Reviewerly/exHarmony huggingface dataset:
Research on Deep Forgery Data Identification and Traceability Technology Based on Blockchain

With the acceleration of the digital transformation of the power system, the electronic data in the power production operation has shown an explosive growth. However, the increasing proliferation of deep forgery technology has brought huge hidden dangers to the electronic data management of power grid enterprises, and it is urgent to build a trusted management system for electronic data. This paper proposes a blockchain-based deep forgery data identification and traceability framework. Firstly, a method of trusted identification of electronic data based on blockchain is proposed, which constructs the unique identification of data and embeds it in the electronic data as a digital watermark. Second, introduce blockchain-based electronic data forensic appraisal technology to conduct authenticity and similarity analysis of electronic data. Finally, a deep forgery data traceability mechanism based on digital identification is designed to realize deep forgery data traceability and dissemination supervision. After comparative analysis, the framework is more secure and efficient in deep forgery data supervision, and can provide key support for building a trusted content system in cyberspace.
Resource Deployment and Task Scheduling Based on Cloud Computing

Cloud computing is a computing model developed from parallel computing and distributed computing. The rapid development of cloud computing technology not only expands the scale of data in the database, but also consumes a lot of resources for data processing, resulting in huge energy costs. In addition, scheduling policies that do not conform to the actual situation will cause uneven task distribution, cause serious waste of resources, and increase cloud data management operating costs. In cloud computing related research, resource deployment and task scheduling have a great impact on the overall performance of the system. In order to solve the above problems, more and more scholars have conducted in-depth research on this problem. Reduce the energy consumption (EC) of cloud data processing, improve data processing efficiency, propose cloud computing architecture, build resource deployment (RD) model and task scheduling (TS) model on this basis. The usefulness of the model is discussed in depth. Aiming at low EC and high-efficiency resource allocation tasks, a TS algorithm based on improved particle swarm optimization (PSO) algorithm is proposed to further improve the performance of cloud computing systems. The experimental results show that the resource deployment and task scheduling model constructed in this paper can consume bad particles, maximize resource utilization, and reduce the EC of cloud computing (CC) systems after being optimized by particle swarm optimization. Compared with the traditional PSO algorithm, the improved PSO algorithm in this paper can effectively avoid the problem of user query resource allocation lag, improve the task execution efficiency, and enhance the stability of the CC system.
A Hybrid Job Recommendation Algorithm for Intelligent Employment System Using User Profile-Based Filtering

With high speed of Internet development, especially in area of college employment system (CES), which are used to searching the Internet for these college graduates to acquire the proper positions of the proper company. Meanwhile, how to find the right candidate from the massive graduate-ability data matrix becomes a hard issue. Traditional campus recruitment includes so many steps which will cost much time and human capital. Thus, our proposed hybrid talent recommendation method can solve this problem by predicting the future proper candidate for the company by comparing the ability data of the present company talent with recent campus graduate who do not have any working experience. Ability similarity and demographic similarity are both considered in our method combined with traditional collaborative filtering making our prediction more precise and suitable for College Employment Systems in real situation.
Predicting the Price Direction of Bitcoin Using Twitter Data and Machine Learning

Bitcoin is a decentralized digital currency that was intro- duced in 2009 and since then, has become increasingly popular as one of the most known and highly valued currencies. Contributing factors to its rise include crypto Twitter influencers. An engaged audience on Twitter seems to have an influence on the cryptocurrency market. In this paper, we analyze the impact that tweets have on the price of Bitcoin. Using word-clouds and candlestick plots, we gain insight into the factors that affect Bitcoin prices. We also use various machine learning techniques to automatically classify the sentiment in Tweets related to cryptocurrencies. We incorporate these and other relevant features to build and compare the performance of multiple machine learning models to predict the direction (increase or decrease) of the price of Bitcoin.
Intelligent Fall Detection System based on Sensor and Image data for Elderly Monitoring

In recent years Internet of Things has gained much attention. Various techniques are being developed to acquire the data from human environment for various smart services and applications. Monitoring elderly people remotely using smart homes is one of the difficult and challenging tasks in lOT due to many accidents that occur during daily activities like falls etc. In elderly people, falling is considered as a main reason for cause of injuries of death. To prevent the fall of elderly people and to identify the fall in smart homes technology need to be increased so that the fall can be prevented and rate of survival can be increased. Currently many loT, wearable devices, mobile phones etc have been developed with advanced technologies which can be used to develop a fall detection technique using loT and Deep Learning. In this paper an loT and Deep learning based elderly fall detection technique is proposed. Deep convolutional Network is used to extract the features obtained using loT devices and to detect the fall using intelligent Deep fall Detection algorithm. Feature extraction is performed using Squeeze Net model by extracting required features. SVM classifier is used for classification of fall and non -fall occurrences. If a fall event is detected an alert message is sent to the smartphone of hospital management. UR fall detection dataset is utilized to perform the experiments. Based on the obtained results it is inferred that proposed method achieved an accuracy of 99.81 % on fall detection dataset.
Hand Sign Recognition using Deep Convolutional Neural Network

