nils-herrmann/clean_scirepeval huggingface dataset:
A wait-list randomized controlled trial of loving-kindness meditation programme for self-criticism.

Self-criticism is a vulnerability risk factor for a number of psychological disorders, and it predicts poor response to psychological and pharmacological treatments. In the current study, we evaluated the efficacy of a loving-kindness meditation (LKM) programme designed to increase self-compassion in a sample of self-critical individuals. Thirty-eight individuals with high scores on the self-critical perfectionism subscale of the Dysfunctional Attitude Scale were randomized to an LKM condition (n = 19) or a wait-list (WL) condition (n = 19). Measures of self-criticism, self-compassion and psychological distress were administered before and immediately following the intervention (LKM or WL). WL participants received the intervention immediately after the waiting period. Both groups were assessed 3 months post-intervention. Intent-to-treat (n = 38) and per-protocol analyses (n = 32) showed significant reductions in self-criticism and depressive symptoms as well as significant increases in self-compassion and positive emotions in the LKM condition compared with the WL condition. A follow-up per-protocol analysis in both groups together (n = 20) showed that these gains were maintained 3 months after the intervention. These preliminary results suggest that LKM may be efficacious in alleviating self-criticism, increasing self-compassion and improving depressive symptoms among self-critical individuals.



Self-criticism plays a major role in many psychological disorders and predicts poor response to brief psychological and pharmacological treatments for depression. The current study shows that loving-kindness meditation, designed to foster self-compassion, is efficacious in helping self-critical individuals become less self-critical and more self-compassionate. The study also suggests that practising loving-kindness may reduce depressive symptoms and increase positive emotions.
Why the sunny side is up: association between affect and vertical position.

Metaphors linking spatial location and affect (e.g., feeling up or down) may have subtle, but pervasive, effects on evaluation. In three studies, participants evaluated words presented on a computer. In Study 1, evaluations of positive words were faster when words were in the up rather than the down position, whereas evaluations of negative words were faster when words were in the down rather than the up position. In Study 2, positive evaluations activated higher areas of visual space, whereas negative evaluations activated lower areas of visual space. Study 3 revealed that, although evaluations activate areas of visual space, spatial positions do not activate evaluations. The studies suggest that affect has a surprisingly physical basis.
Mining (Social) Network Graphs to Detect Random Link Attacks

Modern communication networks are vulnerable to attackers who send unsolicited messages to innocent users, wasting network resources and user time. Some examples of such attacks are spam emails, annoying tele-marketing phone calls, viral marketing in social networks, etc. Existing techniques to identify these attacks are tailored to certain specific domains (like email spam filtering), but are not applicable to a majority of other networks. We provide a generic abstraction of such attacks, called the Random Link Attack (RLA), that can be used to describe a large class of attacks in communication networks. In an RLA, the malicious user creates a set of false identities and uses them to communicate with a large, random set of innocent users. We mine the social networking graph extracted from user interactions in the communication network to find RLAs. To the best of our knowledge, this is the first attempt to conceptualize the attack definition, applicable to a variety of communication networks. In this paper, we formally define RLA and show that the problem of finding an RLA is NP-complete. We also provide two efficient heuristics to mine subgraphs satisfying the RLA property; the first (GREEDY) is based on greedy set-expansion, and the second (TRWALK) on randomized graph traversal. Our experiments with a real-life data set demonstrate the effectiveness of these algorithms.
RiskVis: Supply chain visualization with risk management and real-time monitoring

With increased complexity, supply chain networks (SCNs) of modern era face higher risks and lower efficiency due to limited visibility. Hence, there is an immediate need to provide end-to-end supply chain visibility for efficient management of complex supply chains. This paper proposes a visualization scheme based on multi-hierarchical modular design and develops a supply chain visualization platform with risk management and real-time monitoring, named RiskVis, for realizing better Supply Chain Risk Management (SCRM). A Supply Chain Visualizer (SCV) with a graphical visualization platform is mounted as a part of a SCRM management decision-making dashboard and it provides senior management a clearer view of supply chain operations in a local/regional/global setting. The platform not only displays spatio-temporal connectivity patterns of entities in a supply chain; it also accommodates real-time risk-related data collection and risk monitoring. The proposed platform offers the flexibility to be customized based on the user's requirements - to process and store the supply chain data in the server, visualize the supply chain data, network map, risk alert, and other information needed for SCRM. Supply chain decision makers can deploy it on the desktop or embed it into the company's enterprise applications in a front office environment for better managing risks of their supply chains.
Forecasting at Scale

