Reviewerly/exHarmony huggingface dataset:
Training Data Improvement by Automatic Generation of Semantic Networks for Bias Mitigation

The significance of Bias Detection has increased appreciably, due to the increased application of AI.Although syntactic bias is well explored with statistical techniques, there remains semantic bias challenge like for example, Google's face recognition which excludes colored people.Human expertise is required to detect semantic bias, e.g., for the application of the root-out-bias method.We propose a further automatization to this laborious method, based on the Training Data Improvement for Bias Mitigation (TDIBM).The concept, is to automatically construct a Semantic Network (SN) from the domain description of the training.For the semantic network nouns are extracted.As a second step, synonyms and semantically similar nouns are searched, e.g. in dictionaries, and added to the SNs.As a result, the SN contains nouns that enhances the given domain, with previously unknown knowledge.This SN can be used to check with, e.g., the root-out bias method, whether the training sample is biased, or not.Should the training sample be biased, then the corresponding nouns from the SN can be added to the training sample set to mitigate the bias.The newly developed method, TDIBM is evaluated twofold: Firstly, with the description of the COMPAS system, which is a case management and decision support tool used by U.S. courts to assess the likelihood of a defendant becoming a recidivist.Secondly, an autonomous driving domain is applied, to investigate accidental driving of a Tesla car.Here TDIBM detected among many new features, including one to solve ambiguous scene interpretations for autonomous driving vehicles.
Data Mining and Revealing Hidden Sentiment in Tweets Using Spark

Data science is important and scientific value in our lives because it is multi-fields, it's the science that uses scientific methods, processes, algorithms, and systems for the purpose of extracting knowledge and ideas from data whether this data is organized or not.Data science is called 21st century oil to highlight its importance and scientific value in our lives.We paid great attention in this research paper, where we achieved three main steps in the field of data analysis, collecting data from different sources in the Internet and then storing the data within the system, the second step cleaning the data in order to obtain structured data and then applying the algorithms that are responsible for classifying the data.In pursuit of development, we have collected more than 1600000 tweets about the educational process and the possibility of future online education.We give great attention to this field of Sentiment Analysis (SA) and the use of modern Spark technology, which has achieved great success since its emergence.We have succeeded in using Spark in getting a good result, handling data quickly and accurately, which encouraged us to test it on two algorithms of Machine Learning, Support Vector Machine (SVM) & Maximum Entropy (Max Ent).
On the Key Competencies for China’s Secondary Vocational School English Curriculum Standards

To cope with the challenges in today's world, students' abilities should go well beyond basic reproduction of accumulated knowledge and concomitant acquisition of certain basic skills at school or university.Accordingly, competencies including knowledge, skills, attitudes and values are required for individuals' sustainable development.This paper provides an overview of key competencies for China's Secondary Vocational School English Curriculum Standards.The research panel of 11 members entrusted by the Ministry of Education of China (MOE) carried out a project of the key competencies in secondary vocational English since the official launch of the research project in May, 2017.On this basis, the research panel had China's Secondary Vocational School English Curriculum Standards released in May, 2020.The key terms of competence and competency are first defined.Studies on key competencies abroad and at home are then reviewed.Four key competencies for China's Secondary Vocational School English Curriculum Standards (2020) are then extracted from the review.These are workplace language communication, perception of thinking mode differences, cross-cultural understanding and autonomous learning -which are relatively independent but mutually integrated in that they form an organic whole.The criteria and process of extracting the four key competencies of secondary vocational English are described and the formulation and interpretation of the key competencies are elaborated upon.While the proposal concerning the key competencies provides conceptual support, the transformation from idea to practice needs considerable effort, especially in integrating these key competencies in curriculum design.
An Investigation into the Textual Meaning in Academic IELTS Sample Essays

