Reviewerly/exHarmony huggingface dataset:
Achieving sustainable e-health with information and communication technologies in Nigerian rural communities

Modern information and communication technologies (ICTs) may be used to improve health care. Residents in rural areas appear to be at risk because they may still be plagued by diseases that might have been more easily prevented or managed if they have easy access to online e-health information provided by modern ICTs. Good health is vital for meaningful progress. Achieving good health is the third in the United Nation’s Sustainable Development Goals. However, this goal may not be achieved among the people living in some rural areas because they do not have quick and adequate health information online. A field survey was carried out to sample the opinions of 621 respondents from 14 rural communities in Ota, Western Nigeria, on their access to online services. Since they have little or no access to online health information, their active participation and contribution to achieving sustainable e-health are almost non-existent. This study recommends that government and non-governmental organizations take urgent steps to increase residents’ access to the internet to facilitate their adoption of e-health and, by extension, SDG Goal Three’s speedy achievement.
Term Quantization: Furthering Quantization at Run Time

We present a novel technique, called Term Quantization (TQ), for furthering quantization at run time for improved computational efficiency of deep neural networks (DNNs) already quantized with conventional quantization methods. TQ operates on power-of-two terms in expressions of values. In computing a dot-product computation, TQ dynamically selects a fixed number of largest terms to use from values of the two vectors. By exploiting weight and data distributions typically present in DNNs, TQ has a minimal impact on DNN model performance (e.g., accuracy or perplexity). We use TQ to facilitate tightly synchronized processor arrays, such as systolic arrays, for efficient parallel processing. We evaluate TQ on an MLP for MNIST, multiple CNNs for ImageNet and an LSTM for Wikitext-2. We demonstrate significant reductions in inference computation costs (between 3-10×) compared to conventional uniform quantization for the same level of model performance.
A Video Based Method of Occlusion Vehicle Completion

Aiming at the problem of vehicle partial occlusion in the road monitoring image, the idea of vehicle partial occlusion completion based on depth learning is proposed. Using the characteristics that the appearance of the vehicle is basically unchanged in a short period of time, the same vehicle image that is not occluded in the same equipment or adjacent monitoring video is found, and the image information is extracted to complete the occluded vehicle image. This method mainly involves two key technologies, one is vehicle recognition technology, the other is image smoothing technology. In this paper, based on the mature vehicle recognition technology and image smoothing technology, some improvements are made to effectively solve the technical problems such as occluded vehicle recognition, image smoothing with angle adjustment and so on.
Radar Signal Intra-Pulse Modulation Recognition Based on Contour Extraction

Recent evidence suggests that several methods are focused on changing the convolution neural network (CNN) structure to improve the recognition rate of radar signal intra-pulse modulation. As a result, the CNN structure is becoming significantly complex with poor interpretability. Aiming at this problem, we proposed a recognition method based on contour extraction. The signal binary map is replaced by the contour of signal sending to the CNN for recognition. Because the input is limited to the signal contour, CNN only considers the contour feature extraction, without complex structures. Simulations show that the method can effectively identify five kinds of radar signals when the SNR > 2dB. When the SNR > 4dB, the method has a recognition rate of 2.6% higher than the method without contour extraction.
Design and Internet of Things Development of Network Teaching Resource Base System for Educational Technology

Abstract With the deepening of the application of Internet of Things in education, its role in promoting education and teaching has become increasingly obvious, but the corresponding is that the application of Internet of Things in education has remained in the experimental stage, and can not be applied on a large scale. Therefore, according to the characteristics of the educational application of the Internet of Things, this paper designs and completes a new educational resource database management system of the Internet of Things. The system consists of three parts: the core layer, the access layer and the performance layer. It has good expansibility and shields the complexity of the Internet of Things technology, and meets the needs of ordinary teachers and students to rapidly develop the educational application of the Internet of Things. The research results show that through the analysis of the status quo of network teaching in colleges and universities, a web-based intra-campus network teaching system software platform is established to solve the problems in higher education. At the same time, the experimental system also helps the experimenter to deeply study the basic technology of the Internet of Things.
Privacy Protection Strategy of Vehicle-to-Grid Network Based on Consortium Blockchain and Attribute-based Signature

Abstract V2G(vehicle-to-grid) is an integral part of the smart grid. To deploy a V2G network on a large scale, it is necessary to solve network security and trustworthiness and privacy protection of electric vehicle users (EV). Therefore, this paper proposes a privacy protection strategy for V2G networks based on consortium blockchain and attribute-based signature and designs an overall implementation scheme including system initialization, EV registration, anonymous authentication, optimized scheduling, local services, block construction and consensus mechanism. The strategy also uses the K-anonymity to protect the privacy of the EV’s geographic location, making it well compatible with charging and discharging services. Finally, security and performance analysis proved the feasibility of design scheme.
Adapting to Unknown Conditions in Learning-Based Mobile Sensing

