nils-herrmann/clean_scirepeval huggingface dataset:
A Hybrid Reinforcement Learning Approach to Autonomic Resource Allocation

Reinforcement Learning (RL) provides a promising new approach to systems performance management that differs radically from standard queuing-theoretic approaches making use of explicit system performance models. In principle, RL can automatically learn high-quality management policies without an explicit performance model or traffic model and with little or no built-in system specific knowledge. In our original work , ,  we showed the feasibility of using online RL to learn resource valuation estimates (in lookup table form) which can be used to make high-quality server allocation decisions in a multi-application prototype Data Center scenario. The present work shows how to combine the strengths of both RL and queuing models in a hybrid approach in which RL trains offline on data collected while a queuing model policy controls the system. By training offline we avoid suffering potentially poor performance in live online training. We also now use RL to train nonlinear function approximators (e.g. multi-layer perceptrons) instead of lookup tables; this enables scaling to substantially larger state spaces. Our results now show that in both open-loop and closed-loop traffic, hybrid RL training can achieve significant performance improvements over a variety of initial model-based policies. We also find that, as expected, RL can deal effectively with both transients and switching delays, which lie outside the scope of traditional steady-state queuing theory.
Video-based fingerphoto recognition with anti-spoofing techniques with smartphone cameras

This work is concerned with the acquisition of fingerprints samples on smartphones with the built-in smartphone camera. A novel approach to capture multiple fingerphotos in a videostream with a smartphone camera and the processing of the photos for the finger recognition is discussed in this paper. The proposed technique offers a convenient and efficient way to capture multiple samples of a biometric instance in a short time frame. Due the fact that fingerphotos can be easily replicated with low effort (e.g. print outs with an ordinary printer) and thus are vulnerable to presentation attacks, anti-spoofing algorithms were developed to detect such spoof attempts. The algorithms for the detection and segmentation of the finger as well the preprocessing of the photo with graphical operations and anti-spoofing were implemented in a prototype as application for the Android operating system. User tests are performed to evaluate the usability and to create a database of biometric samples for offline evaluation of the recognition performance. Further tests are done with diverse artefacts such as printed finger images, fake fingers of gelatin, gummy and silicon as well finger replay videos to measure the resistance of the developed solution against presentation attacks.
Internet of Things security: A survey

The Internet of things (IoT) has recently become an important research topic because it integrates various sensors and objects to communicate directly with one another without human intervention. The requirements for the large-scale deployment of the IoT are rapidly increasing with a major security concern. This study focuses on the state-of-the-art IoT security threats and vulnerabilities by conducting an extensive survey of existing works in the area of IoT security. The taxonomy of the current security threats in the contexts of application, architecture, and communication is presented. This study also compares possible security threats in the IoT. We discuss the IoT security scenario and provide an analysis of the possible attacks. Open research issues and security implementation challenges in IoT security are described as well. This study aims to serve as a useful manual of existing security threats and vulnerabilities of the IoT heterogeneous environment and proposes possible solutions for improving the IoT security architecture.
A Multi-cut Formulation for Joint Segmentation and Tracking of Multiple Objects

Recently, Minimum Cost Multicut Formulations have been proposed and proven to be successful in both motion trajectory segmentation and multi-target tracking scenarios. Both tasks benefit from decomposing a graphical model into an optimal number of connected components based on attractive and repulsive pairwise terms. The two tasks are formulated on different levels of granularity and, accordingly, leverage mostly local information for motion segmentation and mostly high-level information for multi-target tracking. In this paper we argue that point trajectories and their local relationships can contribute to the high-level task of multi-target tracking and also argue that high-level cues from object detection and tracking are helpful to solve motion segmentation. We propose a joint graphical model for point trajectories and object detections whose Multicuts are solutions to motion segmentation and multi-target tracking problems at once. Results on the FBMS59 motion segmentation benchmark as well as on pedestrian tracking sequences from the 2D MOT 2015 benchmark demonstrate the promise of this joint approach.
Body Movements for Affective Expression: A Survey of Automatic Recognition and Generation

Body movements communicate affective expressions and, in recent years, computational models have been developed to recognize affective expressions from body movements or to generate movements for virtual agents or robots which convey affective expressions. This survey summarizes the state of the art on automatic recognition and generation of such movements. For both automatic recognition and generation, important aspects such as the movements analyzed, the affective state representation used, and the use of notation systems is discussed. The survey concludes with an outline of open problems and directions for future work.
TenniVis: Visualization for Tennis Match Analysis

