Reviewerly/exHarmony huggingface dataset:
Neural Text Segmentation and its Application to Sentiment Analysis

Text segmentation is a fundamental task in natural language processing. Depending on the levels of granularity, the task can be defined as segmenting a document into topical segments, or segmenting a sentence into elementary discourse units (EDUs). Traditional solutions to the two tasks heavily rely on carefully designed features. The recently proposed neural models do not need manual feature engineering, but they either suffer from sparse boundary tags or cannot efficiently handle the issue of variable size output vocabulary. In light of such limitations, we propose a generic end-to-end segmentation model, namely <inline-formula><tex-math notation="LaTeX">${\mathrm{S}\scriptstyle{\mathrm{EG}}}{\mathrm{B}\scriptstyle{\mathrm{OT}}}$</tex-math></inline-formula> , which first uses a bidirectional recurrent neural network to encode an input text sequence. <inline-formula><tex-math notation="LaTeX">${\mathrm{S}\scriptstyle{\mathrm{EG}}}{\mathrm{B}\scriptstyle{\mathrm{OT}}}$</tex-math></inline-formula> then uses another recurrent neural networks, together with a pointer network, to select text boundaries in the input sequence. In this way, <inline-formula><tex-math notation="LaTeX">${\mathrm{S}\scriptstyle{\mathrm{EG}}}{\mathrm{B}\scriptstyle{\mathrm{OT}}}$</tex-math></inline-formula> does not require any hand-crafted features. More importantly, <inline-formula><tex-math notation="LaTeX">${\mathrm{S}\scriptstyle{\mathrm{EG}}}{\mathrm{B}\scriptstyle{\mathrm{OT}}}$</tex-math></inline-formula> inherently handles the issue of variable size output vocabulary and the issue of sparse boundary tags. In our experiments, <inline-formula><tex-math notation="LaTeX">${\mathrm{S}\scriptstyle{\mathrm{EG}}}{\mathrm{B}\scriptstyle{\mathrm{OT}}}$</tex-math></inline-formula> outperforms state-of-the-art models on two tasks: document-level topic segmentation and sentence-level EDU segmentation. As a downstream application, we further propose a hierarchical attention model for sentence-level sentiment analysis based on the outcomes of <inline-formula><tex-math notation="LaTeX">${\mathrm{S}\scriptstyle{\mathrm{EG}}}{\mathrm{B}\scriptstyle{\mathrm{OT}}}$</tex-math></inline-formula> . The hierarchical model can make full use of both word-level and EDU-level information simultaneously for sentence-level sentiment analysis. In particular, it can effectively exploit EDU-level information, such as the inner properties of EDUs, which cannot be fully encoded in word-level features. Experimental results show that our hierarchical model achieves new state-of-the-art results on the Movie Review and Stanford Sentiment Treebank benchmarks.
Learning Face Image Super-Resolution Through Facial Semantic Attribute Transformation and Self-Attentive Structure Enhancement

Face super-resolution is a domain-specific super-resolution (SR) problem of generating high-resolution (HR) face images from low-resolution (LR) inputs. Even though existing face SR methods have achieved great performance on the global region evaluation, most of them cannot restore local attributes and structure reasonably, especially to ultra-resolve tiny LR face images (16 × 16 pixels) to its larger version (8 × upscaling factor). In this paper, we propose an open source face SR framework based on facial semantic attribute transformation and self-attentive structure enhancement. Specifically, the proposed framework introduces face semantic information (i.e., face attributes) and face structure information (i.e., face boundaries) in a successive two-stage fashion. In the first stage, an Attribute Transformation Network (AT-Net) is established. It upsamples LR face images to HR feature maps and then combines facial attributes with these features to generate the intermediate HR results with rational attributes. In the second stage, a Structure Enhancement Network (SE-Net) is built. It simultaneously extracts face features and estimates facial boundary heatmaps from the inputs, and then fuses them to output the final HR face images. Extensive experiments demonstrate that our method achieves superior super-resolved results and outperforms the state-of-the-art methods.
Beh-Raft-Chain: A Behavior-Based Fast Blockchain Protocol for Complex Networks

