Reviewerly/exHarmony huggingface dataset:
An Efficient Distributed Frequent Itemset Mining Algorithm Based on Spark for Big Data

Frequent item exploration is a fundamental element in many data mining problems aimed at finding interesting models in the data.Recently, the PrePost algorithm, a new algorithm for extraction frequent element sets based on the idea of N-lists, which in most cases surpasses other current state-of-the-art algorithms, has been introduced.The PrePost algorithm's performance deteriorates when it comes to handling big data.Nevertheless, the current existing PrePost algorithms in place implemented with the MapReduce model are not sufficiently powerful for iterative computation.To reduce IO overhead and take advantage of cluster memory, this article offers an enhanced version of PrePost, the Distributed PrePost (DisPrePost), a parallel algorithm built on the Spark framework, which incorporates the concept of resilient distributed datasets and performs in-memory processing to optimize the execution time of operation, that also utilises a HashMap to further refine the N-list creation process.Experience has shown that the DisPrePost algorithm is more efficient and scalable than the two advanced state-of-the-art methods HPrePostPlus and the well-known algorithm HFIM.
A Fast Pyramidal Bayesian Model for Mitosis Detection in Whole-Slide Images

Mitosis detection in Hematoxylin and Eosin images and its quantification for mm $$^2$$ is currently one of the most valuable prognostic indicators for some types of cancer and specifically for the breast cancer. In whole-slide images the main goal is to detect its presence on the full image. This paper makes several contributions to the mitosis detection task in whole-slide in order to improve the current state of the art and efficiency. A new coarse to fine pyramidal model to detect mitosis is proposed. On each pyramid level a Bayesian convolutional neural network is trained to compute class prediction and uncertainty on each pixel. This information is propagated top-down on the pyramid as a constraining mechanism from the above layers. To cope with local tissue and cell shape deformations geometric invariance is also introduced as a part of the model. The model achieves an F1-score of 82.6% on the MITOS ICPR-2012 test dataset when trained with samples from skin tissue. This is competitive with the current state of the art. In average a whole-slide is analyzed in less than 20 s. A new dataset of 8236 mitoses from skin tissue has been created to train our models.
The Effect of the Reliability of Accounting Information Systems on Electronic Disclosures on the Stock Prices: Applied Study on Industrial Companies Listed on Amman Stock Exchange

The study aims at identifying the impact of the reliability of the accounting information systems (security, confidentiality, privacy, the integrity of processes and readiness) on the stock prices of Jordanian industrial companies in Amman Stock Exchange and determining the effect of electronic disclosures (the appropriate timing, predictability and ability to reorganizing) in improving that impact; depending on the descriptive analytical approach, the questionnaire was designed and distributed to the sample of the study composed of directors of financial departments and divisions related to financial issues, monitoring and internal audit and information technology at industrial companies listed on Amman Stock Exchange, with the average of two questionnaires in each company. The number of questionnaires retrieved and statistically analyzed (118), (90.8%) of distributed questionnaires. The results of the multiple regression analysis showed a statistically significant effect on the reliability of accounting information systems in their dimensions (security, confidentiality, privacy, integrity of processes, and readiness) on the stock prices of Jordanian industrial companies in Amman Stock Exchange, where the moral effect appeared in all dimensions, except (security, integrity of processes). The results of the regression analysis showed that electronic disclosures were represented by (the appropriate timing, predictability and ability to reorganizing) which lead to improve the effect of accounting information systems on the stock prices of Jordanian industrial companies in Amman Stock Exchange. The study recommended to increase the level of interest in the reliability of accounting information systems, and to increase the level of its application and activation in the Jordanian industrial companies, because of its role in improving the performance of the company, and maximize their market value through its accounting information to shareholders of accurate, convenience, timely, reliable, consequently ensuring their survival and continuity, and to increase the awareness of the management of Jordanian industrial companies and financial statements preparers about the importance of electronic disclosure and the benefits it achieves in addition to the resulting risks, and adopt effective procedures to verify the security and reliability of accounting information that electronically disclosed and to transmit the information securely by users.
TOmCAT: Target-Oriented Crowd Review Attacks and Countermeasures

Online platforms like Amazon, Yelp, and Regulations.gov give a voice to masses of users through reviews, comments, and ratings. However, this crowd-based feedback is susceptible to manipulation. To tackle this problem, most previous efforts have only indirectly sought to uncover targets of attacks by focusing on manipulation at the review or user level. Instead, this paper focuses on the challenge of countering target-oriented crowd attacks. We introduce a unique ground truth dataset of Amazon products that have been targeted for attack and identify two target-oriented attack patterns: (i) promotion attacks and (ii) restoration attacks. With these attacks in mind, we propose the TOmCAT detection framework based only on the timing and sequencing of product ratings. Although TOmCAT succeeds in uncovering targets of manipulation with high accuracy by addressing existing attacks, strategic attackers potentially can create hard-todetect behavioral patterns by undermining timing-based footprints. Hence, we further propose a complementary approach to TOmCAT called TOmCATSeq which is resistant against strategic manipulation.
Computer Aided Technique to Separate the Red Blood Cells, Categorize them and Diagnose Sickle Cell Anemia