Forecasting is a common data science task that helps organizations with capacity planning, goal setting, and anomaly detection. Despite its importance, there are serious challenges associated with producing reliable and high quality forecasts – especially when there are a variety of time series and analysts with expertise in time series modeling are relatively rare. To address these challenges, we describe a practical approach to forecasting “at scale” that combines configurable models with analyst-in-the-loop performance analysis. We propose a modular regression model with interpretable parameters that can be intuitively adjusted by analysts with domain knowledge about the time series. We describe performance analyses to compare and evaluate forecasting procedures, and automatically flag forecasts for manual review and adjustment. Tools that help analysts to use their expertise most effectively enable reliable, practical forecasting of business time series.
Feature selection for high-dimensional data

This paper offers a comprehensive approach to feature selection in the scope of classification problems, explaining the foundations, real application problems and the challenges of feature selection in the context of high-dimensional data. First, we focus on the basis of feature selection, providing a review of its history and basic concepts. Then, we address different topics in which feature selection plays a crucial role, such as microarray data, intrusion detection, or medical applications. Finally, we delve into the open challenges that researchers in the field have to deal with if they are interested to confront the advent of “Big Data” and, more specifically, the “Big Dimensionality”.
Organizational Chart Inference

Nowadays, to facilitate the communication and cooperation among employees, a new family of online social networks has been adopted in many companies, which are called the "enterprise social networks" (ESNs). ESNs can provide employees with various professional services to help them deal with daily work issues. Meanwhile, employees in companies are usually organized into different hierarchies according to the relative ranks of their positions. The company internal management structure can be outlined with the organizational chart visually, which is normally confidential to the public out of the privacy and security concerns. In this paper, we want to study the IOC (Inference of Organizational Chart) problem to identify company internal organizational chart based on the heterogeneous online ESN launched in it. IOC is very challenging to address as, to guarantee smooth operations, the internal organizational charts of companies need to meet certain structural requirements (about its depth and width). To solve the IOC problem, a novel unsupervised method Create (ChArT REcovEr) is proposed in this paper, which consists of 3 steps: (1) social stratification of ESN users into different social classes, (2) supervision link inference from managers to subordinates, and (3) consecutive social classes matching to prune the redundant supervision links. Extensive experiments conducted on real-world online ESN dataset demonstrate that Create can perform very well in addressing the IOC problem.
Label Refinery: Improving ImageNet Classification through Label Progression

Among the three main components (data, labels, and models) of any supervised learning system, data and models have been the main subjects of active research. However, studying labels and their properties has received very little attention. Current principles and paradigms of labeling impose several challenges to machine learning algorithms. Labels are often incomplete, ambiguous, and redundant. In this paper we study the effects of various properties of labels and introduce the Label Refinery : an iterative procedure that updates the ground truth labels after examining the entire dataset. We show significant gain using refined labels across a wide range of models. Using a Label Refinery improves the state-of-the-art top-1 accuracy of (1) AlexNet from 59.3 to 67.2, (2) MobileNet from 70.6 to 73.39, (3) MobileNet from 50.6 to 55.59, (4) VGG19 from 72.7 to 75.46, and (5) Darknet19 from 72.9 to 74.47.
Exploiting Topic based Twitter Sentiment for Stock Prediction

*This work is supported by a grant from National Science Foundation (IIS-1111092) and a strategic research grant from City University of Hong Kong (7002770) *The work was done when the first author was visiting University of Illinois at Chicago.  The Web has seen a tremendous rise in social media. • People’s experiences and opinions expressed in Social Media (e.g., Twitter, Facebook) contain rich social signals (e.g., about consumer sentiment, stock market, economic conditions, etc.). • The goal of this paper is to exploit public sentiment for stock market prediction. • Topic based sentiment is an important factor in stock prediction because it reflects public opinions about current issues/topics. • We employ a Bayesian non-parametric topic-based sentiment time series approach learned from streaming tweets to predict the S&P100 Stock Index.
Internet Traffic Classification by Aggregating Correlated Naive Bayes Predictions

This paper presents a novel traffic classification scheme to improve classification performance when few training data are available. In the proposed scheme, traffic flows are described using the discretized statistical features and flow correlation information is modeled by bag-of-flow (BoF). We solve the BoF-based traffic classification in a classifier combination framework and theoretically analyze the performance benefit. Furthermore, a new BoF-based traffic classification method is proposed to aggregate the naive Bayes (NB) predictions of the correlated flows. We also present an analysis on prediction error sensitivity of the aggregation strategies. Finally, a large number of experiments are carried out on two large-scale real-world traffic datasets to evaluate the proposed scheme. The experimental results show that the proposed scheme can achieve much better classification performance than existing state-of-the-art traffic classification methods.
Intrusion detection based on k-means clustering and OneR classification

