Reviewerly/exHarmony huggingface dataset:
CARL-D: A vision benchmark suite and large scale dataset for vehicle detection and scene segmentation

Vision-based object detection and scene understanding are becoming key features of environment perception and autonomous driving. In the past couple of years, numerous large-scale datasets for visual object detection and semantic understanding have been released which has enormously benefited the environment perception in self-driving cars. However, these datasets like Kitti and Cityscapes only focus on well organized and urban road traffic scenarios of European countries while ignoring the dense and unpattern traffic conditions of sub-continent countries like Pakistan, India, Bangladesh, and Sri Lanka. Consequently, the environment perception system developed on these datasets cannot efficiently assist self-driving cars in traffic scenarios of sub-continent countries. To this end, we present CARL-D, a large-scale dataset and benchmark suite for develop 2D object detection and instance/pixel-level segmentation methods for self-driving cars. CARL-D comprises large-scale stereo vision-based driving videos captured from more than 100 cities of Pakistan, including motorways, dense and unpattern traffic scenarios of urban, rural, and hilly areas. As a benchmark selection, 15,000 suitable images are labeled for 2D object detection and recognition. Whereas semantic segmentation benchmark contains 2500 images with pixel-level high-quality fine annotations and 5000 coarse-annotated images which could help in enabling the deep neural networks to leverage the weakly labeled data. Alongside the dataset, we also present transfer learning-based 2D vehicle detection and scene segmentation methods to evaluate the performance of existing state-of-the-art deep neural networks on our dataset. Lastly, an extensive experimental evaluation along with the comparative study has been carried out which demonstrates the upper edge of our dataset in terms of interclass-diversity, scene-variability, and annotations richness. The proposed benchmark suite is available at https://carl-dataset.github.io/index/.
Cat-Squirrel Optimization Algorithm for VM Migration in a Cloud Computing Platform

This paper introduces an approach for the VM migration based on optimization algorithm, named CS in cloud. The provider to be selected is carried out with the usage of multiple constraints, like delay, bandwidth, cost, and load. Subsequently, the effective searching criteria are computed for finding the optimal service on the basis of fitness constraints. The searching criteria are formulated as optimization problems, which are tackled using CS. The proposed CS is designed by integrating CSO with the SSA such that the fitness function is evaluated for the optimal VM migration by considering several parameters, such as delay, cost, bandwidth, and load. Thus, the cloud manager will perform the migration of VM in cloud based on proposed CS-based VM migration approach. The performance of the CS-based VM migration is evaluated in terms of delay, cost, and load. The proposed CS-based VM migration method achieves the minimal delay of 0.146, minimal cost of 0.052, and the minimal load of 0.182.
Use of Robotic Platforms as a Tool to Support STEM and Physical Education in Developed Countries: A Descriptive Analysis

The lack of interest of children at school is one of the biggest problems that Mexican education faces. Two important factors causing this lack of interest are the predominant methodology used in Mexican schools and the technology as a barrier for attention. The methodology that institutions have followed has become an issue because of its very traditional approach, with the professor giving all the theoretical material to the students while they listen and memorize the contents, and, if we add the issue of the growing access to technological devices for students, children carrying a phone are more likely to be distracted. This study aims to integrate technology through assistive robots as a beneficial tool for educators, in order to improve the attention span of students by making the learning process in multiple areas of the Mexican curriculum more dynamic, therefore obtaining better results. To prove this, four different approaches were implemented; three in elementary schools and one in higher education: the LEGO® robotic kit and the NAO robot for STEM (science, technology, engineering, and mathematics) teaching, the NAO robot for physical education (PE), and the PhantomX Hexapod, respectively. Each of these technological approaches was applied by considering both control and experimental groups, in order to compare the data and provide conclusions. Finally, this study proves that the attention span is indeed improved as a result of implementing robotic platforms during the teaching process, allowing the children to become more motivated during their PE class and become more proactive and retain more information during their STEM classes.
Assessing the pre-conditions for the pedagogical use of digital tools in the Nigerian higher education sector

Currently, there is a burgeoning interest in digitalization as evidenced in extant literature. Nevertheless, the effect, based on teachers’ own perspectives, of the pedagogical use of digital technologies on learning outcomes in the higher education sector has been under-investigated. Thus, this paper aims to investigate the pre-conditions for the effective adoption of social media tools in the Nigerian higher education sector and to assess the impact of the adoption on specific learning outcomes. A multi-criteria decision-making (MCDM) methodology was proposed for study analysis, aided by views of experts with sufficient teaching experience in Nigerian business school programs. The results indicate that adequate budgetary allocations, technical competence, a sufficient level of privacy, and an effective government regulatory framework are the most important of the investigated pre-conditions. Additionally, the pedagogical use of social media in business school programs is more strongly associated with learning outcomes such as professionalism and strategic thinking, emotional intelligence, and social maturity. Hence, the article offers guidance to decision-makers in the higher education sector on how to actualize the successful adoption of social media for pedagogical use and build effective business strategies at various levels of the digitalization process.
An Advancing Temporal Convolutional Network for 5G Latency Services via Automatic Modulation Recognition

