Reviewerly/exHarmony huggingface dataset:
Blockchain-based Networking Strategy for Privacy-Preserving Demand Side Management

Demand side management (DSM) presents an effective tool to regulate customers’ energy consumption. However, DSM requires customer-utility interaction based on fine-grained data, which jeopardizes the privacy of the customers. To overcome such a threat, several proposals are made based on noise addition, encryption techniques, or extra hardware. Unfortunately, these solutions either impact the integrity of the data, incur additional computational complexity, or add extra cost. Recently, blockchain has been adopted to support smart grid applications while promoting customer’s anonymity. However, special measures are yet to be taken to support both customer’s anonymity and data unlinkability, hence preserving the customer’s privacy. This paper achieves this goal by proposing a novel networking strategy based on a private blockchain. The proposed strategy employs a group signature to ensure the customer’s anonymity and data unlinkability. Further, a novel decentralized random number generation scheme is proposed to support customer-utility interaction for DSM, billing, and auditing while ensuring data unlinkability. We present an implementation of the proposed blockchain-based strategy, investigate its scalability, and provide low-bound empirical expressions on computational time and storage overhead.
Performance of Teleportation-Based Error-Correction Circuits for Bosonic Codes with Noisy Measurements

Bosonic quantum error-correcting codes offer a viable direction towards reducing the hardware overhead required for fault-tolerant quantum information processing. A broad class of bosonic codes, namely rotation-symmetric codes, can be characterized by their phase-space rotation symmetry. However, their performance has been examined to date only within an idealistic noise model. Here, we further analyze the error-correction capabilities of rotation-symmetric codes using a teleportation-based error-correction circuit. To this end, we numerically compute the average gate fidelity, including measurement errors into the noise model of the data qubit. Focusing on physical measurement models, we assess the performance of heterodyne and adaptive homodyne detection in comparison to the previously studied canonical phase measurement. This setting allows us to shed light on the role of different currently available measurement schemes when decoding the encoded information. We find that with the currently achievable measurement efficiencies in microwave optics, bosonic rotation codes undergo a substantial decrease in their break-even potential. In addition, we perform a detailed analysis of Gottesman-Kitaev-Preskill (GKP) codes using a similar error-correction circuit that allows us to analyze the effect of realistic measurement models on different codes. In comparison to RSB codes, we find for GKP codes an even greater reduction in performance together with a vulnerability to photon-number dephasing. Our results show that highly efficient measurement protocols constitute a crucial building block towards error-corrected quantum information processing with bosonic continuous-variable systems.
Collaborative Partially-Observable Reinforcement Learning Using Wireless Communications

Each robot utilizes the reinforcement learning (RL) to control its maneuver and these robots can collaborate to accomplish a common goal to form a collaborative multi-agent system (MAS). Due to the constraints of distributive locations and different poses of robots, in practice, each agent (robot) in such a collaborative MAS can only partially observe the environment and other agents (such as competitive agents), and consequently operate based on its belief of the state(s). The alignment of the beliefs of collaborative agents can be therefore enhanced by adopting wireless communications, but is rarely studied in literature. To explore wireless communications applied to collaborative partially-observable reinforcement learning (PORL), we propose that each collaborative agent predicts the environment dynamics, including the behavior of those agents outside the collaborative MAS, and then constructs the learning-based belief of the world (i.e. global state). To assist such prediction and learning, we modify the RL assisted by the wireless communication functionality into two stages: prediction of the state and local actor-and-critic on global value(s). In other words, while one agent predicts and learns its own policy, another agent can updates critics on the sequence of history to update global value(s) that can further assist to validate the prediction. From numerical experiments, we find that the timing of communication or information exchange among collaborative agents has critical impact on the duration of learning and prediction, and thus the performance of MAS, which suggests the desirable communication for distributed PORL among collaborative agents toward an efficient MAS.
Violence Classification Using Support Vector Machine and Deep Transfer Learning Feature Extraction

Violence detection research is still quite a challenge for researchers and a considerable amount of effort. Before the video can be processed for classification, feature extraction is an important process to obtain important information. Determination of feature extraction and classification algorithms is an important factor for accurate classification results. This study uses deep transfer learning for feature extraction and combining it with the Support Vector Machine (SVM) classifier. The deep transfer learning algorithm in this study is a pre-trained model of Visual Geometry Group Network-16 (VGGNet-16). The video data was extracted using VGGNet-16 and then classified using SVM. Tests were carried out with 5-fold cross-validation with a variety of linear kernel, RBF, and Polynomial functions. The results were also compared with the Principal Component Analysis (PCA) feature extraction algorithm combining with SVM also. The results showed that the combination of deep transfer learning with SVM linear kernel functions resulted in higher accuracy compared to RBF and Polynomial kernel functions, and also compared to PCA combined with SVM.
QoS aware FaaS platform

