Reviewerly/exHarmony huggingface dataset:
A Search Efficient Privacy-Preserving Location-Sharing Scheme in Mobile Online Social Networks

With the advent of intelligent handheld devices, location sharing becomes one of the most popular services in mobile online social networks (mOSNs). In location-sharing services, users can enjoy a better social experience by updating their real-time location information. However, the leakage of private information may hinder the further development of location-sharing services. Although many solutions have been proposed to protect users’ privacy, the privacy-utility trade-offs must be considered. Therefore, we propose a new scheme called search efficient privacy-preserving location-sharing (SELS) system. In our scheme, we create a new approach named associated grids to improve the efficiency of location-sharing systems while maintaining users’ privacy. In addition, by setting the user-defined access control policy proposed in our scheme, users’ flexible privacy-preserving requirements can be satisfied. Detailed complexity and security analysis show that the proposed scheme is a practical and efficient privacy-preserving solution. Extensive simulations are performed to validate the effectiveness and performance of our scheme.
Pattern Prediction using LSTM and ReLU Ensemble

Human beings, like many other cognizant biological organisms, can roughly predict certain patterns in nature, be it with respect to time, or otherwise. In the strive to achieve the global object of achieving a general artificial intelligence, the authors deem it to be of high importance that the intelligence is aware of its surroundings, not just in terms of understanding raw sensory data, but also by learning and being able to predict recurring patterns in the environment. To achieve that result, this paper comes into play by discussing certain neural network models which help in learning from any sort of pattern recognition in a data sequence which can be achieved by either manual input or via data collection from the outer environment. This model is able to predict accurately about 80% of the time, beating state-of-the-art approaches.
Morpho-Geometrical Feature Extraction of Thin Blood Smear Microphotograph for Malaria Plasmodia Species and Life Stage Determination

Malaria is a mosquito-borne disease that is prevalent in tropical areas. The diagnosis of Malaria infection is conducted manually by examining a thin blood smear, conducted by an expert microscopist. Unfortunately, the manual examination of the blood slide can be time consuming and is prone to human errors. Hence, this research aims to develop an algorithm that is based on a priori knowledge of the experts [1]. In this research, a morpho-geometrical approach of feature extraction coupled with Naive Bayes classification are proposed to measure the infected cell's size and shape from a microscopic image, to do species and life stage differentiation of P. falciparum, P. malariae, P. ovale, and P. vivax. This is done with the help of computational geometry, recursive bottleneck detection algorithm, and thresholding with Otsu's algorithm. The proposed algorithm was evaluated using real malaria cases and validated with manual microscopy analysis by a microscopist. The results showed a Positive Predictive Value (PPV) score of 77.14%, sensitivity score of 84.37%, and an F1 score of 80.60%, which shows that the proposed feature extraction method is reliable and faithful to the expert's a priori knowledge.
STGA-LSTM: A Spatial-Temporal Graph Attentional LSTM Scheme for Multi-agent Cooperation

Multi-agent cooperation is one of the attractive aspects in multi-agent systems. However, during the process of cooperation, communication among agents is limited by the distance or the bandwidth. Besides, the agents move around and their neighbors appear or vanish, which makes the agents hard to capture temporal dependences and to learn a stable policy. To address these issues, a Spatial-Temporal Graph Attentional Long Short-Term Memory (LSTM) Scheme (STGA-LSTM), which is composed of spatial capture network and spatiotemporal LSTM network, is proposed. The spatial capture network is designed based on graph attention network to enlarge the agents' communication range and capture the spatial structure of the multi-agent system. Based on the standard LSTM, a spatiotemporal LSTM network, which is in combination with graph convolutional network and attention mechanism, is designed to capture the temporal evolutionary patterns while keeping the spatial structure learned by spatial capture network. The results of simulations including mixed cooperative and competitive tasks indicate that the agents can learn stable and complicated strategies with STGA-LSTM.
Joint Trajectory Prediction of Multi-Linkage Robot Based on Graph Convolutional Network

