nils-herrmann/clean_scirepeval huggingface dataset:
Popularity-driven Caching Strategy for Dynamic Adaptive Streaming over Information-Centric Networks

The growing demand for video streaming is straining the current Internet, and mandating a novel approach to future Internet paradigms. The advent of InformationCentric Networks (ICN) promises a novel architecture for addressing this exponential growth in data-intensive services, of which video streaming is projected to dominate (in traffic size). In this thesis, I present a novel strategy in ICN for adaptive caching of variable video contents tailored to different sizes and bit rates. My objective is to achieve optimal video caching to reduce access time for the maximal requested bit rate for every user. At its core, my approach capitalizes on a rigorous delay analysis and potentiates maximal serviceability for each user. I incorporate predictors for requested video objects based on a popularity index (Zipf distribution). In my proposed model, named DASCache, I present queuing analysis for Round-Trip Time (RTT) of cached objects, providing a cap on expected delay in accessing video content. In DASCache, I present a Binary Integer Programming (BIP) formulation for the cache assignment problem, which operates in rounds based on changes in content requests and popularity scores. DASCache reacts to changes in network dynamics that impact bit rate choices by
Visual Event Summarization on Social Media using Topic Modelling and Graph-based Ranking Algorithms

Due to the increasing popularity of microblogging platforms, the amount of messages (posts) related to public events, especially posts encompassing multimedia content, is steadily increasing. The inclusion of images can convey much more information about the event, compared to their text, which is typically very short (e.g., tweets). Although such messages can be quite informative regarding different aspects of the event, there is a lot of spam and redundancy making it challenging to extract pertinent insights. In this work, we describe a summarization framework that, given a set of social media messages about an event, aims to select a subset of images derived from them, that, at the same time, maximizes the relevance of the selected images and minimizes their redundancy. To this end, we propose a topic modeling technique to capture the relevance of messages to event topics and a graph-based algorithm to produce a diverse ranking of the selected high-relevance images. A user-centered evaluation on a large Twitter dataset around several real-world events demonstrates that the proposed method considerably outperforms a number of state-of-the-art summarization algorithms in terms of result relevance, while at the same time it is also highly competitive in terms of diversity. Namely, we get an improvement of 25% in terms of precision compared to the second best result, and 7% in terms of diversity.
ICTs for Improving Patients Rehabilitation Research Techniques

The world population is rapidly aging and becoming a burden to health systems around the world. In this work we present a conceptual framework to encourage the research community to develop more comprehensive and adaptive ICT solutions for prevention and rehabilitation of chronic conditions in the daily life of the aging population and beyond health facilities. We first present an overview of current international standards in human functioning and disability, and how chronic conditions are interconnected in older age. We then describe innovative mobile and sensor technologies, predictive data analysis in healthcare, and game-based prevention and rehabilitation techniques. We then set forth a multidisciplinary approach for the personalized prevention and rehabilitation of chronic conditions using unobtrusive and pervasive sensors, interactive activities, and predictive analytics, which also eases the tasks of health-related researchers, caregivers and providers. Our proposal represents a conceptual basis for future research, in which much remains to be done in terms of standardization of technologies and health terminology, as well as data protection and privacy legislation.
Energy aware optimal cluster head selection in wireless sensor networks

Wireless sensor network (WSN) consists of spatially distributed autonomous sensors to monitor physical and environmental conditions. Grouping sensor nodes into the cluster can reduce the size of the routing table of the each individual node and conserve communication bandwidth. Sensors in such environments are energy constrained and their batteries cannot be recharged. Therefore, designing energy-aware algorithms becomes an important factor for extending the lifetime of sensors. The proper organization of nodes becomes one of the major techniques to expand the life span of the whole network through aggregating data at the cluster head(CH). LEACH (Low Energy Adaptive Clustering Hierarchy) and PSO (Particle Swarm Optimization) are applied for producing energy-aware clusters with optimal selection of cluster head. The work is simulated using NS-2 and the performance metrics are analyzed.
Combining content-based and collaborative recommendations: A hybrid approach based on Bayesian networks

Recommender systems enable users to access products or articles that they would otherwise not be aware of due to the wealth of information to be found on the Internet. The two traditional recommendation techniques are content-based and collaborative filtering. While both methods have their advantages, they also have certain disadvantages, some of which can be solved by combining both techniques to improve the quality of the recommendation. The resulting system is known as a hybrid recommender system. In the context of artificial intelligence, Bayesian networks have been widely and successfully applied to problems with a high level of uncertainty. The field of recommendation represents a very interesting testing ground to put these probabilistic tools into practice. This paper therefore presents a new Bayesian network model to deal with the problem of hybrid recommendation by combining content-based and collaborative features. It has been tailored to the problem in hand and is equipped with a flexible topology and efficient mechanisms to estimate the required probability distributions so that probabilistic inference may be performed. The effectiveness of the model is demonstrated using the MovieLens and IMDB data sets.
A mobility-based framework for adaptive clustering in wireless ad hoc networks

