Reviewerly/exHarmony huggingface dataset:
Distribution Context Aware Loss for Person Re-identification

To learn the optimal similarity function between probe and gallery images in Person re-identification, effective deep metric learning methods have been extensively explored to obtain discriminative feature embedding. However, existing metric loss like triplet loss and its variants always emphasize pair-wise relations but ignore the distribution context in feature space, leading to inconsistency and sub-optimal. In fact, the similarity of one pair not only decides the match of this pair, but also has potential impacts on other sample pairs. In this paper, we propose a novel Distribution Context Aware (DCA) loss based on triplet loss to combine both numerical similarity and relation similarity in feature space for better clustering. Extensive experiments on three benchmarks including Market-1501, DukeMTMC-reID and MSMT17, evidence the favorable performance of our method against the corresponding baseline and other state-of-the-art methods.
Development of a 3-axis Human Fingertip Tactile Sensor with an Ortho-Planar Spring

Human experts have mastered many manual skills. Transferring those skills to robots or human novices is challenging. Not only the movements, but also the exerted forces are of importance. In this paper we propose a sensor that can measure the force vector acting on the human fingertip, without covering the part of the fingertip that is contact with the touched object. Compared to our previous iterations of this sensor, we use a spring instead of viscoelastic material as the deformable material for the sensor. While it is challenging to include a spring in the given form factor, springs have the benefit of lower hysteresis than viscoelastic materials. This paper presents the integration of the spring in the sensor and shows experimental validation with 8 subjects. Compared to our previous version of the sensor with 2 Hall effect sensors, we achieved improved sensing characteristics.
Improving Robustness of Digital Video Watermarking Scheme through Random Frame Selection Algorithm

Recently, research on digital video watermarking techniques either to permit or deny the use of copyright service has acquired much attention. A video is a sequence of inter-connected frames, thus embedding the watermark should be planned accurately and keep the hacking process more complex is very essential. Selection of random frames for inserting the watermark in a video decides the robustness against frame attacks like frame reduction, frame averaging and frame dropping. This paper proposes a Random Frame Section algorithm to embed a watermark dynamically without compromising its quality during frame selection process and Lucas algorithm to improve its robustness. The experimental analysis demonstrates the effectiveness and applicability of our approach. The outcomes confirm that the proposed method shows optimal result and robust against various frame based attacks.
Advanced SURF Features Based Flexible Object Detection

On the modern assembly line, the posture of flexible products can be distorted or stacked on each other, which is difficult for methods currently in effect to ensure the precision with current methods. To solve this problem, we demonstrate our method on ABB Yumi robot for flexible object detection based on SURF features with color channel prior and marginal feature optimization. Our method achieved 97.99% accuracy, which is 7.67% better than vanilla SURF.
Multiple Description Coding and Forward Error Correction Concealment Methods for ACELP Coders in Packet Networks

In Voice over Iternet Protocol (VoIP) applications, paquet loss is major source of speech impairement. This work consists on using packet loss concealment methods based on Multiple Description Coding (MDC) and Forward Error Correction (FEC) to improve speech quality deterioration caused by packet losses for Code-Excited Linear Prediction (CELP) based coders in packet network. We applied our approach to the standard ITU-T G.722.2 standard speech coder to evaluate its performance. The Perceptual Evaluation of Speech Quality (PESQ) and Enhanced Modified Bark Spectral Distortion (EMBSD) tests under various packet loss conditions confirm that the proposed algorithm, namely MDC is superior to the concealment algorithm embedded in the G.722.2. The performance measures demonstrate that the concealment method based FEC is better than interleaving and MDC methods at the expense of extra delay.
The effect of coherence between two photons on quantum random walk

Quantum random walk with two controlled coherent photon is discussed. Two polarization entangled photons with completely coherence are generated by spontaneous parametric down-conversion (SPDC), and then a half wave plate (HWP) is used to perform Hadamard operation for controlling coherence between two photons through rotating the linear polarization direction of one photon. Quantum random walk with two photons from coherence to incoherence transition is explored. The effect of coherence between two photons on the quantum random walk is investigated through two photons controlled from coherence to incoherence transition.
Local Convex Representation with Pruning for Manifold Clustering