The working accuracy of multi-linkage robot is seriously affected by the errors at the joints caused by the uncertainty factors such as vibration, wear, deformation, and manufacturing clearance. In order to improve the working accuracy, the joint motion prediction including these errors is researched, which can realize the follow-up errors pre-compensation. According to the spatial correlation and time dependence between the joints of the robot, the joints can be represented as a graph. This work proposes a joint trajectory prediction method based on graph convolutional neural network (GCN) and gated recurrent unit (GRU). A real experimental dataset is built to verify the effectiveness, including the uncertain errors. The method is validated by means of Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Accuracy, and R-square. Experimental results demonstrate that the method obtains the highest performance in the joints trajectories prediction, compared with Historical Average (HA), Autoregressive Integrated Moving Average (ARIMA), and Support Vector Regression (SVR). In the case of Accuracy, the accuracy of the proposed method is 91.549%, which is 33.40%, 7.32% and 3.19% higher than that of HA, ARIMA and SVR, respectively. The method can effectively predict the joints trajectories of multi-linkage robot with uncertain error at the joint, and provide theoretical support for further error compensation, obstacle prediction, and obstacle avoidance control of robot.
Optimal Trajectories Synthesis of a Mobile Robots Group Using Cartesian Genetic Programming

The paper is devoted to application of Cartesian Genetic Programming (CGP) for generating optimal trajectories of a mobile robots group. The problem of a control system synthesis for a mobile robots group is solved. The proposed algorithm uses numerical approach from the class of symbolic regression methods to which Cartesian Genetic Programming belonging. It allows to receive a control function in the form of a mathematical expression. We consider several stages to get optimal trajectories for mobile robots group moving along which the robots wouldn’t collide with each other and obstacles. Initially, we solve the problem of synthesis for each robot in order to get the stabilized robot control system relative some point in the state space. At the second stage, spatial trajectories are found along which robots move from the current state to the obtained equilibrium points without collisions. It was proposed to improve an initial algorithm by using the principal of small variation of basic solution. There is considered a group of three robots and the control system for them with phase constraints in the paper.
Fractal analysis of tensile deformation curves of epoxy polymers based on modified epoxy binders

Abstract The paper proposes an approach to study the mechanism of deformation of epoxy polymers based on the methods of fractal calculations. The advantages of the method for determining the fractality index of the deformation curves of polymer samples using the minimum coverage method are shown. An algorithm for the quantitative determination of the location of the “critical” points of the deformation curves of polymer samples under tension is proposed. It is shown that the use of the developed methodology for the fractal analysis of time series on the basis of a set of points of deformation curves of epoxy polymer samples under tension, provides valuable information about the processes occurring in the structure of composite materials affected by mechanical loads and various aggressive factors.
A Simple Multi-Frame Fusion Baseline For Long-Term Multi-Object Tracking

Multi-object tracking (MOT) is an important and practical task related to both surveillance systems and moving camera applications. However, little attention has been paid to keep the stability of tracker in long-term multiple object tracking. In this work, we study the essential reasons behind the failure and accordingly propose two techniques to improve the efficiency of MOT: (1) In order to reduce identity switches (IDS) in tracking, we propose a novel approach by accomplishing the detection and re-identification tasks in a single network, which can exploit historical information and generate more robust frame predictions. (2) For the purpose of increasing the accuracy rate of data association, we also design and train an affinity network to measure the object similarity instead of traditional measurements. Overall, experimental evaluations show that our approach achieves excellent performance, especially under the occluded condition and in the case of long-term tracking, as compared to other state-of-the-art methods.
Attention-Based Fine-Grained Classification of Bone Marrow Cells

Computer aided fine-grained classification of bone marrow cells is a significant task because manual morphological examination is time-consuming and highly dependent on the expert knowledge. Limited methods are proposed for the fine-grained classification of bone marrow cells. This can be partially attributed to challenges of insufficient data, high intra-class and low inter-class variances.In this work, we design a novel framework Attention-based Suppression and Attention-based Enhancement Net (ASAE-Net) to better distinguish different classes. Concretely, inspired by recent advances of weakly supervised learning, we develop an Attention-based Suppression and Attention-based Enhancement (ASAE) layer to capture subtle differences between cells. In ASAE layer, two parallel modules with no training parameters improve the discrimination in two different ways. Furthermore, we propose a Gradient-boosting Maximum-Minimum Cross Entropy (GMMCE) loss to reduce the confusion between subclasses. In order to decrease the intra-class variance, we adjust the hue in a simple way. In addition, we adopt a balanced sampler aiming to alleviate the issue of the data imbalance.Extensive experiments prove the effectiveness of our method. Our approach achieves favorable performance against other methods on our dataset.
Game Strategy of Environmental Regulation and Export Trade Based on Blockchain Algorithm