Automatic modulation recognition (AMR) has received significant attention since its decisive factor for modern non-cooperative communication systems. Meanwhile, the existing works on deep learning technique achieve exceptional accuracy; however, these works dissatisfy real-time requirements for 5G low-latency services. To remedy this flaw, this brief proposes a low-latency AMR method by applying temporal convolutional network (TCN). Furthermore, the principal component analysis (PCA)-based TCN and uniform subsampling-based TCN methods are leveraged to further alleviate the computation complexity and render real-time TCN viable. Experimental results demonstrate that the proposed method can achieve lower complexity and superior recognition accuracy than existing works and pave the way for 5G low-latency services.
Digital masks: screens, selves and symbolic hygiene in online higher education

Given the central role of digital devices and screens in academic work, their use and our relationship to them are under-theorised in mainstream research into digital education. The Covid-19 pandemic, however, rendered the use of digital screens central to life in 'lockdowns'. This paper will consider the relationships between digital screens and anti-epidemic face masks, considering these artefacts in terms of functionality, academic subjectivities and epistemic practices, drawing on sociomaterial perspectives, Goffman's categories of lecturing self, and the history of anti-epidemic mask-wearing. I illustrate this with a vignette of teaching via digital screens, given by a member of faculty in an interview study exploring the impact of the lockdown on university staff. It will conclude that the digital screen may be viewed as a 'digital mask'; carrying out a practical function, but also performing an ideology of hygiene and reason. The implications for digital higher education post-pandemic are discussed.
Self-supervised Test-time Adaptation on Video Data

In typical computer vision problems revolving around video data, pre-trained models are simply evaluated at test time, without adaptation. This general approach clearly cannot capture the shifts that will likely arise between the distributions from which training and test data have been sampled. Adapting a pre-trained model to a new video en-countered at test time could be essential to avoid the potentially catastrophic effects of such shifts. However, given the inherent impossibility of labeling data only available at test-time, traditional "fine-tuning" techniques cannot be lever-aged in this highly practical scenario. This paper explores whether the recent progress in test-time adaptation in the image domain and self-supervised learning can be lever-aged to adapt a model to previously unseen and unlabelled videos presenting both mild (but arbitrary) and severe covariate shifts. In our experiments, we show that test-time adaptation approaches applied to self-supervised methods are always beneficial, but also that the extent of their effectiveness largely depends on the specific combination of the algorithms used for adaptation and self-supervision, and also on the type of covariate shift taking place.
Listening to children voices in early stages of new product development through co-creation – Creative focus group and online platform

To tackle current nutritional issues like obesity, it could be valuable to involve children in the development of healthy food products that they will actively chose and enjoy. The aims of the present exploratory study were (i) to assess a methodology for early-stage idea generation through co-creation, for the development of healthy snacks with pre-adolescents, and (ii) to compare two settings, creative focus groups (CFG) and an online community (ONL). Three steps were defined to allow the gradual exploration of the topic and mutual learning throughout the process: (1) Show &Tell: photo taking and -elicitation to understand what children ate; (2) Reflect: a sorting task of the pictures to discuss and reflect on snacking practices (3) Create: an idea generation step, in which a newspaper article describing an idea for a new healthy snack was created. To increase engagement and creativity, gamification strategies were used. Our results demonstrated that children (preadolescents) can create new food product ideas with the proposed process, using enabling and creative techniques. In the CFG the trained moderator could steer the group to the co-creation goal. The setting facilitated teamwork and group learning, collaborative ideas considering preferences of peers and produced a few detailed and mostly actionable ideas. In the ONL less control over the process was possible. The setting produced many ideas varying in the degree of detail and actionability focusing on individual preferences. The feedback and observations from our study, particularly in the CFG setting, implied that the creative approach was highly engaging for participants. Further research is necessary to assess the potential of initial ideas developed by pre-adolescents.
Self-Guidance: Improve Deep Neural Network Generalization via Knowledge Distillation

