Reviewerly/exHarmony huggingface dataset:
A Unified DNN Weight Pruning Framework Using Reweighted Optimization Methods

To address the large model size and intensive computation requirement of deep neural networks (DNNs), weight pruning techniques have been proposed and generally fall into two categories, i.e., static regularization-based pruning and dynamic regularization-based pruning. However, the former method currently suffers either complex workloads or accuracy degradation, while the latter one takes a long time to tune the parameters to achieve the desired pruning rate without accuracy loss. In this paper, we propose a unified DNN weight pruning framework with dynamically updated regularization terms bounded by the designated constraint. Our proposed method increases the compression rate, reduces the training time and reduces the number of hyper-parameters compared with state-of-the-art ADMM-based hard constraint method.
Hand Gesture Recognition and Tracking Control for a Virtual UR5 Robot Manipulator

Human-machine interfaces (HMIs) have received an increased attention during the past decades, specially due to the use of robots in many fields of the industry. In this paper, we employ a hand gesture recognition (HGR) system and the inertial measurement unit (IMU) integrated in the Myo Armband sensor as a HMI to control the position and orientation of a virtual six-degree-of-freedom (DoF) UR5 robot. As part of the HMI, this work also focuses on solving the trajectory tracking problem for the UR5 robot using two different control strategies: a minimum norm PID and a controller based on linear algebra. Both controllers are designed from the UR5 kinematic model. Finally, we test and compare their performance by computing their IAE performance criterion and the position errors obtained from two experiments: tracking pre-defined input trajectories and commanding the virtual robot by the HGR system plus the Myo Armband's IMU signals.
Ontology Based Semantic File Search Assistant

Many computer users do not give much attention to the organization of files into proper folders when saving them. Even if they do, with the increase of the number of files it becomes very difficult to locate them quickly without extensive search. If the files are saved with proper organization, it will be easier to search for a file. This research takes a different approach to saving and searching for files. It focuses on creating a bot which is integrated into the operating system that takes care of saving, searching and opening files for the user. The bot accepts natural language queries and return file search results by querying an automatically created ontology. Further, the results are ranked by applying a ranking algorithm. Though this concept can be applied to any domain the research will apply this to a set of Request for Comments (RFC) text files.
Mercury

As intelligence is moving from data centers to the edges, intelligent edge devices such as smartphones, drones, robots, and smart IoT devices are equipped with the capability to altogether train a deep learning model on the devices from the data collected by themselves. Despite its considerable value, the key bottleneck of making on-device distributed training practically useful in real-world deployments is that they consume a significant amount of training time under wireless networks with constrained bandwidth. To tackle this critical bottleneck, we present Mercury, an importance sampling-based framework that enhances the training efficiency of on-device distributed training without compromising the accuracies of the trained models. The key idea behind the design of Mercury is to focus on samples that provide more important information in each training iteration. In doing this, the training efficiency of each iteration is improved. As such, the total number of iterations can be considerably reduced so as to speed up the overall training process. We implemented Mercury and deployed it on a self-developed testbed. We demonstrate its effectiveness and show that Mercury consistently outperforms two status quo frameworks on six commonly used datasets across tasks in image classification, speech recognition, and natural language processing.
Synergy of Remote Sensing Data for Exploring Hydrothermal Mineral Resources Using GIS-Based Fuzzy Logic Approach

The Arabian Nubian Shield (ANS) contains a variety of gold deposits in the form of veins and veinlets formed by hydrothermal fluids. Characterizing potential areas of hydrothermal alteration zones therefore provides a significant tool for prospecting for hydrothermal gold deposits. In this study, we develop a model of exploration for hydrothermal mineral resources in an area located in the ANS, Egypt, using multiple criteria derived from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Landsat-Operational Land Imager (OLI), and Sentinel-2 data and field work through GIS-based fuzzy logic approach. The hydrothermal alteration zones (HAZs) map extracted from combining mineral indices, spectral bands, and ratios is consistent with observed argillic alteration zones around the mineralized veins. Combining HAZs and lineament density led to identification of six zones based on their mineralization potential, and provides a tool for successful reconnaissance prospecting for future hydrothermal mineral deposits. The detected zones are labeled as excellent, very high, high, moderate, low, and very low, based on their potential for Au production, and the predictive excellent and very high zones cover about 1.6% of the study area. This model also shows that target prospective zones are quartz veins controlled by NNW-SSE trending fracture/fault zones all crosscutting Precambrian rocks of the ANS. Field observations and petrographic and X-ray diffraction analyses were performed to validate the mineral prospective map and revealed that quartz veins consist of gold–sulfide mineralization (e.g., gold, pyrite, chalcopyrite, and sphalerite). Consistency between the high potential hydrothermal alterations zones (HAZs) and the location of gold mineralization is achieved.
Multi-Moments in Time: Learning and Interpreting Models for Multi-Action Video Understanding