Based upon the knowledge of environmental regulation and export trade in strategic game, we employed the panel data of 30 provinces and cities in China from 2005 to 2018 to analyze the impact of different types of environmental regulations on China's export commodity structure of goods trade by the blockchain GMM estimation method empirically. The studies indicate that: (1) There is a “U”-shaped non-linear relationship between the cost-based environmental regulations and the export commodity structure and an inverted “U”-shaped non-linear relationship between the investment-based environmental regulations and the export commodity structure; (2) The lag effect of the investment-based environmental regulations on the export commodity structure is stronger than that of the cost-based environmental regulations; (3) As indicated by the influence approaches, the effect of investment-based environmental regulations is mainly emphasizing on promoting the innovation in technological sector, while the cost-based environmental regulations have a strong impact on environmental protection costs.
Invited Paper: Homomorphic Operations Techniques Yielding Communication Efficiency

This paper describes our recent results in information theoretically secure homomorphic encryption. The main question that stands in the basis of these works concerns the possibility of modifying encrypted data obliviously. This possibility is useful for various applications, e.g., multiparty computation, outsourcing of computations, and quantum key distribution (QKD). The works presented here consider the scenario in which a user wishes to outsource the storage and computation of confidential data to an untrusted server. The first two works consider the approach of employing multiple servers and distributing secret shares of the data among the servers. The first work introduces a method for evaluating quadratic functions over a dynamic database, with no communication between the servers. The second work allows communication and considers a method for homomorphic evaluation of polynomials of arbitrary degree over non-zero secret shares in a single round of communication. We present protocols that enable the evaluation of multivariate polynomials over shares of a non-zero secret without requiring a secret sharing phase invoked in an offline preprocessing phase, and deal with possibly-zero secrets in several ways. The third work reviewed here considers the approach of employing a single server. That work assumes that the user and server have quantum capabilities, and attempts to enable the homomorphic evaluation of encrypted classical data using quantum devices. The homomorphic encryption scheme presented in that work is used to construct a QKD scheme resilient against weak measurements. Weak measurement based attacks over known QKD schemes are also introduced in the third work, along with the innovative concept of securing entanglement.
Empirical Evaluation of the Impact of Refactoring on Internal Quality Attributes

It is commonly believed that refactoring increases software quality. This paper presents the validation of belief about refactoring effect on internal software quality attributes, notably coupling. High coupling between classes is one of the smells considered dangerous by developers because of its impact, decreasing code maintainability. Some developers believe that refactoring as a method that can improve maintainability by reducing coupling in the source code. Evaluation of the effect of refactoring is done in the experiment way. When data collection from the experiment is done, then statistical analysis could be conducted to see whether refactoring can improve coupling or not. The results show that proper refactoring scenarios significantly reduce the coupling metric in source code, where 86% of samples have zero value of DCH, showing an apparent gain from refactoring. However, developers should pay attention to the code's size as refactoring results in increasing the code's size, especially in NOM, which has increased by 1.81%.
Video Summarization with Self-Attention Based Encoder-Decoder Framework

This paper proposes an efficient supervised video summarization algorithm with self-attention based encoder-decoder network. Given an input video, we implement a Bi-GRU network to encode the contextual information of the video frames using self-attention mechanism, and a GRU network as the decoder, accompanying with a regression network to predict the importance score of every video frame. Experiments and analysis are conducted on the public benchmark datasets TvSum and SumMe, the results validate the superiority of our algorithm.
Decoy-state quantum key distribution with direct modulated commercial off-the-shelf VCSEL lasers

We report on a BB84 decoy-state quantum key distribution (QKD) system constructed using commercial off-the-shelf (COTS) components. Four 794 nm vertical-cavity surface-emitting lasers (VCSELs) are current-modulated at 10 MHz rate with three power levels to form a decoy state transmitter. The COTS VCSELs exhibit long term stability with high polarization extinction ratio, narrow band operation (sub-nanometer bandwidth), and wavelength tunability and stability suitable for constructing four indistinguishable qubit channels. A 780 nm, 10 MHz time-transfer channel is used for transferring the timing information along with a start and end marker for the qubit transfer period. Internally-developed transmitter laser drivers and receiver detectors are controlled and read out with COTS system-on-chip (SoC) boards. We obtain a nominal bit-error-rate (BER) of ~4% for the system. We also report on the development of a synchronous (100 MHz) single photon detector for increasing the repetition rate of our QKD system. This work shows promise for building a COTS-based, small size, weight, and power hardware for space applications.
Combining complementary diversification models for personalized POI recommendations