High-dimensional data in many applications can be considered as samples drawn from a union of multiple lowdimensional manifolds. Assigning data points into their own manifolds is referred to manifold clustering. Inspired by recent advances in subspace clustering, in this paper, we present an efficient approach for manifold clustering, called Local Convex Representation (LCR), in which each data point is represented as a convex combination of other points in the local neighborhood and under some mild conditions the nonzero coefficients are guaranteed to correspond to the data points lying on the same manifold. Moreover, we incorporate the estimated intrinsic dimension of the manifold to prune the minor nonzero coefficients and validate that the pruning step helps LCR yield remarkable improvements. Experiments on synthetic data as well as real world data demonstrate promising performance.
Method for Removing Motion Blur from Images of Harmful Biological Organisms in Power Places Based on Improved Cyclegan

Nowadays, the automatic detection of harmful organisms in power places has attracted attention due to the extensive unattended way of power places. However, surveillance pictures are prone to motion blurring and harmful organisms cannot be effectively detected due to their frequent and fast movements in power places. On the basis of the improved Cycle-Consistent Adversarial Networks (CycleGAN) model, we propose a method for removing motion blur from the images of harmful biological organisms in power places. This method does not require paired blurred and real sharp images for training, which is consistent with actual requirements. In addition, our method improves the classical CycleGAN model by combining cycle consistency and perceptual loss to enhance the detail authenticity of image texture restoration and improve the detection accuracy. The model uses Wasserstein GAN with gradient penalty (WGAN-GP) as a loss function to train the depth model. Given the existence of the GAN itself, the entire real image distribution space is difficult to fill with the generated image distribution space. Experimental results show that the proposed method effectively improves the detection accuracy of harmful organisms in power places.
Implementation of Teaming Behavior in Unmanned Aerial Vehicles

Research in manned-unmanned teaming (MUM-T) addresses the integration of manned and unmanned vehicles within a team. The concept of task-based guidance has been proven to reduce the human pilot’s workload in a MUM-T mission in multiple experiments. In addition to the automated execution of tasks, a mission requires the cooperation between the UAVs in order to increase survivability and mission performance. In this contribution, we describe the implementation of team support for UAVs guided from the cockpit of a manned combat fighter. The implemented agent uses its spare time to escort the team member with the highest priority task. The presented approach was integrated into the MUM-T research simulator at the Institute of Flight Systems and the effects of the implemented teaming behavior on mission effectiveness parameters were analyzed in two scenario simulations.
Deep Residual Optimization for Stereoscopic Image Color Correction

The color correction algorithm is designed to eliminate color discrepancies between image pairs. Compared with the conventional algorithm, color correction for 3D stereoscopic images not only needs to achieve the color consistency of the resulting image and the reference image but also expected to ensure the structural consistency of the resulting image and the target image. For this problem, we propose a stereoscopic image color correction algorithm based on deep residual optimization. First, we get an initial result image by fusing a global color correction image and a dense matching image of the stereo image pair. Then, a residual image optimization scheme is used to improve the structural deformation and color inconsistency of the initial result caused by mismatching and fusion. By combining the target image with the optimized residual image, the structure and clarity of the target image can be retained to the maximum extent. In addition, we use the perceptual loss and per-pixel loss to improve the structural deformation and local color inconsistency while training the optimization network. Experimental results show the effectiveness of our method.
Defying Censorship with Multi-Circuit Tor and Linear Network Coding

Anonymity networks are an increasingly important part of Internet evolution to providing practical mechanisms to enhance anonymity and security of network users. The best known anonymity network, The Onion Routing (Tor), is not only subject to user deanonymization attacks but also subject to censorship attacks by blocking the public and secret Tor entry routers, e.g., bridges, or by jamming Tor traffic to prevent users to access Tor network. To help further evolution of anonymity networks, we focus in this work on censorship attacks, and propose to enhance the well-known multipath Tor technique with linear network coding (LNC). We analyze anonymity, privacy and censorship success considering different types of adversaries and the amount of honest and compromised components in the Tor and in the underlying network. The results show that LNC can significantly improve the robustness of Tor against censorship and maintain sufficient anonymity and privacy levels.
KGMap

In the era of Web of data and Social media, the recent concept of enterprise knowledge graphs (EKG) emerges as a backbone for federating valuable web open information along with the data contained in internal corporate databases. This work proposes KGMap a mapping schema for leveraging knowledge graphs by bridging between heterogeneous Relational, Social and Linked Data Web sources. The KGMap approach connects the knowledge graph schema with the sources metadata elements relying on semantic similarity measures. The implementation of the proposed approach is a configurable middleware for generating enterprise knowledge graphs. We assessed the middleware in a commercial enterprise use case where inputs are integrated on-the-fly from an internal CRM database combined with DBpedia SPARQL endpoint and Facebook Web API. We conducted an empirical study to test the effectiveness of KGMap using different similarity measures. Furthermore, we compared the KGMap with state-of-the-art approaches. The evaluation results illustrate a better precision and recall of the contributed approach, which allowed the data to be more accurately integrated into a knowledge graph.
An Efficient Color Encoding Scheme for HDR Video Compression

