Reviewerly/exHarmony huggingface dataset:
NON-INTELECTIVE PREDICTORS OFMATHEMATICS PERFORMANCE OF THEFRESHMEN STUDENTS OF IICT IN ISABELA STATEUNIVERSITY-CAUAYAN CITY

This study was conducted to determine the non-intellective predictors of mathematics performanceof the freshmen students of the Institute of Information and Communication Technology of Isabela StateUniversity, Cauayan City , Isabela for the School Year 2008-2009 in terms age, sex,type of high school attended,type of place of residence,parentsÂ educational attainment, parents occupation and monthly income and attitudestowards mathematics. The researcher made use of descriptive method through documentary analysis andinferential method to determine the relationship of non-intellective factors to the mathematics performance of thestudents. There were 194 freshmen students ofchosen through random sampling from the four programs of IICTin BSCS, BSIT Ladderized ,BSIT and .The researcher used reliable tools and valid instruments to produce reliableoutput namely: Students Registration Form, Entrance Test Form, and Mathematics Attitude Scale . Datagatheredwere analyzed using frequency and percentage distribution, mean, PearsonÂs Product Moment Correlation,and t test. Findings showed that majority of the respondents were 17 years old 67.43% were female 88.76% weregraduated in public secondary school, 70.10% were living in rural areas, parents were undergraduate,self-employed and their monthly income ranged from 3,000-6,000. final grade ranged from 2.75-3. Of thenon-intellective factors examined in this study, only the respondentsÂ type of residence showed correlation to theMathematics performance of the learners, hence, students whether they spent most of their lifes in urban or ruralareas effectively predicted their mathematics performance. The results imply that age, sex, type of high schoolattended, parentsÂ educational attainment, parentsÂ occupation, parentsÂ monthly income and attitude towardsMathematics do not play a significant role in the performance of learners in Mathematics.
Dataset for: OSIEshort: A small stimulus set can reliably estimate individual differences in semantic salience

This research was supported by ERC StartingGrant 852885318 INDIVISUAL; BdH was further supported by DFG Project No. 222641018 SFB/TRR 135,319 TP A8.
Parallel Computation of Alpha Complexes for Biomolecules

The alpha complex, a subset of the Delaunay triangulation, has been extensively used as the underlying representation for biomolecular structures. We propose a GPU-based parallel algorithm for the computation of the alpha complex, which exploits the knowledge of typical spatial distribution and sizes of atoms in a biomolecule. Unlike existing methods, this algorithm does not require prior construction of the Delaunay triangulation. The algorithm computes the alpha complex in two stages. The first stage proceeds in a bottom-up fashion and computes a superset of the edges, triangles, and tetrahedra belonging to the alpha complex. The false positives from this estimation stage are removed in a subsequent pruning stage to obtain the correct alpha complex. Computational experiments on several biomolecules demonstrate the superior performance of the algorithm, up to a factor of 50 when compared to existing methods that are optimized for biomolecules.
Context-Based Inferences from Probabilistic Conditionals with Default Negation at Maximum Entropy

The principle of maximum entropy (MaxEnt) constitutes a powerful formalism for nonmonotonic reasoning based on probabilistic conditionals. Conditionals are defeasible rules which allow one to express that certain subclasses of some broader concept behave exceptional. In the (common) probabilistic semantics of conditional statements, these exceptions are formalized only implicitly: The conditional (B|A)[p] expresses that if A holds, then B is typically true, namely with probability p, but without explicitly talking about the subclass of A for which B does not hold. There is no possibility to express within the conditional that a subclass C of A is excluded from the inference to B because one is unaware of the probability of B given C. In this paper, we apply the concept of default negation to probabilistic MaxEnt reasoning in order to formalize this kind of unawareness and propose a context-based inference formalism. We exemplify the usefulness of this inference relation, and show that it satisfies basic formal properties of probabilistic reasoning.
The future of legal regulation: the intersection of creativity, technology and biology

November 2019 was the month and year in which the film Blade Runner was set. Anyone familiar with that film will understand the challenges that it presented to us as part of its depiction of the fu...
Development of a General Purpose Sentiment Lexicon for Igbo Language

There are publicly available general purpose sentiment lexicons in some high resource languages but very few exist in the low resource languages. This makes it difficult to directly perform sentiment analysis tasks in such languages. The objective of this work is to create a general purpose sentiment lexicon for Igbo language that can determine the sentiment of documents written in Igbo language without having to translate it to English language. The material used was an automatically translated Liu’s lexicon and manual addition of Igbo native words. The result of this work is a general purpose lexicon – IgboSentilex. The performance was tested on the BBC Igbo news channel. It returned an average polarity agreement of 95% with other general purpose sentiment lexicons.
Combining Imagination and Heuristics to Learn Strategies that Generalize

