nils-herrmann/clean_scirepeval huggingface dataset:
Learning Patch-Based Dynamic Graph for Visual Tracking

Existing visual tracking methods usually localize the object with a bounding box, in which the foreground object trackers/detectors are often disturbed by the introduced background information. To handle this problem, we aim to learn a more robust object representation for visual tracking. In particular, the tracked object is represented with a graph structure (i.e., a set of non-overlapping image patches), in which the weight of each node (patch) indicates how likely it belongs to the foreground and edges are also weighed for indicating the appearance compatibility of two neighboring nodes. This graph is dynamically learnt (i.e., the nodes and edges received weights) and applied in object tracking and model updating. We constrain the graph learning from two aspects: i) the global low-rank structure over all nodes and ii) the local sparseness of node neighbors. During the tracking process, our method performs the following steps at each frame. First, the graph is initialized by assigning either 1 or 0 to the weights of some image patches according to the predicted bounding box. Second, the graph is optimized through designing a new ALM (Augmented Lagrange Multiplier) based algorithm. Third, the object feature representation is updated by imposing the weights of patches on the extracted image features. The object location is finally predicted by adopting the Struck tracker (Hare, Saffari, and Torr 2011). Extensive experiments show that our approach outperforms the state-of-the-art tracking methods on two standard benchmarks, i.e., OTB100 and NUS-PRO.
Search Result Diversification Based on Hierarchical Intents

A large percentage of queries issued to search engines are broad or ambiguous. Search result diversification aims to solve this problem, by returning diverse results that can fulfill as many different information needs as possible. Most existing intent-aware search result diversification algorithms formulate user intents for a query as a flat list of subtopics. In this paper, we introduce a new hierarchical structure to represent user intents and propose two general hierarchical diversification models to leverage hierarchical intents. Experimental results show that our hierarchical diversification models outperform state-of-the-art diversification methods that use traditional flat subtopics.
The role of childhood trauma in the neurobiology of mood and anxiety disorders: preclinical and clinical studies

Epidemiologic studies indicate that children exposed to early adverse experiences are at increased risk for the development of depression, anxiety disorders, or both. Persistent sensitization of central nervous system (CNS) circuits as a consequence of early life stress, which are integrally involved in the regulation of stress and emotion, may represent the underlying biological substrate of an increased vulnerability to subsequent stress as well as to the development of depression and anxiety. A number of preclinical studies suggest that early life stress induces long-lived hyper(re)activity of corticotropin-releasing factor (CRF) systems as well as alterations in other neurotransmitter systems, resulting in increased stress responsiveness. Many of the findings from these preclinical studies are comparable to findings in adult patients with mood and anxiety disorders. Emerging evidence from clinical studies suggests that exposure to early life stress is associated with neurobiological changes in children and adults, which may underlie the increased risk of psychopathology. Current research is focused on strategies to prevent or reverse the detrimental effects of early life stress on the CNS. The identification of the neurobiological substrates of early adverse experience is of paramount importance for the development of novel treatments for children, adolescents, and adults.
Copulas in Machine Learning

Despite overlapping goals of multivariate modeling and dependence identification, until recently, researchers in the fields of machine learning and copulas have been somewhat ignorant of each other. At the same time, complementing strengths of the two fields suggest the great potential of a synergy. In this talk I will present several recent works (not allmine) that give a taste of the kind of benefits that can arise from a symbiosis of the two fields. In particular, I will present copula-based high-dimensional constructions, automated methods for structure learning, and approaches for estimation of information measures. No background in graphical models will be assumed.
Automatic Defect Recognition in X-Ray Testing Using Computer Vision

To ensure safety in the construction of important metallic components for roadworthiness, it is necessary to check every component thoroughly using non-destructive testing. In last decades, X-ray testing has been adopted as the principal non-destructive testing method to identify defects within a component which are undetectable to the naked eye. Nowadays, modern computer vision techniques, such as deep learning and sparse representations, are opening new avenues in automatic object recognition in optical images. These techniques have been broadly used in object and texture recognition by the computer vision community with promising results in optical images. However, a comprehensive evaluation in X-ray testing is required. In this paper, we release a new dataset containing around 47.500 cropped X-ray images of 32 32 pixels with defects and no-defects in automotive components. Using this dataset, we evaluate and compare 24 computer vision techniques including deep learning, sparse representations, local descriptors and texture features, among others. We show in our experiments that the best performance was achieved by a simple LBP descriptor with a SVM-linear classifier obtaining 97% precision and 94% recall. We believe that the methodology presented could be used in similar projects that have to deal with automated detection of defects.
Inductively coupled wireless power transfer with class-E2 DC-DC converter

