nils-herrmann/clean_scirepeval huggingface dataset:
Counting People With Low-Level Features and Bayesian Regression

An approach to the problem of estimating the size of inhomogeneous crowds, which are composed of pedestrians that travel in different directions, without using explicit object segmentation or tracking is proposed. Instead, the crowd is segmented into components of homogeneous motion, using the mixture of dynamic-texture motion model. A set of holistic low-level features is extracted from each segmented region, and a function that maps features into estimates of the number of people per segment is learned with Bayesian regression. Two Bayesian regression models are examined. The first is a combination of Gaussian process regression with a compound kernel, which accounts for both the global and local trends of the count mapping but is limited by the real-valued outputs that do not match the discrete counts. We address this limitation with a second model, which is based on a Bayesian treatment of Poisson regression that introduces a prior distribution on the linear weights of the model. Since exact inference is analytically intractable, a closed-form approximation is derived that is computationally efficient and kernelizable, enabling the representation of nonlinear functions. An approximate marginal likelihood is also derived for kernel hyperparameter learning. The two regression-based crowd counting methods are evaluated on a large pedestrian data set, containing very distinct camera views, pedestrian traffic, and outliers, such as bikes or skateboarders. Experimental results show that regression-based counts are accurate regardless of the crowd size, outperforming the count estimates produced by state-of-the-art pedestrian detectors. Results on 2 h of video demonstrate the efficiency and robustness of the regression-based crowd size estimation over long periods of time.
Subverting system authentication with context-aware, reactive virtual machine introspection

Recent advances in bridging the semantic gap between virtual machines (VMs) and their guest processes have a dark side: They can be abused to subvert and compromise VM file system images and process images. To demonstrate this alarming capability, a context-aware, reactive VM Introspection (VMI) instrument is presented and leveraged to automatically break the authentication mechanisms of both Linux and Windows operating systems. By bridging the semantic gap, the attack is able to automatically identify critical decision points where authentication succeeds or fails at the binary level. It can then leverage the VMI to transparently corrupt the control-flow or data-flow of the victim OS at that point, resulting in successful authentication without any password-guessing or encryption-cracking. The approach is highly flexible (threatening a broad class of authentication implementations), practical (realizable against real-world OSes and VM images), and useful for both malicious attacks and forensics analysis of virtualized systems and software.
Online evolutionary collaborative filtering

Collaborative filtering algorithms attempt to predict a user's interests based on his past feedback. In real world applications, a user's feedback is often continuously collected over a long period of time. It is very common for a user's interests or an item's popularity to change over a long period of time. Therefore, the underlying recommendation algorithm should be able to adapt to such changes accordingly. However, most existing algorithms do not distinguish current and historical data when predicting the users' current interests. In this paper, we consider a new problem - online evolutionary collaborative filtering, which tracks user interests over time in order to make timely recommendations. We extended the widely used neighborhood based algorithms by incorporating temporal information and developed an incremental algorithm for updating neighborhood similarities with new data. Experiments on two real world datasets demonstrated both improved effectiveness and efficiency of the proposed approach.
The Information Bus - An Architecture for Extensible Distributed Systems

Research can rarely be performed on large-scale, distributed systems at the level of thousands of workstations. In this paper, we describe the motivating constraints, design principles, and architecture for an extensible, distributed system operating in such an environment. The constraints include continuous operation, dynamic system evolution, and integration with extant systems. The Information Bus, our solution, is a novel synthesis of four design principles: core communication protocols have minimal semantics, objects are self-describing, types can be dynamically defined, and communication is anonymous. The current implementation provides both flexibility and high performance, and has been proven in several commercial environments, including integrated circuit fabrication plants and brokerage/trading floors.
An online growth mindset intervention in a sample of rural adolescent girls.

Students living in rural areas of the United States exhibit lower levels of educational attainment than their suburban counterparts. Innovative interventions are needed to close this educational achievement gap.



We investigated whether an online growth mindset intervention could be leveraged to promote academic outcomes.


SAMPLE
We tested the mindset intervention in a sample of 222 10th-grade adolescent girls (M age = 15.2; 38% White, 25% Black, 29% Hispanic) from four rural, low-income high schools in the Southeastern United States.



We conducted a randomized controlled trial to test the efficacy of the growth mindset intervention, relative to a sexual health programme. We used random sampling and allocation procedures to assign girls to either the mindset intervention (n = 115) or an attention-matched control programme (n = 107). We assessed participants at pre-test, immediate post-test, and 4-month follow-up.