Deep reinforcement learning can match or exceed human performance in stable contexts, but with minor changes to the environment artificial networks, unlike humans, often cannot adapt. Humans rely on a combination of heuristics to simplify computational load and imagination to extend experiential learning to new and more challenging environments. Motivated by theories of the hierarchical organization of the human prefrontal networks, we have developed a model of hierarchical reinforcement learning that combines both heuristics and imagination into a “stumbler-strategist” network. We test performance of this network using Wythoff’s game, a gridworld environment with a known optimal strategy. We show that a heuristic labeling of each position as hot or cold, combined with imagined play, both accelerates learning and promotes transfer to novel games, while also improving model interpretability
Teacher Cognition about Technological Pedagogical Knowledge of a Teacher of English for ICT Integration in Secondary Schools in Nairobi County, Kenya

English language teaching (ELT) is an area that has changed over the years from very teacher-cantered approaches to learner-cantered ones, and these changes have brought changes in materials and technology devices.This paper examined teacher cognition about technological pedagogical knowledge (TPK) of a teacher of English for ICT integration in secondary schools in Nairobi County, Kenya.The study was guided by the Teacher Cognition and TPACK frameworks viewed from two main landscapes.Teacher cognition included teachers' thoughts about instruction and beliefs about students, classrooms, learning, and their own teaching performances, contextual factors like attitudes, thoughts and beliefs of a teacher about ICT integration.While TPACK covered personal and professional constructs of teachers involving pedagogical technological and content knowledge (TPACK).The study was based on the pragmatic world view, and utilized the convergent parallel mixed methods design embedded within mixed methods approach.Both purposive and simple random sampling techniques were used to select the sample of 19 teachers of English selected from 10 secondary schools.Quantitative data were collected using the questionnaire whereas qualitative data were collected using in-depth interview.Data were sequentially analysed.Both descriptive statistics and factor analysis were used to analyse quantitative data.The descriptive statistics utilized were: frequencies and percentages that were derived using the statistical package for the social science (SPSS) software and data presented in tables, graphs and short descriptions.The results of the study revealed that secondary school teachers of English in Nairobi County, Kenya lack professional and personal factors which include teachers' limited TPACK knowledge, lack of exposure to technology, lack of knowledge about ICT tools and their uses, resulting to limited knowledge and skills required to integrate ICT in instruction.The study concluded that whereas teachers of English appreciate the integration of ICT in the instructional process, did not integrate ICT in their instruction, hence recommends that teachers be provided with required support at different levels to enhance the application of ICT in language teaching; underscoring the importance of reflective practice for continued teacher development in ICT.
Personalized Early Stage Alzheimer’s Disease Detection: A Case Study of President Reagan’s Speeches

Alzheimer's disease (AD)-related global healthcare cost is estimated to be $1 trillion by 2050.Currently, there is no cure for this disease; however, clinical studies show that early diagnosis and intervention helps to extend the quality of life and inform technologies for personalized mental healthcare.Clinical research indicates that the onset and progression of Alzheimer's disease lead to dementia and other mental health issues.As a result, the language capabilities of patient start to decline.In this paper, we show that machine learningbased unsupervised clustering of and anomaly detection with linguistic biomarkers are promising approaches for intuitive visualization and personalized early stage detection of Alzheimer's disease.We demonstrate this approach on 10 year's (1980 to 1989) of President Ronald Reagan's speech data set.Key linguistic biomarkers that indicate early-stage AD are identified.Experimental results show that Reagan had early onset of Alzheimer's sometime between 1983 and 1987.This finding is corroborated by prior work that analyzed his interviews using a statistical technique.The proposed technique also identifies the exact speeches that reflect linguistic biomarkers for early stage AD.
Sentiment Analysis Model for Opinionated Awngi Text: Case of Music Reviews.