This paper proposes an inductive coupled wireless power transfer (WPT) system with class-E2 dc-dc converter along with its design procedure. The proposed WPT system can achieve high power-conversion efficiency at high frequencies because it satisfies the class-E zero-voltage switching and zero-derivative-voltage switching conditions on both the inverter and the rectifier. By using the class-E inverter as a transmitter and the class-E rectifier as a receiver, high power-delivery efficiency can be achieved in the designed WPT system. By using a numerical design procedure proposed in the previous work, it is possible to design the WPT system without considering the impedance matching for satisfying the class-E ZVS/ZDS conditions. The experimental results of the design example showed the overall efficiency of 85.1 % at 100 W output power and 200 kHz operating frequency.
An Integration-Oriented Ontology to Govern Evolution in Big Data Ecosystems

Big Data architectures allow to flexibly store and process heterogeneous data, from multiple sources, in its original format. The structure of those data, commonly supplied by means of REST APIs, is continuously evolving, forcing data analysts using it need to adapt their analytical processes after each release. This gets more challenging when aiming to perform an integrated or historical analysis of multiple sources. To cope with such complexity, in this paper we present the Big Data Integration ontology, the core construct for a data governance protocol that systematically annotates and integrates data from multiple sources in its original format. To cope with syntactic evolution in the sources, we present an algorithm that semi-automatically adapts the ontology upon new releases. A functional evaluation on realworld APIs is performed in order to validate our approach.
Customers ’ perceptions of online retailing service quality and their satisfaction

Online service quality is one of the key determinants of the success of online retailers. This exploratory study revealed some important findings about online service quality. First, the study identified six key online retailing service quality dimensions as perceived by online customers: reliable/prompt responses, access, ease of use, attentiveness, security, and credibility. Second, of the six, three dimensions, notably reliable/prompt responses, attentiveness, and ease of use, had significant impacts on both customers’ perceived overall service quality and their satisfaction. Third, the access dimension had a significant effect on overall service quality, but not on satisfaction. Finally, this study discovered a significantly positive relationship between overall service quality and satisfaction. Important managerial implications and recommendations are also presented.
ORB-SLAM2: An Open-Source SLAM System for Monocular, Stereo, and RGB-D Cameras

We present ORB-SLAM2, a complete simultaneous localization and mapping (SLAM) system for monocular, stereo and RGB-D cameras, including map reuse, loop closing, and relocalization capabilities. The system works in real time on standard central processing units in a wide variety of environments from small hand-held indoors sequences, to drones flying in industrial environments and cars driving around a city. Our back-end, based on bundle adjustment with monocular and stereo observations, allows for accurate trajectory estimation with metric scale. Our system includes a lightweight localization mode that leverages visual odometry tracks for unmapped regions and matches with map points that allow for zero-drift localization. The evaluation on 29 popular public sequences shows that our method achieves state-of-the-art accuracy, being in most cases the most accurate SLAM solution. We publish the source code, not only for the benefit of the SLAM community, but with the aim of being an out-of-the-box SLAM solution for researchers in other fields.
Structure and evolution of online social networks

In this paper, we consider the evolution of structure within large online social networks. We present a series of measurements of two such networks, together comprising in excess of five million people and ten million friendship links, annotated with metadata capturing the time of every event in the life of the network. Our measurements expose a surprising segmentation of these networks into three regions: singletons who do not participate in the network; isolated communities which overwhelmingly display star structure; and a giant component anchored by a well-connected core region which persists even in the absence of stars.We present a simple model of network growth which captures these aspects of component structure. The model follows our experimental results, characterizing users as either passive members of the network; inviters who encourage offline friends and acquaintances to migrate online; and linkers who fully participate in the social evolution of the network.
System design for a solar powered electric vehicle charging station for workplaces q

This paper investigates the possibility of charging battery electric vehicles at workplace in Netherlands using solar energy. Data from the Dutch Meteorological Institute is used to determine the optimal orientation of PV panels for maximum energy yield in the Netherlands. The seasonal and diurnal variation in solar insolation is analyzed to determine the energy availability for EV charging and the necessity for grid connection. Due to relatively low solar insolation in Netherlands, it has been determined that the power rating of the PV array can be oversized by 30% with respect to power rating of the converter. Various dynamic EV charging profiles are compared with an aim to minimize the grid dependency and to maximize the usage of solar power to directly charge the EV. Two scenarios are considered – one where the EVs have to be charged only on weekdays and the second case where EV have to be charged all 7 days/week. A priority mechanism is proposed to facilitate the charging of multiple EV from a single EV–PV charger. The feasibility of integrating a local storage to the EV–PV charger to make it grid independent is evaluated. The optimal storage size that reduces the grid dependency by 25% is evaluated.  This is an openaccess article under the CCBY license (http:// creativecommons.org/licenses/by/4.0/).
Relating effortful control, executive function, and false belief understanding to emerging math and literacy ability in kindergarten.

