nils-herrmann/clean_scirepeval huggingface dataset:
W4: Real-Time Surveillance of People and Their Activities

Ð R is a real time visual surveillance system for detecting and tracking multiple people and monitoring their activities in an outdoor environment. It operates on monocular gray-scale video imagery, or on video imagery from an infrared camera.  R employs a combination of shape analysis and tracking to locate people and their parts (head, hands, feet, torso) and to create models of people's appearance so that they can be tracked through interactions such as occlusions. It can determine whether a foreground region contains multiple people and can segment the region into its constituent people and track them.  R can also determine whether people are carrying objects, and can segment objects from their silhouettes, and construct appearance models for them so they can be identified in subsequent frames.  R can recognize events between people and objects, such as depositing an object, exchanging bags, or removing an object. It runs at 25 Hz for 320Â240 resolution images on a 400 Mhz dual-Pentium II PC.
Human-Level Artificial Intelligence? Be Serious!

68 AI MAGAZINE ■ I claim that achieving real human-level artificial intelligence would necessarily imply that most of the tasks that humans perform for pay could be automated. Rather than work toward this goal of automation by building special-purpose systems, I argue for the development of general-purpose, educable systems that can learn and be taught to perform any of the thousands of jobs that humans can perform. Joining others who have made similar proposals, I advocate beginning with a system that has minimal, although extensive, built-in capabilities. These would have to include the ability to improve through learning along with many other abilities.
Voice Impersonation Using Generative Adversarial Networks

Voice impersonation is not the same as voice transformation, although the latter is an essential element of it. In voice impersonation, the resultant voice must convincingly convey the impression of having been naturally produced by the target speaker, mimicking not only the pitch and other perceivable signal qualities, but also the style of the target speaker. In this paper, we propose a novel neural-network based speech quality- and style-mimicry framework for the synthesis of impersonated voices. The framework is built upon a fast and accurate generative adversarial network model. Given spectrographic representations of source and target speakers' voices, the model learns to mimic the target speaker's voice quality and style, regardless of the linguistic content of either's voice, generating a synthetic spectrogram from which the time-domain signal is reconstructed using the Griffin-Lim method. In effect, this model reframes the well-known problem of style-transfer for images as the problem of style-transfer for speech signals, while intrinsically addressing the problem of durational variability of speech sounds. Experiments demonstrate that the model can generate extremely convincing samples of impersonated speech. It is even able to impersonate voices across different genders effectively. Results are qualitatively evaluated using standard procedures for evaluating synthesized voices.
Query Expansion via structural motifs in Wikipedia Graph

The search for relevant information can be very frustrating for users who, unintentionally, use too general or inappropriate keywords to express their requests. To overcome this situation, query expansion techniques aim at transforming the user request by adding new terms, referred as expansion features, that better describe the real intent of the users. We propose a method that relies exclusively on relevant structures (as opposed to the use of semantics) found in knowledge bases (KBs) to extract the expansion features. We call our method Structural Query Expansion (SQE). The structural analysis of KBs takes us to propose a set of structural motifs that connect their strongly related entries, which can be used to extract expansion features. In this paper we use Wikipedia as our KB, which is probably one of the largest sources of information. SQE is capable of achieving more than 150% improvement over non expanded queries and is able to identify the expansion features in less than 0.2 seconds in the worst case scenario. Most significantly, we believe that we are contributing to open new research directions in query expansion, proposing a method that is orthogonal to many current systems. For example, SQE improves pseudo-relevance feedback techniques up to 13%.
Fraud detection system: A survey

The increment of computer technology use and the continued growth of companies have enabled most financial transactions to be performed through the electronic commerce systems, such as using the Credit card system, Telecommunication system, Healthcare Insurance system, etc. Unfortunately, these systems are used by both legitimate users and fraudsters. In addition, fraudsters utilized different approaches to breach the electronic commerce systems. Fraud prevention systems (FPSs) are insufficient to provide adequate security to the electronic commerce systems. However, the collaboration of FDSs with FPSs might be effective to secure electronic commerce systems. Nevertheless, there are issues and challenges that hinder the performance of FDSs, such as Concept Drift, Supports Real Time Detection, Skewed Distribution, Large Amount of Data etc. This survey paper aims to provide a systematic and comprehensive overview of these issues and challenges that obstruct the performance of FDSs. We have selected five electronic commerce systems; which are Credit card, Telecommunication, Healthcare Insurance, Automobile Insurance and Online auction. The prevalent fraud types in those E-commerce systems are introduced closely. Further, state-of-the-art FDSs approaches in selected E-commerce systems are systematically introduced. Then a brief discussion on potential research trends in the near future and conclusion are presented.
Modeling local and global deformations in Deep Learning: Epitomic convolution, Multiple Instance Learning, and sliding window detection

