Reviewerly/exHarmony huggingface dataset:
AES–CP–IDABE: A Privacy Protection Framework against a DoS Attack in the Cloud Environment with the Access Control Mechanism

Cloud computing technology has revolutionized the field of data management as it has enhanced the barriers of storage restrictions and high-cost establishment for its users. The benefits of the cloud have paved the way for its extensive implementation in large enterprises. However, the data in the cloud have succumbed to various security threats, and its privacy issues remain one of the biggest and topmost concerns for the data owners. Several techniques, such as Attribute-based Encryption (ABE), have been proposed by several researchers to preserve the privacy of the data. However, the issue of security still looms largely over the cloud. In the present work, we introduce the novel encryption model called “Advanced Encryption Standard–Cipher-text-Identity and Attribute-based Encryption” (AES–CP–IDABE) to preserve data privacy along with its access control. In the proposed scheme, the data have been double encrypted initially through the ABE, along with the attributes and the identity of the user. Secondly, the Advanced Encryption Standard (AES) is used to encrypt the encrypted data and provide it to the authorized users. The user access control is established using the digital signature with the help of user ID and security keys. Additionally, the set up includes Denial-of-Service (DoS) detection through IP address monitoring and control. The proposed scheme has also been evaluated for its performance in the communication between the user and the data owner, along with the user’s execution time. From the outcome, it is evident that the proposed scheme was more effective than the existing scheme of ABE over execution, encryption, and decryption time. Additionally, the performance over DoS detection and impact of attribute numbers for the proposed scheme was also studied to prove its effectiveness.
Task Allocation for Mobile Federated and Offloaded Learning with Energy and Delay Constraints

This paper proposes a framework to support federated learning or mobile edge learning (MEL) when there are both, delay constraints on the learning process and constraints on the energy consumed by each device. The aim is to maximize learning accuracy while guaranteeing that the total time taken and energy consumed by each learner in the system are bounded by a preset duration and maximum energy, respectively, while taking into account heterogeneous communication and communication capabilities of the channels and nodes. The problem of interest is shown to be NP-hard and a suggest-and-improve (SAI) approach is proposed based on the solution of the Lagrangian Dual problem (suggest) followed by a local optimizer based on coordinate descent (the improve step). The merits of this proposed solution, which is heterogeneity aware (HA), are exhibited by comparing its performances to both numerical approaches and the heterogeneity unaware (HU) approach.
THE INTEGRATION OF GROUP DISCUSSION METHOD USING AUDIO VISUAL LEARNING MEDIA TOWARD STUDENTS’ LEARNING ACHIEVEMENT ON LISTENING

This research is focus on the integration of group discussion method using audio visual learning media. The aim of conducting this research is to find out how the integration of group discussion method using audio visual learning media toward the students learning achievement on listening. The method of the study used was qualitative with Quasy Experimental design. It was done at Teruna Muda Jurnior High School. The instrument used were a set of test and observation sheets. The result of the research are: a) that the everage result using QA method using the audio visual learning is 74; and b) student learning outcomes using the QA method with audio media is no better than the student learning outcomes provided with conventional learning models using audio media. Then it is concluded that the teachers may implement the QA method or conventional learning in teaching listening to the students in the classroom.
Sphere Fitting with Applications to Machine Tracking

We suggest a provable and practical approximation algorithm for fitting a set P of n points in R d to a sphere. Here, a sphere is represented by its center x ∈ R d and radius r &gt; 0 . The goal is to minimize the sum ∑ p ∈ P ∣ p − x − r ∣ of distances to the points up to a multiplicative factor of 1 ± ε , for a given constant ε &gt; 0 , over every such r and x. Our main technical result is a data summarization of the input set, called coreset, that approximates the above sum of distances on the original (big) set P for every sphere. Then, an accurate sphere can be extracted quickly via an inefficient exhaustive search from the small coreset. Most articles focus mainly on sphere identification (e.g., circles in 2 D image) rather than finding the exact match (in the sense of extent measures), and do not provide approximation guarantees. We implement our algorithm and provide extensive experimental results on both synthetic and real-world data. We then combine our algorithm in a mechanical pressure control system whose main bottleneck is tracking a falling ball. Full open source is also provided.
Reliable IoT Data Management Platform Based on Real-World Cooperation Through Blockchain