The textual meaning, which is embodied in the structure of a clause as a message, is one of three types of meaning that all natural languages possess.Textual meaning relates to the construction of text, the organization of ideas in a coherent text.The research is intended to identify the textual meaning manifested in Academic IELTS sample essays with band scores of 7 -7.5 in the light of Functional Grammar.The linguistic components realizing the textual meaning include cohesion and thematic structure.A descriptive method accompanied by qualitative and quantitative approaches is used to give a detailed description of the textual meaning in relation to the IELTS band descriptors.90 sample essays with band scores of 7.0-7.5 from published books and official IELTS websites are selected as data for text analysis.The findings reveal that cohesive devices are properly used and information is logically arranged in different thematic patterns in the sample essays.It is noticeable that the prevailing types of Themes deployed in the essays are multiple Themes, demonstrating high levels of English proficiency of IELTS writers in constituting a message.It is hoped that the findings of this research will provide insights into salient thematic patterns of Academic IELTS sample essays and appropriate use of cohesive devices, whereby IELTS test-takers can make good use of appropriate strategies in order to produce concise and coherent pieces of writing.
Educational Management in the Covid 19 Era. Case Study: Romanian Universities

The pandemic generated by the SARS-CoV 19 virus had, in addition to the obvious effects on the health of the population, multiple side&#x0D; effects. Of these, the effect on the education system can be found among the most important. The long period of lockdown, coupled with the transition to an online learning system, has been a daunting task for any education system. This article presents the ways to approach educational management in Romania from the perspective of higher education institutions. The research considers both the specific legislation issued during 2019-2021 and the ways of implementing and adapting universities during this period. The obtained results show us that at the level of higher education institutions in Romania there was no consensus on educational management in COVID 19 Era. The conclusion that emerges is that of the need to develop, at the national and institutional level, management plans for such periods of crisis&#x0D; and programs for the concrete implementation of these plans.
RAINBOW algorithm and its ability to resist RBS attacks and third party channels

The essence and possibilities of protecting the Rainbow post-quantum cryptographic algorithm are considered. The main properties of algorithms based on multidimensional quadratic transformations are studied. An assessment is given of what resources and computational energy are needed for the successful use of algorithms based on multidimensional quadratic transformations. The ability to protect the algorithm against attacks by third-party channels is analyzed. It is studied how successfully a cryptographic algorithm based on Rainbow multivariate quadratic transformations can withstand RBS attacks. A detailed description of the steps used to build an attack on a cryptographic algorithm based on Rainbow multivariate quadratic transforms is given. A structural analysis of the Rainbow algorithm is performed. Detailed conclusions are made regarding the performed analyzes. An assessment of the stability and complexity of the cryptographic encryption algorithm and electronic signature based on multivariate quadratic transformations is given.
Study of a new cost function for generating random substitutions of symmetric ciphers

Cryptographic transformations with a secret key play an essential role in providing information and cyber security. Block and stream symmetric ciphers are used in various applications both as a separate cryptographic protection mechanism and as part of other applications (pseudo-random sequence generators, hashing algorithms, electronic signature protocols, etc.). Therefore, the design and study of individual components of symmetric ciphers is a relevant and important scientific task. In this paper we consider and investigates iterative algorithms for generating non-linear substitutions (substitutions, S-boxes), which are used in modern block and stream encryption algorithms with a symmetric key. Cryptographic resistance of symmetric ciphers to statistical, differential, linear and other methods of cryptanalysis is provided by the properties of substitutions. In addition, S-boxes must be random from the point of view of the possibility to use algebraic cryptanalysis. Therefore, the task of quickly generating random S-boxes with the desired cryptographic properties is an urgent, but extremely difficult task. For example, the best known generation algorithm requires more than 65 thousand iterations to find a random bijective 8-bit substitution with a non-linearity of 104. In this paper, we study an iterative algorithm for generating substitutions for hill climbing with different cost functions and propose a new cost function, the use of which can significantly reduce the number of search iterations. In particular, the search for a bijective S-box with nonlinearity 104 requires less than 50 thousand iterations.
THE SIMPLIFYING OF THE SOLUTION OF LINEAR OPTIMIZATION PROBLEMS IN PROJECT MANAGEMENT

