Reviewerly/exHarmony huggingface dataset:
Deep Learning for Activity Recognition Using Audio and Video

Neural networks have established themselves as powerhouses in what concerns several types of detection, ranging from human activities to their emotions. Several types of analysis exist, and the most popular and successful is video. However, there are other kinds of analysis, which, despite not being used as often, are still promising. In this article, a comparison between audio and video analysis is drawn in an attempt to classify violence detection in real-time streams. This study, which followed the CRISP-DM methodology, made use of several models available through PyTorch in order to test a diverse set of models and achieve robust results. The results obtained proved why video analysis has such prevalence, with the video classification handily outperforming its audio classification counterpart. Whilst the audio models attained on average 76% accuracy, video models secured average scores of 89%, showing a significant difference in performance. This study concluded that the applied methods are quite promising in detecting violence, using both audio and video.
Object Detection and Distance Measurement Using AI

To control and manage traffics as well as guide the driver on roads, the lines on the roads are used. In addition, these lines serve as barriers and to ensure the safe, smooth and harmonious flow of traffic. However, in some countries, these lines are missed and causes the driving chaos; and thus, car accidents happen. A car accident is one of the most causes of death and the majority of the accident are due to human error. This research works aims to help driver to provide safety roads and reduces or eliminate care accidents. Object detection is a technique used to find and locate objects in images. In this works, YOLO Version 3 is the network used to detect the object in the frame because of its speed, simplicity, and ability to predict as well as classify objects. In addition, a steering angle circuit is designed and implemented to measure the direction of the car. The steering angle measurements is used with object detection (vehicles and pedestrians) to issue a warning when these objects are close to the driving car (10 meters). After the objects are detected using YOLO V3, the distance of the detected objects is measured using the height of the object. While being affordable and low-cost, the system achieved positive and competitive results, this system can be used at night and in dark environments.
A survey on deep learning based sentiment analysis

This survey focus on sentiment analysis using various Deep learning methodologies namely Convolutional neural network, Recurrent neural network, Long Short Term Memory, Gated Recurrent Unit and its variants. Sentiment analysis is used to analyse opinions or sentiments of people about entities such as products, services, individuals. Currently it has become a very active research area since a vast amount of data is generated daily in various forms such as text, audios and videos in the social media on the world wide web. Sentiment analysis categorizes opinions into positive, negative, or neutral. Deep learning network perform better than SVMs and conventional neural networks for sentiment analysis since it can handle huge amount of data. Out of various deep learning models Recurrent neural networks perform better than Convolutional Neural networks for sentiment analysis. LSTM and GRU both are better than Simple RNN because they can catch Long-Term Dependencies.
An Automatically Layer-Wise Searching Strategy for Channel Pruning Based on Task-Driven Sparsity Optimization

Deep convolutional neural networks (CNNs) have achieved tremendous successes but tend to suffer from high computation costs mainly due to heavy over-parameterization, resulting in the difficulty of directly applying them to the ever-growing application demands based on low-end edge devices with strong power restriction and real-time inference requirement. Recently, there has much research attention devoted to compressing the network via pruning to address this issue. Most of the existing methods rely on some hand-designed pruning rules, which suffer from several limitations. Firstly, manually designed rules are only applicable to limited application scenarios, which can hardly generalize well in a broader scope. And these rules are typically designed based on human experience and via trial and error, and thus highly subjective. Then, channels of different layers in a network may have diverse distributions, which means the same pruning rule is not appropriate for each layer. To address these limitations, we propose a novel channel pruning scheme, in which the task-irrelevant channels are removed in a task-driven manner. Specifically, an adaptively differentiable search module is proposed to find the best pruning rule automatically for different layers in CNNs under sparsity constraints. Besides, we employed knowledge distillation to alleviate the excessive performance loss. Once the training process is finished, a compact network will be obtained by removing channels based on layer-wise pruning rules. We have evaluated the proposed method on some well-known benchmark datasets including CIFAR, MNIST, and ImageNet in comparison to several state-of-the-art pruning methods. Experimental results demonstrate the superiority of our method over the compared ones in terms of both parameters and FLOPs reduction.
A Correlation-Based Anomaly Detection Model for Wireless Body Area Networks Using Convolutional Long Short-Term Memory Neural Network

