nils-herrmann/clean_scirepeval huggingface dataset:
SLANGZY: a fuzzy logic-based algorithm for English slang meaning selection

The text present on online forums and social media platforms conventionally does not follow a standard sentence structure and uses words that are commonly termed as slang or Internet language. Online text mining involves a surfeit of slang words; however, there is a distinct lack of reliable resources available to find accurate meanings of these words. We aim to bridge this gap by introducing SLANGZY, a fuzzy logic-based algorithm for English slang meaning selection which uses a mathematical factor termed as “slang factor” to judge the accuracy of slang word definitions found in Urban Dictionary, the largest Slang Dictionary on the Internet. This slang factor is used to rank definitions of English slang words retrieved from over 4 million unique words on popular social media platforms such as Twitter, YouTube and Reddit. We investigate the usefulness of SLANGZY over Urban Dictionary to find meanings of slang words in social media text and achieve encouraging results due to recognizing the importance of multiple criteria in the calculation of slang factor in the algorithm over successive experiments. The performance of SLANGZY with optimum weights for each criterion is further assessed using the accuracy, error rate, F-Score as well as a difference factor for English slang word definitions. To further illustrate the results, a web portal is created to display the contents of the Slang Dictionary consisting of definitions ranked according to the calculated slang factors.
Learner Modeling for Integration Skills

Complex skill mastery requires not only acquiring individual basic component skills, but also practicing integrating such basic skills. However, traditional approaches to knowledge modeling, such as Bayesian knowledge tracing, only trace knowledge of each decomposed basic component skill. This risks early assertion of mastery or ineffective remediation failing to address skill integration. We introduce a novel integration-level approach to model learners' knowledge and provide fine-grained diagnosis: a Bayesian network based on a new kind of knowledge graph with progressive integration skills. We assess the value of such a model from multifaceted aspects: performance prediction, parameter plausibility, expected instructional effectiveness, and real-world recommendation helpfulness. Our experiments based on a Java programming tutor show that proposed model significantly improves two popular multiple-skill knowledge tracing models on all these four aspects.
Consumer buying behavior towards online shopping stores in Malaysia

The global nature of communication and shopping has as well redefined, seeing that it is the perfect vehicle for online shopping stores. Online convenient shop is mostly reflected in shorter time and less energy spent, including shipping cost reduction, less crowd and queues than real markets, unlimited time and space, which all increase convenience of shopping. Internet shopping for businesses and consumers are being accepted as an alternative shop mode rather than visiting the stores. However, convincing the consumers to shop online is still a challenging task for web retailers in Malaysia. The growth of Internet technology in Malaysia has enormous potential as it reduces the costs of product and service delivery and extends geographical boundaries in bringing buyers and sellers together. This study is conducted to identify factors influencing consumers towards online shopping in Malaysia. The study focused on nine independent variables namely appearance, quick loading, security, sitemap, validity, promotion, attractiveness, believability, and originality. We applied Five-point Likert Scale to measure the influential factors on intention for online shopping.
Learning to Make Predictions on Graphs with Autoencoders

We examine two fundamental tasks associated with graph representation learning: link prediction and semi-supervised node classification. We present a novel autoencoder architecture capable of learning a joint representation of both local graph structure and available node features for the multi-task learning of link prediction and node classification. Our autoencoder architecture is efficiently trained end-to-end in a single learning stage to simultaneously perform link prediction and node classification, whereas previous related methods require multiple training steps that are difficult to optimize. We provide a comprehensive empirical evaluation of our models on nine benchmark graph-structured datasets and demonstrate significant improvement over related methods for graph representation learning. Reference code and data are available at https://github.com/vuptran/graph-representation-learning.
The permeability barrier of nuclear pore complexes appears to operate via hydrophobic exclusion.

Nuclear pore complexes (NPCs) restrict the nucleocytoplasmic flux of most macromolecules, but permit facilitated passage of nuclear transport receptors and their cargo complexes. We found that a simple hydrophobic interaction column can mimic the selectivity of NPCs surprisingly well and that nuclear transport receptors appear to be the most hydrophobic soluble proteins. This suggests that surface hydrophobicity represents a major sorting criterion of NPCs. The rate of NPC passage of cargo-receptor complexes is, however, not dominated just by properties of the receptors. We found that large cargo domains drastically hinder NPC passage and require more than one receptor molecule for rapid translocation. This argues against a rigid translocation channel and instead suggests that NPC passage involves a partitioning of the entire translocating species into a hydrophobic phase, whereby the receptor:cargo ratio determines the solubility in that permeability barrier. Finally, we show that interfering with hydrophobic interactions causes a reversible collapse of the permeability barrier of NPCs, which is consistent with the assumption that the barrier is formed by phenylalanine-rich nucleoporin repeats that attract each other through hydrophobic interactions.
An Assessment of Intrinsic and Extrinsic Motivation on Task Performance in Crowdsourcing Markets

