Reviewerly/exHarmony huggingface dataset:
A Low-Power Keyword Spotting System With High-Order Passive Switched-Capacitor Bandpass Filters for Analog-MFCC Feature Extraction

This paper presents a low-power high-accuracy key-word spotting (KWS) system based on analog passive switched-capacitor (SC) bandpass filters (BPF). The proposed system inno-vatively extracts the Mel-frequency cepstrum coefficient (MFCC) features with all-analog circuits, providing a better spotting performance than the present analog short-time amplitude or energy features under the same condition. At the circuit level, the analog-MFCC extraction includes the low-noise amplifier, BPF, squarer, integrator, and discrete cosine transformer. And thanks to the effective analog-MFCC features, the size of the fully-connected neural network (FCNN) classifier in our KWS system is enormously reduced. A high-order and fully-differential BPF is also proposed, achieving ultra-low power and high dynamic range by combining zero and pole generation stages rather than building stages separately in traditional ways. Fabricated in 0.18- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> CMOS, our filterbank of eight BPFs is measured with a power consumption of 83.2 nW, with a 69.7 dB dynamic range at 5% THD, having advantages over other SC BPFs with similar functions. The total power consumption of our feature extractor is 661.7 nW, achieving an accuracy of 96.6% in two-keyword spotting by an FPGA-based, 15k bit parameter FCNN with a 9.6 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{s}$ </tex-math></inline-formula> latency.
DeepManeuver: Adversarial Test Generation for Trajectory Manipulation of Autonomous Vehicles

Adversarial test generation techniques aim to produce input perturbations that cause a DNN to compute incorrect outputs. For autonomous vehicles driven by a DNN, however, the effect of such perturbations are attenuated by other parts of the system and are less effective as vehicle state evolves. In this work we argue that for adversarial testing perturbations to be effective on autonomous vehicles, they must account for the subtle interplay between the DNN and vehicle states. Building on that insight, we develop DeepManeuver, an automated framework that interleaves adversarial test generation with vehicle trajectory physics simulation. Thus, as the vehicle moves along a trajectory, DeepManeuver enables the refinement of candidate perturbations to: (1) account for changes in the state of the vehicle that may affect how the perturbation is perceived by the system; (2) retain the effect of the perturbation on previous states so that the current state is still reachable and past trajectory is preserved; and (3) result in multi-target maneuvers that require fulfillment of vehicle state sequences (e.g. reaching locations in a road to navigate a tight turn). Our assessment reveals that DeepManeuver can generate perturbations to force maneuvers more effectively and consistently than state-of-the-art techniques by 20.7 percentage points on average. We also show DeepManeuver's effectiveness at disrupting vehicle behavior to achieve multi-target maneuvers with a minimum 52% rate of success.
Correlation-Based Dynamic Allocation Scheme of Fitness Evaluations for Constrained Evolutionary Optimization

Constrained optimization is an active research topic in evolutionary computation. It challenges evolutionary algorithms in allocating fitness evaluations to the minimization of constraint violations and the optimization of objectives. Most existing evolutionary algorithms implement fixed allocation schemes by using non-priority, priority, or priority-complete comparison criteria. This paper argues that different constrained optimization problems should be solved by algorithms with a dynamic allocation scheme, and the key to adjusting the allocation scheme is a judgement on whether the objective optimization or the constraint violation minimization is beneficial to finding the optimum. In this paper, correlations between objectives and constraints are measured for the judgement. Based on the correlations, a dynamic allocation scheme for fitness evaluations is proposed for constrained evolutionary algorithms. The objective priority criterion or the objective priority-complete criterion is dynamically selected to allocate more fitness evaluations to the objective optimization if the optimization of objectives is beneficial. Otherwise, the constraint priority criterion or the constraint priority-complete criterion is selected to allocate fitness evaluations. Experimental results show that algorithms with the selected criterion significantly outperform state-of-the-art algorithms in terms of attaining feasible solutions with better objective values. Furthermore, algorithms with the dynamic allocation scheme have the advantage of consistently finding feasible solutions with better objective values than algorithms based on non-beneficial, fixed, and randomly selected schemes. The results demonstrate the positive impact of the correlation-based dynamic allocation scheme on evolutionary algorithms for solving constrained optimization problems.
A mathematical and neural network-based hybrid technique for detecting the prostate contour from medical image data

