Reviewerly/exHarmony huggingface dataset:
Using Related Text Sources to Improve Classification of Transcribed Speech Data

Today’s content including user generated content is increasingly found in multimedia format. It is known that speech data are sometimes incorrectly transcribed especially when they are spoken by voices on which the transcribers have not been trained or when they contain unfamiliar words. A familiar mining tasks that helps in storage, indexing and retrieval is automatic classification with predefined category labels. Although state-of-the-art classifiers like neural networks, support vector machines (SVM) and logistic regression classifiers perform quite satisfactory when categorizing written text, their performance degrades when applied on speech data transcribed by automatic speech recognition (ASR) due to transcription errors like insertion and deletion of words, grammatical errors and words that are just transcribed wrongly. In this paper, we show that by incorporating content from related written sources in the training of the classification model has a benefit. We especially focus on and compare different representations that make this integration possible, such as representations of speech data that embed content from the written text and simple concatenation of speech and written content. In addition, we qualitatively demonstrate that these representations to a certain extent indirectly correct the transcription noise.
Privacy-Preserving Routing for Wireless Network using OPERA

In wireless network, a routing protocol named privacy-preserving is used which is used to cover-up the actual communication-pair by utilizing the additional traffic. The introduction of dummy traffic is done heuristically that it cannot assure to the latency, transmission cost, etc., in the network topology are optimized. Here, the task is to develop a novel based approach called Optimal Privacy-Preserving Enhancing Routing Algorithm (OPERA) for privacy-preserving and examines the privacy utility problem for wireless networks. In order to implement privacy, we use the statistical decision-making framework called as OPERA. We will use the Bayesian Maximum-a-Posteriori (MAP) which considers the both loosy and looseless observations. We will be solving the privacy-utility tradeoff problem, that can be efficiently solved as a linear program. The simulation that uses OPERA algorithm reduces the probability of adversary detection for up to 5 times compared to baseline schema. OPERA can also perform the theoretic, conventional information and mutual information approach.
A Formal Taxonomy of Temporal Data Defects

Data quality assessment outcomes are essential for analytical processes reliability, especially when they are related to temporal data. Such outcomes depend on efficiency and efficacy of (semi-)automated approaches that are determined by understanding the problem associated with each data defect. Despite the small number of works that describe temporal data defects regarding to accuracy, completeness and consistency, there is a significant heterogeneity of terminology, nomenclature, description depth and number of examined defects. To cover this gap, this work reports a taxonomy that organizes temporal data defects according to a five-step methodology. The proposed taxonomy enhances the descriptions and coverage of defects with regard to the related works, and also supports certain requirements of data quality assessment, including the design of visual analytics solutions to support data quality assessment.
Mechanisms Fostering the Misuse of Information Systems for Corrupt Practices in the Nigerian Public Sector

The paper explores the misuse of information systems for corrupt practices in the Nigerian public sector as a phenomenon under study. Routine Activity theory, Model of Emergent IT Use, and Normalization theory were used as lenses. Danermark et al.'s six stage framework with a single case-study was adopted as the critical realist methodology. The anti-corruption and regulatory agency, the Economic and Financial Crimes Commission of Nigeria, is the case in focus. Semi-structured interviews, archival documents and press media were used as data sources. By analyzing the data, we identified motivated offender, suitable target, and the absence of a capable guardian as the entities/factors that characterized the phenomenon. Political clientelism patronage, socialization, embeddedness of corrupt routine into IT artifacts and rationalization were identified as causal mechanisms with culture as the enabling conditions. Dysfunctional structures of the Nigerian public sector were also identified as structures that breed the mechanisms. The study findings contribute to theory, practice, and the methodology of critical realism.
Recommendations and privacy in the arXiv system: A simulation experiment using historical data

Recommender systems may accelerate knowledge discovery in many fields. However, their users may be competitors guarding their ideas before publication or for other reasons. We describe a simulation experiment to assess user privacy against targeted attacks, modeling recommendations based on co‐access data. The analysis uses an unusually long (14 years) set of anonymized historical data on user‐item accesses. We introduce the notions of “visibility” and “discoverability.” We find, based on historical data, that the majority of the actions of arXiv users would be potentially “visible” under targeted attack. However, “discoverability,” which incorporates the difficulty of actually seeing a “visible” effect, is very much lower for nearly all users. We consider the effect of changes to the settings of the recommender algorithm on the visibility and discoverability of user actions and propose mitigation strategies that reduce both measures of risk.
Gamma Corrected Fusion Framework of Dominant Orientation and Edge based Textural Flistogram Equalization for Image Enhancement