By facilitating multiple independent owners to jointly control a distributed network, blockchain can be used to solve the problem of device collaboration in complex networks (e.g., 5G, health care industries) through a distributed consensus mechanism. However, the state-of-the-art blockchain-based solutions cannot meet the demand of high transaction rate for those applications, due to the unavoidable data synchronization cost in decentralized systems. To address this issue, recent research splits blockchain nodes into multiple groups as parallel shardings to improve scalability at the cost of increased communication and storage per node. This paper proposes a fast and secure distributed blockchain protocol to reduce the traffic complexity while enhancing the transaction rates and the capability of fault-toleration. We introduce Proof-of-Behavior (PoB), a behavior-based incentive mechanism, for stimulating honest behavior and neutralizing malicious attacks. We design a blockchain protocol by integrating PoB with Raft, another classic consensus protocol with supervision, called Beh-Raft-Chain. Our approach replaces Practical Byzantine Fault Tolerance (PBFT) with Behavior-based Raft to lower the traffic complexity to O(n) and boost the capability of fault-toleration from n/4 to n/3, where n is the scale of blockchain. In our solution, we weigh all nodes based on their money and behaviors, and then set an adjustment parameter to increase the probability of candidate nodes being chosen beyond only a few nodes with the highest weight, in order to incentivize honest behavior in our mechanism. Our comparative experiments confirm Beh-Raft-Chain's theoretical low complexity and high fault-toleration properties.
Nuisance parameter problem in quantum estimation theory: tradeoff relation and qubit examples

In this paper, we analyze quantum-state estimation problems when some of the parameters are of no interest to be estimated. In classical statistics, these irrelevant parameters are called nuisance parameters and this problem is of great importance in many practical applications of statistics. However, little is known regarding the effects of nuisance parameters in quantum-state estimation problems. The main contribution of this paper is first to formulate the nuisance parameter problem for the quantum-state estimation theory, then to propose a method of how to eliminate the nuisance parameters to obtain an estimation error bound for the parameters of interest. We also develop useful methods of dealing with the nuisance parameter problem in the quantum case and reveal the significant difference from the classical case. In particular, we clarify an intrinsic tradeoff relation to estimate the nuisance parameters and parameters of interest. The general qubit model is examined in detail to emphasize that we cannot ignore the effects of the nuisance parameters in general. Several examples in qubit systems are worked out to illustrate our findings.
Efficient File Dissemination in Data Center Networks With Priority-Based Adaptive Multicast

In today's data center networks (DCN), cloud applications commonly disseminate files from a single source to a group of receivers for service deployment, data replication, software upgrade, etc. For these group communication tasks, recent advantages of software-defined networking (SDN) provide bandwidth-efficient ways-they enable DCN to establish and control a large number of explicit multicast trees on demand. Yet, the benefits of data center multicast are severely limited, since there does not exist a scheme that could prioritize multicast transfers respecting the performance metrics wanted by today's cloud applications, such as pursuing small mean completion times or meeting soft-time deadlines with high probability. To this end, we propose PAM (Priority-based Adaptive Multicast), a preemptive, decentralized, and ready-deployable rate control protocol for data center multicast. At the core, switches in PAM explicitly control the sending rates of concurrent multicast transfers based on their desired priorities and the available link bandwidth. With different policies of priority generation, PAM supports a range of scheduling goals. We not only prototype PAM upon the emerged P4-based programmable switch with novel approximation designs, but also evaluate its performance with ns3-based extensive simulations. Results imply that PAM is ready-deployable; it converges very fast, has negligible impacts on coexisting TCP traffic, and always performs near-optimal priority-based multicast scheduling.
Global DIC approach guided by a cross-correlation based initial guess for HR-EBSD and on-axis HR-TKD

Digital image correlation (DIC) techniques usually performed on deforming speckle patterns are applied here on electron diffraction patterns (EDP) in order to map disorientations with high angular resolution, as well as geometrically necessary dislocation densities and elastic strains. The proposed approach relies on a DIC analysis which is conducted independently from the microscope calibration. It registers EDP as a whole through a single and large region of interest whose relative deformation with respect to a reference EDP is described by a first-order homography. Subpixel registration is performed iteratively in the spatial domain using an inverse-compositional Gauss-Newton (IC-GN) algorithm which integrates the correction of the optical distortions. Its robustness against large orientation changes as well as its computational efficiency are improved by mean of an automated and path-independent initial guess which fairly captures the effects of large rotations on the EDP. Using successive Fourier-Mellin and Fourier transforms based cross-correlation techniques, the initial guess measures the in-plane rotation and translation between the reference and the target EDP, respectively. The performances of the technique are illustrated in markedly plastically deformed steels using two electron diffraction techniques in the scanning electron microscope. A 15% deformed interstitial free steel is investigated by means of electron backscattered diffraction (EBSD) while a quenched and tempered oxide dispersed strengthened steel subjected to martensitic transformation is characterized using a recently developed on-axis Transmission Kikuchi Diffraction (TKD) configuration.
Multiple Swarm Fruit Fly Optimization Algorithm Based Path Planning Method for Multi-UAVs