Extraction of prostate contour on transrectal ultrasound (TRUS) is significant in prostate cancer diagnosis. With the low-contrast images of TRUS and the existence of imaging artifacts, detecting the prostate accurately from TRUS images becomes more and more difficult. In this paper, we present four strategies to achieve improved segmentation precision on difficult images. First, to achieve the data sequence, we adopt a variant of the principal curve-based algorithm, where a small part of prior point information is utilized as the approximate initialization. Secondly, we devise an evolution neural network to assist in finding the optimal neural network. Thirdly, we train the fractional-order-based neural network using data sequence as input, thereby decreasing the model error and increasing the precision of results during training. Finally, a smooth and explainable mathematical function of organ boundary is represented by the parameters of a fractional-order-based neural network. The performance of our method was evaluated on two clinical datasets with 1,338 TRUS prostate images using Accuracy (ACC), Dice similarity coefficient (DSC), and Jaccard similarity coefficient (OMG). Results show that our model 1) achieves better performance than the state-of-the-art segmentation models; the corresponding ACC, DSC, and OMG of our method are 95.3 ± 2.2 %, 95.9 ± 2.3 %, and 94.9 ± 2.4 %, respectively, and 2) detects unseen or vague regions satisfactorily.
DCMF-AFNet: An anchor-free photovoltaic hot-spot fault detection network based on deformable context transformer and bi-branch multi-level feature fusion

In the process of photovoltaic hot-spot detection by thermal infrared sensors, the fault features cannot be effectively represented due to low pixel ratios and complex environmental interference, which makes it difficult for the detection network to accurately detect hot-spot faults. Therefore, an anchor-free photovoltaic hot-spot fault detection algorithm based on deformable context Transformer and bi-branch multi-level feature fusion is proposed. First, to improve the feature extraction ability of the backbone network for small-scale hot-spot faults, a deformable context Transformer module is constructed. By building an offset network in a multi-headed self-attention mechanism, dynamic and static context information with small-scale fault features can be explored from shallow feature maps. Second, to solve the problem of low target saliency due to the complex background interference, a bi-branch multi-level feature fusion module is designed to aggregate global and local multi-level features in a parallel fusion manner, enabling the detection network to rapidly focus on the fault target region in complex environments. Then, an anchor-free mechanism is introduced, and a dynamic task alignment prediction module is proposed to avoid feature misalignment in the classification and localization tasks and further improve the algorithm detection accuracy. Finally, to verify the superiority of the proposed network, seven detection algorithms are selected for comparison experiments. The experimental results show that the DCMF-AFNet network can accurately detect multi-scale hot-spot targets under various harsh conditions, and the detection accuracy can reach 87.3%.
Non-homogeneous denoising for virtual reality in real-time path tracing rendering

Real time Path-tracing is becoming an important approach for the future of games, digital entertainment, and virtual reality applications that require realism and immersive environments. Among different possible optimizations, denoising Monte Carlo rendered images is necessary in low sampling densities. When dealing with Virtual Reality devices, other possibilities can also be considered, such as foveated rendering techniques. Hence, this work proposes a novel and promising rendering pipeline for denoising a real-time path-traced application in a dual-screen system such as head-mounted display (HMD) devices. Therefore, we leverage characteristics of the foveal vision by computing G-Buffers with the features of the scene and a buffer with the foveated distribution for both left and right screens. Later, we path trace the image within the coordinates buffer generating only a few initial rays per selected pixel, and reconstruct the noisy image output with a novel non-homogeneous denoiser that accounts for the pixel distribution. Our experiments showed that this proposed rendering pipeline could achieve a speedup factor up to 1.35 compared to one without our optimizations.
Assessment of Varied User Representations and XR Environments in Consumer-Grade XR Telemeetings

Audiovisual telemeetings realized through the use of extended reality (XR) can provide remote users with an enhanced sense of immersion and presence, offering more natural and dynamic interaction techniques than conventional videoconferencing tools. However, the majority of existing solutions are inaccessible to the average consumer. Using a portable and affordable hardware setup and a state-of-the-art software solution (Holonet), we conducted a study (N=38) focused on investigating user preferences regarding user representation (avatar or video cutout) in the context of virtual (VR) and augmented (AR) reality. With results indicating that participants preferred avatars over video representation, and VR over AR, this paper highlights the importance of considering user preferences when choosing user representations and XR environments for XR telemeetings. Furthermore, we discuss user preferences for video representations without a visible head-mounted display (HMD), in anticipation of further advancements in HMD-removal software.
BRICS in Creating Digital Educational Environments: Social and Legal Aspects of ‘A New Normal’

