nils-herrmann/clean_scirepeval huggingface dataset:
Part-Activated Deep Reinforcement Learning for Action Prediction

In this paper, we propose a part-activated deep reinforcement learning (PA-DRL) method for action prediction. Most existing methods for action prediction utilize the evolution of whole frames to model actions, which cannot avoid the noise of the current action, especially in the early prediction. Moreover, the loss of structural information of human body diminishes the capacity of features to describe actions. To address this, we design the PA-DRL to exploit the structure of the human body by extracting skeleton proposals under a deep reinforcement learning framework. Specifically, we extract features from different parts of the human body individually and activate the action-related parts in features to enhance the representation. Our method not only exploits the structure information of the human body, but also considers the saliency part for expressing actions. We evaluate our method on three popular action prediction datasets: UT-Interaction, BIT-Interaction and UCF101. Our experimental results demonstrate that our method achieves the performance with state-of-the-arts.
Pathological Internet use, cyberbullying and mobile phone use in adolescence: a school-based study in Greece.

This study investigated the prevalence of Internet addiction (IA) and cyberbullying and examined profiles of adolescents with increased risk to develop pathological behaviors.  In this cross-sectional, school-based study, 8053 students of 30 middle and 21 high schools (12-18 years old) were invited to participate, based on a multistage stratified random sampling technique. The Internet aiddiction test (IAT) was used along with information on socio-demographics, Internet activities and cyberbullying experience.  Five thousand five hundred and ninety students participated (response rate 69.4%). Pathological Internet use (IAT ≥50) was found in 526 (10.1%), while 403 (7.3%) experienced cyberbullying as victims and 367 (6.6%) as perpetrators during the last year. In multivariable models, the odds of IA increased with online hours on mobile phones and Internet use during weekends, Internet café visits, chatrooms usage and engagement in cyberbullying. Cyberbullying victims were more likely to be older, female, Facebook and chatrooms users, while perpetrators were more likely to be male, older Internet users and fans of pornographic sites. A perpetrator was significantly more likely to have also been a victim [odds ratio (OR) = 5.51, confidence interval (CI): 3.92-7.74]. Hours of daily Internet use on a mobile phone was independently associated with IA and cyberbullying (OR) 1.41, 95% CI 1.30, 1.53 and OR 1.11, 95% CI 1.01, 1.21, respectively.  Cyberbullying is associated with IA and the hours spent online on a mobile phone affected both conditions. The increasing Internet access through mobile phones or other devices should be accompanied by the appropriate education of both parents and adolescents on safe Internet use.
A Survey of Securing Networks Using Software Defined Networking

Software Defined Networking (SDN) is rapidly emerging as a new paradigm for managing and controlling the operation of networks ranging from the data center to the core, enterprise, and home. The logical centralization of network intelligence presents exciting challenges and opportunities to enhance security in such networks, including new ways to prevent, detect, and react to threats, as well as innovative security services and applications that are built upon SDN capabilities. In this paper, we undertake a comprehensive survey of recent works that apply SDN to security, and identify promising future directions that can be addressed by such research.
Efficient calibration for rssi-based indoor localization by bayesian experimental design on multi-task classification

RSSI-based indoor localization is getting much attention. Thanks to a number of researchers, the localization accuracy has already reached a sufficient level. However, it is still not easy-to-use technology because of its heavy installation cost. When an indoor localization system is installed, it needs to collect RSSI data for training classifiers. Existing techniques need to collect enough data at each location. This is why the installation cost is very heavy. We propose a technique to gather data efficiently by using machine learning techniques. Our proposed algorithm is based on multi-task learning and Bayesian optimization. This algorithm can remove the need to collect data of all location labels and select location labels to acquire new data efficiently. We verify this algorithm by using a Wi-Fi RSSI dataset collected in a building. The empirical results suggest that the algorithm is superior to an existing algorithm applying single-task learning and Active Class Selection.
KDD meets Big Data

Cross-Industry Standard process model (CRISP-DM) was developed in the late 90s by a consortium of industry participants to facilitate the end-to-end data mining process for Knowledge Discovery in Databases (KDD). While there have been efforts to better integrate with management and software development practices, there are no extensions to handle the new activities involved in using big data technologies.  Edge (DSE) is an enhanced process model to accommodate big data technologies and data science activities. In recognition of the changes, the author promotes the use of a new term, Knowledge Discovery in Data Science (KDDS) as a call for the community to develop a new industry standard data science process model.
Sexual abuse of boys: definition, prevalence, correlates, sequelae, and management.

