Reviewerly/exHarmony huggingface dataset:
Multi-feature optimization strategies for target classification using seismic and acoustic signatures

Perimeter monitoring systems have become one of the most researched topics in recent times. Owing to the increasing demand for using multiple sensor modalities, the data for processing is becoming high dimensional. These representations are often too complex to visualize and decipher. In this paper, we will investigate the use of feature selection and dimensionality reduction strategies for the classification of targets using seismic and acoustic signatures. A time-slice classification approach with 43 numbers of features extracted from multi-domain transformations has been evaluated on the SITEX02 military vehicle dataset consisting of tracked AAV and wheeled DW vehicle. Acoustic signals with SVM-RBF resulted in an accuracy of 93.4%, and for seismic signals, the ensemble of decision trees classifier with bagging approach resulted in an accuracy of 90.6 %. Further principal component analysis (PCA) and neighborhood component analysis (NCA) based feature selection approach has been applied to the extracted features. NCA based approach retained only 20 features that obtained classification accuracy ~ 94.7% for acoustic and ~ 90.5% for seismic. An increase of ~2% to 4% is observed for NCA when compared to PCA based feature transformation approach. A further fusion of individual seismic and acoustic classifier posterior probabilities increases the classification accuracy to 97.7%. Further, a comparison with PCA and NCA based feature optimization strategies have also been validated on CSIO experimental datasets comprising of moving civilian vehicles and anthropogenic activities.
Lissajous scanned variable structured illumination for dynamic stereo depth map

Structured illumination plays an important role in advanced photographic and microscopic imaging applications. Here we report variable structured illumination (VSI) using Lissajous scanning techniques. The variable structured illumination module comprises Lissajous scanning micromirror and fiber-based diode pumped solid state (DPSS) laser with intensity modulation, combined with a stereo camera for dynamic stereo depth map. The micromirror projects static and discrete patterns by modulating the intensity of a laser beam at the least common multiple (LCM) of two scanning frequencies. The pattern density is increased by either decreasing the greatest common divisor (GCD) of scanning frequencies or decreasing the duty rate of the laser modulation. The scanning amplitude also controls the field-of-view (FOV) for the exact illumination of a target object for dynamic stereo depth map. The variable structured illumination module provides a new route for advanced imaging applications such as high-quality depth map, super-resolution, or motion recognition.
Data-Driven Redundant Transform Based on Parseval Frames

The sparsity of images in a certain transform domain or dictionary has been exploited in many image processing applications. Both classic transforms and sparsifying transforms reconstruct images by a linear combination of a small basis of the transform. Both kinds of transform are non-redundant. However, natural images admit complicated textures and structures, which can hardly be sparsely represented by square transforms. To solve this issue, we propose a data-driven redundant transform based on Parseval frames (DRTPF) by applying the frame and its dual frame as the backward and forward transform operators, respectively. Benefitting from this pairwise use of frames, the proposed model combines a synthesis sparse system and an analysis sparse system. By enforcing the frame pair to be Parseval frames, the singular values and condition number of the learnt redundant frames, which are efficient values for measuring the quality of the learnt sparsifying transforms, are forced to achieve an optimal state. We formulate a transform pair (i.e., frame pair) learning model and a two-phase iterative algorithm, analyze the robustness of the proposed DRTPF and the convergence of the corresponding algorithm, and demonstrate the effectiveness of our proposed DRTPF by analyzing its robustness against noise and sparsification errors. Extensive experimental results on image denoising show that our proposed model achieves superior denoising performance, in terms of subjective and objective quality, compared to traditional sparse models.
The Effect of Emotional Intelligence, Student’s Motivation toward Student’s Achievement

This study aimed at determining the effect of emotional intelligence and students’ motivation toward student’s achievement. This research used quantitative. The population was 172 students of class XI (eleven) SMA Negeri 1 Sungai Rotan and the sample was 43 people. Data were analyzed using multiple linear regression analysis techniques. The results showed that (1) emotional intelligence had a positive and significant effect on student’s achievement; (2) student motivation has a positive and significant effect on student’s achievement; (3) emotional intelligence and student’s motivation together positively and significantly influences student’s achievement.
Robust Syndrome-Trellis Codes for Fault-Tolerant Steganography

Syndrome-Trellis Codes (STCs) have been widely used in adaptive steganography due to their high embedding efficiency. However, this type of steganographic codes is sensitive to stego damage, which may be incurred by compression, channel noise, active attacks, and so on, in practical covert communication. In this paper, a construction of robust STCs is proposed to achieve a good balance between robustness and embedding efficiency. The encoder of the proposed scheme performs a specified Error Correction Code (ECC) on the STC’s intermediate outputs. Further, a Viterbi algorithm is suggested to effectively find the optimum stego vector. At the extraction phase, the received stego vector is first decoded by the decoder of the employed ECC and then sent to the STC decoder, which gives the extracted message bits. Experimental results show the proposed scheme presents a good robustness performance, meanwhile the stego remains an acceptable detection resistance against steganalyzers.
FCN-Based 3D Reconstruction with Multi-Source Photometric Stereo

