nils-herrmann/clean_scirepeval huggingface dataset:
Teaching virtual reality with HTC vive and leap motion

Creating high quality virtual reality (VR) experience takes time and requires extensive practice. Although, multiple virtual and augmented reality courses existed for years all over the world, high costs of the equipment were always in the way of building up the up to date knowledge. Rapid development of the technology caused the new VR boom and exposed a serious lack of the experienced VR/AR developers.
 In this paper, we present our introductory VR courses for master students. The aim of the courses is to provide introduction to the basics of VR and AR and supporting technology. We move the teaching focus from the specifics of a particular VR system to the skill build-up and development of the VR user experience. For that we provide the access to the up to date consumer hardware, such as HTC Vive and Leap Motion. We discuss the structure of the courses and methodology, and provide the teaching materials. Moreover, we discuss in details our updated practical course that unites the development of a low-cost desktop and a high-quality immersive VR applications using only off-the-shelf consumer equipment. Furthermore, we discuss the overall course evaluation by the students and further opportunities for their professional growth, as well as consecutive changes that will be made next.
Progress in the gut microbiota in exploring shrimp disease pathogenesis and incidence

It is now recognized that gut microbiota contributes indispensable roles in safeguarding host health. Shrimp is being threatened by newly emerging diseases globally; thus, understanding the driving factors that govern its gut microbiota would facilitate an initial step to reestablish and maintain a “healthy” gut microbiota. This review summarizes the factors that assemble the shrimp gut microbiota, which focuses on the current progresses of knowledge linking the gut microbiota and shrimp health status. In particular, I propose the exploration of shrimp disease pathogenesis and incidence based on the interplay between dysbiosis in the gut microbiota and disease severity. An updated research on shrimp disease toward an ecological perspective is discussed, including host–bacterial colonization, identification of polymicrobial pathogens and diagnosing disease incidence. Further, a simple conceptual model is offered to summarize the interplay among the gut microbiota, external factors, and shrimp disease. Finally, based on the review, current limitations are raised and future studies directed at solving these concerns are proposed. This review is timely given the increased interest in the role of gut microbiota in disease pathogenesis and the advent of novel diagnosis strategies.
Regenerative Endodontics.

Regenerative endodontics has been defined as "biologically based procedure designed to replace damaged structures, including dentin and root structures, as well as cells of the pulp-dentin complex." This is an exciting and rapidly evolving field of human endodontics for the treatment of immature permanent teeth with infected root canal systems. These procedures have shown to be able not only to resolve pain and apical periodontitis but continued root development, thus increasing the thickness and strength of the previously thin and fracture-prone roots. In the last decade, over 80 case reports, numerous animal studies, and series of regenerative endodontic cases have been published. However, even with multiple successful case reports, there are still some remaining questions regarding terminology, patient selection, and procedural details. Regenerative endodontics provides the hope of converting a nonvital tooth into vital one once again.
A distributed control path architecture for VLIW processors

VLIW architectures are popular in embedded systems because they offer high-performance processing at low cost and energy. The major problem with traditional VLIW designs is that they do not scale efficiently due to bottlenecks that result from centralized resources and global communication. Multicluster designs have been proposed to solve the scaling problem of VLIW datapaths, while much less work has been done on the control path. In this paper, we propose a distributed control path architecture for VLIW processors (DVLIW) to overcome the scalability problem of VLIW control paths. The architecture simplifies the dispersal of complex VLIW instructions and supports efficient distribution of instructions through a limited bandwidth interconnect, while supporting compressed instruction encodings. DVLIW employs a multicluster design where each cluster contains a local instruction memory that provides all intra-cluster control. All clusters have their own program counter and instruction sequencing capabilities, thus instruction execution is completely decentralized. The architecture executes multiple instruction streams at the same time, but these streams collectively function as a single logical instruction stream. Simulation results show that DVLIW processors reduce the number of cross-chip control signals by approximately two orders of magnitude while incurring a small performance overhead to explicitly manage the instruction streams.
A 200 MHz CMOS pipelined multiplier-accumulator using a quasi-domino dynamic full-adder cell design

AbstmctA bit-level pipelined 12 x 12-b two’s-complement multiplier with a 27-b accumulator has been designed and fabricated in a 1.0-pm p-well CMOS technology. A new “quasi N-P domino logic” structure has been adopted to increase the throughput rate, and special pipeline structures were used in the accumulator to reduce the total latency. The chip complexity is approximately 10 OOO transistors and the die area is 2.5 x 3.7 mm’. The measured maximum clock rate is 200 MHz (i.e., 200 million multiply-accumulate operations per second), and the power-speed ratio is 6.5 mWIMHz. An unique output buffer design was also adopted to achieve 200-MHz off-chip communication while maintaining full CMOS logic levels.
Online recurrent extreme learning machine and its application to time-series prediction

