nils-herrmann/clean_scirepeval huggingface dataset:
Reducing the human overhead in text categorization

Many applications in text processing require significant human effort for either labeling large document collections (when learning statistical models) or extrapolating rules from them (when using knowledge engineering). In this work, we describe away to reduce this effort, while retaining the methods' accuracy, by constructing a hybrid classifier that utilizes human reasoning over automatically discovered text patterns to complement machine learning. Using a standard sentiment-classification dataset and real customer feedback data, we demonstrate that the resulting technique results in significant reduction of the human effort required to obtain a given classification accuracy. Moreover, the hybrid text classifier also results in a significant boost in accuracy over machine-learning based classifiers when a comparable amount of labeled data is used.
A Compact Wideband Dual-Polarized Slot Antenna With Five Resonances

This letter presents the design and analysis of a compact dual-polarized slot antenna using a triple-mode stepped slot-line resonator and a dual-mode cross-shaped monopole. The orthogonal polarization is achieved by feeding the two ports symmetrically at the opposite positions of the slot. All the five resonance frequencies can be flexibly adjusted by tuning different dimension parameters, so by increasing the frequency difference between the five resonance modes, a very wide common operating band (1.63–2.93 GHz) is obtained and also with a high port isolation (< $-$32 dB). Finally, by loading two grounded lumped capacitors on the cusp sections of the monopole, its second resonance frequency can be independently reconfigured.
RLgraph: Flexible Computation Graphs for Deep Reinforcement Learning

Reinforcement learning (RL) tasks are challenging to implement, execute and test due to algorithmic instability, hyper-parameter sensitivity, and heterogeneous distributed communication patterns. We argue for the separation of logical component composition, backend graph definition, and distributed execution. To this end, we introduce RLgraph, a library for designing and executing high performance RL computation graphs in both static graph and define-by-run paradigms. The resulting implementations yield high performance across different deep learning frameworks and distributed backends.
How Many Subjects Does It Take To Do A Regression Analysis.

Numerous rules-of-thumb have been suggested for determining the minimum number of subjects required to conduct multiple regression analyses. These rules-of-thumb are evaluated by comparing their results against those based on power analyses for tests of hypotheses of multiple and partial correlations. The results did not support the use of rules-of-thumb that simply specify some constant (e.g., 100 subjects) as the minimum number of subjects or a minimum ratio of number of subjects (N) to number of predictors (m). Some support was obtained for a rule-of-thumb that N ≥ 50 + 8 m for the multiple correlation and N ≥104 + m for the partial correlation. However, the rule-of-thumb for the multiple correlation yields values too large for N when m ≥ 7, and both rules-of-thumb assume all studies have a medium-size relationship between criterion and predictors. Accordingly, a slightly more complex rule-of thumb is introduced that estimates minimum sample size as function of effect size as well as the number of predictors. It is argued that researchers should use methods to determine sample size that incorporate effect size.
Let's play chinese characters: mobile learning approaches via culturally inspired group games

In many developing countries such as India and China, low educational levels often hinder economic empowerment. In this paper, we argue that mobile learning games can play an important role in the Chinese literacy acquisition process. We report on the unique challenges in the learning Chinese language, especially its logographic writing system. Based on an analysis of 25 traditional Chinese games currently played by children in China, we present the design and implementation of two culturally inspired mobile group learning games, Multimedia Word and Drumming Strokes. These two mobile games are designed to match Chinese children's understanding of everyday games. An informal evaluation reveals that these two games have the potential to enhance the intuitiveness and engagement of traditional games, and children may improve their knowledge of Chinese characters through group learning activities such as controversy, judgments and self-correction during the game play.
Grasping the Intentions of Others: The Perceived Intentionality of an Action Influences Activity in the Superior Temporal Sulcus during Social Perception

An explication of the neural substrates for social perception is an important component in the emerging field of social cognitive neuroscience and is relevant to the field of cognitive neuroscience as a whole. Prior studies from our laboratory have demonstrated that passive viewing of biological motion (Pelphrey, Mitchell, et al., 2003; Puce et al., 1998) activates the posterior superior temporal sulcus (STS) region. Furthermore, recent evidence has shown that the perceived context of observed gaze shifts (Pelphrey, Singerman, et al., 2003; Pelphrey et al., 2004) modulates STS activity. Here, using event-related functional magnetic resonance imaging at 4 T, we investigated brain activity in response to passive viewing of goal- and non-goal- directed reaching-to-grasp movements. Participants viewed an animated character making reaching-to-grasp movements either toward (correct) or away (incorrect) from a blinking dial. Both conditions evoked significant posterior STS activity that was strongly right lateralized. By examining the time course of the blood oxygenation level-dependent response from areas of activation, we observed a functional dissociation. Incorrect trials evoked significantly greater activity in the STS than did correct trials, while an area posterior and inferior to the STS (likely corresponding to the MT/V5 complex) responded equally to correct and incorrect movements. Parietal cortical regions, including the superior parietal lobule and the anterior intraparietal sulcus, also responded equally to correct and incorrect movements, but showed evidence for differential responding based on the hand and arm (left or right) of the animated character used to make the reaching-to-grasp movement. The results of this study further suggest that a region of the right posterior STS is involved in analyzing the intentions of other people's actions and that activity in this region is sensitive to the context of observed biological motions.
Teaching a lay theory before college narrows achievement gaps at scale.