The global challenges brought about by the recent pandemic outbreak of COVID-19 have forced many countries to seek out eﬀective digital tools for supporting higher education. The issue of not only the technological complexity of the digitalization of education but also the necessity to develop and standardize the social and legal frameworks of E-education in the BRICS countries has become acute. This article examines how the BRICS countries regulate the digital transformation of higher education and discusses the changes that need to be made to social and legal regulation in order to accommodate the process of digital transformation. The authors’ research leads them to the conclusion that the process of social and legal modiﬁcation of higher education in the aspect of its digital transformation is patchy. On the one hand, it is forced by unpredictable global challenges like the pandemic outbreak. In this aspect, the digital transformation across the BRICS countries tends to be rather international. On the other hand, the absence of common settings and digital standards within the BRICS countries could intensify the digital stratiﬁcation among universities and lead to a decrease in the quality of higher education. The authors propose the establishment of a set of common digital standards that comprise a uniﬁed ecosystem of digital tools and services, a common model for a “digital university,” uniﬁed standards of digital competences and educational services, frameworks and standards of technical modernization as the basis of digital transformation and the creation of a common technical landscape.
Design of oral English teaching model based on multi-modal perception of the Internet of Things and improved conventional neural networks

Oral English instruction plays a pivotal role in educational endeavors. The emergence of online teaching in response to the epidemic has created an urgent demand for a methodology to evaluate and monitor oral English instruction. In the post-epidemic era, distance learning has become indispensable for educational pursuits. Given the distinct teaching modality and approach of oral English instruction, it is imperative to explore an intelligent scoring technique that can effectively oversee the content of English teaching. With this objective in mind, we have devised a scoring approach for oral English instruction based on multi-modal perception utilizing the Internet of Things (IoT). Initially, a trained convolutional neural network (CNN) model is employed to extract and quantify visual information and audio features from the IoT, reducing them to a fixed dimension. Subsequently, an external attention model is proposed to compute spoken English and image characteristics. Lastly, the content of English instruction is classified and graded based on the quantitative attributes of oral dialogue. Our findings illustrate that our scoring model for oral English instruction surpasses others, achieving the highest rankings and an accuracy of 88.8%, outperforming others by more than 2%.
Guiding the retraining of convolutional neural networks against adversarial inputs

When using deep learning models, one of the most critical vulnerabilities is their exposure to adversarial inputs, which can cause wrong decisions (e.g., incorrect classification of an image) with minor perturbations. To address this vulnerability, it becomes necessary to retrain the affected model against adversarial inputs as part of the software testing process. In order to make this process energy efficient, data scientists need support on which are the best guidance metrics for reducing the adversarial inputs to create and use during testing, as well as optimal dataset configurations.We examined six guidance metrics for retraining deep learning models, specifically with convolutional neural network architecture, and three retraining configurations. Our goal is to improve the convolutional neural networks against the attack of adversarial inputs with regard to the accuracy, resource utilization and execution time from the point of view of a data scientist in the context of image classification.We conducted an empirical study using five datasets for image classification. We explore: (a) the accuracy, resource utilization, and execution time of retraining convolutional neural networks with the guidance of six different guidance metrics (neuron coverage, likelihood-based surprise adequacy, distance-based surprise adequacy, DeepGini, softmax entropy and random), (b) the accuracy and resource utilization of retraining convolutional neural networks with three different configurations (one-step adversarial retraining, adversarial retraining and adversarial fine-tuning).We reveal that adversarial retraining from original model weights, and by ordering with uncertainty metrics, gives the best model w.r.t. accuracy, resource utilization, and execution time.Although more studies are necessary, we recommend data scientists use the above configuration and metrics to deal with the vulnerability to adversarial inputs of deep learning models, as they can improve their models against adversarial inputs without using many inputs and without creating numerous adversarial inputs. We also show that dataset size has an important impact on the results.
Security Model for Cloud Computing: Case Report of Organizational Vulnerability

Cloud computing services have quickly become a mainstay in business, leading to success as a business model and numerous advantages from the client’s point of view. Ease and amount of storage and computational services provisions were not previously accessible or affordable. However, parallel to this explosion has been significant security risk concerns. Thus, it is important to understand and define these security risks in a cybersecurity framework. This paper will take a case study approach to approach past security risks and propose a model that can be followed by organizations to eliminate the risk of Cloud-related cyberattacks. The main aims of this systematic literature review (SLR) are to (1) address security risks/vulnerabilities that can target cloud environments, (2) define tools that can be used by organizations to defend their cloud environment against those security risks/vulnerabilities, and (3) analyze case studies of significant cyberattacks and provide recommendations for organizations to mitigate such cyberattacks. This paper will propose a novel cloud cybersecurity model from a two-pronged offensive and defensive perspective for implementation by organizations to enhance their security infrastructure.
Evaluation and improvement of student learning experience in the post-COVID world: A lean six-sigma DMAIC study