Online sequential extreme learning machine (OS-ELM) is an online learning algorithm training single-hidden layer feedforward neural networks (SLFNs), which can learn data one-by-one or chunk-by-chunk with fixed or varying data size. Due to its characteristics of online sequential learning, OS-ELM is popularly used to solve time-series prediction problem, such as stock forecast, weather forecast, passenger count forecast, etc. OS-ELM, however, has two fatal drawbacks: Its input weights cannot be adjusted and it cannot be applied to learn recurrent neural network (RNN). Therefore we propose a modified version of OS-ELM, called online recurrent extreme learning machine (OR-ELM), which is able to adjust input weights and can be applied to learn RNN, by applying ELM-auto-encoder and a normalization method called layer normalization (LN). Proposed method is used to solve a time-series prediction problem on NewYork City passenger count dataset, and the results show that R-ELM outperforms OS-ELM and other online-sequential learning algorithms such as hierarchical temporal memory (HTM) and online long short-term memory (online LSTM).
Cache Simulation for Instruction Set Simulator QEMU

In embedded system design, there is an increasing demand for modeling techniques that can provide both accurate measurements of delay and fast simulation speed. Modeling latency effects of a cache can greatly increase accuracy of the simulation and assist developers to optimize their software. Current solutions have not succeeded in balancing three important factors: speed, accuracy and usability. In this research, we created a cache simulation module inside a well-known instruction set simulator QEMU. Our implementation can simulate various cases of cache configuration and obtain every memory access. In full system simulation, speed is kept at around 73 MIPS on a personal host computer which is close to native execution of ARM Cortex-M3(125 MIPS at 100 MHz). Compared to the widely used cache simulation tool, Valgrind, our simulator is three time faster.
ClaimBuster: The First-ever End-to-end Fact-checking System

Naeemul Hassan1 Gensheng Zhang2 Fatma Arslan2 Josue Caraballo2 Damian Jimenez2 Siddhant Gawsane2 Shohedul Hasan2 Minumol Joseph2 Aaditya Kulkarni2 Anil Kumar Nayak2 Vikas Sable2 Chengkai Li2 Mark Tremayne3 1Department of Computer and Information Science, University of Mississippi 2Department of Computer Science and Engineering, University of Texas at Arlington 3Department of Communication, University of Texas at Arlington
Passive position finding of stationary targets: Based on circulation and the least square concept

A method for two dimensional position finding of stationary targets whose bearing measurements suffers from indeterminable bias and random noise has been proposed. The algorithm uses convex optimization to minimize an error function which has been calculated based on circular as well as linear loci of error. Taking into account a number of observations, certain modifications have been applied to the initial crude method so as to arrive at a faster, more accurate method. Simulation results of the method illustrate up to 30% increase in accuracy compared with the well-known least square filter.
DIFFERENCES IN UPPER BODY POSTURE AND POSTURAL MUSCLE ACTIVATION IN FEMALES WITH LARGER BREAST SIZES

The following individuals read and discussed the thesis submitted by student Mindy Elizabeth Bennett, and they also evaluated her presentation and response to questions during the final oral examination. They found that the student passed the final oral examination, and that the thesis was satisfactory for a master's degree and ready for any final modifications that they explicitly required. iii ACKNOWLEDGEMENTS During my time of study at Boise State University, I have received an enormous amount of academic support and guidance from a number of different individuals. I would like to take this opportunity to thank everyone who has been instrumental in the completion of this degree. Without the continued support and guidance of these individuals, this accomplishment would not have been possible. I would also like to thank the following individuals for generously giving their time to provide me with the help and support needed to complete this study. Without them, the completion of this study would not have been possible. Breast hypertrophy is a common medical condition whose morbidity has increased over recent decades. Symptoms of breast hypertrophy often include musculoskeletal pain in the neck, back and shoulders, and numerous psychosocial health burdens. To date, reduction mammaplasty (RM) is the only treatment shown to significantly reduce the severity of the symptoms associated with breast hypertrophy. However, due to a lack of scientific evidence in the medical literature justifying the medical necessity of RM, insurance companies often deny requests for coverage of this procedure. Therefore, the purpose of this study is to investigate biomechanical differences in the upper body of women with larger breast sizes in order to provide scientific evidence of the musculoskeletal burdens of breast hypertrophy to the medical community Twenty-two female subjects (average age 25.90, ± 5.47 years) who had never undergone or been approved for breast augmentation surgery, were recruited to participate in this study. Kinematic data of the head, thorax, pelvis and scapula was collected during static trials and during each of four different tasks of daily living. Surface electromyography (sEMG) data from the Midcervical (C-4) Paraspinal, Upper Trapezius, Lower Trapezius, Serratus Anterior, and Erector Spinae muscles were recorded in the same activities. Maximum voluntary contractions (MVC) were used to normalize the sEMG data, and %MVC during each task in the protocol was analyzed. Kinematic data from the tasks of daily living were normalized to average static posture data for each subject. Subjects were …
Concealed Weapon Detection Using Image Processing

