nils-herrmann/clean_scirepeval huggingface dataset:
Representation Sharing for Prolog

Representation sharing can reduce the memory footprint of a program by sharing one representation between duplicate terms. The most common implementation of representation sharing in functional programming systems is known as hash-consing. In the contex t of Prolog, representation sharing has been given little attention. Some current techniques th at deal with representation sharing are reviewed. The new contributions are: (1) an easy implementa tio of input sharingfor findall/3; (2) a description of asharermodule that introduces representation sharing at runtime. Th ir realization is shown in the context of the WAM as implemented by hProlog. B oth can be adapted to any WAMlike Prolog implementation. The sharer works independentl y of the garbage collector, but it can be made to cooperate with the garbage collector. Benchmark res ults how that the sharer has a cost comparable to the heap garbage collector, that its effectiv ness is highly application dependent, and that its policy must be tuned to the collector.
Design of a Blockchain-Based Lottery System for Smart Cities Applications

Among the smart cities applications, optimizing lottery games is one of the urgent needs to ensure their fairness and transparency. The emerging blockchain technology shows a glimpse of solutions to fairness and transparency issues faced by lottery industries. This paper presents the design of a blockchain-based lottery system for smart cities applications. We adopt the smart contracts of blockchain technology and the cryptograph blockchain model, Hawk , to design the blockchain-based lottery system, FairLotto, for future smart cities applications. Fairness, transparency, and privacy of the proposed blockchain-based lottery system are discussed and ensured.
Exploiting Emotion on Reviews for Recommender Systems

Review history is widely used by recommender systems to infer users’ preferences and help find the potential interests from the huge volumes of data, whereas it also brings in great concerns on the sparsity and cold-start problems due to its inadequacy. Psychology and sociology research has shown that emotion information is a strong indicator for users’ preferences. Meanwhile, with the fast development of online services, users are willing to express their emotion on others’ reviews, which makes the emotion information pervasively available. Besides, recent research shows that the number of emotion on reviews is always much larger than the number of reviews. Therefore incorporating emotion on reviews may help to alleviate the data sparsity and cold-start problems for recommender systems. In this paper, we provide a principled and mathematical way to exploit both positive and negative emotion on reviews, and propose a novel framework MIRROR, exploiting eMotIon on Reviews for RecOmmendeR systems from both global and local perspectives. Empirical results on real-world datasets demonstrate the effectiveness of our proposed framework and further experiments are conducted to understand how emotion on reviews works for the proposed framework.
Health-aware Food Recommender System

With the rapid changes in the food variety and lifestyles, many people are facing the problem of making healthier food decisions to reduce the risk of chronic diseases such as obesity and diabetes. To this end, our recommender system not only offers recipe recommendations that suit the user's preference but is also able to take the user's health into account. It is developed on a mobile platform by considering that our application may be directly used in the kitchen. This demo paper summarizes the complete human-computer interaction design, the implemented health-aware recommendation algorithm and preliminary user feedback.
A Simple yet Effective Model for Zero-Shot Learning

Zero-shot learning has tremendous application value in complex computer vision tasks, e.g. image classification, localization, image captioning, etc., for its capability of transferring knowledge from seen data to unseen data. Many recent proposed methods have shown that the formulation of a compatibility function and its generalization are crucial for the success of a zero-shot learning model. In this paper, we formulate a softmax-based compatibility function, and more importantly, propose a regularized empirical risk minimization objective to optimize the function parameter which leads to a better model generalization. In comparison to eight baseline models on four benchmark datasets, our model achieved the highest average ranking. Our model was effective even when the training set size was small and significantly outperforming an alternative state-of-the-art model in generalized zero-shot recognition tasks.
To Link or Not to Link? A Study on End-to-End Tweet Entity Linking

Information extraction from microblog posts is an important task, as today microblogs capture an unprecedented amount of information and provide a view into the pulse of the world. As the core component of information extraction, we consider the task of Twitter entity linking in this paper. In the current entity linking literature, mention detection and entity disambiguation are frequently cast as equally important but distinct problems. However, in our task, we find that mention detection is often the performance bottleneck. The reason is that messages on micro-blogs are short, noisy and informal texts with little context, and often contain phrases with ambiguous meanings. To rigorously address the Twitter entity linking problem, we propose a structural SVM algorithm for entity linking that jointly optimizes mention detection and entity disambiguation as a single end-to-end task. By combining structural learning and a variety of firstorder, second-order, and context-sensitive features, our system is able to outperform existing state-of-the art entity linking systems by 15% F1.
Development of Employee Attendance and Payroll System using Fingerprint Biometrics