As a classical method widely used in 3D reconstruction tasks, the multi-source Photometric Stereo can obtain more accurate 3D reconstruction results compared with the basic Photometric Stereo, but its complex calibration and solution process reduces the efficiency of this algorithm. In this paper, we propose a multi-source Photometric Stereo 3D reconstruction method based on the fully convolutional network (FCN). We first represent the 3D shape of the object as a depth value corresponding to each pixel as the optimized object. After training in an end-to-end manner, our network can efficiently obtain 3D information on the object surface. In addition, we added two regularization constraints to the general loss function, which can effectively help the network to optimize. Under the same light source configuration, our method can obtain a higher accuracy than the classic multi-source Photometric Stereo. At the same time, our new loss function can help the deep learning method to get a more realistic 3D reconstruction result. We have also used our own real dataset to experimentally verify our method. The experimental results show that our method has a good effect on solving the main problems faced by the classical method.
The Technology of Professional Teaching The Simulative Company as a Complex of Imitative Teaching Methods

The characteristics of the competitive specialist and requirements for their training served the basis for the development of the generalized specialist model. A new imitative tutorial The Simulative Company, which is a systemized complex of operation teaching methods, is worked out and implemented. The research provides the algorithm of the teaching method The Simulative Company development realized and implemented by the authors in student groups on the specialties Management and Economy and Bookkeeping. The algorithm comprises the following stages: the launch of The Simulative Company (presenting The Simulative Company, the analysis of the environment); the functioning of The Simulative Company (recruitment, professional activity appraisals, partnering); the creation of an automated workplace. It is proved that in secondary professional educational institutions, active teaching methods should be given precedence to, which will increase the student's learning activity, facilitate the development of their initiative and personal potential, generate their creative approach, as well as will allow solving important teaching and organizational tasks. The established specifics of imitative teaching methods usage enable: to put into practice the models of the competitive specialist within the educational process; to realize the teacher’s potential in improving his/her pedagogical excellence; to implement the system of new imitative teaching methods which is presented by the new technology The Simulative Company.
On a conjecture concerning the Bruhat order

Let R and S be two sequences of positive integers in nonincreasing order having the same sum. Let A(R,S) be the class of all (0,1)-matrices with row sum vector R and column sum vector S. If A(R,S) is nonempty, an inversion in A∈A(R,S) consists of two entries of A equal to 1, one of them is located to the top-right of the other. Let ν(A) be the total number of inversions in A. The Bruhat order is a partial order defined on A(R,S) and denoted by ⪯B. In this paper, we prove the conjecture: "If A,C∈A(R,S), A≠C and A⪯BC then ν(A)<ν(C)".
Algorithm Comparation of Naive Bayes and Support Vector Machine based on Particle Swarm Optimization in Sentiment Analysis of Freight Forwarding Services

The needs of the community for freight forwarding are now starting to increase with the marketplace. User opinion about freight forwarding services is currently carried out by the public through many things one of them is social media Twitter. By sentiment analysis, the tendency of an opinion will be able to be seen whether it has a positive or negative tendency. The methods that can be applied to sentiment analysis are the Naive Bayes Algorithm and Support Vector Machine (SVM). This research will implement the two algorithms that are optimized using the PSO algorithms in sentiment analysis. Testing will be done by setting parameters on the PSO in each classifier algorithm. The results of the research that have been done can produce an increase in the accreditation of 15.11% on the optimization of the PSO-based Naive Bayes algorithm. Improved accuracy on the PSO-based SVM algorithm worth 1.74% in the sigmoid kernel.
Hybrid Cloud: A Cost Optimised Solution To Cloud Interoperability

A hybrid cloud computing environment is a perfect combination of security features of private cloud and easy and fast access of public clouds. The powerful hybrid cloud can be considered as an effective solution for interoperability issue in cloud computing environment. Interoperability issue remains an understudied area. Interoperability can be implemented with the help of service brokers. This paper deals with hybrid cloud and study the interoperability by taking into consideration the combination of server broker policy and load balancing algorithm. This paper uses combination of either of three broker policies available along with throttled load balancing algorithm and hence study the impact of hybrid cloud on the Memory Usage cost and bandwidth usage cost. Memory usage cost and bandwidth usage cost is collectively called debt value. Cloudsim simulator is used to model a hybrid cloud and it's impact on grand total cost and debt value is studied. The aim of study is to find the cost optimized solution in terms of grand total cost and debt value.
RNN -based Human Motion Prediction via Differential Sequence Representation

