Reviewerly/exHarmony huggingface dataset:
Group of spectators behavior mining: Integrating TV ratings with Multimedia content

Television appraisals are a generally utilized pointer in the TV broadcasting field. While TV evaluations are for the most part utilized in publicizing, they can likewise be utilized as a social sensor that mirrors the interests of individuals. This paper introduces a structure for finding group of spectators conduct through the mining of TV evaluations. We have set up a system that empowers revelation of various examples of group of spectators conduct from TV appraisals. Utilized alongside other sight and sound substance, for example, video and content, it empowers different sorts of learning to be semi-naturally found, for example, what kinds of news projects are of most premium and what are the key visual highlights for getting high TV appraisals. The disclosure of group of spectators conduct is accomplished by concentrating on the change focuses in the rating information, i.e., the focuses in time where numerous individuals switch the station or turn the TV on or off. Rich depictions that describe these focuses are extricated from mixed media substance, and afterward different sifting systems are utilized to remove explicit examples of premium. A few utilizations of this system for finding learning showed that it can viably extricate different kinds of group of spectators conduct. As far as we could possibly know, this work is the principal work to break down the utilization of evaluations information in blend with video and other mixed media information.
Facilitating of knowledge transfer in a software development company

Abstract In an organization, knowledge transfer between its members is vital. Most studies conducted up to this date have focused on knowledge transfer in the field of industrial production. If in this branch of knowledge, where operations and processes have a certain degree of standardization and repeatability, this is done through initial and possibly periodic training, in the matter of software development things are more complicated. Tasks do not resemble with each other, most are unique and often require documentation before being resolved. Obtaining documentation from external environments often involves work on adapting to the organization’s requirements and to the project’s requirements also. In an organization without a constant direct interaction between the members, the transfer of knowledge requires much labor. In this paper, we propose the design and implementation of a software solution that will facilitate the transfer of knowledge between the developers of a software development company. This internal database will include the solutions identified, adapted, and used by programmers to solve non-familiar tasks. Also, a collection will be created with the errors encountered throughout the development and implementation process and their resolution, useful for all members of the organization. Every time a team will meet an unfamiliar functionality in an assigned task, it will consult the internal database. In this way, the solution will be easier reached and adapted from the source. External sources of documentation and research will only be used if the internal search has not returned a solution to the problem. In this case, following the solving steps, the familiar solution will be added to the internal database for future use. Finally, we will measure the benefits of the proposed and implemented solution. Ultimately, the rate of use of a solution stored in the database over a period of time will be measured, along with the average time of solving a task in the development process, when making use of the internal knowledge and solutions base.
Efficient counter-factual type error debugging

Providing effective error messages in response to type errors continues to be a challenge in functional programming. Type error messages often point to bogus error locations or lack sufficient information for removing the type error, making error debugging ineffective. Counter-factual typing (CFT) addressed this problem by generating comprehensive error messages with each message includes a rich set of information. However, this comes with a cost of huge computations, making it too slow for interactive use. In particular, our recent study shows that programmers usually have to go through multiple iterations of updating and recompiling programs to remove a type error. Interestingly, our study also finds that program updates are minor in each iteration during type error debugging. We exploit this fact and develop eCFT, an efficient version of CFT, which doesn't recompute all error fixes from scratch for each updated program but only recomputes error fixes that are changed in response to the update. Our key observation is that minor program changes lead to minor error suggestion changes. eCFT is based on principal typing, a typing scheme more amenable to reuse previous typing results. We have evaluated our approach and found it is about 12.4× faster than CFT in updating error fixes.
Random variate generation for the truncated negative gamma distribution

We provide a uniformly efficient and simple random variate generator for the truncated negative gamma distribution restricted to any interval.
A Comparative Analysis and Evaluation of MapReduce Cloud Computing Simulators

The application of MapReduce cloud computing simulators for research and development is becoming popular, due to their efficiency and ease of utilization. This ignited the development of several cloud simulators for algorithm testing and performance analysis of dynamic MapReduce environments. The selection of appropriate simulator for a specific research remains a challenge. We have designed a MapReduce classification framework to guide cloud and big data researchers towards suitable tools. We have reviewed eleven MapReduce specific simulators. Our evaluation first revealed thirty general functional requirements for more widely applicable cloud simulators. Then, we focused on specific concerns of MapReduce related simulations and filtered the general requirements down to the most relevant thirteen. Our evaluation highlighted the strengths and weakness of several MapReduce simulators. IoT-based applications, stream processing and replaying of production cluster workloads are key criteria absent from many simulators. Therefore, we identified these as gaps, that simulator developers could focus on when extending their works towards MapReduce oriented simulations. Finally, researchers simulating dynamic behaviors of Hadoop clusters should select simulators efficient in parameter tuning.
The Phases Based Approach for Regeneration of Timeline in Digital Forensics