Previous experiments have shown that college students benefit when they understand that challenges in the transition to college are common and improvable and, thus, that early struggles need not portend a permanent lack of belonging or potential. Could such an approach-called a lay theory intervention-be effective before college matriculation? Could this strategy reduce a portion of racial, ethnic, and socioeconomic achievement gaps for entire institutions? Three double-blind experiments tested this possibility. Ninety percent of first-year college students from three institutions were randomly assigned to complete single-session, online lay theory or control materials before matriculation (n > 9,500). The lay theory interventions raised first-year full-time college enrollment among students from socially and economically disadvantaged backgrounds exiting a high-performing charter high school network or entering a public flagship university (experiments 1 and 2) and, at a selective private university, raised disadvantaged students' cumulative first-year grade point average (experiment 3). These gains correspond to 31-40% reductions of the raw (unadjusted) institutional achievement gaps between students from disadvantaged and nondisadvantaged backgrounds at those institutions. Further, follow-up surveys suggest that the interventions improved disadvantaged students' overall college experiences, promoting use of student support services and the development of friendship networks and mentor relationships. This research therefore provides a basis for further tests of the generalizability of preparatory lay theories interventions and of their potential to reduce social inequality and improve other major life transitions.
ACES: Automatic Compartments for Embedded Systems

Securing the rapidly expanding Internet of Things (IoT) is critical. Many of these “things” are vulnerable baremetal embedded systems where the application executes directly on hardware without an operating system. Unfortunately, the integrity of current systems may be compromised by a single vulnerability, as recently shown by Google’s P0 team against Broadcom’s WiFi SoC. We present ACES (Automatic Compartments for Embedded Systems), an LLVM-based compiler that automatically infers and enforces inter-component isolation on bare-metal systems, thus applying the principle of least privileges. ACES takes a developer-specified compartmentalization policy and then automatically creates an instrumented binary that isolates compartments at runtime, while handling the hardware limitations of baremetal embedded devices. We demonstrate ACES’ ability to implement arbitrary compartmentalization policies by implementing three policies and comparing the compartment isolation, runtime overhead, and memory overhead. Our results show that ACES’ compartments can have low runtime overheads (13% on our largest test application), while using 59% less Flash, and 84% less RAM than the Mbed μVisor—the current state-of-theart compartmentalization technique for bare-metal systems. ACES ‘ compartments protect the integrity of privileged data, provide control-flow integrity between compartments, and reduce exposure to ROP attacks by 94.3% compared to μVisor.
Min-BDeu and Max-BDeu Scores for Learning Bayesian Networks

This work presents two new score functions based on the Bayesian Dirichlet equivalent uniform (BDeu) score for learning Bayesian network structures. They consider the sensitivity of BDeu to varying parameters of the Dirichlet prior. The scores take on the most adversary and the most beneficial priors among those within a contamination set around the symmetric one. We build these scores in such way that they are decomposable and can be computed efficiently. Because of that, they can be integrated into any state-of-the-art structure learning method that explores the space of directed acyclic graphs and allows decomposable scores. Empirical results suggest that our scores outperform the standard BDeu score in terms of the likelihood of unseen data and in terms of edge discovery with respect to the true network, at least when the training sample size is small. We discuss the relation between these new scores and the accuracy of inferred models. Moreover, our new criteria can be used to identify the amount of data after which learning is saturated, that is, additional data are of little help to improve the resulting model.
A generalized capacitor voltage balancing scheme for flying capacitor multilevel converters

Multilevel power electronic converters are the converter of choice in medium-voltage applications due to their reduced switch voltage stress, better harmonic performance, and lower switching losses. Although it has received little attention, the flying-capacitor multilevel converter has a distinct advantage in terms of its ease of capacitor voltage balancing. A number of techniques have been presented in the literature for capacitor voltage balancing, some relying on “self-balancing” properties. However, self balancing cannot guarantee balancing of capacitor voltages in practical applications. Other researchers present closed-loop control schemes which force voltage balancing of capacitors. In this paper, a new closed loop control scheme is proposed which regulates the capacitor voltages for a multilevel flying capacitor converter. The proposed scheme is based on the converter equations and involves implementing simple rules. In particular, multiple duty cycles are defined and modulated in direct response to the capacitor voltages. Through simulation, the method is shown to work on four, eight and nine-level flying capacitor inverters.
Anti-counterfeiting Using Memory Spots