Deep Convolutional Neural Networks (DCNNs) achieve invariance to domain transformations (deformations) by using multiple `max-pooling' (MP) layers. In this work we show that alternative methods of modeling deformations can improve the accuracy and efficiency of DCNNs. First, we introduce epitomic convolution as an alternative to the common convolution-MP cascade of DCNNs, that comes with the same computational cost but favorable learning properties. Second, we introduce a Multiple Instance Learning algorithm to accommodate global translation and scaling in image classification, yielding an efficient algorithm that trains and tests a DCNN in a consistent manner. Third we develop a DCNN sliding window detector that explicitly, but efficiently, searches over the object's position, scale, and aspect ratio. We provide competitive image classification and localization results on the ImageNet dataset and object detection results on Pascal VOC2007.
What is best practice for training intensity and duration distribution in endurance athletes?

Successful endurance training involves the manipulation of training intensity, duration, and frequency, with the implicit goals of maximizing performance, minimizing risk of negative training outcomes, and timing peak fitness and performances to be achieved when they matter most. Numerous descriptive studies of the training characteristics of nationally or internationally competitive endurance athletes training 10 to 13 times per week seem to converge on a typical intensity distribution in which about 80% of training sessions are performed at low intensity (2 mM blood lactate), with about 20% dominated by periods of high-intensity work, such as interval training at approx. 90% VO2max. Endurance athletes appear to self-organize toward a high-volume training approach with careful application of high-intensity training incorporated throughout the training cycle. Training intensification studies performed on already well-trained athletes do not provide any convincing evidence that a greater emphasis on high-intensity interval training in this highly trained athlete population gives long-term performance gains. The predominance of low-intensity, long-duration training, in combination with fewer, highly intensive bouts may be complementary in terms of optimizing adaptive signaling and technical mastery at an acceptable level of stress.
Self-discipline outdoes IQ in predicting academic performance of adolescents.

In a longitudinal study of 140 eighth-grade students, self-discipline measured by self-report, parent report, teacher report, and monetary choice questionnaires in the fall predicted final grades, school attendance, standardized achievement-test scores, and selection into a competitive high school program the following spring. In a replication with 164 eighth graders, a behavioral delay-of-gratification task, a questionnaire on study habits, and a group-administered IQ test were added. Self-discipline measured in the fall accounted for more than twice as much variance as IQ in final grades, high school selection, school attendance, hours spent doing homework, hours spent watching television (inversely), and the time of day students began their homework. The effect of self-discipline on final grades held even when controlling for first-marking-period grades, achievement-test scores, and measured IQ. These findings suggest a major reason for students falling short of their intellectual potential: their failure to exercise self-discipline.
Secondary Frequency and Voltage Control of Islanded Microgrids via Distributed Averaging

In this paper, we present new distributed controllers for secondary frequency and voltage control in islanded microgrids. Inspired by techniques from cooperative control, the proposed controllers use localized information and nearest-neighbor communication to collectively perform secondary control actions. The frequency controller rapidly regulates the microgrid frequency to its nominal value while maintaining active power sharing among the distributed generators. Tuning of the voltage controller provides a simple and intuitive tradeoff between the conflicting goals of voltage regulation and reactive power sharing. Our designs require no knowledge of the microgrid topology, impedances, or loads. The distributed architecture allows for flexibility and redundancy, eliminating the need for a central microgrid controller. We provide a voltage stability analysis and present extensive experimental results validating our designs, verifying robust performance under communication failure and during plug-and-play operation.
Swarm robotics reviewed

Jan Carlo Barca and Y. Ahmet Sekercioglu Robotica / Volume 31 / Issue 03 / May 2013, pp 345 ­ 359 DOI: 10.1017/S026357471200032X,  2012 Link to this article:  Jan Carlo Barca and Y. Ahmet Sekercioglu (2013). Swarm robotics reviewed. Robotica, 31, pp 345­359 doi:10.1017/ S026357471200032X Request Permissions : Click here
OmniTrack: A Flexible Self-Tracking Approach Leveraging Semi-Automated Tracking