Anemia is a disease caused due to the lack of healthy Red blood cells. Red blood cells are mainly responsible for carrying oxygen to different parts of the body. It releases generated red blood cells into the bloodstream after the proper maturation. Anemia is primarily classified as chronic or acute. Chronic anemia continues to exist for a long time. Acute anemia occurs rapidly. It causes sickle cell anemia due to red blood cells that are in shape of a sickle or a crescent moon. The irregular shape makes it get stuck in the bloodstream which leads to a reduced flow of oxygen. The primary aim of this work is to overcome the problems involved in the manual process of detection of anemia. Manual processes require proper pipetting skills and hence is imprecise. In the proposed work, an efficient technique using a decision system is proposed that helps the pathologists to identify the disease accurately. The decision system is built by deriving the rules from the decision tree. The granulometric analysis is carried out to separate the Red blood cells from the other components of the blood. Modified Watershed transform algorithm is used to isolate cluttered cells. The work defines a systematic approach to categorize the red blood cells with central pallor and without central pallor. It puts forward the comparison of the work with state-of-the-art methods. It shows the role of the accurate segregation of Red blood cells in getting the blood cell count. The precise separation of the cluttered cells improved the accuracy of the system. Results show that the work achieved an overall accuracy of 98.5% in the identification of Sickle cell anemia and an efficiency of 97.6% in the separation of RBCs.
TMLab SRPOL at SemEval-2019 Task 8: Fact Checking in Community Question Answering Forums

The article describes our submission to SemEval 2019 Task 8 on Fact-Checking in Community Forums.The systems under discussion participated in Subtask A: decide whether a question asks for factual information, opinion/advice or is just socializing.Our primary submission was ranked as the second one among all participants in the official evaluation phase.The article presents our primary solution: Deeply Regularized Residual Neural Network (DRR NN) with Universal Sentence Encoder embeddings.This is followed by a description of two contrastive solutions based on ensemble methods.
The Assessment of Multimedia Technology in the Teaching of Mathematics in Secondary Schools in Abuja-Nigeria

Most students especially at the secondary school level have little or no interest in the subject 'Mathematics'.Mathematics teachers have sought for better ways to present the subject so as to captivate the interest of students in order to improve their performance in Mathematics.In this paper, the study on the assessment of multimedia technology method of teaching Mathematics in secondary schools was carried out in Abuja using a public and private secondary school at different locations.The students were taught using the multimedia technology method and the traditional chalk and talk method.The students taught with the multimedia method were termed the experimental group while the students taught with traditional method were termed the control group.Questionnaires were being distributed to the students to fill after which a post-test of the same questions was administered to the students.The responses from the questionnaire were analyzed using frequencies and percentages to find out the method of teaching the students preferred most.Also, the analysis of the post-test result with a paired sample t-test clearly showed there was a significant difference in the mean scores of both teaching methods.The multimedia technology teaching method impacted greatly on the interest and performance of the students in Mathematics.
The Determinants Of Forming A Valuable Attitude To Maternity Among Female Students

At present, the revival of the value attitude to motherhood is perceived as one of the priority national tasks requiring its significant rethinking.Studies show that university students produce a wide range of value positions.Attitude to motherhood contributes to the development of a woman's personal values, value orientations that contribute to the social regulation of her behavior, as well as building relationships with other people.It is shown that more than half of the respondents have a high level of value attitude towards motherhood.The results of the study noted that the majority of these respondents are not ready for maternity.Awareness of the importance of motherhood and its importance in raising a child allows modern girls to adequately assess their own abilities and readiness for the realization of maternal functions.The data obtained in the course of the experiment led to the conclusion that the determinants of the formation of a valuable attitude to motherhood among female students are the psychological acceptance by the mother in the process of maturity and the formation of such value orientations as a happy family life, mental and physical health, love, upbringing, education and responsibility.It is noted that the purposeful development of value orientations which contribute to the formation of a valuable attitude to motherhood among female students will be a powerful incentive in preparing the girl for motherhood and in her personal development.
Balancing Cooling and IT Airflow with Dampers in Ceiling-Ducted Hot-Aisle Containment in Data Centers

