Reviewerly/exHarmony huggingface dataset:
Parallax-Estimation-Enhanced Network With Interweave Consistency Feature Fusion for Binocular Salient Object Detection

Salient object detection (SOD) has received extensive attention in recent years, and many models have been developed. However, most SOD models only consider monocular images and not binocular images, which resemble the human vision and can better reflect human perception for distinguishing salient objects. To leverage the information in binocular images, we propose herein a first-of-its-kind parallax-estimation-enhanced network (PEENet) for binocular SOD. More specifically, we use a weighted binocular fusion module and a parallax correlation fusion module to explore the complementary and different information in binocular images. In addition, a parallax enhancing module and interweave consistency fusion use complementary saliency information and parallax information to enhance saliency and parallax representations. Finally, a transformation module avoids global and local information loss during decoding. Experiments were performed to validate the effectiveness and robustness of the proposed PEENet, which outperforms 10-RGB/RGB-D SOD methods on two binocular SOD datasets.
Converting UML-Based Ontology Conceptualizations to OWL with Chowlk

During the ontology conceptualization activity, developers usually generate preliminary models of the ontology in the form of diagrams. Such models drive the ontology implementation activity, where the models are encoded using an implementation language, typically by means of ontology editors. The goal of this demo is to take advantage of the developed ontology conceptualizations in order to accelerate the ontology implementation activity. For doing so we present Chowlk, a converter to transform digital UML-based ontology diagrams into OWL. This system aims at supporting users in the generation of the first versions of ontologies by reusing the ontology conceptualization output.
Transformer text recognition with deep learning algorithm

The transformer is a vital equipment in the power system, which is used in large quantities and replaced frequently in industrial projects. Therefore, it is essential to find an efficient automatic detection and recognition method for the text information of the transformer nameplate. At present, the text information of the transformer nameplate is collected manually, which is inefficient. On the other hand, the complex text features of transformer nameplates are a challenge to the existing text recognition algorithms. Therefore, we propose a two-stage network based on deep learning to recognize the nameplate text content automatically. At the same time, we establish a transformer nameplate dataset due to the particularity of the data characteristics of the transformer nameplate. The dataset is used to train our network to improve its sensitivity of the transformer nameplate information. The experimental results show that our model achieves a recognition accuracy of 71% in the transformer nameplate dataset. The test performance of our network on the transformer nameplate dataset is comparable with the state-of-the-art text recognition algorithms.
Tele-Immersive Improv

Performers acutely need but lack tools to remotely rehearse and create live theatre, particularly due to global restrictions on social interactions during the Covid-19 pandemic. No studies, however, have heretofore examined how remote video-collaboration affects performance. This paper presents the findings of a field study with 16 domain experts over six weeks investigating how tele-immersion affects the rehearsal and performance of improvisational theatre. To conduct the study, an original media server was developed for co-locating remote performers into shared virtual 3D environments which were accessed through popular video conferencing software. The results of this qualitative study indicate that tele-immersive environments uniquely provide performers with a strong sense of co- presence, feelings of physical connection, and an increased ability to enter the social-flow states required for improvisational theatre. Based on our observations, we put forward design recommendations for video collaboration tools tailored to the unique demands of live performance.
Detection of Machine Generated Multimedia Elements Using Deep Learning

With recent advancements in Deep Learning [DL], many useful tasks like text recognition, speech recognition and many more tasks can be easily done using machines. Such advancements have led to the development of deepfakes (coined from combining the words "Deep learning" and "fake"), face swaps, facial motion transfer, and other such media content. Deepfakes are synthetically generated media in which a person in an image or video is replaced with someone else’s likeness. Deepfakes and other such content were intended to help in the field of cinematography, conceal the identity of the witness, and many such positive uses, but they can easily be used to spread false news, spread fake propaganda, and spread hate. There has come a time now that the ill uses of such technologies heavily outweigh the positive uses. With the passing time, deepfakes are becoming more and more realistic and it is becoming increasingly difficult to identify such media from real ones. This paper intends to present a system that would make its users to distinguish such media from authentic media. The user can simply select the image or video file which they would like to authenticate, and the system would predict them to be doctored or pristine.
Dynamic Hand Gesture Pattern Recognition Using Probabilistic Neural Network

