Reviewerly/exHarmony huggingface dataset:
Theory-Based Problem Formulation and Ideation in mHealth

This article reports on an investigation into how to improve problem formulation and ideation in Design Science Research (DSR) within the mHealth domain. A Systematic Literature Review of problem formulation in published mHealth DSR papers found that problem formulation is often only weakly performed, with shortcomings in stakeholder analysis, patient-centricity, clinical input, use of kernel theory, and problem analysis. The study proposes using Coloured Cognitive Mapping for DSR (CCM4DSR) as a tool to improve problem formulation in mHealth DSR. A case study using CCM4DSR found that using CCM4DSR provided a more comprehensive problem formulation and analysis, highlighting aspects that, until CCM4DSR was used, weren’t apparent to the research team and which served as a better basis for mHealth feature ideation.
A Tool for Automatic Question Generation for Teaching English to Beginner Students

We present a tool for the automatic generation of questions from texts for ESL teaching, using resources and methods from the Natural Language Processing (NLP) field. The approach presented in this paper is based on symbolic methods, using hand-crafted rules. The tool uses linguistic information, such as semantic roles, co-references and named entities, to generate questions and answers from a text selected by a teacher. The generated questions are ranked in order to offer the teacher a varied set to create an exercise. All the elements can be edited in case any error is produced, since NLP tools and methods are not completely accurate. When solving an exercise, the students are given immediate feedback based on the expected answers. A primary evaluation showed promising results, especially for “what” and “who” questions.
Detection of Seashore Debris with Fixed Camera Images using Computer Vision and Deep learning

Marine debris is impacting coastal landscapes majorly by affecting biodiversity, impairing recreational uses, causing losses to fishing industries, maritime industries, etc. In this paper, a model is being proposed to detect seven types of debris categories on custom dataset using instance segmentation with shape matching network which can then be cleaned timely and efficiently. This method resulted in the improvement of misclassification of masks for objects with different illuminations, shape, occlusion and viewpoints. The manually constructed dataset for this system is created with fixed camera images, by annotating them using CV AT with seven types of labels. A pre-trained HOG shape feature extractor is being used on LIBSVM along with template matching to improve the predicted masked images obtained via Mask R-CNN training. This system intends to timely alert the cleanup organizations with the recorded debris data.
A Circuit-embedded Reservoir Computer for Smart Noise Reduction of MCG Signals

With the COVID-19 pandemic, it has become necessary to monitor cardiac activities, not only for heart patients but for everyone. However, the traditional way to use heavy machines which are non-portable, intrusive, to check the electrocardiography (ECG) is not possible for everyone. As an alternative, there are sensors that can collect magnetocardiography (MCG) signals by measuring the magnetic field produced by the electrical currents in the heart and can be converted into ECG signals. The sensor for MCG is very sensitive, consume low power, portable, and can be a good alternative to check cardiac activities. But the challenging part of these sensors would be the noise at the low frequencies because the heart also oscillates at the low frequencies. As the relevant signal and noise share the same spectral properties, standard linear filtering techniques are not efficient. In this paper, we propose a physical reservoir computing technique using a circuit that can act as a reservoir and a lightweight machine learning model. The output is modeled to reduce the noise and extract the ECG signals out of the MCG ones.
Tremors: Privacy-Breaching Inference of Computing Tasks Using Vibration-Based Condition Monitors

We propose the adaptation of vibration-based condition monitoring systems and techniques, popularly used in industrial condition-based maintenance, for identifying the possibility of compromising the privacy of personal computing systems. This work exploits the automated fan-based heat dissipation features and read/write operations of disk-based storage, commonly present in personal computers, to read computing task-specific vibration signatures on the computer’s cabinet/case. These vibration signatures are then used to identify the broad classes of tasks being executed on a separate computer without ever needing to log into the monitored machine. This work builds upon the premise that heterogeneous tasks have distinct computing requirements, which translates to variations in the amount of heat generated by the computer’s processor, eventually leading to variations in the computer’s heat control fan speed. The variations in the fan’s speed and the frequency of read/write operations to disk-based storage create unique vibration signatures, which maps uniquely to the computer’s processing operations, leading to a breach of privacy of the computer. Our work’s preliminary results suggest that computer-based tasks can be mapped from their vibration signatures with an accuracy of at least <inline-formula><tex-math notation="LaTeX">$70\%$</tex-math></inline-formula> . We additionally study the task identification granularity of such an approach.
Developing an integrated platform to enable hardware-in-the-loop for synchronous VR, traffic simulation and sensor interactions

