Reviewerly/exHarmony huggingface dataset:
Parametric PH Curves Model Based Kinematic Control of the Shape of Mobile Soft Manipulators in Unstructured Environment

Mobile soft continuum manipulator (MSCM) is more and more used in various applications of everyday life, such as logistics, farming, medical diagnosis, medical therapy, bakery, human collaboration, etc. However, the control of its shape remains a major challenge, especially when it is navigating in an unstructured environment. In this contribution, a kinematic-model-based shape control is designed in the space configuration for an MSCM, allowing its autonomous navigation in the presence of obstacles. For that purpose, the shape kinematic modeling is realized using a parametric spatial Pythagorean hodograph (PH) curve with a predefined length, whereas the artificial potential field algorithm acts on the control points of the PH curve for shape adaptation in presence of obstacles. The novelty of the proposed work resides in the use of parametric curve formalism to model the shape of an infinite degree of freedom mobile soft manipulator robot and its kinematic control by optimizing its bending potential energy during its motion. The shape optimization based on a minimal bending energy during the collision-free path is respecting a sliding mode strategy applied to the PH curve control points. Experimental results are obtained using a class of MSCM called RobotinoXT. These experiments show the advantages of using a reduced kinematic model based on PH curve to control the MSCM shape in dynamic motion.
HIBAF: A data security scheme for fog computing

Fog computing complemented cloud computing integration services in the Internet of Things (IoT) and the web of real-time interactivity. Fog offers faster computing and other services facilities sitting close to user applications. However, secure data transfer in the fog is still a challenging issue requiring attention and efficient deployment of a secure data security scheme. We present an Identity Based Encryption (IBE) scheme to secure data security and transmission in fog clouds and IoT ecosystems. We devise and develop a four-level Hierarchical Identity Based Architecture for Fog Computing (HIBAF) data security scheme to enhance data security. We also analyze the system’s performance regarding response time, CPU utilization, run-time encryption-decryption, and key generation time in the fog computing paradigm to an increasing number of users data-loads. Moreover, we evaluate our scheme and compare the outcomes with different cryptography structures to discern our scheme’s effectiveness. Furthermore, we also evaluate secret key updating time, re-encrypted key updating time, and file revoking time by launching DDoS attacks both in the cloud and fog computing environment to compare improvements of HIBAF in the fog computing paradigm. Finally, through this overall evaluation, we have found that the developed HIBAF scheme provides a 33% performance enhancement in a fog environment in terms of data processing, provision, and management compared to the cloud environment.
<scp>Fast‐RRT</scp>*: An Improved Motion Planner for Mobile Robot in Two‐Dimensional Space

Abstract Path planning for mobile robot aims to solve the problem of creating a collision‐free path from the start state to the goal state in the given space, which is a key supporting technology for unmanned work. In order to solve the problems of the asymptotic optimal rapidly extended random trees star (RRT*) algorithm, such as its slow convergence rate, the low efficiency of planning and the high cost of path, an improved motion planner (Fast‐RRT*) was proposed based on hybrid sampling strategy and choose parent based on backtracking. Firstly, the goal bias strategy and constraint sampling are combined in the sampling stage to reduce the blindness of sampling. Secondly, to obtain a path with lower cost than the RRT* algorithm, the ancestor of the nearest node is considered until the initial state in the process of choose parent for new node. To ensure the feasibility of the path, the path is smoothed by cubic B‐spline curve. The effectiveness of Fast‐RRT* algorithm was verified based on MATLAB and V‐rep platform. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.
Global Convergence of Stochastic Gradient Hamiltonian Monte Carlo for Nonconvex Stochastic Optimization: Nonasymptotic Performance Bounds and Momentum-Based Acceleration

Nonconvex Stochastic Optimization Nonconvex stochastic optimization problems arise in many machine learning problems, including deep learning. The stochastic gradient Hamiltonian Monte Carlo (SGHMC) is a variant of stochastic gradients with a momentum method in which a controlled and properly scaled Gaussian noise is added to the stochastic gradients to steer the iterates toward a global minimum. SGHMC has shown empirical success in practice for solving nonconvex stochastic optimization problems. In “Global convergence of stochastic gradient Hamiltonian Monte Carlo for nonconvex stochastic optimization: Nonasymptotic performance bounds and momentum-based acceleration,” Gao, Gürbüzbalaban, and Zhu provide, for the first time, the finite-time performance bounds for the global convergence of SGHMC in the context of both population and empirical risk minimization problems and show that acceleration with momentum is possible in the context of global nonconvex stochastic optimization.
It's Not What It Looks Like: Manipulating Perceptual Hashing based Applications