Sign language is the most complex language to understand by the end-user without knowing the meaning of the sign and it depends on the special gesture motion. The gesture marks are characterized by hands with aid by facial appearance and body position. In this article, Gesture recognition is proposed for static sign language using Deep Learning with image processing. The involvement contains dual solutions to the problem. One is resized with Bicubic static American Sign Language binary images. Besides that, good recognition results in of detection the borderline hand using the various edge detection techniques. Another solution is to classify the alpha characters of sign language using a Convolution Neural Network (CNN). Owing to the anticipated method, test accuracy of 96% and F1-Score of 99% have achieved for 10 different hand gesture classifications.
Providing Post-Hoc Explanation for Node Representation Learning Models Through Inductive Conformal Predictions

Learning with graph-structured data, such as social, biological, and financial networks, requires effective low-dimensional representations to handle their large and complex interactions.Recently, with the advances of neural networks and embedding algorithms, many unsupervised approaches have been proposed for many downstream tasks with promising results; however, there has been limited research on interpreting the unsupervised representations and, specifically, on understanding which parts of the neighboring nodes contribute to the representation of a node.To mitigate this problem, we propose a statistical framework to interpret the learned representations.Many of the existing works, which are designed for supervised node presentation models, compute the difference in prediction scores after perturbing the edges of a candidate explanation node; however, our proposed framework leverages a conformal prediction (CP)-based statistical test to verify the importance of the candidate node in each node representation.In our evaluation, our proposed framework was verified in many experimental settings and presented promising results compared to those of the recent baseline methods.
Eyes on me: Investigating the role and influence of eye-tracking data on user modeling in virtual reality

Research has shown that sensor data generated by a user during a VR experience is closely related to the user's behavior or state, meaning that the VR user can be quantitatively understood and modeled. Eye-tracking as a sensor signal has been studied in prior research, but its usefulness in a VR context has been less examined, and most extant studies have dealt with eye-tracking within a single environment. Our goal is to expand the understanding of the relationship between eye-tracking data and user modeling in VR. In this paper, we examined the role and influence of eye-tracking data in predicting a level of cybersickness and types of locomotion. We developed and applied the same structure of a deep learning model to the multi-sensory data collected from two different studies (cybersickness and locomotion) with a total of 50 participants. The experiment results highlight not only a high applicability of our model to sensor data in a VR context, but also a significant relevance of eye-tracking data as a potential supplement to improving the model's performance and the importance of eye-tracking data in learning processes overall. We conclude by discussing the relevance of these results to potential future studies on this topic.
Challenges of integration of blockchain into internet of things (IoT): A survey

The Internet of Things(IoT) has changed the world into a smart and autonomous world. The way in which we interact with each other and with the environment has changed.The IoT is experiencing exponential growth in research and industry, but still suffers from privacy and security issues. The integration of blockchain technology with Internet of Things (IoT) has resolved many challenges faced by Internet of Things (IoT) such as decentralization, poor interoperability, privacyand security vulnerabilities. Blockchain when combined with IoT can solve many challenges of IoT systems. This integration will come out to be the better system for the environment in many aspects specially security which is the most important aspect to be considered. Also, the distributed ledger of Blockchain which when used in IoT can resolve many issues like scalability and authenticity. However, the integration of Blockchain technology with IoT originates various challenges like storage capacity and scalability, security, anonymity and data privacy etc. We first introduce Blockchain with Internet of Things (IoT) and key characteristic of this integration. We will also concentrate the issued resolved by blockchain technology in Internet of Things (IoT). The main concern of this paper is to discuss the various challenges of integration of blockchain into IoT.
Joint Optimization of Jamming Link and Power Control in Communication Countermeasures: A Multiagent Deep Reinforcement Learning Approach

