Reviewerly/exHarmony huggingface dataset:
Synchronization of the vector state estimation methods with unmeasurable coordinates for intelligent water quality monitoring systems in the river

One of the main problems of signal processing is the issue of estimation, which consists in reproducing a useful signal on the basis of another one, e.g. one burdened with unwanted interference. The use of classical filtering to eliminate disturbances carries the risk of removing an important part of the information about the useful signal. In order to avoid this phenomenon, optimal filters are used, utilizing the statistical properties of signals. Such a case of consideration concerns the issue of estimation for stochastic systems. An example is the Kalman filter in which the main aspect of research is the differential equation of the state estimate in which there is an amplification factor dependent on the covariance matrix of the estimation error. The value of this factor is determined from the non-linear Riccati differential equation, depending on the characteristics of signals affecting the object under investigation and the measurement. The paper proposes an adaptive algorithm to determine the value of the filter amplification factor which allows to avoid the inconveniences resulting from the need to estimate the characteristics of signals affecting the object under investigation. The presented approach to estimation issues does not require the need to characterize random signals due to the application of the adaptive method of state estimation. The analyzed process refers to an object described by ordinary differential equations with continuous measurements representing the state of pollution of the river using the so-called interpretation “along the characteristics”. The method used in the module of the intelligent analytical system allows for quick determination of hard-to-measure parameters of the quality of water in the river and to react to upcoming ecological threats without any delays dangerous for the system.
A Gene-Inspired Malware Detection Approach

Malware detection is an important topic in cyber security. The research presented in this paper mainly studies on disassembly codes of Windows executable files, learns from the research route of bioinformatics and proposes the concept of software gene. A distance-based method is also proposed to measure the difference of genes and the dimensionality reduction based on a modified clustering algorithm of biological phylogenetic model. Finally a gene-inspired malware detector is constructed using Random Forest model. The software gene extraction proposed in this paper is more flexible and generates less data than the widely-used n-gram method. The detector based on genes also performs better. The clustering-based dimensionality reduction retains more comprehensive features and maintains the interpretability in software analysis area. The detector based on gene-inspired malware detection approach can reach the precision 96.14%, which is better than traditional methods.
Improving 3D Human Pose Estimation Via 3D Part Affinity Fields

3D human pose estimation from monocular images has become a heated area in computer vision recently. For years, most deep neural network based practices have adopted either an end-to-end approach, or a two-stage approach. An end-to-end network typically estimates 3D human poses directly from 2D input images, but it suffers from the shortage of 3D human pose data. It is also obscure to know if the inaccuracy stems from limited visual under-standing or 2D-to-3D mapping. Whereas a two-stage directly lifts those 2D keypoint outputs to the 3D space, after utilizing an existing network for 2D keypoint detections. However, they tend to ignore some useful contextual hints from the 2D raw image pixels. In this paper, we introduce a two-stage architecture that can eliminate the main disadvantages of both these approaches. During the first stage we use an existing state-of-the-art detector to estimate 2D poses. To add more con-textual information to help lifting 2D poses to 3D poses, we propose 3D Part Affinity Fields (3D-PAFs). We use 3D-PAFs to infer 3D limb vectors, and combine them with 2D poses to regress the 3D coordinates. We trained and tested our proposed framework on Human3.6M, the most popular 3D human pose benchmark dataset. Our approach achieves the state-of-the-art performance, which proves that with right selections of contextual information, a simple regression model can be very powerful in estimating 3D poses.
You are Missing a Concept! Enhancing Ontology-Based Data Access with Evolving Ontologies

In the last years, enterprises increase their effort to collect large amounts of data from many heterogeneous data sources and store it in modern architectures like data lakes. However, this approach faces different drawbacks for finding and understanding data sources. Ontology-Based Data Access (OBDA) originating from the Semantic Web enables a homogeneous access to the data sources by using a mapping, called semantic model, between a data source and a target ontology. However, OBDA requires a detailed ontology, which is usually created by ontology engineers and domain experts resulting along with high effort for designing and maintaining. To overcome these limitations, we develop an approach consisting of a knowledge graph, which features an internal growing ontology and linked data-source specific semantic models. The ontology continuously evolves on-demand based on newly added data sources along with their corresponding semantic models, which are created by domain experts. To ensure the knowledge graph's stability, we develop an intuitive user-oriented assistant and combine it with a semi-supervised evolving strategy that assists the user with the help of external knowledge bases. We evaluate accuracy and usability of our approach by conducting a user study with a heterogeneous group of participants that define semantic models upon pre-defined data sets. The results show that semantic models become more objective and consistent with our provided user assistant and thus lead to a knowledge graph with higher interconnectivity and stability.
An Investigation of the Impact of Data Breach Severity on the Readability of Mandatory Data Breach Notification Letters: Evidence From U.S. Firms