This study examined the role of self-regulation in emerging academic ability in one hundred and forty-one 3- to 5-year-old children from low-income homes. Measures of effortful control, false belief understanding, and the inhibitory control and attention-shifting aspects of executive function in preschool were related to measures of math and literacy ability in kindergarten. Results indicated that the various aspects of child self-regulation accounted for unique variance in the academic outcomes independent of general intelligence and that the inhibitory control aspect of executive function was a prominent correlate of both early math and reading ability. Findings suggest that curricula designed to improve self-regulation skills as well as enhance early academic abilities may be most effective in helping children succeed in school.
HCI issues in computer games

The mid 20 century witnessed some serious attempts in studies of play and games with an emphasis on their importance within culture. Most prominently, Johan Huizinga (1944) maintained in his book Homo Ludens that the earliest stage of culture is in the form of play and that culture proceeds in the shape and the mood of play. He also claimed that some elements of play crystallised as knowledge such as folklore, poetry and philosophy as culture advanced.
Model Composition from Interchangeable Components

Following the increasing demand to make the creation and manipulation of 3D geometry simpler and more accessible, we introduce a modeling approach that allows even novice users to create sophisticated models in minutes. Our approach is based on the observation that in many modeling settings users create models which belong to a small set of model classes, such as humans or quadrupeds. The models within each class typically share a common component structure. Following this observation, we introduce a modeling system which utilizes this common component structure allowing users to create new models by shuffling interchangeable components between existing models. To enable shuffling, we develop a method for computing a compatible segmentation of input models into meaningful, interchangeable components. Using this segmentation our system lets users create new models with a few mouse clicks, in a fraction of the time required by previous composition techniques. We demonstrate that the shuffling paradigm allows for easy and fast creation of a rich geometric content.
A high-swing, high-impedance MOS cascode circuit

|A simple cascode circuit with the gate voltage of the cascode transistor being controlled by a feedback ampli er and thus named `regulated cascode' is presented. In comparison to the standard cascode circuit the minimum output voltage is lower by about 30 to 60% while the output conductance and the feedback capacitance are lower by about 100 times. An analytical large-signal, small-signal, and noise analysis is carried out. Some applications like current mirrors and voltage ampli ers are discussed. Finally, experimental results con rming the theory are presented.
Associated Activation-Driven Enrichment: Understanding Implicit Information from a Cognitive Perspective

In this paper, we propose a novel text representation paradigm and a set of follow-up text representation models based on cognitive psychology theories. The intuition of our study is that the knowledge implied in a large collection of documents may improve the understanding of single documents. Based on cognitive psychology theories, we propose a general text enrichment framework, study the key factors to enable activation of implicit information, and develop new text representation methods to enrich text with the implicit information. Our study aims to mimic some aspects of human cognitive procedure in which given stimulant words serve to activate understanding implicit concepts. By incorporating human cognition into text representation, the proposed models advance existing studies by mining implicit information from given text and coordinating with most existing text representation approaches at the same time, which essentially bridges the gap between explicit and implicit information. Experiments on multiple tasks show that the implicit information activated by our proposed models matches human intuition and significantly improves the performance of the text mining tasks as well.
Using anomaly detection based techniques to detect HTTP-based botnet C&C traffic

HTTP is becoming the most preferred channel for command and control (C&C) communication of botnets. One of the main reasons is that it is very easy to hide the C&C traffic in the massive amount of browser generated Web traffic. However, detecting these HTTP-based C&C packets which constitute only a minuscule portion of the overall everyday HTTP traffic is a formidable task. In this paper, we present an anomaly detection based approach to detect HTTP-based C&C traffic using statistical features based on client generated HTTP request packets and DNS server generated response packets. We use three different unsupervised anomaly detection techniques to isolate suspicious communications that have a high probability of being part of a botnet's C&C communication. Results indicate that our method can achieve more than 90% detection rate while maintaining a reasonably low false positive rate.
Challenges on Large Scale Surveillance Video Analysis