Many applications utilize sensors on mobile devices and apply deep learning for diverse applications. However, they have rarely enjoyed mainstream adoption due to many different <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">individual conditions</i> users encounter. Individual conditions are characterized by users’ unique behaviors and different devices they carry, which collectively make sensor inputs different. It is impractical to train countless individual conditions beforehand and we thus argue meta-learning is a great approach in solving this problem. We present <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MetaSense</i> that leverages “seen” conditions in training data to adapt to an “unseen” condition (i.e., the target user). Specifically, we design a meta-learning framework that learns “how to adapt” to the target via iterative training sessions of adaptation. MetaSense requires very few training examples from the target (e.g., one or two) and thus requires minimal user effort. In addition, we propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">similar condition detector</i> (SCD) that identifies when the unseen condition has similar characteristics to seen conditions and leverages this hint to further improve the accuracy. Our evaluation with 10 different datasets shows that MetaSense improves the accuracy of state-of-the-art transfer learning and meta learning methods by 15 and 11 percent, respectively. Furthermore, our SCD achieves additional accuracy improvement (e.g., 15 percent for human activity recognition).
Statistical parameter estimation of multimode multiphoton-subtracted thermal states of light

Thermal states of light are widely used in quantum optics for testing of various quantum phenomena. Particularly, they can be utilized for characterization of photon creation and photon annihilation operations. During the last decade the problem of photon subtraction from multimode quantum states has become of much significance. Therefore in this paper we present a technique for statistical parameter estimation of multimode multiphoton-subtracted thermal states of light, which can be used for the multimode photon annihilation test.
Research on the Innovation of College Basketball Information Teaching Mode under the Background of Internet

Abstract Currently, in most college physical education curriculum, college basketball teaching is based on students’ interests, but different students have different interests and hobbies, which leads to some students’ resistance to basketball knowledge and lack of learning interest in the actual teaching process. In recent years, the rapid development of Internet technology has made it widely used in all fields of life and work, which leads to the reform of traditional basketball teaching. On this basis, combined with the current teaching mode of colleges and universities in China, the concept of network teaching is introduced into basketball teaching. In this paper, the experimental class and the control class were established. The experimental class adopts the college basketball information teaching under the background of Internet, while the control class adopts the existing college basketball teaching mode. After the end of the semester, comparative analysis shows that the college basketball class designed in this paper can better play the enthusiasm of students and improve students’ interest in basketball class.
A local method for identifying causal relations under Markov equivalence

Causality is important for designing interpretable and robust methods in artificial intelligence research. We propose a local approach to identify whether a variable is a cause of a given target under the framework of causal graphical models of directed acyclic graphs (DAGs). In general, the causal relation between two variables may not be identifiable from observational data as many causal DAGs encoding different causal relations are Markov equivalent. In this paper, we first introduce a sufficient and necessary graphical condition to check the existence of a causal path from a variable to a target in every Markov equivalent DAG. Next, we provide local criteria for identifying whether a variable is a cause/non-cause of a target based only on the local structure instead of the entire graph. Finally, we propose a local learning algorithm for this causal query via learning the local structure of the variable and some additional statistical independence tests related to the target. Simulation studies show that our local algorithm is efficient and effective, compared with other state-of-art methods.
Learning Mathematics in Madrasah Aliyah Muhammadiyah 2 Banjarmasin during the covid-19 pandemic era

Abstract In 2020, the world is being plagued by the Covid-19 epidemic, Indonesians are required to follow health protocols established by the central government and WHO. One of the protocols for the world of education is not held face-to-face teaching to avoid more transmission. Instead, learning is done through online media and students learn from their homes. The purpose of this article was to describe the learning of mathematics that took place at Madrasah Aliyah Muhammadiyah 2 Banjarmasin during the Covid-19 pandemic era, difficulties experienced by teachers and how to overcome them and their impact on student learning outcomes. The research method used was quantitative and qualitative where data is taken from students’ scores before and during the co-19 pandemic as well as questionnaires and interviews with mathematics teachers. To see differences in student learning outcomes before and when a pandemic, the statistical test (t-test) was used. The results obtained were the teacher used several learning methods with several different software, including using Quizizz for interactive quizzes to reduce student boredom in online learning. Student learning outcomes had improved, this was because when students took exams from home students did not experience anxiety because they were not supervised directly by the exam supervisor, also before the exam was carried out students had been guided by parents and students also learnt independently through online media to get information added. In addition, it was also described the difficulties experienced by teachers during carrying out online learning as well as the ways to overcome them along with the results obtained.
Learning Consistency Pursued Correlation Filters for Real-Time UAV Tracking

