Reviewerly/exHarmony huggingface dataset:
Enhanced Secret Image Sharing Using POB Number System

In 2017, Singh et al. constructed a secure cloud-based (2, 2) secret image sharing with the capability of tampering detection using (r, n) Permutation Ordered Binary (POB) number system. This scheme can solve the problem of secure storage of image-shares at cloud servers, and is also capable of authenticating image-shares at pixel level. However, Singh et al.’s scheme assumes that participants already know the parameters r in (r, n) POB, on which they can recover each image pixel efficiently. In fact, the distribution of parameters r is not that easy as described in their scheme, it needs secure channels and the storage of r is almost same as the share. In Singh et al.’s scheme, the authors did not show how to share the information of r among participants. In this paper, we proposed a new secret image sharing using (r, n) POB number system which is extended from Singh et al.’s framework. In our scheme, the parameters r for all image pixels are identical and are publicly published. Thus, the problem of Singh et al.’s scheme is solved. In addition, our scheme can also authenticate tampered shares at pixel level.
Tensor Decomposition for Salient Object Detection in Images

The fundamental challenge of salient object detection is to find the decision boundary that separates the salient object from the background. Low-rank recovery models address this challenge by decomposing an image or image feature-based matrix into a low-rank matrix representing the image background and a sparse matrix representing salient objects. This method is simple and efficient in finding salient objects. However, it needs to convert high-dimensional feature space into a two-dimensional matrix. Therefore, it does not take full advantage of image features in discovering the salient object. In this article, we propose a tensor decomposition method which considers spatial consistency and tries to make full use of image feature information in detecting salient objects. First, we use high-dimensional image features in tensor to preserve spatial information about image features. Following this, we use a tensor low-rank and sparse model to decompose the image feature tensor into a low-rank tensor and a sparse tensor, where the low-rank tensor represents the background and the sparse tensor is used to identify the salient object. To solve the tensor low-rank and sparse model, we employed a heuristic strategy by relaxing the definition of tensor trace norm and tensor l1-norm. Experimental results on three saliency benchmarks demonstrate the effectiveness of the proposed tensor decomposition method.
Better Late Than Never

A method of detecting malicious intrusions and runtime faults in software is proposed, which replicates untrusted computations onto two diverse but often co-located instruction architectures: CPU and GPU. Divergence between the replicated computations signals an intrusion or fault, such as a zero-day exploit. A prototype implementation for Java demonstrates that the approach is realizable in practice, and can successfully detect exploitation of Java VM and runtime system vulnerabilities even when the vulnerabilities are not known in advance to defenders. To achieve acceptable performance, it is shown that GPU parallelism can be leveraged to rapidly validate CPU computations that would otherwise exhibit unacceptable performance if executed on GPU alone. The resulting system detects anomalies in CPU computations on a short delay, during which the GPU replica quickly validates many CPU computation fragments in parallel in order to catch up with the CPU computation. Significant differences between the CPU and GPU computational models lead to high natural diversity between the replicas, affording detection of large exploit classes without laborious manual diversification of the code.
On the Complexity of Baroque Music and Implications on Robotics and Creativity

In this paper we propose a perspective of baroque music as a phenomenon emerging from the interaction between a set of instructions (the score) and a frame of constraints in which to apply the instructions (the performance practice). This perspective provides a more principled setting for addressing the issue of estimating music complexity and suggests new ways towards artificial creativity through incompleteness. The considerations we elaborate on baroque music are extended to other complex processes, such as robotic behaviour.
Explaining Results of Data-Driven Applications

Albert Einstein once said "If you can't explain it simply, you don't understand it well enough". This is not only true for physical theories but also highly relevant to the results of data-driven applications. Data science is extensively used in various domains, achieving high performance at different tasks, e.g. question answering, and image recognition. Unfortunately, its results generally remain unexplainable, leaving its users to wonder why a specific result was returned instead of the other. My Ph.D. thesis focuses on providing explanations for data-driven applications. This paper demonstrates approaches for interpretability in two applications: Natural Language Queries, and Machine Learning Classifiers, followed by a discussion of open problems and future work.
Weight Distributions of a Class of Linear Codes

Linear codes with a few weights have many applications in secret sharing schemes, authentication codes, association schemes and strongly regular graphs, and they are also of importance in consumer electronics, communications and data storage systems. In this literature, based on the theory of defining sets, we present a class of five-weight linear codes over F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> (p is an odd prime). Then, we use exponential sums to determine its weight distributions.
An Improving Data Stream Classification Algorithm Based on BP Neural Network