To clarify the definition of sexual abuse of boys, update estimates of its prevalence, and explore critically its reported correlates, sequelae, and management.



Studies from 1985 to 1997 were identified using OVID-MEDLINE and OVID-CINAHL. The search terms used were sexual abuse, sexual victimization, and sexual assault. Constraints applied included English language, human male subjects, age younger than 19 years, and North American samples.



Publications were included for review if they appeared in peer-reviewed journals; had clear research designs; reported results for at least 20 male subjects; and were not reviews, perspectives, theoretical treatises, editorials, or letters.



Study types and sampling methods were categorized using well-established definitions. Preference was given to studies with large samples, with case-control or cohort designs, and/or with adjustment for effect modifiers or confounders.



We identified 166 studies representing 149 sexual abuse samples. Studies were methodologically limited and definitions of sexual abuse varied widely. Prevalence estimates varied widely (by definition used and population studied), ranging from 4% to 76%. Boys at highest risk were younger than 13 years, nonwhite, of low socioeconomic status, and not living with their fathers. Perpetrators tended to be known but unrelated males. Abuse frequently occurred outside the home, involved penetration, and occurred more than once. Sequelae included psychological distress, substance abuse, and sexually related problems. Evaluation of management strategies was limited.



Sexual abuse of boys appears to be common, underreported, underrecognized, and undertreated. Future study requires clearer definitions of abuse, improved sampling, more rigorous data collection, more sophisticated data analyses, and better assessment of management and treatment strategies. Regardless, health care professionals should be more aware of and sensitive to the possibility of sexual abuse in their male patients.
Tax Fraud Detection for Under-Reporting Declarations Using an Unsupervised Machine Learning Approach

Tax fraud is the intentional act of lying on a tax return form with intent to lower one's tax liability. Under-reporting is one of the most common types of tax fraud, it consists in filling a tax return form with a lesser tax base. As a result of this act, fiscal revenues are reduced, undermining public investment.
 Detecting tax fraud is one of the main priorities of local tax authorities which are required to develop cost-efficient strategies to tackle this problem. Most of the recent works in tax fraud detection are based on supervised machine learning techniques that make use of labeled or audit-assisted data. Regrettably, auditing tax declarations is a slow and costly process, therefore access to labeled historical information is extremely limited. For this reason, the applicability of supervised machine learning techniques for tax fraud detection is severely hindered.
 Such limitations motivate the contribution of this work. We present a novel approach for the detection of potential fraudulent tax payers using only unsupervised learning techniques and allowing the future use of supervised learning techniques. We demonstrate the ability of our model to identify under-reporting taxpayers on real tax payment declarations, reducing the number of potential fraudulent tax payers to audit. The obtained results demonstrate that our model doesn't miss on marking declarations as suspicious and labels previously undetected tax declarations as suspicious, increasing the operational efficiency in the tax supervision process without needing historic labeled data.
Exploring decision-making processes in Python

The process by which norms are developed to become policies, the normative decision-making process, is not often explicit to stakeholders of Open Source Software (OSS) projects. Understanding the normative decision-making process is crucial for members if such projects are to evolve and succeed. In this paper, we investigated aspects of the normative decision-making processes of OSS development through the use of Python Enhancement Proposals (PEPs). We compared extracted process models with those that are advertised by the Python community to evaluate the extent to which those processes overlap. In addition, we assess members' involvement and contribution to these processes. Our work used structural and behavioral analysis techniques, and social network analysis metrics. We found that there were differences between the extracted processes and Python's advertised process, with the extracted processes being significantly more complex. These differences also extended to granular models used for managing social and technical aspects of the Python project. Furthermore, some key members were largely responsible for PEPs' success. Our extracted models could go a far way in helping the Python community to quickly understand decision-making processes in Python.
Tracing traitors

We give cryptographic schemes that help trace the source of leaks when sensitive or proprietary data is made available to a large set of parties. A very relevant application is in the context of pay television, where only paying customers should be able to view certain programs. In this application, the programs are normally encrypted, and then the sensitive data is the decryption keys that are given to paying customers. If a pirate decoder is found, it is desirable to reveal the source of its decryption keys. We describe fully resilient schemes which can be used against any decoder which decrypts with nonnegligible probability. Since there is typically little demand for decoders which decrypt only a small fraction of the transmissions (even if it is nonnegligible), we further introduce threshold tracing schemes which can only be used against decoders which succeed in decryption with probability greater than some threshold. Threshold schemes are considerably more efficient than fully resilient schemes.
Decision support system for servitization of industrial SMEs: a modelling and simulation approach