Crowdsourced labor markets represent a powerful new paradigm for accomplishing work. Understanding the motivating factors that lead to high quality work could have significant benefits. However, researchers have so far found that motivating factors such as increased monetary reward generally increase workers’ willingness to accept a task or the speed at which a task is completed, but do not improve the quality of the work. We hypothesize that factors that increase the intrinsic motivation of a task – such as framing a task as helping others – may succeed in improving output quality where extrinsic motivators such as increased pay do not. In this paper we present an experiment testing this hypothesis along with a novel experimental design that enables controlled experimentation with intrinsic and extrinsic motivators in Amazon’s Mechanical Turk, a popular crowdsourcing task market. Results suggest that intrinsic motivation can indeed improve the quality of workers’ output, confirming our hypothesis. Furthermore, we find a synergistic interaction between intrinsic and extrinsic motivators that runs contrary to previous literature suggesting “crowding out” effects. Our results have significant practical and theoretical implications for crowd work.
Combined Star-Delta Winding Analysis

The combined star-delta winding has been attracting the attention of industry because of its potential to improve the efficiency of electrical machines. In order to show the efficiency benefits of this type of winding, many authors have used methods to determine the improvements in winding factors, published in different papers, which have not been validated yet. Some of these methods contradict each other. Furthermore, as a demerit of this winding, a unidirectional rotation has been reported. In this paper, a set of new formulas to calculate the winding factors for combined star-delta windings is proposed. In order to check the validity of the proposed formulas, a 2-D FEM and experimental tests on a salient pole synchronous machine are presented, which allows different combined star-delta winding connections. In contrast to other publications, the measurements show that combined windings can be applied for both directions of rotation without any problems.
Ladder Variational Autoencoders

Variational autoencoders are powerful models for unsupervised learning. However deep models with several layers of dependent stochastic variables are difficult to train which limits the improvements obtained using these highly expressive models. We propose a new inference model, the Ladder Variational Autoencoder, that recursively corrects the generative distribution by a data dependent approximate likelihood in a process resembling the recently proposed Ladder Network. We show that this model provides state of the art predictive log-likelihood and tighter log-likelihood lower bound compared to the purely bottom-up inference in layered Variational Autoencoders and other generative models. We provide a detailed analysis of the learned hierarchical latent representation and show that our new inference model is qualitatively different and utilizes a deeper more distributed hierarchy of latent variables. Finally, we observe that batch normalization and deterministic warm-up (gradually turning on the KL-term) are crucial for training variational models with many stochastic layers.
Blood Exosomes Endowed with Magnetic and Targeting Properties for Cancer Therapy.

Exosomes are a class of naturally occurring nanoparticles that are secreted endogenously by mammalian cells. Clinical applications for exosomes remain a challenge because of their unsuitable donors, low scalability, and insufficient targeting ability. In this study, we developed a dual-functional exosome-based superparamagnetic nanoparticle cluster as a targeted drug delivery vehicle for cancer therapy. The resulting exosome-based drug delivery vehicle exhibits superparamagnetic behavior at room temperature, with a stronger response to an external magnetic field than individual superparamagnetic nanoparticles. These properties enable exosomes to be separated from the blood and to target diseased cells. In vivo studies using murine hepatoma 22 subcutaneous cancer cells showed that drug-loaded exosome-based vehicle delivery enhanced cancer targeting under an external magnetic field and suppressed tumor growth. Our developments overcome major barriers to the utility of exosomes for cancer application.
Predicting Rare Events In Temporal Domains

Temporal data mining aims at finding patterns in historical data. Our work proposes an approach to extract temporal patterns from data to predict the occurrence of target events, such as computer attacks on host networks, or fraudulent transactions in financial institutions. Our problem formulation exhibits two major challenges: 1) we assume events being characterized by categorical features and displaying uneven inter-arrival times; such an assumption falls outside the scope of classical time-series analysis, 2) we assume target events are highly infrequent; predictive techniques must deal with the class-imbalance problem. We propose an efficient algorithm that tackles the challenges above by transforming the event prediction problem into a search for all frequent eventsets preceding target events. The class imbalance problem is overcome by a search for patterns on the minority class exclusively; the discrimination power of patterns is then validated against other classes. Patterns are then combined into a rule-based model for prediction. Our experimental analysis indicates the types of event sequences where target events can be accurately predicted.
Circuit for Shor's algorithm using 2n+3 qubits