With the continuous development of science and technology, many application fields of data belong to the data stream type. Data stream classification is one of the most important analysis methods of data stream processing. The neural network algorithms have no complicated models and reasoning, and have great advantages in data stream classification. In this paper, data stream classification, neural network algorithm, and improved BP neural network algorithm are studied. The neural network toolbox provided by MATLAB is used for data stream classification and simulation.
Evolution of Police Leadership Enhancement

Leadership is a word that seemingly needs no definition but is generally misunderstood or misused as a concept. Confusion is common between the functions of leadership and those of management. However, the immediate assistants reported a gap between the chiefs’ values and behaviors, particularly with respect to the leadership items. Changing times call for changing approaches to leadership and leadership development. Educational institutions have broadened applied science degree programs that incorporate cybercrime and public leadership as featured majors. Along with these progressions came a shift in police leadership typology. Police culture is unique in that it operates and is responsible to specific government policies, yet can only fulfill its mission by allowing officers to operate with the greatest amount of discretion. Properly trained police psychologists, especially those with backgrounds in industrial/organizational psychology, can be very useful in devising and improving leadership development programs in law enforcement agencies.
Selective Multi Keys to Modify RSA Algorithm

The use of the internet and communication technology noticeably contributes to development in all branches of science. The data that is transferred to communication channel are unsecured. Cryptography including security and integrity is a domain that provides policies for having privacy of the data. Protection of the data transferred through the internet is very important since such data when transferred through an unprotected channel may be attacked by a third party. RSA represents one of the public key cryptography methods that generate the modulus using two primes. These two primes represent the weakness of RSA benefited from and used by attackers. This paper proposes a new modified RSA method using selective multi keys encryption and decryption for the same modulus instead of using two keys, one for encryption and the other for decryption. This new method guarantees and increases the security of RSA.
Privacy Issues in Holistic Recommendations

In this paper we point out some relevant issues in relation to privacy when providing holistic recommendations. We emphasize that a holistic recommender should be fair, explainable and privacy-preserving to ensure the ethicality of the recommendation process. Further, we point out relevant research questions that should be addressed in the future, as well as propose some preliminary suggestions to face the emergent issues with reference to privacy in the recommendation domain.
Web Surfing in Anonymity Status

Due to conflicting objective of the stake holders, the privacy of the user in the Internet is difficult to achieve. There are some laws in privacy which are implemented by several national governments. The data usage of Internet is still gathered by ISP's, even with data breaches risks. Data breaches are very harmful since it affects several well-developed organizations and its impact is more on future. In this, Users availing anonymous services such as Tor who were stooping the users with P2P network are rare to identify. However, there are also negative sides of Tor that its legitimate user are viewed suspiciously by the law governing authorities due to use of services by criminals in negative aspects. This paper delivers, A protocol with internet access is introduced to compromise the conflicting stake holders' objectives for user anonymity. The protocol in the paper allows the analysis of web data usage only by the interested parties (i.e. ISP) and the criminal abuses are greatly reduced and also supports the law enforcement by providing flexibility. The methods and approach used in our projected protocol were: (i) Onion Routing Protocol, which is used for disguising web user's identity, (ii) An Authentic Computation, which creates a common communication between ISP and User keeping those information's private. (iii) Digital Certification of Licenses, which ensures that the anonymous status is provided for users only by the authorised web content providers and spoofing of service providers for licensing to provide anonymous status can't be done.
Nonlocal means variants filtering methods for speckle noise reduction in digital speckle pattern interferometric fringes

Nonlocal means (NLM) and its variant filtering methods such as nonlocal means-average (NLM-av), nonlocal meanslocal polynomial regression (NLM-LPR), and nonlocal means-shape adaptive patches (NLM-SAP) for speckle noise reduction in digital speckle pattern interferometric (DSPI) fringes are presented. The performance of these filtering methods is appraised by several criteria such as peak signal-to-noise ratio (PSNR), the quality index (Q), and the edge preservation index (EPI). The obtained filtering results corroborate the effectiveness of NLM and its variant filtering methods for speckle noise reduction in DSPI fringes.
Adaptive Fusion of Sub-band Particle Filters for Robust Tracking of Multiple Objects in Video