Decision support system for servitization of industrial SMEs: a modelling and simulation approach M. Chalal, X. Boucher & G. Marques To cite this article: M. Chalal, X. Boucher & G. Marques (2015) Decision support system for servitization of industrial SMEs: a modelling and simulation approach, Journal of Decision Systems, 24:4, 355-382,  To link to this article: http://dx.doi.org/10.1080/12460125.2015.1074836
Image Descriptors from ConvNets: Comparing Global Pooling Methods for Image Retrieval

A major component of a generic image retrieval pipeline is producing concise and effective descriptors for each image. Previous works have shown impressive results in image retrieval when using descriptors from the black-box output of the fully-connected stage of pretrained Convolutional Neural Networks (ConvNets). However, previous work on descriptors pooled from the deep feature maps from late convolutional layers can produce more discriminative descriptors for generic image retrieval, while being relatively concise. When planning to globally pool such feature maps from a ConvNet, some options to consider are (1) the depth of the network, (2) choice of layer to pool, and (3) the level of dimension reduction. The previous work on global pooling methods uses differing techniques without a clear consensus on which method is best. This motivates us to establish a baseline pipeline from which to compare these options and their effect on retrieval results. Our contribution is a systematic and comprehensive experimental study of different pooling strategies of deep features for image retrieval, and the various options. Our results show that the nature of the dataset (object- heavy or scene-heavy) warrants a different pooling strategy. Significantly, we visualise the level of image discrimination brought by the different pooling methods on the datasets, and show that pooling need not have a priori spatial weights to effectively find objects within the image. The results underline the need to consider the context of the image dataset when developing image retrieval pipelines using ConvNets.
Business Analytics in (a) Blink

The Blink project’s ambitious goal is to answer all Business Intelligence (BI) queries in mere seconds, regardless of the database size, with an extremely low total cost of ownership. Blink is a new DBMS aimed primarily at read-mostly BI query processing that exploits scale-out of commodity multi-core processors and cheap DRAM to retain a (copy of a) data mart completely in main memory. Additionally, it exploits proprietary compression technology and cache-conscious algorithms that reduce memory bandwidth consumption and allow most SQL query processing to be performed on the compressed data. Blink always scans (portions of) the data mart in parallel on all nodes, without using any indexes or materialized views, and without any query optimizer to choose among them. The Blink technology has thus far been incorporated into two IBM accelerator products generally available since March 2011. We are now working on the next generation of Blink, which will significantly expand the “sweet spot” of the Blink technology to much larger, disk-based warehouses and allow Blink to “own” the data, rather
Shape reconstruction from gradient data.

We present a generalized method for reconstructing the shape of an object from measured gradient data. A certain class of optical sensors does not measure the shape of an object but rather its local slope. These sensors display several advantages, including high information efficiency, sensitivity, and robustness. For many applications, however, it is necessary to acquire the shape, which must be calculated from the slopes by numerical integration. Existing integration techniques show drawbacks that render them unusable in many cases. Our method is based on an approximation employing radial basis functions. It can be applied to irregularly sampled, noisy, and incomplete data, and it reconstructs surfaces both locally and globally with high accuracy.
A topological hierarchy for functions on triangulated surfaces

We combine topological and geometric methods to construct a multiresolution representation for a function over a two-dimensional domain. In a preprocessing stage, we create the Morse-Smale complex of the function and progressively simplify its topology by cancelling pairs of critical points. Based on a simple notion of dependency among these cancellations, we construct a hierarchical data structure supporting traversal and reconstruction operations similarly to traditional geometry-based representations. We use this data structure to extract topologically valid approximations that satisfy error bounds provided at runtime.
Static secure page allocation for light-weight dynamic information flow tracking

Dynamic information flow tracking (DIFT) is an effective security countermeasure for both low-level memory corruptions and high-level semantic attacks. However, many software approaches suffer large performance degradation, and hardware approaches have high logic and storage overhead. We propose a flexible and light-weight hardware/software co-design approach to perform DIFT based on secure page allocation. Instead of associating every data with a taint tag, we aggregate data according to their taints, i.e., putting data with different attributes in separate memory pages. Our approach is a compiler-aided process with architecture support. The implementation and analysis show that the memory overhead is little, and our approach can protect critical information, including return address, indirect jump address, and system call IDs, from being overwritten by malicious users.
Interpreting Dimensions of Consumer Trust in E-Commerce