Modern mathematic models of project management processes description can be use in many cases to linear optimization problems. Simplification algorithms provide an efficient method of searching for solution of an optimization problem. If we project a multidimensional process onto a two-dimensional plane, this method will enable graphic visualization of the problem solution matrixes. A significant simplification of the algorithms for preparing the linear optimization problem in computer calculations can be achieved using the concept of duality in linear optimization problems. The linear optimization problem forms are equivalent. This can be achieved provided that transformation techniques are used to move from one form of tasks to another. To simplify the transformation of linear optimization problems, the transition from maximizing to minimizing the objective function is used. This research has proposed a method of simplifying the combinatorial solution of a discrete optimization problem. It is based on decomposition of the system representing a system of constraints of a five-dimensional initial problem into the two-dimensional coordinate plane. There was a model example considered for solving a five-dimensional linear optimization problem based on such projecting of a multidimensional space onto the two-dimensional one. The paper is concerned with construction of a chain of efficient algorithms to simplify the primary mathematic model of problem and realization its computer-aided calculation. Applied value of the proposed approach consists in using the scientific result for enabling the possibility to improve canonical methods of optimization problem solution and, respectively, for simplification of computer-assisted calculation.
अष्टाध्यायी व्याकरणमा सूत्रशैली {The Formulation Style of the Sutra in Ashtadhyayi Grammar}

प्रस्तुत लेख अष्टाध्यायी व्याकरणमा सूत्र शैलीको अध्ययनमा केन्द्रित छ । अष्टाध्यायी पाणिनि (इ. पू. ४५०—४००) द्वारा वैदिक र लौकिक संस्कृतमा भएका भाषिक व्यवस्थालाई नियमन गरिएको व्याकरण हो । करिब चार हजार सूत्रलाई पाणिनिले तिनको प्रकार्यका आधारमा प्रस्तुत गरेका छन् । यी सूत्रले व्याकरणमा विशेष शैलीगत स्वरूप प्राप्त गरेका छन् । यस व्याकरणको अन्य पक्षबाट अध्ययन भए पनि सूत्रको प्रकार तथा तिनका बिचको पारस्परिक अन्तरनिर्भरताका विषयमा अध्ययन भएको छैन । त्यही रिक्तताको परिपूर्तिका लागि अध्ययन गर्नु यस लेखको मुख्य उद्देश्य हो । त्यसका लागि सामग्री सङ्कलन पुस्तकालयीय कार्यद्वारा गरिएको छ । सङ्कलित सामग्रीको विश्लेषण वर्णनात्मक तथा व्याख्यात्मक विधि प्रयोग गरिएको छ । सूत्र, तिनका पदार्थगत अन्वय र सङ्क्षिप्त नियमनका स्वरूप दिएर शोध्य विषयलाई सामान्यीकरण गरिएकाले विश्लेषण निगमनात्मक पद्धतिमा आधारित छ । अष्टाध्यायीमा प्रस्तुत गरिएका संज्ञा, परिभाषा, विधि, नियम, अतिदेश, अधिकार गरी छ ओटा सूत्रका प्रकार एवं अध्याहार र अनुवृत्ति सूत्रकाबिचका प्रविधिगत अन्तर्सम्बद्धता हो भन्ने यस लेखको निष्कर्ष हो । {The present article focuses on the study of the formulation style of the sutra in Ashtadhyayi grammar. Ashtadhyayi is the grammar regulated on the linguistic system in Vedic and classical Sanskrit by Panini (450-400 BC). Panini has presented about four thousand sutrasbased on their functions. These sutras have acquired a special stylistic form in grammar. Although other aspects of this grammar have been studied, the type of sutra and the interdependence between them have not been studied. The main purpose of this article is to study to fill that critical gap. For that, data has been collected through library work. The descriptive and explanatory method of analysis of the collected data has been used. The analysis is based on the deductive method, as the subject matter is generalized by giving the form of the sutras, its semantic coherence, and brief regulation. This article concludes in Ashtadhyayi that there is a technical interrelationship between the types of six sutras, sanjna, paribhasha, vidhi, niyama, atidesha, adhikara and adhyahara and anuvritti.}
Firm-Level Determinants of Corporate Social Responsibility