Due to the nonconvexity feature of optimal controlling such as jamming link selection and jamming power allocation issues, obtaining the optimal resource allocation strategy in communication countermeasures scenarios is challenging. Thus, we propose a novel decentralized jamming resource allocation algorithm based on multiagent deep reinforcement learning (MADRL) to improve the efficiency of jamming resource allocation in battlefield communication countermeasures. We first model the communication jamming resource allocation problem as a fully cooperative multiagent task, considering the cooperative interrelationship of jamming equipment (JE). Then, to alleviate the nonstationarity feature and high decision dimensions in the multiagent system, we introduce a centralized training with decentralized execution framework (CTDE), which means all JEs are trained with global information and rely on their local observations only while making decisions. Each JE obtains a decentralized policy after the training process. Subsequently, we develop the multiagent soft actor-critic (MASAC) algorithm to enhance the exploration capability of agents and accelerate the learning of cooperative policies among agents by leveraging the maximum policy entropy criterion. Finally, the simulation results are presented to demonstrate that the proposed MASAC algorithm outperforms the existing centralized allocation benchmark algorithms.
Corpus Linguistics and Technical Editing: How Corpora Can Help Copy Editors Adopt a Rhetorical View of Prescriptive Usage Rules

Scholars have long argued that technical editing should be viewed as a rhetorical practice in which copy editors take “a situational approach to each individual task” (Buehler, 1980/2003, p. 458). Yet many editing pedagogies still treat some language-level editing tasks, like those that involve prescriptive usage rules, as mechanical rather than rhetorical. This article discusses how empirical data from corpora can help copy editors adopt a more rhetorical view of prescriptive usage rules and introduces corpus linguistics as a methodology that can contribute to technical editing pedagogy.
Metaheuristic Optimization Algorithm for Optimal Design of Type-2 Fuzzy Controller

The utilization of Le`vy flight to create new candidate solutions is one of the most powerful elements of CS. Candidate solutions are modified using this method by making a lot of minor modifications and a few big jumps. As a result, CS will be able to significantly increase the link between exploration and exploitation while also improving its search capabilities. The cuckoo search optimization (CSO) algorithm is applied to interval type-2 fuzzy logic controller (IT2FLC) in this research to determine the optimal parameters of membership functions (MFs) of interval type-2 fuzzy logic systems (IT2FLSs). The study takes into account two forms of MFs: triangular and trapezoidal. When perturbations are applied during the execution of each control issue, the CSO algorithm's performance and efficiency improve significantly. The proposed approach is tested using two benchmark control problems: water tank controller and inverted pendulum controller.
Circuit quantum electrodynamics with dressed states of a superconducting artificial atom

A dynamical control of the coupling strengths between dressed states and probe photon states is demonstrated with a transmon-like artificial atom coupled to two closely spaced resonant modes. When the atom is driven with one mode, the atom state and driving photon states form the so-called dressed states. Dressed states with sideband index up to 3 were prepared and probed via the strong coupling to the other resonant mode. Spectroscopy reveals that the coupling strengths are "dressed" and can be modulated by the power and sideband index of the driving. The transmission of the probe tone is modulated by the driving microwave amplitude with a Bessel behavior, displaying multi-photon process associated with the inter-atomic level transitions.
Deep Hash Remote-Sensing Image Retrieval Assisted by Semantic Cues

With the significant and rapid growth in the number of remote-sensing images, deep hash methods have become a research topic. The main work of deep hash method is to build a discriminate embedding space through the similarity relation between sample pairs and then map the feature vector into Hamming space for hashing retrieval. We demonstrate that adding a binary classification label as a kind of semantic cue could further improve the retrieval performance. In this work, we propose a new method, which we called deep hashing, based on classification label (DHCL). First, we propose a network architecture, which can classify and retrieve remote-sensing images under a unified framework, and the classification labels are further utilized as the semantic cues to assist in network training. Second, we propose a hash code structure, which can integrate the classification results into the hash-retrieval process to improve accuracy. Finally, we validate the performance of the proposed method on several remote-sensing image datasets and show the superiority of our method.
seL4 Microkernel for Virtualization Use-Cases: Potential Directions towards a Standard VMM

Virtualization plays an essential role in providing security to computational systems by isolating execution environments. Many software solutions, called hypervisors, have been proposed to provide virtualization capabilities. However, only a few were designed for being deployed at the edge of the network in devices with fewer computation resources when compared with servers in the Cloud. Among the few lightweight software that can play the hypervisor role, seL4 stands out by providing a small Trusted Computing Base and formally verified components, enhancing its security. Despite today being more than a decade with seL4 microkernel technology, its existing userland and tools are still scarce and not very mature. Over the last few years, the main effort has been to increase the maturity of the kernel itself, and not the tools and applications that can be hosted on top. Therefore, it currently lacks proper support for a full-featured userland Virtual Machine Monitor, and the existing one is quite fragmented. This article discusses the potential directions to a standard VMM by presenting our view of design principles and the feature set needed. This article does not intend to define a standard VMM, we intend to instigate this discussion through the seL4 community.
Prioritized Hindsight with Dual Buffer for Meta-Reinforcement Learning

