Reviewerly/exHarmony huggingface dataset:
Self Error Detection and Correction for Noisy Labels Based on Error Correcting Output Code in Convolutional Neural Networks

When using convolutional neural networks in different applications, human errors may occur in labeling the data samples. To solve this problem, a self error detection and correction based on Error Correcting Output Code (SEDC-ECOC) method is proposed in this paper. The SEDC-ECOC method works in two stages. In the first stage, the distance between each sample and each class is measured using ECOC, which provides the base of error detection and correction. In the second stage, SVM ECOC conducts further correction as well as plays the role of classification layer in deep networks. Having the advantages of simple construction and independence from deep networks, the SEDC-ECOC method can be applied with different convolutional neural networks. The experimental results show that the proposed method achieves high correction performance for MNIST and CIFAR-10 datasets. Up to 56.09% and 92.11% erroneous sample labels are corrected by applying the proposed method once and twice respectively to noisy labels.
Symptom check for plagiarism become students of economic specialties directions and training programs

The article deals with the issues of the quality requirements of the content of scientific articles of students of economic universities, represented for participation in scientific conferences, subject to a high percentage of the uniqueness of the text. The idea of this article is to get an answer to the most important question: what can a student write today, only mastering the basics of the profession, studying various courses provided by the curriculum, forming their professional competence, on any chosen topic of research, observing the established standard of uniqueness. On the example of articles on the problems of taxation, accounting, auditing, the impossibility of ensuring the semantic content of the text without the use of a large number of legal sources, and, therefore, the inadmissibility of the installation by universities of any percent of uniqueness, acting as a criterion for the quality of the article. The main task of higher education, according to the author, is to teach the student to determine the topic of interest, highlight unresolved problems in the context of this topic, find and group available in the economic literature approaches to its solution, to analyze existing proposals and recommendations for compliance with current tax, accounting and audit legislation, taking into account the specifics of the object of study.
A New Langage for Adaptatif System Structured Decision Network Language (SDNL)

Based on adaptatif system and decision theory, this paper introduces a new language to manage Decision Network for proposing services to a dynamic event after an adaptation. The new approach presents several solutions to critiques situations but the most problem was how we can manage and structure the decisions in the database. To solve this problem, we propose a Structured Decision Network Language (SDNL) to define all the operations that can be used like add a decision, update, delete and link the decisions. The new language is some instructions to execute for having some operations. The main objective of this article is to present the new language and the instruction of this language. We also propose a case study on which we apply the instructions of the language. For that, we suggest a network for the managements of accidents during pilgrimage and we take part of the network to show how to construct a network decision by the language SDNL.
A High-Reliability Approach to Risk Management in an IT Project

This article describes how an organization successfully managed a particularly high-risk and complex IT implementation project using an approach found in High-Reliability Organizations (HROs). HROs include organizations such as nuclear power plants and air traffic control systems, which typically operate within high-complexity, high-risk, and high-cost-of-failure environments. As such, HROs develop organizational cultures and management strategies designed to mitigate the risks inherent in their environments and deliver value to their clientele. The organization in this study, a public utility that had been sold to a private investment firm, faced the challenge of rebuilding its information technology and systems infrastructure while confronting highly rigid and aggressive time and quality of service requirements. We identify the specific HRO-like environmental elements and risks faced by IT management in this organization and link those elements to the strategies they employed in successfully completing the project. Those linkages in turn serve as a model to guide IT management facing similar challenges.
Investigation of Passage Based Ranking Models to Improve Document Retrieval

Passage retrieval deals with identifying and retrieving small but explanatory portions of a document that answers a user’s query. In this paper, we focus on improving the document ranking by using different passage based evidence. Several similarity measures were evaluated and a more in-depth analysis was undertaken into the effect of varying specific. We have also explored the notion of query difficulty to understand whether the best performing passage-based approach helps to improve, or not, the performance of certain queries. Experimental results indicate that for the passage level technique, the worst-performing queries are damaged slightly and the those that perform well are boosted for the WebAp collection. However, our rank-based similarity function boosted the performance of the difficult queries in the Ohsumed collection.
Parallel String Matching for Urdu Language Text