The field of digital forensics has developed to enable security experts to look at proof from the expanding plenty of computerized gadgets to help figure out what people may have done before. In this paper, we present a methodology based on the abstraction of information provided about the timeline of events and artefacts at four different levels such as event high level, event low level, artefact high level and artefact low level for the enhancement of timeline analysis. In our approach, two command-based digital forensics tools are also used such as log2timeline and psort to collect data from various files systems found on the typical computer for implementing the developed methodology. From results, it is observed that developed methodology can enhance the timeline of events and artefacts by increasing the visualization of the timeline, realistic of data and minimize the time utilized for collection and understanding of digital evidence from digital devices.
Remaining Useful Life Prediction Based on Stacked Sparse Autoencoder and Echo State Network

Nowadays, the prediction of industry components' remaining useful life (RUL) has already become a hot topic. In this paper, a RUL prediction method based on stacked sparse autoencoder (SAE) and echo state network (ESN) is proposed. Autoencoder (AE) is an unsupervised learning method that can be used for feature extraction to obtain a health index (HI). To enhance the performance of the HI, a sparse constraint is added and a stacked structure is used to increase depth. The RUL of the industry components is then predicted using a method that maps directly to the RUL value based on the health factor curve. With the capability of encapsulating dynamic time behavior and saving historical information of input data, ESN is selected as the prediction network. The proposed method is verified using the C-MPASS dataset. The experimental results show that the method has better performance than the stacked AE based method.
Combined Radiology and Pathology Based Classification of Tumor Types

Computer Aided Detection plays a crucial role in the early detection of deadly diseases such as cancer (or) tumor. Pathology and radiology images form the core of tumor diagnosis. Pathology images provide clinical information about the tissues whereas the radiology images can be used for locating the lesions. This work aims at proposing a classification model which categorizes the tumor as oligodendroglioma (benign tumors) (or) astrocytoma (Malignant tumors) using features of both the radiology and pathology images. Dataset from MICCAI Computational Precision Medicine Challenge is used for model building. The proposed model uses dedicated workflows for processing the pathology and radiology images. The feature descriptors of the images are obtained using pre-trained Inception v3 model. The resulting vectors are then used as input to the linear SVM (Support Vector Machine) classification model. The SVM model provided an accuracy of 75% on the blind folded test dataset provided in the competition.
PRESENT Crypto-core as Closely-coupled Coprocessor for Efficient Embedded SoCs

PRESENT cipher is a lightweight block cipher best suited for securing low-cost hardware having single-chip solutions or SoCs for enhanced system-level efficiency. This paper focuses to further enhance the efficiency of such SoCs by demonstrating closely-coupled integration of PRESENT cipher hardware to LEON3 processor. Our closely-coupled integration achieved more than 70% reduction in machine cycles. Variation in instruction per cycle has been restricted within 3rd decimal digit which provides precise performance estimation. Also, the proposed integration is frugal in terms of interface hardware requirement.
Have I Been Exploited?

Ethereum Smart Contracts, also known as Decentralized Applications (DApps), are small programs which orchestrate financial transactions. Though beneficial in many cases, such contracts can and have been exploited, leading to a history of financial losses in the millions of dollars for those who have invested in them. It is critical that users be able to trust the contract code they place their money into. One way for verifying a program's integrity is Symbolic Execution. Unfortunately, while the information derived from symbolic execution is beneficial, performing it is often financially and technically infeasible for users to do. To address this problem, this paper describes the design and implementation of a registry of vulnerable Ethereum contracts. The registry compiles the results of exhaustive application of symbolic analysis to deployed contracts and makes it available to users seeking to understand the risks associated with contracts they are intending to utilize.
Decentralized Self-adaptation in Large-Scaled Systems of Systems

Today’s distributed applications require steady maintenance. To tackle this problem, so-called self-adaptive systems (SAS) can be used to change the behaviour automatically to adapt to a changing environment and context. Open challenges remain when those SAS get combined with Systems of Systems (SoS). SoS can get partitioned in multiple sub-parts as a result of errors or connection faults which rises the need for a decentralized self-adaptation approach in SoS. In this doctoral paper, those open challenges are discussed and explained using a scenario of self-driving vehicles. Ideas for solving the problems are presented and the evaluation method of using the Webots simulation environment is explained. Solving the problems of self-adaptive SoS will enable robust adaptations in large-scale systems.
Colorizing and Captioning Images Using Deep Learning Models and Deploying Them Via IoT Deployment Tools

