Reviewerly/exHarmony huggingface dataset:
DP-Hybrid: A Two-Layer Consensus Protocol for High Scalability in Permissioned Blockchain

The permissioned blockchain has attracted the attention of multiple industries like the supply chain due to its decentralization and data tamper resistance. In these industries applications, the permissioned blockchain maintained by multiple participants often has a large number of nodes. The PBFT consensus is commonly used in the permissioned blockchain, but it requires a large amount of message transmission to reach consensus, resulting in poor scalability. In this paper, we propose DP-Hybrid, a novel two-layer consensus protocol, to reduce the communication costs and improve scalability. Specifically, nodes use PBFT to establish K autonomous systems at the bottom layer, and then participate at the top layer with Constrained PoW consensus protocol. DP-Hybrid reduces the communication costs from PBFT’s $$O(N^2)$$ to $$O(N^2/K^2)$$ . The experiment results show that DP-Hybrid’s throughput is always about 10 times that of PBFT when the number of nodes increases.
The regulation of virtual currencies in comparative perspective: new private money or niche technological innovation?

Purpose This paper aims to situate virtual currencies (VCs) in a landscape of regulatory questions that help orient the direction and purpose of a possible legal approach, vis-à-vis this relatively recent technological and financial phenomenon. Design/methodology/approach The triangulation of historical overview and comparative examination of regulatory interventions allows to situate VCs in relation to a range of regulatory topics: from monetary policy, to fundraising and money laundering. First, the paper charts the emergence of VCs in time, and situates this innovation on a continuum with historically observed forms of private money. Second, it provides an overview of different regulatory approaches that can be observed on a comparative landscape. Findings At present, several features of VC schemes (particularly their deflationary character and fixed supply) prevent them from working as private money, competitive with sovereign currency. Instead, three specific kinds of uses – as security tokens, utility tokens and currency tokens – offer a more realistic picture of the risks and potentials associated with different forms of use. Originality/value The paper puts forth an integrated framework for devising a more sensitive regulatory approach towards VCs.
Performance Analysis of Software with a Variant NoSQL Data Schemes

The popularity of web applications and platforms for collecting big data has set the goal of efficient data storage. NoSQL databases have become widely used for data storage and scaling. However, there are also a number of problems associated with the choice of efficient data storage and data schemes. The solution of these problems requires an understanding of the runtime conditions and possible loading of the system, which relies primarily on dedicated computing resources. The same data storage schemes can show different efficiency while under different critical load conditions or different computing infrastructures. In this paper, we propose a framework and experiment methodology for two NoSQL storage schemes that allow us to evaluate the most preferred option in the design of the developed application. The paper presents an analysis of the efficiency of two database schemas for maintaining a reliable saving of semi-structured data stream with specified characteristics.
MobileHand: Real-Time 3D Hand Shape and Pose Estimation from Color Image

We present an approach for real-time estimation of 3D hand shape and pose from a single RGB image. To achieve real-time performance, we utilize an efficient Convolutional Neural Network (CNN): MobileNetV3-Small to extract key features from an input image. The extracted features are then sent to an iterative 3D regression module to infer camera parameters, hand shapes and joint angles for projecting and articulating a 3D hand model. By combining the deep neural network with the differentiable hand model, we can train the network with supervision from 2D and 3D annotations in an end-to-end manner. Experiments on two publicly available datasets demonstrate that our approach matches the accuracy of most existing methods while running at over 110 Hz on a GPU or 75 Hz on a CPU.
Deep Cross-modal Proxy Hashing

Due to the high retrieval efficiency and low storage cost for cross-modal search tasks, cross-modal hashing methods have attracted considerable attention from the researchers. For the supervised cross-modal hashing methods, how to make the learned hash codes sufficiently preserve semantic information contained in the label of datapoints is the key to further enhance the retrieval performance. Hence, almost all supervised cross-modal hashing methods usually depend on defining similarities between datapoints with the label information to guide the hashing model learning fully or partly. However, the defined similarity between datapoints can only capture the label information of datapoints partially and misses abundant semantic information, which then hinders the further improvement of retrieval performance. Thus, in this paper, different from previous works, we propose a novel cross-modal hashing method without defining the similarity between datapoints, called Deep Cross-modal Proxy Hashing (DCPH). Specifically, DCPH first trains a proxy hashing network to transform each category information of a dataset into a semantic discriminative hash code, called proxy hash code. Each proxy hash code can preserve the semantic information of its corresponding category well. Next, without defining the similarity between datapoints to supervise the training process of the modality-specific hashing networks, we propose a novel <i>margin-dynamic-softmax loss</i> to directly utilize the proxy hashing codes as supervised information. Finally, by minimizing the novel <i>margin-dynamic-softmax loss</i>, the modality-specific hashing networks can be trained to generate hash codes that can simultaneously preserve the cross-modal similarity and abundant semantic information well. Extensive experiments on three benchmark datasets show that the proposed method outperforms the state-of-the-art baselines in the cross-modal retrieval tasks.
Learnability with Indirect Supervision Signals