Human motion prediction-to predict the future motion sequences given the past sequences using 3D skeleton data-is a classical research field in computer vision. With the deep learning method has been successful in several computer vision tasks, recent attempts on this field suggested to use RNN-based methods to model the movement of human in the temporal domain. In this kind of task, as the error will accumulate with time steps, the stability of human body movement data will affect the accuracy of prediction. In this paper, aiming at the stability of human body data, we focus on the motion of human body can be represented by velocity and acceleration instead of posture to obtain more stable sequence to be predicted. We propose a framework based on the velocity and acceleration and obtain better results.
Texture Classification Using Gradient Features with Artificial Neural Network

Texture Analysis is the technique usinga small number of measurable features to represent complex textures. It provides many important discriminating characteristics that are related with extracting features and coding images which are used in various patterns recognition applications and classification texture. This research studies the extraction of discriminating characteristics for various texture images from using Absolute Gradient Matrix (AGM) and a comparative study of conventional texture-analysis method and the proposed methods. Four different sets of features have been considered: the first set consists of simple features extracted from the traditional AGM, the second set consists of new features which are derived using AGM and intensity values (pAGM); the third feature set consists of two sub-sets of features extracted from two AGM calculated using two displacement values and intensity values (CtdAGM). The fourth method implies the extracted set of AGM through Artificial Neural Network (ANN) for classification purpose. The considered method was applied on 13 classes of textures belonging to three sets of Salzburg Texture Image Database (i.e., bark, marble and woven fabric), each set holds 16 images per class, thus 208 images were tested in total. The attained average accuracy of classification proved that the proposed methods were superior to the conventional texture-analysis method with respect to classification accuracy and computational complexity.
Rapid Review of Contact Tracing Methods for COVID-19

This rapid review explores the question “How can we undertake ‘people-powered’ contact tracing activity at scale? What types of options are available?”. The main focus of the report will be on investigating any methods and evidence around digitally enhanced people-powered contact tracing. The spectrum of available methodologies for contact tracing range from paper-based methods to fully automated, anonymous digital contact tracing using mobile phone Apps and Bluetooth or geolocation services. Selected examples are provided.
Security Vulnerabilities of Four Signature Schemes From NTRU Lattices and Pairings

Certificateless cryptography solves the certificate management problem in public-key cryptography and the key-escrow problem in identity-based cryptography. Xie et al. proposed a certificateless signature scheme using NTRU lattices. They proved that their scheme was existential unforgeable for Type I and Type II adversaries under the intractability of the Small Integer Solution (SIS) problem on NTRU lattices in the random oracle model. Subsequently, Hung et al. proposed a revocable certificateless signature scheme on NTRU lattices that gave a revocation method via public channels to revoke illegal or compromised users. They also proved that their scheme was existential unforgeable for Type I, Type II and Type III adversaries under the intractability of the SIS problem. Recently, Wang et al. proposed a certificateless aggregate signature scheme from bilinear pairings and Rezaeibagha et al. proposed a new lightweight certificateless scheme for Industrial Internet of Things from bilinear pairings. The security of their schemes was proven for Type I and Type II adversaries under the intractability of some mathematical problems. In this paper, we show that the two schemes on NTRU lattices are insecure against Type I or Type II adversaries. We also point out invalidity of Wang et al.'s scheme and vulnerability of Rezaeibagha et al.'s scheme against Type I attacks. We then suggest some improvements to prevent our attacks.
Effective natural interaction with our sensorized smart homes

Smart environments are becoming quite popular in the home setting consisting of a broad range of connected devices. While offering a novel set of possibilities, this also contributes to the complexity of the environment, posing new challenges to allowing the full potential of a sensorized home to be made available to users. In the scope of our project, “Smart Green Homes,” we have been addressing how humans can interact successfully with their wireless devices. We examine how an architecture supporting multimodal interaction along with a house assistant can blend the multiple devices into an interactive ecosystem accessible by different audiences. This chapter presents current results, including a first deployment of the architecture of the smart home equipped with a multimodal interactive home assistant that encompasses conversational features.
Contracting in Brazilian public administration: A machine learning approach