The aim of this article is to investigate the impact of data breach severity on the readability of mandatory data breach notification letters. Using a content analysis approach to determine data breach severity attributes (measured by the total number of breached records, type of data accessed, the source of the data breach, and how the data were used), in conjunction with readability measures (reading complexity, numerical intensity, length of letter, word size, and unique words), 512 data breach incidents from 281 U.S. firms across the 2012–2015 period were examined. The results indicate that data breach severity has a positive impact on reading complexity, length of letter, word size, and unique words, and a negative impact on numerical terms. Interpreting the results collectively through the lens of impression management, it can be inferred that business managers may be attempting to obfuscate bad news associated with high data breach severity incidents by manipulating syntactical features of the data breach notification letters in a way that makes the message difficult for individuals to comprehend. The study contributes to the information studies and impression management behavior literatures by analyzing linguistic cues in notifications following a data breach incident.
Medical image retrieval using Resnet-18 for clinical diagnosis

Advances in medical imaging technologies have led to the generation of large databases with high-resolution image volumes. To retrieve images with pathology similar to the one under examination, we propose a content- based image retrieval framework (CBIR) for medical image retrieval using deep Convolutional Neural Network (CNN). We present retrieval results for medical images using a pre-trained neural network, ResNet-18. A multi- modality dataset that contains twenty-three classes and four modalities including (Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Mammogram (MG), and Positron Emission Tomograph (PET)) are used for demonstrating our method. We obtain an average classification accuracy of 92% and the mean average precision of 0.90 for retrieval. The proposed method can assist in clinical diagnosis and training radiologist.
Person Recognition for Access Logging

The goal of this project is to develop a complete hardware and software system for access monitoring into a secured facility using modern facial recognition technology (FRT) and lightweight, inexpensive components. FRT detects and recognizes individuals as they enter and exit the secured area, providing accessible logs for a visualization of this activity. The project uses FaceNet FRT which takes a deep-learning approach to increase facial recognition accuracy. With input from two portable cameras, the FaceNet FRT is implemented on a Raspberry Pi using the Intel Movidius Neural Compute system.
Remote Sensing Image Stripe Detecting and Destriping Using the Joint Sparsity Constraint with Iterative Support Detection

Remote sensing images have been applied to a wide range of fields, but they are often degraded by various types of stripes, which affect the image visual quality and limit the subsequent processing tasks. Most existing destriping methods fail to exploit the stripe properties adequately, leading to suboptimal performance. Based on a full consideration of the stripe properties, we propose a new destriping model to achieve stripe detection and stripe removal simultaneously. In this model, we adopt the unidirectional total variation regularization to depict the directional property of stripes and the weighted ℓ 2 , 1 -norm regularization to depict the joint sparsity of stripes. Then, we combine the alternating direction method of multipliers and iterative support detection to solve the proposed model effectively. Comparison results on simulated and real data suggest that the proposed method can remove and detect stripes effectively while preserving image edges and details.
Effect of behavioral variables on organizational citizenship behavior (OCB), with job satisfaction as moderating among Jordanian five-star hotels

Purpose In the current competitive world of business, it is important that every individual strives to be competitive to elevate his/her life status and creates a bright-looking future. The purpose of this study is to lay emphasis on the Jordanian hospitality industry in light of the investigation as to how organizational commitment, leadership style and organizational culture influence organizational citizen behavior (OCB) with the moderating role of job satisfaction in the relationship. Design/methodology/approach The study explores the determinants based on the responses obtained from the employees working in five-star hotels in Jordan. A small sample was exposed to SPSS software analysis for instrument validity, reliability and data normality. Findings The study validated the reliability of the instrument in conducting a preliminary study, obtaining reasonable normality and highly reliable coefficients of measures (0.753-0.938). Originality/value The study validated the effectiveness of the instrument that examined different specific antecedents of OCB.
Digital conversion model for hand-filled forms using optical character recognition (OCR)