We propose a new hardware and software solution for anticounterfeiting that puts product authentication directly into the hands of end-users, enabling them to be confident of the authenticity of a product regardless of the integrity of the distribution chain. This is made possible by a new type of tamper-resistant hardware chip, called “memory spot”, which has a unique combination of small size, very fast on-air data rate, relatively large memory, and integrated security features, in conjunction with a novel authentication protocol. In addition, the low cost of these new chips makes our proposed solution even more compelling than possible alternatives. Example applications include pharmaceutical anti-counterfeiting, asset tracking, secure-ID, brand protection and warranty fraud prevention. We will take pharmaceutical anti-counterfeiting as an example to explain our solution. A prototype system has been built to demonstrate the feasibility of the proposed system.
An Anonymous Electronic Voting Protocol for Voting Over The Internet

In this work we propose a secure electronic voting protocol that is suitable for large scale voting over the Internet. The protocol allows a voter to cast his or her ballot anonymously, by exchanging untraceable yet authentic messages. The protocol ensures that (i) only eligible voters are able to cast votes, (ii) a voter is able to cast only one vote, (iii) a voter is able to verify that his or her vote is counted in the final tally, (iv) nobody, other than the voter, is able to link a cast vote with a voter, and (v) if a voter decides not to cast a vote, nobody is able to cast a fraudulent vote in place of the voter. The protocol does not require the cooperation of all registered voters. Neither does it require the use of complex cryptographic techniques like threshold cryptosystems or anonymous channels for casting votes. This is in contrast to other voting protocols that have been proposed in the literature. The protocol uses three agents, other than the voters, for successful operation. However, we do not require any of these agents to be trusted. That is, the agents may be physically co-located or may collude with one another to try to commit a fraud. If a fraud is committed, it can be easily detected and proven, so that the vote can be declared null and void. Although we propose the protocol with electronic voting in mind, the protocol can be used in other applications that involve exchanging an untraceable yet authentic message. Examples of such applications are answering confidential questionnaire anonymously or anonymous financial transactions.
Deep Belief Networks for Electroencephalography: A Review of Recent Contributions and Future Outlooks

Deep learning, a relatively new branch of machine learning, has been investigated for use in a variety of biomedical applications. Deep learning algorithms have been used to analyze different physiological signals and gain a better understanding of human physiology for automated diagnosis of abnormal conditions. In this paper, we provide an overview of deep learning approaches with a focus on deep belief networks in electroencephalography applications. We investigate the state-of-the-art algorithms for deep belief networks and then cover the application of these algorithms and their performances in electroencephalographic applications. We covered various applications of electroencephalography in medicine, including emotion recognition, sleep stage classification, and seizure detection, in order to understand how deep learning algorithms could be modified to better suit the tasks desired. This review is intended to provide researchers with a broad overview of the currently existing deep belief network methodology for electroencephalography signals, as well as to highlight potential challenges for future research.
Optimal Mass Transport: Signal processing and machine-learning applications

Transport-based techniques for signal and data analysis have recently received increased interest. Given their ability to provide accurate generative models for signal intensities and other data distributions, they have been used in a variety of applications, including content-based retrieval, cancer detection, image superresolution, and statistical machine learning, to name a few, and they have been shown to produce state-of-the-art results. Moreover, the geometric characteristics of transport-related metrics have inspired new kinds of algorithms for interpreting the meaning of data distributions. Here, we provide a practical overview of the mathematical underpinnings of mass transport-related methods, including numerical implementation, as well as a review, with demonstrations, of several applications. Software accompanying this article is available from [43].
Asymmetrical Duty Cycle-Controlled LLC Resonant Converter With Equivalent Switching Frequency Doubler

In the conventional full-bridge LLC converter, the duty cycle is kept as 0.5 and the phase-shift angle of the half-bridge modules is 0° to be a symmetrical operation, which makes the resonant tank operating frequency only equal to the switching frequency of the power devices. By regulating the duty cycles of the upper and lower switches in each half-bridge module to be 0.75 and 0.25 and the phase-shift angle of the half-bridge modules to be 180°, the asymmetrical duty cycle controlled full-bridge LLC resonant converter is derived. The proposed asymmetrical duty cycle controlled scheme halves the switching frequency of the primary switches. As a result, the driving losses are effectively reduced. Compared with the conventional full-bridge LLC converter, the soft-switching condition of the derived asymmetrical controlled LLC converter becomes easier to reduce the resonant current. Consequently, the conduction losses are decreased and the conversion efficiency is improved. The asymmetrical control scheme can be also extended to the stacked structure for high input voltage applications. Finally, two LLC converter prototypes both with 200-kHz resonant frequency for asymmetrical and symmetrical control schemes are built and compared to validate the effectiveness of the proposed control strategy.
An Attack Strategy based on Hyperbolic Coordinates