Hardware-in-the-loop (HIL) is a testing paradigm where physical sensors (e.g., monitoring sensors such as cameras and proximity sensors, and alarm sensors such as screaming traffic cones) are connected to a virtual test system that simulates reality (e.g., virtual work zone with simulated dangerous situations). This paradigm is well-suited for conducting user studies for work zone safety because virtual test systems can be implemented using virtual reality (VR), which allows for safe and realistic testing of a sensing system without putting workers in danger along with avoiding high upfront costs needed to generate physical research testbeds. However, when recreating physical work zones in VR, researchers face various challenges while representing traffic patterns in VR, such as the lack of bi-directional communication between traffic simulation platforms and user behaviors in VR, hardware compatibility and integration issues, and customization inflexibility during implementation. Researchers, who need to develop such platforms for research studies that involve high-risk exposure to participants, are in need of evaluating the options available for bringing together the components of such platforms. This study provides an overview of alternative ways to integrate components of platforms that enable hardware-in-the-loop for synchronous VR, traffic simulation, and sensor interactions to position researchers to make decisions based on the pros and cons of each alternative. This paper also presents the implementation of such an integrated platform that allows a two-way interface between traffic simulation and VR environments for work zone safety analysis. Outcomes of this work will lay out the steps in implementing the integrated and immersive platform to be used in work zone safety studies based on the guidance presented.
Mutually unbiased bases in 3 and 4 dimensions semi-quantum key distribution protocol

Semi-quantum key distribution is traditionally based on two-level quantum systems. In this paper, an unconditional security of a semi quantum key distribution protocol based on higher-dimensional systems using various mutually unbiased bases is presented. We first consider the three dimensional case using three and four mutually unbiased bases and derive a lower bound for the key rate as a function of the quantum channel's noise. We then generalize the result to a semi-quantum key distribution protocol that employs different number of mutually unbiased bases for four-dimensional states. It is found that basing the semi-quantum key distribution protocol on higher-dimensional mutually unbiased bases can increase the tolerable threshold of the noise and the maximum achievable value of the secret key rate.
argXtract: Deriving IoT Security Configurations via Automated Static Analysis of Stripped ARM Cortex-M Binaries

Recent high-profile attacks on the Internet of Things (IoT) have brought to the forefront the vulnerabilities in “smart” devices, and have revealed poor device configuration to be the root cause in many cases. This has resulted in IoT technologies and devices being subjected to numerous security analyses. For the most part, automated analyses have been confined to IoT hub or gateway devices, which tend to feature traditional operating systems such as Linux or VxWorks. However, most IoT peripherals, by their very nature of being resource-constrained, lacking traditional operating systems, implementing a wide variety of communication technologies, and (increasingly) featuring the ARM Cortex-M architecture, have only been the subject of smaller-scale analyses, typically confined to a certain class or brand of device. We bridge this gap with argXtract, a framework for performing automated static analysis of stripped Cortex-M binaries, to enable bulk extraction of security-relevant configuration data. Through a case study of 200+ Bluetooth Low Energy binaries targeting Nordic Semiconductor chipsets, as well as smaller studies against STMicroelectronics BlueNRG binaries and Nordic ANT binaries, argXtract has discovered widespread security and privacy issues in IoT, including minimal or no protection for data, weakened pairing mechanisms, and potential for device and user tracking.
Reversible image authentication scheme with blind content reconstruction based on compressed sensing

Reversible image authentication (RIA) schemes embed fragile watermarks to protect images against tampering attacks. If a marked image is not attacked, watermarks can be completely extracted and eliminated to obtain the original cover image. Tampered image regions can be reconstructed by including recovery information as part of the watermarks. In this paper, we introduce a novel blind RIA scheme for tampering localization and reconstruction. The proposed scheme is based on compressed sensing technique to obtain reference values, which are embedded into the cover image to perform sparse signal reconstruction using only the embedded information. Signal sparsity is enhanced in the discrete cosine transform (DCT) domain by omitting some high-frequency coefficients. Experimental results indicate that better sparse signal approximations can be obtained when some DCT coefficients are omitted, improving image reconstructions. The proposed scheme outperforms similar state-of-the-art works when reconstructing tampered image regions, especially when the damaged area size is increased.
Efficient Black-Box Adversarial Attacks for Deep Driving Maneuver Classification Models

