nils-herrmann/clean_scirepeval huggingface dataset:
Scalable Hashing-Based Network Discovery

Discovering and analyzing networks from non-network data is a task with applications in fields as diverse as neuroscience, genomics, energy, economics, and more. In these domains, networks are often constructed out of multiple time series by computing measures of association or similarity between pairs of series. The nodes in a discovered graph correspond to time series, which are linked via edges weighted by the association scores of their endpoints. After graph construction, the network may be thresholded such that only the edges with stronger weights remain and the desired sparsity level is achieved. While this approach is feasible for small datasets, its quadratic time complexity does not scale as the individual time series length and the number of compared series increase. Thus, to avoid the costly step of building a fully-connected graph before sparsification, we propose a fast network discovery approach based on probabilistic hashing of randomly selected time series subsequences. Evaluation on real data shows that our methods construct graphs nearly 15 times as fast as baseline methods, while achieving both network structure and accuracy comparable to baselines in task-based evaluation.
A random-effects regression model for meta-analysis.

Many meta-analyses use a random-effects model to account for heterogeneity among study results, beyond the variation associated with fixed effects. A random-effects regression approach for the synthesis of 2 x 2 tables allows the inclusion of covariates that may explain heterogeneity. A simulation study found that the random-effects regression method performs well in the context of a meta-analysis of the efficacy of a vaccine for the prevention of tuberculosis, where certain factors are thought to modify vaccine efficacy. A smoothed estimator of the within-study variances produced less bias in the estimated regression coefficients. The method provided very good power for detecting a non-zero intercept term (representing overall treatment efficacy) but low power for detecting a weak covariate in a meta-analysis of 10 studies. We illustrate the model by exploring the relationship between vaccine efficacy and one factor thought to modify efficacy. The model also applies to the meta-analysis of continuous outcomes when covariates are present.
Treatment of a skeletal Class II malocclusion using fixed functional appliance with miniplate anchorage

Based on our literature search, we found that the use of miniplate anchorage with Forsus fatigue-resistance device (FRD) has not yet been reported. Therefore, the aim of the present case report was to present the treatment of a patient with skeletal Class II malocclusion with mandibular retrusion using Forsus FRD with miniplate anchorage. Fixed appliances with 0.022-inch slots were attached to the maxillary teeth and after 8 months of the leveling and alignment of the upper arch, 0.019 × 0.025-inch stainless steel archwire was inserted and cinched back. Two weeks after the placement of the miniplates bilaterally at the symphysis of the mandible, Forsus FRD was adjusted to the miniplates with a 35-mm length of rod chosen. Nine months after the skeletal anchored Forsus worn, Class I canine and molar relations were achieved and overjet was eliminated.
Return of the Devil in the Details: Delving Deep into Convolutional Nets

The latest generation of Convolutional Neural Networks (CNN) have achieved impressive results in challenging benchmarks on image recognition and object detection, significantly raising the interest of the community in these methods. Nevertheless, it is still unclear how different CNN methods compare with each other and with previous state-of-the-art shallow representations such as the Bag-of-Visual-Words and the Improved Fisher Vector. This paper conducts a rigorous evaluation of these new techniques, exploring different deep architectures and comparing them on a common ground, identifying and disclosing important implementation details. We identify several useful properties of CNN-based representations, including the fact that the dimensionality of the CNN output layer can be reduced significantly without having an adverse effect on performance. We also identify aspects of deep and shallow methods that can be successfully shared. In particular, we show that the data augmentation techniques commonly applied to CNN-based methods can also be applied to shallow methods, and result in an analogous performance boost. Source code and models to reproduce the experiments in the paper is made publicly available.
Image based Arabic Sign Language Recognition System

Through history, humans have used many ways of communication such as gesturing, sounds, drawing, writing, and speaking. However, deaf and speaking impaired people cannot use speaking to communicate with others, which may give them a sense of isolation within their societies. For those individuals, sign language is their principal way to communicate. However, most people (who can hear) do not know the sign language. In this paper, we aim to automatically recognize Arabic Sign Language (ArSL) alphabets using an image-based methodology. More specifically, various visual descriptors are investigated to build an accurate ArSL alphabet recognizer. The extracted visual descriptors are conveyed to One-Versus-All Support Vector Machine (SVM). The analysis of the results shows that Histograms of Oriented Gradients (HOG) descriptor outperforms the other considered descriptors. Thus, the ArSL gesture models that are learned by One-Versus-All SVM using HOG descriptors are deployed in the proposed system. Component; Arabic sign language; image; visual descriptor; recognition
An algebra for fine-grained integration of XACML policies