Consumer trust in an Internet vendor is an issue commanding ever more attention. Based on an extensive review of literature, this paper proposes dimensions of trust in an Internet vendor. These are competence, integrity and benevolence. Competence refers to a company’s ability to fulfill promises made with the consumers. Integrity suggests that a company acts in a consistent, reliable, and honest manner. Benevolence is the ability of a company to hold consumer interests ahead of its own self-interest and indicates sincere concern for the welfare of the customers. In a further analysis various sources where trust might reside are also identified. Drawing on the literature in marketing and general management, the sources of trust are classified as characteristics of the consumer, the firm, the website and the interaction between the consumer and the firm. Given the dimensions and sources of trust, a path model for developing consumer trust in E-commerce is suggested. This research makes a contribution to the development of a theoretical understanding of trust in E-commerce. Although the concepts presented in this paper can be used to carry out further empirical research, they can also be used by practitioners to identify particular trust characteristics for realizing the potential of business to consumer E-commerce venture.
Deep Graph Laplacian Regularization for Robust Denoising of Real Images.

Recent developments in deep learning have revolutionized the paradigm of image restoration. However, its applications on real image denoising are still limited, due to its sensitivity to training data and the complex nature of real image noise. In this work, we combine the robustness merit of model-based approaches and the learning power of data-driven approaches for real image denoising. Specifically, by integrating graph Laplacian regularization as a trainable module into a deep learning framework, we are less susceptible to overfitting than pure CNNbased approaches, achieving higher robustness to small datasets and cross-domain denoising. First, a sparse neighborhood graph is built from the output of a convolutional neural network (CNN). Then the image is restored by solving an unconstrained quadratic programming problem, using a corresponding graph Laplacian regularizer as a prior term. The proposed restoration pipeline is fully differentiable and hence can be end-to-end trained. Experimental results demonstrate that our work is less prone to overfitting given small training data. It is also endowed with strong cross-domain generalization power, outperforming the state-of-the-art approaches by a remarkable margin.
Supervising the New with the Old: Learning SFM from SFM

Recent work has demonstrated that it is possible to learn deep neural networks for monocular depth and ego-motion estimation from unlabelled video sequences, an interesting theoretical development with numerous advantages in applications. In this paper, we propose a number of improvements to these approaches. First, since such selfsupervised approaches are based on the brightness constancy assumption, which is valid only for a subset of pixels, we propose a probabilistic learning formulation where the network predicts distributions over variables rather than specific values. As these distributions are conditioned on the observed image, the network can learn which scene and object types are likely to violate the model assumptions, resulting in more robust learning. We also propose to build on dozens of years of experience in developing handcrafted structure-from-motion (SFM) algorithms. We do so by using an off-the-shelf SFM system to generate a supervisory signal for the deep neural network. While this signal is also noisy, we show that our probabilistic formulation can learn and account for the defects of SFM, helping to integrate different sources of information and boosting the overall performance of the network.
Promoting Student Metacognition

Imagine yourself as the instructor of an introductory undergraduate biology course. Two students from your course independently visit your office the week after the first exam. Both students are biology majors. Both regularly attend class and submit their assignments on time. Both appear to be eager, dedicated, and genuine students who want to learn biology. During each of their office hours visits, you ask them to share how they prepared for the first exam. Their stories are strikingly different (inspired by Ertmer and Newby, 1996). During office hours, Josephina expresses that she was happy the exam was on a Monday, because she had a lot of time to prepare the previous weekend. She shares that she started studying after work on Saturday evening and did not go out with friends that night. When queried, she also shares that she reread all of the assigned textbook material and made flashcards of the bold words in the text. She feels that she should have done well on the test, because she studied all Saturday night and all day on Sunday. She feels that she did everything she could do to prepare. That said, she is worried about what her grade will be, and she wants you to know that she studied really hard, so she should get a good grade on the exam. Later in the week, Maya visits your office. When asked how she prepared for the first exam, she explains that she has regularly reviewed the PowerPoint slides each evening after class since the beginning of the term 4 weeks ago. She also read the assigned textbook pages weekly, but expresses that she spent most of her time comparing the ideas in the PowerPoint slides with the information in the textbook to see how they were similar and different. She found several places in which things seemed not to agree, which confused her. She kept a running list of these confusions each week. When you ask what she did with these confusions, she shares that she