Flex Sensor and Gyroscope based hand gloves are being used widely to detect gesture-based sign language patterns to provide aid for speech-impaired people. However, detecting dynamic gestures is not an easy task due to the variability and redundant implementation precautions. This article approaches a dynamic hand gesture recognition system by using Probabilistic Neural Network (PNN) deliberately focused on practical usage of dynamic gestures on day-to-day life. In this experiment, 10 gesture patterns have generated by using a data glove. For each gesture, 360 training input vectors are generated to train the PNN model, and the output has provided via a speaker.
Quranic Education and Technology: Reinforcement learning System for Non-Native Arabic Children

We built a simulator based on reinforcement learning to improve teaching experience in Quranic and Islamic education for non-native Arabic speakers to evaluate their strength and weaknesses and allow the system to help improving the child in one hand, and provide an accurate actual report for each child on the other hand.
The security and privacy of blockchain-enabled EMR storage management scheme

The Electronic Medical Record (EMR) management system currently used by healthcare institutions has security concerns for data sharing and access control. To address these issues, we have proposed a security and privacy management scheme for blockchain-based EMR called GAC-PSPR (Granular Access Control supporting Privilege Separation and Property Revocation). The GAC-PSPR scheme uses a dual chain structure of private blockchain and consortium blockchain. The technology of permission separation and property revocation ensures that data cannot be arbitrarily modified, thus solving the security problem of access control. Protect data privacy by protecting against quantum attacks with Number Theory Research Unit (NTRU) encryption. Besides, we have proposed a ring signature solution based on the lattice to address the issue of too large signatures and the key lengths that secures the sharing of EMR data. We provide extensive security analysis and performance analysis to demonstrate that the GAC-PSPR scheme is effective and secure.
Hydrogeochemical Processes Governing Uranium Mobility: Inferences from the Anthropogenically Disturbed, Semi-arid Region of India

Khetri Copper Belt, Rajasthan, is anthropogenically active and geologically belongs to the Delhi super-group. A study was designed to understand the geochemical processes controlling the elemental mobility in the groundwater. Sampling sites were divided into three zones, i.e. copper, quartzite and granite mine zones depending on the type of mineral excavated. A total of 32 representative groundwater samples were collected and analysed for heavy metals and radionuclide (U) using ICP–MS. A maximum U concentration (average 87 µgL−1) is observed in the quartzite mine zone, and minimum (average 13 µgL−1) is found in the copper mine zone samples. A high concentration of U (maximum of 430 µgL−1) in groundwater is attributed to mineral dissolution due to geogenic and anthropogenic activities. Despite the presence of Jaspura and Gothra granitoid in the copper mine zone, the abundance of U is low suggesting the scavenging of U by sulphides or iron oxides. Additionally, at the confluence of two geological groups, Fe concentration is found high with a low concentration of U which further confirms scavenging of U. It is evident from the results that in the absence of iron-bearing sulphides, U concentration in groundwater would be very high compared to the current concentration. It also indicates low concentration of U in the copper mine zone is due to dissolution of Fe sulphide-rich waste. The present study recommends further research to understand the feasibility of mining waste for the removal of U contamination from groundwater.
A Deep Neural Network Training Architecture With Inference-Aware Heterogeneous Data-Type

As deep learning applications often encounter accuracy degradation due to the distorted inputs from a variety of environmental conditions, training with personal data has become essential for the edge devices. Hence, &#x2018;training on edge&#x2019; by supporting a trainable deep learning accelerator has been actively studied. Nevertheless, previous research does not consider the fundamental datapath for training and the importance of retaining the high performance for inference tasks. In this work, we propose NeuroFlix, a deep neural network training accelerator supporting heterogeneous data-type of floating- and fixed-point for input operands. From two perspectives: 1) separate precision decision for each input data, 2) maintenance of high performance on inference, we configure the data with low-bit fixed-point of activation/weight and floating-point based error gradient securing up to half-precision. A novel MAC architecture is designed to compute low- and high-precision modes for the different input combinations. By substituting a high-cost floating-point based addition to brick-level separate accumulations, we realize both area-efficient architecture and high throughput for low-precision computation. Consequently, NeuroFlix outperforms the previous architectures of state-of-the-art configurations proving its high efficiency in both training and inference. By also comparing with the off-the-shelf bfloat16-based accelerator, it achieves 1.2 &#x00D7;/2.0 &#x00D7; of speedup/energy-efficiency at training and further enhancement of 3.6 &#x00D7;/4.5 &#x00D7; at inference.
Effects of Language Familiarity in Simulated Natural Dialogue with a Virtual Crowd of Digital Humans on Emotion Contagion in Virtual Reality