The human visual system does not perceive all colors the same way, being sensitive to changes in some colors more than others. In this work, we propose a chroma processing scheme that takes advantage of this characteristic of the human eye to reduce the color errors generated by the quantization and subsampling processes in the HDR video delivery pipeline. Our method redistributes chroma code-words in favor of those colors that our eyes are most sensitive to. Objective evaluations have shown that the proposed chroma processing can significantly improve the color accuracy of the tested HDR video content. Furthermore, compression performance evaluations showed that the proposed method improves the compression efficiency of the existing approach by 0.6% and 13.3% on average, in terms of tPSNR XYZ and tOSNR XYZ, respectively.
The aspects of destructive influence of technical means and technologies of telecommunications on the person and society as a whole

In the context of the Fourth Industrial Revolution, information processing and information and communication technologies (ICT) are actively used in all areas of socio-economic relations. In fact, they are the driver of the formation and dynamic development of the global information society. Information systems, hardware and software systems, mobile devices and applications today allow you to successfully solve many important tasks of practical activity in the economy, social sphere, medicine, education, management infrastructure of megacities and even in politics. The consequence of the effective use of ICT is the formation of completely new social, economic and political activities of mankind, striving for progress and ensuring its existence in harmony with nature. However, the above-mentioned technological innovations and trends, despite the many positive aspects of creating the conditions for the evolution of society as a whole and each individual, can have a very negative impact on the immediate ICT user, on his well-being, on health and performance. A number of studies of such destructive effects have now been carried out. But, many nuances have not yet been fully explored. The authors believe that it is necessary to conduct a systematic analysis of the relevant processes and develop tools for the rapid identification of negative factors, as well as on a scientific basis to form methodical recommendations for the implementation of the complex measures to protect people from the destructive effects of ICT in the formation of modern information and environmental systems. The authors attempt to organize and classify elements of negative impact of ICT on people and society, to assess risks, to determine the likely consequences.
DEFINE: Friendship Detection Based on Node Enhancement

Network representation learning (NRL) plays a vital role in a variety of tasks such as node classification and link prediction. It aims to learn low-dimensional vector representations for nodes based on network structures or node attributes. While embedding techniques on complete networks have been intensively studied, in real-world applications, it is still a challenging task to collect complete networks. To bridge the gap, in this paper, we propose a Deep Incomplete Network Embedding method, namely DINE. Specifically, we first complete the missing part including both nodes and edges in a partially observable network by using the expectation-maximization framework. To improve the embedding performance, we consider both network structures and node attributes to learn node representations. Empirically, we evaluate DINE over three networks on multi-label classification and link prediction tasks. The results demonstrate the superiority of our proposed approach compared against state-of-the-art baselines.
NKNet: A Deep Neural Network Framework with Defensing Against Adversarial Attacks

Adversarial attacks are a major security risk for deep neural networks (DNN) and other machine learning models, which can substantially reduce the performance of the model and make security issues on the application of the model. Hence, in order to use DNNs as a reliable method, this paper proposes a DNN framework with the capability to defensive adversarial attacks. In the framework every single DNN model is designed to be differentiate. And decisions made by the framework rely on a part of DNNs in the framework which are chosen random. This makes the framework heterogeneous, dynamic, randomly and general. In the paper, we design a verification instance. In the instance, every DNN has different data transform and the structure is unique to each other. The results of the experiment demonstrate that the framework can giving predictions accurately and reliably in different kinds of adversarial and clean samples. This indicates that the framework is an acceptable method to defense adversarial attacks.
Nebula: A Blockchain Based Decentralized Sharing Computing Platform

Nowadays, there is a considerable amount of idle computers whose computing resources are partially wasted. On the other hand, the demand of resources is rapidly growing, since the explosion of data and the complexity of algorithms. To settle the contradictions, we develop Nebula, a decentralized platform based on blockchain for sharing computing resources. Nebula leverages blockchain to gather the scattered computing resources and provide a secure and vibrant computation trading market. Compared to traditional cloud platform, Nebula guarantees extra security because all transactions in this platform are validated by smart contracts. No one can tamper the transaction orders which are recorded by a widely distributed ledger. In Nebula, the resource consumer can order resources from resource providers with a very simple declarative script. When a deal is done, consumers can submit jobs to suppliers with a docker instance. Moreover, we model the order matching procedure of users' requests into a global maximum matching problem in a bipartite graph. We adopt the Hungarian algorithm to find an order matching policy, bringing an 10% increase to the matching rate in our best case. Moreover, we leverage the Proof of Authority (PoA) consensus algorithm called Clique, rather than Proof of Work (PoW) to increase the efficiency of Nebula, which provides nearly no less security but requires negligible computation on reaching consensus. To our best knowledge, we are the first to propose a general blockchain based platform for sharing computing resources, which fully utilizes the features of blockchain to achieve the scalability, the optimal order matching and a high performance.
K-MACE and Kernel K-MACE Clustering

