nils-herrmann/clean_scirepeval huggingface dataset:
The Effectiveness of Whole-School Antibullying Programs : A Synthesis of Evaluation Research

Bullying is a serious problem in schools, and school authorities need effective solutions to resolve this problem. There is growing interest in the wholeschool approach to bullying. Whole-school programs have multiple components that operate simultaneously at different levels in the school community. This article synthesizes the existing evaluation research on whole-school programs to determine the overall effectiveness of this approach. The majority of programs evaluated to date have yielded nonsignificant outcomes on measures of self-reported victimization and bullying, and only a small number have yielded positive outcomes. On the whole, programs in which implementation was systematically monitored tended to be more effective than programs without any monitoring. show little empathy for their victims (Roberts & Morotti, 2000). Bullying may be a means of increasing one’s social status and access to valued resources, such as the attention of opposite-sex peers (Pellegrini, 2001). Victims tend to be socially isolated, lack social skills, and have more anxiety and lower self-esteem than students in general (Olweus, 1997). They also tend to have a higher than normal risk for depression and suicide (e.g., Sourander, Helstelae, Helenius, & Piha, 2000). A subgroup of victims reacts aggressively to abuse and has a distinct pattern of psychosocial maladjustment encompassing both the antisocial behavior of bullies and the social and emotional difficulties of victims (Glover, Gough, Johnson, & Cartwright, 2000). Bullying is a relatively stable and long-term problem for those involved, particularly children fitting the profile Bullying is a particularly vicious kind of aggressive behavior distinguished by repeated acts against weaker victims who cannot easily defend themselves (Farrington, 1993; Smith & Brain, 2000). Its consequences are severe, especially for those victimized over long periods of time. Bullying is a complex psychosocial problem influenced by a myriad of variables. The repetition and imbalance of power involved may be due to physical strength, numbers, or psychological factors. Both bullies and victims evidence poorer psychological adjustment than individuals not involved in bullying (Kumpulainen, Raesaenen, & Henttonen, 1999; Nansel et al., 2001). Children who bully tend to be involved in alcohol consumption and smoking, have poorer academic records than noninvolved students, display a strong need for dominance, and
Prior matters: simple and general methods for evaluating and improving topic quality in topic modeling

Latent Dirichlet Allocation (LDA) models trained without stopword removal often produce topics with high posterior probabilities on uninformative words, obscuring the underlying corpus content. Even when canonical stopwords are manually removed, uninformative words common in that corpus will still dominate the most probable words in a topic. In this work, we first show how the standard topic quality measures of coherence and pointwise mutual information act counter-intuitively in the presence of common but irrelevant words, making it difficult to even quantitatively identify situations in which topics may be dominated by stopwords. We propose an additional topic quality metric that targets the stopword problem, and show that it, unlike the standard measures, correctly correlates with human judgements of quality. We also propose a simple-to-implement strategy for generating topics that are evaluated to be of much higher quality by both human assessment and our new metric. This approach, a collection of informative priors easily introduced into most LDA-style inference methods, automatically promotes terms with domain relevance and demotes domain-specific stop words. We demonstrate this approach’s effectiveness in three very different domains: Department of Labor accident reports, online health forum posts, and NIPS abstracts. Overall we find that current practices thought to solve this problem do not do so adequately, and that our proposal offers a substantial improvement for those interested in interpreting their topics as objects in their own right.
A Tool for Checking ANSI-C Programs

We present a tool for the formal verification of ANSI-C programs using Bounded Model Checking (BMC). The emphasis is on usability: the tool supports almost all ANSI-C language features, including pointer constructs, dynamic memory allocation, recursion, and the float and double data types. From the perspective of the user, the verification is highly automated: the only input required is the BMC bound. The tool is integrated into a graphical user interface. This is essential for presenting long counterexample traces: the tool allows stepping through the trace in the same way a debugger allows stepping through a program.
A Tableau Prover for Natural Logic and Language

Modeling the entailment relation over sentences is one of the generic problems of natural language understanding. In order to account for this problem, we design a theorem prover for Natural Logic, a logic whose terms resemble natural language expressions. The prover is based on an analytic tableau method and employs syntactically and semantically motivated schematic rules. Pairing the prover with a preprocessor, which generates formulas of Natural Logic from linguistic expressions, results in a proof system for natural language. It is shown that the system obtains a comparable accuracy (≈81%) on the unseen SICK data while achieving the stateof-the-art precision (≈98%).
Generative Temporal Models with Spatial Memory for Partially Observed Environments