Function as a Service (FaaS), a form of serverless computing, is one of the recent cloud computing service offerings, which abstracts and automates the management and provisioning of resources, and deployment of applications. It provides powerful abstractions to compose applications as stateless functions and triggers their executions through events. The platform offers autonomous scaling for applications and pay-as-you-go sub-second billing model. However, contemporary FaaS platforms provide limited features in stating resource requirements. They often lack specifications to express application specificities and resource requirements associated with Quality of Service (QoS). Such specifications can effectively guide the resource provisioning and function deployment at the resource provider, leading to efficient resource utilization and cost savings. This research exploration motivates the need for a QoS specification framework for FaaS and proposes ideas for realizing an initial QoS aware FaaS platform. Experimental results based on real-world workload trace show the cost savings and efficient resource utilization that QoS can bring in FaaS platforms.
Research on the Construction Method of Knowledge Graph for Power Grid Education Resources

Knowledge graph has been a research hotspot in the field of knowledge services in recent years. Constructing a power grid knowledge graph with a reasonable hierarchy and structure is an important driving force to promote intelligent education in the grid field and the development and innovation of grid technology. Based on the analysis of the gaps between the requirements of data understanding and the technologies of the knowledge graph, a 2-layer power grid knowledge graph framework is designed for data visualization and analysis. Then the weighted average of the word vector similarity is used to match the technical framework with the acquired power grid education resource entities to build a more complete grid technology knowledge graph, making knowledge expression closer to the structure of human cognition. One case study based on this structure is performed, which shows how this power grid knowledge graph works and the effectiveness of it in power grid education resource management. The research provides an efficient analysis and mining environment for the development of research and education intelligence in the field of power grids.
Decentralized Path Planning Approach for Crowd Surveillance using Drones

The problem of periodical surveillance of multiple moving targets using multiple drones in a decentralized manner is addressed in this paper. The strategy proposes a traverse order generation scheme by which the sequence in which targets are tracked can be determined. To cover all the targets by visiting them sequentially, the predicted targets' future position is used in path planning. The proposed algorithm takes care of drones' assignment to targets and eventual hand-off to neighboring drones as targets move from one region to another. The proposed strategy is used for a crowd monitoring system, and it attempts to detect the anomalous activity of a crowd via target trajectories. Complete surveillance architecture is validated using simulation for tracking of multiple walking agents using multi drones.
A Compact &amp; Low Power Architecture of XXTEA192 Lightweight block cipher

RFID and IoT security threats have been increased due to the enormous usage of ubiquitous computing devices. Providing security to RFID tags is one of perplexing tasks as it has to meet rigid area and power requirements. This paper has proposed a compact serial architecture for XXTEA-192 block cipher which can be used in RFID applications. The RFID tags are constrained with limited area and power. XXTEA is the most secured version among the ciphers of TEA family. This cipher works on variable length message with a minimum message length of 62-bit and should be a multiple of 32-bit words. This feature enables the cipher to encrypt larger block sizes. Serial architecture of XXTEA-192 has been proposed that performs 32-bit operations like addition, XOR and shifting in each clock cycle. The proposed design has been implemented using verilog. Analysis of performance metrics like area, throughput, efficiency, power and energy has been performed on different families of FPGA devices like Spartan 6, Virtex 6 and Virtex 7. The proposed design has achieved 45 slices and a throughput of 498.44 Mbps on Spartan 6 making the architecture area and speed efficient for RFID applications.
Patchwise Generative ConvNet: Training Energy-Based Models from a Single Natural Image for Internal Learning

Exploiting internal statistics of a single natural image has long been recognized as a significant research paradigm where the goal is to learn the internal distribution of patches within the image without relying on external training data. Different from prior works that model such a distribution implicitly with a top-down latent variable model (e.g., generator), this paper proposes to explicitly represent the statistical distribution within a single natural image by using an energy-based generative framework, where a pyramid of energy functions, each parameterized by a bottom-up deep neural network, are used to capture the distributions of patches at different resolutions. Meanwhile, a coarse-to-fine sequential training and sampling strategy is presented to train the model efficiently. Besides learning to generate random samples from white noise, the model can learn in parallel with a self-supervised task (e.g., recover the input image from its corrupted version), which can further improve the descriptive power of the learned model. The proposed model is simple and natural in that it does not require an auxiliary model (e.g., discriminator) to assist the training. Besides, it also unifies internal statistics learning and image generation in a single framework. Experimental results presented on various image generation and manipulation tasks, including super-resolution, image editing, harmonization, style transfer, etc, have demonstrated the effectiveness of our model for internal learning.
VR Implementation in User-Interactive Simulation Environments