Videos capture events that typically contain multiple sequential, and simultaneous, actions even in the span of only a few seconds. However, most large-scale datasets built to train models for action recognition in video only provide a single label per video. Consequently, models can be incorrectly penalized for classifying actions that exist in the videos but are not explicitly labeled and do not learn the full spectrum of information present in each video in training. Towards this goal, we present the Multi-Moments in Time dataset (M-MiT) which includes over two million action labels for over one million three second videos. This multi-label dataset introduces novel challenges on how to train and analyze models for multi-action detection. Here, we present baseline results for multi-action recognition using loss functions adapted for long tail multi-label learning, provide improved methods for visualizing and interpreting models trained for multi-label action detection and show the strength of transferring models trained on M-MiT to smaller datasets.
Innovative and Additive Outlier Robust Kalman Filtering With a Robust Particle Filter

In this paper, we propose CE-BASS, a particle mixture Kalman filter which is robust to both innovative and additive outliers, and able to fully capture multi-modality in the distribution of the hidden state. Furthermore, the particle sampling approach re-samples past states, which enables CE-BASS to handle innovative outliers which are not immediately visible in the observations, such as trend changes. The filter is computationally efficient as we derive new, accurate approximations to the optimal proposal distributions for the particles. The proposed algorithm is shown to compare well with existing approaches and is applied to both machine temperature and server data.
2-D LIDAR-Based Approach for Activity Identification and Fall Detection

Activity detection is a key task in the monitoring of elderly people living alone. This is because it helps locate them and identify any accident that might occur to them. In this article, we propose a novel approach that uses 2-D light detection and ranging (LIDAR) and deep learning to perform activity detection. In a first step, our approach processes and interpolates the data collected using the 2-D LIDAR following an algorithm we propose to locate the person and identify the useful data points. In the next steps, the data are transformed into two types of representations: 1) a time-series type and 2) an image type. The time-series data are used to train different long short-term memory (LSTM) networks to identify the person and to recognize his/her activity, while the image type is used to fine-tune a convolutional neural network (CNN) for fall detection. Throughout our experiments, we show that our approach allows for the identification of people from their gait, and the detection of unsteady gait or unstable walk (i.e., when the person is about to fall or feeling dizzy) as well as the detection of up to four activities: 1) walking; 2) standing; 3) sitting; and 4) falling. The results obtained from our experiment show that the proposed method reaches an accuracy equal to 94.1% for multiclass activity detection, 98.6% for fall detection, 93.2% for person identification (for three different people), and 92.5% for unsteady walk detection.
UAV Dynamic Path Planning Based on Obstacle Position Prediction in an Unknown Environment

The application of unmanned aerial vehicle (UAV) has been increasingly popular for its advantages such as convenience and mobility. Thus, its application scenarios have been more and more complex. The UAV must avoid not only stationary obstacles but also dynamic obstacles. Typical UAV path planning algorithms perform well in avoiding static obstacles but poor in dynamic ones. A new dynamic path planning algorithm based on obstacles’ position prediction and modified artificial potential field - HOAP is proposed in this paper. The Markov prediction model is employed to predict the obstacles’ future position with an obstacle grid map. And to resolve the local minima of the typical APF algorithm, a new virtual obstacle method is put forward. What’s more, the attractive force gain coefficient gradient increase method is proposed to solve local oscillation. Simulation results show that the UAV can fly a safe path with high accuracy under static or uncertain dynamic obstacles environments.
Attention guided contextual feature fusion network for salient object detection

In recent years, the Convolutional Neural Network (CNN) has been widely used in various visual tasks because of its powerful feature extraction ability. Salient object detection methods based on CNN have also achieved great performance. Although a large number of feature information can be obtained through CNN, the key to improve the quality of the saliency maps is how to make full use of the high and low-level features and their relationships. Some previous works merged high and low-level features without processing the features, which resulted in the blurring of the saliency map, and even the inability to distinguish the foreground from the background in a complex environment. In order to solve the above problem, we propose an Attention guided Contextual Feature Fusion Network (ACFFNet) for salient object detection. There are mainly three modules in the proposed ACFFNet, including the Multi-field Channel Attention (MCA) module, Contextual Feature Fusion (CFF) module, and the feature Self-Refinement (SR) module. The MCA module selects features from different receptive fields, the CFF module can efficiently aggregate contextual features, and the SR module is able to repair the holes in the prediction maps caused by the contradictory response of different layers. In addition, we propose a Cross-Consistency Enhancement (CCE) loss to guide the network to focus on more detailed information and highlight the difference between foreground and background. Experimental results on six benchmark datasets show that the proposed method outperforms the state-of-the-art methods.
Mixtures of Matrix-Variate Contaminated Normal Distributions