This investigation compared the emotional impact caused by a crowd of affective virtual humans (VHs) that communicated in the users' native or foreign language. We evaluated the users' affective reactions to a crowd of VHs that exhibited distinct emotional expressions. A total of four emotions were presented, which were Positive, Negative, Neutral, and Mixed. The VHs performed verbal and non-behaviors accordingly. Under the Mixed condition, the VHs were divided into three groups equally and each group was uniquely assigned one of the three emotions (i.e., positive, negative, and neutral). Users collected ten items from a virtual reality market. To complete the tasks, they interacted using natural speech with the emotional VHs. Three language conditions were investigated: one condition in USA and another two conditions in Taiwan. The group of participants in USA interacted with the VHs in English; and the two groups of participants in Taiwan interacted with the VHs using a foreign (English) language and a native (Mandarin) language respectively. We discovered that the medium of communication or language familiarity had a strong influence on participants' emotional reactions. When participants interacted in a foreign language with VHs with a positive emotional disposition, we found their positive emotional reactions were subdued and negative reactions were elevated. However, this was not the case when participants interacted with VHs in their native language, as their emotional reactions were contingent on the emotional disposition of the VHs.
Vehicles Detection for Smart Roads Applications on Board of Smart Cameras: A Comparative Analysis

Video analytics can be profitably adopted in smart roads environments to automatically detect abnormal situations. Within this context, vehicle detection is the first and foremost stage, and its accuracy is crucial, since any detection error will affect the performance of any subsequent step. Furthermore, in smart road environments it is often preferred to perform the video analysis directly on board of smart surveillance cameras, in order to reduce bandwidth usage and eliminate the cost of setup and maintenance of powerful processing servers; on the flip side, processing on board of smart cameras implies the detection algorithm to be fast and slim, since the resources available on this kind of embedded device are limited. In the era of deep learning, it seems that the question <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">what is the best method for vehicle detection?</i> may have a trivial answer, since this class of methods includes some very accurate ones. Anyway, according to the above consideration, the best suited method for this application is not necessarily the most accurate one, but for sure the most accurate one running on the available hardware at a given resolution and frame rate. Starting from the above considerations, in this paper we perform an analysis of the methods available in the literature for vehicle detection, by comparing them in terms of accuracy and computational burden, with the aim to answer the following question: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">what is the best method for vehicles detection when working with smart cameras?</i>
Detection and Correspondence Matching of Corneal Reflections for Eye Tracking Using Deep Learning

Eye tracking systems that estimate the point-of-gaze are essential in extended reality (XR) systems as they enable new interaction paradigms and technological improvements. It is important for these systems to maintain accuracy when the headset moves relative to the head (known as device slippage) due to head movements or user adjustment. One of the most accurate eye tracking techniques, which is also insensitive to shifts of the system relative to the head, uses two or more infrared (IR) light emitting diodes to illuminate the eye and an IR camera to capture images of the eye. An essential step in estimating the point-of-gaze in these systems is the precise determination of the location of two or more corneal reflections (virtual images of the IR-LEDs that illuminate the eye) in images of the eye. Eye trackers tend to have multiple light sources to ensure at least one pair of reflections for each gaze position. The use of multiple light sources introduces a difficult problem: the need to match the corneal reflections with the corresponding light source over the range of expected eye movements. Corneal reflection detection and matching often fail in XR systems due to the proximity of camera and steep illumination angles of light sources with respect to the eye. The failures are caused by corneal reflections having varying shape and intensity levels or disappearance due to rotation of the eye, or the presence of spurious reflections. We have developed a fully convolutional neural network, based on the UNET architecture, that solves the detection and matching problem in the presence of spurious and missing reflections. Eye images of 25 people were collected in a virtual reality headset using a binocular eye tracking module consisting of five infrared light sources per eye. A set of 4,000 eye images were manually labelled for each of the corneal reflections, and data augmentation was used to generate a dataset of 40,000 images. The network is able to correctly identify and match 91% of corneal reflections present in the test set. This is comparable to a state-of-the-art deep learning system, but our approach requires 33 times less memory and executes 10 times faster. The proposed algorithm, when used in an eye tracker in a VR system, achieved an average mean absolute gaze error of 1°. This is a significant improvement over the state-of-the-art learning-based XR eye tracking systems that have reported gaze errors of 2-3°.
Restraints as a Mechanic for Bodily Play

