Reviewerly/exHarmony huggingface dataset:
Gradient Methods for Solving Stackelberg Games

Stackelberg Games are gaining importance in the last years due to the raise of Adversarial Machine Learning (AML). Within this context, a new paradigm must be faced: in classical game theory, intervening agents were humans whose decisions are generally discrete and low dimensional. In AML, decisions are made by algorithms and are usually continuous and high dimensional, e.g. choosing the weights of a neural network. As closed form solutions for Stackelberg games generally do not exist, it is mandatory to have efficient algorithms to search for numerical solutions. We study two different procedures for solving this type of games using gradient methods. We study time and space scalability of both approaches and discuss in which situation it is more appropriate to use each of them. Finally, we illustrate their use in an adversarial prediction problem.
Human-Computer Insurrection: Notes on an Anarchist HCI

The HCI community has worked to expand and improve our consideration of the societal implications of our work and our corresponding responsibilities. Despite this increased engagement, HCI continues to lack an explicitly articulated politic, which we argue re-inscribes and amplifies systemic oppression. In this paper, we set out an explicit political vision of an HCI grounded in emancipatory autonomy - an anarchist HCI, aimed at dismantling all oppressive systems by mandating suspicion of and a reckoning with imbalanced distributions of power. We outline some of the principles and accountability mechanisms that constitute an anarchist HCI. We offer a potential framework for radically reorienting the field towards creating prefigurative counterpower - systems and spaces that exemplify the world we wish to see, as we go about building the revolution in increment.
Spolni stereotipi v informativno-zabavnih spletnih straneh in revijah

Stereotypes are an integral part of processing information; therefore, they belong to the human mental world, which co-shapes the social world, including by means of media texts with informational and entertainment content and communicative intent. Despite third-wave feminism, gender stereotypes can still often be found in these texts, thus maintaining the binarity of both genders and their characteristic (social) roles.
Search Algorithms for Mastermind

his paper presents two novel approaches to solving the classic board game mastermind, including a variant of simulated annealing (SA) and a technique we term maximum expected reduction in consistency (MERC). In addition, we compare search results for these algorithms to two baseline search methods: a random, uninformed search and the method of minimizing maximum query partition sets as originally developed by both Donald Knuth and Peter Norvig.
RFID security system in libraries: An overview

Circulation and racking of the reading materials in a library is a significant manageable work which takes more often than not of the library staff. RFID gives a clarification for such an issue, by reducing the measure of the time required to perform circulation functions. The paper covers the parts and specialized highlights of a cutting-edge RFID library framework, its points of interest and issues identified with the utilization of RFID in libraries.
Estimating Local Function Complexity via Mixture of Gaussian Processes

Real world data often exhibit inhomogeneity, e.g., the noise level, the sampling distribution or the complexity of the target function may change over the input space. In this paper, we try to isolate local function complexity in a practical, robust way. This is achieved by first estimating the locally optimal kernel bandwidth as a functional relationship. Specifically, we propose Spatially Adaptive Bandwidth Estimation in Regression (SABER), which employs the mixture of experts consisting of multinomial kernel logistic regression as a gate and Gaussian process regression models as experts. Using the locally optimal kernel bandwidths, we deduce an estimate to the local function complexity by drawing parallels to the theory of locally linear smoothing. We demonstrate the usefulness of local function complexity for model interpretation and active learning in quantum chemistry experiments and fluid dynamics simulations.
Cryptanalysis of Homomorphic Encryption Schemes based on the Aproximate GCD Problem

Economies of scale make cloud computing an attractive option for small and medium enterprises. However, loss of data integrity or data theft remain serious concerns. Homomorphic encryption which performs computations in the encrypted domain is a possible solution to address these concerns. Many partially homomorphic encryption schemes that trade off functionality for lower storage and computation cost have been proposed. However, not all these schemes have been adequately investigated from the security perspective. This paper analyses a suite of such proposed schemes based on the hardness of the Approximate GCD problem. We show that two of these schemes are vulnerable to the Orthogonal Lattice attack. The execution time of the attack is a function of various parameters including message entropy. For the recommended set of parameters, the execution time of the attack is no greater than 1 day on a regular laptop.
An Investigation of Classroom Management Strategy ( A Case Study of Tenth Grade English Teachers at SMAN 2 Kota Bengkulu)

