Reviewerly/exHarmony huggingface dataset:
A Practical Method to Reduce Privacy Loss When Disclosing Statistics Based on Small Samples

We develop a simple method to reduce privacy loss when disclosing statistics such as OLS regression estimates based on samples with small numbers of observations. We focus on the case where the dataset can be broken into many groups (“cells”) and one is interested in releasing statistics for one or more of these cells. Building on ideas from the differential privacy literature, we add noise to the statistic of interest in proportion to the statistic's maximum observed sensitivity, defined as the maximum change in the statistic from adding or removing a single observation across all the cells in the data. Intuitively, our approach permits the release of statistics in arbitrarily small samples by adding sufficient noise to the estimates to protect privacy. Although our method does not offer a formal privacy guarantee, it generally outperforms widely used methods of disclosure limitation such as count-based cell suppression both in terms of privacy loss and statistical bias. We illustrate how the method can be implemented by discussing how it was used to release estimates of social mobility by Census tract in the Opportunity Atlas. We also provide a step-by-step guide and illustrative Stata code to implement our approach.
An Improved Skeleton Extraction Method via Multi-Task and Variable Coefficient Loss Function in Natural Images

Extracting object skeleton and its scale features in natural images is helpful for object detection and recognition in computer vision. In order to advance the location accuracy of object skeleton pixels, a new method via multi-task and variable coefficient loss function is proposed in this paper. Adopting the hierarchical integration mechanism to mutually refine captured features at different network layers; a specific variable coefficient loss function is designed for multi-class imbalanced data handling problem, such as the skeleton pixels in natural images are always far less than the non-skeleton pixels; the regression algorithm is an added deep learning branch in the skeleton extraction network assisting the improvement of recognition accuracy. Besides, not only the skeleton pixels and its classification can be obtained, but also its scales are predicted without disturbing skeleton acquisition process. The experimental results verify that both the skeleton accuracy and the generalization abilities are promoted benefiting from the regression task and the new loss function in the new method, as satisfactory results are achieved on three public datasets, i.e., SK-LARGE, SK-SMALL, and WH-SYMMAX, which are indicated by F-measures and precision/recall curves. The results further demonstrate that the proposed method is superior to the best skeleton extraction method available currently.
An Analysis of Optical Character Recognition (OCR) Methods

This survey paper presents a comprehensive study of Urdu Optical Character Recognition (OCR) methodologies.The main focus of the study is detail investigation of the techniques used to recognize the Nastaliq, Naskh and other similar scripts fonts.These script fonts are used to write Urdu, Arabic, Pashto and Sindhi etc. languages.Several methods of text recognition and classification of Urdu like cursive scripts are discussed.The survey contains the comparison and description of each method in a brief way which identifies handwritten, printed and online text recognition as well.For each optical character recognition (OCR) the phases of pre-processing, segmentation, feature extraction, classification and finally recognition are discussed.After the comprehensive analysis of all methodologies critics and future work in Urdu cursive scripts, i.e.Naskh and Nastaliq scripts are also proposed.
Alfabetización informacional y digital mediante b-learning

Analyzing the factors involved in the development of online search, selection and use of information skills in teachers in training is fundamental, these skills contribute to their personal development, professional training and lifelong learning. The purpose of this article is to present an analysis of the academic and technological factors that influence Primary Education Degree (LEP) students to develop digital literacy and information management skills within a learning environment with blended learning technology (b-learning). It is posed that b-learning has become a necessary alternative to the classroom training processes that still prevail in the majority of teacher training institutions in Mexico. Based on the experience with a group of LEP students who attended a b-learning workshop, it is shown that under this scheme students are allowed to put their literacy and information management skills into play. The findings that resulted from this experience show that the academic factors that most influence the development of digital and informational skills are: the contents and interaction with the module coordinator in the face-to-face sessions, and the decisive technological factors: discussion forums, virtual interaction with the teachers and the resources that are integrated to the digital platform in which the course is found.
High Level Speaker Specific Features as an Efficiency Enhancing Parameters in Speaker Recognition System