In this paper, an optimal approach is proposed for the enhancement of textural images. This objective is achieved by introducing an optimally weighted framework. It is framed by harvesting the collective benefits of gamma corrected interim channel along with dominant orientation based texture dependent interim intensity channel. In this manner both the textural features and illumination based features of the image can be improved in an efficient manner. Collective contribution of both edge-thresholding based intensity equalization and dominant-orientation based textural equalization, leads to processing of textural and edge components separately. In this paper, cuckoo search optimizer is employed for attaining the desired level of enhancement, by exploring the 2-D search space in an efficient manner. Comparative performance evaluation is presented so that the outperformance of proposed approach can be underlined.
Select the Best for Me: Privacy-Preserving Polynomial Evaluation Algorithm over Road Network

Recent years have witnessed the rapid advances in location based services (LBSs) with the fast development of mobile devices and communication technologies. Outsourcing spacial databases to Cloud provides an economical and flexible way for the LBS provider to deploy services. For an LBS enterprise, the collected data might be its most valuable strategic asserts. However, in the outsourced database paradigm, the third-party database server at cloud is not completely trustworthy, therefore, protecting data privacy is critical. A polynomial evaluation algorithm over road network returns top-k results (restaurant) to a user (tourist). It is a fundamental and important query mode has been widely used in LBS applications. In this paper, we extend the Order-Revealing Encryption (ORE) to design a privacy-preserving polynomial evaluation algorithm over the spacial data with security guarantee. To reduce the computation overhead, we introduce an influence model to store the encrypted point data (restaurant). Besides the stronger security, this work is a practical polynomial evaluation algorithm over the road network. The practicality is mainly manifested in two aspects: evaluation over multiple attributes, and the dynamic evaluation function rather than fixed function in advance. We formally prove the security of our scheme in the random oracle model. Finally, we implement a prototype to evaluate the performance of our scheme. The experimental results over the real road network dataset demonstrate that the proposed scheme is an efficient and practical polynomial evaluation algorithm for the LBS applications.
FAST

Availability of Internet technologies has formed a new virtual job paradigm, called as, freelancing or remote work. The world has adopted it as an employment model in the modern era. The flexible online platforms, low development cost, and fast delivery, make freelancing increasingly popular. However, online freelance marketplaces have limited services available and suffer from several issues. New innovations are needed in the freelancing area to overcome these shortcomings. This article presents a detailed comparison and analysis of twelve freelance platforms in terms of their providing features and using technologies. Along with that, this proposes refined foresight suggestions for new freelance platform models.
Wand-Like Interaction with a Hand-Held Tablet Device—A study on Selection and Pose Manipulation Techniques

Current hand-held smart devices are supplied with powerful processors, high resolution screens, and sharp cameras that make them suitable for Augmented Reality (AR) applications. Such applications commonly use interaction techniques adapted for touch, such as touch selection and multi-touch pose manipulation, mapping 2D gestures to 3D action. To enable direct 3D interaction for hand-held AR, an alternative is to use the changes of the device pose for 6 degrees-of-freedom interaction. In this article we explore selection and pose manipulation techniques that aim to minimize the amount of touch. For this, we explore and study the characteristics of both non-touch selection and non-touch pose manipulation techniques. We present two studies that, on the one hand, compare selection techniques with the common touch selection and, on the other, investigate the effect of user gaze control on the non-touch pose manipulation techniques.
Measurement Affecting Errors in Digital Image Correlation

Optical full-field measurement methods such as Digital Image Correlation (DIC) are increasingly used in the area of experimental mechanics. The reliability for each measurement technique depends on the knowledge of its uncertainty and the sources of errors of the results. The aim of this work is to systemize the sources of errors related to digital image correlation (DIC) technique applied to strain measurements. The paper is concluded by some suggestions proposed in order to minimize the errors.
How to Understand the Fundamental Laws of Information

The purpose of this paper is to prove the fundamental laws used in the three types of information processing through knowledge content and language form. Thus, the ability showing human-computer as twin smart system of information co-processing is analyzed, and its core intelligence is targeted to produce ambiguity or to reduce ambiguity. The method includes three steps: first, to explore logical rules of thinking process followed by human brain, second, to find out mathematical principles of information processing followed by computer, third, to accumulate a series of translation rules of bilingual information processing followed by human-machine collaboration. The result is that the fundamental laws of information processing are proved by both mental program contents and language expression symbols, including logical rules for content information processing, mathematical rules for formal information processing, generalized translation rules for all kinds of bilingual information processing. Its significance is that it not only clarifies the three rules of human-machine as the generalized bilingual information co-processing, but also finds sequencing and positioning in logic as smart system of language, knowledge, software, hardware, and as super-development environment for big production of knowledge, and all supporting with the generalized text gene information processing within smart system as the common focus collaborated with education, management, learning, using or application.
Single Tone Background Removal Applying Dual Threshold and Dynamic Clustering