Biometric technology offers an advanced verification of human identity used in most schools and companies for recording the daily attendance (login and logout) and generating the payroll of the employees. This study uses the biometric technology to address the problems of many companies or institutions such as employees doing the proxy attendance for their colleagues, stealing company time, putting in more time in the daily time record (DTR), and increasing the amount of gross payroll resulted of buddy punching. The researcher developed a system for employee’s attendance and processing of payroll with the use of fingerprint reader and the webcam device. The employee uses one finger to record his or her time of arrival and departure from the office through the use of the fingerprint reader. The DTR of employees is recorded correctly by the system; the tardiness and under time in the morning and in the afternoon of their official time is also computed. The system was developed using the Microsoft Visual C# 2008 programming language, MySQL 5.1 database software, and Software Development Kit (SDK) for the fingerprint reader and the webcam device. The data were analyzed using the percentage technique and arithmetic mean. The study was tested for 30 employees using the fingerprint reader for biometric fingerprint scanning (login and logout), and 50 employees were recorded and used for processing the payroll, and the proposed system. Results of biometric fingerprint scanning for the login and logout revealed that 90% of the employees have been accepted for the first attempt, 5.84% for the second attempt, 3.33% and 0.83% for the third and more than four attempts, respectively. The result of processing the advanced payroll (permanent, substitute, temporary & casual employees) and regular payroll (job order and contract of service employees) is 17.07 s and 5.08 s respectively. The Employee Attendance and Payroll System (EAPS) showed that the verification and identification of the employees in the school campus using the biometric technology provides a reliable and accurate recording in the daily attendance, and generate effectively the monthly payroll.
A dissociation between social mentalizing and general reasoning

It has recently been suggested that brain areas crucial for mentalizing, including the medial prefrontal cortex (mPFC), are not activated exclusively during mentalizing about the intentions, beliefs, morals or traits of the self or others, but also more generally during cognitive reasoning including relational processing about objects. Contrary to this notion, a meta-analysis of cognitive reasoning tasks demonstrates that the core mentalizing areas are not systematically recruited during reasoning, but mostly when these tasks describe some human agency or general evaluative and enduring traits about humans, and much less so when these social evaluations are absent. There is a gradient showing less mPFC activation as less mentalizing content is contained in the stimulus material used in reasoning tasks. Hence, it is more likely that cognitive reasoning activates the mPFC because inferences about social agency and mind are involved.
Bilingualism protects anterior temporal lobe integrity in aging

Cerebral gray-matter volume (GMV) decreases in normal aging but the extent of the decrease may be experience-dependent. Bilingualism may be one protective factor and in this article we examine its potential protective effect on GMV in a region that shows strong age-related decreases-the left anterior temporal pole. This region is held to function as a conceptual hub and might be expected to be a target of plastic changes in bilingual speakers because of the requirement for these speakers to store and differentiate lexical concepts in 2 languages to guide speech production and comprehension processes. In a whole brain comparison of bilingual speakers (n = 23) and monolingual speakers (n = 23), regressing out confounding factors, we find more extensive age-related decreases in GMV in the monolingual brain and significantly increased GMV in left temporal pole for bilingual speakers. Consistent with a specific neuroprotective effect of bilingualism, region of interest analyses showed a significant positive correlation between naming performance in the second language and GMV in this region. The effect appears to be bilateral though because there was a nonsignificantly different effect of naming performance on GMV in the right temporal pole. Our data emphasize the vulnerability of the temporal pole to normal aging and the value of bilingualism as both a general and specific protective factor to GMV decreases in healthy aging.
An Introduction to Adversarial Machine Learning

Machine learning based system are increasingly being used for sensitive tasks such as security surveillance, guiding autonomous vehicle, taking investment decisions, detecting and blocking network intrusion and malware etc. However, recent research has shown that machine learning models are venerable to attacks by adversaries at all phases of machine learning (e.g., training data collection, training, operation). All model classes of machine learning systems can be misled by providing carefully crafted inputs making them wrongly classify inputs. Maliciously created input samples can affect the learning process of a ML system by either slowing the learning process, or affecting the performance of the learned model or causing the system make error only in attacker’s planned scenario. Because of these developments, understanding security of machine learning algorithms and systems is emerging as an important research area among computer security and machine learning researchers and practitioners. We present a survey of this emerging area named Adversarial machine learning.
A STATISTICAL MODEL OF CRIMINAL BEHAVIOR