String matching is one of the essential problems in computer science. The language used in Pakistan is Urdu. For Urdu language texts, its characters are encoded by utf-8, and the utf-8 is a length-variable encoding. If we implement string matching algorithms for Urdu language texts by ASCII encoding, the correct matched positions may not be obtained. This paper analyzes the characteristics of Urdu language texts and studies the character encoding presentation for Urdu language texts and recognizes that the correct matched positions can be obtained when the wchar_t type and Unicode encoding is used to process Urdu language texts, then, this paper implements parallel algorithms for Boyer-Moore string matching, Knuth-Morris-Pratt string matching, and Sunday string matching for Urdu language texts and evaluate the execution performance of these four string matching algorithms on a large number of Urdu language patterns and text strings via experimental testing.
Investigation of Improving Load Balancing and Data Fragmentation in Cloud Computing Performance

Objective: In this study we have investigated the affecting parameters in cloud computing in information technology.To this end, security and load balancing in cloud computing is still considered to be one of the most important views owe tothe sensitive and confidential information stored in the cloud by the users.Methods: This study has as a prime focus on the matters related to thesecurity and load balancing aspects of CC.Furthermore, we have tried to review the researches about cloud computing, improving load balancing and data fragmentation in cloud computing and related matters in regard of its performance.Findings: As well, this research paper has shown all of aspect of investigations in recent years.In fact, this study has located novel ways for finding new paths in this field for future researches.Application: In the latest part of the paper we have presented some future themes as follows: Load balancing with novel load balancing algorithms for better efficiency, and also security issues considering some aspects such as data fragmentation and encrypted in CC are unknown for research.
Identifying Finding Sentences in Conclusion Subsections of Biomedical Abstracts

Segmenting scientific abstracts and full-text based on their rhetorical function is an essential task in text classification. Small rhetorical segments can be useful for fine-grained literature search, summarization, and comparison. Current effort has been focusing on segmenting documents into general sections such as introduction, method, and conclusion, and much less on the roles of individual sentences within the segments. For example, not all sentences in the conclusion section are describing research findings. In this work, we developed rule-based and machine learning methods and compared their performance in identifying the finding sentences in conclusion subsections of biomedical abstracts. 1100 conclusion subsections with observational and randomized clinical trials study designs covering five common health topics were sampled from PubMed to develop and evaluate the methods. The rule-based method and the bag-of-words based machine learning method both achieved high accuracy. The better performance by the simple rule-based approach shows that although advanced machine learning approaches could capture the main patterns, human expert may still outperform on such a specialized task.
ON NON-QUADRATIC PENALTY FUNCTION FOR NON-LINEAR PROGRAMMING PROBLEM WITH EQUALITY CONSTRAINTS

Purpose: The present paper focuses on the Non-Linear Programming Problem (NLPP) with equality constraints. NLPP with constraints could be solved by penalty or barrier methods.&#x0D; Methodology: We apply the penalty method to the NLPP with equality constraints only. The non-quadratic penalty method is considered for this purpose. We considered a transcendental i.e. exponential function for imposing the penalty due to the constraint violation. The unconstrained NLPP obtained in this way is then processed for further solution. An improved version of evolutionary and famous meta-heuristic Particle Swarm Optimization (PSO) is used for the same. The method is tested with the help of some test problems and mathematical software SCILAB. The solution is compared with the solution of the quadratic penalty method.&#x0D; Results: The results are also compared with some existing results in the literature.
Is Pose Really Solved? A Frontalization Study On Off-Angle Face Matching

Recently, impressive results have been achieved on many large-scale face recognition benchmarks, such as IJB-A Janus and Janus CS3. These datasets were designed to test robustness to nuisance transformations simultaneously such as pose, illumination, expression etc. We present a study paper, where we find that despite this goal in evaluation, there exists a significant frontal bias in yaw pose in these datasets. Therefore, high-performance on these recent datasets is misleading and does not reflect robustness to extreme pose in yaw. Moreover, many real-world applications only allow for a single frontal enrollment in a gallery (law enforcement, immigration etc.). As we show in our study, face recognition in this highly constrained setting with extreme pose variation in the probe images remains a highly challenging problem. Traditional approaches, performing well on datasets such as IJB-A Janus, perform much worse on older but highly controlled datasets such as CMU MPIE. To aid our study, we present a simple and practical method to handle pose variation in face recognition pipelines designed to deal with extremely off-angle faces. Our approach is to ignore the half of the face with any self-occlusion. This method allows our models to be highly robust to pose, and helps us achieve state-of-the-art results on several protocols using the CMU MPIE dataset as well as very accurate results on the CFP dataset, outperforming recent efforts using the same training data.
Viability of Viola-Jones method for the problem of image classification