We have recently witnessed the series of bomb blasts in Mumbai which killed and left many injured, left the world in shell shock and the Indians in terror. This situation is not limited to India but it can happen anywhere and anytime in the world. Here we show you the technology which predicts the suicide bombers and explosion of weapons through, IMAGING FOR CONCEALED WEAPON DETECTION. Manual screening procedures for detecting concealed weapons such as handguns, knives, and explosives are common in controlled access settings like airports, entrances to sensitive buildings, and public events. The detection of weapons concealed underneath a person's clothing is an important obstacle to the improvement of the security of the general public as well as the safety of public assets like airports and buildings. It is desirable to be able to detect concealed weapons from a standoff distance, especially when it is impossible to arrange the flow of people through a controlled procedure. The goal is the eventual deployment of automatic detection and recognition of concealed weapons. It is a technological challenge that requires innovative solutions in sensor technologies and image processing. A number of sensors based on different phenomenology as wel l as image processing support are being developed to observe objects underneath people's clothing. In ‘CONCEALEAD WEAPON DETECTION’ the sensor improvements, how the imaging place techniques for simultaneous noise suppression, object enhancement of video data and describes mathematical results[1].
The Spatial Structure of Transnational Human Activity

Starting from conflictive predictions of hitherto disconnected debates in the natural and social sciences, this article examines the spatial structure of transnational human activity (THA) worldwide (a) across eight types of mobility and communication and (b) in its development over time. It is shown that the spatial structure of THA is similar to that of animal displacements and local-scale human motion in that it can be approximated by Lévy flights with heavy tails that obey power laws. Scaling exponent and power-law fit differ by type of THA, being highest in refuge-seeking and tourism and lowest in student exchange. Variance in the availability of resources and opportunities for satisfying associated needs appears to explain these differences. Over time (1960-2010), the Lévy-flight pattern remains intact and remarkably stable, contradicting the popular notion that socio-technological trends lead to a "death of distance." Humans have not become more "global" over time, they rather became more mobile in general, i.e. they move and communicate more at all distances. Hence, it would be more adequate to speak of "mobilization" than of "globalization." Longitudinal change occurs only in some types of THA and predominantly at short distances, indicating regional rather than global shifts.
Uniform Concentric Circular Arrays With Frequency-Invariant Characteristics—Theory, Design, Adaptive Beamforming and DOA Estimation

This paper proposes a new digital beamformer for uniform concentric circular arrays (UCCAs) having nearly frequency-invariant (FI) characteristics. The basic principle is to transform the received signals to the phase mode and remove the frequency dependency of the individual phase mode through the use of a digital beamforming or compensation network. As a result, the far-field pattern of the array, which is governed by a set of variable beamformer weights, is electronically steerable, and it is approximately invariant over a wider range of frequencies than conventional uniform circular arrays (UCAs). This also makes it possible to design the compensation network and the beamformer weights separately. The design of the compensation network is formulated as a second order cone programming (SOCP) problem and is solved optimally for minimax criterion. By employing the beamspace approach using the outputs of a set of fixed UCCA frequency-invariant beamformers (FIBs), a new beamspace MUSIC algorithm is proposed for estimating the direction-of-arrivals (DOAs) of broadband sources. Since the beampatterns of the UCCA-FIB is approximately invariant with frequency and is governed by a small set of weights, a very efficient adaptive beamformer using the minimum variance beamforming (MVB) approach can be developed. Simulation results using broadband Gaussian and multisinusoidal inputs show that the proposed adaptive UCCA-FIB is numerically better conditioned than the conventional broadband tapped-delay-line-based adaptive beamformers, due to the FI property and significantly fewer numbers of adaptive parameters. Consequently, a higher output signal-to-inference-plus-noise ratio over the conventional tapped-delay-line approach is observed. The usefulness of the proposed UCCA-FIB in broadband DOA estimation is also verified by computer simulation
Combating online in-auction fraud: Clues, techniques and challenges

The number of Internet auction shoppers is rapidly growing. However, online auction customers may suffer from auction fraud, sometimes without even noticing it. In-auction fraud differs from preand post-auction fraud in that it happens in the bidding period of an active auction. Since the in-auction fraud strategies are subtle and complex, it makes the fraudulent behavior more difficult to discover. Researchers from disciplines such as computer science and economics have proposed a number of methods to deal with in-auction fraud. In this paper, we summarize commonly seen indicators of in-auction fraud, provide a review of significant contributions in the literature of Internet in-auction fraud, and identify future challenging research tasks.
Decision Tree-Based Data Characterization for Meta-Learning