&lt;p&gt;In this paper, I present high-level speaker specific feature extraction considering intonation, linguistics rhythm, linguistics stress, prosodic features directly from speech signals. I assume that the rhythm is related to language units such as syllables and appears as changes in measurable parameters such as fundamental frequency ( ), duration, and energy. In this work, the syllable type features are selected as the basic unit for expressing the prosodic features. The approximate segmentation of continuous speech to syllable units is achieved by automatically locating the vowel starting point. The knowledge of high-level speaker’s specific speakers is used as a reference for extracting the prosodic features of the speech signal. High-level speaker-specific features extracted using this method may be useful in applications such as speaker recognition where explicit phoneme/syllable boundaries are not readily available. The efficiency of the particular characteristics of the specific features used for automatic speaker recognition was evaluated on TIMIT and HTIMIT corpora initially sampled in the TIMIT at 16 kHz to 8 kHz. In summary, the experiment, the basic discriminating system, and the HMM system are formed on TIMIT corpus with a set of 48 phonemes. Proposed ASR system shows 1.99%, 2.10%, 2.16% and 2.19 % of efficiency improvements compared to traditional ASR system for and of 16KHz TIMIT utterances.&lt;/p&gt;
Pseudolikelihood Reranking with Masked Language Models.

We rerank with scores from pretrained masked language models like BERT to improve ASR and NMT performance. These log-pseudolikelihood scores (LPLs) can outperform large, autoregressive language models (GPT-2) in out-of-the-box scoring. RoBERTa reduces WER by up to 30% relative on an end-to-end LibriSpeech system and adds up to +1.7 BLEU on state-of-the-art baselines for TED Talks low-resource pairs, with further gains from domain adaptation. In the multilingual setting, a single XLM can be used to rerank translation outputs in multiple languages. The numerical and qualitative properties of LPL scores suggest that LPLs capture sentence fluency better than autoregressive scores. Finally, we finetune BERT to estimate sentence LPLs without masking, enabling scoring in a single, non-recurrent inference pass.
Business recommendation based on collaborative filtering and feature engineering – aproposed approach

Business decisions for any service or product depend on sentiments by people. We get these sentiments or rating on social websites like twitter, kaggle. The mood of people towards any event, service and product are expressed in these sentiments or rating. The text of sentiment contains different linguistic features of sentence. A sentiment sentence also contains other features which are playing a vital role in deciding the polarity of sentiments. If features selection is proper one can extract better sentiments for decision making. A directed preprocessing will feed filtered input to any machine learning approach. Feature based collaborative filtering can be used for better sentiment analysis. Better use of parts of speech (POS) followed by guided preprocessing and evaluation will minimize error for sentiment polarity and hence the better recommendation to the user for business analytics can be attained.
Implementation of FOAF, AIISO and DOAP ontologies for creating an academic community network using semantic frameworks

Web 2.0 delivers the information which is then displayed in human readable content, omitting the crucial information which can be drawn from the data by the applications. Web 3.0 or semantic web is an extension to the current web, with an ambition to determine the drawbacks of the current web. The semantic web has already proven its influence in several communities around the globe, such as social media, music industry, healthcare domain, online blogs or articles, etc.; Among the several tools and technologies, ontologies or vocabularies are the foundation pillar for the semantic web. In this paper, the developed system aims at improving the collaboration and academic relations among staff which is directly related to our education community by providing a better networking platform which lets the agents discuss their achievements, titles, domain interests, and various other activities. Results have been analyzed to show how new facts, information can be implied from the presented knowledge of several agents and help generate a relationship graph by utilizing various semantic tools. The system discussed in this paper processes all the information in a format which can be understood by both humans and the machines, to interpret the underlying meaning about it and provide effective results.
Elliptic curve and pseudo-inverse matrix based cryptosystem for wireless sensor networks

Applying asymmetric key security to wireless sensor network (WSN) has been challenging task for the researcher of this field. One common trade-off is that asymmetric key architecture does provide good enough security than symmetric key but on the other hand, sensor network has some resource limitations to implement asymmetric key approach. Elliptic curve cryptography (ECC) has significant advantages than other asymmetric key system like RSA, D-H etc. The most important feature of ECC is that it has much less bit requirement and at the same time, ensures better security compared to others. Hence, ECC can be a better option for implementing asymmetric key approach for sensor network. We propose a new cryptosystem which is based on Pseudo-inverse matrix and Elliptic Curve Cryptography. We establish a relationship between these two different concepts and evaluate our proposed system on the basis of the results of similar works as well as our own simulation done in TinyOS environment.
Fractional revival of threshold graphs under Laplacian dynamics