Determining the correct number of clusters (CNC) is an important task in data clustering and has a critical effect on nalizing the partitioning results. K-means is one of the popular methods of clustering that requires CNC. Validity index methods use an additional optimization procedure to estimate the CNC for K-means. We propose an alternative validity index approach denoted by k-Minimizing Average Central Error (KMACE). Average Central Error (ACE) is the average error between the unavailable cluster center and the estimated cluster center for each sample data. Kernel K-MACE is kernel K-means that is equipped with the proposed CNC estimator. In addition, kernel K-MACE includes an automatically tuned procedure for choosing the Gaussian kernel parameters. Simulation results for both synthetic and real data show superiority of K-MACE and kernel K-MACE over the conventional clustering methods not only in CNC estimation but also in the partitioning procedure.
Two-Level Text Summarization with Natural Language Processing

Text summarization is the process of shortening a text document in order to create a summary covering important points, aspects of the original document. Text summarization methods are based on extractive model and abstractive model. Two-level text summarization is used to form summary of different news articles. In the first level, multiple news articles are read and first level summary is generated. These multiple summaries are then analyzed and a single summary concerning the news topic is generated in second-level. TextRank with TF-IDF algorithm is used which is an extractive summarization technique to create news summary. The performance of the summary is evaluated using ROUGE matrix.
An 8-bit Compact Architecture of Lesamnta-LW Hash Function for Constrained Devices

A very compact architecture for the lightweight 256-bit hash function Lesamnta-LW is presented in this paper. The proposed architecture has an 8-bit datapath and requires very few hardware resources and, therefore, can be a prime candidate for resource constrained devices. The proposed architecture was designed and implemented on the BASYS3 board (Artix 7 XC7A35T) using the VERILOG hardware description language. The implementation utilizes only 434 FPGA LUTs, 474 FFs and reaches a throughput equal to 50 Mbps at 161 MHz clock frequency.
Design and Implementation of Information Reconstruction System

At present, the representation of information shows the characteristics of isolated and fragmented. The original text information describing information objects exists widely in various information carriers, which results in the occurrence, development and outcome of the information object being not displayed intuitively and systematically, and the information of other related information objects and their interrelationship can't be obtained, so the reconstruction of information organization form is of great significance in today's era. In this paper, the information reconstruction model and information retrieval mechanism are designed, and a set of information reconstruction system based on distributed storage is implemented, which realizes the reconstruction function of original information samples, the retrieval editing function and visualization service of information object evolution and association in the database.
Resource Provisioning in IaaS Clouds; Auto-Scale RAM memory issue

In the Infrastructure-as-a-Service model of the cloud computing paradigm, virtual machines are deployed on bare-metal servers called hosts. The host is responsible for the allocation of required resources such as CPU, RAM memory, and network bandwidth for the virtual machine. Thus, the problem of resource allocation reduces to how to place the virtual machines on physical hosts. In this paper, we propose CTMC modeling based on the birth-death process of the queueing systems for the performance of the data center. We will focus on RAM allocation for virtual machines. In this architecture, a job is defined as RAM assignment for a virtual machine. Job arrivals and their service times are assumed to be based on the Poisson process and exponential distribution, respectively. The purpose of this modeling is to keep the number of running hosts minimal in a scalable datacenter while the quality of service in terms of response time is acceptable due to system utilization.
Factors that Determine the Adoption of Scientific Indicators in Community-Based Monitoring of Mangrove Ecosystems in Tanzania

Abstract This article reveals factors that need to be considered by facilitating institutions and organisations prior to adoption of scientific indicators in community-based monitoring of mangrove ecosystems; as a necessary route towards achieving effective participation and meaningful experiential learning processes. It employs an Experiential Learning Intervention Workshop (ELIW) as a key methodological tool and a useful space for analysing conditions that are necessary for adoption of scientific frameworks in the Tanzanian coastal area. ELIW also offers an opportunity for local people to share knowledge and decide the kind of input required for monitoring mangroves and fisheries.