This paper presents a novel framework for dynamically organizing mobile nodes in wireless ad hoc networks into clusters in which the probability of path availability can be bounded. The purpose of the( ; t) cluster is to help minimize the far-reaching effects of topological changes while balancing the need to support more optimal routing. A mobility model for ad hoc networks is developed and is used to derive expressions for the probability of path availability as a function of time. It is shown how this model provides the basis for dynamically grouping nodes into clusters using an efficient distributed clustering algorithm. Since the criteria for cluster organization depends directly upon path availability, the structure of the cluster topology is adaptive with respect to node mobility. Consequently, this framework supports an adaptive hybrid routing architecture that can be more responsive and effective when mobility rates are low and more efficient when mobility rates are high.
Recognizing Fine-Grained and Composite Activities Using Hand-Centric Features and Script Data

Activity recognition has shown impressive progress in recent years. However, the challenges of detecting fine-grained activities and understanding how they are combined into composite activities have been largely overlooked. In this work we approach both tasks and present a dataset which provides detailed annotations to address them. The first challenge is to detect fine-grained activities, which are defined by low inter-class variability and are typically characterized by fine-grained body motions. We explore how human pose and hands can help to approach this challenge by comparing two pose-based and two hand-centric features with state-of-the-art holistic features. To attack the second challenge, recognizing composite activities, we leverage the fact that these activities are compositional and that the essential components of the activities can be obtained from textual descriptions or scripts. We show the benefits of our hand-centric approach for fine-grained activity classification and detection. For composite activity recognition we find that decomposition into attributes allows sharing information across composites and is essential to attack this hard task. Using script data we can recognize novel composites without having training data for them.
A honeycomb-shaped planar monopole antenna for broadband millimeter-wave applications

This paper investigates a planar monopole antenna for fifth generation (5G) wireless communication networks. The proposed antenna has an ultra-wide band impedance response in millimeter wave (mmW) spectrum, 25–39 GHz covering Ka band. The antenna has unique structural layout resembling hexagonal honeycomb and has low profile (8×7 mm2) on 0.254 mm thick Rogers substrate, enabling the design for incorporation into future mobile phones. This antenna provides peak gain of 4.15 dBi along with 90% efficiency in the working band. The design is also extended to an 8×1 element array presenting maximum gain of 12.7 dBi at central frequency of the antenna.
Information Security Policy Compliance: An Empirical Study of Rationality-Based Beliefs and Information Security Awareness

Frequency Percentage Has your employer established information security policies? Yes No 464 0 100 0 To what extent are you aware of the regulations prescribed by the information security policy (ISP) of your organization? 1 (Completely Unaware) 2 3 4 5 6 7 (Completely Aware) 0 0 50 87 101 114 112 0 0 11 19 22 24 24
A snake-based method for segmentation of intravascular ultrasound images and its in vivo validation.

Image segmentation for detection of vessel walls is necessary for quantitative assessment of vessel diseases by intravascular ultrasound. A new segmentation method based on gradient vector flow (GVF) snake model is proposed in this paper. The main characteristics of the proposed method include two aspects: one is that nonlinear filtering is performed on GVF field to reduce the critical points, change the morphological structure of the parallel curves and extend the capture range; the other is that balloon snake is combined with the model. Thus, the improved GVF and balloon snake can be automatically initialized and overcome the problem caused by local energy minima. Results of 20 in vivo cases validated the accuracy and stability of the segmentation method for intravascular ultrasound images.
Formalising Suspense from Immersive Environments

Survival horror video-games have acquired great acceptance in recent years. Immersion, ease of play, virtual reality gadgets and emotional intensity have helped to popularise them. Famous youtubers still perform their videos based on these games, counting millions of viewers even if the design of them is very similar in terms of atmosphere, limited actions and mapping. Usually conceived as suspense-evoking threatening environments to explore and escape, this similarity may lead to think about automatising the generation of products of this genre. In this paper, suspense definitions are reviewed from the view of emotional e↵ects. Further, we propose a formula relating suspense to dimensions valence, arousal and dominance. Finally, we present an experiment that supports its application for a horror game.
Astrocyte-Neuron Lactate Transport Is Required for Long-Term Memory Formation