The objective of this research was to find out the classroom management strategies implemented by the English teachers and the reason in applying the most often strategy than others strategies. This study employed the descriptive qualitative method, the subject of the research were the English teachers (2 persons) at SMAN 2 Kota Bengkulu.. The data was collected by using observations checklist and interview. The result of the research revealed there are 5 strategies used by the teachers in managing the classroom; 1) Strategies to arrange the classroom, 2) Strategies to build positive relation with the student, 3) Strategies to create positive classroom environment, 4) Strategies to prevent misbehavior and5) Strategies to handle the students’ discipline situation. In 5 meetings the total aspects of the first strategy applied by the teacher 1 and teacher 2 was about 33 and 37. The second strategy was 30 and 31. The third strategy was 26 and 24. The fourth strategy was 25 and 28. The last strategy was 25 and 24. Furthermore, the reasons of why the teachers applying the most often strategy than others strategies because it helps the teachers in the teaching and learning process to be more conducive. Beside that the strategies also help the teachers to create an effective teaching and learning process. Therefore, it could be concluded that the English teachers of SMAN 2 Kota Bengkulu used all the 5 strategies asmentioned by Marzano (2003) in managing classroom but not all aspects they used in teaching and learning process. The dominant strategy implemented by Teacher I and Teacher 2 were strategy to arrange the classroom. Meanwhile, the least strategy implemented by both teachers were the strategy to handle the students discipline situation.
Consolidate Your Library's Data using Google Data Studio & Alma Analytics API Connector

This session presents the implementation of a shared and straightforward data consolidation platform using the free Google Data Studio dashboards and API connection to Alma Analytics.Our initial objectives were:Facilitate the use of data from isolated data sourcesEncourage evidence-based decision-makingCommunicate how UM Libraries’ activities support student learning and faculty researchBenefit from data in a consistent, library-wide mannerImplement a data consolidation solution tool that is (1) familiar, (2) flexible, (3) enables customized sharing options, and (4) reasonably pricedGoogle Data Studio’s core function is creating visual dashboards, where users can interpret multiple and diverse data sources in a clear and visually engaging manner. Data Studio can automatically ingest data from either pre-built data connectors (Google Analytics, MySQL, and Google Sheets) or any source with an API, allowing for real-time dashboards that aggregate information from various sources. All data sources can be easily imported manually as well.As part of the initiative, an Alma Analytics API connector to Google Data Studio infrastructure was built and deployed. A “flexible schema” type of Data Studio Connector was chosen, enabling connection to datasets where the structure of the data is unknown. This ensures access to all Alma Analytics subject areas and fields. Now statistics from Alma Analytics, Google Analytics, and many other University of Miami Libraries’ data sources are filtered and displayed into a single report or even a single chart.
An Alternative Model for Motivation in the Academe: An Exploratory Factor Analysis

What works best to motivate employee and engage them toward commitment have been an unending inquiry in the workplace. The interest in the quest for â€žappropriateâ€Ÿ motivation models is strengthened by widespread assumptions that a motivated workforce is a critical antecedent to the organizationâ€Ÿs productivity. This study verified Haefnerâ€Ÿs Fourth Theory of Motivation further after having used it as a program theory in redesigning the faculty evaluation scheme project of the University five years back where it saw that when team members are engaged, they contributed to the substantial outcome of the project. The first stage of this study has been completed after having classified the qualitative responses of 100 professionals in the academe. Using the guidelines of Heppner and Heppner (2004), the responses were analyzed into core ideas and were categorized as general, typical, and variant. Core ideas were fitted into the constructs within each of the three- motivation queues: leadership, work environment and individual psychology . Results of the qualitative responses confirmed most of the constructs in the motivational queues.Â Â The second stage of this study developed scales for each motivational queue. The scales were floated to 300 academics of three HEIs to test the validity and reliability of the constructs in the subsystems using exploratory factor analysis. Factors were clustered and confirmed most of the constructs in Haefnerâ€Ÿs motivational queues. To further validate the theoretical assumptions of the scales, confirmatory factor analysis is recommended. Keywords : Motivation, leadership, work environment, individual psychology
Peranan LAPER BE-ON (Layanan Perpustakaan On line) untuk pendidikan jarak jauh dalam era globalisasi

This journal discusses how be-on services (online libraries) initiated by researchers can influence distance learning which was developed as a form of adaptation to the age of globalization. This distance education requires an interactive telecommunication system to connect the two and various resources needed in it. The learning done in this distance education program is through online which specifically combines electronic technology and internet-based technology. Through this library service, students can obtain information easily and practically and can conduct meetings virtually by utilizing information technology. besides being able to meet lecture meetings with no face-to-face meetings, students can also obtain information on assignments given and can obtain books, journals or other media that can be accessed through online library services using the internet.
Research on Intelligent Writing Poetry Model Based on Neural Network