In 2020, the COVID-19 pandemic necessitated a shift to remote work-from-home (WFH) setups, including in the education sector. This transition had a significant impact on the interaction between students and instructors. To address this, our study aims to investigate the effects of the sudden transition to online learning on teaching methodology and to propose improvements to enhance its quality. We have developed a scoring system to evaluate teaching quality in the post-COVID-19 world. The scoring function incorporates various metrics, including students’ performance, sentiment towards the course (course material, teaching method, communication, etc.), feedback scores for weekly lectures, and students’ retention scores for recorded/live lecture videos. Following the Lean Six Sigma (LSS) procedure (Define, Measure, Analyze, Improve, and Control—DMAIC), we assessed the overall quality of online courses. The undergraduate courses demonstrated an increase in overall score from 86.67% during the online transition to 90.0% after implementing the suggested improvements. For graduate courses, the initial face-to-face lecture score was 55.81%, which dropped to 50.28% during the first online transition. However, after a year, the score improved to 61.59%, indicating successful improvement efforts. Upon careful analysis of the data, this paper provides suggestions to enhance students’ online learning experience during situations similar to the COVID-19 pandemic. The outcomes of the study aim to improve the quality of online learning experiences for students.
A wearable system for sign language recognition enabled by a convolutional neural network

Sign language recognition is of great significance to connect the hearing/speech impaired and non-sign language communities. Compared to isolated word recognition, sentence recognition is more practical in real-world scenarios, but is also more complicated because continuous, high-quality sign data with distinct features must be collected and isolated signs must be identified with high accuracy. Here, we propose a wearable sign language recognition system enabled by a convolutional neural network (CNN) that integrates stretchable strain sensors and inertial measurement units attached to the body to perceive hand postures and movement trajectories. Forty-eight Chinese sign language words commonly used in daily life were collected and used to train the CNN model, and an isolated sign language word recognition accuracy of 95.85% was achieved. For sentence-level sign language recognition, we proposed a method that combines multiple sliding windows and uses correlation analysis to improve the CNN recognition performance, achieving a correct rate of 84% for 50 sign language sentence samples, showing good extendibility.
Learning high-level robotic manipulation actions with visual predictive model

Abstract Learning visual predictive models has great potential for real-world robot manipulations. Visual predictive models serve as a model of real-world dynamics to comprehend the interactions between the robot and objects. However, prior works in the literature have focused mainly on low-level elementary robot actions, which typically result in lengthy, inefficient, and highly complex robot manipulation. In contrast, humans usually employ top–down thinking of high-level actions rather than bottom–up stacking of low-level ones. To address this limitation, we present a novel formulation for robot manipulation that can be accomplished by pick-and-place, a commonly applied high-level robot action, through grasping. We propose a novel visual predictive model that combines an action decomposer and a video prediction network to learn the intrinsic semantic information of high-level actions. Experiments show that our model can accurately predict the object dynamics (i.e., the object movements under robot manipulation) while trained directly on observations of high-level pick-and-place actions. We also demonstrate that, together with a sampling-based planner, our model achieves a higher success rate using high-level actions on a variety of real robot manipulation tasks.
Logical connectives for two-state semantics

AbstractIn this note we define a new two-state logic, called 2neg-logic (logic with two negations) and compare its expressive power with other two-state logics, such as the logic of here-and-there, contingency logic, and bi-state logic.Keywords: Logic of ‘here-and-there’equilibrium logicbi-state logic AcknowledgmentsThe authors wish to thank the anonymous referees of this work for their helpful comments. In particular, the translation presented in Section 2.2 is due to their suggestion. Beyond being simpler than the reduction originally proposed by the authors, it can also help to understand the differences between 2neg-logic and the logic of ‘here-and-there’.Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 This mapping will be recalled in Section 3.3.
Machine Learning Algorithms in Identifying Balanced Diet Plan for Healthy Life style

The present generation of all ages is terribly facing the challenges of obesity in recent times. The people suffering from this disorder practice different diet plans for weight reduction without considering the balanced proportion of nutrients in their diet. This paper aims in highlighting the ill effects of unbalanced diet plans and proposes a machine learning (ML) model based on support vector machine to make decisions on the balanced nature of the diet. The efficiency of the proposed ML model is compared with other ML algorithms. The accuracy results of the proposed model are more convincing in comparison with other ML algorithms. The proposed ML model is applied to deterministic type of secondary data sets and this shall be extended by applying to fuzzy data sets. This research work applies the algorithms of machine learning to health-based decision-making systems