Sharing prior knowledge across multiple robotic manipulation tasks is a challenging research topic. Although the state-of-the-art deep reinforcement learning (DRL) algorithms have shown immense success in single robotic tasks, it is still challenging to extend these algorithms to be applied directly to resolve multi-task manipulation problems. This is mostly due to the problems associated with efficient exploration in high-dimensional state and continuous action spaces. Furthermore, in multi-task scenarios, the problem of sparse reward and sample inefficiency of DRL algorithms is exacerbated. Therefore, we propose a method to increase the sample efficiency of the soft actor-critic (SAC) algorithm and extend it to a multi-task setting. The agent learns a prior policy from two structurally similar tasks and adapts the policy to a target task. We propose a prioritized hindsight with dual experience replay to improve the data storage and sampling technique, which, in turn, assists the agent in performing structured exploration that leads to sample efficiency. The proposed method separates the experience replay buffer into two buffers to contain real trajectories and hindsight trajectories to reduce the bias introduced by the hindsight trajectories in the buffer. Moreover, we utilize high-reward transitions from previous tasks to assist the network in easily adapting to the new task. We demonstrate the proposed method based on several manipulation tasks using a 7-DoF robotic arm in RLBench. The experimental results show that the proposed method outperforms vanilla SAC in both a single-task setting and multi-task setting.
Parallel Learning of Dynamics in Complex Systems

Dynamics always exist in complex systems. Graphs (complex networks) are a mathematical form for describing a complex system abstractly. Dynamics can be learned efficiently from the structure and dynamics state of a graph. Learning the dynamics in graphs plays an important role in predicting and controlling complex systems. Most of the methods for learning dynamics in graphs run slowly in large graphs. The complexity of the large graph’s structure and its nonlinear dynamics aggravate this problem. To overcome these difficulties, we propose a general framework with two novel methods in this paper, the Dynamics-METIS (D-METIS) and the Partitioned Graph Neural Dynamics Learner (PGNDL). The general framework combines D-METIS and PGNDL to perform tasks for large graphs. D-METIS is a new algorithm that can partition a large graph into multiple subgraphs. D-METIS innovatively considers the dynamic changes in the graph. PGNDL is a new parallel model that consists of ordinary differential equation systems and graph neural networks (GNNs). It can quickly learn the dynamics of subgraphs in parallel. In this framework, D-METIS provides PGNDL with partitioned subgraphs, and PGNDL can solve the tasks of interpolation and extrapolation prediction. We exhibit the universality and superiority of our framework on four kinds of graphs with three kinds of dynamics through an experiment.
Latency-constrained DNN architecture learning for edge systems using zerorized batch normalization

Deep learning applications have been widely adopted on edge devices, to mitigate the privacy and latency issues of accessing cloud servers. Deciding the number of neurons during the design of a deep neural network to maximize performance is not intuitive. Particularly, many application scenarios are real-time and have a strict latency constraint, while conventional neural network optimization methods do not directly change the temporal cost of model inference for latency-critical edge systems. In this work, we propose a latency-oriented neural network learning method to optimize models for high accuracy while fulfilling the latency constraint. For efficiency, we also introduce a universal hardware-customized latency predictor to optimize this procedure to learn a model that satisfies the latency constraint by only a one-shot training process. The experiment results reveal that, compared to state-of-the-art methods, our approach can well-fit the 'hard' latency constraint and achieve high accuracy. Under the same training settings as the original model and satisfying a 34 ms latency constraint on the ImageNet-100 dataset, we reduce GoogLeNet's latency from 40.32 ms to 34 ms with a 0.14% accuracy reduction on the NVIDIA Jetson Nano. When coupled with quantization, our method can be further improved to only 0.04% drop for GoogLeNet. On the NVIDIA Jetson TX2, we compress VGG-19 from 119.98 ms to 34 ms and even improve its accuracy by 0.5%, and we scale GoogLeNet up from 20.27 ms to 34 ms and achieve higher accuracy by 0.78%. We also open source this framework at https://github.com/ntuliuteam/ZeroBN.