The purpose of this research paper is to document some of the ongoing developments with virtual reality in the technology industry, as well as the commercial market today. Virtual reality technology has a varied number of purposes ranging from entertainment purposes to training simulations for safety critical operations. Despite the increasing popularity in virtual reality, several limitations still exist that limit the potentials of the technology to achieve sustainable user immersion. This paper discusses the problems facing the virtual reality medium, and ways in which they can be solved. Two major cases of industry-level uses of the technology are highlighted in order to better demonstrate the themes presented from this paper's research. This paper emphasizes the problems facing simulation environments in virtual reality experiences today, and concludes by explaining how they can be treated in the future.
Differential Privacy Principal Component Analysis for Support Vector Machines

In big data era, massive and high-dimensional data is produced at all times, increasing the difficulty of analyzing and protecting data. In this paper, in order to realize dimensionality reduction and privacy protection of data, principal component analysis (PCA) and differential privacy (DP) are combined to handle these data. Moreover, support vector machine (SVM) is used to measure the availability of processed data in our paper. Specifically, we introduced differential privacy mechanisms at different stages of the algorithm PCA-SVM and obtained the algorithms DPPCA-SVM and PCADP-SVM. Both algorithms satisfy <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"><mfenced open="(" close=")" separators="|"><mrow><mi>ε</mi><mo>,</mo><mn>0</mn></mrow></mfenced></math> -DP while achieving fast classification. In addition, we evaluate the performance of two algorithms in terms of noise expectation and classification accuracy from the perspective of theoretical proof and experimental verification. To verify the performance of DPPCA-SVM, we also compare our DPPCA-SVM with other algorithms. Results show that DPPCA-SVM provides excellent utility for different data sets despite guaranteeing stricter privacy.
Image Processing for Enhanced OMR Answer Matching Precision

Optical Mark Recognition (OMR) is used to automate answer matching especially in the education sector. OMR marking machine is costly and limited to specific OMR paper design, thus launching researchers using image processing to find less costly solutions. However, studies so far have achieved relatively low accuracy and poor consistency unless a fixed OMR form design is used. Accuracy drops with more OMR questions. Therefore, this study investigate means to improve OMR marking accuracy using enhanced algorithm designed for OMR marking. The results were compared against manual marking as the control and existing image processing algorithms. The metrics used are F1 score and error percentage for accuracy of detected answer options and marking fault respectively. The result is encouraging with consistent full accuracy for up to 90 questions as compared to previous works.
Achieving Blockchain-based Privacy-Preserving Location Proofs under Federated Learning

Federated learning-based navigation has received much attention in vehicular IoT. The intention is to employ a big number of end-users for data collection along different trajectories and perform local training of a global learning model to substitute the global positioning system (GPS) in urban areas. The prerequisites for its commercialization, however, lie in the location-dependent input data trustworthiness and participants’ privacy preservation. In this paper, we propose a privacy-preserving proof-of-location mechanism using blockchain to meet these conditions. Specifically, the proposed scheme utilizes a Threshold Identity-Based Encryption (TIBE) system for the generation of secret shares, such that each anonymous location proof can only be verified with at least a threshold number of participants. In addition, the proposed scheme exploits a cuckoo filter for the secure and efficient maintenance and dissemination of location proofs. Systematic security analysis is conducted to demonstrate the fulfillment of harsh security requirements. Performance evaluations are carried out to validate the computation efficiency in comparison with an oblivious transfer (OT) protocol, which has been widely adopted for secure data acquisition.
Ananya Algorithm: A Simple and New Optimization Algorithm for Engineering Optimization

Optimization is the act of making the best or most effective use of a situation or resource. It involves maximizing or minimizing a mathematical function. A new and simple metaheuristic optimization algorithm is developed and proposed in this paper as Ananya Algorithm. Simplicity is the beauty of the proposed algorithm. Ananya algorithm is one of the simplest optimization algorithms to implement, among all optimization techniques. This algorithm has only two candidates hence it avoids large calculations. This algorithm moves towards a better solution with the difference between the mean of variables and the best variable. This algorithm works on simple calculations and does not involve any complicated calculations. This algorithm is tested for thirty unconstrained benchmark functions like sphere function, Beale function, Goldstein-Price function, Booth Function, Matyas Function, and convergence graph shown for the same. Every time this algorithm got successful to achieve an optimum solution. It takes a little CPU time to optimize. Ananya algorithm is compared to particle swarm optimization (PSO) and genetic algorithm (GA). It required lesser mean functional evaluation to achieve an optimal solution, hence the Ananya algorithm has better performance than the two algorithms.
A Multi-Agent Approach to Combine Reasoning and Learning for an Ethical Behavior