We now see an increasing number of self-tracking apps and wearable devices. Despite the vast number of available tools, however, it is still challenging for self-trackers to find apps that suit their unique tracking needs, preferences, and commitments. Furthermore, people are bounded by the tracking tools’ initial design because it is difficult to modify, extend, or mash up existing tools. In this paper, we present OmniTrack, a mobile self-tracking system, which enables self-trackers to construct their own trackers and customize tracking items to meet their individual tracking needs. To inform the OmniTrack design, we first conducted semi-structured interviews (N = 12) and analyzed existing mobile tracking apps (N = 62). We then designed and developed OmniTrack as an Android mobile app, leveraging a semi-automated tracking approach that combines manual and automated tracking methods. We evaluated OmniTrack through a usability study (N = 10) and improved its interfaces based on the feedback. Finally, we conducted a 3-week deployment study (N = 21) to assess if people can capitalize on OmniTrack’s flexible and customizable design to meet their tracking needs. From the study, we showed how participants used OmniTrack to create, revise, and appropriate trackers—ranging from a simple mood tracker to a sophisticated daily activity tracker. We discuss how OmniTrack positively influences and supports self-trackers’ tracking practices over time, and how to further improve OmniTrack by providing more appropriate visualizations and sharable templates, incorporating external contexts, and supporting researchers’ unique data collection needs.
A Kronecker-factored approximate Fisher matrix for convolution layers

Second-order optimization methods such as natural gradient descent have the potential to speed up training of neural networks by correcting for the curvature of the loss function. Unfortunately, the exact natural gradient is impractical to compute for large models, and most approximations either require an expensive iterative procedure or make crude approximations to the curvature. We present Kronecker Factors for Convolution (KFC), a tractable approximation to the Fisher matrix for convolutional networks based on a structured probabilistic model for the distribution over backpropagated derivatives. Similarly to the recently proposed Kronecker-Factored Approximate Curvature (K-FAC), each block of the approximate Fisher matrix decomposes as the Kronecker product of small matrices, allowing for efficient inversion. KFC captures important curvature information while still yielding comparably efficient updates to stochastic gradient descent (SGD). We show that the updates are invariant to commonly used reparameterizations, such as centering of the activations. In our experiments, approximate natural gradient descent with KFC was able to train convolutional networks several times faster than carefully tuned SGD. Furthermore, it was able to train the networks in 10-20 times fewer iterations than SGD, suggesting its potential applicability in a distributed setting.
Impact of pre-sowing magnetic field exposure of seeds to stationary magnetic field on growth, reactive oxygen species and photosynthesis of maize under field conditions

Impact of pre-sowing exposure of seeds to static magnetic field were studied on 1 month old maize [Zea mays . var: HQPM.1] plants under field conditions. Pre-standardized magnetic field strength of 100 mT (2 h) and 200 mT (1 h), which were proven best for improving different seedling parameters under laboratory condition, were used for this study. Magnetic field treatment altered growth, superoxide radical level, antioxidant enzymes and photosynthesis. Among the different growth parameters, leaf area and root length were the most enhanced parameters (78–40%, respectively), over untreated plants. Electron paramagnetic resonance spectroscopy study showed that superoxide radical was reduced and hydroxyl radical was unaffected after magnetic field treatment. With decrease in free radical content, antioxidant enzymes like superoxide dismutase and peroxidase were also reduced by 43 and 23%, respectively, in plants that emerged from magnetically treated seeds. Measurement of Chlorophyll a fluorescence by plant efficiency analyzer showed that the potential of processing light energy through photosynthetic machinery was enhanced by magnetic field treatment. Performance index of the plant enhanced up to two-fold and phenomenological leaf model showed more active reaction centers after magnetic field treatment. Among the two field strengths used, 200 mT (1 h) was more effective in altering all these parameters. It is concluded that pre-sowing magnetic field treatment can be effectively used for improving plant growth and development under field conditions.
A Fast and Scalable Payment Network with Bitcoin Duplex Micropayment Channels

Bitcoin does not scale, because its synchronization mechanism, the blockchain, limits the maximum rate of transactions the network can process. However, using off-blockchain transactions it is possible to create long-lived channels over which an arbitrary number of transfers can be processed locally between two users, without any burden to the Bitcoin network. These channels may form a network of payment service providers (PSPs). Payments can be routed between any two users in real time, without any confirmation delay. In this work we present a protocol for duplex micropayment channels, which guarantees end-to-end security and allow instant transfers, laying the foundation of the PSP network.
Fast and Space-Efficient Entity Linking for Queries