Monochrome solid background removal is a common practice in the field of motion pictures, video game and news casting that is known as green screening. It is the basis of CG (Computer Graphics) employed for creation of special visual effects either in images or, more aptly, in motion pictures. The bewildering success of this process in weaving out a synthetic reality is more often than not the outcome of intricate and elaborate combination of illumination, color selection, capturing device settings, post production non-linear editing and repetitive arduous rendering. Even though it is very tempting to imagine experiences wherein a person can also be subjected to experience similar environments in real-time, the state of the art technology is yet to attain that efficacy at acceptable commercial level. Museums, which thrive upon creating amazing experiences for communicating knowledge, always aspire to embed wow factors of CG-created motion pictures in real-time situations where the visitor walks into similar environments be it that of the ancient Egypt or the internal structure of molecular crystals. To virtually transport the visitor inside such CG created environments, monochrome background subtraction is a very potential first shot. Yet, the approach suffers from twofold difficulties - the illumination in a museum gallery cannot be customized to suit the current approaches of green screening technologies and secondly its visitors are uninitiated to handle the methods involved in pre-processing captures and post-production renderings that require professional judgments not common to the visitors coming from different walks of life. This paper, by adopting a unique background subtraction algorithm avoids the custom illumination hurdle and that automatically helps in automated capture and on the fly rendering.
Modeling ophthalmic surfaces using Zernike, Bessel and Chebyshev type functions

In this paper the application of Zernike, Bessel and Chebyshev functions is studied and the results are compared when modeling ophthalmic surfaces in visual optics. The total RMS error is presented when addressing capability of these functions in fitting with different surfaces. It is shown that Chebyshev polynomials could be appropriate alternatives of the Zernike polynomials to represent complete anterior corneal surfaces.
Optimal Path Planning for Unmanned Aircraft Target Observation Through Constrained Urban Environments

The work herein determines the optimal flight path for a small unmanned aircraft system through a constrained urban environment. The implementation of unavoidable keep-out regions is evaluated by first solving the optimal flight path through a constrained simplex corridor and, second, minimizing incursions to keep-out regions given the same constraint field and ending on a defined orbit around a target of interest. Direct orthogonal collocation methods are combined with fast geometric path planning techniques where a triangulated mesh is used to produce a hybrid control routine, resulting in optimal flight paths through a defined triangulated corridor. Physical constraints are eliminated from the nonlinear program search space, while keep-out regions are modeled within the objective function of the optimal control problem and avoided according to a weighted distribution of the objective components. A scenario is presented for a small unmanned aircraft system to advance at constant altitude and constant speed through city building constraints while minimizing time in unavoidable keep-out regions. The path terminates outside the triangulated corridor on an orbit, encircling the target location. Results illustrate an optimal path solution through 37 polygonal constraints and 2 nonlinear, unavoidable keep-out regions with computational times on the order of 90 s.
Semi-Automatic Feature Extraction to Assess Solar Potential for Rural India

The over-exploitation of non-renewable resources for energy demands is a serious issue. Convergence towards renewable resources such as solar energy is need of the day. Solar energy is the cleanest form of energy available on Earth. The objective of this research is to extract the building rooftop from the satellite images using a k-means clustering algorithm to identify the usable area for solar potential assessment. The scenes of WorldView-3 and Google Earth are segmented into nine parts and the algorithm implemented in Matlab is applied to the individual parts for better utilization of computing resources. This approach has been applied to the parts of northern states of India for solar potential assessment in a fast and accurate manner. The Global Horizontal Irradiance (GHI) data obtained from the database of National Renewable Energy Laboratory (NREL), United States have been used in the solar potential assessment. For the validation purpose, the above-mentioned algorithm has been compared with the digitization in QGIS software. The results obtained from the above-mentioned algorithm developed have extracted 85% to 90% of the features in the satellite image. The developed algorithm has given best results with the WorldView-3 (high-resolution image) than the other coarser resolution scenes. The developed approach is helpful in evaluating the feasibility of the large areas for solar potential assessment. This methodology is useful for the implementation of different government's solar energy generation schemes for rural and hilly areas. It helped in estimating the solar potential of the large hilly area for electricity generation. This approach is useful for a larger area as it computes the usable area by dividing the scenes into smaller parts and applies the algorithm individually to each part of the scene. Keywords: k-means clustering, GHI, rooftop, solar potential, Google Earth, WorldView-3
Secure Cyber-Physical Systems for Improving Transportation Facilities in Smart Cities and Industry 4.0