Abstract The analysis of sentiments is imperative to make a decision for individuals, organizations, and governments. Due to the rapid growth of Awngi (Agew) text on the web, there is no available corpus annotated for sentiment analysis. In this paper, we present a SA model for the Awngi language spoken in Ethiopia, by using a supervised machine learning approach. We developed our corpus by collecting around 1500 posts from online sources. This research is begun to build and evaluate the model for opinionated Awngi music reviews. Thus, pre-processing techniques have been employed to clean the data, to convert transliterations to the native Ethiopic script for accessibility and convenience to typing and to change the words to their base form by removing the inflectional morphemes. After pre-processing, the corpus is manually annotated by three the language professional for giving polarity, and rate, their level of confidence in their selection and sentiment intensity scale values. To improve the calculation method of feature selection and weighting and proposed a more suitable SA algorithm for feature extraction named CHI and weight calculation named TF IDF, increasing the proportion and weight of sentiment words in the feature words. We employed Support Vector Machines (SVM), Naive Bayes (NB) and Maximum Entropy (MxEn) machine learning algorithms. Generally, the results are encouraging, despite the morphological challenge in Awngi, the data cleanness and small size of data. We are believed that the results could improve further with a larger corpus.
Accommodating a Massive Surge in Absentee Voting

This fall, most states are likely to see a massive surge in absentee voting. The significantly greater burdens absentee ballots impose on election administration, compared to in-person voting, are not widely appreciated. Unless certain key dates in the election calendar are changed to take this unprecedented surge into account, the structural integrity and legitimacy of the election could be called into doubt.The date changes suggested here have three specific objectives. First, to enable as many absentee ballots as possible to be counted on (rather than after) Election Night. If tens of thousands of votes cannot be counted until many days after the election, with the front-runner on Election Night turning out to lose, this increases the risk of the loser and his supporters declaring the process rigged. Minimizing that risk must be a key focal point of election-administration this fall. Second, to give election officials enough time to complete the complicated task of processing all these ballots fairly and properly. Third, to give voters an adequate opportunity to vote in the novel circumstances this fall might pose.The dates changes identified in this essay involve state law and the federal law that governs the final stage of the presidential election, The Electoral Count Act. The date the electoral college votes should be pushed back to accommodate the longer time states are likely to need to process properly levels of absentee voting that might be an order of magnitude greater than in normal times.
The methodology to use scientific electronic libraries for information and research competence development of scientific and pedagogical professionals

Tekhnologizatsiya sovremennogo lichnostno-oriyentirovannogo vuzovskogo obrazovaniya [Technologization of a modern personalityoriented university education].9. Makhotin, D.A. (2017) Razvitiye tekhnologicheskogo obrazovaniya shkol'nikov na perekhode k novomu tekhnologicheskomu kladu.[The development of technological education for schoolchildren in the transition to a new technological treasure].10.Paton, B.YE. (2012) Natsional´na paradyhma staloho rozvytku Ukrayiny [The National Paradigm of Sustainable Development of Ukraine].11.Proyekt derzhavnoho standartu bazovoyi seredn´oyi osvity (5-9 klasy) [Draft National Standard of Basic Secondary Education (grades 5-9)].12. Stadnyk, O.D. (2015) Rozvytok nanoosvityodyn iz chynnykiv zabezpechennya perekhodu na shostyy tekhnolohichnyy uklad [Development of nanoeducation is one of the factors of ensuring the transition to the sixth technological way].13.Korsak, K. (2014) Teoretyko-metodolohichni osnovy modernizatsiyi pryrodnychoyi y inzhenernoyi vyshchoyi osvity v umovakh innovatsiyno-tekhnolohichnoho rozvytku suspilstva: monohrafiya (rukopys) [Theoretical and methodological foundations of the modernization of natural and engineering higher education in the conditions of innovative and technological development of society: monograph (manuscript)].14.Filippov, L.I. (2018) Shestoy tekhnologicheskiy uklad: yego osobennosti i mesto pedagogiki v nom [Sixth technological structure: its features and the place of pedagogy in it].15. Sadovyi Mykola (2019).Digitization of the experiment in natural sciences as a means of information and digital competence formation of specialists in professional education.Katowice.16.Tryfonova Olena (2019).Development of information and digital competence of future specialists of computer technologies in the study of the physical
Manifold-Learning-Based Feature Extraction

Manifold-learning-based algorithms have widely studied in the last two decades and have been considered as powerful tools for feature learning. The theory of differential geometry shows that the intrinsic geometry of manifolds can be fully determined by the local metric and the infinitesimal neighborhood information. In this chapter, we give a brief introduction to manifold learning and then mainly focus on two linear manifold-learning-based feature extraction methods, neighborhood preserving embedding (NPE) and locality preserving projection (LPP). Different from principal component analysis (PCA), both NPE and LPP aim to preserve the local manifold structure of the data. NPE can be viewed as a linear approximation to locally linear embedding (LLE), while LPP can be considered as a linear approximation to Laplacian Eigenmap. Both NPE and LPP can naturally evaluate the obtained transformations on new data points to obtain low-dimensional features. For LPP, we also show its relationship to PCA.
DEVICE FOR EJECTING MULTIPLE STREAMS