Relative to the control condition, students assigned to the mindset intervention reported stronger growth mindsets at immediate post-test and 4-month follow-up. Although the intervention did not have a total effect on academic attitudes or grades, it indirectly increased motivation to learn, learning efficacy and grades via the shifts in growth mindsets.



Results indicate that this intervention is a promising method to encourage growth mindsets in rural adolescent girls.
Human Activity Recognition on Smartphones with Awareness of Basic Activities and Postural Transitions

Postural Transitions (PTs) are transitory movements that describe the change of state from one static posture to another. In several Human Activity Recognition (HAR) systems, these transitions cannot be disregarded due to their noticeable incidence with respect to the duration of other Basic Activities (BAs). In this work, we propose an online smartphone-based HAR system which deals with the occurrence of postural transitions. If treated properly, the system accuracy improves by avoiding fluctuations in the classifier. The method consists of concurrently exploiting Support Vector Machines (SVMs) and temporal filters of activity probability estimations within a limited time window. We present the benefits of this approach through experiments over a HAR dataset which has been updated with PTs and made publicly available. We also show the new approach performs better than a previous baseline system, where PTs were not taken into account.
Fuzzy Graph Theory : A Survey

A fuzzy graph (f-graph) is a pair G : ( σ, μ) where σ is a fuzzy subset of a set S and μ is a fuzzy relation on σ. A fuzzy graph H : ( τ, υ) is called a partial fuzzy subgraph of G : (σ, μ) if τ (u) ≤ σ(u) for every u and υ (u, v) ≤ μ(u, v) for every u and v . In particular we call a partial fuzzy subgraph H : ( τ, υ) a fuzzy subgraph of G : ( σ, μ ) if τ (u) = σ(u) for every u in τ * and υ (u, v) = μ(u, v) for every arc (u, v) in υ*. A connected f-graph G : ( σ, μ) is a fuzzy tree(f-tree) if it has a fuzzy spannin g subgraph F : (σ, υ), which is a tree, where for all arcs (x, y) not i n F there exists a path from x to y in F whose strength is more than μ(x, y). A path P of length n is a sequence of disti nct nodes u0, u1, ..., un such that μ(ui−1, ui) > 0, i = 1, 2, ..., n and the degree of membershi p of a weakest arc is defined as its strength. If u 0 = un and n≥ 3, then P is called a cycle and a cycle P is called a fuzzy cycle(f-cycle) if it cont ains more than one weakest arc . The strength of connectedness between two nodes x and y is efined as the maximum of the strengths of all paths between x and y and is denot ed by CONNG(x, y). An x − y path P is called a strongest x − y path if its strength equal s CONNG(x, y). An f-graph G : ( σ, μ) is connected if for every x,y in σ ,CONNG(x, y) > 0. In this paper, we offer a survey of selected recent results on fuzzy graphs.
News recommendation via hypergraph learning: encapsulation of user behavior and news content

Personalized news recommender systems have gained increasing attention in recent years. Within a news reading community, the implicit correlations among news readers, news articles, topics and named entities, e.g., what types of named entities in articles are preferred by users, and why users like the articles, could be valuable for building an effective news recommender. In this paper, we propose a novel news personalization framework by mining such correlations. We use hypergraph to model various high-order relations among different objects in news data, and formulate news recommendation as a ranking problem on fine-grained hypergraphs. In addition, by transductive inference, our proposed algorithm is capable of effectively handling the so-called cold-start problem. Extensive experiments on a data set collected from various news websites have demonstrated the effectiveness of our proposed algorithm.
Adaptive Security for Multi-layer Ad-hoc Networks

Secure communication is critical in military environments where the network infrastructure is vulnerable to various attacks and compromises. A conventional centralized solution breaks down when the security servers are destroyed by the enemies. In this paper we design and evaluate a security framework for multi-layer ad-hoc wireless networks with unmanned aerial vehicles (UAVs). In battlefields, the framework adapts to the contingent damages on the network infrastructure. Depending on the availability of the network infrastructure, our design is composed of two modes. In infrastructure mode, security services, specifically the authentication services, are implemented on UAVs that feature low overhead and flexible managements. When the UAVs fail or are destroyed, our system seamlessly switches to infrastructureless mode, a backup mechanism that maintains comparable security services among the surviving units. In the infrastructureless mode, the security services are localized to each node’s vicinity to comply with the ad-hoc communication mechanism in the scenario. We study the instantiation of these two modes and the transitions between them. Our implementation and simulation measurements confirm the effectiveness of our design.
Virtual reality exposure therapy in anxiety disorders: a systematic review of process-and-outcome studies.