&#x0D; &#x0D; &#x0D; This paper examined firm-level determinants of social performance among small and medium-sized enterprises (SMEs) in Ghana, using specific traits, such as firm age, firm size, financial performance, leverage and managerial competence. It contributes to our knowledge on how firm-level characteristics influence the corporate social responsibility (CSR) initiatives among SMEs. The study analysed primary data of 423 SMEs, using regression analysis. It documented positive relationships between managerial competence, financial performance and CSR, and a negative relationship between leverage and CSR. The thrust of the study is that, besides managerial competence, financial performance variables were far more important determinants of the social performance of SMEs than non-financial ones. Therefore, it recommended that policy makers that desire active participation of the SME sector in addressing societal problems should institute measures that will improve their financial performance. These could include providing cheaper alternatives to accessing funds and maintaining a sustainable macroeconomic environment that support the financial prosperity of this sector. Finally, since it has been proven that managerial competence improves social performance, SME owner/managers are being encouraged to invest a lot of time and resources in sharpening their managerial competencies.&#x0D; &#x0D; &#x0D;
FSEE: A Forward Secure End-to-End Encrypted Message Transmission System for IoT

Leakage of long-term secrets is a major concern when cryptographic schemes are implemented on devices with weak protection capability, especially for resource-constrained IoT devices. Forward secrecy is a means to minimize the damage when such an event takes place. For pub-/sub-based IoT systems, several end-to-end (from publisher to subscriber) encrypted message transmission schemes have been proposed to tackle the confidentiality problems brought by malicious message brokers. But none of them provide forward secrecy. This article presents FSEE, a forward secure end-to-end encrypted message transmission system for pub-/sub-based IoT. To support FSEE, we design a novel group key exchange protocol BA-GKE, which relies on a semi-trusted key exchange server to provide forward secrecy and support asynchronous communication between group members. We prove its forward secrecy by ProVerif. The core idea of FSEE is to establish a forward secure symmetric key per device using BA-GKE asynchronously, and this device-specific key is shared with the device and its authorized subscribers for encrypting messages securely. By adding a semi-trusted key exchange server to realize BA-GKE in the current IoT architecture, FSEE does not need to change the existing message broker and could be deployed incrementally. The experimental results show that FSEE has comparable performance to existing prominent research and provides higher security.
THE CONSTRAINTS OF CROSS-LINGUISTIC TRANSFER ON BIPA LEARNING

One of the interesting issues regarding the BIPA learning process is how the similarities and differences of the source and target languages (Indonesian language) are related to students’ understanding of the Indonesian language. This study is related to what is known as a cross-linguistic transfer. This paper reviews theoretically cross-linguistic transfer in learning in BIPA (Indonesian Language for Foreign Speakers). This study employed the descriptive-qualitative method. A qualitative method was used to describe the patterns of similarity between Indonesian language as the target language and the learner’s native language in BIPA learning using a cross-linguistic transfer approach. The result of the study revealed that the discussion about cross-linguistic transfer in the process of learning the Indonesian language for Foreign Speakers (BIPA) was not a monolithic matter. The similarities and differences in word forms and word meanings were closely related to how quickly speakers of other languages learned the target language.
A Reconfigurable Source of Entangled Frequency bins

We present a highly-efficient integrated reconfigurable source of entangled frequency bins. Leveraging the flexibility offered by the silicon photonics platform, we demonstrate the on-chip generation of all the main two-qubit quantum states.
Improved rf Tolerance of Trap-Integrated Superconducting Nanowire Single-Photon Detectors for Trapped-Ion Qubit State Readout