As the Internet of Healthcare Things (IoHT) concept emerges today, Wireless Body Area Networks (WBAN) constitute one of the most prominent technologies for improving healthcare services. WBANs are made up of tiny devices that can effectively enhance patient quality of life by collecting and monitoring physiological data and sending it to healthcare givers to assess the criticality of a patient and act accordingly. The collected data must be reliable and correct, and represent the real context to facilitate right and prompt decisions by healthcare personnel. Anomaly detection becomes a field of interest to ensure the reliability of collected data by detecting malicious data patterns that result due to various reasons such as sensor faults, error readings and possible malicious activities. Various anomaly detection solutions have been proposed for WBAN. However, existing detection approaches, which are mostly based on statistical and machine learning techniques, become ineffective in dealing with big data streams and novel context anomalous patterns in WBAN. Therefore, this paper proposed a model that employs the correlations that exist in the different physiological data attributes with the ability of the hybrid Convolutional Long Short-Term Memory (ConvLSTM) techniques to detect both simple point anomalies as well as contextual anomalies in the big data stream of WBAN. Experimental evaluations revealed that an average of 98% of F1-measure and 99% accuracy were reported by the proposed model on different subjects of the datasets compared to 64% achieved by both CNN and LSTM separately.
A High-Performance SIKE Hardware Accelerator

Supersingular isogeny key encapsulation (SIKE) is a promising candidate in the NIST postquantum cryptography (PQC) standardization process, which has the smallest key lengths. It is the only isogeny-based cryptographic scheme in the NIST list that leverages the traditional elliptic curve cryptography (ECC) arithmetic; however, the high computational complexity is one of its limiting factors. In this work, we proposed a high-performance hardware architecture for the SIKE protocol. The architecture includes an improved multiplier based on the high-performance finite field multiplication (HFFM) algorithm which is 15%–20.7% faster than the previous multiplier based on the HFFM algorithm and a unified adder/subtractor with radix <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3^{b}$ </tex-math></inline-formula> . In addition, it also comprises an efficient scheduler strategy that decomposes all the functions of SIKE into finite field <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F_{p}$ </tex-math></inline-formula> and then effectively schedules through optimized multiplication chains for maximal performance. The proposed architecture is synthesized and implemented on Xilinx Virtex-7 FPGA for all the four variants of SIKE having security levels from 1 to 5 and achieved 2.6%–7.8% faster speeds as well as consumed less equivalent number of slices (ENS) than the state-of-the-art designs. In the comparison of area and time (AT), the proposed architecture is 14.2%–34.5% lower than the previous architecture.
Visual Surveillance for Human Fall Detection in Healthcare IoT

This article designs a visual surveillance framework for human fall detection. In order to solve the conventional issues in fall detection, such as unsatisfactory feature generalization, low recall rates, and large computational time, we design a model that incorporates the deep convolutional neural network and the aggregated heuristic visual features in detecting the occurrence of falls. First, the convolutional neural network (Openpose model) is utilized to extract human skeleton in the image. Second, the hand-crafted spatial features, such as the angle of human shank inclination, are aggregated to determine the fall presence. It should be noticed that our fall detection method has been integrated to healthcare Internet of Things (IoT) video surveillance architecture, which has multiple graphic processing unit groups to perform real-time monitoring and alarming for the elderly in need. The experimental results prove that our method is able to accurately distinguish fall and nonfall activities with a competitive false-alarm rate.
The life and times of university teachers in the era of digitalization: A tragedy

Although digitalization has become an everyday phenomenon in higher education (HE), there is still limited understanding on how HE teachers experience the digitalized work environment. In this article we utilize narrative research methodology to share HE teachers’ voices, addressing the following questions: What is the lived experience of being a teacher in digitalizing higher education? How has digitalization shaped the work environment in higher education? What meaning do HE teachers make of digitalization? The research participants are six university teachers with significant work experience. The findings are reported in the form of a classic tragedy narrative, which reveals that the rise of managerialism and neoliberalization in HE has changed teachers’ work and the role of digital tools play in it, and teachers are experiencing an increasing sense of alienation. In conclusion, we elaborate these experiences theoretically and paint a picture of today’s digitalized work environment in HE.
YOLO V4 with hybrid dilated convolution attention module for object detection in the aerial dataset

Object detection is an important part of computer vision. Besides, small object detection is a challenging task in object detection. Most existing methods have difficulty locating small objects and classification. In this paper, we propose a new method to solve the problem. On the one hand, we improve the YOLO V4 network with ASPP. On the other hand, we propose a Hybrid Dilated Convolution Attention (HDCA) module to focus on the important position in images. The Hybrid Dilated Convolution (HDC) module is redesigned for parameter-efficient in the HDCA module. We also design a Translational Dilated Convolution (TDC) to solve the 'gridding issueʻ of the HDC and enlarge the receptive field at the same time. The experiments are based on the DOTA dataset, and our method achieves 2.31% mAP improvement compared with the original YOLO V4. Besides, our method achieves the best improvement in the class of basketball court, which reaches 81.03% of AP. Compared with the state-of-the-art method, our method achieves a 3.99% improvement on the mAP criterion. We put other attention modules in the YOLO V4 architecture at the same place as our method. And our method achieves 0.78% mAP improvement compared with the BAM module, which is the second place in the competition.
The Public Perception of the #GeneEditedBabies Event Across Multiple Social Media Platforms: Observational Study