This article proposes a new data management platform using blockchain that contributes to verify the integrity of the real-world data obtained from the IoT (Internet of Things) devices (e.g., camera, smart meter, smartphone) deployed on the real world. The existing blockchain-based platform guarantees the integrity of only the data registered inside the blockchain but does not detect the situation that the data is tampered and/or spoofed outside the blockchain. The proposed platform is designed based on the characteristic of the IoT system that the real-world data is continuously generated from the origin IoT device and there may exist various sensing devices (e.g., smartphone, sensor node) in the vicinity of the origin device. In the integrity verification mechanism, the origin device requests the surrounding devices to cooperate to verify the integrity of the real-world data. The tendency of the real-world data of the origin device should be almost the same as the data generated by the cooperator devices if the data tampering/spoofing did not occur. Therefore, by comparing the real-world data generated by the origin and the cooperator devices on the blockchain, the proposed platform verifies the integrity of the real-world data on the whole part of the IoT system.
FlowControl: Optical Flow Based Visual Servoing

One-shot imitation is the vision of robot programming from a single demonstration, rather than by tedious construction of computer code. We present a practical method for realizing one-shot imitation for manipulation tasks, exploiting modern learning-based optical flow to perform real-time visual servoing. Our approach, which we call FlowControl, continuously tracks a demonstration video, using a specified foreground mask to attend to an object of interest. Using RGB-D observations, FlowControl requires no 3D object models, and is easy to set up. FlowControl inherits great robustness to visual appearance from decades of work in optical flow. We exhibit FlowControl on a range of problems, including ones requiring very precise motions, and ones requiring the ability to generalize.
Twitter Users Opinion Classification of Smart Farming in Indonesia

Abstract In the past year, Indonesia has digitalized agriculture called “Smart Farming” or “Agriculture 4.0” to follow the era of the industrial revolution 4.0. Public opinion on social media is a strong factor in determining whether or not smart farming is implemented in Indonesia. So the purpose of this research is to find data from Twitter and then analyzed using the sentiment analysis method, which will be classified using Naïve Bayes method. This process starts with searching data on Twitter, preprocessing text, classification, and finally testing. The accuracy testing results gives a value of 0.97, and Recall produces a value of 1.0, F1 produces a value of 0.98 and an AUC value of 0.5.
Blockchain-Based Multi-Tier Double Auctions for Smart Energy Distribution Grids

Future Smart Grids will need to integrate thousands of independently operated intelligent energy devices into one organizational unit. Distributed ledger technologies can provide the means for a business-driven consensus between supply and demand of electrical power. This approach is limited by the computing power of the participating devices. We describe a way to increase the scalability of this approach by a) aggregating power consumption and generation, thus reducing the computational load on the blockchain and by b) applying the concept of edge computing to increase reliability and reaction time. We propose a hierarchical clustering approach for smart grid power balancing, which aims to advance our understanding of architectures for future real-world smart grids.
Accurate Graph Filtering in Wireless Sensor Networks

Wireless sensor networks (WSNs) are considered as a major technology enabling the Internet-of-Things (IoT) paradigm. The recent emerging graph signal processing field can also contribute to enabling the IoT by providing key tools, such as graph filters (GFs), for processing the data associated with the sensor devices. GFs can be performed over WSNs in a distributed manner by means of a certain number of communication exchanges among the nodes. But, WSNs are often affected by interferences and noise, which leads to view these networks as directed, random and time-varying graph topologies. Most of the existing works neglect this problem by considering an unrealistic assumption that claims the same probability of link activation in both directions when sending a packet between two neighboring nodes. This work focuses on the problem of operating graph filtering in random asymmetric WSNs. We show first that graph filtering with finite impulse response GFs (node-invariant and node-variant) requires having equal connectivity probabilities for all the links in order to have an unbiased filtering, which cannot be achieved in practice in random WSNs. After this, we characterize the graph filtering error and present an efficient strategy to conduct graph filtering tasks over random WSNs with node-variant GFs by maximizing accuracy, that is, ensuring a small bias-variance tradeoff. In order to enforce the desired accuracy, we optimize the filter coefficients and design a cross-layer distributed scheduling algorithm (CDSA) at the MAC layer. Extensive numerical experiments are presented to show the efficiency of the proposed solution as well as the CDSA for the denoising application.
A polynomial algorithm for balanced clustering via graph partitioning