Neural networks and IoT are some top fields of research in computer science nowadays. Inspired by this, this article works on using and creating an efficient neural networks model for colorizing images and transports them to remote systems through IoT deployment tools. This article develops two models, Alpha and Beta, for the colorization of the greyscale images. Efficient models are developed to lessen the loss rate to around 0.005. Further, it also develops an efficient model for the captioning of an image. The paper then describes the use of tools like AWS Greengrass and Docker for the deployment of different neural networks models, providing a comparative analysis among them, combining neural networks with IoT deployment tools.
A Secure Verification Item Requesting Technique by way of Adopting Paillier Encryption Algorithm

A defended exploration strategies over encrypted cloud statistics permit an authorized consumer to query records files of interest with the aid of using filing encrypted query key phrases to the cloud server in a privateness-retaining manner. However, in exercise, the lower again query effects can also moreover furthermore be incorrect or incomplete inside the dishonest cloud environment. For instance, the cloud server also can intentionally miss a few certified consequences to computing sources and conversation overhead. Thus, a first-class-functional defended query framework want to provide a question results verification mechanism that lets in the statistics user to confirm consequences. On this paper, we construct a included, successfully unified, and notable-grained query effects authentication framework, by means of which, given an encrypted query outcomes set, the query character not most effective can affirm the correctness of every information document inside the set however can also similarly take a look at how many or which certified records documents aren't rebound if the set is incomplete earlier than decryption. The verification scheme is unfastened-coupling to concrete at ease are attempting to find techniques and may be very consequences easily included into any relaxed query scheme. We obtain the intention thru building defended verification item for encrypted cloud facts. Furthermore, a quick signature technique with extensively small garage charge is planned to guarantee the authenticity of verification object and a verification item request technique is offered to allow the question consumer to securely achive the favored verification item performance evaluation shows that the planned schemes are feasible and adequate.
Cones, Negation, and All That

This paper summarizes results on embedding ontologies expressed in the $$\mathcal {ALC}$$ description logic into a real-valued vector space, comprising restricted existential and universal quantifiers, as well as concept negation and concept disjunction. The main result states that an $$\mathcal {ALC}$$ ontology is satisfiable in the classical sense iff it is satisfiable by a partial faithful geometric model based on cones. The line of work to which we contribute aims to integrate knowledge representation techniques and machine learning. The new cone-model of $$\mathcal {ALC}$$ proposed in this work gives rise to conic optimization techniques for machine learning, extending previous approaches by its ability to model full $$\mathcal {ALC}$$ . This is an extended abstract of the paper “Cone Semantics for Logics with Negation” to be published in the proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI 2020).
Neural Nets to Approach Optimal Receivers for High Speed Optical Communication

We show via simulation that two nonlinear neural network solutions can achieve performance close to that of the maximum likelihood sequence estimator for experimental frequency responses of a silicon photonic modulator and coherent detection.
Mining Frequent Differences in File Collections

Collections of textual files, or documents, with substantial inter-document similarities are common in diverse domains. A practically significant class of such similarities, and the dual differences, are well characterized by edit scripts, or colloquially diffs, that use a simple sequence model for documents. The study of such diffs provides valuable insights into the inter-document relationships within a collection and can guide data integration within and across collections. This paper describes a framework for such study that is based on frequently occurring inter-document differences. It motivates and defines a general problem of mining frequent differences and outlines some specific instances. It presents the design and implementation of a prototype system for interactively discovering and visualizing frequent differences. A notable feature of this method is its use of difference-components, or deltas, to bootstrap the discovery of interesting structure in file collections. The paper describes a preliminary experimental evaluation of the method and implementation on a widely used corpus of file-collections.
A Homomorphic verifiable Group Signature Mechanism for Shared Cloud Data

Nowadays, cloud storages will become one of the important service that which allows employees to be more flexible with data in their work practices. The reliability of public cloud is weak to hardware, software or human mistakes. To make certain the reliability of the shared information, a few techniques were planned to permit public verifiers to efficaciously audit information integrity with protecting customer's information. Unlikely, community audit going on the reliability of collective information may also display records proprietors' vital data to the third party assessor. Here, we suggest a new Privacy-Aware Public Auditing system designed for shared cloud facts through building a similar demonstrable institution signature. Dissimilar the accessible answers, our plan require as a minimum grouping managers to get better a trace key considerately eliminates misuse of single-authority influence there by presents non frame ability. Furthermore, our plan guarantees that group customers can hint facts adjustments thru certain binary tree and can get better modern day correct facts block when the information block is broken. In addition, the protection evaluation and experimental analysis suggest that our scheme is protected and competent.
Air Traffic Network Generation for UAVs at a Low Altitude Based on Digital Maps