Poetry is a special and important cultural heritage in human history. The arrival of the era of big data and the development of deep learning technology have promoted the innovation of algorithm design ideas in the field of artificial intelligence. Deep learning has also developed greatly in poetry generation technology. Even to a certain extent, poetry produced may be difficult for ordinary people to distinguish. However, at present, poetry generation technology still focus on poetry avoid of thoughts and feelings and human spirituality. The work of machine poetry began in the 1970s. The traditional method of poetry generation relies heavily on the professional knowledge of the field of poetry. It requires experts to design a large number of artificial rules to constrain the rhythm and quality of the generated poetry. With the development of deep learning technology, the study of poetry generation has entered a new stage. Many methods of poetry generation based on deep learning have been proposed, such as the method based on RNN language model, the framework based on encoder-decoder, and the method of poetry generation based on GAN. This paper needs to modify this reality from human writing. Based on the iterative polishing mechanism proposed by Rui Yan et al., it adds a more perfect poetry evaluation system and proposes intelligent writing based on deep learning that can be automatically modified for multiple times. The poetry model, after trial and manual evaluation, proves that the method has certain effects.
Keefektifan Model Pembelajaran Tipe Think Pair Share terhadap Prestasi Belajar Matematika Ditinjau dari Self Efficacy Siswa

This research is aimed to know: (1) Which learning did give better mathematical achievement, cooperative learning which is typed Think Pair Share (TPS) or conventional learning. (2) Did the students with higher self efficacy have better learning achievement in mathematics than the students don’t. (3) Are there any differences in learning achievement in mathematics from the students with higher self efficacy and the students with lower self efficacy in TPS learning model and conventional learning model. This research is quasi-experimental. The populations are all students grade X mechanical automotive in Sukoharjo region. The technique of data analysis used is test analysis variation two ways with unequal cells. The conclusions are that: (1) TPS learning model produced better learning achiement than conventional model; (2) there are any differences learning achievement in mathematics from the sudents that have higher self efficacy, averege self efficacy and lower self efficacy; (3) There is no interaction between learning model with the students’ sefl efficacy toward learning achievement in mathematics.
A Comparative Study between Collaborative Filtering Techniques and Generate Personalized Story Recommendations for the Vixio Application

Interactive fiction (or text-based game) is a game that consists of texts which are used to bring interactivity to a story. Interactive fiction shows the potential to improve reading behaviour and engage the player with reading materials. In continuing to explore more benefits in reading, creating, and sharing interactive fiction, a web application called Vixio is developed as a platform, where users can develop and distribute interactive fiction. To engage and to feed the users with more interactive stories, a recommender system is applied to provide recommendations of stories that would be suitable to the reader’s interest. This paper is focused on developing a recommender system which can generate personalized story recommendations for the Vixio web application. This paper also discusses determining which techniques are better to be implemented inside the recommender system by conducting a comparative study between five collaborative filtering techniques, which are: Three Matrix Factorizations (SVD, SVD++, and NMF), Slope One, and Co-clustering. To compare each technique with one another, 5-fold cross-validation and response time were measured. Based on these two evaluations, it is shown that there is no technique which has a superior accuracy over the others. However, Slope One algorithm is eminent in terms of fit time and mean response time.
Yewno Discover: Make Your Content Discoverable and Visualise Your Repository

Meet Yewno Discover, the AI-powered platform which uses machine learning to harness millions of semantic connections across interdisciplinary topics, and draws from over 200 million scholarly books and journals. Because Discover is able to connect multiple concepts within the same ongoing visual search, an environment of serendipity fuels user engagement for an exciting and deep research experience. Yewno Discover users have compared the difference between traditional and Discover as the difference between black-and-white and color - or going from two dimensions to 3-D. Yewno Co-Founder Ruth Pickering will explain how you can introduce this new discovery tool easily into your existing environment and also use it to visualize your institutional repository, so that you can see who's published on what, and direct future research funding strategically. Specifically, Discover's widget can be integrated into Ex Libris' search tools to provide a visual search environment.
A narrative move towards the exploration of gauging of image quality

Image quality assessment (IQA) acting as a noteworthy part in a variety of image processing applications. Manipulative eminence of an image is essential predicament in image and record handling and a range of procedure have been anticipated for IQA.widespread psychological substantiation shows that humans favor to conduct evaluations qualitatively comparative than numerical. However most frequently used IQA metrics are not reliable fine with the individual judgments of image quality. For the majority of the applications, the perceptual momentous compute is the one which can routinely estimate the worth of images or videos involving reliable behavior. This article explains about the various methods and their behavior towards the assessment of image quality.
Asymptotic Enumeration of Binary Orthogonal Arrays*