We consider Laplacian fractional revival between two vertices of a graph X. Assume that it occurs at time τ between vertices 1 and 2. We prove that for the spectral decomposition L = q r=0 θ r E r of the Laplacian matrix L of X, for each r = 0, 1, . . ., q, either E r e 1 = E r e 2 , or E r e 1 = -E r e 2 , depending on whether e iτ θr equals to 1 or not.That is to say, vertices 1 and 2 are strongly cospectral with respect to L. We give a characterization of the parameters of threshold graphs that allow for Laplacian fractional revival between two vertices; those graphs can be used to generate more graphs with Laplacian fractional revival.We also characterize threshold graphs that admit Laplacian fractional revival within a subset of more than two vertices.Throughout we rely on techniques from spectral graph theory.
Getting E-Books into Courses: How Libraries Can Partner with Faculty to Ease the Textbook Affordability Crisis

Academic libraries have implemented various initiatives to help reduce the cost students pay for learning materials.Popular initiatives including promoting open educational resources (OER), inclusive access programs, and curriculumbased collection development.A recent survey conducted by Library Journal/EBSCO identified several barriers to faculty engagement with e-books in courses.This paper will discuss those barriers, as well as the efforts at two Louisiana universities, under the leadership of the statewide academic library consortium (LOUIS), to promote both OER and library-purchased e-books, and address challenges to faculty and student engagement with these materials.In addition to these, some libraries have implemented programs to purchase books assigned to courses as e-books and made them available to students free of charge.One such program has been in place at Louisiana State University (LSU) since 2014.This paper will describe that program, including its efforts to engage faculty members, as well as similar efforts at the University of New Orleans (UNO).Among the efforts to engage faculty is LSU's "e-Textbooks for Faculty" portal; a website that enables faculty members to search for relevant e-books.A similar tool, created by EBSCO to serve a state consortium, will also be discussed.Finally, user testing was conducted to understand whether an existing library e-textbook product (EBSCO) sufficiently supported the course-reading workflow; this paper will discuss that testing as well as recommendations for platforms seeking to support this use case.
The impact of training sessions on security awareness: measuring the security knowledge, attitude and behaviour of employees

In recent years, many companies stepped up security efforts in chemical and process facilities. The need for the implementation of technological and organizational measures is accompanied by an increased attention for the human factor of the company's security policy. In order to create a strong security culture, a high level of security awareness among employees is needed. The aim of this paper is to measure the impact of a training session on the level of security awareness. Based on the results of a quantitative research, the effectiveness regarding the security knowledge, attitude and behavior.of employees is measured.
Data Center Job Scheduling Algorithm Based on Temperature Prediction

Improved energy efficiency of data center is in hotspot. Prevailing data center energy-conservation measures are intended for computing devices, but ignoring the potential energy savings from cooling equipment whose energy consumption accounts for about 40% of the total energy consumption of data center. In addition cooling equipment is often set to an excessively low temperature to ensure the thermal safety of the data center, resulting in energy waste. In this paper, we propose a neural network-based distributed temperature prediction algorithm including an inter-server joint modeling framework based on the thermal locality principle, which significantly reduces the training time of the temperature prediction model and make the proposed algorithm be easily extended to large data centers. Furthermore, we propose a job scheduling algorithm based on the proposed temperature prediction algorithm. The job scheduling algorithm monitors the server inlet temperature in real time and controls the load of each server using feedback control. It guarantees that thermal reliability of servers and attempts to avoid the creation of a hot point. It selects the best job scheduling strategy based on the result of the temperature prediction algorithm. The two proposed algorithms are evaluated on a small data center. Our results show that the average prediction error of the proposed temperature prediction algorithm is only 0.28 °C in a 10-min predicted field of view. The proposed job scheduling algorithm can achieve approximately 10% cooling energy consumption compared with the load balancing algorithm while ensuring the thermal reliability of the data center.
Completeness theorems for σ–additive probabilistic semantics

We study propositional probabilistic logics (LPP–logics) with probability operators of the form P≥r ("the probability is at least r") with σ–additive semantics. For regular infinite cardinals κ and λ, the probabilistic logic LPPκ,λ has λ propositional variables, allows conjunctions of <κ formulas, and allows iterations of probability operators. LPPκ,λ,2 denotes the fragment of LPPκ,λ where iterations of probability operators is not allowed. Besides the well known non-compactness of LPP–logics, we show that LPPκ,λ,2–logics are not countably compact for any λ≥ω1 and any κ, and that are not 2ℵ0+–compact for κ≥ω1 and any λ. We prove the equivalence of our adaptation of the Hoover's continuity rule (Rule (5) in [13]) and Goldblat's Countable Additivity Rule [9] and show their necessity for complete axiomatization with respect to the class of all σ–additive models. The main result is the strong completeness theorem for countable fragments LPPA and LPPA,2 of LPPω1,ω.
The Qualification of Teachers in Bulgaria – Necessity or Obligation