Large scale surveillance video analysis is one of the most important components in the future artificial intelligent city. It is a very challenging but practical system, consists of multiple functionalities such as object detection, tracking, identification and behavior analysis. In this paper, we try to address three tasks hosted in NVIDIA AI City Challenge contest. First, a system that transforming the image coordinate to world coordinate has been proposed, which is useful to estimate the vehicle speed on the road. Second, anomalies like car crash event and stalled vehicles can be found by the proposed anomaly detector framework. Third, multiple camera vehicle re-identification problem has been investigated and a matching algorithm is explained. All these tasks are based on our proposed online single camera multiple object tracking (MOT) system, which has been evaluated on the widely used MOT16 challenge benchmark. We show that it achieves the best performance compared to the state-of-the-art methods. Besides of MOT, we evaluate the proposed vehicle re-identification model on VeRi-776 dataset and it outperforms all other methods with a large margin.
How many child deaths can we prevent this year?

This is the second of five papers in the child survival series. The first focused on continuing high rates of child mortality (over 10 million each year) from preventable causes: diarrhoea, pneumonia, measles, malaria, HIV/AIDS, the underlying cause of undernutrition, and a small group of causes leading to neonatal deaths. We review child survival interventions feasible for delivery at high coverage in low-income settings, and classify these as level 1 (sufficient evidence of effect), level 2 (limited evidence), or level 3 (inadequate evidence). Our results show that at least one level-1 intervention is available for preventing or treating each main cause of death among children younger than 5 years, apart from birth asphyxia, for which a level-2 intervention is available. There is also limited evidence for several other interventions. However, global coverage for most interventions is below 50%. If level 1 or 2 interventions were universally available, 63% of child deaths could be prevented. These findings show that the interventions needed to achieve the millennium development goal of reducing child mortality by two-thirds by 2015 are available, but that they are not being delivered to the mothers and children who need them.
A 94 GHz, 1.4 dB Insertion Loss Single-Pole Double-Throw Switch Using Reverse-Saturated SiGe HBTs

This work demonstrates two 94 GHz SPDT quarter-wave shunt switches using saturated SiGe HBTs. A new mode of operation, called reverse saturation, using the emitter at the RF output node of the switch, is utilized to take advantage of the higher emitter doping and improved isolation from the substrate. The switches were designed in a 180 nm SiGe BiCMOS technology featuring 90 nm SiGe HBTs (selective emitter shrink) with fT/fmax of 250/300+ GHz. The forward-saturated switch achieves an insertion loss and isolation at 94 GHz of 1.8 dB and 19.3 dB, respectively. The reverse-saturated switch achieves a similar isolation, but reduces the insertion loss to 1.4 dB. This result represents a 30% improvement in insertion loss in comparison to the best CMOS SPDT at 94 GHz.
Attack taxonomies for the Modbus protocols

The Modbus protocol and its variants are widely used in industrial control applications, especially for pipeline operations in the oil and gas sector. This paper describes the principal attacks on the Modbus Serial and Modbus TCP protocols and presents the corresponding attack taxonomies. The attacks are summarized according to their threat categories, targets and impact on control system assets. The attack taxonomies facilitate formal risk analysis efforts by clarifying the nature and scope of the security threats on Modbus control systems and networks. Also, they provide insights into potential mitigation strategies and the relative costs and benefits of implementing these strategies.
Violent Video Games , Delinquency , and Youth Violence : New Evidence

Violent video game playing is correlated with aggression, but its relation to antisocial behavior in correctional and juvenile justice samples is largely unknown. Based on a data from a sample of institutionalized juvenile delinquents, behavioral and attitudinal measures relating to violent video game playing were associated with a composite measure of delinquency and a more specific measure of violent delinquency after controlling for the effects of screen time, years playing video games, age, sex, race, delinquency history, and psychopathic personality traits. Violent video games are associated with antisociality even in a clinical sample, and these effects withstand the robust influences of multiple correlates of juvenile delinquency and youth violence most notably psychopathy.
Testing for the Consecutive Ones Property, Interval Graphs, and Graph Planarity Using PQ-Tree Algorithms

A data structure called a PQ-tree is introduced. PQ-trees can be used to represent the permutations of a set U in which various subsets of U occur consecutively. Efficient algorithms are presented for manipulating PQ-trees. Algorithms using PQ-trecs are then given which test for the consecutive ones property in matrices and for graph planarity. The consecutive ones test is extended to a test for interval graphs using a recently discovered fast recognition algorithm for chordal graphs. All of these algorithms require a number of steps linear in the size of their input.