Unmanned Aerial Vehicle (UAV) has a rapid growth in recent years. An efficient air traffic system for UAVs working at a low altitude which is rich of obstacles is desired. However, as far as we know, the work on the generation of such an air traffic network at a low altitude lacks attention. It is expected to develop an algorithm in order to generate a traffic network automatically similar to the complex road traffic network by taking a matrix or image marked with obstacles as input. Considering those situations above, this paper addresses (i) automatic air traffic network generation that is suitable for UAVs working at a low altitude, (ii) several line segments (airlines) fitting for a curve, and (iii) a graph modeling. A simple experiment is also performed to show the effectiveness of the method proposed.
직무 소진이 조직몰입과 조직시민행동에 미치는 영향: 가족친화 조직인식의 조절 효과를 중심으로

Objectives: Work-Family Balance (WFB) is a significant social issue in Korea. We examined the effects of employees’ burnout on organizational performances by determining the moderating effect of family-friendly organizational culture in firefighter’s organization. Methods: To test the hypothesis, data were collected from firefighters who are working at five fire stations in Deajeon and Chungnam province. Based on quantitative survey from 489 respondents, hierarchical regression analyses were performed. Results: The analysis revealed that job burnout had a negative effect on both organizational commitment (OC) and organizational citizen behavior (OCB). Family-Supportive Organization Perception (FSOP) negatively moderated the relationship between burnout and OCB. On the other hand, there was no significant moderating effect of FSOP on the relationship between burnout and OC. Conclusions: This study raises the importance of creating an organizational culture that gives its members a belief that the organization guarantees and supports the work-family balance system.
Hydrogeochemical features of the North Ossetian artesian basin

Abstract The article considers the hydro chemical features of the North Ossetian artesian basin and the changes in its qualitative characteristics over the past 30 years. An increase in the rigidity and, accordingly, mineralization of groundwater in the southeastern part of the Ossetian artesian basin, which occurs against the intensive production background, both directly from the basin and beyond it in the nutrition zone of the aquifer complex is noted. The tendency to increase the rigidity and mineralization of groundwater in the Industrial zone of the Vladikavkaz city has been recorded since 1991, and goes on up to this day. The main version of aquifer pollution is justified by the presence of industrial waste from the plants of “Electrozinc” OJSC and “Pobedit” OJSC, as well as an intensive underground water production from the southeastern part of the North Ossetian artesian basin - about 50 production wells with a capacity of about 1000 m3 / day. Among the many conditions and factors affecting the water chemical composition formation, one of the most important is the active, in most cases, haphazard exploitation of aquifers both within the territory under consideration, in the north-eastern part of the city of Vladikavkaz, as well as outside it, where the recharge comes from the mining level.
On Solving <i>Link-a-Pix</i> Picture Puzzles

The Link-a-Pix puzzle, which is also known as Piczle, PathPix, Pictlink, Number Net, or Paint by Pairs, is a popular picture logic puzzle game where the player paints a grid by linking the hint points with number-color labels to obtain the solution as a pixel art picture. In this article, we propose a joint depth first searching and linear programming aided algorithm for the sake of automatically solving the Link-a-Pix puzzle. The experiments implemented on 40 puzzles with various types verify the effectiveness and feasibility of our proposed solver, which is conducive to both designing and solving the Link-a-Pix puzzles and related applications.
An In-class Teaching Comprehensive Evaluation Model Based on Statistical Modelling and Ensemble Learning

Education is the foundation of our cities' and society's development. The realization of Smart City is inseparable from the construction of Smart Education. The development of science and technology has promoted the popularity and booming of information technology in the education field. With the rise of Smart Education, intelligent learning and evaluation have provided new ideas for in-class teaching evaluation. The model proposed in this article is based on existing multi-dimensional and multi-modal data set from in-class audio and video recognition, as well as movement perception and interaction analysis. We firstly designed a statistical model and an ensemble learning model for in-class teaching evaluation, which are based on Analytic Hierarchy Process - Entropy Weight Method and AdaBoost algorithm respectively. Then, we designed experiments to assess the performance of the proposed statistical model and ensemble learning model. Finally, we compared and selected better models through experiments in different evaluation indicators and combined them into our In-class Teaching Comprehensive Evaluation Model with outstanding performance.