In November 2018, a Chinese researcher reported that his team had applied clustered regularly interspaced palindromic repeats or associated protein 9 to delete the gene C-C chemokine receptor type 5 from embryos and claimed that the 2 newborns would have lifetime immunity from HIV infection, an event referred to as #GeneEditedBabies on social media platforms. Although this event stirred a worldwide debate on ethical and legal issues regarding clinical trials with embryonic gene sequences, the focus has mainly been on academics and professionals. However, how the public, especially stratified by geographic region and culture, reacted to these issues is not yet well-understood.The aim of this study is to examine web-based posts about the #GeneEditedBabies event and characterize and compare the public's stance across social media platforms with different user bases.We used a set of relevant keywords to search for web-based posts in 4 worldwide or regional mainstream social media platforms: Sina Weibo (China), Twitter, Reddit, and YouTube. We applied structural topic modeling to analyze the main discussed topics and their temporal trends. On the basis of the topics we found, we designed an annotation codebook to label 2000 randomly sampled posts from each platform on whether a supporting, opposing, or neutral stance toward this event was expressed and what the major considerations of those posts were if a stance was described. The annotated data were used to compare stances and the language used across the 4 web-based platforms.We collected >220,000 posts published by approximately 130,000 users regarding the #GeneEditedBabies event. Our results indicated that users discussed a wide range of topics, some of which had clear temporal trends. Our results further showed that although almost all experts opposed this event, many web-based posts supported this event. In particular, Twitter exhibited the largest number of posts in opposition (701/816, 85.9%), followed by Sina Weibo (968/1140, 84.91%), Reddit (550/898, 61.2%), and YouTube (567/1078, 52.6%). The primary opposing reason was rooted in ethical concerns, whereas the primary supporting reason was based on the expectation that such technology could prevent the occurrence of diseases in the future. Posts from these 4 platforms had different language uses and patterns when they expressed stances on the #GeneEditedBabies event.This research provides evidence that posts on web-based platforms can offer insights into the public's stance on gene editing techniques. However, these stances vary across web-based platforms and often differ from those raised by academics and policy makers.
Social Robots in Applied Settings: A Long-Term Study on Adaptive Robotic Tutors in Higher Education

Learning in higher education scenarios requires self-directed learning and the challenging task of self-motivation while individual support is rare. The integration of social robots to support learners has already shown promise to benefit the learning process in this area. In this paper, we focus on the applicability of an adaptive robotic tutor in a university setting. To this end, we conducted a long-term field study implementing an adaptive robotic tutor to support students with exam preparation over three sessions during one semester. In a mixed design, we compared the effect of an adaptive tutor to a control condition across all learning sessions. With the aim to benefit not only motivation but also academic success and the learning experience in general, we draw from research in adaptive tutoring, social robots in education, as well as our own prior work in this field. Our results show that opting in for the robotic tutoring is beneficial for students. We found significant subjective knowledge gain and increases in intrinsic motivation regarding the content of the course in general. Finally, participation resulted in a significantly better exam grade compared to students not participating. However, the extended adaptivity of the robotic tutor in the experimental condition did not seem to enhance learning, as we found no significant differences compared to a non-adaptive version of the robot.
Searching part-specific neural fabrics for human pose estimation

Neural architecture search (NAS) has emerged in many domains to jointly learn the architectures and weights of neural networks. The core spirit behind NAS is to automatically search neural architectures for target tasks with better performance-efficiency trade-offs. However, existing approaches emphasize on only searching a single architecture with less human intervention to replace a human-designed neural network, yet making the search process almost independent of the domain knowledge. In this paper, we aim to apply NAS for human pose estimation and we ask: when NAS meets this localization task, can the articulated human body structure help to search better task-specific architectures? To this end, we first design a new neural architecture search space, Cell-based Neural Fabric (CNF), to learn micro as well as macro neural architecture using a differentiable search strategy. Then, by viewing locating human parts as multiple disentangled prediction sub-tasks, we exploit the compositionality of human body structure as guidance to search multiple part-specific CNFs specialized for different human parts. After the search, all these part-specific neural fabrics have been tailored with distinct micro and macro architecture parameters. The results show that such knowledge-guided NAS-based model outperforms a hand-crafted part-based baseline model, and the resulting multiple part-specific architectures gain significant performance improvement against a single NAS-based architecture for the whole body. The experiments on MPII and COCO datasets show that our models1 achieve comparable performance against the state-of-the-art methods while being relatively lightweight.
Continuous-variable quantum repeaters based on bosonic error-correction and teleportation: architecture and applications