Perceptual hashing is widely used to search or match similar images for digital forensics and cybercrime study. Unfortunately, the robustness of perceptual hashing algorithms is not well understood in these contexts. In this paper, we examine the robustness of perceptual hashing and its dependent security applications both experimentally and empirically. We first develop a series of attack algorithms to subvert perceptual hashing based image search. This is done by generating attack images that effectively enlarge the hash distance to the original image while introducing minimal visual changes. To make the attack practical, we design the attack algorithms under a black-box setting, augmented with novel designs (e.g., grayscale initialization) to improve the attack efficiency and transferability. We then evaluate our attack against the standard pHash as well as its robust variant using three different datasets. After confirming the attack effectiveness experimentally, we then empirically test against real-world reverse image search engines including TinEye, Google, Microsoft Bing, and Yandex. We find that our attack is highly successful on TinEye and Bing, and is moderately successful on Google and Yandex. Based on our findings, we discuss possible countermeasures and recommendations.
Research on Aerobics Training and Evaluation Method Based on Artificial Intelligence-Aided Modeling

Traditional aerobics training methods have the problems of lack of auxiliary teaching conditions and low-training efficiency. With the in-depth application of artificial intelligence and computer-aided training methods in the field of aerobics teaching and practice, this paper proposes a local space-time preserving Fisher vector (FV) coding method and monocular motion video automatic scoring technology. Firstly, the gradient direction histogram and optical flow histogram are extracted to describe the motion posture and motion characteristics of the human body in motion video. After normalization and data dimensionality reduction based on the principal component analysis, the human motion feature vector with discrimination ability is obtained. Then, the spatiotemporal pyramid method is used to embed spatiotemporal features in FV coding to improve the ability to identify the correctness and coordination of human behavior. Finally, the linear model of different action classifications is established to determine the action score. In the key frame extraction experiment of the aerobics action video, the ST-FMP model improves the recognition accuracy of uncertain human parts in the flexible hybrid joint human model by about 15 percentage points, and the key frame extraction accuracy reaches 81%, which is better than the traditional algorithm. This algorithm is not only sensitive to human motion characteristics and human posture but also suitable for sports video annotation evaluation, which has a certain reference significance for improving the level of aerobics training.
Barriers and facilitators of using mobile devices as an educational tool by nursing students: a qualitative research

Although the use of mobile devices can facilitate the learning process, there may be barriers to using them for learning purposes. This study aimed to identify and investigate the barriers and facilitators of using mobile devices as an educational device from the perspective of nursing students.This qualitative descriptive study was conducted in 2020 on undergraduate nursing students of the Nursing and Midwifery Faculty, Tehran University of Medical Sciences. A total of 22 undergraduate nursing students were selected by purposive sampling with maximum variability. Inclusion criteria were Undergraduate nursing education, having mobile devices, and willingness to participate in research. Data were collected through semi-structured individual interviews for 45-75 min in the proposed environment of students until the data were saturated. Data analysis was performed manually using the framework analysis method with the steps: familiarization, identifying a thematic framework, indexing, Charting and Synthesis, Mapping, and Interpretation. Trustworthiness was determined by methods of Credibility, Dependability, Conformability, and Transferability.The majority of participants (45.45%) were 21 years old, 63.63% were women, and 36.36% studied in the 8th semester. Findings from the analysis of interviews showed that barriers to the use of mobile devices were classified into 4 main categories (barriers related to mobile devices, barriers related to Internet access, barriers related to information literacy, cultural-environmental barriers) and 15 subcategories, and facilitating the use of mobile devices was divided into 2 main categories (easy to use mobile devices and easy access to information) and 6 subcategories.The results of this study help educational managers and curriculum planners to adapt to technological change, to focus on the many benefits of mobile devices as an educational tool, and to plan to overcome barriers to mobile device use, and use mobile devices to teach theoretical topics and clinical nursing skills effectively.
Evaluation of the teachers’ digital competences in primary and secondary education in Portugal with DigCompEdu CheckIn in pandemic times

The current pandemic crisis the world is living in has brought new and emerging challenges to teachers, making it essential to acquire digital skills, especially in virtual learning environments and online technologies. In this sense, from the DigCompEdu CheckIn self-assessment questionnaire, validated for the Portuguese population by Dias-Trindade, Moreira, and Nunes (2019), the research presented in this paper aims to identify the most fragile and robust areas of digital skills of primary and secondary education (ISCED) perceived by teachers in Portugal. The quantitative methodological approach emphasizes teachers' perception of their digital skills in three dimensions: teachers’ professional competences, teachers’ pedagogical competences and students' competences and involved 434 teachers from mainland Portugal and the Autonomous Regions. The results allow us to conclude that teachers have an overall moderate level of digital proficiency – level B1 - Integrators – and the dimensions pedagogical competences and students’ competences are those where teachers have more weaknesses than in other levels. From a panorama observed before the onset of the COVID-19 pandemic, it is possible to understand their needs regarding work that involves digital technologies at different moments in the teaching activity. The results thus, show the need for teachers to increase their level of digital competence through specific training and the importance of developing public policies that prepare teachers for a more digital school.
Three-way multi-granularity learning towards open topic classification