Video tracking is a relevant research topic because of its many surveillance, robotics, and biomedical applications. Although remarkable progress was made on this topic the capability to track objects precisely in video frames that contain difficult conditions, such as an abrupt variation in scene illumination, incomplete object camouflage, background motion and shadow, presence of objects with distinct sizes and contrasts, and presence of noise in the video frame, is still considered a vital research problem. To overcome the presence of these difficult conditions, we proposed a robust multi-scale tracker that used different sub-bands frame in the wavelet domain to express a captured video frame. Then N independent particle filters are employed to a selected subset of these sub-bands, where the selection of this wavelet sub-bands varies with every captured frame. Finally, the output position paths of these N independent particle filters were fused to obtain more precise position paths for moving objects in the video. To show the robustness of the proposed multi-scale video tracker, we employed it to various example videos that have different challenges. Opposed to a standard full-resolution particle filter-based tracker and a single wavelet sub-band (LL)2 based tracker, the proposed multi-scale tracker shows greater tracking performance.
Frontiers in plasma catalysis (ISPCEM 2018)

Herein we provide an overview of science and technology involved in remote sensing, and outlines some practical constraints in applications to geological problems. We further summarize diagnostic spectral features of important geological material that can be detected using satellite- and air-borne remote sensing. Finally, the papers contained in the special issue are briefly introduced.
MyDigiBuddy: An Android Based Student Organizer

Objective: This study is focused on the development of an android based application that served as a digital student companion.Methods/Statistical Analysis: Rapid Application Development (RAD) model and the android framework served as the foundation in the development of the mobile application.A Software Quality Model (SQM) evaluation tool based on ISO 9126 was utilized to assess the system in terms of the following metrics: 1. Functionality, 2. Reliability, 3. Usability, 4. Efficiency, 5. Maintainability, and 6. portability.Findings: The testing resulted to an over-all mean of 4.61 interpreted as Strongly Acceptable.This means that the application was free from errors during testing, the experts were able to accomplish their tasks using the system, there were few issues during the installation of the mobile application and the pool of experts used the system with ease.The strong acceptability of the application also means that the system adhered to software quality metrics based on ISO 9126.Application/Improvements: MyDigiBuddy will greatly help students organize their day to day activities.Taking down notes, scheduling activities for the entire week, and creating reminders usually done using separate application can now be accessed and run through a single application.
Rover

Rover is a mechatronic imaging device inserted into quotidian space, transforming the sights and sounds of the everyday through its peculiar modes of machine perception. Using computational light field photography and machine listening it creates a kind of cinema following the logic of dreams: suspended but mobile, familiar yet infinitely variable in detail. Rover draws on diverse traditions of robotic exploration, landscape and still life depiction, and audio field recording to create a hybrid between photography and cinema. Rover won the Best Art Paper award at SIGGRAPH 2017. For ACM Designing Interactive Systems / Creativity and Cognition 2019, Rover is installed on the 70 megapixel VRoom display in the black box theater in CalIT2 at UC San Diego. We have developed new functionality where image synthesis is distributed across the panels of the tiled display, visual and sonic trajectories are shaped using the unique hardware and software capabilities at UCSD. The result is an ultra-high resolution audio-visual experience with 4 channel surround sound.
System Design and Experimental Analysis of an All-environment Mobile Robot

Due to complexity of the structure, uncertainty of the dynamic model and poor adaptability of the environment of the traditional mobile robot, this paper proposes an all-environment mobile robot based on coaxial eight-rotor which can adapt to the three environments of water, land and air. This paper first describes the mechanical structure and hardware design of AEM (All-environment Mobile) robot, and then aerodynamics model and hydrodynamic model of the robot are formulated by Newton Euler equations. In the end, the physical prototype is assembled, and experiments are conducted to verify that the AEM robot could achieve the expected three motion modes.
Improvement of stereo matching algorithm based on guided filtering and Kernel Regression

Abstract Stereo matching is one of the most dynamic fields in computer vision. Though its relevant research has already stepped into a mature stage, there are still certain challenges to obtain real-time and high-precision disparity maps from stereo image pairs. This paper presents a novel local stereo matching algorithm with better performance in edge preserving. In the first stage, this paper measures matching cost through combining truncated absolute differences (TAD) of the color and gradient. In the cost aggregation stage, this paper is creatively to combined the weighted guided filtering and adaptive steering kernel regression algorithm, which effectively preserves image edge and depth information. In the final stage, an adaptive steering kernel regression algorithm is employed in interpolation to refine the final disparity map. According to the Middlebury benchmark experiments, the algorithm proposed in this paper could have better performance than other local stereo matching algorithms.
Autonomous Indoor Navigation for Wheelchairs using Signboards