Physical containment is often used to separate cold and hot airflow streams for reliable and efficient cooling in data centers. In a common implementation, all (or most) data-center-pod hot aisles are ducted directly to the ceiling plenum. Since each contained IT pod typically has different IT airflow and leakage characteristics, the amount of cooling airflow drawn through each pod varies significantly. Therefore, it is usually necessary to oversupply cooling airflow at the data-center level to ensure that sufficient airflow is drawn through each pod. This practice wastes considerable energy due to increased cooling-fan power and reduced cooling-system efficiency. A much closer match between IT and cooling airflow can be achieved by employing adjustable dampers installed between the pod hot aisle and the ceiling plenum. In order to analyze the efficiency improvements afforded by the damper systems, we developed a software tool based on a computationally efficient flow network model (FNM) of the airflow in ceiling-ducted containment systems. This paper describes the FNM and a closed-form technique for predicting optimal damper settings which balance pod airflows. Finally, the FNM can also be used in the development of damper-control systems based on pressure measurements for real facilities.
Restriction‐based fragmentation of business processes over the cloud

Summary Despite the elasticity and pay‐per‐use benefits of cloud computing (aka fifth utility computing), organizations adopting clouds could be locked into single cloud providers, which is not always a “pleasant” experience when these providers stop operations. This is a serious concern for those organizations that who would like to deploy (core) business processes on the cloud along with tapping into these two benefits. To address the lock‐into concern, this paper proposes an approach for decomposing business processes into fragments that would run over multiple clouds and hence multiple providers. To develop fragments, the approach considers both restrictions over owners of business processes and potential competition among cloud providers. On the one hand, restrictions apply to each task in a business process and are specialized into budget to allocate, deadline to meet, and exclusivity to request. On the other hand, competition leads cloud providers to offer flexible pricing policies that would cater to the needs and requirements of each process owner. A policy handles certain clouds' properties referred to as limitedness, non‐renewability, and non‐shareability that impact the availability of cloud resources and hence the whole fragmentation. For instance, a non‐shareable resource could delay other processes should the current process do not release this resource on time. During fragmentation, interactions between owners of processes and providers of clouds happen according to two strategies referred to as global and partial. The former collects offers about cloud resources from all providers, while the latter collects such details from particular providers. To evaluate these strategies' pros and cons, a system implementing them, as well as demonstrating the technical feasibility of the fragmentation approach using credit‐application case study, is also presented in the paper. The system extends BPMN2‐modeler Eclipse plugin and supports interactions of processes' owners with clouds' providers that result to identifying the necessary fragments with focus on cost optimization.
Breast Cancer Detection using SVM Classifier with Grid Search Technique

Medical science is a boon to mankind.The technological advancement has widened the scope of curing and fighting with the diseases.It is essential to diagnose the symptoms and identify the disease timely.It has been observed that breast cancer cases are the most reported cases among women around the world and the second most common cancer overall.According to [1] World Cancer Research Fund International, London has shared that there were over 2 million new cases in 2018.In the year 2012, the BCRF (Breast Cancer Research Foundation) has reported nearly 1.7 million new breast cancer cases [2].With the help of technology, medical science is trying to predict cancer; which can significantly increase the chances of survival.In this research paper, the authors have illustrated the model to predict breast cancer with Support Vector Machine using Grid search.First Support vector machine model is tested without a grid search.Later, Support vector machine model is tested with grid search.Finally, the comparative analysis was done and based on the result; a new model was built.The new model designed is based on grid search on data before fitting it for prediction, which enhances the outcome.
Big Data Prosecution and Brady

Prosecutors are joining the big data revolution, adopting “intelligence-driven” strategies to target crime patterns. Centralized big data systems now track offenders, places, and groups allowing prosecutors to link crimes by time, place, associations, or other connections. Adding to these types of formalized, structured databases are growing sources of raw, unstructured big data from digital surveillance technologies like video cameras, police body cameras, and automated license plate readers. The prosecutors of the future will sit on a wealth of valuable investigative insights – all searchable and potentially relevant for a more aggressive and proactive investigation strategy. But as helpful as these new forms of centralized data collection might be for investigators, there remains a critical open issue: the systems were not designed to identify the exculpatory and impeaching material prosecutors are required to disclose under Brady v. Maryland. The information exists in the government’s possession, but cannot be obtained because of the way the systems were designed. This Article examines the design flaw at the core of the intelligence-driven prosecution model – a flaw that creates a due process problem that threatens to undermine the legality and legitimacy of this innovation. It is an urgent examination because intelligence-driven prosecution is being promoted nationally as the future of prosecution. The Article also explores how big data prosecution necessitates a new theory of Brady. The shift away from an individualized, reactive approach to crime requires a parallel shift in Brady theory. In an intelligence-driven prosecution system, Brady should be understood to include a proactive search for relationships and patterns, a broader quantitatively search through shared systems, a structured process for qualitative assessments, and even the possibility of predictive analytics to flag potential Brady material. This Article develops a new Brady theory consistent with the changed technology. Fortunately, precisely because of the networked technology at issue, big data information systems can be reengineered to flag, link, evaluate, and predict relevant data for prosecutors. This Article offers a way to engineer a theoretical and technological solution to current Brady practice consistent with due process principles.
New Information Management Dimension in Blockchain

