-realistic images progressively. TISRM is introduced to enhance the semantic consistency of the text-image pairs by regenerating the text descriptions from the generated images. Extensive experiments on three datasets indicate that our method can generate text-matched and more diverse images without quality degradation compared to the state-of-the-art approaches.
Learning-Based Transmission Protocol Customization for VoD Streaming in Cybertwin-Enabled Next-Generation Core Networks

Next-generation core networks are expected to achieve service-oriented traffic management for diversified Quality-of-Service (QoS) provisioning based on software-defined networking (SDN) and network function virtualization (NFV). In this article, a learning-based transmission protocol customized for Video-on-Demand (VoD) streaming services is proposed for a Cybertwin-enabled next-generation core network, which provides caching-based congestion control and throughput enhancement functionalities at the edge of the core network based on traffic prediction. The per-slot traffic load of a VoD streaming service at an ingress edge node is predicted based on the autoregressive integrated moving average (ARIMA) model. To balance the tradeoff between network congestion and throughput enhancement, a multiarmed bandit (MAB) problem is formulated to maximize the expected overall network performance in a long run, by capturing the relationship between transmission control actions and QoS provisioning. A comprehensive transmission protocol operation framework is also presented with in-network congestion control and throughput enhancement modules. Simulation results are presented to validate the efficacy of the proposed protocol in terms of packet delay, goodput ratio, throughput, and resource utilization.
oneVFC—A Vehicular Fog Computation Platform for Artificial Intelligence in Internet of Vehicles

We are witnessing the evolution from Internet of Things (IoT) to Internet of Vehicles (IoV).Internet connected vehicles can sense, communicate, analyze and make decisions.Rich vehicle-related data collection allows to apply artificial intelligence (AI) such as machine learning and deep learning (DL) to develop advanced services in Intelligent Transportation Systems (ITS).However, AI/DL-based ITS applications require intensive computation, both for model training and deployment.The exploitation of the huge computational power obtained through aggregation of resources present in individual vehicles and ITS infrastructure brings an efficient solution.In this work, oneVFC, a tangible vehicular fog computing (VFC) platform based on oneM2M is proposed.It benefits from the oneM2M standard to facilitate interoperability as well as hierarchical resource organization.oneVFC manages the distributed resources, orchestrates information flows and computing tasks on vehicle fog nodes and feeds back results to the application users.On a lab scale model consisting of Raspberry Pi modules and laptops, we demonstrate how oneVFC manages the AI-driven applications running on various machines and how it succeeds in significantly reducing application processing time, especially in cases with high workload or with requests arriving at high pace.We also show how oneVFC facilitates the deployment of AI model training in Federated Learning (FL), an advanced privacy preserving and communication saving training approach.Our experiments deployed in an outdoor environment with mobile fog nodes participating in the computation jobs confirm the feasibility of oneVFC for IoV environments whenever the communication links among fog nodes are guaranteed by V2X technology.
Measure-resend authenticated semi-quantum key distribution with single photons

This paper proposes a new measure-resend ASQKD protocol. The proposed ASQKD protocol uses only single photons, needs fewer pre-shared keys and even provides better qubit efficiency than the state-of-the-art ASQKD protocols. The security proof shows the robustness of the proposed protocol under the collective attack.
Temporal Self-Ensembling Teacher for Semi-Supervised Object Detection

This paper focuses on the semi-supervised object detection (SSOD) which makes good use of unlabeled data to boost performance. We face the following obstacles when adapting the knowledge distillation (KD) framework in SSOD. (1) The teacher model serves a dual role as a teacher and a student, such that the teacher predictions on unlabeled images may limit the upper bound of the student. (2) The data imbalance issue caused by the large quantity of consistent predictions between the teacher and student hinders an efficient knowledge transfer between them. To mitigate these issues, we propose a novel SSOD model called Temporal Self-Ensembling Teacher (TSET). Our teacher model ensembles its temporal predictions for unlabeled images under stochastic perturbations. Then, our teacher model ensembles its model weights with those of the student model by an exponential moving average. These ensembling strategies ensure data and model diversity, and lead to better teacher predictions for unlabeled images. In addition, we adapt the focal loss to formulate the consistency loss for handling the data imbalance issue. Together with a thresholding method, the focal loss automatically reweights the inconsistent predictions, which preserves the knowledge for difficult objects to detect in the unlabeled images. The mAP of our model reaches 80.73&#x0025; and 40.52&#x0025; on the VOC2007 test set and the COCO2014 <i>minival5k</i> set, respectively, and outperforms a strong fully supervised detector by 2.37&#x0025; and 1.49&#x0025;, respectively. Furthermore, the mAP of our model (80.73&#x0025;) sets a new state-of-the-art performance in SSOD on the VOC2007 test set.
Lightweight 2D Human Pose Estimation for Fitness Coaching System