Collaborative and distributed applications, such as dynamic coalitions and virtualized grid computing, often require integrating access control policies of collaborating parties. Such an integration must be able to support complex authorization specifications and the fine-grained integration requirements that the various parties may have. In this paper, we introduce an algebra for fine-grained integration of sophisticated policies. The algebra, which consists of three binary and two unary operations, is able to support the specification of a large variety of integration constraints. To assess the expressive power of our algebra, we introduce a notion of completeness and prove that our algebra is complete with respect to this notion. We then propose a framework that uses the algebra for the fine-grained integration of policies expressed in XACML. We also present a methodology for generating the actual integrated XACML policy, based on the notion of Multi-Terminal Binary Decision Diagrams.
Online Dominant and Anomalous Behavior Detection in Videos

We present a novel approach for video parsing and simultaneous online learning of dominant and anomalous behaviors in surveillance videos. Dominant behaviors are those occurring frequently in videos and hence, usually do not attract much attention. They can be characterized by different complexities in space and time, ranging from a scene background to human activities. In contrast, an anomalous behavior is defined as having a low likelihood of occurrence. We do not employ any models of the entities in the scene in order to detect these two kinds of behaviors. In this paper, video events are learnt at each pixel without supervision using densely constructed spatio-temporal video volumes. Furthermore, the volumes are organized into large contextual graphs. These compositions are employed to construct a hierarchical codebook model for the dominant behaviors. By decomposing spatio-temporal contextual information into unique spatial and temporal contexts, the proposed framework learns the models of the dominant spatial and temporal events. Thus, it is ultimately capable of simultaneously modeling high-level behaviors as well as low-level spatial, temporal and spatio-temporal pixel level changes.
Component-Based Robotic Engineering (Part II)

This article discusses the role of software components as architectural units of large, possibly distributed, software-intensive robotic systems. The focus is on technologies to manage the heterogeneity of hardware, computational, and communication resources and on design techniques to assemble components into systems .A component-based system is a composition of components, and the way components interact with other components and with the computational environment greatly affects the flexibility of the entire system and the reusability of individual functionality.
Anycast vs. DDoS: Evaluating the November 2015 Root DNS Event

Distributed Denial-of-Service (DDoS) attacks continue to be a major threat on the Internet today. DDoS attacks overwhelm target services with requests or other traffic, causing requests from legitimate users to be shut out. A common defense against DDoS is to replicate a service in multiple physical locations/sites. If all sites announce a common prefix, BGP will associate users around the Internet with a nearby site, defining the catchment of that site. Anycast defends against DDoS both by increasing aggregate capacity across many sites, and allowing each site's catchment to contain attack traffic, leaving other sites unaffected. IP anycast is widely used by commercial CDNs and for essential infrastructure such as DNS, but there is little evaluation of anycast under stress. This paper provides the first evaluation of several IP anycast services under stress with public data. Our subject is the Internet's Root Domain Name Service, made up of 13 independently designed services ("letters", 11 with IP anycast) running at more than 500 sites. Many of these services were stressed by sustained traffic at 100× normal load on Nov. 30 and Dec. 1, 2015. We use public data for most of our analysis to examine how different services respond to stress, and identify two policies: sites may absorb attack traffic, containing the damage but reducing service to some users, or they may withdraw routes to shift both good and bad traffic to other sites. We study how these deployment policies resulted in different levels of service to different users during the events. We also show evidence of collateral damage on other services located near the attacks.
Goal-Directed Evaluation of Binarization Methods

This paper presenlts a methodology for evaluation of low-level image analysis methods, using binarization (two-level thresholding) as an example. Isinarization of scanned gray scale images is the first step in most document image analysis systems. Selection of an appropriate biniarization method for an input image domain is a difficult problem. Typically, a human expert evaluates the binarized images according to hislher visual criteria. However, to conduct an objective evaluation, one needs to investigate how well the subsequent image analysis steps will perform on the binarized image. We call this approach goal-directed evaluation, and it can be used to evialuate other low-level image processing methods as well. Our evaluation of binarization methods is in the context of digit recognilion, so we define the performance of the character recognition module as the objective measure. Eleven different locally adalptive binarization methods were evaluated, and Niblack’s method gave the best performance.
Implementing Neural Networks Efficiently