The article considers an original device for ejecting multiple streams, based on the principle of flow distribution in a slot gap and designed to increase the degree of rarefaction of ejected liquid and gaseous media.
Quantitative assessment of distant recurrence risk in early stage breast cancer using a nonlinear combination of pathological, clinical and imaging variables

Abstract Use of genomic assays to determine distant recurrence risk in patients with early stage breast cancer has expanded and is now included in the American Joint Committee on Cancer staging manual. Algorithmic alternatives using standard clinical and pathology information may provide equivalent benefit in settings where genomic tests, such as OncotypeDx, are unavailable. We developed an artificial neural network (ANN) model to nonlinearly estimate risk of distant cancer recurrence. In addition to clinical and pathological variables, we enhanced our model using intraoperatively determined global mammographic breast density (MBD) and local breast density (LBD). LBD was measured with optical spectral imaging capable of sensing regional concentrations of tissue constituents. A cohort of 56 ER+ patients with an OncotypeDx score was evaluated. We demonstrated that combining MBD/LBD measurements with clinical and pathological variables improves distant recurrence risk prediction accuracy, with high correlation ( r = 0.98) to the OncotypeDx recurrence score.
The Dynamic Epistemic Logic for Actual Knowledge

The dynamic epistemic logic for actual knowledge models the phenomenon of actual knowledge change when new information is received. In contrast to the systems of dynamic epistemic logic which have been discussed in the past literature, our system is not burdened with the problem of logical omniscience, that is, an idealized assumption that the agent explicitly knows all classical tautologies and all logical consequences of his or her knowledge. We provide a sound and complete axiomatization for this logic.
Modular Termination for Second-Order Computation Rules and Application to Algebraic Effect Handlers

We present a new modular proof method of termination for second-order computation, and report its implementation SOL. The proof method is useful for proving termination of higher-order foundational calculi. To establish the method, we use a variation of semantic labelling translation and Blanqui's General Schema: a syntactic criterion of strong normalisation. As an application, we apply this method to show termination of a variant of call-by-push-value calculus with algebraic effects and effect handlers. We also show that our tool SOL is effective to solve higher-order termination problems.
Facilitating Physics Classroom Instruction Through the Use of Modern Electronic Device

The use of modern electronic devices (MED) in teaching and learning in classrooms has become imperative especially at this period of Covid-19 pandemic. This necessitated this paper which is to show the uses of Modern Electronic Device (MED) as an educational tool for impacting and prospecting in Physics Education and to highlight a few devices that can be used in the areas of Reading and Writing. Through the use of low-tech and high-tech devices, MED will be able to help students become better readers and writers. In the area of Reading, the use of MED will help students improve upon and build on their spelling abilities, and also better students’ decoding, listening, and oral skills. In the area of Writing, the use of MED will enable students to write neatly and legibly, to form letters correctly, and to write sentences that are grammatically correct. Electronic-learning tools such as internet, computers, e-mail facilities, multimedia, scanner, printer, VCD player and digital camera were discussed. The prospects of electronic learning in secondary schools were identified. The paper conclude that if teachers plan carefully and use modern electronic devices to enhance instruction throughout the school curriculum, Basic Science, reading and writing will not be the only subjects that students will benefit. It was suggested among other things that, the government should embark on computer training program for teachers. Teachers should be trained and retrained through in-service training, seminars, workshops and conferences for acquisition of the knowledge and skills needed for e-learning application in secondary schools in Nigeria. It was suggested that teachers need also to explore other avenues in their instructional curriculum where they can enhance learning through the use of MED.
Low Rank Feature Learning

Low-rank representation (LRR), which constructs a robust low rank representation for data processing, has attracted much attention in the past decades. It is assumed that the data points lie on a low-dimensional subspace and the representation matrix of the data points is low-rank. In real applications, the collected data might be corrupted by noises or errors. The low-rank learning methods can recover the noisy data and capture the global information of the data. In this chapter, we introduce the low-rank learning methods and their extensions. The main idea, objective function, and optimization of these methods are introduced in detail. Finally, we make a numerical experiment on two publicly image databases to verify the performance of these methods. It is shown that the LRE and LRLE converge very fast and obtain competitive performances against other methods. However, how to further increase performances is still a challenging problem.
REFLECTING ON VIOLENCE. ABOUT CLASSIC THEMES AND CONTEMPORARY ISSUES

Arguing about violence means dealing with a classic theme which, however, today, with the complicity of new technologies, has taken on more nuanced faces and underground forms. Virtualized and dematerialized, today’s violence occurs above all in that particular cocooning that is Cyberspace, forcing the jurist to deal with controversial issues ranging from cyber-terrorism to hate speech.