nils-herrmann/clean_scirepeval huggingface dataset:
Interpretable Partitioned Embedding for Customized Fashion Outfit Composition

Intelligent fashion outfit composition becomes more and more popular in these years. Some deep learning based approaches reveal competitive composition recently. However, the unexplainable characteristic makes such deep learning based approach cannot meet the the designer, businesses and consumers’ urge to comprehend the importance of different attributes in an outfit composition. To realize interpretable and customized fashion outfit compositions, we propose a partitioned embedding network to learn interpretable representations from clothing items. The overall network architecture consists of three components: an auto-encoder module, a supervised attributes module and a multi-independent module. The auto-encoder module serves to encode all useful information into the embedding. In the supervised attributes module, multiple attributes labels are adopted to ensure that different parts of the overall embedding correspond to different attributes. In the multi-independent module, adversarial operation are adopted to fulfill the mutually independent constraint. With the interpretable and partitioned embedding, we then construct an outfit composition graph and an attribute matching map. Given specified attributes description, our model can recommend a ranked list of outfit composition with interpretable matching scores. Extensive experiments demonstrate that 1) the partitioned embedding have unmingled parts which corresponding to different attributes and 2) outfits recommended by our model are more desirable in comparison with the existing methods.
STEM education K-12: perspectives on integration

This commentary was stimulated by Yeping Li’s first editorial (2014) citing one of the journal’s goals as adding multidisciplinary perspectives to current studies of single disciplines comprising the focus of other journals. In this commentary, I argue for a greater focus on STEM integration, with a more equitable representation of the four disciplines in studies purporting to advance STEM learning. The STEM acronym is often used in reference to just one of the disciplines, commonly science. Although the integration of STEM disciplines is increasingly advocated in the literature, studies that address multiple disciplines appear scant with mixed findings and inadequate directions for STEM advancement. Perspectives on how discipline integration can be achieved are varied, with reference to multidisciplinary, interdisciplinary, and transdisciplinary approaches adding to the debates. Such approaches include core concepts and skills being taught separately in each discipline but housed within a common theme; the introduction of closely linked concepts and skills from two or more disciplines with the aim of deepening understanding and skills; and the adoption of a transdisciplinary approach, where knowledge and skills from two or more disciplines are applied to real-world problems and projects with the aim of shaping the total learning experience. Research that targets STEM integration is an embryonic field with respect to advancing curriculum development and various student outcomes. For example, we still need more studies on how student learning outcomes arise not only from different forms of STEM integration but also from the particular disciplines that are being integrated. As noted in this commentary, it seems that mathematics learning benefits less than the other disciplines in programs claiming to focus on STEM integration. Factors contributing to this finding warrant more scrutiny. Likewise, learning outcomes for engineering within K-12 integrated STEM programs appear under-researched. This commentary advocates a greater focus on these two disciplines within integrated STEM education research. Drawing on recommendations from the literature, suggestions are offered for addressing the challenges of integrating multiple disciplines faced by the STEM community.
Algorithms for Advanced Battery-Management Systems

Lithium-ion (Li-ion) batteries are ubiquitous sources of energy for portable electronic devices. Compared to alternative battery technologies, Li-ion batteries provide one of the best energy-to-weight ratios, exhibit no memory effect, and have low self-discharge when not in use. These beneficial properties, as well as decreasing costs, have established Li-ion batteries as a leading candidate for the next generation of automotive and aerospace applications. In the automotive sector, increasing demand for hybrid electric vehicles (HEVs), plug-in HEVs (PHEVs), and EVs has pushed manufacturers to the limits of contemporary automotive battery technology. This limitation is gradually forcing consideration of alternative battery technologies, such as Li-ion batteries, as a replacement for existing leadacid and nickel-metal-hydride batteries. Unfortunately, this replacement is a challenging task since automotive applications demand large amounts of energy and power and must operate safely, reliably, and durably at these scales. The article presents a detailed description and model of a Li-ion battery. It begins the section "Based Batteries" by providing an intuitive explanation of the fundamentals behind storing energy in a Li-ion battery. In the sections "Modeling Approach" and "Li-Ion Battery Model," it present equations that describe a Li-ion cell's dynamic behavior. This modeling is based on using electrochemical principles to develop a physics-based model in contrast to equivalent circuit models. A goal of this article is to present the electrochemical model from a controls perspective.
Substrate Integrated Waveguide (SIW) Monopulse Slot Antenna Array