Neural networks and machine learning algorithms in general require a flexible environment where new algorithm prototypes and experiments can be set up as quickly as possible with best possible computational performance. To that end, we provide a new framework called Torch7, that is especially suited to achieve both of these competing goals. Torch7 is a versatile numeric computing framework and machine learning library that extends a very lightweight and powerful programming language Lua. Its goal is to provide a flexible environment to design, train and deploy learning machines. Flexibility is obtained via Lua, an extremely lightweight scripting language. High performance is obtained via efficient OpenMP/SSE and CUDA implementations of low-level numeric routines. Torch7 can also easily be interfaced to third-party software thanks to Lua’s light C interface. Runtime efficiency is probably perceived as the most important topic when considering an efficient neural network implementation. One should however not under-estimate the time spent in designing the right neural network for a given task, or even the amount of work put into feeding data to the neural network properly. Designing the right network for a given task in a short amount of time requires a flexible development environment and a properly designed neural network toolbox. Several efficient (in terms of runtime execution) neural network libraries for very specific needs are freely available. QuickNet is a good example in the speech recognition community: it implements most commonly used algorithms, that is multi-layer perceptrons with few layers. However, flexible libraries are quite rare. It is not a trivial task to implement a library supporting a wide range of complex networks (such as convolutional networks for images, text or speech...), any type of connectivity (full connectivity, shared weights, order in layers...), or several type of training algorithms (stochastic gradient, batch, second order like LBFGS...). It is even more difficult to find a unified environment where one can 5 http://www.icsi.berkeley.edu/Speech/qn.html.
Prisoner's dilemma game model for e-commerce

This study investigates how the user’s exchanges affect people’s trust in electronic commerce and empower them to interact with foreign customers. Online exchanges often occur between strangers who cannot rely on past behavior or the prospect of future interactions to establish mutual trust. Game theorists have formalized this problem as a prisoner’s dilemma and predict mutual noncooperation. In this paper, we introduce a social network based model to promote the cooperation in the prisoner’s dilemma game. For this study, we implemented an agent-based simulation framework, which models different types of behaviors in online auctions. Agents who represent buyers or sellers are located in the nodes of a small world graph. Each link weight between the agent and its neighbor symbolizes how much it trusts this neighbor. A link with trust inferior to some cut-link threshold will be removed from the graph. Our results show that the evolved structure of the graph induces considerable variation in the level of cooperation and the profit in the e-commerce system. This shows that the outcome of our co-evolutionary game, in terms of cooperative behavior, strongly depends on topology but also on the update rule used in the trust between users. Co-evolution of game dynamics in interconnected networks is also studied, where two networks are interconnected, and players have interactions not only with others in the same network, but also with players in the other network. We will demonstrate that the interdependence between networks promotes the cooperation in both networks. But, the degree of promotion changes as a function of interdependence
Large scale metric learning from equivalence constraints

In this paper, we raise important issues on scalability and the required degree of supervision of existing Mahalanobis metric learning methods. Often rather tedious optimization procedures are applied that become computationally intractable on a large scale. Further, if one considers the constantly growing amount of data it is often infeasible to specify fully supervised labels for all data points. Instead, it is easier to specify labels in form of equivalence constraints. We introduce a simple though effective strategy to learn a distance metric from equivalence constraints, based on a statistical inference perspective. In contrast to existing methods we do not rely on complex optimization problems requiring computationally expensive iterations. Hence, our method is orders of magnitudes faster than comparable methods. Results on a variety of challenging benchmarks with rather diverse nature demonstrate the power of our method. These include faces in unconstrained environments, matching before unseen object instances and person re-identification across spatially disjoint cameras. In the latter two benchmarks we clearly outperform the state-of-the-art.
Towards a questionnaire for measuring affective benefits and costs of communication technologies

As CSCW creates and investigates technologies for social communication, it is important to understand the emotional benefits and costs of these systems. We propose the Affec-tive Benefits and Costs of Communication Technologies (ABCCT) questionnaire to supplement traditional qualita-tive methods of understanding communication media. We discuss the pilots of this survey with 45 children and 110 adults measuring the inter-item reliability of this instru-ment. We present the results of interviews with 14 children and 14 adults, which help confirm that the ABCCT measures the same constructs that may emerge through interview investigations. Finally, we demonstrate that the ABCCT is sensitive enough to discriminate between differ-ent communication technologies and has shown promise in some of its early adoption. Though the ABCCT is not with-out limitations, it may provide a way to compare technolo-gies in field deployments, draw findings across investiga-tions, and quantify the impact of specific design decisions.
ESD reliability improvement of the 0.25-µm 60-V power nLDMOS with discrete embedded SCRs separated by STI structures