Deep Neural Network (DNN) models are expected to be widely used in self-driven autonomous vehicles to understand surrounding environments and enhance driving safety. In this paper, we propose a Fast Black-box Adversarial (FBA) attack for time-series DNN models in connected autonomous vehicle (CAV) scenarios. In this attack, an attacker sends false driving signals to a vehicle to misclassify its DNN model (e.g., maintaining speed is misclassified to stopping). Though different black-box adversarial attacks have been proposed previously, they are mainly for image classification, which cannot be directly adopted in the CAV scenarios due to two challenges. First, the attack needs to be generated in near real time. Second, it should not be noticeable based on the driving time-series signals. To handle these two challenges, FBA consists of two steps for the adversarial signal generation: offline and online. First, based on our real data analysis observation that each driving maneuver has maneuver-specific similar patterns (in the time-series) regardless of drivers or vehicles, FBA finds the influential input portion for each maneuver as the offline adversarial signal portion. Second, given a benign driving signal input, FBA replaces its influential input portion with the offline adversarial signal portion and smooths the signals, and uses this input as the initial solution to find the optimal perturbation (that leads to successful attack while minimizing the perturbation values) online using the zeroth-order gradient descent method. It significantly reduces the time to find the optimal perturbation since the initial solution is closer to the optimal solution. Our experiments based on real-driving datasets show the effectiveness of FBA in dealing with the two challenges compared with the existing black-box adversarial attacks.
Artificial Neuronal Networks for Empowering Radio Transceivers: Opportunities and Challenges

With the advances in wireless communications towards beyond 5G (B5G) and 6G networks, new signal processing and resource management methods need to be explored to overcome the channel impairments and other radio and computing obstacles. In contrast to the conventional methods which are based on classic digital communications structures, B5G and 6G will leverage artificial intelligence (AI) to configure or adapt the radios and networks to the operational context. This requires the ability to reformulate legacy transceiver structures and drive research, development and standardization that can leverage the amount of data that is available and that can be processed with the available computing technology. This paper describes this vision and discusses successful research that justifies it as well as the remaining challenges. We numerically analyze some of the tradeoffs when replacing the physical layer receiver processing with an artificial neural network (ANN).
A novel end-to-end model for steering behavior prediction of autonomous ego-vehicles using spatial and temporal attention mechanism

In recent years, autonomous vehicles have attracted many researchers from academia and industry. Their efforts include object detection and tracking in the fields of computer vision, probabilistic-fusion and decision-making algorithms, etc. But most current methods that directly map from image pixels to steering behavior are not able to generate accurate results, especially under scenarios such as light and weather change drastically; this is largely due to the fact that these methods ignore the temporal relationship between frames. In this paper, we propose a novel end-to-end deep learning framework based on temporal and spatial attention mechanism, which aims to solve the problem of inaccurate vehicle steering angle prediction in complex environments and the difficulty of model interpretation. First, we use video sequence and historical steering angle sequence as inputs to the model, instead of just using a single frame as input. Second, we design a temporal attention mechanism to capture the long- and short-term memory in input visual information, and a spatial attention mechanism to capture key objects in the image and obtain their position information. This is achieved by inserting carefully-designed SE-Net, ConvLSTM and CNN layers into the appropriate layers of the network framework. Finally, we demonstrate the feasibility of the proposed model with public Comma2k19, with comparison to current advanced methods. Experimental results show that compared with state-of-the-art methods, the average absolute error (MAE) values of our model on the training set and testing set are reduced by 10.2% and 6.3%, respectively, and has more accurate steering prediction performance. In addition, we explain the trigger mechanism of steering behavior prediction by visualizing the spatial attention map and temporal attention score on Comma2k19 and Udacity datasets, which further demonstrates that the proposed model can learn human-like driving behavior.
Research on Head-up and Down Behavior Computer Detection by Deep Learning and Artificial Intelligence

The use of deep learning technology to recognize human behavior in video scenes is one of the current research hotspots in the field of computer vision. In recent years, it has received extensive attention from academia and engineering. Human behavior recognition related technologies are used in intelligent monitoring, human-computer interaction, and virtual Reality and content-based video retrieval and interpretation have broad application prospects and potential economic value. At the same time, in the classroom scene, identifying students' classroom behavior has important reference significance for the evaluation of students' learning status, learning quality and teachers' teaching effects. This article applies deep learning technology to college classroom scenes, from the perspective of students looking down and raising their heads, to detect students and identify their behavioral states.
A Lossless Data-Hiding based IoT Data Authenticity Model in Edge-AI for Connected Living

Edge computing is an emerging technology for the acquisition of Internet-of-Things (IoT) data and provisioning different services in connected living. Artificial Intelligence (AI) powered edge devices (edge-AI) facilitate intelligent IoT data acquisition and services through data analytics. However, data in edge networks are prone to several security threats such as external and internal attacks and transmission errors. Attackers can inject false data during data acquisition or modify stored data in the edge data storage to hamper data analytics. Therefore, an edge-AI device must verify the authenticity of IoT data before using them in data analytics. This article presents an IoT data authenticity model in edge-AI for a connected living using data hiding techniques. Our proposed data authenticity model securely hides the data source’s identification number within IoT data before sending it to edge devices. Edge-AI devices extract hidden information for verifying data authenticity. Existing data hiding approaches for biosignal cannot reconstruct original IoT data after extracting the hidden message from it (i.e., lossy) and are not usable for IoT data authenticity. We propose the first lossless IoT data hiding technique in this article based on error-correcting codes (ECCs). We conduct several experiments to demonstrate the performance of our proposed method. Experimental results establish the lossless property of the proposed approach while maintaining other data hiding properties.
Comparison of Binary Particle Swarm Optimization And Binary Dragonfly Algorithm for Choosing the Feature Selection