In model-based reinforcement learning, generative and temporal models of environments can be leveraged to boost agent performance, either by tuning the agent’s representations during training or via use as part of an explicit planning mechanism. However, their application in practice has been limited to simplistic environments, due to the difficulty of training such models in larger, potentially partially-observed and 3D environments. In this work we introduce a novel action-conditioned generative model of such challenging environments. The model features a non-parametric spatial memory system in which we store learned, disentangled representations of the environment. Low-dimensional spatial updates are computed using a state-space model that makes use of knowledge on the prior dynamics of the moving agent, and high-dimensional visual observations are modelled with a Variational Auto-Encoder. The result is a scalable architecture capable of performing coherent predictions over hundreds of time steps across a range of partially observed 2D and 3D environments.
A3Net: Adversarial-and-Attention Network for Machine Reading Comprehension

In this paper, we introduce Adversarial-and-attention Network (A3Net) for Machine Reading Comprehension. This model extends existing approaches from two perspectives. First, adversarial training is applied to several target variables within the model, rather than only to the inputs or embeddings. We control the norm of adversarial perturbations according to the norm of original target variables, so that we can jointly add perturbations to several target variables during training. As an effective regularization method, adversarial training improves robustness and generalization of our model. Second, we propose a multi-layer attention network utilizing three kinds of high-efficiency attention mechanisms. Multi-layer attention conducts interaction between question and passage within each layer, which contributes to reasonable representation and understanding of the model. Combining these two contributions, we enhance the diversity of dataset and the information extracting ability of the model at the same time. Meanwhile, we construct A3Net for the WebQA dataset. Results show that our model outperforms the state-ofthe-art models (improving Fuzzy Score from 73.50% to 77.0%).
Explicit Pose Deformation Learning for Tracking Human Poses

We present a method for human pose tracking that learns explicitly about the dynamic effects of human motion on joint appearance. In contrast to previous techniques which employ generic tools such as dense optical flow or spatiotemporal smoothness constraints to pass pose inference cues between frames, our system instead learns to predict joint displacements from the previous frame to the current frame based on the possibly changing appearance of relevant pixels surrounding the corresponding joints in the previous frame. This explicit learning of pose deformations is formulated by incorporating concepts from human pose estimation into an optical flow-like framework. With this approach, state-of-the-art performance is achieved on standard benchmarks for various pose tracking tasks including 3D body pose tracking in RGB video, 3D hand pose tracking in depth sequences, and 3D hand gesture tracking in RGB video.
USSD: The Third Universal App

In this paper we argue for the use of Unstructured Supplementary Service Data (USSD) as a platform for universal cell phone applications. We examine over a decade of ICT4D research, analyzing how USSD can extend and complement current uses of IVR and SMS for data collection, messaging, information access, social networking and complex user initiated transactions. Based on these findings we identify situations when a mobile based project should consider using USSD with increasingly common third party gateways over other mediums. This analysis also motivates the design and implementation of an open source library for rapid development of USSD applications. Finally, we explore three USSD use cases, demonstrating how USSD opens up a design space not available with IVR or SMS.
A Sensorimotor Circuit in Mouse Cortex for Visual Flow Predictions

The cortex is organized as a hierarchical processing structure. Feedback from higher levels of the hierarchy, known as top-down signals, have been shown to be involved in attentional and contextual modulation of sensory responses. Here we argue that top-down input to the primary visual cortex (V1) from A24b and the adjacent secondary motor cortex (M2) signals a prediction of visual flow based on motor output. A24b/M2 sends a dense and topographically organized projection to V1 that targets most neurons in layer 2/3. By imaging the activity of A24b/M2 axons in V1 of mice learning to navigate a 2D virtual environment, we found that their activity was strongly correlated with locomotion and resulting visual flow feedback in an experience-dependent manner. When mice were trained to navigate a left-right inverted virtual environment, correlations of neural activity with behavior reversed to match visual flow. These findings are consistent with a predictive coding interpretation of visual processing.
Active ROCOF Relay for Islanding Detection

Intentional frequency perturbation by recently researched active islanding detection techniques for inverter based distributed generation (DG) define new threshold settings for the frequency relays. This innovation has enabled the modern frequency relays to operate inside the non-detection zone (NDZ) of the conventional frequency relays. However, the effect of such perturbation on the performance of the rate of change of frequency (ROCOF) relays has not been researched so far. This paper evaluates the performance of ROCOF relays under such perturbations for an inverter interfaced DG and proposes an algorithm along with the new threshold settings to enable it work under the NDZ. The proposed algorithm is able to differentiate between an islanding and a non-islanding event. The operating principle of relay is based on low frequency current injection through grid side voltage source converter (VSC) control of doubly fed induction generator (DFIG) and therefore, the relay is defined as “active ROCOF relay”. Simulations are done in MATLAB.
Magnetic Resonance Contrast Prediction Using Deep Learning