We try to minimize the number of qubits needed to factor an integer of n bits using Shor’s algorithm on a quantum computer. We introduce a circuit which uses 2n+3 qubits and O(nlg(n)) elementary quantum gates in a depth of O(n) to implement the factorization algorithm. The circuit is computable in polynomial time on a classical computer and is completely general as it does not rely on any property of the number to be factored.
Partial-Observation Stochastic Games: How to Win when Belief Fails

In two-player finite-state stochastic games of partial observation on graphs, in every state of the graph, the players simultaneously choose an action, and their joint actions determine a probability distribution over the successor states. The game is played for infinitely many rounds and thus the players construct an infinite path in the graph. We consider reachability objectives where the first player tries to ensure a target state to be visited almost-surely (i.e., with probability 1) or positively (i.e., with positive probability), no matter the strategy of the second player.
 We classify such games according to the information and to the power of randomization available to the players. On the basis of information, the game can be one-sided with either (a) player 1, or (b) player 2 having partial observation (and the other player has perfect observation), or two-sided with (c) both players having partial observation. On the basis of randomization, (a) the players may not be allowed to use randomization (pure strategies), or (b) they may choose a probability distribution over actions but the actual random choice is external and not visible to the player (actions invisible), or (c) they may use full randomization.
 Our main results for pure strategies are as follows: (1) For one-sided games with player 2 having perfect observation we show that (in contrast to full randomized strategies) belief-based (subset-construction based) strategies are not sufficient, and we present an exponential upper bound on memory both for almost-sure and positive winning strategies; we show that the problem of deciding the existence of almost-sure and positive winning strategies for player 1 is EXPTIME-complete and present symbolic algorithms that avoid the explicit exponential construction. (2) For one-sided games with player 1 having perfect observation we show that nonelementary memory is both necessary and sufficient for both almost-sure and positive winning strategies. (3) We show that for the general (two-sided) case finite-memory strategies are sufficient for both positive and almost-sure winning, and at least nonelementary memory is required. We establish the equivalence of the almost-sure winning problems for pure strategies and for randomized strategies with actions invisible. Our equivalence result exhibit serious flaws in previous results of the literature: we show a nonelementary memory lower bound for almost-sure winning whereas an exponential upper bound was previously claimed.
E-FFC: an enhanced form-focused crawler for domain-specific deep web databases

A key problem of retrieving, integrating and mining rich and high quality information from massive Deep Web Databases (WDBs) online is how to automatically and effectively discover and recognize domain-specific WDBs’ entry points, i.e., forms, in the Web. It has been a challenging task because domain-specific WDBs’ forms with dynamic and heterogeneous properties are very sparsely distributed over several trillion Web pages. Although significant efforts have been made to address the problem and its special cases, more effective solutions remain to be further explored towards achieving both the satisfactory harvest rate and coverage rate of domain-specific WDBs’ forms simultaneously. In this paper, an Enhanced Form-Focused Crawler for domain-specific WDBs (E-FFC) has been proposed as a novel framework to address existing solutions’ limitations. The E-FFC, based on the divide and conquer strategy, employs a series of novel and effective strategies/algorithms, including a two-step page classifier, a link scoring strategy, classifiers for advanced searchable and domain-specific forms, crawling stopping criteria, etc. to its end achieving the optimized harvest rate and coverage rate of domain-specific WDBs’ forms simultaneously. Experiments of the E-FFC over a number of real Web pages in a set of representative domains have been conducted and the results show that the E-FFC outperforms the existing domain-specific Deep Web Form-Focused Crawlers in terms of the harvest rate, coverage rate and crawling robustness.
A multiagent system for intelligent material handling

The goal of the paper is to outline a multiagent robotic system employed in the task of material handling in an unstructured, indoor environment. In the research described, manipulators, observers, vehicles, sensors, and human operators are considered to be agents. Alternatively, an agent can be a general-purpose agent. Mobility is considered to be essential-if an agent is not mobile, it must be possible for it to 'piggy-back' on another agent which is mobile. In addition there is a central station which is stocked with a variety of additional sensors, means of illumination, special effectors or tools, that the agents can employ depending on the environment, task and the outcome of the execution of the task. Some possible applications of such a material handling system and examples of the tasks and environments are outlined.<<ETX>>
Experimental and Quasi-Experimental Designs for Generalized Causal Inference

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Obesity, fat distribution, and weight gain as risk factors for clinical diabetes in men.

To investigate the relation between obesity, fat distribution, and weight gain through adulthood and the risk of non-insulin-dependent diabetes mellitus (NIDDM).