Intrusion detection system (IDS) is used to detect various kinds of attacks in interconnected network. Many machine learning methods have also been introduced by researcher recently to obtain high accuracy and detection rate. Unfortunately, a potential drawback of all those methods is the rate of false alarm. However, our proposed approach shows better results, by combining clustering (to identify groups of similarly behaved samples, i.e. malicious and non-malicious activity) and classification techniques (to classify all data into correct class categories). The approach, KM+1R, combines the k-means clustering with the OneR classification technique. The KDD Cup '99 set is used as a simulation dataset. The result shows that our proposed approach achieve a better accuracy and detection rate, particularly in reducing the false alarm.
Thumb Motor Performance Varies by Movement Orientation, Direction, and Device Size During Single-Handed Mobile Phone Use

The aim of this study was to determine if thumb motor performance metrics varied by movement orientation, direction, and device size during single-handed use of a mobile phone device.



With the increased use of mobile phones, understanding how design factors affect and improve performance can provide better design guidelines.



A repeated measures laboratory experiment of 20 right-handed participants measured the thumb tip's 3-D position relative to a phone during reciprocal tapping tasks across four phone designs and four thumb tip movement orientations. Each movement orientation included two movement directions: an "outward" direction consisting in CMC (carpometacarpal) joint flexion or abduction movements and an "inward" direction consisting in CMC joint extension or adduction movements. Calculated metrics of the thumb's motor performance were Fitts' effective width and index of performance.



Index of performance varied significantly across phones, with performance being generally better for the smaller devices. Performance was also significantly higher for adduction-abduction movement orientations compared to flexion-extension, and for "outward" compared to "inward" movement directions.



For single-handed device use, adduction-abduction-type movements on smaller phones lead to better thumb performance.



The results from this study can be used to design new mobile phone devices and keypad interfaces that optimize specific thumb motions to improve the user-interface experience during single-handed use.
Decomposing Generalization: Models of Generic, Habitual, and Episodic Statements

We present a novel semantic framework for modeling linguistic expressions of generalization – generic, habitual, and episodic statements – as combinations of simple, real-valued referential properties of predicates and their arguments. We use this framework to construct a dataset covering the entirety of the Universal Dependencies English Web Treebank. We use this dataset to probe the efficacy of typelevel and token-level information – including hand-engineered features and contextual and non-contextual word embeddings – for predicting expressions of generalization.
Ka band spatial power-combining amplifier structures

2.24 w power-amplifier (PA) module at 35 GHz presented using broad-band spatial power-combining system. The combiner can accommodate more monolithic microwave integrated-circuit (MMIC) PA with stagger placement structure on limited microstrip space in Ka-band waveguide structure with good return losses, and heat can dissipated into aluminum carrier quickly. This combiner is based on a slotline-to-microstrip transition structure, which also serves as a four-way power combiner. The proposed 2*2 combining structure combined by vertical stacking inside the waveguide was analyzed and optimized by finite-element-method (FEM) simulations and experiments.
Physics-Based Planning for Planetary Exploration

Recently a planetary rover returned important scientific information from Mars. More ambitious missions are planned. New planning methods are required that allow rovers to explore challenging areas with a high level of autonomy. This paper presents a planning methodology based on a physics-based model of the rover and environment. Plans are developed that allow a rover to perform a mission while explicitly considering constraints such as power, actuator, wheel slip, and vehicle stability limits. Results obtained from detailed rover simulations are presented.
Involuntary covert orienting is contingent on attentional control settings.

Four experiments tested a new hypothesis that involuntary attention shifts are contingent on the relationship between the properties of the eliciting event and the properties required for task performance. In a variant of the spatial cuing paradigm, the relation between cue property and the property useful in locating the target was systematically manipulated. In Experiment 1, invalid abrupt-onset precues produced costs for targets characterized by an abrupt onset but not for targets characterized by a discontinuity in color. In Experiment 2, invalid color precues produced greater costs for color targets than for abrupt-onset targets. Experiment 3 provided converging evidence for this pattern. Experiment 4 investigated the boundary conditions and time course for attention shifts elicited by color discontinuities. The results of these experiments suggest that attention capture is contingent on attentional control settings induced by task demands.
Association of host genome with intestinal microbial composition in a large healthy cohort

Intestinal microbiota is known to be important in health and disease. Its composition is influenced by both environmental and host factors. Few large-scale studies have evaluated the association between host genetic variation and the composition of microbiota. We recruited a cohort of 1,561 healthy individuals, of whom 270 belong in 123 families, and found that almost one-third of fecal bacterial taxa were heritable. In addition, we identified 58 SNPs associated with the relative abundance of 33 taxa in 1,098 discovery subjects. Among these, four loci were replicated in a second cohort of 463 subjects: rs62171178 (nearest gene UBR3) associated with Rikenellaceae, rs1394174 (CNTN6) associated with Faecalibacterium, rs59846192 (DMRTB1) associated with Lachnospira, and rs28473221 (SALL3) associated with Eubacterium. After correction for multiple testing, 6 of the 58 associations remained significant, one of which replicated. These results identify associations between specific genetic variants and the gut microbiome.
Analysis of Magnetic Interaction in Remotely Controlled Magnetic Devices and its Application to a Capsule Robot for Drug Delivery