Traditional topic classification usually adopts the closed-world assumption that all the test topics have been seen in training. However, in open dynamic environments, the potential new topics may appear in testing due to the evolution of text data over time. Considering the uncertainty and multi-granularity of dynamic text data, such open topic classification needs to detect unseen topics by mining the boundary region continually, and incrementally update the previous models by knowledge accumulation. To address these challenge issues, this paper introduces a unified framework of three-way multi-granularity learning to open topic classification based on the fusion of three-way decision and granular computing. First, we propose the multilevel granular structure of tasks from the temporal-spatial multi-granularity perspective. Then, we construct an adaptive decision boundary and use the centroids and the corresponding radius to discover unknowns by the reject option. Subsequently, we further explore the unknown topics by three-way enhanced clustering and the uncertain instances will be re-investigated in the next stage. Besides, we design a built-in knowledge base represented as the centroid of each topic to store the topic knowledge. Finally, the experiments are conducted to compare the performances of proposed models and the efficiency of knowledge accumulation with classic models.
A Suspicious Multi-Object Detection and Recognition Method for Millimeter Wave SAR Security Inspection Images Based on Multi-Path Extraction Network

There are several major challenges in detecting and recognizing multiple hidden objects from millimeter wave SAR security inspection images: inconsistent clarity of objects, similar objects, and complex background interference. To address these problems, a suspicious multi-object detection and recognition method based on the Multi-Path Extraction Network (MPEN) is proposed. In MPEN, You Only Look Once (YOLO) v3 is used as the base network, and then the Multi-Path Feature Pyramid (MPFP) module and modified residual block distribution are proposed. MPFP is designed to output the deep network feature layers separately. Then, to distinguish similar objects more easily, the residual block distribution is modified to improve the ability of the shallow network to capture details. To verify the effectiveness of the proposed method, the millimeter wave SAR images from the laboratory’s self-developed security inspection system are utilized in conducting research on multi-object detection and recognition. The detection rate (probability of detecting a target) and average false alarm (probability of error detection) rate of our method on the target are 94.6% and 14.6%, respectively. The mean Average Precision (mAP) of recognizing multi-object is 82.39%. Compared with YOLOv3, our method shows a better performance in detecting and recognizing similar targets.
Investigating Hostile Post Detection in Hindi

Hostile content on Social Media platforms is becoming a problem for governments and organizations. There is a need for AI based intervention which can filter hostile content at scale. The challenge lies in ambiguity of language, absence of training data and local context. In this paper, we investigate Hostile Post Detection for the Hindi Language, which is the topmost language in the Indian Subcontinent in terms of speaker population and third in the world. We extend our prior work in this area along the dimensions of (i) Representations (ii) Data and (iii) Architecture, exploring approaches like Transformers and Multi Task Learning among others, along the way. In this highly experimental study, comparisons are drawn, trends are discovered and insights are presented. We manage to improve on the baseline by 16.5% and 29.77% on the two evaluation metrics viz. Coarse Grained F1 Score and Fine Grained F1 Score. We are also able to beat our prior work results by 0.93% and 9.18% on these two evaluation metrics respectively. Experiments performed by us number 60 which is larger than the number reported in any other work for Hostility Detection in Hindi, to the best of our knowledge.
Reputation-aware Hedonic Coalition Formation for Efficient Serverless Hierarchical Federated Learning

Amid growing concerns on data privacy, Federated Learning (FL) has emerged as a promising privacy preserving distributed machine learning paradigm. Given that the FL network is expected to be implemented at scale, several studies have proposed system architectures towards improving the network scalability and efficiency. Specifically, the Hierarchical FL (HFL) network utilizes cluster heads, e.g., base stations, for the intermediate aggregation and relay of model parameters. Serverless FL is also proposed recently, in which the data owners, i.e., workers, exchange the local model parameters among a neighborhood of workers. This decentralized approach reduces the risk of a single point of failure but inevitably incurs significant communication overheads. To achieve the best of both worlds, we propose the Serverless Hierarchical Federated Learning (SHFL) framework in this paper. The SHFL framework adopts a two-layer system architecture. In the lower layer, the FL workers are grouped into clusters under cluster heads. In the upper layer, the cluster heads exchange the intermediate parameters with their one-hop neighbors without the aid of a central server. To improve the sustainable efficiency of the FL system while taking into account the incentive design for workers marginal contributions in the system, we propose the reputation-aware hedonic coalition formation game in this paper. Specifically, the workers are rewarded for their marginal contribution to the cluster, whereas the reputation opinions of each cluster head is updated in a decentralized manner, thereby deterring malicious behaviors by the cluster head. This improves the performance of the network since cluster heads with higher reputation scores are more reliable in relaying the intermediate model parameters. The simulation results show that our proposed hedonic coalition formation algorithm converges to a Nash-stable partition and improves the network efficiency.
Encrypt DNS Traffic: Automated Feature Learning Method for Detecting DNS Tunnels