The main aim of this strategic research proposal is to develop a model of secure transportation system using efficient CPS which not only reduce the unnecessary accident rates but also increase safety system that enhances the livability of smart cities and Industry 4.0. Although the main focus is efficient security solutions, dynamic and intelligent approaches of the future security solutions will be able to detect the evolving threats and cyberattacks during the data or signal transmission between the users and service providers.
Attention and Convolution Enhanced Memory Network for Sequential Recommendation

The sequential recommendation, which models sequential behavioral patterns among users for the recommendation, plays a critical role in recommender systems. Conventionally, user general taste and recent demand are combined to promote recommendation performance. However, existing methods usually neglect that user long-term preference keeps evolving over time and only use a static user embedding to model the general taste. Moreover, they often ignore the feature interactions when modeling short-term sequential patterns and integrate user-item or item-item interactions through a linear way, which limits the capability of model. To this end, we propose an $$\mathbf {A}$$ttention and $$\mathbf {C}$$onvolution enhanced memory network for $$\mathbf {S}$$equential $$\mathbf {R}$$ecommendation (ACSR) in this paper. Specifically, an attention layer learns user's general preference, while the convolutional layer searches for feature interactions and sequential patterns to capture user's sequential preference. Moreover, the outputs of the attention layer and the convolutional layer are concatenated and fed into a fully-connected layer to generate the recommendation. This approach provides a unified and flexible network structure for capturing both general taste and sequential preference. Finally, we evaluate our model on two real-world datasets. Extensive experimental results show that our model ACSR outperforms the state-of-the-art approaches.
A methodology to forecast some attributes of an automatic storyteller’s outputs

This paper reports an extension of the program MEXICA, an automatic plot generator. The purpose of this project is to build a framework that permits studying the relations between MEXICA’s processes, its knowledge structures and the features of the produced narratives. We describe a methodology to analyse the features of the agent’s knowledge-base, to further establish correlations between such features and a set of general characteristics of the tales that they produce. Next, we make use of those correlations to forecast some properties of the future tales to be developed by different MEXICAs agents with different knowledge-bases. For this task, we introduce the S-graphs, representations of the similarity and organisation of the knowledge-structures. The results we obtained indicate that we are able to correctly forecast some of the features of tales to be produced; however, much more work is required.
SecureCSearch: Secure Searching in PDF Over Untrusted Cloud Servers

The usage of cloud for data storage has become ubiquitous. To prevent data leakage and hacks, it is common to encrypt the data (e.g. PDF files) before sending it to a cloud. However, this limits the search for specific files containing certain keywords over an encrypted cloud data. The traditional method is to take down all files from a cloud, store them locally, decrypt and then search over them, defeating the purpose of using a cloud. In this paper, we propose a method, called SecureCSearch, to perform keyword search operations on the encrypted PDF files over cloud in an efficient manner. The proposed method makes use of Shamir's Secret Sharing scheme in a novel way to create encrypted shares of the PDF file and the keyword to search. We show that the proposed method maintains the security of the data and incurs minimal computation cost.
Distributions on the circle group

In this paper, we extend the definition of a random angle and the definition of a probability distribution of a random angle. We expand P. Lévy’s researches related to wrapping the probability distributions defined on R. We determine a relation between quasi-lattice probability distributions on R and lattice probability distributions on the unit circle S. We use the Bergström identity for comparison of a convolution of probability distributions of random angles. We also prove an inverse formula for lattice probability distributions on S.
Adaptive Cutoff Distance Based Density Peak Pivot for Metric Space Outlier Detection

Outlier Detection is a key technique to discover abnormal patterns from mass data. Pivot based metric space outlier detection algorithm is designed to improve the cutoff value of outlier degree faster and terminate the detection program earlier, for saving detection time. However, due to the lack of efficient pivot selection method, the performance of existing related algorithms is not as good as it might have been. In this paper, we propose an adaptive cutoff distance based density peak pivot selection algorithm (ADP) to get a suitable pivot quickly. Moreover, we also develop an improved outlier detection algorithm based on ADP algorithm (ADPOD). Experimental results indicate that ADPOD can effectively overcome the existing relate algorithm, and achieves a 2.67 speed up over it on average and, in certain cases, up to 8.06. The distance calculation times are reduced by 53.39% on average and up to 94.84%, as well as acceptable indexing time and the universality of metric space.