In this paper, we present the 3-D analysis of the torque transmitted to a small permanent magnet that is embedded in a capsule robot to achieve targeted drug delivery. This analysis is carried out by using analytical models and experimental results for the magnetic field created by multiple arc-shaped permanent magnets (ASMs), and the torque imparted to a magnetic capsule robot that has an arbitrary position and orientation. Our experimental results, which are in agreement with the analytical results, show that the on-board drug release mechanism can be actuated at any position and with large inclinations of the capsule robot without having to make positional adjustments in the external magnetic system. We have established analytical expressions describing the relationship between the rotating magnetic field created by the ASMs and the transmitted torque. Such relationships are essential to establish real-time control strategies for the actuation of magnetically articulated devices such as drug delivery in capsule robots.
Varieties of learning automata: an overview

Automata models of learning systems introduced in the 1960s were popularized as learning automata (LA) in a survey paper by Narendra and Thathachar (1974). Since then, there have been many fundamental advances in the theory as well as applications of these learning models. In the past few years, the structure of LA, has been modified in several directions to suit different applications. Concepts such as parameterized learning automata (PLA), generalized learning,automata (GLA), and continuous action-set learning automata (CALA) have been proposed, analyzed, and applied to solve many significant learning problems. Furthermore, groups of LA forming teams and feedforward networks have been shown to converge to desired solutions under appropriate learning algorithms. Modules of LA have been used for parallel operation with consequent increase in speed of convergence. All of these concepts and results are relatively new and are scattered in technical literature. An attempt has been made in this paper to bring together the main ideas involved in a unified framework and provide pointers to relevant references.
MobileDeepPill: A Small-Footprint Mobile Deep Learning System for Recognizing Unconstrained Pill Images

Correct identification of prescription pills based on their visual appearance is a key step required to assure patient safety and facilitate more effective patient care. With the availability of high-quality cameras and computational power on smartphones, it is possible and helpful to identify unknown prescription pills using smartphones. Towards this goal, in 2016, the U.S. National Library of Medicine (NLM) of the National Institutes of Health (NIH) announced a nationwide competition, calling for the creation of a mobile vision system that can recognize pills automatically from a mobile phone picture under unconstrained real-world settings. In this paper, we present the design and evaluation of such mobile pill image recognition system called MobileDeepPill. The development of MobileDeepPill involves three key innovations: a triplet loss function which attains invariances to real-world noisiness that deteriorates the quality of pill images taken by mobile phones; a multi-CNNs model that collectively captures the shape, color and imprints characteristics of the pills; and a Knowledge Distillation-based deep model compression framework that significantly reduces the size of the multi-CNNs model without deteriorating its recognition performance. Our deep learning-based pill image recognition algorithm wins the First Prize (champion) of the NIH NLM Pill Image Recognition Challenge. Given its promising performance, we believe MobileDeepPill helps NIH tackle a critical problem with significant societal impact and will benefit millions of healthcare personnel and the general public.
Cognition as a dynamic system : Principles from embodiment

Traditional approaches to cognitive development concentrate on the stability of cognition and explain that stability via concepts segregated from perceiving acting. A dynamic systems approach in contrast focuses on the self-organization of behavior in tasks. This article uses recent results concerning the embodiment of cognition to argue for a dynamic systems approach. The embodiment hypothesis is the idea that intelligence emerges in the interaction of an organism with an environment and as a result of sensory-motor activity. The continual coupling of cognition to the world through the body both adapts cognition to the idiosyncrasies of the here and now, makes it relevant, and provides the mechanism for developmental change.
X-band two-stage high-efficiency switched-mode power amplifiers

This paper presents efficiency optimization of X-band two-stage microwave power amplifiers (PAs) in which the output stage is designed to operate in class-E mode. A hybrid PA which uses the same MESFET devices in both stages achieves 16 dB of saturated gain with an output power of 20 dBm and total power added efficiency (PAE) of 52% at 10 GHz. A broadband monolithic two-stage double heterojunction bipolar transistor PA, fabricated by Northrop Grumman Space Technology, with a class-AB first stage and class-E second stage achieves 24.6 dBm of output power with 24.6-dB gain and total PAE of 52% at 8 GHz. The design is performed starting from class-E theory and using load-pull measurements and/or nonlinear simulations.