The performance of ion-trap-integrated superconducting nanowire single-photon detectors for trapped-ion qubit readout is significantly i mpacted b y r f trapping fields. We demonstrate an electrically-shielded trap-integrated detector with substantially improved rf tolerance.
Improving Mortality Prediction in ICU by Learning Long-Tailed Population of Patients Through Graph-Domain Aggregation

Early detection of mortality in intensive care units (ICUs) is significant to improve patient survival. Since heterogeneous data can be collected from the intensive care unit, there are meaningful static features (e.g., ICU type, gender, and ethnicity) that can enhance the performance of early detection of mortality. Although the characteristics of physiological representation for each patient can be different by static features, recently proposed mortality prediction models overlook these features and only rely on a one-size-fits-all model. In this paper, we propose a simple yet effective domain encoder named G-DAM (Graph-Domain Aggregation Module) that relies on a relational graph between each static feature to adapt to patient groups and capture the relationship. We show the importance of utilizing static features to predict ICU mortality through extensive experimental results. The experimental results demonstrate that our proposed G-DAM outperforms existing baseline methods for encoding static features not only in the major domains with dense populations but also in the minor domains with sparse populations. The ablative study also shows different sequential models can perform better combined with G-DAM.
Demonstration of Single-Photon Synapses

We present a single-photon-sensitive synapse realized by the monolithic integration of superconducting-nanowire single-photon detectors with Josephson electronics. Data show the utility of the synapse for achieving synaptic weighting and integration of pulse trains.
How good is your algorithm: the significance of data diversity

Artificial intelligence (AI) algorithms permeate all spheres of human lives to a great extent. Their influence is expected to only grow in the future. Autonomous driving is no exception and is not possible without AI algorithms. The quality of AI algorithms closely depends on the data used for their training, testing, and validation. Due to the specific nature of how they are collected, the data sets used for autonomous driving algorithms often lack diversity. The majority of the data is collected under ideal weather conditions. The performance of most algorithms during adverse weather conditions is thus unknown. We present an overview of physics-based algorithms used for transforming images and videos taken during ideal weather conditions into ones with haze, fog, and rain. We show that performance algorithms for deep object detection and semantic segmentation algorithms decrease with the increase of weather-induced noise in images and videos. Our survey confirms the need to introduce diverse datasets with well-controlled noise levels for training, testing, and validation of autonomous driving algorithms.
Cloud Architecture Principles

There are tons of different Cloud Architecture styles, patterns and principles. In this talk I'll touch some of them, that I found most useful and applicable in most of the cases. At the end of the session you gonna have a checklist to apply at your project to not just use a Cloud, but thrive in the Cloud!
Pulmonary Emergencies

The amount of data in health care is rapidly rising, leading to multiple datasets generated for any given individual. Data integration involves mapping variables in different datasets together to form a combined dataset which can then be used to conduct different types of analyses. However, with increasing numbers of variables, manual mapping of a dataset can become inefficient. Another approach is to use text classification through machine learning to classify the variables to a schema.Our aim was to create and evaluate the use of machine learning methods for the integration of data from datasets across health information-seeking behavior (HISB) databases.Four online databases relevant to the research field were selected for integration. Two experiments were designed for dataset mapping: intra-database mapping using the one data source, and inter-database mapping to map datasets between the four databases. We compared logistic regression (LR), a random forest classifier (RFC), and neural network (NN) models by F1-score for two methods of integration. A third experiment was an ablation study that used all the available data to create a model for classifying HISB variables in a dataset.In intra-database mapping, the mean F1 score for an LR classifier (0.787) was better than the RFC score (0.767) and fully connected NN (0.735). In inter-database mapping, the LR (0.245) scored best, however, this was dependent on which database was used as a training source. Using all the databases, these top three models were able to correctly classify 90–91% of the variables. Removing one dataset improved scores and resulted in a model able to correctly classify 95–96% of the HISB variables.As part of data integration, a neural network can be used as an approach to map the variables of a dataset. The developed models can be used to classify the HISB terms in a database.