Location-Based Social Networks (LBSNs) have become important tools for people interested in exploring new places. And, similar to traditional recommendation domains, handling the trade-off between accuracy and diversity is a major challenge to provide useful recommendations. However, this domain adds an equally relevant dimension to this challenge: the geographical distance between users and each point-of-interest (POI). Besides understanding how the characteristics of services offered by each POI fit the user needs, realizing how far users are willing to move to fulfill these needs is of paramount relevance. Moreover, the users present distinct levels of interest in diversification. In this paper, we propose a strategy to provide POI recommendations linearly combining categorical and geographical diversifications in a personalized way. Indeed, our strategy is able to prioritize quality dimensions that better suit the personalized needs of each user, with gains up to 10% when compared with unpersonalized versions.
Architecture Design of Overlap-Add FFT on the Convolution Neural Networks

As the computation demand of CNNs is dominated by convolution layers, some researches exploit the duality between spatial domain and frequency domain through fast Fourier transform (FFT) to replace convolutions with pointwise multiplications. However, the FFT approach requires zero padding to enlarge the filter kernel to be the same size of input feature map. In this paper, we apply the overlap-add FFT algorithm to resolve the large zero padding problem in full FFT model. We propose an architecture design to implement the overlap-add FFT algorithm with 2D FFT technique. Our design targets to filter size 3x3 ~ 7x7 and is implement with four 8x4 PE architecture. Experiments on ResNet-34 shows that in average, our overlap-add FFT scheme achieves near to 80% of multiplication reduction in comparison with the convolution approach.
A conceptual model of agumented virtual and reality in cadet training

Through digital communication in the educational process, its stakeholders, professors and students, become creators of new information practices, which in involve modern technologies augmented reality (AR) virtual reality (VR). The research objective was aimed at detecting AR and VR information concepts important for determining students' digital intelligence in the digital creativity domain. In the education of the cadets, some issues have been observed acknowledged, particularly the issues of defining the set of information which is necessary to be transferred to the cadets by AR and VR and the issue of detecting feedback expressed through the learning outcomes previously acquired. The main research challenge is correlation of information sets and how to structure them into a standard cadet training information model. The research was conducted by using the method of content analysis of software solutions and the questionnaire method. The cadets and expert at military academy have been selected as the research sample as they undergo through both the process of acquiring theoretical knowledge as well as practical training. The paper presents the research aimed at VR prototype development and its evaluation with two cadets generations and military professionals, who were introduced to VR technology in different ways. The obtained research results present basis for an AR / VR conceptual model to be further defined, that will contain set of abstract elements, relationships, and information that depict a complex real cadet training system. In this sense, this paper represents an initial step in defining the framework of a standard AR / VR information model in the military training that can be further developed by creating additional categories of data that do not appear in any of the existing information models.
RE-Net: A Relation Embedded Deep Model for AU Occurrence and Intensity Estimation

Facial action units (AUs) recognition is a multi-label classification problem, where regular spatial and temporal patterns exist in AU labels due to facial anatomy and human’s behavior habits. Exploiting AU correlation is beneficial for obtaining robust AU detector or reducing the dependency of a large amount of AU-labeled samples. Several related works have been done to apply AU correlation to model’s objective function or the extracted features. However, this may not be optimal as all the AUs still share the same backbone network, requiring to update the model as a whole. In this work, we present a novel AU Relation Embedded deep model (RE-Net) for AU detection that applies the AU correlation to the model’s parameter space. Specifically, we format the multi-label AU detection problem as a domain adaptation task and propose a model that contains both shared and AU specific parameters, where the shared parameters are used by all the AUs, and the AU specific parameters are owned by individual AU. The AU relationship based regularization is applied to the AU specific parameters. Extensive experiments on three public benchmarks demonstrate that our method outperforms the previous work and achieves state-of-the-art performance on both AU detection task and AU intensity estimation task.
MaskBot: Real-time Robotic Projection Mapping with Head Motion Tracking

The projection mapping systems on the human face are limited by the process latency and the users’ movement. The area of the projection is restricted by the position of the projectors and cameras. We are introducing MaskBot, a real-time projection mapping system guided by a 6 Degrees of Freedom (DoF) collaborative robot. The collaborative robot locates the projector and camera in front of the user’s face to increase the projection area and reduce the system’s latency. A webcam is used to detect the face orientation and to measure the robot-user distance. Based on this information we modify the projection size and orientation. MaskBot projects different images on the user’s face, such as face modifications, make-up, and logos. In contrast to the existing methods, the presented system is the first that introduces a robotic projection mapping. One of the prospective applications is to acquire a dataset of adversarial images to challenge face detection DNN systems, such as Face ID.