Learning from indirect supervision signals is important in real-world AI applications when, often, gold labels are missing or too costly. In this paper, we develop a unified theoretical framework for multi-class classification when the supervision is provided by a variable that contains nonzero mutual information with the gold label. The nature of this problem is determined by (i) the transition probability from the gold labels to the indirect supervision variables and (ii) the learner's prior knowledge about the transition. Our framework relaxes assumptions made in the literature, and supports learning with unknown, non-invertible and instance-dependent transitions. Our theory introduces a novel concept called \emph{separation}, which characterizes the learnability and generalization bounds. We also demonstrate the application of our framework via concrete novel results in a variety of learning scenarios such as learning with superset annotations and joint supervision signals.
Artificial Potential Field for Dynamic Obstacle Avoidance with MPC-Based Trajectory Tracking for Multiple Quadrotors

This paper's objective is to navigate multiple quadrotors using Artificial Potential Field (APF) technique for obstacle avoidance in a known environment. The idea is to use the dynamic potential field concept that does not depend on the quadrotor's dynamics to ensure avoiding collisions among quadrotors. Each quadrotor navigates with repulsion force surrounding it to avoid obstacles including other quadrotors. The quadrotor follows its free-collision path using Model Predictive Control (MPC) as the trajectory tracker. The MPC controller task is to control each quadrotor independently to follow the planned path.
Building Undetectable Covert Channels Over Mobile Networks with Machine Learning

Covert channel is an important way to transmit covert message and implement covert communication through the network. However, the existing research on covert channel cannot meet the security requirements of covert communication in the complex mobile networks. There are problems such as low transmission capacity, insufficient adaptability to network complexity, and difficulty in countering the detection of covert channels by adversaries. In this paper, we preprocess video traffics over mobile network, and extract traffic features to build a target model. We analysis traffic data by machine learning method to improve the undetectability of the covert channel. Based on the characteristics of real-time interactive communication, gray code and interval block are employed to improve the robustness of covert communication in the complex network environment. A cover channel over VoLTE video traffic, which is based on video packet reordering supported by machine learning algorithms, is proposed to realize the awareness and confrontation of detection attacks on the network side. The covert channel is built over mobile network to ensure end-to-end reliable covert communication under complex network conditions.
Facial recognition and 3D non-rigid registration

One of the most efficient tool for human face recognition is neural networks. However, the result of recognition can be spoiled by facial expressions and other deviation from the canonical face representation. In this paper, we propose a resampling method of human faces represented by 3D point clouds. The method is based on a non-rigid Iterative Closest Point (ICP) algorithm. To improve the facial recognition performance, we use a combination of the proposed method and convolutional neural network (CNN). Computer simulation results are provided to illustrate the performance of the proposed method.
Superradiant phase transition in quantum Rabi dimer with staggered couplings

We study the quantum phase transitions and the critical behavior of the quantum Rabi dimer model, where the two cavities are not equivalent. In our model the coupling strength in each cavity is individually tuned as what usually happens in experimental systems. In the frequency-ratio limit with an infinite ratio of the atomic transition frequency to the cavity frequency, the model is analytically solved so that we find a superradiant phase transition and the order parameter of the system is found to be the normal mode of the linear combination of the two cavity modes. Thus we can determine precisely the critical points and depict the associated phase diagram. We also extract the critical exponents through calculating the universal scaling function and thus conclude that the phase transition of the model belongs to the mean-field universality class. The von Neumann entropy and l1 norm coherence are adopted to analyze the quantum phase transition and the associate critical phenomena. The critical points and critical exponents extracted from these information measures agree with ones distilled by the order parameter. We expect this work could stimulate the further study on the multi-cavity models with disorder, where a series of nonequivalent cavities are cascaded.
A Data Privacy-preserving Method for Students’ Physical Health Monitoring by Using Smart Wearable Devices