Appropriate selection of learning algorithms is essential for the success of data mining. Meta-learning is one approach to achieve this objective by identifying a mapping from data characteristics to algorithm performance. Appropriate data characterization is, thus, of vital importance for the meta-learning. To this effect, a variety of data characterization techniques, based on three strategies including simple measure, statistical measure and information theory based measure, have been developed, however, the quality of them is still needed to be improved. This paper presents new measures to characterise datasets for meta-learning based on the idea to capture the characteristics from the structural shape and size of the decision tree induced from the dataset. Their effectiveness is illustrated by comparing to the results obtained by the classical data characteristics techniques, including DCT that is the most wide used technique in meta-learning and Landmarking that is the most recently developed method and produced better performance comparing to DCT.
Mobile Payments Market: Towards Another Clash of the Titans?

In this paper we assess developments in the mobile payments in light of new technologies (e.g., NFC) and solution frameworks (e.g., Android). With the recent announcements of Google and other players to enter the mobile payment markets the already dynamic arena of mobile payments is getting more complex and competitive. Everyone is jockeying to new positions by announcing novel payment solutions based e.g. on NFC systems. We argue, however, that significant and recurrent social, institutional, and business challenges remain to be solved for successful mobile payment platforms to emerge. In order to get these multi-sided platforms to diffuse multiple stakeholders have to be simultaneously convinced for related value propositions and more workable economic arrangements need to be forged.
Towards visual smart metering exploiting wM-Bus and DLMS/COSEM

Automatic Meter Reading (AMR) is one of the key issues in modern energy efficiency technologies, which are expected to bring on great benefits to both customers and utility companies (electricity, gas and water). Several efforts have been recently made in developing suitable communication protocols for metering data management and transmission. In order to match the cost requirements for the water market, an original solution represented by a visual sensor is proposed as an add-on module for the smart meter. The adoption of developed electronic device and ad-hoc protocol at application layer within the last mile of a multiservice Advanced Metering Infrastructure is investigated in terms of energy issues and interoperability. This study also analyses the communication performance that can be expected when using wireless M-Bus technology to send DLMS/COSEM messages and complete an image file transmission from the smart meter to data concentrator unit.
Social Media Marketing

Angesichts der wachsenden Bedeutung von Social Media Plattformen für Marketing-Aktivitäten von Unternehmen will der Beitrag eine erste datenschutzrechtliche Orientierung für Unternehmen geben, die sich in diesem Umfeld bewegen oder bewegen wollen. Besondere Berücksichtigung findet dabei die Social Media Plattform Facebook.
Image Caption with Endogenous–Exogenous Attention

Automatically generating captions of an image is a fundamental problem in computer vision and natural language processing, which translates the content of the image into natural language with correct grammar and structure. Attention-based model has been widely adopted for captioning tasks. Most attention models generate only single certain attention heat map for indicating eyes where to see. However, these models ignore the endogenous orienting which depends on the interests, goals or desires of the observers, and constrain the diversity of captions. To improve both the accuracy and diversity of the generated sentences, we present a novel endogenous–exogenous attention architecture to capture both the endogenous attention, which indicates stochastic visual orienting, and the exogenous attention, which indicates deterministic visual orienting. At each time step, our model generates two attention maps, endogenous heat map and exogenous heat map, and then fuses them into hidden state of LSTM for sequential word generation. We evaluate our model on the Flickr30k and MSCOCO datasets, and experiments show the accuracy of the model and the diversity of captions it learns. Our model achieves better performance over state-of-the-art methods.
BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain

Deep learning-based techniques have achieved stateof-the-art performance on a wide variety of recognition and classification tasks. However, these networks are typically computationally expensive to train, requiring weeks of computation on many GPUs; as a result, many users outsource the training procedure to the cloud or rely on pre-trained models that are then fine-tuned for a specific task. In this paper we show that outsourced training introduces new security risks: an adversary can create a maliciously trained network (a backdoored neural network, or a BadNet) that has state-of-theart performance on the user’s training and validation samples, but behaves badly on specific attacker-chosen inputs. We first explore the properties of BadNets in a toy example, by creating a backdoored handwritten digit classifier. Next, we demonstrate backdoors in a more realistic scenario by creating a U.S. street sign classifier that identifies stop signs as speed limits when a special sticker is added to the stop sign; we then show in addition that the backdoor in our US street sign detector can persist even if the network is later retrained for another task and cause a drop in accuracy of 25% on average when the backdoor trigger is present. These results demonstrate that backdoors in neural networks are both powerful and—because the behavior of neural networks is difficult to explicate— stealthy. This work provides motivation for further research into techniques for verifying and inspecting neural networks, just as we have developed tools for verifying and debugging