Abstract The risk of non‐fulfilment of a contract can harm public administration or even interrupt public services. Therefore, models that assist manager decision making in the audit and control of contracts with a higher disqualification risk may be important tools, with economic and even social repercussions. In this article, public contracts are classified with respect to the risk of non‐compliance with their terms of delivery. The quantitative tools used are statistical and machine learning models, similar to credit risk rating of loans. As dependent variables, the models use data found in electronic databases present in e‐government implementations. A previously classified listing of suspended companies is used as a proxy for risky contracts, as it contains private companies which failed with their contractual obligations. The classification techniques utilized are logistic regression, k‐nearest neighbours, discriminant analysis, support vector machine and random forests. Although the methods can be applied to any government with electronic procurement and contracts systems, Brazilian data is used to illustrate the benefits of contract governance for emerging economies. It is concluded that the credit rating techniques used directly apply to contractual risk in public administration. Considering real public administration contract data, the classification algorithm that generates the best performance is k‐nearest neighbours.
Inpainting of Ring Artifacts on Microtomographic Images by 3D CNN

Ring artifacts are inevitable in microtomographic images. In a Digital Rock workflow, such defects might affect the subsequent segmentation and flow simulation. We propose a correction of ring artifacts in reconstructed microtomographic images by inpainting. Our blind inpainting method uses a 3D convolutional network U-net. For the creation of training and validation datasets, we suggest an algorithm for transferring real ring artifacts to an arbitrary place in the undistorted slices of 8 big images of sandstones and sand. The parameters of the deep neural network and loss functions are analyzed. A loss function based on the multi-scale structural similarity index (MS-SSIM) allows to achieve the best performance. The developed solution corrects ring artifacts perfectly from a point of view of visual assessment and outperforms existing inpainting methods according to quality metrics based on MS-SSIM and mean absolute error (MAE).
Secure and Efficient Nearest Neighbor Query for an Outsourced Database

Cloud computing enables resource-constrained clients to outsource their computation-intensive data storage and computation tasks to a cloud server. Although it creates many benefits, data outsourcing causes some security challenges for clients. In this paper, we propose the secure and efficient query (SecNN) scheme to address the secure nearest neighbor query problem, which is popular in academia and industry. Compared with the state-of-the-art scheme, the proposed scheme can significantly decrease the number of communications between the clients and the cloud server. Furthermore, it achieves the desired security in the random oracle model. Moreover, the extending scheme described can verify the correctness and completeness of query results and database updates by using the Merkle hash tree technique. Finally, an experimental evaluation demonstrates the high efficiency of our scheme.
Artificial Intelligence Accidentally Learned Ecology through Video Games

An advanced artificial intelligence (AI) system defeated the best human players in StarCraft II, a popular real-time strategy game. In a virtual ecosystem, players compete for habitats and resources, unintentionally reproducing many ecological phenomena. We propose to repurpose this AI to test ecological hypotheses that have been intractable using traditional approaches.
Comparison of Key Entities Within Bibliographic Conceptual Models and Implementations: Definitions, Evolution, and Relationships

With the introduction of FRBR (Functional Requirements of a Bibliographic Record) in 1998, IFLA (the International Federation of Library Associations and Institutes) introduced a new conceptual entity relationship model. FRBR was soon followed by FRAD (Functional Requirements of Authority Data) and FRSAD (Functional Requirements of Subject Authority Data). With LRM (IFLA Library Reference Model) and two descriptive standards, the RDA Toolkit and BIBFRAME to follow, it helps catalogers to have a greater understanding of the entity relationship models they use for bibliographic description. The authors compare the models and descriptive standards. Differences among the entities, their definitions, and properties are examined and analyzed.
Intelligent traffic analysis: A heuristic high-dimensional image search algorithm based on spatiotemporal probability for constrained environments

However, most heuristic algorithms are solely based on image features, due to the lack of diverse attributes of samples. Rather than rely on the existing datasets, this paper fully processes the original data. Firstly, the original videos were collected from several intelligent traffic surveillance cameras (monitoring nodes). Then, the three categories of objects in all videos were classified by a video image classifier. For each object in the critical class of cars, four attributes were identified, including ID, region of interest (ROI), sampling time and sampling position (position of sampling node). Next, the moving attitude (i.e. moving direction and speed) of each object was estimated through moving target tracking, and added to the preprocessed data. On this basis, a basic hyper-spherical hash search algorithm was created for the initial identification of similar objects. Furthermore, 93% of the objects were accurately identified through the recognition of license plate. Thereafter, a spatiotemporal correlation Bayesian network (STCBN) was established to predict the probability of each object to appear at a certain node in a period of time. Based on the probability, the heuristic factor was designed and used to enhance the basic hyper-spherical hash search algorithm. The enhancement improves the search performance, while reducing the environmental requirements of a single node. Therefore, the enhanced algorithm can be deployed effectively in edge computing systems. Our method was proved effective through experiment on a number of actual monitoring nodes.