We present Self-Guidance, a simple way to train deep neural networks via knowledge distillation. The basic idea is to train sub-network to match the prediction of the full network, so-called "Self-Guidance". Under the "teacher-student" framework, we construct both teacher and student within the same target network. Student network is the sub-networks that randomly skip some portions of the full network. The teacher network is the full network, can be considered as the ensemble of all possible student networks. The training process is performed in a closed-loop: (1) Forward prediction contains two passes that generate student and teacher predictions. (2) Backward distillation allows knowledge transfer from the teacher back to students. Comprehensive evaluations show that our approach improves the generalization ability of deep neural networks to a significant margin. The results prove our superior performance in both image classification on CIFAR10, CIFAR100, and facial expression recognition on FER-2013 and RAF.
Reinforcement Learning-Based Reactive Obstacle Avoidance Method for Redundant Manipulators

Redundant manipulators are widely used in fields such as human-robot collaboration due to their good flexibility. To ensure efficiency and safety, the manipulator is required to avoid obstacles while tracking a desired trajectory in many tasks. Conventional methods for obstacle avoidance of redundant manipulators may encounter joint singularity or exceed joint position limits while tracking the desired trajectory. By integrating deep reinforcement learning into the gradient projection method, a reactive obstacle avoidance method for redundant manipulators is proposed. We establish a general DRL framework for obstacle avoidance, and then a reinforcement learning agent is applied to learn motion in the null space of the redundant manipulator Jacobian matrix. The reward function of reinforcement learning is redesigned to handle multiple constraints automatically. Specifically, the manipulability index is introduced into the reward function, and thus the manipulator can maintain high manipulability to avoid joint singularity while executing tasks. To show the effectiveness of the proposed method, the simulation of 4 degrees of planar manipulator freedom is given. Compared with the gradient projection method, the proposed method outperforms in a success rate of obstacles avoidance, average manipulability, and time efficiency.
Deep Learning: Parameter Optimization Using Proposed Novel Hybrid Bees Bayesian Convolutional Neural Network

Deep Learning (DL) is a type of machine learning used to model big data to extract complex relationship as it has the advantage of automatic feature extraction. This paper presents a review on DL showing all its network topologies along with their advantages, limitations, and applications. The most popular Deep Neural Network (DNN) is called a Convolutional Neural Network (CNN), the review found that the most important issue is designing better CNN topology, which needs to be addressed to improve CNN performance further. This paper addresses this problem by proposing a novel nature inspired hybrid algorithm that combines the Bees Algorithm (BA), which is known to mimic the behavior of honey bees, with Bayesian Optimization (BO) in order to increase the overall performance of CNN, which is referred to as BA-BO-CNN. Applying the hybrid algorithm on Cifar10DataDir benchmark image data yielded an increase in the validation accuracy from 80.72% to 82.22%, while applying it on digits datasets showed the same accuracy as the existing original CNN and BO-CNN, but with an improvement in the computational time by 3 min and 12 s reduction, and finally applying it on concrete cracks images produced almost similar results to existing algorithms.
The efficiency of fractal techniques in geochemical anomaly delineation within BLEG and &lt;180 μm stream sediments in Western Turkey

In regional exploration for Au mineralization using stream sediment geochemistry, multielement analysis following cyanide leaching of bulk samples or aqua regia digestion of the <180 μm fraction are the two most common sampling and analytical methods. Using data from extensive regional stream sediment and rock chip surveys in Western Turkey a comparison is made between the efficiency of fractal modelling of various spatial and frequency-based statistical methods to isolate patterns or populations related to known mineral deposits. This includes different combinations of pre-processing raw data, such as logistic-transformations and principal component analysis, together with fractal, U-spatial statistics and singularity index modelling. Due to variations in regional geology and the effects of a substantial number of base and precious metal in the region, all approaches indicate the presence of multiple geochemical populations with clearly defined thresholds. The efficiency in detecting known deposits is higher for spatially based methods than for simpler frequency distribution-based techniques, especially if weak geochemical anomalies are excluded to limit the risk of generating false positives. Maximum efficiency (between 70 and 80%) in linking the spatial distribution of anomalous geochemical populations with known Au ± Ag mineral deposits, in both in BLEG and the <180 μm fraction, was obtained using classification based on singularity indices, or logistical-transformed data with fractal analysis or U-spatial statistics. The results demonstrate the advantage of spatially based anomaly detection methods applied to multivariate data over simpler frequency distribution methods applied to univariate data. • Comparison between various anomaly-detection applied to regional geochemical data • Spatial methods generally more efficient than frequency-based techniques • Singularity index mapping the preferred method for BLEG and <180 μm stream sediments
Zero trust in edge computing environment: a blockchain based practical scheme