Orthogonal arrays are basic combinatorial structures, which appear in various cryptographic applications designed to ensure information security in electronic financial services.Asymptotic estimates for the number of orthogonal arrays are proved in this paper.
Signal denoising based on the Schrödinger operator's eigenspectrum and a curvature constraint

Recently, a new Signal processing method, named Semi-Classical Signal Analysis (SCSA), has been proposed for denoising Magnetic Resonance Spectroscopy (MRS) signals. It is based on the Schr\"odinger Operator's eigenspectrum. It allows an efficient noise reduction while preserving MRS signal's peaks. In this paper, we propose to extend this approach to different signals, in particular pulse shaped signals, by including an optimization that considers curvature constraints. The performance of the method is measured by analyzing noisy signal data and comparing with other denoising methods. Results indicate that the proposed method not only produces good denoising performance but also guarantees the peaks are well preserved in the denoising process.
PENERAPAN MODEL COOPERATIVE LEARNING TIPE NUMBER HEAD TOGETHER DALAM PEMBELAJARAN PPKN UNTUK MENINGKATKAN PARTISIPASI BELAJAR PESERTA DIDIK: Penelitian Tindakan Kelas Terhadap Peserta Didik Kelas XI IPS 3 SMAN 7 Bandung

At present the learning process emphasizes the focus on students, the teacher in the current learning process is only as a facilitator, so the teacher's role is important in facilitating students to be able to participate in the learning process, so the teacher must be more creative in planning learning because what is found in school there is still a lot of learning that is done as it is, where the learning process that is carried out is only transferring information from the teacher to students, thus making students become passive and less participating. So to overcome these problems applied the Cooperative Learning Models Type Number Head Together in the learning process to increase student learning participation. This study uses a qualitative approach with classroom action research methods, the purpose of classroom action research is to improve and improve the learning process. The stages in this study include planning, action, observation and reflection. Data obtained in this study include teacher observation sheets, and student observation sheets. The results of the observation indicators of student participation in the action of cycle 1 obtained the category less, in the action of cycle 2 the category good and the action of cycle 3 obtained the category very good. Based on the results of observations it can be concluded that applying Cooperative Learning Models Type Number Head Together in PPKn learning can increase student learning participation.
EyeNet: A Multi-Task Network for Off-Axis Eye Gaze Estimation and User Understanding

Eye gaze estimation and simultaneous semantic understanding of a user through eye images is a crucial component in Virtual and Mixed Reality; enabling energy efficient rendering, multi-focal displays and effective interaction with 3D content. In head-mounted VR/MR devices the eyes are imaged off-axis to avoid blocking the user's gaze, this view-point makes drawing eye related inferences very challenging. In this work, we present EyeNet, the first single deep neural network which solves multiple heterogeneous tasks related to eye gaze estimation and semantic user understanding for an off-axis camera setting. The tasks include eye segmentation, blink detection, emotive expression classification, IR LED glints detection, pupil and cornea center estimation. To train EyeNet end-to-end we employ both hand labelled supervision and model based supervision. We benchmark all tasks on MagicEyes, a large and new dataset of 587 subjects with varying morphology, gender, skin-color, make-up and imaging conditions.
Comparison of Shadow Detection based on HSV and YCbCr Color Space

The shadow detection of moving vehicle is a prominent task for all system of vision by computer. Therefore, this study is analyzed the image pixel values of shadows and vehicles based on HSV and YC b C r color spaces and is compared these two color models for getting higher shadow detection rate. The HSV and YC b C r color spaces are evaluated by Thresholding Method using the MATLAB programming. The foreground and background objects are detected by using HSV and YC b C r Color Space. According to the result, The HSV color Space is detected shadows more effectively than YC b C r even though applying auto Thresholding Method in both color spaces.
THE ECONOMICS OF SOFTWARE 2.0

Software 2.0 is completely focused on the expected behavior of a program, writing an application being about training a neural network rather than writing the code line by line.The programmers of tomorrow will most probably be data scientists rather than code writers.Artificial intelligence will very soon be accountable for the execution of the most of our repetitive tasks, while the humans will use their creativity for complex assignments for a greater economic value and an enhanced level of productivity.The first place in any ranking related to the artificial intelligence frameworks and libraries is occupied by TensorFlow.From an economic point of view, TensorFlow is one of the best choices for companies and individuals, having a significant contribution for the better world of tomorrow.