In recent years, attacks on the DNS continue to proliferate due to the lack of security mechanisms. DNS over HTTPS (DoH) is a standard developed for encrypting plaintext DNS to protect user privacy, but attackers may use this protocol to bypass enterprise firewalls and exfiltrate private data through the establishment of DNS tunnels. Traditional DNS tunnel detection methods based on packet inspection are no longer applicable because of DNS encryption. Although popular machine learning methods are widely used, it require expert knowledge to extract statistical feature sets which are complicated. In order to solve the problem of detecting encrypted DNS tunnels, we propose an end-to-end anomaly detection model based on a variational autoencoder which incorporates the attention mechanism. By modeling the raw flow sequence data at the flow-level, we use bidirectional GRU-based network to automatically learn the feature representations and detect anomalies via reconstruction error. We conducted experiments on the public dataset, which contains raw normal DoH traffic and abnormal DNS tunnel traffic. The results show that our model achieves excellent performance, and has the ability to identify unknown DNS tunnels.
Towards Realistic Few-Shot Relation Extraction

In recent years, few-shot models have been applied successfully to a variety of NLP tasks.Han et al. ( 2018) introduced a few-shot learning framework for relation classification, and since then, several models have surpassed human performance on this task, leading to the impression that few-shot relation classification is solved.In this paper we take a deeper look at the efficacy of strong few-shot classification models in the more common relation extraction setting, and show that typical few-shot evaluation metrics obscure a wide variability in performance across relations.In particular, we find that state of the art few-shot relation classification models overly rely on entity type information, and propose modifications to the training routine to encourage models to better discriminate between relations involving similar entity types.
Deep learning for human activity recognition

Human Activity Recognition (HAR) based on wearable devices has always been a hot topic in health applications, human-object interaction, and smart homes. Despite significant improvements achieved by convolutional neural networks, long short-term memory networks, transformer networks and various hybrid models, there are still two fundamental issues. First, the spatial–temporal dependencies of sensor signals are difficult to be effectively modeled. Second, in multimodal environments, sensors placed on various body positions contribute distinctly to the classification results. In this work, we propose a self-attention based Two-stream Transformer Network (TTN). In view of the former issue, we use two streams, named temporal stream and spatial stream, respectively, to extract the readings-over-time and time-over-readings features from sensor signals. These features extracted from two streams are complementary since the time-over-readings features are able to express additional information which cannot be captured from sensor signals directly. To deal with the latter issue, we assign attention weights to each sensor axis in the spatial channel based on their classification scores. It makes sense that different axis-readings with distinct recognition contributions caused by data heterogeneity be treated unequally. Extensive experiments on four available benchmark datasets (PAMAP2, Opportunity, USC–HAD, and Skoda) reveal that our proposed model is better suited for multimodal HAR than previous state-of-the-art methods.
Unpaired Image Dehazing With Physical-Guided Restoration and Depth-Guided Refinement

Most existing single image dehazing methods aim to learn supervised models from paired synthetic data, which often limits their generalization ability in real-world applications. Besides, due to ignoring the merits of physical model in visibility restoration and the properties of depth features in clarity improvement, we observe that only relying on the transfer capability of unpaired adversarial learning will suffer from low-quality recovery. To this end, we develop an effective end-to-end unpaired image dehazing method by integrating a physical-guided restoration stage and a depth-guided refinement stage in a GAN framework, named as PDR-GAN. Specifically, the dark channel prior is embedded in the restoration stage to provide constraints for the network, and the preliminary dehazed image is first generated. For the refinement stage, we excavate the potential relationship between the depth and transmission map to better refine the results of the previous stage and further recover the distant area details. Our framework benefits from the stage-wise learning strategy of model-based restoration and feature-based reconstruction, which is especially helpful for image dehazing when paired data is not available. Experimental results show that our method is superior to the current unpaired dehazing approaches in terms of both quantitative and qualitative.