The process of manual data entry used by several industries garners a high error rate. This is because the manual process relies too heavily on a human's capability to interpret handwritten forms. To reduce the high error rate of data entry, the researchers explored the different processes that comprise optical character recognition (OCR) and used it on a novel digital conversion model for hand-filled forms. The OCR process is made up of 4 major phases. The techniques for each stage are as follows: Sauvola binarization for image pre-processing; blob analysis for character segmentation; pre-trained Convolutional Neural Networks: GoogLeNet, AlexNet, and VGG16 for feature extraction, and Support Vector Machines (SVM), K-Nearest Neighbor (KNN), and Naïve Bayes for classification. The novel combination of Convolutional Neural Networks for feature extraction coupled with SVM for character classification showed promising results, going up to 98.62% in accuracy and 65.31% in F-Score.
Framework modeling for User privacy in cloud computing

Many organizations around the world recognize the vitality of cloud computing. However, some concerns make organizations reluctant to adopting cloud computing. These include data security, privacy, and trust issues. It is very important that these issues are addressed to meet client concerns and to encourage the wider adoption of cloud computing. This paper develops a user privacy framework based upon on emerging security model that includes access control, encryption and protection monitor schemas in the cloud environment.
Pano-RSOD: A Dataset and Benchmark for Panoramic Road Scene Object Detection

Panoramic images have a wide range of applications in many fields with their ability to perceive all-round information. Object detection based on panoramic images has certain advantages in terms of environment perception due to the characteristics of panoramic images, e.g., lager perspective. In recent years, deep learning methods have achieved remarkable results in image classification and object detection. Their performance depends on the large amount of training data. Therefore, a good training dataset is a prerequisite for the methods to achieve better recognition results. Then, we construct a benchmark named Pano-RSOD for panoramic road scene object detection. Pano-RSOD contains vehicles, pedestrians, traffic signs and guiding arrows. The objects of Pano-RSOD are labelled by bounding boxes in the images. Different from traditional object detection datasets, Pano-RSOD contains more objects in a panoramic image, and the high-resolution images have 360-degree environmental perception, more annotations, more small objects and diverse road scenes. The state-of-the-art deep learning algorithms are trained on Pano-RSOD for object detection, which demonstrates that Pano-RSOD is a useful benchmark, and it provides a better panoramic image training dataset for object detection tasks, especially for small and deformed objects.
Encoding transformation-based differential evolution algorithm for solving knapsack problem with single continuous variable

The knapsack problem with a single continuous variable (KPC) is an extension of the standard 0–1 knapsack problem. It is an especial combinatorial optimization problem with continuous variable S so that its solution is more difficult. In this paper, a novel differential evolution algorithm is proposed based on the encoding transformation technique, which is named Encoding Transformation-based Differential Evolution algorithm (ETDE). In ETDE, the individual is represented as an (n + 1)-dimensional vector in which the first n components are transformed into an n-dimensional 0–1 vector by using a special surjection, and the last component is as the value of continuous variable S. From this, a potential solution of KPC is obtained. Moreover, the Gauss-Seidel method is used to accelerate the convergence rate of ETDE, and an effective repair and optimization method is used to handle the infeasible solutions. In order to verify the performance of ETDE, we use it and the exact algorithm, approximation algorithm, particle swarm optimization, and artificial bee colony to solve four classes of large-scale KPC instances, respectively. The computational results show that ETDE not only has the fast speed, but also the calculation result is better than other algorithms. Consequently, ETDE is more suitable for quickly and efficiently solving the KPC problem than other algorithms in practical applications.
Implementation of Portable Executable File Analysis Framework (PEFAF)

The Portable executable file format is the format of executables, object code and DLL's (Dynamic Link Library) in Microsoft Windows Operating system. It is the standard of how executable files are organized within file system in Microsoft Windows. The Portable executable file format was designed for Windows NT 3.1 that released in 1993. Many of its features are inherited from COFF (Common object file format) used in Unix Operating systems. It consists of header and sections; headers are rules that tell windows loader how the section should be mapped and loaded into memory. The section are nothing but the data or content. Different sections hold different kind of data for example code section contains executable code while bss (Block Started by Symbol) sections contains uninitialized data. Portable executable file could be used in a way to dent the security of computer, therefore great care should be taken while downloading and running Portable executable files. In our work we develop a static malware analysis tool called `Portable Executable File Analysis Framework (PEFAF)' using data mining techniques. A collection of 8 thousand benign and 7 thousand malicious files were used in this work. We extracted 60 features, analyzed them and found that 34 of them are significant for the detection of malware threats. Based on these 34 indicators our tool classifies input file into malicious or non-malicious.
Measures of Scatter and Fisher Discriminant Analysis for Uncertain Data