A Ka-band compact single layer substrate integrated waveguide monopulse slot array antenna for the application of monopulse tracking system is designed, fabricated and measured. The feeding network as well as the monopulse comparator and the subarrays is integrated on the same dielectric with the size of 140 mmtimes130 mm. The bandwidth ( S11 < -10 dB) of the antenna is 7.39% with an operating frequency range of 30.80 GHz-33.14 GHz. The maximum gain at 31.5 GHz is 18.74 dB and the maximum null depth is -46.3 dB. The sum- and difference patterns of three planes: H-plane, E-plane and diagonal plane are measured and presented.
Analog Chaotic Maps with Sample-and-Hold Errors ∗

Though considerable effort has recently been devoted to hardware realization of one-dimensional chaotic systems, the influence of implementation inaccuracies is often underestimated and limited to non-idealities in the non-linear map. Here we investigate the consequences of sample-and-hold errors. Two degrees of freedom in the design space are considered: the choice of the map and the sample-and-hold architecture. Current-mode systems based on Bernoulli Shift, on Tent Map and on Tailed Tent Map are taken into account and coupled with an order-one model of sample-and-hold to ascertain error causes and suggest implementation improvements. key words: chaotic systems, analog circuits, sample-and-hold errors
The textcat Package for n-Gram Based Text Categorization in R

Identifying the language used will typically be the first step in most natural language processing tasks. Among the wide variety of language identification methods discussed in the literature, the ones employing the Cavnar and Trenkle (1994) approach to text categorization based on character n-gram frequencies have been particularly successful. This paper presents the R extension package textcat for n-gram based text categorization which implements both the Cavnar and Trenkle approach as well as a reduced n-gram approach designed to remove redundancies of the original approach. A multi-lingual corpus obtained from the Wikipedia pages available on a selection of topics is used to illustrate the functionality of the package and the performance of the provided language identification methods.
Detection of Websites Based on PhishingWebsites Characteristics

Phishing is a web-based attack that uses social engineering techniques to exploit internet users and acquire sensitive data. Most phishing attacks work by creating a fake version of the real site's web interface to gain the user's trust.. We applied different methods for detecting phishing using known as well as new features. In this we used the heuristic-based approach to handle phishing attacks, in this approached several website features are collected and used to identify the type of the website. The heuristic-based approach can recognize newly created fake websites in real-time. One intelligent approach based on genetic algorithm seems a potential solution that may effectively detect phishing websites with high accuracy and prevent it by blocking them.
Factors Affecting Junior High School Students' Interest in Physics.

We report the results of a study on students’ interest in physics at the end of their compulsory schooling in Israel carried out in the framework of the ROSE Project. Factors studied were their opinions about science classes, their out-of-school experiences in physics, and their attitudes toward science and technology. Students’ overall interest in physics was “neutral” (neither positive nor negative), with boys showing a higher interest than girls. We found a strong correlation between students’ “neutral” interest in physics and their negative opinions about science classes. These findings raise serious questions about the implementation of changes made in the Israeli science curriculum in primary and junior high school, especially if the goal is to prepare the young generation for life in a scientific-technological era. A more in-depth analysis of the results led us to formulate curricular, behavioral, and organizational changes needed to reach this goal.
Effects of caffeine on human health.

Caffeine is probably the most frequently ingested pharmacologically active substance in the world. It is found in common beverages (coffee, tea, soft drinks), in products containing cocoa or chocolate, and in medications. Because of its wide consumption at different levels by most segments of the population, the public and the scientific community have expressed interest in the potential for caffeine to produce adverse effects on human health. The possibility that caffeine ingestion adversely affects human health was investigated based on reviews of (primarily) published human studies obtained through a comprehensive literature search. Based on the data reviewed, it is concluded that for the healthy adult population, moderate daily caffeine intake at a dose level up to 400 mg day(-1) (equivalent to 6 mg kg(-1) body weight day(-1) in a 65-kg person) is not associated with adverse effects such as general toxicity, cardiovascular effects, effects on bone status and calcium balance (with consumption of adequate calcium), changes in adult behaviour, increased incidence of cancer and effects on male fertility. The data also show that reproductive-aged women and children are 'at risk' subgroups who may require specific advice on moderating their caffeine intake. Based on available evidence, it is suggested that reproductive-aged women should consume </=300 mg caffeine per day (equivalent to 4.6 mg kg(-1) bw day(-1) for a 65-kg person) while children should consume </=2.5 mg kg(-1) bw day(-1).
TOWARD A “ GRAND UNIFYING THEORY ” OF LEADERSHIP : IMPLICATIONS FOR CONSULTING PSYCHOLOGY