The path planning of unmanned aerial vehicles (UAVs) in the threat and countermeasure region is a constrained nonlinear optimization problem with many static and dynamic constraints. The fruit fly optimization algorithm (FOA) is widely used to handle this kind of nonlinear optimization problem. In this paper, the multiple swarm fruit fly optimization algorithm (MSFOA) is proposed to overcome the drawback of the original FOA in terms of slow global convergence speed and local optimum, and then is applied to solve the coordinated path planning problem for multi-UAVs. In the proposed MSFOA, the whole fruit fly swarm is divided into several sub-swarms with multi-tasks in order to expand the searching space to improve the searching ability, while the offspring competition strategy is introduced to improve the utilization degree of each calculation result and realize the exchange of information among various fruit fly sub-swarms. To avoid the collision among multi-UAVs, the collision detection method is also proposed. Simulation results show that the proposed MSFOA is superior to the original FOA in terms of convergence and accuracy.
Control of Robot Arm Motion Using Trapezoid Fuzzy Two-Degree-of-Freedom PID Algorithm

Symmetries play very important in the dynamics of robot systems. The relevant control of robot arm motion with fault diagnosis including the optimized fuzzy algorithm based on the error rate adjustment P, I, D value (Fuzzy PID algorithm) model relies on symmetry principles. A robot is a kind of mechanical device that can program and perform certain operations and mobile tasks under automatic control. The manipulator is a very complex multi-input multi-output non-linear system and the main actuator of the robot. This paper focuses on the design of a control algorithm for a two-degree-of-freedom (2-DOF) manipulator. First, the mathematical model of a 2-DOF articulated manipulator is established, that is, the functional relationship between the input driving force vector and the output rotation angle vector of a 2-DOF manipulator. Then, a set of trajectory planning algorithms are designed by using gradient model control, which can calculate the trajectory of the end-effector of a 2-DOF manipulator according to the user’s task requirements. The experimental results verify the effectiveness of the proposed algorithm.
GFNet: Gate Fusion Network With Res2Net for Detecting Salient Objects in RGB-D Images

The performance of recent RGB-D salient object detectors has significantly improved owing to their integration of convolutional neural networks (CNNs). However, most existing salient object detection (SOD) methods represent features using a VGGNet backbone, which lacks the ability to retain complete RGB and depth modals and must compensate by applying several skip connections. In this letter, we propose a gate fusion network (GFNet) with Res2Net architecture to solve this problem. GFNet consists of two interacting Res2Net block encoder streams and four gate fusion block (GFB) decoders to interconnect the streams and fuse features. Res2Net blocks have a robust feature retention mechanism to ensure that the decoders can learn complete information, while the GFB formulates the interdependences of the encoders and eliminates noise via a gate mechanism. We evaluated GFNet using two popular RGB-D salient detection benchmark datasets (NJU2000 and NLPR) and achieved state-of-the art performance.
Two-Stage Human Activity Recognition Using 2D-ConvNet

There is huge requirement of continuous intelligent monitoring system for human activity recognition in various domains like public places, automated teller machines or healthcare sector.Increasing demand of automatic recognition of human activity in these sectors and need to reduce the cost involved in manual surveillance have motivated the research community towards deep learning techniques so that a smart monitoring system for recognition of human activities can be designed and developed.Because of low cost, high resolution and ease of availability of surveillance cameras, the authors developed a new two-stage intelligent framework for detection and recognition of human activity types inside the premises.This paper, introduces a novel framework to recognize single-limb and multi-limb human activities using a Convolution Neural Network.In the first phase single-limb and multi-limb activities are separated.Next, these separated single and multi-limb activities have been recognized using sequence-classification.For training and validation of our framework we have used the UTKinect-Action Dataset having 199 actions sequences performed by 10 users.We have achieved an overall accuracy of 97.88% in real-time recognition of the activity sequences.
Multiple element mapping and in-situ S isotopes of Au-carrying pyrite of Shuiyindong gold deposit, southwestern China using NanoSIMS: Constraints on Au sources, ore fluids, and mineralization processes