Existing research efforts into tennis visualization have primarily focused on using ball and player tracking data to enhance professional tennis broadcasts and to aid coaches in helping their students. Gathering and analyzing this data typically requires the use of an array of synchronized cameras, which are expensive for non-professional tennis matches. In this paper, we propose TenniVis, a novel tennis match visualization system that relies entirely on data that can be easily collected, such as score, point outcomes, point lengths, service information, and match videos that can be captured by one consumer-level camera. It provides two new visualizations to allow tennis coaches and players to quickly gain insights into match performance. It also provides rich interactions to support ad hoc hypothesis development and testing. We first demonstrate the usefulness of the system by analyzing the 2007 Australian Open men's singles final. We then validate its usability by two pilot user studies where two college tennis coaches analyzed the matches of their own players. The results indicate that useful insights can quickly be discovered and ad hoc hypotheses based on these insights can conveniently be tested through linked match videos.
NONVERBAL BEHAVIOR AND NONVERBAL COMMUNICATION: WHAT DO CONVERSATIONAL HAND GESTURES TELL US?

graphic designs The messages describing novel graphic designs and synthesized sounds obtained in the experiments by Krauss, et al. (in press) were coded into categories of description types, and the rate of gesturing associated with these description types was examined. For the novel designs, we used a category system developed by Fussell and Krauss (1989a) for descriptionsof these figures that partitions the descriptions into three categories: Literal descriptions, in which a design was characterized in terms of its geometric elements — as a collection of lines, arcs, angles, etc.; Figurative descriptions, in which a design was described in terms of objects or images it suggested; Symbol descriptions, in which a design was likened to a familiar symbol, typically one or more numbers or letters.21 When a message contained more than one type of description (as many did), it was coded for the type that predominated. Overall, about 60% of the descriptions were coded as figurative, about 24% as literal and the remaining 16% as symbols. For the descriptions of the graphic designs, a one-way ANOVA was performed with description type (literal, figurative or symbol) as the independent variable and gesture rate as the dependent variable to determine whether gesturing varied as a function of the kind of content. A significant effect was found F (2, 350) = 4.26, p = .015. Figurative descriptions were accompanied by slightly more gestures than literal descriptions; both were accompanied by more gestures than were the symbol descriptions (14.6 vs. 13.7 vs. 10.6 gestures per m, respectively). Both figurative and literal descriptions tended to be formulated in spatial terms. Symbol descriptions tended to be brief and static—essentially a statement of the resemblance.
Hidden sentiment association in chinese web opinion mining

The boom of product review websites, blogs and forums on the web has attracted many research efforts on opinion mining. Recently, there was a growing interest in the finer-grained opinion mining, which detects opinions on different review features as opposed to the whole review level. The researches on feature-level opinion mining mainly rely on identifying the explicit relatedness between product feature words and opinion words in reviews. However, the sentiment relatedness between the two objects is usually complicated. For many cases, product feature words are implied by the opinion words in reviews. The detection of such hidden sentiment association is still a big challenge in opinion mining. Especially, it is an even harder task of feature-level opinion mining on Chinese reviews due to the nature of Chinese language. In this paper, we propose a novel mutual reinforcement approach to deal with the feature-level opinion mining problem. More specially, 1) the approach clusters product features and opinion words simultaneously and iteratively by fusing both their content information and sentiment link information. 2) under the same framework, based on the product feature categories and opinion word groups, we construct the sentiment association set between the two groups of data objects by identifying their strongest n sentiment links. Moreover, knowledge from multi-source is incorporated to enhance clustering in the procedure. Based on the pre-constructed association set, our approach can largely predict opinions relating to different product features, even for the case without the explicit appearance of product feature words in reviews. Thus it provides a more accurate opinion evaluation. The experimental results demonstrate that our method outperforms the state-of-art algorithms.
Ischiofemoral impingement in an 11-year-old girl.