Uncertain data objects are objects that can be characterized by either a probability density function (PDF) or with multiple points. Because of existing levels of uncertainty for uncertain data objects, the scatter of this type of objects might be very different than the scatter of certain data objects. Measures of scatter for uncertain objects have not been defined before. In this paper, we define covariance matrix, within scatter matrix, and between scatter matrix as the measures of scatter for uncertain data objects. Also, in this paper, we extend the idea of Fisher linear discriminant analysis for uncertain objects (UFLDA). We also develop kernel Fisher discriminant analysis for uncertain objects (UKFDA). The developed uncertain kernel Fisher discriminants are for two cases: 1) when the uncertain objects are given with PDF and 2) when the uncertain objects are given with multiple points. We compare the performance of our developed uncertain Fisher discriminants (UFLDA and UKFDA) with a few other methods in classification of uncertain data objects through several examples on both simulated and real-world data. We will show in the experiments that our developed uncertain Fisher discriminants outperform other methods in classifying uncertain data objects.
Methods of machine-readable zone recognition results post-processing

In this paper a task of correction (post-processing) of machine-readable zone recognition results is discussed. A survey is presented for existing approaches of recognition error correction methods and an algorithm is proposed for applying these methods for machine-readable zone post-processing. Experimental results are shown for the described methods.
Persuasion of voices: The effects of a speaker’s voice characteristics and gender on consumers’ responses

Voices are present in most communications. Yet, the literature on voice persuasion is astonishingly limited and fragmented, focusing on certain voice characteristics (e.g. pitch), contexts, and providing mixed results. This research attempts to integrate the various constructs and mechanisms involved in voice persuasion as a result of the cross-fertilization of the disciplines having studied voice (psychoacoustics, cognitive psychology, anthropology, psycho-sociology, marketing, and politics). Study 1 manipulates via acoustic software the key voice characteristics (i.e. pitch, roughness, and brightness) and gender of a speaker heard in a radio advertisement for a neutral, non-gendered product category. Study 2 explores a potential boundary condition of the effects of voice, the presence of context-specific expectations toward the speaker (i.e. gender and competence level), by manipulating the voice of a political candidate. The effects of the voice characteristics are consistent in both contexts: speakers with low- (vs high-) pitched, dull (vs bright), and smooth (vs rough) voices are the most effective. Speakers with high-pitched, dull, and smooth voices are perceived as the most competent. Finally, speaker gender plays a secondary persuasive role; listener gender only plays a role in the absence of context-specific expectations toward the speaker. Implications for voice and speaker persuasion as well as for voice casting and coaching are discussed.
Tissue segmentation in volumetric laser endomicroscopy data using FusionNet and a domain-specific loss function

Volumetric Laser Endomicroscopy (VLE) is a promising balloon-based imaging technique for detecting early neoplasia in Barretts Esophagus. Especially Computer Aided Detection (CAD) techniques show great promise compared to medical doctors, who cannot reliably find disease patterns in the noisy VLE signal. However, an essential pre-processing step for the CAD system is tissue segmentation. At present, tissue is segmented manually but is not scalable for the entire VLE scan consisting of 1,200 frames of 4,096 &times; 2,048 pixels. Furthermore, the current CAD methods cannot use the VLE scans to their full potential, as only a small segment of the esophagus is selected for further processing, while an automated segmentation system results in significantly more available data. This paper explores the possibility of automatically segmenting relevant tissue for VLE scans using FusionNet and a domain-specific loss function. The contribution of this work is threefold. First, we propose a tissue segmentation algorithm for VLE scans. Second, we introduce a weighted ground truth that exploits the signal-to-noise ratio characteristics of the VLE data. Third, we compare our algorithm segmentation against two additional VLE experts. The results show that our algorithm annotations are indistinguishable from the expert annotations and therefore the algorithm can be used as a preprocessing step for further classification of the tissue.
Multi-fidelity deep neural networks for adaptive inference in the internet of multimedia things

Internet of Things (IoT) infrastructures are more and more relying on multimedia sensors to provide information about the environment. Deep neural networks (DNNs) could extract knowledge from this audiovisual data but they typically require large amounts of resources (processing power, memory and energy). If all limitations of the execution environment are known beforehand, we can design neural networks under these constraints. An IoT setting however is a very heterogeneous environment where the constraints can change rapidly. We propose a technique allowing us to deploy a variety of different networks at runtime, each with a specific complexity-accuracy trade-off but without having to store each network independently. We train a sequence of networks of increasing size and constrain each network to contain the parameters of all smaller networks in the sequence. We only need to store the largest network to be able to deploy each of the smaller networks. We experimentally validate our approach on different benchmark datasets for image recognition and conclude that we can build networks that support multiple trade-offs between accuracy and computational cost.