A succinct overview of some of the major research approaches to the study of leadership is provided as a foundation for the introduction of a multicomponent model of leadership that draws on those findings, complexity theory, and the concept of emergence. The major aspects of the model include: the personal characteristics and capacities, thoughts, feelings, behaviors, and human working relationships of leaders, followers, and other stake holders, the organization’s systems, including structures, processes, contents, and internal situations, the organization’s performance and outcomes, and the external environment(s), ecological niches, and external situations in which an enterprise functions. The relationship between this model and other approaches in the literature as well as directions in research on leadership and implications for consulting practice are discussed.
Lingua Franca English , Multilingual Communities

Firth and Wagner (1997) questioned the dichotomies nonnative versus native speaker, learner versus user , and interlanguage versus target language , which reflect a bias toward innateness, cognition, and form in language acquisition. Research on lingua franca English (LFE) not only affirms this questioning, but reveals what multilingual communities have known all along: Language learning and use succeed through performance strategies, situational resources, and social negotiations in fluid communicative contexts. Proficiency is therefore practicebased, adaptive, and emergent. These findings compel us to theorize language acquisition as multimodal, multisensory, multilateral, and, therefore, multidimensional. The previously dominant constructs such as form, cognition, and the individual are not ignored; they get redefined as hybrid, fluid, and situated in a more socially embedded, ecologically sensitive, and interactionally open model.
Indonesian Twitter Cyberbullying Detection using Text Classification and User Credibility

Cyberbullying is a repeated act that harasses, humiliates, threatens, or hassles other people through electronic devices and online social networking websites. Cyberbullying through the internet is more dangerous than traditional bullying, because it can potentially amplify the humiliation to an unlimited online audience. According to UNICEF and a survey by the Indonesian Ministry of Communication and Information, 58% of 435 adolescents do not understand about cyberbullying. Some of them might even have been the bullies, but since they did not understand about cyberbullying they could not recognise the negative effects of their bullying. The bullies may not recognise the harm of their actions, because they do not see immediate responses from their victims. Our study aimed to detect cyberbullying actors based on texts and the credibility analysis of users and notify them about the harm of cyberbullying. We collected data from Twitter. Since the data were unlabelled, we built a web-based labelling tool to classify tweets into cyberbullying and non-cyberbullying tweets. We obtained 301 cyberbullying tweets, 399 non-cyberbullying tweets, 2,053 negative words and 129 swear words from the tool. Afterwards, we applied SVM and KNN to learn about and detect cyberbullying texts. The results show that SVM results in the highest f1-score, 67%. We also measured the credibility analysis of users and found 257 Normal Users, 45 Harmful Bullying Actors, 53 Bullying Actors and 6 Prospective Bullying Actors.
Robust Machine Comprehension Models via Adversarial Training

It is shown that many published models for the Stanford Question Answering Dataset (Rajpurkar et al., 2016) lack robustness, suffering an over 50% decrease in F1 score during adversarial evaluation based on the AddSent (Jia and Liang, 2017) algorithm. It has also been shown that retraining models on data generated by AddSent has limited effect on their robustness. We propose a novel alternative adversary-generation algorithm, AddSentDiverse, that significantly increases the variance within the adversarial training data by providing effective examples that punish the model for making certain superficial assumptions. Further, in order to improve robustness to AddSent’s semantic perturbations (e.g., antonyms), we jointly improve the model’s semantic-relationship learning capabilities in addition to our AddSentDiversebased adversarial training data augmentation. With these additions, we show that we can make a state-of-the-art model significantly more robust, achieving a 36.5% increase in F1 score under many different types of adversarial evaluation while maintaining performance on the regular SQuAD task.
A Transformer-Based Multi-Source Automatic Post-Editing System

This paper presents our English–German Automatic Post-Editing (APE) system submitted to the APE Task organized at WMT 2018 (Chatterjee et al., 2018). The proposed model is an extension of the transformer architecture: two separate self-attention-based encoders encode the machine translation output (mt) and the source (src), followed by a joint encoder that attends over a combination of these two encoded sequences (encsrc and encmt) for generating the post-edited sentence. We compare this multi-source architecture (i.e, {src, mt} → pe) to a monolingual transformer (i.e., mt → pe) model and an ensemble combining the multi-source {src, mt} → pe and singlesource mt → pe models. For both the PBSMT and the NMT task, the ensemble yields the best results, followed by the multi-source model and last the singlesource approach. Our best model, the ensemble, achieves a BLEU score of 66.16 and 74.22 for the PBSMT and NMT task, respectively.
Two Infinite Families of Nonadditive Quantum Error-Correcting Codes