Analysis of matrix-variate data is becoming ever more prevalent in the literature, especially in the area of clustering and classification. Real data, including real matrix-variate data, are often contaminated by potential outlying observations. Their detection, as well as the development of models insensitive to their presence, is particularly important for this type of data because of the practical issues concerning their effective visualization. Herein, the matrix-variate contaminated normal distribution is discussed and then utilized in the mixture model paradigm for clustering. One key advantage of the proposed model is the ability to automatically detect potential outlying matrices by computing their a posteriori probability of being typical or atypical. Such detection is currently unavailable using existing matrix-variate methods. An expectation conditional maximization algorithm is used for parameter estimation, and both simulated and real data are used for illustration. Supplementary files for this article are available online.
What Went Wrong? Predictors of Contact Tracing Adoption in Italy during COVID-19 Pandemic

Together with vaccines, contact tracing systems (CTS) have proved to be one of the best strategies to deal with the current COVID-19 pandemic. However, the adoption of such systems has been quite limited in EU countries, and Italy was no exception. The present research aimed to investigate the factors drawn from the most relevant psychological models in the literature, most associated with the adoption of CTS. The data analysis of the 501 surveyed answers (329 from CTS adopters) showed that knowing important others who have downloaded the CTS, CTS attitudes, CTS perceived efficacy, COVID-19 risk perception, and trust in the government and its actions influenced the adoption of the Italian CTS (52% of explained variance). These factors defined a new specific model that can be used to more effectively promote CTS adoption and thus increase the protective potential of these technologies, whose effectiveness is inevitably linked to adoption.
Development and Application of an Augmented Reality Oyster Learning System for Primary Marine Education

Marine knowledge is such an important part of education that it has been integrated into various subjects and courses across educational levels. Previous research has indicated the importance of AR assisted students’ learning during the learning process. This study proposed the AR Oyster Learning System (AROLS) that integrates mobile AR with a marine education teaching strategy for teachers in primary schools. To evaluate the effectiveness of the proposed approach, an experiment was conducted in a primary school natural science course regarding oysters. The participants consisted of 22 fourth-grade students. The experimental group comprised 11 students who learned with the AROLS learning approach and the control group comprised 11 students who learned with the conventional multimedia learning approach. The results indicate that (1) students were interested in the AR learning approach, (2) students’ learning achievement and motivation were improved by the AR learning approach, (3) students acquired the target knowledge through the oyster course, and (4) students learned the importance of sustainability when taking online courses at home during the pandemic.
User Experience and Technical Communication: Beyond Intertwining

In this special issue, we reflect on the past and current connections between TC and UX, it is important to recognize the value the two fields bring to one another. The articles in this collection illustrate a move into new spaces, incorporating new methods, and forging new connections and provide us an opportunity to conceptualize the continuously evolving relationship between the fields.
Bullseye Polytope: A Scalable Clean-Label Poisoning Attack with Improved Transferability

A recent source of concern for the security of neural networks is the emergence of clean-label dataset poisoning attacks, wherein correctly labeled poison samples are injected into the training dataset. While these poison samples look legitimate to the human observer, they contain malicious characteristics that trigger a targeted misclassification during inference. We propose a scalable and transferable clean-label poisoning attack against transfer learning, which creates poison images with their center close to the target image in the feature space. Our attack, Bullseye Polytope, improves the attack success rate of the current state-of-the-art by 26.75% in end-to-end transfer learning, while increasing attack speed by a factor of 12. We further extend Bullseye Polytope to a more practical attack model by including multiple images of the same object (e.g., from different angles) when crafting the poison samples. We demonstrate that this extension improves attack transferability by over 16% to unseen images (of the same object) without using extra poison samples.
A Hard Label Black-box Adversarial Attack Against Graph Neural Networks