In this paper we study combination of Viola-Jones classifier with deep convolutional neural network as an approach to the problem of object detection and classification. It is well known that Viola-Jones detectors are fast and accurate in detection of vast variety of different objects. On the other hand, methods based on neural network usage demonstrate high accuracy in the problems of image classification. The main goal of this paper is to study viability of Viola-Jones classifier in problem of image classification. The first part of both algorithms is the same: we will use Viola-Jones classifier to find object bounding rectangle in the image. The second part of the algorithms is different: we will compare usage of Viola-Jones classifier with convolutional neural network-based classifier. We will provide speed and accuracy comparison between these two algorithms.
A Hybrid Method for Remote Eye Tracking using RGB-IR Camera

Methods for eye tracking using images can be divided largely into two categories: Methods using a near-infrared image and methods using a visible image. These images have been used independently in conventional eye-tracking methods; however, each category of methods have different advantageous features. Therefore, we propose using these images simultaneously to compensate for the weak points in each technique, and an RGB-IR camera, which can capture visible and near-infrared images simultaneously, is employed. Pupil detection can yield better results than iris detection because the eyelid often occludes the iris. On the other hand, the iris area can be used for model fitting because the iris size is constant. The model fitting can be automated at initialization; thus, the relationship between the 3D eyeball model and eye camera is solved. Additionally, the positions of the eye and gaze vectors are estimated continuously using these images for tracking. We conducted several experiments for evaluating the proposed method and confirmed its feasibility.
BitoBody

In this research, we propose a novel human body contact detection and projection system with dynamic mesh collider. We use motion capture camera and generated human 3D models to detect the contact between user's bodies. Since it is difficult to update human mesh collider every frame, a special algorithm that divides body meshes into small pieces of polygons to do collision detection is developed and detected hit information will be dynamically projected according to its magnitude of damage. The maximum deviation of damage projection is about 7.9cm under a 240-fps optitrack motion capture system and 12.0cm under a 30-fps Kinect camera. The proposed system can be used in various sports where bodies come in contact and it allows the audience and players to understand the context easier.
Fuzzy Semantic-Based Similarity and Big Data for Detecting Multilingual Plagiarism in Arabic Documents

Plagiarism (intelligent-monolingual) is a complicated fuzzy process, adding translation and making it a cross language problem turn thing to be more obfuscated, what pose difficulties to current plagiarism detection methods. Multilingual plagiarism nature could be more complicated than simple copy + translate and paste, it is defined as the unacknowledged reuse of a text involving its translation from one natural language to another without proper referencing to the original source. Before the detecting process several NLP techniques were used to characterize input texts (tokenization, stop words removal, post-tagging, and text segmentation). In this paper, fuzzy semantic similarity between words is studied using WordNet-based similarity measures Wu & Palmer and Lin. In any data processing system the common problem is efficient large-scale text comparison, especially fuzzy-based semantic similarity to reveal dishonest practices in Arabic documents, first due to the complexity of the Arabic language and the increase in the number of publications and the rate of suspicious documents sources of plagiarism. To remedy this, vague concepts and fuzzy techniques in a big data environment will be used. The work is done in a parallel way using Apache Hadoop with its distributed file system HDFS and the MapReduce programming model. The proposed approach was evaluated on 400 English and Arabic cases of different sources (news, articles, tweets, and academic works), including 25% machine based translated plagiarism cases, and 75% translated (machine and human based) with a percentage of obfuscated plagiarism e.g. handmade paraphrases and back-translation. We effectuate some experimental verifications and comparisons showing that results and running time of Fuzzy-WuP are better than Fuzzy-Lin. Results are evaluated based on three testing parameters: precision, recall and F-measure.
Neural Network Based Target User Recognition Model for Network Community