Ischiofemoral impingement (IFI) is the entrapment of the quadratus femoris muscle (QFM) between the trochanter minor of the femur and the ischium-hamstring tendon. Patients with IFI generally present with hip pain, which may radiate toward the knee. Although there is no specific diagnostic clinical test for this disorder, the presence of QFM edema/fatty replacement and narrowing of the ischiofemoral space and the quadratus femoris space on magnetic resonance imaging (MRI) are suggestive of IFI. The optimal treatment strategy of this syndrome remains obscure. Patients may benefit from a conservative treatment regimen that includes rest, activity restriction, nonsteroidal anti-inflammatory drugs, and rehabilitation procedures, just as with other impingement syndromes. Herein we report an 11-year-old girl with IFI who was successfully treated conservatively. To our knowledge, our case is the youngest patient reported in the English literature. MRI remains an important tool in the diagnosis of IFI, and radiologists should be aware of the specific features of this entity.
Those Who Understand : Knowledge Growth in Teaching

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The pandemic of physical inactivity: global action for public health

Physical inactivity is the fourth leading cause of death worldwide. We summarise present global efforts to counteract this problem and point the way forward to address the pandemic of physical inactivity. Although evidence for the benefits of physical activity for health has been available since the 1950s, promotion to improve the health of populations has lagged in relation to the available evidence and has only recently developed an identifiable infrastructure, including efforts in planning, policy, leadership and advocacy, workforce training and development, and monitoring and surveillance. The reasons for this late start are myriad, multifactorial, and complex. This infrastructure should continue to be formed, intersectoral approaches are essential to advance, and advocacy remains a key pillar. Although there is a need to build global capacity based on the present foundations, a systems approach that focuses on populations and the complex interactions among the correlates of physical inactivity, rather than solely a behavioural science approach focusing on individuals, is the way forward to increase physical activity worldwide.
TouchViz: a case study comparing two interfaces for data analytics on tablets

As more applications move from the desktop to touch devices like tablets, designers must wrestle with the costs of porting a design with as little revision of the UI as possible from one device to the other, or of optimizing the interaction per device. We consider the tradeoffs between two versions of a UI for working with data on a touch tablet. One interface is based on using the conventional desktop metaphor (WIMP) with a control panel, push buttons, and checkboxes -- where the mouse click is effectively replaced by a finger tap. The other interface (which we call FLUID) eliminates the control panel and focuses touch actions on the data visualization itself. We describe our design process and evaluation of each interface. We discuss the significantly better task performance and preference for the FLUID interface, in particular how touch design may challenge certain assumptions about the performance benefits of WIMP interfaces that do not hold on touch devices, such as the superiority of gestural vs. control panel based interaction.
Learning Visual Reasoning Without Strong Priors

Achieving artificial visual reasoning — the ability to answer image-related questions which require a multi-step, high-level process — is an important step towards artificial general intelligence. This multi-modal task requires learning a questiondependent, structured reasoning process over images from language. Standard deep learning approaches tend to exploit biases in the data rather than learn this underlying structure, while leading methods learn to visually reason successfully but are hand-crafted for reasoning. We show that a general-purpose, Conditional Batch Normalization approach achieves state-ofthe-art results on the CLEVR Visual Reasoning benchmark with a 2.4% error rate. We outperform the next best end-to-end method (4.5%) and even methods that use extra supervision (3.1%). We probe our model to shed light on how it reasons, showing it has learned a question-dependent, multi-step process. Previous work has operated under the assumption that visual reasoning calls for a specialized architecture, but we show that a general architecture with proper conditioning can learn to visually reason effectively.
Learning Image and User Features for Recommendation in Social Networks

Good representations of data do help in many machine learning tasks such as recommendation. It is often a great challenge for traditional recommender systems to learn representative features of both users and images in large social networks, in particular, social curation networks, which are characterized as the extremely sparse links between users and images, and the extremely diverse visual contents of images. To address the challenges, we propose a novel deep model which learns the unified feature representations for both users and images. This is done by transforming the heterogeneous user-image networks into homogeneous low-dimensional representations, which facilitate a recommender to trivially recommend images to users by feature similarity. We also develop a fast online algorithm that can be easily scaled up to large networks in an asynchronously parallel way. We conduct extensive experiments on a representative subset of Pinterest, containing 1,456,540 images and 1,000,000 users. Results of image recommendation experiments demonstrate that our feature learning approach significantly outperforms other state-of-the-art recommendation methods.
No More Pesky Learning Rate Guessing Games