We report that, in the rat hippocampus, learning leads to a significant increase in extracellular lactate levels that derive from glycogen, an energy reserve selectively localized in astrocytes. Astrocytic glycogen breakdown and lactate release are essential for long-term but not short-term memory formation, and for the maintenance of long-term potentiation (LTP) of synaptic strength elicited in vivo. Disrupting the expression of the astrocytic lactate transporters monocarboxylate transporter 4 (MCT4) or MCT1 causes amnesia, which, like LTP impairment, is rescued by L-lactate but not equicaloric glucose. Disrupting the expression of the neuronal lactate transporter MCT2 also leads to amnesia that is unaffected by either L-lactate or glucose, suggesting that lactate import into neurons is necessary for long-term memory. Glycogenolysis and astrocytic lactate transporters are also critical for the induction of molecular changes required for memory formation, including the induction of phospho-CREB, Arc, and phospho-cofilin. We conclude that astrocyte-neuron lactate transport is required for long-term memory formation.
Design and In Vitro Interference Test of Microwave Noninvasive Blood Glucose Monitoring Sensor

A design of a microwave noninvasive continuous blood glucose monitoring sensor and its interference test results are presented. The novelty of the proposed sensor is that it comprises two spatially separated split-ring resonators, where one interacts with the change in glucose level of a sample under test while the other ring is used as a reference. The reference ring has a slightly different resonant frequency and is desensitized to the sample owing to its location, thus allowing changes in temperature to be calibrated out. From an oral glucose tolerance test with two additional commercially available sensors (blood strip and continuous glucose monitor) in parallel, we obtained encouraging performance for our sensor comparable with those of the commercial sensors. The effects of endogenous interferents common to all subjects, i.e., common sugars, vitamins (ascorbic acid), and metabolites (uric acid) have also been investigated by using a large Franz cell assembly. From the interference test, it is shown that the change in sensor response is dominated by changes in glucose level for concentrations relevant to blood, and the effects of interferents are negligible in comparison.
Design of an Assistive Communication Glove Using Combined Sensory Channels

This paper presents a new design of a wireless sensor glove developed for American Sign Language finger spelling gesture recognition. Five contact sensors are installed on the glove, in addition to five flex sensors on the fingers and a 3D accelerometer on the back of the hand. Each pair of flex and contact sensors are combined into the same input channel on the BSN node in order to save the number of channels and the installation area. After which, the signal is analyzed and separated back into flex and contact features by software. With electrical contacts and wirings made of conductive fabric and threads, the glove design has become thinner and more flexible. For validation, ASL finger spelling gesture recognition experiments have been performed on signals collected from six speech-impaired subjects and a normal subject. With the new sensor glove design, the experimental results have shown a significant increase in classification accuracy.
Equation of State Calculations by Fast Computing Machines

1087 instead, only water molecules with different amounts of excitation energy. These may follow any of three paths: (a) The excitation energy is lost without dissociation into radicals (by collision, or possibly radiation, as in aromatic hydrocarbons). (b) The molecules dissociate, but the resulting radicals recombine without escaping from the liquid cage. (c) The molecules dissociate and escape from the cage. In this case we would not expect them to move more than a few molecular diameters through the dense medium before being thermalized. paths (a) and (b) can be designated H 2 0* and those following path (c) can be designated H 2 0t. It seems reasonable to assume for the purpose of these calculations that the ionized H 2 0 molecules will become the H 20 t molecules, but this is not likely to be a complete correspondence. In conclusion we would like to emphasize that the qualitative result of this section is not critically dependent on the exact values of the physical parameters used. However, this treatment is classical, and a correct treatment must be wave mechanical; therefore the result of this section cannot be taken as an a priori theoretical prediction. The success of the radical diffusion model given above lends some plausibility to the occurrence of electron capture as described by this crude calculation. Further work is clearly needed. A general method, suitable for fast computing machines, for investigatiflg such properties as equations of state for substances consisting of interacting individual molecules is described. The method consists of a modified Monte Carlo integration over configuration space. Results for the two-dimensional rigid-sphere system have been obtained on the Los Alamos MANIAC and are presented here. These results are compared to the free volume equation of state and to a four-term virial coefficient expansion.
Massively Parallel A* Search on a GPU

A* search is a fundamental topic in artificial intelligence. Recently, the general purpose computation on graphics processing units (GPGPU) has been widely used to accelerate numerous computational tasks. In this paper, we propose the first parallel variant of the A* search algorithm such that the search process of an agent can be accelerated by a single GPU processor in a massively parallel fashion. Our experiments have demonstrated that the GPU-accelerated A* search is efficient in solving multiple real-world search tasks, including combinatorial optimization problems, pathfinding and game solving. Compared to the traditional sequential CPU-based A* implementation, our GPU-based A* algorithm can achieve a significant speedup by up to 45x on largescale search problems.
Data Quality and Data Cleaning: An Overview