The objective of clustering is to discover natural groups in datasets and to identify geometrical structures which might reside there, without assuming any prior knowledge on the characteristics of the data. The problem can be seen as detecting the inherent separations between groups of a given point set in a metric space governed by a similarity function. The pairwise similarities between all data objects form a weighted graph whose adjacency matrix contains all necessary information for the clustering process. Consequently, the clustering task can be formulated as a graph partitioning problem. In this context, we propose a new cluster quality measure which uses the ratio of intra- and inter-cluster variance and allows us to compute the optimal clustering under the min-max principle in polynomial time. Our algorithm can be applied to both partitional and hierarchical clustering.
The configuration and experience mapping of an accessible VR environment for effective design reviews

Abstract This paper presents a new VR interaction environment for the evaluation of digital prototypes, specifically in designer–client review sessions, and documents its implementation via experience mapping. Usability of VR controllers and basic manipulation remains a barrier for lay users, and a range of typical implementations are reviewed, highlighting the need for an easily accessible interface for this setting. The resulting interface configuration – the Control Carousel – demonstrates how the appropriate use of familiar mechanisms can increase VR accessibility. Three case studies using the Carousel in commercial design projects are described, and the subsequent interface refinements outlined. Finally, the development of an experience map describing the logistical, interactive, and emotive factors affecting the Carousel's implementation is documented. This provides insights on how experience mapping can be used as part of a human-centred design process to ensure VR environments are attuned to the requirements of users, in this instance delivering improved collaborative reviews.
An Open Ecosystem for Pervasive Use of Persistent Identifiers

Persistent identifiers (PIDs) are essential for making data Findable, Accessible, Interoperable, and Reusable, or FAIR. While the advantages of PIDs for data publication and citation are well understood, and Digital Object Identifiers (DOIs) are increasingly applied to data, there are two gaps in the current identifier ecosystem: 1) services that provide a consistent baseline of capabilities encompassing key aspects of the research data lifecycle, including canonical landing pages and machine-readable metadata via the same URL; and 2) support for identifiers to be applied to ephemeral data, particularly as data move across system boundaries, such as during workflows. To address these gaps, we have implemented the FAIR Research Identifiers service. This service supports multiple identifier providers (ARK, Handle, DOIs via DataCite, etc.) and uses Globus Auth to implement a rich user- and group-based authorization model for identifier creation. This paper summarizes the current identifier ecosystem, presents best-practices recommendations for identifier use, and describes our FAIR Research Identifiers service.
360 Panorama Synthesis from a Sparse Set of Images on a Low-Power Device

A full 360 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</sup> × 180 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</sup> image provides an unlimited field of view (FOV) and an immersive experience for the users without any loss of information of the surrounding. In this study, a deep learning based approach is proposed to synthesize a 360 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</sup> image from a sparse set of images captured with a limited FOV. The proposed network consists of a cascade of the FOV estimation network and the panorama synthesis network. We propose a hierarchical generative network to synthesize high quality 360 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</sup> panorama images. The design of progressive multi-scale generator and multiple discriminator reduces the high frequency artifact which is commonly observed in image synthesis using generative networks. The network is further compressed to run on a low-power device such as a smartphone using our proposed size and latency optimization. Experimental result demonstrates that the proposed method produces 360 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</sup> panorama with satisfactory image quality of up to 512 × 1024 resolution. It is also shown that the proposed method outperforms the alternative method and can be generalized for non-panoramic scenes and real images captured by a smartphone camera.
A low-complexity psychometric curve-fitting approach for the objective quality assessment of streamed game videos

The increasing popularity of video gaming competitions, the so called eSports, has contributed to the rise of a new type of end-user: the passive game video streaming (GVS) user. This user acts as a passive spectator of the gameplay rather than actively interacting with the content. This content, which is streamed over the Internet, can suffer from disturbing network and encoding impairments. Therefore, assessing the user's perceived quality, i.e the Quality of Experience (QoE), in real-time becomes fundamental. For the case of natural video content, several approaches already exist that tackle the client-side real-time QoE evaluation. The intrinsically different expectations of the passive GVS user, however, call for new real-time quality models for these streaming services. Therefore, this paper presents a real-time Reduced-Reference (RR) quality assessment framework based on a low-complexity psychometric curve-fitting approach. The proposed solution selects the most relevant, low-complexity objective feature. Afterwards, the relationship between this feature and the ground-truth quality is modelled based on the psychometric perception of the human visual system (HVS). This approach is validated on a publicly available dataset of streamed game videos and is benchmarked against both subjective scores and objective models. As a side contribution, a thorough accuracy analysis of existing Objective Video Quality Metrics (OVQMs) applied to passive GVS is provided. Furthermore, this analysis has led to interesting insights on the accuracy of low-complexity client-based metrics as well as to the creation of a new Full-Reference (FR) objective metric for GVS, i.e. the Game Video Streaming Quality Metric (GVSQM).
Fusion Drive: End-to-End Multi Modal Sensor Fusion for Guided Low-Cost Autonomous Vehicle