Entity linking deals with identifying entities from a knowledge base in a given piece of text and has become a fundamental building block for web search engines, enabling numerous downstream improvements from better document ranking to enhanced search results pages. A key problem in the context of web search queries is that this process needs to run under severe time constraints as it has to be performed before any actual retrieval takes place, typically within milliseconds.
 In this paper we propose a probabilistic model that leverages user-generated information on the web to link queries to entities in a knowledge base. There are three key ingredients that make the algorithm fast and space-efficient. First, the linking process ignores any dependencies between the different entity candidates, which allows for a O(k2) implementation in the number of query terms. Second, we leverage hashing and compression techniques to reduce the memory footprint. Finally, to equip the algorithm with contextual knowledge without sacrificing speed, we factor the distance between distributional semantics of the query words and entities into the model.
 We show that our solution significantly outperforms several state-of-the-art baselines by more than 14% while being able to process queries in sub-millisecond times---at least two orders of magnitude faster than existing systems.
Global burden of cardiovascular diseases: Part II: variations in cardiovascular disease by specific ethnic groups and geographic regions and prevention strategies.

This two-part article provides an overview of the global burden of atherothrombotic cardiovascular disease. Part I initially discusses the epidemiological transition which has resulted in a decrease in deaths in childhood due to infections, with a concomitant increase in cardiovascular and other chronic diseases; and then provides estimates of the burden of cardiovascular (CV) diseases with specific focus on the developing countries. Next, we summarize key information on risk factors for cardiovascular disease (CVD) and indicate that their importance may have been underestimated. Then, we describe overarching factors influencing variations in CVD by ethnicity and region and the influence of urbanization. Part II of this article describes the burden of CV disease by specific region or ethnic group, the risk factors of importance, and possible strategies for prevention.
Anomaly detection of web-based attacks

Web-based vulnerabilities represent a substantial portion of the security exposures of computer networks. In order to detect known web-based attacks, misuse detection systems are equipped with a large number of signatures. Unfortunately, it is difficult to keep up with the daily disclosure of web-related vulnerabilities, and, in addition, vulnerabilities may be introduced by installation-specific web-based applications. Therefore, misuse detection systems should be complemented with anomaly detection systems. This paper presents an intrusion detection system that uses a number of different anomaly detection techniques to detect attacks against web servers and web-based applications. The system correlates the server-side programs referenced by client queries with the parameters contained in these queries. The application-specific characteristics of the parameters allow the system to perform focused analysis and produce a reduced number of false positives. The system derives automatically the parameter profiles associated with web applications (e.g., length and structure of parameters) from the analyzed data. Therefore, it can be deployed in very different application environments without having to perform time-consuming tuning and configuration.
Six-Phase Fractional-Slot-per-Pole-per-Phase Permanent-Magnet Machines With Low Space Harmonics for Electric Vehicle Application

This paper discusses the development of new winding configuration for six-phase permanent-magnet (PM) machines with 18 slots and 8 poles, which eliminates and/or reduces undesirable space harmonics in the stator magnetomotive force. The proposed configuration improves power/torque density and efficiency with a reduction in eddy-current losses in the rotor permanent magnets and copper losses in end windings. To improve drive train availability for applications in electric vehicles (EVs), this paper proposes the design of a six-phase PM machine as two independent three-phase windings. A number of possible phase shifts between two sets of three-phase windings due to their slot-pole combination and winding configuration are investigated, and the optimum phase shift is selected by analyzing the harmonic distributions and their effect on machine performance, including the rotor eddy-current losses. The machine design is optimized for a given set of specifications for EVs, under electrical, thermal and volumetric constraints, and demonstrated by the experimental measurements on a prototype machine.
Session State: Beyond Soft State

The cost and complexity of administration of large systems has come to dominate their total cost of ownership. Stateless and soft-state components, e.g. Web servers or network routers, are easy to manage: capacity can be scaled incrementally by adding more nodes, rebalancing of load after failover is easy, and reactive or proactive (“rolling”) reboots can be used to handle transient failures. We show that it is possible to achieve the same ease of management for the state-storage subsystem by subdividing persistent state according to the specific guarantees needed by each type. While other systems [19, 17] have addressed persistent-untildeleted state, we describe SSM, a store for a previously unaddressed class of state – user-session state – that exhibits the same manageability properties as stateless nodes while providing firm storage guarantees. Any node can be proactively or reactively rebooted at any time to recover from transient faults, without impacting online performance or losing data. We exploit this simplified manageability by pairing SSM with an application-generic, statistical-anomalybased framework that detects crashes, hangs, and performance failures, and automatically attempts to recover from them by rebooting faulty nodes. Although the detection techniques generate some false positives, the cost of recovery is so low that the false positives have low impact. We provide microbenchmarks to demonstrate SSM’s built-in overload protection, failure management and self-tuning. We benchmark SSM integrated into a production enterprise-scale interactive service to demonstrate that these benefits need not come at the cost of significantly decreased throughput or response time.