In recent years, virtual reality exposure therapy (VRET) has become an interesting alternative for the treatment of anxiety disorders. Research has focused on the efficacy of VRET in treating anxiety disorders: phobias, panic disorder, and posttraumatic stress disorder. In this systematic review, strict methodological criteria are used to give an overview of the controlled trials regarding the efficacy of VRET in patients with anxiety disorders. Furthermore, research into process variables such as the therapeutic alliance and cognitions and enhancement of therapy effects through cognitive enhancers is discussed. The implications for implementation into clinical practice are considered.
Experimental Comparison of Skid Steering Vs. Explicit Steering for a Wheeled Mobile Robot

Robotic tasks call for a range of steering activity: one extreme is highway driving with negligible turning for hundreds of kilometers; another is forklift handling, which calls for agile turning. The scope of this thesis considers steady state turning of a wheeled vehicle on natural terrain with slow but capable locomotors characteristic of planetary robotic vehicles. Experiments are performed using a single vehicle that can exhibit both skid and explicit steering while driving steady state circles. Skid steering is accomplished by creating a differential velocity between the inner and outer wheels. Explicit steering is accomplished by changing the heading of the wheels to cause a change in heading of the vehicle. Experimental results are gathered to provide information regarding power draw, individual wheel torque, and position information. The experimental results show that power and torque for skid and explicit turning degenerate to equal values at infinite radius (straight driving). As the turn radius decreases from straight driving to a point turn, greater power and torque are required as a greater sideslip angle is encountered. For all turns skid steering requires greater power and torque than for explicit turning because sideslip angles are greater in all cases. In the limiting case of a point turn, the power for skid steering is approximately double that of an explicit point turn. The primary contribution of this research is the experimental quantification of the power and torque requirements over turn radii from zero to infinity.
Internet addiction or excessive internet use.

Problematic Internet addiction or excessive Internet use is characterized by excessive or poorly controlled preoccupations, urges, or behaviors regarding computer use and Internet access that lead to impairment or distress. Currently, there is no recognition of internet addiction within the spectrum of addictive disorders and, therefore, no corresponding diagnosis. It has, however, been proposed for inclusion in the next version of the Diagnostic and Statistical Manual of Mental Disorder (DSM).



To review the literature on Internet addiction over the topics of diagnosis, phenomenology, epidemiology, and treatment.



Review of published literature between 2000-2009 in Medline and PubMed using the term "internet addiction.



Surveys in the United States and Europe have indicated prevalence rate between 1.5% and 8.2%, although the diagnostic criteria and assessment questionnaires used for diagnosis vary between countries. Cross-sectional studies on samples of patients report high comorbidity of Internet addiction with psychiatric disorders, especially affective disorders (including depression), anxiety disorders (generalized anxiety disorder, social anxiety disorder), and attention deficit hyperactivity disorder (ADHD). Several factors are predictive of problematic Internet use, including personality traits, parenting and familial factors, alcohol use, and social anxiety.



Although Internet-addicted individuals have difficulty suppressing their excessive online behaviors in real life, little is known about the patho-physiological and cognitive mechanisms responsible for Internet addiction. Due to the lack of methodologically adequate research, it is currently impossible to recommend any evidence-based treatment of Internet addiction.
Psychological well-being: meaning, measurement, and implications for psychotherapy research.

A model of positive psychological functioning that emerges from diverse domains of theory and philosophy is presented. Six key dimensions of wellness are defined, and empirical research summarizing their empirical translation and sociodemographic correlates is presented. Variations in well-being are explored via studies of discrete life events and enduring human experiences. Life histories of the psychologically vulnerable and resilient, defined via the cross-classification of depression and well-being, are summarized. Implications of the focus on positive functioning for research on psychotherapy, quality of life, and mind/body linkages are reviewed.
A data mining approach to analysis and prediction of movie