We analyzed data from a cohort of 51,529 U.S. male health professionals, 40-75 years of age in 1986, who completed biennial questionnaires sent out in 1986, 1988, 1990, and 1992. During 5 years of follow-up (1987-1992), 272 cases of NIDDM were diagnosed among men without a history of diabetes, heart disease, and cancer in 1986 and who provided complete health information. Relative risks (RRs) associated with different anthropometric measures were calculated controlling for age, and multivariate RRs were calculated controlling for smoking, family history of diabetes, and age.



We found a strong positive association between overall obesity as measured by body mass index (BMI) and risk of diabetes. Men with a BMI of > or = 35 kg/m2 had a multivariate RR of 42.1 (95% confidence interval [CI] 22.0-80.6) compared with men with a BMI < 23.0 kg/m2. BMI at age 21 and absolute weight gain throughout adulthood were also significant independent risk factors for diabetes. Fat distribution, measured by waist-to-hip ratio (WHR), was a good predictor of diabetes only among the top 5%, while waist circumference was positively associated with the risk of diabetes among the top 20% of the cohort.



These data suggest that waist circumference may be a better indicator than WHR of the relationship between abdominal adiposity and risk of diabetes. Although early obesity, absolute weight gain throughout adulthood, and waist circumference were good predictors of diabetes, attained BMI was the dominant risk factor for NIDDM; even men of average relative weight had significantly elevated RRs.
Intelligent accident identification system using GPS , GSM modem

Recently technological and population development, the usage of vehicles are rapidly increasing and at the same time the occurrence accident is also increased. Hence, the value of human life is ignored. No one can prevent the accident, but can save their life by expediting the ambulance to the hospital in time. A new vivid scheme called Intelligent Transportation System (ITS) is introduced. The objective of this scheme is to minimize the delay caused by traffic congestion and to provide the smooth flow of emergency vehicles. The concept of this scheme is to green the traffic signal in the path of ambulance automatically with the help of RF module. So that the ambulance can reach the spot in time and human life can be saved and the accident location is identified sends the accident location immediately to the main server. The main server finds the nearest ambulance to the accident zone and sends the exact accident location to the emergency vehicle. The control unit monitors the ambulance and provides the shortest path to the ambulance at the same time it controls the traffic light according to the ambulance location and thus arriving at the hospital safely. This scheme is fully automated, thus it locates the accident spot accurately, controls the traffic lights, provide the shortest path to reach the location and to the hospital in time.
Learning to Teach with Dynamic Loss Functions

Teaching is critical to human society: it is with teaching that prospective students are educated and human civilization can be inherited and advanced. A good teacher not only provides his/her students with qualified teaching materials (e.g., textbooks), but also sets up appropriate learning objectives (e.g., course projects and exams) considering different situations of a student. When it comes to artificial intelligence, treating machine learning models as students, the loss functions that are optimized act as perfect counterparts of the learning objective set by the teacher. In this work, we explore the possibility of imitating human teaching behaviors by dynamically and automatically outputting appropriate loss functions to train machine learning models. Different from typical learning settings in which the loss function of a machine learning model is predefined and fixed, in our framework, the loss function of a machine learning model (we call it student) is defined by another machine learning model (we call it teacher). The ultimate goal of teacher model is cultivating the student to have better performance measured on development dataset. Towards that end, similar to human teaching, the teacher, a parametric model, dynamically outputs different loss functions that will be used and optimized by its student model at different training stages. We develop an efficient learning method for the teacher model that makes gradient based optimization possible, exempt of the ineffective solutions such as policy optimization. We name our method as “learning to teach with dynamic loss functions” (L2T-DLF for short). Extensive experiments on real world tasks including image classification and neural machine translation demonstrate that our method significantly improves the quality of various student models.
Triggered instructions: a control paradigm for spatially-programmed architectures

In this paper, we present triggered instructions, a novel control paradigm for arrays of processing elements (PEs) aimed at exploiting spatial parallelism. Triggered instructions completely eliminate the program counter and allow programs to transition concisely between states without explicit branch instructions. They also allow efficient reactivity to inter-PE communication traffic. The approach provides a unified mechanism to avoid over-serialized execution, essentially achieving the effect of techniques such as dynamic instruction reordering and multithreading, which each require distinct hardware mechanisms in a traditional sequential architecture.
 Our analysis shows that a triggered-instruction based spatial accelerator can achieve 8X greater area-normalized performance than a traditional general-purpose processor. Further analysis shows that triggered control reduces the number of static and dynamic instructions in the critical paths by 62% and 64% respectively over a program-counter style spatial baseline, resulting in a speedup of 2.0X.