In this paper, the electrostatic-discharge (ESD) robustness improvement by modulating the drain-side embedded SCR of an HV nLDMOS device is investigated via a TSMC 0.25 μm 60 V process. After a systematic layout design and data analysis, it can be found that the holding voltage (Vh) of an nLDMOS with a parasitic SCR “npn”-arranged type & thin oxide (OD) discrete (i.e. separated by the shallow-trench isolation (STI) structure) distribution in the drain-side have greatly increased with the parasitic SCR OD decreasing. Therefore, a high Vh value in the OD discrete parameter 2 (DIS-2) can be obtained about 13.3 V. On the other hand, the trigger voltage (Vt1) values and the holding voltage (Vh) values of the nLDMOS DUTs with a parasitic SCR “pnp”-arranged type & OD discrete distribution in the drain-side are slowly increased with the parasitic-SCR OD decreasing. The best Vh value in the “pnp”-arranged type & OD discrete parameter 2 (DIS-2) is about 14.4 V. Meanwhile, the secondary breakdown current (It2) values of this type are greater than 7 A except for the OD discrete parameter 2 and 3. Therefore, an appropriate layout of nLDMOS embedded with a drain-side “pnp” arranged type & OD discrete distribution that can yield high ESD and latch-up (LU) robustness levels.
Humidity Control for Front Opening Unified Pod after Opening Its Door in a Mini-Environment

Wafers are usually placed in a front opening unified pod (FOUP) while waiting in the line of manufacturing processes; a conventional way for preventing (or removing) contamination is to purge an FOUP with a gas, so as to maintain the cleanliness level. This study investigates the effect of the flow rate of clean dry air (CDA), supplied to the purge system and air curtain, on the moisture prevention performance, while opening the FOUP door in a mini-environment. An initial relative humidity (RH) in the pre-purged FOUP was approximately 0%, whereas an RH in the mini-environment was about 43%. Conventional, diffuser, and panel purge approaches were tested. The results showed that the moisture in the surroundings rapidly entered the FOUP when the door was opened, without using either the purge system or air curtain. When the CDA was then supplied to both the purge system and the air curtain, the RH values decreased significantly; the drop slope of the RH for the diffuserand panel-purge processes was much larger than that of the conventional-purge process. In addition, the moisture prevention performance when the developed panel-purge system was used was slightly lower than when using the diffuser-purge system. However, the use of the panel-purge method could sufficiently suppress the vibration problem. In the present study, the case with CDA flow rates of 150 L/min to the air curtain and 250 L/min to the panel-purge device had the best moisture prevention performance, because of the smallest values for both the mean and maximum RH values (i.e., 1.3% and 9.5%, respectively) in the FOUP.
On the Relationship between Sum-Product Networks and Bayesian Networks

In this paper, we establish some theoretical connections between Sum-Product Networks (SPNs) and Bayesian Networks (BNs). We prove that every SPN can be converted into a BN in linear time and space in terms of the network size. The key insight is to use Algebraic Decision Diagrams (ADDs) to compactly represent the local conditional probability distributions at each node in the resulting BN by exploiting context-specific independence (CSI). The generated BN has a simple directed bipartite graphical structure. We show that by applying the Variable Elimination algorithm (VE) to the generated BN with ADD representations, we can recover the original SPN where the SPN can be viewed as a history record or caching of the VE inference process. To help state the proof clearly, we introduce the notion of normal SPN and present a theoretical analysis of the consistency and decomposability properties. We conclude the paper with some discussion of the implications of the proof and establish a connection between the depth of an SPN and the tree-width of its corresponding BN.
20 Machine Analysis of Facial Expressions

The human face is the site for major sensory inputs and major communicative outputs. It houses the majority of our sensory apparatus as well as our speech production apparatus. It is used to identify other members of our species, to gather information about age, gender, attractiveness, and personality, and to regulate conversation by gazing or nodding. Moreover, the human face is our preeminent means of communicating and understanding somebody’s affective state and intentions on the basis of the shown facial expression (Keltner & Ekman, 2000). Thus, the human face is a multi-signal input-output communicative system capable of tremendous flexibility and specificity (Ekman & Friesen, 1975). In general, the human face conveys information via four kinds of signals. (a) Static facial signals represent relatively permanent features of the face, such as the bony structure, the soft tissue, and the overall proportions of the face. These signals contribute to an individual’s appearance and are usually exploited for person identification. (b) Slow facial signals represent changes in the appearance of the face that occur gradually over time, such as development of permanent wrinkles and changes in skin texture. These signals can be used for assessing the age of an individual. Note that these signals might diminish the distinctness of the boundaries of the facial features and impede recognition of the rapid facial signals.  2000) and personality traits (Ambady & Rosenthal, 1992). More specifically, types of messages