M. B. SHORT∗, M. R. D’ORSOGNA†, V. B. PASOUR∗, G. E. TITA‡, P. J. BRANTINGHAM§, A. L. BERTOZZI∗ and L. B. CHAYES∗ ∗Department of Mathematics, University of California — Los Angeles, Los Angeles, CA 90095, USA †Department of Mathematics, California State University — Northridge, Los Angeles, CA 91330, USA ‡Department of Criminology, Law & Society, University of California — Irvine, Irvine, CA 92697, USA §Department of Anthropology, University of California — Los Angeles, Los Angeles, CA 90095, USA
Machine Learning Techniques in Clinical Vision Sciences.

This review presents and discusses the contribution of machine learning techniques for diagnosis and disease monitoring in the context of clinical vision science. Many ocular diseases leading to blindness can be halted or delayed when detected and treated at its earliest stages. With the recent developments in diagnostic devices, imaging and genomics, new sources of data for early disease detection and patients' management are now available. Machine learning techniques emerged in the biomedical sciences as clinical decision-support techniques to improve sensitivity and specificity of disease detection and monitoring, increasing objectively the clinical decision-making process. This manuscript presents a review in multimodal ocular disease diagnosis and monitoring based on machine learning approaches. In the first section, the technical issues related to the different machine learning approaches will be present. Machine learning techniques are used to automatically recognize complex patterns in a given dataset. These techniques allows creating homogeneous groups (unsupervised learning), or creating a classifier predicting group membership of new cases (supervised learning), when a group label is available for each case. To ensure a good performance of the machine learning techniques in a given dataset, all possible sources of bias should be removed or minimized. For that, the representativeness of the input dataset for the true population should be confirmed, the noise should be removed, the missing data should be treated and the data dimensionally (i.e., the number of parameters/features and the number of cases in the dataset) should be adjusted. The application of machine learning techniques in ocular disease diagnosis and monitoring will be presented and discussed in the second section of this manuscript. To show the clinical benefits of machine learning in clinical vision sciences, several examples will be presented in glaucoma, age-related macular degeneration, and diabetic retinopathy, these ocular pathologies being the major causes of irreversible visual impairment.
On cryptographic properties of LFSR-based pseudorandom generators

Pseudorandom generators (PRGs) are used in modern cryptography to transform a small initial value into a long sequence of seemingly random bits. Many designs for PRGs are based on linear feedback shift registers (LFSRs), which can be constructed in such a way as to have optimal statistical and periodical properties. This thesis discusses construction principles and cryptanalytic attacks against LFSR-based PRGs. After providing a full survey of existing cryptanalytical results, we introduce and analyse the dynamic linear consistency test (DLCT), a search-tree based method for reconstructing the inner state of a PRG. We conclude by discussing the role of the inner state size in PRG design, giving lower bounds as well as examples from practice that indicate the necessary size of a secure PRG.
Learning from Paper, Learning from Screens: Impact of Screen Reading and Multitasking Conditions on Reading and Writing among College Students

Electronic screens on laptop and tablet computers are being used for reading text, often while multitasking. Two experimental studies with college students explored the effect of medium and opportunities to multitask on reading (Study 1) and report writing (Study 2). In study 1, participants (N = 120) read an easy and difficult passage on paper, a laptop, or tablet, while either multitasking or not multitasking. Neither multitasking nor medium impacted reading comprehension, but those who multitasked took longer to read both passages, indicating loss of efficiency with multitasking. In Study 2, participants (N = 67) were asked to synthesize source material in multiple texts to write a one-page evidence-based report. Participants read the source texts either on (1) paper, (2) computer screen without Internet or printer access, or (3) computer screen with Internet and printer access (called the “real-world” condition). There were no differences in report quality or efficiency between those whose source materials were paper or computer. However, global report quality was significantly better when participants read source texts on a computer screen without Internet or printer access, compared with when they had Internet and printer access. Active use of paper for note-taking greatly reduced the negative impact of Internet and printer access in the real-world condition. Although participants expressed a preference for accessing information on paper, reading the texts on paper did not make a significant difference in report quality, compared with either of the two computer conditions. Implications for formal and informal learning are discussed.
A Strong Lexical Matching Method for the Machine Comprehension Test