Nowadays, physical fitness of young people has been widely concerned by all fields of research. Using smart wearable devices to monitor students' physical fitness level is a good choice. However, due to its limited computing performance, the use of smart wearable devices faces the risk of privacy disclosure. We propose a privacy-preserving framework for smart wearable devices, and an improved algorithm based on differential privacy according to the characteristics of smart wearable devices. Through a shielding condition, we choose the appropriate data to publish, which reduces the possibility of attackers to obtain users' privacy. In the following tests, we take approximate entropy as the indicator to reflect the security of results, and it finally conclude that compared with the traditional method, the algorithm proposed in this paper has a better ability of privacy preserving.
MAXENT3D_PID: An Estimator for the Maximum-Entropy Trivariate Partial Information Decomposition

Chicharro (2017) introduced a procedure to determine multivariate partial information measures within the maximum entropy framework, separating unique, redundant, and synergistic components of information. Makkeh, Theis, and Vicente (2018) formulated the latter trivariate partial information measure as Cone Programming. In this paper, we present MAXENT3D_PID, a production-quality software that computes the trivariate partial information measure based on the Cone Programming model. We describe in detail our software, explain how to use it, and perform some experiments reflecting its accuracy in estimating the trivariate partial information decomposition.
BGP Route Leaks Detection Using Supervised Machine Learning Technique

The route leaks problem is considered one of the unsolved Border Gateway Protocol problems for more than fifteen years ago. It has a large negative impact on global internet stability and reliability. This problem is hard to be prevented due to human errors and misconfigurations, and hard to be detected due to the confidentiality of autonomous systems relationships.The paper proposes a new taxonomy to the different types of route leaks depending on their effects on the Border Gateway Protocol traffic, the first real route leaks incidents dataset, and a complete real-time detection system based on a supervised learning classification method. The work compares three classifiers (Decision Tree, Random Forest Trees, and Support Vector Machines). The proposed system prototype can detect and classify route leaks from normal updates with an accuracy of 87% and time complexity of O(NM), where N is the number of prefixes each with M prefix length.
Competence-Level Prediction and Resume &amp; Job Description Matching Using Context-Aware Transformer Models

This paper presents a comprehensive study on resume classification to reduce the time and labor needed to screen an overwhelming number of applications significantly, while improving the selection of suitable candidates.A total of 6,492 resumes are extracted from 24,933 job applications for 252 positions designated into four levels of experience for Clinical Research Coordinators (CRC).Each resume is manually annotated to its most appropriate CRC position by experts through several rounds of triple annotation to establish guidelines.As a result, a high Kappa score of 61% is achieved for interannotator agreement.Given this dataset, novel transformer-based classification models are developed for two tasks: the first task takes a resume and classifies it to a CRC level (T1), and the second task takes both a resume and a job description to apply and predicts if the application is suited to the job (T2).Our best models using section encoding and multi-head attention decoding give results of 73.3% to T1 and 79.2% to T2.Our analysis shows that the prediction errors are mostly made among adjacent CRC levels, which are hard for even experts to distinguish, implying the practical value of our models in real HR platforms.
A 3D Path Planning Algorithm Based on PSO for Autonomous UAVs Navigation

In this paper, a new three-dimensional path planning approach with obstacle avoidance for UAVs is proposed. The aim is to provide a computationally-fast on-board sub-optimal solution for collision-free path planning in static environments. The optimal 3D path is an NP (non-deterministic polynomial-time) hard problem which may be solved numerically by global optimization algorithms such as the Particle Swarm Optimization (PSO). Application of PSO to the 3D path planning class of problems faces typical challenges such slow convergence rate. It is shown that the performance may be improved markedly by implementing a novel parallel approach and incorporation of new termination conditions. Moreover, the exploration and exploitation parameters are optimized to find a reasonably short, smooth, and safe path connecting the way-points. As an additional precaution to avoid collisions, obstacle dimensions are artificially slightly enlarged. To verify the robustness of the algorithm, several simulations are carried out by varying the number of obstacles, their volume and location in space. A certain number of simulations exploiting the random nature of PSO are performed to highlight the computational efficiency, and the robustness of this new approach.
Analysis of Autopsy Mobile Forensic Tools against Unsent Messages on WhatsApp Messaging Application