Correlation filter (CF)-based methods have demonstrated exceptional performance in visual object tracking for unmanned aerial vehicle (UAV) applications, but suffer from the undesirable boundary effect. To solve this issue, spatially regularized correlation filters (SRDCF) proposes the spatial regularization to penalize filter coefficients, thereby significantly improving the tracking performance. However, the temporal information hidden in the response maps is not considered in SRDCF, which limits the discriminative power and the robustness for accurate tracking. This work proposes a novel approach with dynamic consistency pursued correlation filters, i.e., the CPCF tracker. Specifically, through a correlation operation between adjacent response maps, a practical consistency map is generated to represent the consistency level across frames. By minimizing the difference between the practical and the scheduled ideal consistency map, the consistency level is constrained to maintain temporal smoothness, and rich temporal information contained in response maps is introduced. Besides, a dynamic constraint strategy is proposed to further improve the adaptability of the proposed tracker in complex situations. Comprehensive experiments are conducted on three challenging UAV benchmarks, i.e., UAV123@10FPS, UAVDT, and DTB70. Based on the experimental results, the proposed tracker favorably surpasses the other 25 state-of-the-art trackers with real-time running speed (~43FPS) on a single CPU.
Efficient revocable identity-based encryption with short public parameters

Revocation functionality is vital to real-world cryptographic systems for managing their reliability. In the context of identity-based encryption (IBE), Boldyreva, Goyal, and Kumar (ACM CCS 2008) first showed an efficient revocation method for IBE, and such an IBE scheme with the scalable revocation method is called revocable IBE (RIBE). Seo and Emura (PKC 2013) introduced a new security notion, called decryption key exposure resistance (DKER), which is a desirable security notion for RIBE. However, all existing RIBE schemes that achieve adaptive security with DKER require long public parameters or composite-order bilinear groups. In this paper, we first show an RIBE scheme that (1) satisfies adaptive security; (2) achieves DKER; (3) realizes constant-size public parameters; and (4) is constructed over prime-order bilinear groups. Our core technique relies on Seo and Emura's one (PKC 2013), which transform the Waters IBE (EUROCRYPT 2005) to the corresponding RIBE scheme. Specifically, we construct an IBE scheme that satisfies constant-size public parameters over prime-order groups and some requirements for the Seo-Emura technique, and then transform the IBE scheme to an RIBE scheme. We also discuss how to extend the proposed RIBE scheme to a chosen-ciphertext secure one and server-aided one (ESORICS 2015).
Sample Contribution Pattern Based Big Data Mining Optimization Algorithms

As is the case in many big data mining scenarios with a large scale of samples, the heavy computation cost hinders the application of machine learning, which has to iteratively compute by passing over the whole dataset without considering the roles of different samples in training computation.However, we argue that most of the samples dominating computation resources contribute little to the gradient-based model update, particularly when the model is close to convergence.We define this observation as the Sample Contribution Pattern (SCP) in machine learning.This paper proposes two approaches to exploit SCP by detecting gradient characteristics and triggering the reuse of outdated gradients.In particular, this paper reports research results in (1) the definition and description of SCP to reveal an intrinsic gradient contribution pattern of different samples; (2) a novel SCP-based optimizing algorithm (SCPOA) that outperforms alternative tested algorithms in terms of computation overhead; (3) a variant of SCPOA that incorporates discarding-recovering mechanisms to carefully tradeoff between model accuracy and computation cost; (4) the implementation and evaluation of two algorithms based on popular distributed big data mining platforms running typical sample-sets; (5) intuitive convergence proof of the algorithms.Our experimental results illustrate that the proposed approaches can significantly reduce the computation cost with competitive accuracy.
A survey on matching strategies for boundary image comparison and evaluation

Most of the strategies for boundary image evaluation involve the comparison of computer-generated images with ground truth solutions. While this can be done in different manners, recent years have seen a dominance of techniques based on the use of confusion matrices. That is, techniques that, at the evaluation stage, interpret boundary detection as a classification problem. These techniques require a correspondence between the boundary pixels in the candidate image and those in the ground truth; that correspondence is further used to create the confusion matrix, from which evaluation statistics can be computed. The correspondence between boundary images faces different challenges, mainly related to the matching of potentially displaced boundaries. Interestingly, boundary image comparison relates to many other fields of study in literature, from object tracking to biometrical identification. In this work, we survey all existing strategies for boundary matching, we propose a taxonomy to embrace them all, and perform a usability-driven quantitative analysis of their behaviour.
FPGA-Based Inter-layer Pipelined Accelerators for Filter-Wise Weight-Balanced Sparse Fully Convolutional Networks with Overlapped Tiling