A key strength of magnetic resonance imaging (MRI) is its ability to measure different tissue contrasts. However, each contrast is typically collected in series, making clinical MRI a slow and expensive procedure. In this paper, we present a novel approach for learning transformations from one MRI contrast to another. We train a fully convolutional neural network that takes T1-weighted MRI images as input, and generates the corresponding T2-weighted images as output. To evaluate our results, we compare different network depths, input features, and numbers of training subjects. We find that a nine-layer 2D convolutional neural network (CNN) using T1-intensity with eight tissue masks as input generates the best output images with lowest L2 error. Furthermore, the results can continue to improve with even larger training datasets. These results confirm the feasibility of using deep learning to predict one MRI contrast from another and accelerate clinical MRI acquisition.
3D Object Detection and Localization Using Multimodal Point Pair Features

Object detection and localization is a crucial step for inspection and manipulation tasks in robotic and industrial applications. We present an object detection and localization scheme for 3D objects that combines intensity and depth data. A novel multimodal, scale- and rotation-invariant feature is used to simultaneously describe the object's silhouette and surface appearance. The object's position is determined by matching scene and model features via a Hough-like local voting scheme. The proposed method is quantitatively and qualitatively evaluated on a large number of real sequences, proving that it is generic and highly robust to occlusions and clutter. Comparisons with state of the art methods demonstrate comparable results and higher robustness with respect to occlusions.
Auto-Encoding Scene Graphs for Image Captioning

We propose Scene Graph Auto-Encoder (SGAE) that incorporates the language inductive bias into the encoderdecoder image captioning framework for more human-like captions. Intuitively, we humans use the inductive bias to compose collocations and contextual inference in discourse. For example, when we see the relation “person on bike”, it is natural to replace “on” with “ride” and infer “person riding bike on a road” even the “road” is not evident. Therefore, exploiting such bias as a language prior is expected to help the conventional encoder-decoder models less likely overfit to the dataset bias and focus on reasoning. Specifically, we use the scene graph — a directed graph (G) where an object node is connected by adjective nodes and relationship nodes — to represent the complex structural layout of both image (I) and sentence (S). In the textual domain, we use SGAE to learn a dictionary (D) that helps to reconstruct sentences in the S → G → D → S pipeline, where D encodes the desired language prior; in the vision-language domain, we use the shared D to guide the encoder-decoder in the I → G → D → S pipeline. Thanks to the scene graph representation and shared dictionary, the inductive bias is transferred across domains in principle. We validate the effectiveness of SGAE on the challenging MS-COCO image captioning benchmark, e.g., our SGAE-based single-model achieves a new state-of-theart 127.8 CIDEr-D on the Karpathy split, and a competitive 125.5 CIDEr-D (c40) on the official server even compared to other ensemble models.
Numerical Analysis of Turbulent Fluid Flow and Drag Coefficient for Optimizing the AUV Hull Design

Autonomous Underwater Vehicles (AUVs) are robots able to perform tasks without human intervention (remote operators). Research and development of this class of vehicles has growing, due to the excellent characteristics of the AUVs to operate in different situations. Therefore, this study aims to analyze turbulent single fluid flow over different geometric configurations of an AUV hull, in order to obtain test geometry that generates lower drag force, which reduces the energy consumption of the vehicle, thereby increasing their autonomy during operation. In the numerical analysis was used ANSYS-CFX® 11.0 software, which is a powerful tool for solving problems involving fluid mechanics. Results of the velocity (vectors and streamlines), pressure distribution and drag coefficient are showed and analyzed. Optimum hull geometry was found. Lastly, a relationship between the geometric parameters analyzed and the drag coefficient was obtained.
Mental Health Problems in University Students : A Prevalence Study

A survey of mental health problems of university students was carried out on 1850 participants in the age range 19-26 years.  As far as the individual dimensions are concerned, 17% respondents comprising sample of the present study fall in very severe category Sense of Being Dysfunctional, followed by Loss of Confidence (16%), Lack of Self Regulation (14%) and Anxiety Proneness (12%). These findings are in lying with similar other studies on mental health of students. The role of variables like sample characteristics, the measure used, cultural and contextual factors are discussed in determining rates as well as their implications for student counseling service in prevention and intervention.
Mining big data: current status, and forecast to the future

Big Data is a new term used to identify datasets that we can not manage with current methodologies or data mining software tools due to their large size and complexity. Big Data mining is the capability of extracting useful information from these large datasets or streams of data. New mining techniques are necessary due to the volume, variability, and velocity, of such data. The Big Data challenge is becoming one of the most exciting opportunities for the years to come. We present in this issue, a broad overview of the topic, its current status, controversy, and a forecast to the future. We introduce four articles, written by influential scientists in the field, covering the most interesting and state-of-the-art topics on Big Data mining.
A Very Brief Introduction to Machine Learning With Applications to Communication Systems