For multi-dimensional feature of user data in network community, available methods mainly use Rank scoring algorithm or user classification algorithm for target user recognition. However, Rank method has a low performance, and the classification algorithm needs high construction cost. Therefore, this paper uses a target user recognition model integrating the user content analysis and the user behavior analysis to improve performance and speed of the target user recognition by a neural network content analysis model with a single-layer neural network and N-gram features for discovering automatically the user feature. The proposed method outperforms the current Rank scoring and classification methods in terms of performance, in which the F value reaches 0.89 and the accuracy reaches 0.91. Moreover, avoiding the cost of manual design dependent on specific tasks shortens the training time. Ten thousand data can be modeled in one minute.
Synthesis and texture manipulation of screening mammograms using conditional generative adversarial network

Annotated data availability has always been a major limiting f actor for the development of algorithms in the field of computer aided diagnosis. The purpose of this study is to investigate the feasibility of using a conditional generative adversarial network (GAN) to synthesize high resolution mammography images with semantic control. We feed a binary mammographic texture map to the generator to synthesize a full-field digital-mammogram (FFDM). Our results show the generator quickly learned to grow anatomical details around the edges within the texture mask. However, we found the training unstable and the quality of generated images unsatisfactory due to the inherent limitation of latent space and sample space mapping by the pix2pix framework. In order to synthesize high resolution mammography images with semantic control, we identified the critical challenge is to build the efficient mappings of binary textures with a great variety of pattern realizations with the image domain.
What's Old Is Marketable

This chapter will provide direction to special collections and archives looking to craft a social media strategy by including steps to guide archivists and librarians through the planning process. It will provide an adaptable approach to engaging special collections and archives' audiences with the unique treasures in the collection holdings and in initiating conversations about the opportunities and challenges associated with social media at their respective institutions. This chapter will also assist archivists and librarians in creating a practical framework to make informed decisions for their social media accounts, in crafting goals that advance their organizational mission and values, and in making social media outreach sustainable by considering questions of purpose, audience, platform, messaging, tone, content, and assessment.
Testing-based Model Learning Approach for Legacy Components

Operating, maintaining, and upgrading legacy systems are the foremost challenges which are being faced by many organizations today. Usually, these systems are based on outdated technologies, have limited documentation, and actual developers are unavailable. It is risky to upgrade black-box legacy systems without knowing their internal structures. In this paper, we have proposed an approach which is based on the state of the art dynamic analysis technique known as Model Learning, a reverse engineering approach, to infer the behavioral models of legacy systems. We prepared and utilized our test-bed for black-box vending machines (considered as legacy systems) to learn the behavioral models of all the software modules embedded in vending machines. The in-depth analysis of learned models is helpful in the operation, up-gradation, and maintenance of the legacy system. The experimental results reveal that our proposed approach is very auspicious to modernize the legacy components and explore the concealed structures of the black-box systems automatically.
The Two-Point Fano and Ideal Binary Clutters

Let $$\mathbb{F}$$ be a binary clutter. We prove that if $$\mathbb{F}$$ is non-ideal, then either $$\mathbb{F}$$ or its blocker $$b(\mathbb{F})$$ has one of $$\mathbb{L}_7,\mathbb{O}_5,\mathbb{LC}_7$$ as a minor. $$\mathbb{L}_7$$ is the non-ideal clutter of the lines of the Fano plane, $$\mathbb{O}_5$$ is the non-ideal clutter of odd circuits of the complete graph K5, and the two-point Fano $$\mathbb{LC}_7$$ is the ideal clutter whose sets are the lines, and their complements, of the Fano plane that contain exactly one of two fixed points. In fact, we prove the following stronger statement: if $$\mathbb{F}$$ is a minimally non-ideal binary clutter different from $$\mathbb{L}_7,\mathbb{O}_5,b(\mathbb{O}_5)$$ , then through every element, either $$\mathbb{F}$$ or $$b(\mathbb{F})$$ has a two-point Fano minor.
transFORM - A Cyber-Physical Artefact Augmenting Social Interaction in Residual Public Spaces

The emergence of social networks and apps has reduced the importance of physical space as a locus for social interaction. In response, we introduce transFORM, a cyber-physical environment installed in under-used, outdoor, public spaces. transFORM embodies our understanding of how a responsive, cyber-physical architecture can augment social relationship and increase place attachment. In this paper we critically examine the social interaction problem in the context of our increasingly digital society, present our ambition, and introduce our prototype, which we will iteratively design, and test. Cyber-physical interventions at large scale in public spaces are an inevitable future, and this paper serves to establish the fundamental terms of this frontier.