Abstract Convolutional neural networks (CNNs) exhibit state-of-the-art performance while performing computer-vision tasks. CNNs require high-speed, low-power, and high-accuracy hardware for various scenarios, such as edge environments. However, the number of weights is so large that embedded systems cannot store them owing to their limited on-chip memory. A different method is used to minimize the input image size, for real-time processing, but it causes a considerable drop in accuracy. Although pruned sparse CNNs and special accelerators are proposed, the requirement of random access incurs a large number of wide multiplexers for a high degree of parallelism, which becomes more complicated and unsuitable for FPGA implementation. To address this problem, we propose filter-wise pruning with distillation and block RAM (BRAM)-based zero-weight skipping accelerator. It eliminates weights such that each filter has the same number of nonzero weights, performing retraining with distillation, while retaining comparable accuracy. Further, filter-wise pruning enables our accelerator to exploit inter-filter parallelism , where a processing block for a layer executes filters concurrently, with a straightforward architecture. We also propose an overlapped tiling algorithm , where tiles are extracted with overlap to prevent both accuracy degradation and high utilization of BRAMs storing high-resolution images. Our evaluation using semantic-segmentation tasks showed a 1.8 times speedup and 18.0 times increase in power efficiency of our FPGA design compared with a desktop GPU. Additionally, compared with the conventional FPGA implementation, the speedup and accuracy improvement were 1.09 times and 6.6 points, respectively. Therefore, our approach is useful for FPGA implementation and exhibits considerable accuracy for applications in embedded systems.
Task Scheduling in Cloud Computing Environment Using Bumble Bee Mating Algorithm

Tasks scheduling in cloud computing environment plays an important role for both Cloud Service Providers (CSPs) and the users of the services provided. Therefore, designing an efficient task scheduling algorithm, which fulfill the requirements of CSPs and their clients is essential. Several scheduling algorithms are proposed by various researchers for task scheduling in cloud computing environments. This paper introduces an alternative method for cloud task scheduling problem, which aims to minimize makespan of executing a number tasks on different Virtual Machines (VMs). This method is based on Bumble Bee Mating Optimization (BBMO) algorithm. BBMO is powered by the features of swarm intelligence and local search algorithms. The performance of BBMO is compared to two existing algorithms, Honey Bee Mating Optimization (HBMO) algorithm and Genetic Algorithm (GA). Finally, we analyze the performance of the proposed algorithm with other two algorithms using different scenarios of experiments. The results show that the proposed algorithm (BBMO) outperforms other algorithms.
Multi-Agents Path Planning for a Swarm of Unmanned Aerial Vehicles

The objective of swarms is to enable multiple agents to collaborate toward a common goal, as one would find in a remote sensing setting. In this paper we focus on swarms of unmanned aerial vehicles (UAVs), which for instance have an objective to optimize the survey of a prescribed area and/or the detection of a specific object. Instructing each individual agent from a central command control quickly becomes inefficient, even for small groups of agents. Agreement protocol is done locally by the multi-agents without external user input. Because of the wide variety of conditions UAVs can face, the algorithm needs to be robust despite external disturbances.
Griddly: A platform for AI research in games

<h2>Abstract</h2> Griddly is an open-source project aimed to be a all-encompassing platform for grid-world based research. Griddly provides a highly optimized game state and rendering engine with a flexible high-level interface for configuring environments. Not only does Griddly offer simple interfaces for single, multi-player and RTS games, but also multiple methods of rendering, configurable partial observability and interfaces for procedural content generation.
A fast-moving horizon estimation method based on the symplectic pseudospectral algorithm

In this paper, a fast-moving horizon state estimation algorithm for nonlinear continuous systems with measurement noises and model disturbances is developed. The optimization problem required to be solved at each sampling instant is formulated into a backward nonlinear optimal control problem over the finite past. Once prior knowledge of the observed system is available, constraints can be further imposed. The highly efficient and accurate symplectic pseudospectral algorithm is taken as the core solver, which leads to the symplectic pseudospectral moving horizon estimation (SP-MHE) method. The developed SP-MHE is first evaluated by numerical simulations for a hovercraft. Then the developed method is extended to parameter estimation and applied to a chaotic system with an unknown parameter. Simulation results show that the SP-MHE can generate accurate estimations even under large sampling periods or large noise where regular filters fail. In addition, the SP-MHE exhibits excellent online efficiency, suggesting it can be used for scenarios where the sampling period is relatively small.