Given the unprecedented availability of data and computing resources, there is widespread renewed interest in applying data-driven machine learning methods to problems for which the development of conventional engineering solutions is challenged by modeling or algorithmic deficiencies. This tutorial-style paper starts by addressing the questions of why and when such techniques can be useful. It then provides a high-level introduction to the basics of supervised and unsupervised learning. For both supervised and unsupervised learning, exemplifying applications to communication networks are discussed by distinguishing tasks carried out at the edge and at the cloud segments of the network at different layers of the protocol stack, with an emphasis on the physical layer.
Stacked Convolutional Auto-Encoders for Hierarchical Feature Extraction

We present a novel convolutional auto-encoder (CAE) for unsupervised feature learning. A stack of CAEs forms a convolutional neural network (CNN). Each CAE is trained using conventional on-line gradient descent without additional regularization terms. A max-pooling layer is essential to learn biologically plausible features consistent with those found by previous approaches. Initializing a CNN with filters of a trained CAE stack yields superior performance on a digit (MNIST) and an object recognition (CIFAR10) benchmark.
Self-regulation and depletion of limited resources: does self-control resemble a muscle?

The authors review evidence that self-control may consume a limited resource. Exerting self-control may consume self-control strength, reducing the amount of strength available for subsequent self-control efforts. Coping with stress, regulating negative affect, and resisting temptations require self-control, and after such self-control efforts, subsequent attempts at self-control are more likely to fail. Continuous self-control efforts, such as vigilance, also degrade over time. These decrements in self-control are probably not due to negative moods or learned helplessness produced by the initial self-control attempt. These decrements appear to be specific to behaviors that involve self-control; behaviors that do not require self-control neither consume nor require self-control strength. It is concluded that the executive component of the self--in particular, inhibition--relies on a limited, consumable resource.
Anomalies The Endowment EfFect , Loss Aversion , and Status Quo Bias

Economics can be distinguished from other social sciences by the belief that most (all?) behavior can be explained by assuming that agents have stable, well-defined preferences and make rational choices consistent with those preferences in markets that (eventually) clear. An empirical result qualifies as an anomaly if it is difficult to "rationalize," or if implausible assumptions are necessary to explain it within the paradigm. This column presents a series of such anomalies. Readers are invited to suggest topics for future columns by sending a note with some reference to (or better yet copies oO the relevant research. Comments on anomalies printed here are also welcome. The address is: Richard Thaler, c/o Journal of Economic Perspectives, Johnson Graduate School of Management, Malott Hall, Cornell University, Ithaca, NY 14853. After this issue, the "Anomalies" column will no longer appear in every issue and instead will appear occasionally, when a pressing anomaly crosses Dick Thaler's desk. However, suggestions for new columns and comments on old ones are still welcome. Thaler would like to quash one rumor before it gets started, namely that he is cutting back because he has run out of anomalies. Au contraire, it is the dilemma of choosing which juicy anomaly to discuss that lakes so much time.
Network service orchestration standardization: A technology survey

Network services underpin operator revenues, and value-added services provide income beyond core (voice and data) infrastructure capability. Today, operators face multiple challenges: a need to innovate and offer a wider choice of value-added services, whilst increasing network scale, bandwidth and flexibility. They must also reduce operational costs, and deploy services far faster in minutes rather than days or weeks. In the recent years, the network community, motivated by the aforementioned challenges, has developed production network architectures and seeded technologies, like Software Defined Networking, Application-based Network Operations and Network Function Virtualization. These technologies enhance the highly desired properties for elasticity, agility and cost-effectiveness in the operator environment. A key requirement to fully exploit the benefits of these new architectures and technologies is a fundamental shift in management and control of resources, and the ability to orchestrate the network infrastructure: coordinate the instantiation of high-level network services across different technological domains and automate service deployment and re-optimization. This paper surveys existing standardization efforts for the orchestration automation, coordination, and management of complex set of network and function resources (both physical and virtual), and highlights the various enabling technologies, strengths and weaknesses, adoption challenges for operators, and areas where further research is required.
Vehicle Price Prediction System using Machine Learning Techniques

This paper presents a vehicle price prediction system by using the supervised machine learning technique. The research uses multiple linear regression as the machine learning prediction method which offered 98% prediction precision. Using multiple linear regression, there are multiple independent variables but one and only one dependent variable whose actual and predicted values are compared to find precision of results. This paper proposes a system where price is dependent variable which is predicted, and this price is derived from factors like vehicle’s model, make, city, version, color, mileage, alloy rims and power steering.