Edge computing offloads the data processing capacity to the user side, provides flexible and efficient computing services for the development of smart city, and brings many security challenges. Aiming at the problems of fuzzy boundary security protection and dynamic identity authentication in the edge computing environment in smart city, the zero trust architecture based on blockchain is studied, and a digital identity model and dynamic authentication scheme of edge computing nodes based on distributed ledger are proposed. Firstly, a digital identity model of two-way authentication between edge computing node and sensing terminal is established to realize fine-grained authorization and access control in edge computing. Secondly, based on the identity data and behavior log bookkeeping on the chain, the quantification of trust value, trust transmission and update are realized, and the traceability of security events is improved. Finally, based on the improved RAFT consensus algorithm, the multi-party consensus and consistency accounting in the authentication process are realized. Simulation results show that this scheme can meet the requirements of zero trust verification in edge computing environment, and has good efficiency and robustness.
On Efficiency Enhancement of SHA-3 for FPGA-Based Multimodal Biometric Authentication

Synchronized padder block and a compact-dynamic round constant (RC) generator to achieve highly efficient Keccak architecture are proposed in this work. The proposed design yields high security with an option of 1024 bits as capacity “ <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$c$ </tex-math></inline-formula> ,” while limiting the round count to less than 12 for the base design. Fusion schemes are adapted as a cost-effective approach in the base design to explore and arrive at the best efficient architecture for biometric access control application. The hybrid architecture designed as a pipeline structure with ≤2 stages eliminated the need for on-chip digital signal processor (DSP) and block random access memory (BRAM) slices. Though fusion schemes might lead to the increase in area, the minimized structural RC design coupled with a low cost architecture, ensures to achieve moderately low area. Among the proposed architectures, dual round function (Dual-f) design performed better in terms of throughput and operating frequency. Thus, when implemented, Dual-f achieved the highest efficiency of all with 12.85 Mb/s/slices and 15.11 Mb/s/slices on Virtex-5 and Virtex-7 devices, respectively. The miniature and high-speed features of the Dual-f Keccak design are found to be adequate for multimodal biometric authentication applications.
Deep Online Fused Video Stabilization

We present a deep neural network (DNN) that uses both sensor data (gyroscope) and image content (optical flow) to stabilize videos through unsupervised learning. The network fuses optical flow with real/virtual camera pose histories into a joint motion representation. Next, the LSTM cell infers the new virtual camera pose, which is used to generate a warping grid that stabilizes the video frames. We adopt a relative motion representation as well as a multi-stage training strategy to optimize our model without any supervision. To the best of our knowledge, this is the first DNN solution that adopts both sensor data and image content for video stabilization. We validate the proposed framework through ablation studies and demonstrate that the proposed method outperforms the state-of-art alternative solutions via quantitative evaluations and a user study. Check out our video results, code and dataset at our website.
Verifiable Fuzzy Multi-keyword Search over Encrypted Data with Adaptive Security

To ensure the security of outsourced data without affecting data availability, one can use Symmetric Searchable Encryption (SSE) to achieve search over encrypted data. Considering that query users may search with misspelled words, the fuzzy search should be supported. However, conventional privacy-preserving fuzzy multi-keyword search schemes are incapable of achieving the result verification and adaptive security. To solve the above challenging issues, in this paper we propose a Verifiable Fuzzy multi-keyword Search scheme with Adaptive security (VFSA). VFSA first employs the locality sensitive hashing to hash the misspelled and correct keywords to the same positions, then designs a twin Bloom filter for each document to store and mask all keywords contained in the document, next constructs an index tree based on the graph-based keyword partition algorithm to achieve adaptive sublinear retrieval, finally combines the Merkle hash tree structure with the adapted multiset accumulator to check the correctness and completeness of search results. Our formal security analysis shows that VFSA is secure under the IND-CKA2 model and achieves query authentication. Our empirical experiments using the real-world dataset demonstrate the practicality of VFSA.
Omnidirectional Galvanic Vestibular Stimulation in Virtual Reality

In this paper we propose omnidirectional galvanic vestibular stimulation (GVS) to mitigate cybersickness in virtual reality applications. One of the most accepted theories indicates that Cybersickness is caused by the visually induced impression of ego motion while physically remaining at rest. As a result of this sensory mismatch, people associate negative symptoms with VR and sometimes avoid the technology altogether. To reconcile the two contradicting sensory perceptions, we investigate GVS to stimulate the vestibular canals behind our ears with low-current electrical signals that are specifically attuned to the visually displayed camera motion. We describe how to calibrate and generate the appropriate GVS signals in real-time for pre-recorded omnidirectional videos exhibiting ego-motion in all three spatial directions. For validation, we conduct an experiment presenting real-world 360° videos shot from a moving first-person perspective in a VR head-mounted display. Our findings indicate that GVS is able to significantly reduce discomfort for cybersickness-susceptible VR users, creating a deeper and more enjoyable immersive experience for many people.