The recent field of Machine Ethics is experiencing rapid growth to answer the societal need for Artificial Intelligence (AI) algorithms imbued with ethical considerations, such as benevolence toward human users and actors. Several approaches already exist for this purpose, mostly either by reasoning over a set of predefined ethical principles (Top-Down), or by learning new principles (Bottom-Up). While both methods have their own advantages and drawbacks, only few works have explored hybrid approaches, such as using symbolic rules to guide the learning process for instance, combining the advantages of each. This paper draws upon existing works to propose a novel hybrid method using symbolic judging agents to evaluate the ethics of learning agents' behaviors, and accordingly improve their ability to ethically behave in dynamic multi-agent environments. Multiple benefits ensue from this separation between judging and learning agents: agents can evolve (or be updated by human designers) separately, benefiting from co-construction processes; judging agents can act as accessible proxies for non-expert human stakeholders or regulators; and finally, multiple points of view (one per judging agent) can be adopted to judge the behavior of the same agent, which produces a richer feedback. Our proposed approach is applied to an energy distribution problem, in the context of a Smart Grid simulator, with continuous and multi-dimensional states and actions. The experiments and results show the ability of learning agents to correctly adapt their behaviors to comply with the judging agents' rules, including when rules evolve over time.
APVAS+: A Practical Extension of BGPsec with Low Memory Requirement

BGPsec is a protocol that utilizes digital signatures to guarantee the validity of routing information on the Internet. However, it is impractical because its use of digital signatures requires significant memory that is beyond the memory capacity of current routers. The latest extension of BGPsec based on an aggregate signature scheme, which aggregates individual signatures into a single short signature, has been proposed in the recent years, but its memory requirement is still impractical. In this paper, we present APVAS+, a protocol that reduces the memory consumption of routers compared to state-of-the-art protocols. The memory requirement of APVAS+ is almost within the memory capacity of real-world routers. While the latest BGPsec protocol can only aggregate signatures generated on a single linear network topology, APVAS+ can aggregate signatures generated on any network topology by using a novel aggregate signature scheme. We also show a prototype implementation of APVAS+ by extending a router software called BIRD. Using this prototype, we conducted experiments on a full route information, i.e., about 800,000 routes. We consider that APVAS+ can be optimized to further reduce its memory requirement, and our promising results show that the memory consumption of routers running APVAS+ is lower than that of routers running other protocols by more than half when guaranteeing the validity of routing information.
A Dense RNN for Sequential Four-Chamber View Left Ventricle Wall Segmentation and Cardiac State Estimation

The segmentation of the left ventricle (LV) wall in four-chamber view cardiac sequential image is significant for cardiac disease diagnosis and cardiac mechanisms study; however, there is no successful reported work on sequential four-chambered view LV wall segmentation due to the complex four-chamber structure and diversity of wall motion. In this article, we propose a dense recurrent neural network (RNN) algorithm to achieve accurately LV wall segmentation in a four-chamber view MRI time sequence. In the cardiac sequential LV wall process, not only the sequential accuracy but also the accuracy of each image matters. Thus, we propose a dense RNN to provide compensation for the first long short-term memory (LSTM) cells. Two RNNs are combined in this work, the first one aims at providing information for the first image, and the second RNN generates segmentation result. In this way, the proposed dense RNN improves the accuracy of the first frame image. What is more is that, it improves the effectiveness of information flow between LSTM cells. Obtaining more competent information from the former cell, frame-wise segmentation accuracy is greatly improved. Based on the segmentation result, an algorithm is proposed to estimate cardiac state. This is the first time that deals with both cardiac time-sequential LV segmentation problems and, robustly, estimates cardiac state. Rather than segmenting each frame separately, utilizing cardiac sequence information is more stable. The proposed method ensures an Intersection over Union (IoU) of 92.13%, which outperforms other classical deep learning algorithms.
High-Density Salt and Pepper Noise Removal from Colour Images by Introducing New Enhanced Filter

Any image captured by a camera or scanner contains some unwanted particles, which are known as noise or salt and pepper. They are black and white color dots that are present in some pixels. The value of those pixels is either 0 or 255. There exist different predefined methods to remove salt and pepper or noise from an image in Matlab. This paper presents an effective salt and pepper filtering and removal algorithm, which has accuracy greater than some of the predefined filters in Matlab like Median Filter, Adaptive Median Filter, MDBUTMF, MDBPTGMF, and Adaptive Weighted Median Filter. The Peak Signal to Noise Ratio (PSNR) value for each algorithm has been compared with this proposed algorithm. A comparative analysis has been carried out in this paper between these predefined methods and the proposed algorithm. The tests have been done on few images and the results have been displayed in tabular format.