This is a conceptual paper is to describe the new era of Information Management through block-chain technology.The study reviews literature on block-chain technology focusing on the characteristics, advantages, and achievement of blockchain.The paper goes on to analyze the role and principles of blockchain to Information Management (IM).Conclusions from the literature are drawn that states the impact of blockchain to IM should be taken into consideration.The paper ends with recommendations on future research to investigate the existing loopholes in blockchain technology.
P204 Comparing Website Data From Food Newsletter Subscribers with General “Organic” Search Traffic Data

time (ST) from elementary to middle school among children and whether there are disparities by sex and weight status.Study Design, Settings, Participants: Lower-income children who participated in the FLEX Study during grades 4 and 5 were re-recruited into this supplementary prospective cohort study, FLEX+ during middle school (grade 6).Measurable Outcome/Analysis: Demographic data were collected via parent questionnaire.Child assessments included height, weight, and 7-day accelerometry (Actigraph GT3X+) in 4th, 5th, and 6th grade.The association between grade and changes in sMVPA and ST were examined using mixed effects models adjusting for sex, race, BMI status, free/reduced price lunch eligibility (FPRL), and mean weekly ambient temperature.Individual-level clustering was adjusted for as a random intercept.Interactions were tested to examine disparities by sex and BMI status.Results: Eighty eight children (grade 4: 9.2 § 0.5 years, 42% male, 38% non-white, 39% FRPL, 33% overweight/ obese, sMVPA = 19.9§ 8.5 min/day) had valid accelerometer wear-time (> 3 days, >10 hrs/day).There was a decline in children meeting the 30-minute sMVPA recommendation from grades 4 to 6 (14.8%, 7.5%, and 3.5%, respectively; P < .05);however, the decline in sMVPA was not significant (P > .05).Notably, the change in ST reflects an increase in grade 5 (b = 16.5 mins/day; 95% CI: 8.4, 24.6; P < .001)and grade 6 (b = 61.7 mins/day; 95% CI: 46.4,77.0; P < .001)compared to grade 4.There were no significant effects of sex or BMI status on changes in sMVPA or ST (P > .05).Conclusion: Policies and programs that promote movement time during school hours are urgently needed to counteract the steep increase in sedentary behaviors that occur in middle school.
A new neighborhood topology for QUAntum particle swarm optimization (QUAPSO)

Swarm Intelligence (SI) is a behavior, used first by Beni and Wang, corresponding to a system working with single and self-organized agents, interacting the ones with each other. This operating concept is implemented in many algorithms. Developed by Kennedy, Eberhart and Shi, Particle Swarm Optimization (PSO) is one of them. Its behavior is based on the movements of birds swarm, and its effectiveness, for looking for the optimal solution of a given problem, is well established. Nevertheless, PSO is known for its weakness in local search. Moreover, the behavior of PSO strongly depends on internal parameters settings. In order to address these problems, we propose a new type of self-adaptive Quantum PSO (QPSO), called QUAntum Particle Swarm Optimization (QUAPSO), based on quantum superposition to set the velocity parameters and hybridized with a Kangaroo Algorithm in order to optimize its efficiency in local search. QUAPSO was compared with five known algorithms from the literature (classical PSO, Kangaroo Algorithm, Simulated Annealing Particle Swarm Optimization, Bat Algorithm and Simulated Annealing Gaussian Bat Algorithm) on a set of 19 test functions. The results show that QUAPSO outperforms the competing algorithms.
A study on mediating effect of institute reputation on relationship between institute social responsibility and student loyalty: Exploring concerns in Pakistani private HEIs

The aim of this study is to determine the mediating role of the institute reputation between institute social responsibility and student loyalty.The present study uses a multi-stage sampling technique, which first creates four clusters; including Karachi, Quetta, Peshawar, and Lahore, and picks one cluster; namely Lahore, randomly for the purpose of the study.Second, proportionate sampling technique is used to calculate the proportionate values of every individual educational institute.Finally, systematic sampling is used to compute the sampling interval k th number of individual universities.Data are collected from the provincial capital city of Punjab province (Lahore) and a total of 511 questionnaires are distributed among postgraduate students under business education discipline of private HEIs, 206 questionnaires are excluded from the sample due to some missing and misleading values, and final analysis is run by using SmartPLS 3.2.8 on 345 questionnaires.Findings elucidate that institutes social responsibility did not have any direct relationship with student loyalty.However, institute social responsibility has indirect relationship with student loyalty thru the mediating variable of institute reputation.Institute reputation also has a significant and positive influence on student loyalty.