We construct explicitly two infinite families of genuine nonadditive 1-error correcting quantum codes and prove that their coding subspaces are 50% larger than those of the optimal stabilizer codes of the same parameters via the linear programming bound. All these nonadditive codes can be characterized by a stabilizer-like structure, and thus, their encoding circuits can be designed in a straightforward manner.
COMPONENTS OF PERFECTIONISM AND PROCRASTINATION IN COLLEGE STUDENTS

The present research examined the relations between individual differences in perfectionism and procrastinatory behavior in college students. A sample of 131 students (56 males, 75 females) completed measures of self-oriented, other-oriented, and socially prescribed perfectionism, as well as measures of academic procrastination and general procrastination. Subjects also completed ratings of factors related to procrastination (i.e., fear of failure, task aversiveness). Correlational analyses revealed it was the socially prescribed perfectionism dimension that was most closely correlated with both generalized procrastinat ion and academic procrastination, especially among males. There were few significant correlations involving self-oriented and other-oriented perfectionism. However, the fear of failure component of procrastination was associated broadly with all the perfectionism dimensions. Overall, the results suggest that procrastination stems, in part, from the anticipation of social disapproval from individuals with perfectionistic standards for others.
Evaluating Domain Adversarial Neural Networks on Multi-Genre Natural Language Inference

The recently published MULTINLI dataset introduces the domain adaptation challenge for the Natural Language Inference (NLI) task. In this work, we address this challenge by using a domain adversarial approach, which has been previously applied for multiple Natural Language Processing (NLP) tasks. We extend the setup used in prior work, to support training with multiple source domains, compare between the two baselines and analyze the adversarial component performance. Finally, we conduct a feature-based analysis of the data, considering the relation between key features of each domain and the performance of the adversarial approach.
Reinforcement Learning on Web Interfaces Using Workflow-Guided Exploration

Reinforcement learning (RL) agents improve through trial-and-error, but when reward is sparse and the agent cannot discover successful action sequences, learning stagnates. This has been a notable problem in training deep RL agents to perform web-based tasks, such as booking flights or replying to emails, where a single mistake can ruin the entire sequence of actions. A common remedy is to “warmstart” the agent by pre-training it to mimic expert demonstrations, but this is prone to overfitting. Instead, we propose to constrain exploration using demonstrations. From each demonstration, we induce high-level “workflows” which constrain the allowable actions at each time step to be similar to those in the demonstration (e.g., “Step 1: click on a textbox; Step 2: enter some text”). Our exploration policy then learns to identify successful workflows and samples actions that satisfy these workflows. Workflows prune out bad exploration directions and accelerate the agent’s ability to discover rewards. We use our approach to train a novel neural policy designed to handle the semi-structured nature of websites, and evaluate on a suite of web tasks, including the recent World of Bits benchmark. We achieve new state-of-the-art results, and show that workflow-guided exploration improves sample efficiency over behavioral cloning by more than 100x.
High-speed micro-crack detection of solar wafers with variable thickness

A new approach for high-speed micro-crack detection of solar wafers with variable thickness is proposed. Using a pair of laser displacement sensors, wafer thickness is measured and the lighting intensity is automatically adjusted to compensate for loss in NIR transmission due to varying thickness. In this way, the image contrast is maintained relatively uniform for the entire size of a wafer. An improved version of Niblack segmentation algorithm is developed for this application. Experimental results show the effectiveness of the system when tested with solar wafers with thickness ranging from 125 to 170 μm. Since the inspection is performed on the fly, therefore, a high throughput rate of more than 3600 wafers per hour can easily be obtained. Hence, the proposed system enables rapid in-line monitoring and real-time measurement.
A Vision-Based Counting and Recognition System for Flying Insects in Intelligent Agriculture

Rapid and accurate counting and recognition of flying insects are of great importance, especially for pest control. Traditional manual identification and counting of flying insects is labor intensive and inefficient. In this study, a vision-based counting and classification system for flying insects is designed and implemented. The system is constructed as follows: firstly, a yellow sticky trap is installed in the surveillance area to trap flying insects and a camera is set up to collect real-time images. Then the detection and coarse counting method based on You Only Look Once (YOLO) object detection, the classification method and fine counting based on Support Vector Machines (SVM) using global features are designed. Finally, the insect counting and recognition system is implemented on Raspberry PI. Six species of flying insects including bee, fly, mosquito, moth, chafer and fruit fly are selected to assess the effectiveness of the system. Compared with the conventional methods, the test results show promising performance. The average counting accuracy is 92.50% and average classifying accuracy is 90.18% on Raspberry PI. The proposed system is easy-to-use and provides efficient and accurate recognition data, therefore, it can be used for intelligent agriculture applications.