Several studies have shown that people with disabilities benefit substantially from access to a means of independent mobility. While the requirements of many individuals with disabilities can be satisfied with manual or powered wheelchairs, a segment of the disabled community finds it difficult or impossible to use wheelchairs independently. This paper presents an autonomous indoor navigation system for wheelchairs based on sign board recognition. The system uses a deep learning model to detect signboards from surroundings and Azure Text Analytics API is used to extract the text from the signboard images. The system runs on a Raspberry Pi minicomputer and can be installed on any powered wheelchair. Experimental results and comparisons prove the efficiency of the proposed system.
Implementation of scalable and automatic Earth observation ground system

Satellite Ground System (GS) is one of the most important components of Earth Observation (EO) infrastructure with functions of antenna control, data reception, data processing, archiving, data transmission and distribution etc. The GS architecture has evolved from the dedicated satellite receiving system to the multi-mission comprehensive system. The new generation GS should support comprehensive EO applications based on inter-agency cross-system interoperability between different EO application systems. To deal with the performance enhancement of EO satellites, many studies have been done to enhance the capacity and scalability of the new generation EO GSs. The former tightly coupled GS architecture had dedicated software for specific tasks and poor scalability that was difficult to implement the inter-agency cross-system interoperability between different EO application systems. Web services and open standards make it possible to develop the open and interoperable EO GS. This paper proposes an architectural framework for interoperable GS system implementation based on open standards. The multi-layer architecture was used to decouple the tightly integrated GS and implement the function modules into GS web services. The architecture consists of physical resource layer, module implementation layer, the interoperability layer, business logic layer, and user interface layer. Service-based workflow technology was used to compose and chain the processing process of the EO ground service into the complete business process. An experiment GS test bed was developed to test the GS architecture proposed in the paper.
Minimum Precision Requirements of General Margin Hyperplane Classifiers

Margin hyperplane classifiers such as support vector machines have achieved considerable success in various classification tasks. Their simplicity makes them suitable candidates for the design of embedded intelligent systems. Precision is an effective parameter to trade-off accuracy and resource utilization. We present analytical bounds on the precision requirements of general margin hyperplane classifiers. In addition, we propose a principled precision reduction scheme based on the trade-off between input and weight precisions. We present simulation results that support our analysis and illustrate the gains of our approach in terms of reducing resource utilization. For instance, we show that a linear margin classifier with precision assignment dictated by our approach and applied to the “two versus four” task of the MNIST dataset is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim 2\times $ </tex-math></inline-formula> more accurate than a standard 8 bits low precision implementation in spite of using <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim 2\times 10^{4}$ </tex-math></inline-formula> fewer 1 bit full adders and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim 2\times 10^{3}$ </tex-math></inline-formula> fewer bits for data and weight representation.
Implementation of Creativity Problem Solving Model Using E-Learning in Applied Graph Theory Course

Abstract One of learning alternatives that can be applied to improve students’ activity and creativity is e-learning.. Implementation of e-learning for applied graph theory course used e-module for course material, e-portfolio for student assignments, and online-based tasks that can be accessed by other students, hence it is could be as a pioneer of wider implementation of online learning. In applied graph theory course, students needed to have direct experience in solving problems in related industries/institutions. From direct application experience in the field, students raise problems, model problems in the graph, and determine appropriate methods to solve the problem. Creative thinking process could be identified from students’ problem solving task by using Creative Problem Solving (CPS) model. There are four indicators of main step (understanding the challenge, generating ideas, preparing for action, and planning approach) and eight minor step indicators (constructing opportunities, exploring data, problem framing, generating ideas, developing solutions, building acceptance, appraising tasks, and designing process) in the recent version. Purpose of the paper is to analyze result of CPS model implementation, and to discuss student’s responses to the implementation. By implementing the CPS model through e-learning, it could be identified that student’ s creativity in real problem solving were good. The questionnaire indicated that the student response strongly agree with the implementation of this learning to develop creativity.
Geochemical Aureoles of Ore-Bearing Areas of Nizhne-Taezhnyi Ore Node (East Sikhote-Alin)

Abstract Nizhne-Taezhnyi ore node of Northern Primorye is a complex object containing tin-polymetal-silver, polymetal-silver, and proper silver mineralization silver-porphyry mineralization is possible to be found also. Analysis geogimicl element distribution showed that Ag and Sb with good correlation between each other manifested a weak tendency to decrease with depth. The highest concentrations of Sn, Pb, and Zn are fixed at a depth of about 250 m and decrease upwards and downwards from this level, but Mo and Cu contents essentially increase at low levels (Fig. The revealed tendencies of variability of the chalcophile element concentration both laterally and vertically can be explained by the availability of a vertical mineralogical-geochemical zonality of the mineralization.