This paper presents restraints - directly imposed restrictions on players' bodily movements, as a mechanic for bodily play in HCI. While this is a familiar mechanic in non-digital movement-based games, its potential in designing bodily play experiences in HCI has been scarcely explored. Three types of restraints observed in non-digital movement-based games, are explored here: fixating body parts, excluding body parts and depriving/manipulating bodily senses. Then, we investigate the experiential dynamics of restraints as a bodily play mechanic bridging a phenomenological perspective on bodily movement with theories on play. These investigations form the theoretical framework for the subsequent analysis of five digital body game examples. Building on this analysis and theoretical framework, we formulate five design strategies for implementing restraints as a mechanic for bodily play in HCI. We propose restraints as a generative resource for researchers and designers interested in understanding and designing bodily play experiences in HCI.
Latent Space Motion Analysis for Collaborative Intelligence

When the input to a deep neural network (DNN) is a video signal, a sequence of feature tensors is produced at the intermediate layers of the model. If neighboring frames of the input video are related through motion, a natural question is, "what is the relationship between the corresponding feature tensors?" By analyzing the effect of common DNN operations on optical flow, we show that the motion present in each channel of a feature tensor is approximately equal to the scaled version of the input motion. The analysis is validated through experiments utilizing common motion models.
Contrastive Self-Supervised Learning for Wireless Power Control

We propose a new approach for power control in wireless networks using self-supervised learning. We partition a multi-layer perceptron that takes as input the channel matrix and outputs the power control decisions into a backbone and a head, and we show how we can use contrastive learning to pre-train the backbone so that it produces similar embeddings at its output for similar channel matrices and vice versa, where similarity is defined in an information-theoretic sense by identifying the interference links that can be optimally treated as noise. The backbone and the head are then fine-tuned using a limited number of labeled samples. Simulation results show the effectiveness of the proposed approach, demonstrating significant gains over pure supervised learning methods in both sum-throughput and sample efficiency. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Semi-Automated Test-Case Propagation in Fork Ecosystems

Forking provides a flexible and low-cost strategy for developers to adapt an existing project to new requirements, for instance, when addressing different market segments, hardware constraints, or runtime environments. Then, small ecosystems of forked projects are formed, with each project in the ecosystem maintained by a separate team or organization. The software quality of projects in fork ecosystems varies with the resources available as well as team experience, and expertise, especially when the forked projects are maintained independently by teams that are unaware of the evolution of other's forks. Consequently, the quality of forked projects could be improved by reusing test cases as well as code, thereby leveraging community expertise and experience, and commonalities between the projects. We propose a novel technique for recommending and propagating test cases across forked projects. We motivate our idea with a pre-study we conducted to investigate the extent to which test cases are shared or can potentially be reused in a fork ecosystem. We also present the theoretical and practical implications underpinning the proposed idea, together with a research agenda.
Reweighted Dynamic Group Convolution

Computational efficiency of modern deep convolutional networks is always desired in real world applications. Various types of group convolutions have been widely used to reduce the complexity of the networks. Inspired by the previous work, a new reweighted dynamic group convolution (RDGC) structure, including a reweighted pruning module and a survival loss, is proposed in this work for more precise channel pruning. Specifically, a layer-wise pruning rate adjustment strategy is employed with an intra-cluster loss to guide the learning of self-attention implicitly. The proposed model is more capable to select the suitable channels to construct the filter groups. Its effectiveness is demonstrated by extensive experiments on four public datasets, namely CIFAR-10, CIFAR-100 and two fine-grained image datasets (Stanford Dogs and Stanford Cars).
Continuous Sign Language Recognition with Iterative Spatiotemporal Fine-tuning

This paper aims to develop a deep neural network for Continuous Sign Language Recognition (CSLR) with iterative Gloss Recognition (GR) fine-tuning. CSLR has been a popular research field in the last few years and iterative optimization methods are well established. This paper introduces our proposed architecture involving Spatiotemporal feature-extraction model to segment useful “gloss-unit” features and BiLSTM with CTC as a sequence model. Spatiotemporal Feature Extractor is used for both image features extraction and sequence length reduction. To this end, we compare different architectures for feature extraction and sequence model. In addition, we iteratively fine-tune feature extractor on gloss-unit video segments with alignments from the end2end model. During the iterative training, we use novel alignment correction technique, which is based on minimum transformations of Levenshtein distance. All the experiments are conducted on the RWTH-PHOENIX-Weather-2014 dataset.