The report presents the main priorities of the ongoing training of pedagogical specialists in Bulgaria in terms of educational reforms. It traces the development of the main goals, the challenges and the opportunities for improving the competence of teachers. The report analyses the state educational standard status and professional development of teachers, directors and other pedagogical specialists in the system of preschool and school education. Paper presents a part of a study aimed at examining the state of the system of teacher training, opportunities for optimizing the qualification system, the degree of teachers’ motivation to participate in In-service training. More specifically, the object of the study is the satisfaction of the teachers of the programs and projects for training, their preferred organizational forms, motives for participation / non-participation in training, incentives that would attract young people to the teaching profession and others. The main research question in the study is „What are the factors that are most highly influence teachers to participate in training activities to enhance their competencies and personal and professional development?". Anonymous survey conducted among teachers from schools and kindergartens in Bulgaria (Pernik, Sofia region, Stara Zagora and Pazardzhik) is used as a research method. The main conclusions from the survey are: capabilities of different organizational forms to enhance personal and professional competencies of teachers are not used to the maximum extent; factors that motivate teachers to participate in training activities are useful training that challenge their creativity and innovation, and the achievement of their personal educational goals; forthcoming evaluation of teachers has a positive impact on the level of motivation for professional development; teachers are insufficiently involved in research and forums. In this context, they are made some recommendations for the use of all qualification forms that are included in the State educational standard for teachers' professional development (including participation in research and forums) and taking into account specific needs of teachers relevant and important topics, forms and methods of training.
A data mining approach for desire and intention to participate in virtual communities

&lt;span lang="EN-US"&gt;The purpose of this study is to investigate performances of some of the data mining approaches while understanding desire and intention to participate in virtual communities and its antecedents. A research model has been developed following the literature review and the model was tested afterwards. In research part of the study, some of the data mining approaches as JRip, Part, OneR Method, Multilayer Perceptron (Neural Networks), Bayesian Networks have been used. Based on the analysis conducted it has been found out that Multilayer Neural Network had the best correct classification rate and lowest RMSE.&lt;/span&gt;
An optimization of facial feature point detection program by using several types of convolutional neural network

&lt;p&gt;Detection of facial feature points is an important technique used for biometric authentication and facial expression estimation. A facial feature point is a local point indicating both ends of the eye, holes of the nose, and end points of the mouth in the face image. Many researches on face feature point detection have been done so far, but the accuracy of facial organ point detection is improving by the approach using&lt;br /&gt;Convolutional Neural Network (CNN). However, CNN not only takes time to learn but also the neural network becomes a complicated model, so it is necessary to improve learning time and detection accuracy. In this research, the improvement of the detection accuracy of the learning speed is improved by increasing the convolution layer.&lt;/p&gt;
Feature Extraction Techniques in Associate with Opposition Based Whale Optimization Algorithm

Near-duplicate video (NDV) detection is an important issue of copyright protection.However, the traditional detection methods are very imprecise and complex.To solve the problem, this paper introduces the opposition-based solution generation strategy into the conventional whale optimization algorithm (OWOA), creating a novel NDV detection method called the OWOA.The author detailed how to use the hybrid method to extract different types of features, ranging from color, shape to texture, and compared the OWOA with traditional feature extraction methods through experiments.The comparison shows that the OWOA achieved the optimal performance in DNV detection.The research findings can greatly assist regulatory authorities in monitoring and detecting edited contents.
Analysis of Upper Primary Students‘ Critical Reading Skills in Surakarta Based on School Accreditation

This study aims to describe the critical reading skills of upper primary students in elementary schools of Surakarta City.The researchers also analyzed the differences in critical reading skills of upper primary students from A-accredited and B-accredited elementary schools.This type of research is descriptive qualitative research.Informants in the study come from 4 A-accredited elementary schools and 4 B-accredited elementary schools consisting of 2 fourth grade students, 2 fifth grade students and 2 sixth grade students per school (6 students per school).Critical reading ability was measured by using a written test instrument followed by in-depth interviews to explore six aspects of critical reading skills, namely: accuracy, clarity, precision, depth, relevance and logic.Competency analysis of students refers to Bahasa Indonesia Basic Competencies for primary students of Indonesian Ministry of Education and Culture Number 37 of 2018.The result of the study shows that the students in A-accredited schools have better critical reading skills in the aspects of accuracy, clarity and relevance.Students in sixth grade master the aspects of critical reading more than the students in fourth grade and fifth grade.