In this paper, we present a supervised learning-based mixed-input sensor fusion neural network for autonomous navigation on a designed track referred to as Fusion Drive. The proposed method combines RGB image and LiDAR laser sensor data for guided navigation along the track and avoidance of learned as well as previously unobserved obstacles for a low-cost embedded navigation system. The proposed network combines separate CNN-based sensor processing into a fully combined network that learns throttle and steering angle labels end-to-end. The proposed network outputs navigational commands with similar learned behavior from the human demonstrations. Performed experiments with validation data-set and in real environment exhibit desired behavior. Recorded performance shows improvement over similar approaches.
Parallel self‐testing for device‐independent verifiable blind quantum computation

With advances in experimental quantum computing, the requirement for verifying the correctness of quantum computation is urgent. The recent protocols of device-independent verifiable blind quantum computation provide a fruitful solution. However, all existing approaches have relatively high overhead. In this paper, we present a parallel self-testing technology to extract the presence of tensor products of Pauli observables on maximally entangled state. We then utilize our parallel self-testing to propose a device-independent verification protocol. Finally, compared to other existing protocols, our scheme has a lower overhead, which costs Bell pairs, where n is the size of original computation.
Multi-Person Pose Estimation Under Complex Environment Based on Progressive Rotation Correction and Multi-Scale Feature Fusion

The research of multi-person pose estimation has been largely improved recently. However, multi-person pose estimation in complex environments is still challenging. For example, the following two situations cannot be handled well by existing pose estimation methods: first, there are pedestrians that are not upright or even inverted in the image, and pedestrians of different scales appear in the same image. To solve these problems, the Progressive rotation correction module (PRCM) and Scale-invariance module (SIM) based on multi-scale feature fusion are proposed. First of all, the PRCM was proposed to address the situation where pedestrians appear rotated or even inverted in the image. This module is divided into three stages, with the aim of gradually correcting the inverted human to an upright one. Besides, SIM is designed to handle multi-scale problems. In this module, dilated convolutions with different receptive field are used to extract multi-scale information. Then, the extracted multi-scale features (different semantic information in different feature maps) will be fused to solve the multi-scale problem. The experimental results show that our algorithm can reach an AP value of 72.0% when tested on the COCO2017 dataset. Demonstrates that the proposed method is superior to state-of-the-art methods.
The impact of situated learning activities on technology university students' learning outcome

Purpose The purpose of this study was to investigate 3D virtual reality (VR) situated activity, preschool reality and how the lecture teaching method affects technology university students' learning outcome. Design/methodology/approach The quasi-experimental design is used. Participants are 144 students in three classes who all take Child Development Assessment course. Research instruments include 3D VR animation, preschool live video and child development as the case. One class attended 3D VR situated activities, another observed preschool live video and the other takes a traditional lecture class. Learning outcomes were measured by two paper-and-pencil tests in different times and with one performance assessment. In the writing test, mechanical and meaningful questions were included. Findings Major findings of this study are, first, that the auxiliary learning of 3D VR is better than the real-life situation. Second, situational learning activities can enhance participant performance in context-based questions. In summary, this study found that well-organized 3D VR animation is more effective than live situation learning, especially for context-based course content. Research limitations/implications The lack of random assignment into test groups leads to non-equivalent test groups which can limit the generalizability of the results to other student population. Practical implications The findings of this study suggest that teachers can gradually arrange learning activities, from 3D VR to a real applied workplace; situated learning activities are more likely to support the transfer knowledge to real-life problem solving. Originality/value The findings suggest that teachers in arranging the classroom context activities can be the first to use 3D VR before actual reality to avoid novices getting lost in the complicated real situations. If learning activities can be arranged gradually, from 3D VR to real applied workplace, situated learning activities can help students to deploy their newly acquired knowledge and skills in real-life problem solving.