In this paper, we describe lightweight 2D human pose estimation for a fitness coaching system. To achieve real-time inference speed on mobile devices, we propose the online pose distillation learning strategy that trains large teacher networks and small student networks simultaneously. The proposed lightweight model requires significantly lower computation as well as shows competitive pose estimation accuracy on the COCO keypoint detection dataset. In addition, the model was fine-tuned on a fitness dataset for the fitness coaching application where various fitness postures were accessed.
Quarter Laplacian Filter For Edge Aware Image Processing

This paper presents a quarter Laplacian filter that can preserve corners and edges during image smoothing. Its support region is $2\times 2$, which is smaller than the $3\times 3$ support region of the classical Laplacian filter. Thus, it is more local. Moreover, this filter can be implemented via the classical box filter, leading to high performance for real time applications. Finally, we show its edge preserving property in several image processing tasks, including image smoothing, texture enhancement, and low-light image enhancement. The proposed filter can be adopted in a wide range of image processing applications.
Supporting features updating of apps by analyzing similar products in App stores

Facing the increasingly fierce competition, app developers have to update features of their products continually. In this process, developers need to not only consider users' demands but also pay attention to what other similar apps do so that they can stay one step ahead in the competition. App stores provide large-scale useable data for achieving this goal while how to use them efficiently becomes a new challenge for developers. In this paper, we aim at helping developers make feature updating strategies of their apps by analyzing data of similar products in App stores. Firstly, we identify similar apps by using texts in app descriptions and UI. Then, we gain and integrate the information of updated features in these apps from their release texts. Furthermore, we match reviews with the related updated features, which helps developers predict the payback if they adopt a similar updating strategy. To validate the proposed approach, we conducted a series of experiments based on Google Play. The results show that our approach can analyze the data reasonably and provide useful information for developers making feature updating strategy in the evolution of their own products.
Adversarial Examples Generation Algorithm through DCGAN

In recent years, due to the popularization of deep learning technology, more and more attention has been paid to the security of deep neural networks. A wide variety of machine learning algorithms can attack neural networks a... | Find, read and cite all the research you need on Tech Science Press
Semantic and detail collaborative learning network for salient object detection

To obtain more accurate saliency maps, current methods mainly focus on aggregating multi-level features with structures like U-Net and introducing edge information as auxiliary supervision. Different from the focus of existing methods, in the paper, we study the different roles of semantics and details in saliency detection. The task is decomposed into two parallel sub-tasks: internal semantic estimation and boundary detail prediction, and these sub-goals are optimized simultaneously via explicit constraints. Specifically, we propose a novel semantic and detail collaborative learning network (SDCLNet) for salient object detection. To this end, a backbone network (e.g., VGG-16) with an additional layer is first adopted as a shared encoder to extract features from each image. And then two asymmetric decoders without bells and whistles are designed, in which the semantic decoder generates a coarse semantic mask, and the detail decoder generates a fine-grained object boundary. Finally, a collaborative learning block with some meaningful design adaptively selects the discriminative features to undertake the task of saliency prediction. In this way, both the semantic and detailed information can effectively fused, and the final accurate and consistent saliency maps are generated. SDCLNet is easy to be trained in an end-to-end style, and it does not need any post-processing. Extensive experiments demonstrate the effectiveness and superiority of the proposed method in terms of both subjective visual perception and objective evaluation metrics on six benchmark datasets. SDCLNet achieves a real-time speed of 51 FPS when it is run on one GPU.
Secure exchange of cyber threat intelligence using TAXII and distributed ledger technologies - application for electrical power and energy system

The energy sector has been, in recent years, the target of sophisticated cyberattacks. Although the importance of collaborative cyber-security consciousness, expressed as extensive cyber threat intelligence sharing, is undoubted, the standardization of the means of exchanging cyber threat information efficiently and securely has been inadequately addressed and is mostly expressed by the emergence of the Trusted Automated eXchange of Indicator Information (TAXIITM) protocol which faces major deficiencies when it comes to data integrity assurance and suitability for event-driven architectures. This paper presents a novel approach enabling secure and real-time exchange of cyber threat information, by extending the technological capacity of the TAXII framework and addressing its deficiencies through the integration of Distributed Ledger Technologies (DLT) and a generalized publish-subscribe middleware. The applicability of the proposed solution has been validated in several use cases addressing the real needs of Electrical Power and Energy Systems.