It is known that the learning rate is the most important hyper-parameter to tune for training deep convolutional neural networks (i.e., a “guessing game”). This report describes a new method for setting the learning rate, named cyclical learning rates, that eliminates the need to experimentally find the best values and schedule for the learning rates. Instead of setting the learning rate to fixed values, this method lets the learning rate cyclically vary within reasonable boundary values. This report shows that training with cyclical learning rates achieves near optimal classification accuracy without tuning and often in many fewer iterations. This report also describes a simple way to estimate “reasonable bounds” by linearly increasing the learning rate in one training run of the network for only a few epochs. In addition, cyclical learning rates are demonstrated on training with the CIFAR-10 dataset and the AlexNet and GoogLeNet architectures on the ImageNet dataset. These methods are practical tools for everyone who trains convolutional neural networks.
Soil Classification From Large Imagery Databases Using a Neuro-Fuzzy Classifier

In this paper, we propose a neuro-fuzzy (NF) classification technique to determine various soil classes from large imagery soil databases. The technique looks at the feature-wise degree of belongings of the imagery databases to obtainable soil classes using a fuzzification method. The fuzzification method builds a membership matrix with an element count equal to the mathematical product of the number of data records and soil classes present. The elements of this matrix are the input to a neural network model. We apply our technique to three UCI databases, namely, Statlog Landsat Satellite, Forest Covertype, and Wilt for soil classification. The paper aims to find out soil classes using the proposed technique, and then compare its performance with four well-known classification algorithms, namely, radial basis function network, k-nearest neighbor, support vector machine, and adaptive NF inference system. Numerous measures, for example, root-mean-square error, kappa statistic, accuracy, false positive rate, true positive rate, precision, recall, F-measure, and area under the curve, are used for evaluating the quantitative analysis of the simulated results. All these evaluation measures approve the supremacy of the proposed NF method.
BALSA: Bluetooth Low Energy Application Layer Security Add-on

Bluetooth Low Energy (BLE) is ideally suited to exchange information between mobile devices and Internet-of-Things (IoT) sensors. It is supported by most recent consumer mobile devices and can be integrated into sensors enabling them to exchange information in an energy-efficient manner. However, when BLE is used to access or modify sensitive sensor parameters, exchanged messages need to be suitably protected, which may not be possible with the security mechanisms defined in the BLE specification. Consequently we contribute BALSA, a set of cryptographic protocols, a BLE service and a suggested usage architecture aiming to provide a suitable level of security. In this paper we define and analyze these components and describe our proof-of-concept, which demonstrates the feasibility and benefits of BALSA.
A bound on the label complexity of agnostic active learning

We study the label complexity of pool-based active learning in the agnostic PAC model. Specifically, we derive general bounds on the number of label requests made by the A2 algorithm proposed by Balcan, Beygelzimer & Langford (Balcan et al., 2006). This represents the first nontrivial general-purpose upper bound on label complexity in the agnostic PAC model.
A dynamic modularity based community detection algorithm for large-scale networks: DSLM

In this work, a new fast dynamic community detection algorithm for large scale networks is presented. Most of the previous community detection algorithms are designed for static networks. However, large scale social networks are dynamic and evolve frequently over time. To quickly detect communities in dynamic large scale networks, we proposed dynamic modularity optimizer framework (DMO) that is constructed by modifying well-known static modularity based community detection algorithm. The proposed framework is tested using several different datasets. According to our results, community detection algorithms in the proposed framework perform better than static algorithms when large scale dynamic networks are considered.
The impact of cognitive absorption on perceived usefulness and perceived ease of use in on-line learning: an extension of the technology acceptance model

Internet-based learning systems are being used in many universities and firms but their adoption requires a solid understanding of the user acceptance processes. Our effort used an extended version of the technology acceptance model (TAM), including cognitive absorption, in a formal empirical study to explain the acceptance of such systems. It was intended to provide insight for improving the assessment of on-line learning systems and for enhancing the underlying system itself. The work involved the examination of the proposed model variables for Internet-based learning systems acceptance. Using an on-line learning system as the target technology, assessment of the psychometric properties of the scales proved acceptable and confirmatory factor analysis supported the proposed model structure. A partial-least-squares structural modeling approach was used to evaluate the explanatory power and causal links of the model. Overall, the results provided support for the model as explaining acceptance of an on-line learning system and for cognitive absorption as a variable that influences TAM variables.