Artificial life is a living behavior that comes from animals and humans which is currently used as inspiration in making algorithms and is usually used to look for patterns such as classification, feature selection, optimization, and many more. Binary Particle Swarm Optimization (BPSO) and Binary Dragonfly Algorithm (BDA) are algorithms derived from Artificial Life and Genetic Algorithms. The purpose of this research is to compare Binary Particle Swarm Optimization (BPSO), Binary Dragonfly Algorithm (BDA). The dataset used is breast cancer from the UCI public dataset. The results of the feature selection will be used in the K-NN (K Nearest Neighbor) classification algorithm to get the Accuracy, Precision, Recall values. The BPSO algorithm is still superior to the BDA algorithm because the iteration process of BPSO algorithm is more centralized in solving existing problems. the BPSO algorithm produces 97.5% accuracy, 97% precision, and 98.98% recall compared to the BDA algorithm whose value is still below BPSO.
Improved subspace-based and angle-based outlier detections for fuzzy datasets with a real case study

To overcome curse of dimensionality for outlier detecting in high dimensional dataset, axis-parallel subspace (SOD) and angle-based outlier detection (ABOD) methods were presented. These methods are also friendly used distance-based to detect outliers. In this regard, based on the reality of fuzzy data for explaining the world phenomena, this paper introduces an extended version of both methods for fuzzy dataset. First, the basic concepts of both methods are explained. Next we provide two metrics based on Euclidean and analytic distance to measure distance between fuzzy objects; also Cosine similarity measure formula for calculating the cosine of angle between two difference vectors in high-dimensional fuzzy dataset is illustrated. Then the algorithms to determine outliers of fuzzy datasets by using these metrics and Cosine similarity measure, based on ABOD and SOD algorithms, are presented. Some numerical experimental examples are also presented, in which both real and synthesis datasets are used, For a real numerical examination, we have applied proposed algorithms to data from 15 Iranian petrochemical companies in a fully fuzzy environment. The obtained results show the significant properties of the new methods in detecting outliers.
Goal-Oriented Navigation with Avoiding Obstacle based on Deep Reinforcement Learning in Continuous Action Space

Obstacle avoidance problems using Deep Reinforcement Learning (DRL) are becoming possible solutions for autonomous mobile robots. In real-world situations with stationary and moving obstacles, mobile robots must be able to navigate to a goal and safely avoid collisions. This work is an extension of ongoing research on the navigation approach for a mobile robot. We show that through the proposed DRL, a goal-oriented collision avoidance model can be trained end-to-end without manual turning or supervision by a human operator. We suggest performing the obstacle avoidance algorithm of the mobile robot in both simulated environments and continuous action space of the real world. Finally, we measure and evaluate the obstacle avoidance capability through data collection of hit ratio metrics during robot execution.
Design of FIR Filter With Several Window Functions

Digital filter is an important component of signal processing such as image processing and spectrum analysis. In this paper, FIR low-pass filter is analyzed and designed by window function method. The first part introduces several commonly used window functions, and analyzes the necessity of using window functions from a mathematical point of view; According to the basic principle of FIR digital filter, the second part realizes the design of FIR filter with different window functions such as Hamming window, Haining window and Blackman window, simulates on MATLAB platform, and compares and analyzes the influence of different window functions on the design performance of FIR filter. Finally, the conclusion is drawn, and the principle of using different window functions in the design of FIR filter is discussed.
Per-frame Sign Language Gloss Recognition

Sign language is a language for communicating among the deaf and hard of hearing people and the hearing people. Sign language has its own grammatical system which is different from spoken or written languages. A sentence of sign language consists of glosses as morphemes, and the meaning of sign language depends on the movements of a body, hands, finger shapes, and facial expressions. Previous methods can translate sign language sentences into a sequence of glosses or written language but are weak for the out-of-vocabulary problem such as variations of word order. This is because the model only considers a sign language sentence as an inseparable sequence, even though the sentence consists of multiple glosses. In this paper, we propose a method that predicting every gloss for each video frame of Korean sign language using transformers. Predicted frame-by-frame gloss information can be used to transform a video of a sign language sentence into a gloss sequence, even if the model has not learned that pattern.