Graph Neural Networks (GNNs) have achieved state-of-the-art performance in various graph structure related tasks such as node classification and graph classification. However, GNNs are vulnerable to adversarial attacks. Existing works mainly focus on attacking GNNs for node classification; nevertheless, the attacks against GNNs for graph classification have not been well explored.
Collision Detection for Robot Manipulators Using Unsupervised Anomaly Detection Algorithms

Collaborative robot manipulators operating in dynamic and unstructured environments shared with humans require fast and accurate detection of collisions. When using model-based detection methods without joint torque sensors, proper treatment for friction in the motors is required, such as accurate modeling of friction parameters. In our previous work, we have presented two supervised learning-based detection methods, which successfully compensate for uncertain dynamic effects, including unmodeled friction. However, robot collision data are required for training and only the scenarios that are learned as collisions can be robustly detected. In this article, we present two collision detection methods using unsupervised anomaly detection algorithms—a one-class support vector machine, and an autoencoder—for robot manipulators. Only the motor current measurements together with a robot dynamics model are required while no additional external sensors, friction modeling, or manual tuning of multiple detection thresholds are needed. Extensive experiments with a robot collision dataset collected with a six-joint robot manipulator validate that our unsupervised detection algorithms can robustly detect a variety of hard and soft collisions with very light computation using only the data for collision-free motions and without the knowledge of every possible type of collision that can occur.
Understanding digital contact tracing app continuance: Insights from India

Digital contact tracing (DCT) was touted as an effective alternative to lockdown and other restrictive measures in controlling the spread of the COVID-19 pandemic. Despite considerable investments in research and development, the usage of DCT apps was found to be phenomenally low across the world. In this context, the current study investigates the factors influencing citizens’ continuance intentions to use the DCT app. A theoretical framework was developed by extending the Expectation-confirmation model (ECM) of Information system continuance with Technology trust theory and a contextual factor perceived security and privacy to predict citizens’ continuance intentions to use the DCT app. The model was empirically tested using data from a field survey of 206 actual users of a DCT app implemented in India. The findings reveal that user satisfaction, trust in government, and trust in technology are significant predictors of citizens’ continuance intention. The model demonstrates high explanatory power by explaining 57.8% of the variance of continuance intention. It also validates the role of perceived security and privacy and trust in technology in determining user satisfaction. The study makes a theoretical contribution by extending the ECM framework to predict DCT app continuance behavior. The insights from the study could be helpful for developers and policymakers in crafting strategies to improve the usage of DCT apps during future disease outbreaks.
Secure Software Leasing Without Assumptions

Quantum cryptography is known for enabling functionalities that are unattainable using classical information alone. Recently, Secure Software Leasing (SSL) has emerged as one of these areas of interest. Given a target circuit $C$ from a circuit class, SSL produces an encoding of $C$ that enables a recipient to evaluate $C$, and also enables the originator of the software to verify that the software has been returned -- meaning that the recipient has relinquished the possibility of any further use of the software. Clearly, such a functionality is unachievable using classical information alone, since it is impossible to prevent a user from keeping a copy of the software. Recent results have shown the achievability of SSL using quantum information for a class of functions called compute-and-compare (these are a generalization of the well-known point functions). These prior works, however all make use of setup or computational assumptions. Here, we show that SSL is achievable for compute-and-compare circuits without any assumptions. Our technique involves the study of quantum copy-protection, which is a notion related to SSL, but where the encoding procedure inherently prevents a would-be quantum software pirate from splitting a single copy of an encoding for $C$ into two parts, each of which enables a user to evaluate $C$. We show that point functions can be copy-protected without any assumptions, for a novel security definition involving one honest and one malicious evaluator; this is achieved by showing that from any quantum message authentication code, we can derive such an honest-malicious copy-protection scheme. We then show that a generic honest-malicious copy-protection scheme implies SSL; by prior work, this yields SSL for compute-and-compare functions.
Privacy-Preserving Serverless Computing Using Federated Learning for Smart Grids

The smart power grid is a critical energy infrastructure where real-time electricity usage data is collected to predict future energy requirements. The existing prediction models focus on the centralized frameworks, where the collected data from various home area networks (HANs) are forwarded to a central server. This process leads to cybersecurity threats. This article proposes a federated learning based model with privacy preservation of smart grids data using serverless cloud computing. The model considers the blockchain-enabled dew servers in each HAN for local data storage and local model training. Advanced perturbation and normalization techniques are used to reduce the inverse impact of irregular workload on the training results. The experiment conducted on benchmarks datasets demonstrates that the proposed model minimizes the computation and communication costs, attacking probability, and improves the test accuracy. Overall, the proposed model enables smart grids with robust privacy preservation and high accuracy.