This paper discusses the new feature implemented in most social media messaging applications: the unsent feature, where the sender can delete the message he sent both in the sender and the recipient devices. This new feature poses a new challenge in mobile forensic, as it could potentially delete sent messages that can be used as evidence without the means to retrieve it. This paper aims to analyze how well Autopsy open-source mobile forensics tools in extracting and identifying the deleted messages, both that are sent or received. The device used in this paper is a Redmi Xiaomi Note 4, which has its userdata block extracted using linux command, and the application we're using is WhatsApp. Autopsy will analyze the extracted image and see what information can be extracted from the unsent messages. From the result of our experiment, Autopsy is capable of obtaining substantial information, but due to how each vendor and mobile OS store files and databases differently, only WhatsApp data can be extracted from the device. And based on the WhatsApp data analysis, Autopsy is not capable of retrieving the deleted messages. However it can detect the traces of deleted data that is sent from the device. And using sqlite3 database browser, the author can find remnants of received deleted messages from the extracted files by Autopsy.
Automated analysis with Apriori algorithm

research-article Share on Automated analysis with Apriori algorithm: deriving guidance for working-level cybersecurity curricula Authors: Honggoo Kang Korea University School of Cybersecurity/Defense Security Support Command Korea University School of Cybersecurity/Defense Security Support CommandView Profile , Sangjin Lee Korea University School of Cybersecurity Korea University School of CybersecurityView Profile Authors Info & Claims ACM InroadsVolume 11Issue 4December 2020 pp 20–26https://doi.org/10.1145/3410471Online:12 November 2020Publication History 0citation62DownloadsMetricsTotal Citations0Total Downloads62Last 12 Months20Last 6 weeks6 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Type-aware Convolutional Neural Networks for Slot Filling

The slot filling task aims at extracting answers for queries about entities from text, such as "Who founded Apple". In this paper, we focus on the relation classification component of a slot filling system. We propose type-aware convolutional neural networks to benefit from the mutual dependencies between entity and relation classification. In particular, we explore different ways of integrating the named entity types of the relation arguments into a neural network for relation classification, including a joint training and a structured prediction approach. To the best of our knowledge, this is the first study on type-aware neural networks for slot filling. The type-aware models lead to the best results of our slot filling pipeline. Joint training performs comparable to structured prediction. To understand the impact of the different components of the slot filling pipeline, we perform a recall analysis, a manual error analysis and several ablation studies. Such analyses are of particular importance to other slot filling researchers since the official slot filling evaluations only assess pipeline outputs. The analyses show that especially coreference resolution and our convolutional neural networks have a large positive impact on the final performance of the slot filling pipeline. The presented models, the source code of our system as well as our coreference resource is publicly available.
Approximate minimization of weighted tree automata

This paper studies the following approximate minimization problem: given a minimal weighted tree automaton A with n states recognizing a weighted tree language f, can we construct a smaller automaton Aˆ with nˆ<n states recognizing a language fˆ that is a good approximation of f? The corresponding problem for weighted automata on strings was recently studied by Balle et al. [16], [15], where the authors introduced a new canonical form for weighted automata called singular value automata inspired by spectral methods, and showed that truncating such canonical form yields a solution for the problem satisfying a certain approximation criteria. In this paper we take a similar approach and show that in the tree case one can obtain an analogous canonical form that we call singular value tree automata, and use it to study the approximate minimization problem for weighted tree automata. We first establish the existence of this canonical form for weighted tree automata and then provide bounds on the quality of the resulting approximation method based on truncation. We also study the problem of computing the canonical form given a minimal weighted tree automaton and show that in the tree case this task is considerably more complicated than in the string case. In particular, computing the canonical form reduces to solving a system of polynomial equations. By further reducing this problem to the computation of generalized partition functions for weighted tree automata, we propose and analyze two methods for computing the canonical form based on iterative methods: fixed point iteration and Newton's method. Our analysis of Newton's method unveils a connection between iterative methods and sequences of sets of trees satisfying a certain technical condition that might be of independent interest.
Biometric Authentication System for Industrial Applications using Speaker Recognition

Biometric authentication has gained popularity in recent years as knowledge-based authentication methods overburden users. This is because users are required to remember distinct and secure passwords for each system where they are registered. As speech recognition systems are embraced the smart technologies of the future, speaker recognition is going to be a natural authentication mechanism. This paper presents a biometric authentication system for industrial applications using speaker recognition. The system uses an autocorrelation voice activity detector and gaussian mixture models to identify users of the system. The equal error rate of the overall designed system was found to be 12.1% and the average verification time was found to be 4.67 seconds.