In this paper, from the view of community structure and information flow, a new attack strategy NCLIC is proposed based on hyperbolic coordinates in the complex network. Firstly, a centrality index LIC is introduced in the hyperbolic coordinates which can reflect the amount of information flow of a node, and the performance of LIC is analyzed under different hyperbolic mapping methods. Then some nodes in the network are named "bridge node" which play an important role in the community structure of network. Based on the LIC and the bridge node, a new attack strategy NCLIC is proposed. To verify that the NCLIC can significantly decompose a network, it has been compared with other existing attack strategies. In the experiments, and it has been found that the attack effects of NCLIC is better than others when the destroyed nodes reach to a certain amount, especially for networks with obvious community structure.
Mini-implant anchorage for en-masse retraction of maxillary anterior teeth: a clinical cephalometric study.

This study was conducted to determine the efficiency of mini-implants as intraoral anchorage units for en-masse retraction of the 6 maxillary anterior teeth when the first premolars are extracted compared with conventional methods of anchorage reinforcement.



Thirty patients requiring high anchorage after extraction of the maxillary first premolars were selected for this study. They were divided into 2 groups of 15 each. In the first group (G1), mini-implants were used for en-masse retraction; in the second group (G2), conventional methods of anchorage preservation were followed. Horizontal, vertical, and angular positions of the maxillary first molar and central incisor were evaluated cephalometrically before and after orthodontic retraction.



The maxillary first molars in the G1 patients showed net distal movement of 0.55 mm, and mesial movement of 1.95 mm was found in G2. The differences were statistically significant. Distal tipping of the first molar of -0.13 degrees+/-3.63 degrees was seen in G1, and mesial tipping of 3.7 degrees+/-3.9 degrees was observed in G2. No significant differences were found in the rates of incisor retraction between the 2 groups. However, G1 showed more than 2 mm of incisor intrusion; this was statistically significant.



Mini-implants are efficient for intraoral anchorage reinforcement for en-masse retraction and intrusion of maxillary anterior teeth. No anchorage loss was seen in either the horizontal or the vertical direction in G1 when compared with G2. However, a statistically significant decrease in intermolar width was noted in G1.
Learning to Understand Phrases by Embedding the Dictionary

Distributional models that learn rich semantic word representations are a success story of recent NLP research. However, developing models that learn useful representations of phrases and sentences has proved far harder. We propose using the definitions found in everyday dictionaries as a means of bridging this gap between lexical and phrasal semantics. Neural language embedding models can be effectively trained to map dictionary definitions (phrases) to (lexical) representations of the words defined by those definitions. We present two applications of these architectures: reverse dictionaries that return the name of a concept given a definition or description and general-knowledge crossword question answerers. On both tasks, neural language embedding models trained on definitions from a handful of freely-available lexical resources perform as well or better than existing commercial systems that rely on significant task-specific engineering. The results highlight the effectiveness of both neural embedding architectures and definition-based training for developing models that understand phrases and sentences.
Towards zero-shot learning for human activity recognition using semantic attribute sequence model

Understanding human activities is important for user-centric and context-aware applications. Previous studies showed promising results using various machine learning algorithms. However, most existing methods can only recognize the activities that were previously seen in the training data. In this paper, we present a new zero-shot learning framework for human activity recognition that can recognize an unseen new activity even when there are no training samples of that activity in the dataset. We propose a semantic attribute sequence model that takes into account both the hierarchical and sequential nature of activity data. Evaluation on datasets in two activity domains show that the proposed zero-shot learning approach achieves 70-75% precision and recall recognizing unseen new activities, and outperforms supervised learning with limited labeled data for the new classes.
DemNet: A Convolutional Neural Network for the detection of Alzheimer's Disease and Mild Cognitive Impairment

The early diagnosis of Alzheimer's Disease (AD) and its prodromal form, Mild Cognitive Impairment (MCI), has been the subject of extensive research in recent years. Some recent studies have shown promising results in the diagnosis of AD and MCI using structural Magnetic Resonance Imaging (MRI) scans. In this paper, we propose the use of a Convolutional Neural Network (CNN) in the detection of AD and MCI. In particular, we modified the 16-layered VGGNet for the 3-way classification of AD, MCI and Healthy Controls (HC) on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset achieving an overall accuracy of 91.85% and outperforming several classifiers from other studies.