Data quality is a serious concern in any data-driven enterprise, often creating misleading findings during data mining, and causing process disruptions in operational databases. The manifestations of data quality problems can be very expensive- "losing" customers, "misplacing" billions of dollars worth of equipment, misallocated resources due to glitched forecasts, and so on. Solving data quality problems typically requires a very large investment of time and energy -- often 80% to 90% of a data analysis project is spent in making the data reliable enough that the results can be trusted.In this tutorial, we present a multi disciplinary approach to data quality problems. We start by discussing the meaning of data quality and the sources of data quality problems. We show how these problems can be addressed by a multidisciplinary approach, combining techniques from management science, statistics, database research, and metadata management. Next, we present an updated definition of data quality metrics, and illustrate their application with a case study. We conclude with a survey of recent database research that is relevant to data quality problems, and suggest directions for future research.
Signal-to-interference ratio improvement by using a phased array antenna of switched-beam elements

A phased array antenna of switched-beam elements is proposed to improve the signal-to-interference ratio (SIR). By using 1-bit phase shifters, the main beam of the array is switched to desired signal direction, and with the switched-beam patch antenna elements the null patterns are changeable. The results of radiation patterns show that the interference suppression can be improved. Moreover, the SIR measurement is also set up in multipath environment to ensure the array performance. It is found that the SIR is significantly affected by the multipath environment; however, the SIR improvement of more than 10 dB can be obtained by changing the null patterns.
Estimating and exploring the product form design space using deep generative models

Product forms in quantitative design methods are typically expressed with a mathematical representation such as vectors, trees, graphs, and grammars. Such formal representations are restrictive in terms of realism or flexibility, and this limits their utility for human designers who typically create product forms in a design space that is restricted by the medium (e.g., free-hand sketching) and by their cognitive skills (e.g., creativity and experience). To increase the value of formal representations to human designers, this paper proposes to represent the design space as designs sampled from a statistical distribution of form and estimate a generative model of this distribution using a large set of images and design attributes of previous designs. This statistical representation approach is both flexible and realistic, and is estimated using a deep (multi-layer) generative model. The value of the representation is demonstrated in a study of two-dimensional automobile body forms. Using 180,000 form data of automobile designs over the past decade, we can morph a vehicle form into different body types and brands, thus offering human designers potential insights on realistic new design possibilities. ∗Address all correspondence to this author.
An Introduction to Boosting and Leveraging

We provide an introduction to theoretical and practical aspects of Boosting and Ensemble learning, providing a useful reference for researchers in the field of Boosting as well as for those seeking to enter this fascinating area of research. We begin with a short background concerning the necessary learning theoretical foundations of weak learners and their linear combinations. We then point out the useful connection between Boosting and the Theory of Optimization, which facilitates the understanding of Boosting and later on enables us to move on to new Boosting algorithms, applicable to a broad spectrum of problems. In order to increase the relevance of the paper to practitioners, we have added remarks, pseudo code, “tricks of the trade”, and algorithmic considerations where appropriate. Finally, we illustrate the usefulness of Boosting algorithms by giving an overview of some existing applications. The main ideas are illustrated on the problem of binary classification, although several extensions are discussed. 1 A Brief History of Boosting The underlying idea of Boosting is to combine simple “rules” to form an ensemble such that the performance of the single ensemble member is improved, i.e. “boosted”. Let h1, h2, . . . , hT be a set of hypotheses, and consider the composite ensemble hypothesis
Automatic Defect Detection for TFT-LCD Array Process Using Quasiconformal Kernel Support Vector Data Description

Defect detection has been considered an efficient way to increase the yield rate of panels in thin film transistor liquid crystal display (TFT-LCD) manufacturing. In this study we focus on the array process since it is the first and key process in TFT-LCD manufacturing. Various defects occur in the array process, and some of them could cause great damage to the LCD panels. Thus, how to design a method that can robustly detect defects from the images captured from the surface of LCD panels has become crucial. Previously, support vector data description (SVDD) has been successfully applied to LCD defect detection. However, its generalization performance is limited. In this paper, we propose a novel one-class machine learning method, called quasiconformal kernel SVDD (QK-SVDD) to address this issue. The QK-SVDD can significantly improve generalization performance of the traditional SVDD by introducing the quasiconformal transformation into a predefined kernel. Experimental results, carried out on real LCD images provided by an LCD manufacturer in Taiwan, indicate that the proposed QK-SVDD not only obtains a high defect detection rate of 96%, but also greatly improves generalization performance of SVDD. The improvement has shown to be over 30%. In addition, results also show that the QK-SVDD defect detector is able to accomplish the task of defect detection on an LCD image within 60 ms.