This paper details our analysis of the Internet Movie Database (IMDb), a free, user-maintained, online resource of production details for over 390,000 movies, television series and video games, which contains information such as title, genre, box-office taking, cast credits and user's ratings. We gather a series of interesting facts and relationships using a variety of data mining techniques. In particular, we concentrate on attributes relevant to the user ratings of movies, such as discovering if big-budget films are more popular than their low budget counterparts, if any relationship between movies produced during the "golden age" (i.e. Citizen Kane, It’s A Wonderful Life, etc.) can be proved, and whether any particular actors or actresses are likely to help a movie to succeed. The paper also reports on the techniques used, giving their implementation and usefulness. We have found that the IMDb is difficult to perform data mining upon, due to the format of the source data. We also found some interesting facts, such as the budget of a film is no indication of how well-rated it will be, there is a downward trend in the quality of films over time, and the director and actors/actresses involved in a film are the most important factors to its success or lack thereof. The data used in this paper is not freely distributable, but remains copyright to the Internet Movie Database inc. It is used here within the terms of their copying policy. Further distribution of the source data used in this paper may be prohibited.
Feature Distillation: DNN-Oriented JPEG Compression Against Adversarial Examples

Deep Neural Networks (DNNs) have achieved remarkable performance in a myriad of realistic applications. However, recent studies show that welltrained DNNs can be easily misled by adversarial examples (AE) – the maliciously crafted inputs by introducing small and imperceptible input perturbations. Existing mitigation solutions, such as adversarial training and defensive distillation, suffer from expensive retraining cost and demonstrate marginal robustness improvement against the stateof-the-art attacks like CW family adversarial examples. In this work, we propose a novel low-cost “feature distillation” strategy to purify the adversarial input perturbations of AEs by redesigning the popular image compression framework “JPEG”. The proposed “feature distillation” wisely maximizes the malicious feature loss of AE perturbations during image compression while suppressing the distortions of benign features essential for high accurate DNN classification. Experimental results show that our method can drastically reduce the success rate of various state-of-the-art AE attacks by ∼ 60% on average for both CIFAR-10 and ImageNet benchmarks without harming the testing accuracy, outperforming existing solutions like default JPEG compression and “feature squeezing”.
Power electronics as efficient interface in dispersed power generation systems

The global electrical energy consumption is rising and there is a steady increase of the demand on the power capacity, efficient production, distribution and utilization of energy. The traditional power systems are changing globally, a large number of dispersed generation (DG) units, including both renewable and nonrenewable energy sources such as wind turbines, photovoltaic (PV) generators, fuel cells, small hydro, wave generators, and gas/steam powered combined heat and power stations, are being integrated into power systems at the distribution level. Power electronics, the technology of efficiently processing electric power, play an essential part in the integration of the dispersed generation units for good efficiency and high performance of the power systems. This paper reviews the applications of power electronics in the integration of DG units, in particular, wind power, fuel cells and PV generators.
Whose Space? Differences Among Users and Non-Users of Social Network Sites

Are there systematic differences between people who use social network sites and those who stay away, despite a familiarity with them? Based on data from a survey administered to a diverse group of young adults, this article looks at the predictors of SNS usage, with particular focus on Facebook, MySpace, Xanga, and Friendster. Findings suggest that use of such sites is not randomly distributed across a group of highly wired users. A person's gender, race and ethnicity, and parental educational background are all associated with use, but in most cases only when the aggregate concept of social network sites is disaggregated by service. Additionally, people with more experience and autonomy of use are more likely to be users of such sites. Unequal participation based on user background suggests that differential adoption of such services may be contributing to digital inequality.
Deep Representation for Abnormal Event Detection in Crowded Scenes

Abnormal event detection is extremely important, especially for video surveillance. Nowadays, many detectors have been proposed based on hand-crafted features. However, it remains challenging to effectively distinguish abnormal events from normal ones. This paper proposes a deep representation based algorithm which extracts features in an unsupervised fashion. Specially, appearance, texture, and short-term motion features are automatically learned and fused with stacked denoising autoencoders. Subsequently, long-term temporal clues are modeled with a long short-term memory (LSTM) recurrent network, in order to discover meaningful regularities of video events. The abnormal events are identified as samples which disobey these regularities. Moreover, this paper proposes a spatial anomaly detection strategy via manifold ranking, aiming at excluding false alarms. Experiments and comparisons on real world datasets show that the proposed algorithm outperforms state of the arts for the abnormal event detection problem in crowded scenes.