This study employed high-sensitivity and high-resolution secondary ionizing mass spectrometry (NanoSIMS) to determine the distribution of Au, As, Cu, Se, and S isotopes of pyrite from the Shuiyindong Au deposit, southwestern China. NanoSIMS maps show that Au occurs as solid solution, and heterogeneously distributes at micron scale in the ore pyrite, which is the overgrowth of the preexisting synsedimentary or hydrothermal pyrite core, commonly rimmed again by the As- and Au-poor outermost zone. There is no detectable Au in the synsedimentary pyrite. Invisible Au was observed to fill in the submicron-scale cracks of the ore pyrite. Meanwhile, element line profile analysis shows that Au occurs in three discrete climaxes in the ore pyrite. In-situ S isotopic analyses identified two types of S sources. The synsedimentary pyrites have large negative δ34S values ranging from −38.3‰ to −18.8‰, which is a typical biogenetic S generated by bacterial reduction of sea water sulfate. The hydrothermal pyrites with zoned texture have a narrow δ34S range of −3.5‰～−7.4‰ with a median of 2.3‰, similar to the recently published sulfur isotope data of the Southwest China’s Carlin-type gold deposits except for the Lannigou Mine. Combining the distribution of Au and S isotopes in pyrite indicates that the Au-carrying zones have lower δ34S values than that of the ore-unrelated zones. These NanoSIMS observations indicate that the Au mineralization was closely associated with strong deformation of the host rock. The Au forming the deposit was exotic, instead of the reactivated Au in the preexisting pyrite formed in the synsedimentary basin. The mineralization progress was multistage. The distribution of Au and S isotopes is hard to interpret by the viewpoint of metamorphic fluid or fluid pressure fluctuation. Instead, it is more consistent with magmatic fluid, likely contaminated by sedimentary sulfur during its ascending to the surface. In addition, the multistage ore formation pattern discovered in this study poses new challenges to determine the mineralization age of the Au deposits in southwestern China.
Digital Maturity: Definition and Model

Digital maturity is a phenomenon that has emerged along with the digital economy and Industry 4.0.Till now there is no common definition and understating of this process, so this research topic has plenty of room to study and explore.This article contains the definition of digital maturity based on the analysis of existing definitions, as well as overview and analysis of digital maturity models.The new model of digital maturity is as well represented, alongside with the first results received from applying it to clothing factory and oil and gas equipment manufacturing organization.Recommendations for these two companies are given based on results, recommendations were developed to help companies achieve a higher level of digital maturity and improve their performance, help them to achieve their operational goals, etc.
What’s really ‘Happning’? A forensic analysis of Android and iOS Happn dating apps

With today's world revolving around online interaction, dating applications (apps) are a prime example of how people are able to discover and converse with others that may share similar interests or lifestyles, including during the recent COVID-19 lockdowns. To connect the users, geolocation is often utilized. However, with each new app comes the possibility of criminal exploitation. For example, while apps with geolocation feature are intended for users to provide personal information that drive their search to meet someone, that same information can be used by hackers or forensic analysts to gain access to personal data, albeit for different purposes. This paper examines the Happn dating app (versions 9.6.2, 9.7, and 9.8 for iOS devices, and versions 3.0.22 and 24.18.0 for Android devices), which geographically works differently compared to most notable dating apps by providing users with profiles of other users that might have passed by them or in the general radius of their location. Encompassing both iOS and Android devices along with eight varying user profiles with diverse backgrounds, this study aims to explore the potential for a malicious actor to uncover the personal information of another user by identifying artifacts that may pertain to sensitive user data.
Detection of Abandoned and Stolen Objects Based on Dual Background Model and Mask R-CNN

Dual background models have been widely used for detecting stationary objects in video surveillance systems.However, there is a problem that both abandoned and stolen objects are equally detected as stationary objects, making it difficult to distinguish them.Another problem is the ghost region created by shadow shift or light changes, which makes the discrimination issue more complicated.In this paper, we present an efficient method to distinguish abandoned objects, stolen objects, and ghost regions in the surveillance video.This method contains two main strategies: the first one is the dual background model for extracting candidate stationary objects, the second one is object segmentation based on mask regions with CNN features (Mask R-CNN) for providing the object mask information.The basic idea is: given a candidate stationary object from the background model, it is checked whether a corresponding segmented object exists in the current video frame or the previous background frame to take into account the current and past situations.And the final state of the candidate stationary object is determined by considering various situations through the comparative analysis technique presented in this paper.The proposed algorithm has qualitatively experimented with our own dataset focusing on the discrimination issue, which generated satisfactory results.Therefore, it is expected to be widely applied to automatic detection of stolen objects as well as abandoned objects in open environments such as exhibition halls and public parks where existing intrusion detection-based security services are difficult to be deployed.
Deep subspace clustering to achieve jointly latent feature extraction and discriminative learning

Deep subspace clustering based on autoencoder and self-expression layer has become popular for clustering method. However, these models only focus on feature extraction by minimizing reconstruction error rather than specific clustering task, leading to unsatisfactory performance. To overcome the above shortcoming, we propose a deep subspace clustering framework which jointly extracts features via the embedding neural network and performs subspace learning. The model contains three modules, an autoencoder, a self-expression layer and a supervised competitive feature learning module. The proposed model is highly capable of capturing characteristic features to guide the clustering task by using relative entropy to minimize probabilistic cluster assignments and the target variables. The three modules are consolidated to be jointly trained and optimized competitively. Experimental results on five benchmark datasets demonstrate the effectiveness of the proposed deep subspace clustering by comparing with eleven baseline methods.