Abstract Quantum repeater is an essential ingredient for quantum networks that link distant quantum modules such as quantum computers and sensors. Motivated by distributed quantum computing and communication, quantum repeaters that relay discrete-variable quantum information have been extensively studied; while continuous-variable (CV) quantum information underpins a variety of quantum sensing and communication application, a quantum-repeater architecture for genuine CV quantum information remains largely unexplored. This paper reports a CV quantum-repeater architecture based on CV quantum teleportation assisted by the Gottesman–Kitaev–Preskill code to significantly suppress the physical noise. The designed CV quantum-repeater architecture is shown to significantly improve the performance of entanglement-assisted communication, target detection based on quantum illumination and CV quantum key distribution, as three representative use cases for quantum communication and sensing.
EVCT: An Efficient VM Deployment Algorithm for a Software-Defined Data Center in a Connected and Autonomous Vehicle Environment

Software-defined data centers (SDDC) are an emerging softwarized model that can monitor the virtual machines’ allocation atop the cloud servers. SDDC consists of softwarized entities like Virtual Machine (VM) and hardware entities like servers and connected switches. SDDCs apply VM deployment algorithms to preserve efficient placement and processing data traffic generated from the Connected and Autonomous Vehicles (CAV). To enhance user satisfaction, SDDC providers are always looking for an intellectual model to monitor large-scale incoming traffics, such as the Internet of Things (IoT) and CAV applications, by optimizing service quality and service level agreement (SLA). This paper is motivated by this, raising an energy-efficient VM cluster placement algorithm named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EVCT</i> to handle service quality and SLA issues in an SDDC in a CAV environment. EVCT algorithm leverages the similarity between VMs and models the problem of VM deployment into a weighted directed graph. Based on the amount of traffic between VM, EVCT adopts the “maximum flow and minimum cut theory” to cut the directed graph and achieve high energy-efficient placement for VMs. The proposed algorithm can efficiently reduce the energy consumption cost, provide a high quality of services (QoS) to users, and have good scalability for the variable workload. We have also carried out a series of experiments to use the real-world workload to evaluate the performance of the EVCT. The results illustrate that the EVCT surpasses the state-of-the-art algorithms in terms of energy consumption cost and efficiency.
Developing a ‘Virtual Go mode’ on a mobile app to enhance primary students’ vocabulary learning engagement: an exploratory study

This article reports on an exploratory study on developing and adopting a ‘Virtual Go Mode’ feature embedded in a mobile vocabulary learning app, VocabGo, to enhance students’ vocabulary learning engagement. Distinguished from other virtual reality (VR) or augmented reality (AR) applications, the Virtual Go Mode is designed for students to practice and consolidate their newly learned words via virtual location-based tasks with 360° Google Street View or pre-photographed panoramas. Google Cloud Vision, using an AI model, is embedded to identify objects and their corresponding tasks for students. A total of 15 primary students in Hong Kong participated in this exploratory study for one week. Both quantitative and qualitative data were collected and analyzed. The results showed that students’ vocabulary learning engagement was high when using the Virtual Go Mode. It is suggested that future research focus on empirical studies when designing and implementing virtual location-based tasks supported by VR technologies in class to provide opportunities for students to practice and consolidate newly learned words and enhance their learning engagement after the COVID-19 pandemic.
A Spatio-Temporal Motion Network for Action Recognition Based on Spatial Attention

Temporal modeling is the key for action recognition in videos, but traditional 2D CNNs do not capture temporal relationships well. 3D CNNs can achieve good performance, but are computationally intensive and not well practiced on existing devices. Based on these problems, we design a generic and effective module called spatio-temporal motion network (SMNet). SMNet maintains the complexity of 2D and reduces the computational effort of the algorithm while achieving performance comparable to 3D CNNs. SMNet contains a spatio-temporal excitation module (SE) and a motion excitation module (ME). The SE module uses group convolution to fuse temporal information to reduce the number of parameters in the network, and uses spatial attention to extract spatial information. The ME module uses the difference between adjacent frames to extract feature-level motion patterns between adjacent frames, which can effectively encode motion features and help identify actions efficiently. We use ResNet-50 as the backbone network and insert SMNet into the residual blocks to form a simple and effective action network. The experiment results on three datasets, namely Something-Something V1, Something-Something V2, and Kinetics-400, show that it out performs state-of-the-arts motion recognition networks.