Machine comprehension of text is the overarching goal of a great deal of research in natural language processing. The Machine Comprehension Test (Richardson et al., 2013) was recently proposed to assess methods on an open-domain, extensible, and easy-to-evaluate task consisting of two datasets. In this paper we develop a lexical matching method that takes into account multiple context windows, question types and coreference resolution. We show that the proposed method outperforms the baseline of Richardson et al. (2013), and despite its relative simplicity, is comparable to recent work using machine learning. We hope that our approach will inform future work on this task. Furthermore, we argue that MC500 is harder than MC160 due to the way question answer pairs were created.
Invariant Information Distillation for Unsupervised Image Segmentation and Clustering

We present a new method that learns to segment and cluster images without labels of any kind. A simple loss based on information theory is used to extract meaningful representations directly from raw images. This is achieved by maximising mutual information of images known to be related by spatial proximity or randomized transformations, which distills their shared abstract content. Unlike much of the work in unsupervised deep learning, our learned function outputs segmentation heatmaps and discrete classifications labels directly, rather than embeddings that need further processing to be usable. The loss can be formulated as a convolution, making it the first end-to-end unsupervised learning method that learns densely and efficiently (i.e. without sampling) for semantic segmentation. Implemented using realistic settings on generic deep neural network architectures, our method attains superior performance on COCO-Stuff and ISPRS-Potsdam for segmentation and STL for clustering, beating state-of-the-art baselines.
Spacing Repetitions Over Long Timescales: A Review and a Reconsolidation Explanation

Recent accounts of the spacing effect have proposed molecular explanations that explain spacing over short, but not long timescales. In the first half of this paper, we review research on the spacing effect that has employed spaces of 24 h or more across skill-related tasks, language-related tasks and generalization for adults and children. Throughout this review, we distinguish between learning and retention by defining learning (or acquisition) as performance at the end of training and retention as performance after a delay period. Using this distinction, we find age- and task-related differences in the manifestation of the spacing effect over long timescales. In the second half of this paper, we discuss a reconsolidation account of the spacing effect. In particular, we review the evidence that suggests the spacing of repetitions influences the subsequent consolidation and reconsolidation processes; we explain how a reconsolidation account may explain the findings for learning; the inverted-U curve for retention; and compare the reconsolidation account with previous consolidation accounts of the spacing effect.
Design and control of a gas-electric hybrid quad tilt-rotor UAV with morphing wing

In this work, design and control system development of a gas-electric hybrid quad tilt-rotor UAV with morphing wing are presented. The proposed aircraft has an all carbon-composite body, gas-electric hybrid electric generation system for 3 hours hovering or up to 10 hours of horizontal flight, a novel configuration for VTOL and airplane-like flights with minimized aerodynamic costs and mechanical morphing wings for both low speed and high speed horizontal flights. The mechanical design of the vehicle is performed to achieve a strong and light-weight structure, whereas the aerodynamic and propulsion system designs are aimed for accomplishing both fixed wing and rotary wing aircraft flights with maximized flight endurance. A detailed dynamic model of the aerial vehicle is developed including the effects of tilting rotors, variable fuel weight, and morphing wing lift-drag forces and pitching moments. Control system is designed for both flight regimes and flight simulations are carried out to check the performance of the proposed control system.
Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data

We introduce Deep Variational Bayes Filters (DVBF), a new method for unsupervised learning of latent Markovian state space models. Leveraging recent advances in Stochastic Gradient Variational Bayes, DVBF can overcome intractable inference distributions by means of variational inference. Thus, it can handle highly nonlinear input data with temporal and spatial dependencies such as image sequences without domain knowledge. Our experiments show that enabling backpropagation through transitions enforces state space assumptions and significantly improves information content of the latent embedding. This also enables realistic long-term prediction.
A robotics-based approach to foster programming skills and computational thinking: Pilot experience in the classroom of early childhood education

This document has the purpose to present some results obtained in the pilot experience, play and program with Bee-Bot. The activities were developed in the framework of the doctoral research project whose purpose is the design and integration of learning activities with robotics to foster programming skills and computational thinking in the classroom of early childhood. Teachers and students of the second cycle of early childhood education of a concerted school participated in the experience during 2016-2017 academic period. School is in Salamanca, Spain. The activity allowed students to solve programming challenges using the Bee-Bot floor robot. Instruments were used to collect data, such as: questionnaires, interviews, rubrics and field diary. In general terms, the results obtained were positive. The technical, pedagogical and social aspects proposed in this research have received the favorable acceptance of teachers and students. Therefore, the information generated allowed to strengthen the design, structure and evaluation of the robotics program would be used in later stages of the investigation.