nils-herrmann/clean_scirepeval huggingface dataset:
Context-Aware QoE Modelling, Measurement, and Prediction in Mobile Computing Systems

Quality of Experience (QoE) as an aggregate of Quality of Service (QoS) and human user-related metrics will be the key success factor for current and future mobile computing systems. QoE measurement and prediction are complex tasks as they may involve a large parameter space such as location, delay, jitter, packet loss, and user satisfaction just to name a few. These tasks necessitate the development of practical context-aware QoE models that efficiently determine relationships between user context and QoE parameters. In this paper, we propose, develop, and validate a novel decision-theoretic approach called CaQoEM for QoE modelling, measurement, and prediction. We address the challenge of QoE measurement and prediction where each QoE parameter can be measured on a different scale and may involve different units of measurement. CaQoEM is context-aware and uses Bayesian networks and utility theory to measure and predict users' QoE under uncertainty. We validate CaQoEM using extensive experimentation, user studies and simulations. The results soundly demonstrate that CaQoEM correctly measures range-defined QoE using a bipolar scale. For QoE prediction, an overall accuracy of 98.93% was achieved using 10-fold cross validation in multiple diverse network conditions such as vertical handoffs, wireless signal fading and wireless network congestion.
How the brain separates sounds

In everyday life we often listen to one sound, such as someone's voice, in a background of competing sounds. To do this, we must assign simultaneously occurring frequency components to the correct source, and organize sounds appropriately over time. The physical cues that we exploit to do so are well-established; more recent research has focussed on the underlying neural bases, where most progress has been made in the study of a form of sequential organization known as "auditory streaming". Listeners' sensitivity to streaming cues can be captured in the responses of neurons in the primary auditory cortex, and in EEG wave components with a short latency (< 200ms). However, streaming can be strongly affected by attention, suggesting that this early processing either receives input from non-auditory areas, or feeds into processes that do.
A 2.5 V 12 b 5 MSample/s pipelined CMOS ADC

This 1.2 /spl mu/m, 33 mW analog-to-digital converter (ADC) demonstrates a family of power reduction techniques including a commutated feedback capacitor switching (CFCS), sharing of the second stage of an op amp between adjacent stages of a pipeline, reusing the first stage of an op amp as the comparator pre-amp, and exploiting parasitic capacitance as common-mode feedback capacitors.
Development of a Mobile Robot Test Platform and Methods for Validation of Prognostics-Enabled Decision Making Algorithms

As fault diagnosis and prognosis systems in aerospace applications become more capable, the ability to utilize information supplied by them becomes increasingly important. While certain types of vehicle health data can be effectively processed and acted upon by crew or support personnel, others, due to their complexity or time constraints, require either automated or semi-automated reasoning. Prognostics-enabled Decision Making (PDM) is an emerging research area that aims to integrate prognostic health information and knowledge about the future operating conditions into the process of selecting subsequent actions for the system. The newly developed PDM algorithms require suitable software and hardware platforms for testing under realistic fault scenarios. The paper describes the development of such a platform, based on the K11 planetary rover prototype. A variety of injectable fault modes are being investigated for electrical, mechanical, and power subsystems of the testbed, along with methods for data collection and processing. In addition to the hardware platform, a software simulator with matching capabilities has been developed. The simulator allows for prototyping and initial validation of the algorithms prior to their deployment on the K11. The simulator is also available to the PDM algorithms to assist with the reasoning process. A reference set of diagnostic, prognostic, and decision making algorithms is also described, followed by an overview of the current test scenarios and the results of their execution on the simulator. Edward Balaban et.al.
The self-programming thermostat: optimizing setback schedules based on home occupancy patterns

Programmable thermostats offer large potential energy savings without sacrificing comfort, but only if setback schedules are defined correctly. We present the concept of a self-programming thermostat that automatically creates an optimal setback schedule by sensing the occupancy statistics of a home. The system monitors occupancy using simple sensors in the home, similar to those already used in typical security systems, and the user defines the desired balance between energy and comfort using a single, intuitive knob. Our preliminary results show that this approach can reduce heating and cooling demand by up to 15% over the default setback schedule recommended by EnergyStar.
Two neural correlates of consciousness

Neuroscientists continue to search for "the" neural correlate of consciousness (NCC). In this article, I argue that a framework in which there are at least two distinct NCCs is increasingly making more sense of empirical results than one in which there is a single NCC. I outline the distinction between phenomenal NCC and access NCC, and show how they can be distinguished by experimental approaches, in particular signal-detection theory approaches. Recent findings in cognitive neuroscience provide an empirical case for two different NCCs.
Characteristics of resonance in heart rate variability stimulated by biofeedback.

As we previously reported, resonant frequency heart rate variability biofeedback increases baroreflex gain and peak expiratory flow in healthy individuals and has positive effects in treatment of asthma patients. Biofeedback readily produces large oscillations in heart rate, blood pressure, vascular tone, and pulse amplitude via paced breathing at the specific natural resonant frequency of the cardiovascular system for each individual. This paper describes how resonance properties of the cardiovascular system mediate the effects of heart rate variability biofeedback. There is evidence that resonant oscillations can train autonomic reflexes to provide therapeutic effect. The paper is based on studies described in previous papers. Here, we discuss the origin of the resonance phenomenon, describe our procedure for determining an individual's resonant frequency, and report data from 32 adult asthma patients and 24 healthy adult subjects, showing a negative relationship between resonant frequency and height, and a lower resonant frequency in men than women, but no relationship between resonant frequency and age, weight, or presence of asthma. Resonant frequency remains constant across 10 sessions of biofeedback training. It appears to be related to blood volume.
Sequential scalar quantization of color images

An efficient algorithm for color image quantization is proposed based on a new vector quantization technique that we call sequential scalar quantization. The scalar components of the 3-D color vector are individually quantized in a predetermined sequence. With this technique, the color palette is designed very efficiently, while pixel mapping is performed with no computation. To obtain an optimal allocation of quantization levels along each color coordinate, we appeal to the asymptotic quantization theory, where the number of quantization levels is assumed to be very large. We modify this theory to suit our application, where the number of quantization 1evels is typically small. To utilize the properties of the human visual system (HVS), the quantization is performed in a luminancechrominance color space. A luminance-chrominance weighting is introduced to account for the greater sensitivity of the HVS to luminance than to chrominance errors. A spatial activity measure is also incorporated to reflect the increased sensitivity of the HVS to quantization errors in smooth image regions. The algorithm yields highquality images and is significantly faster than existing quantization algorithms.
Choosy: max-min fair sharing for datacenter jobs with constraints

Min Fairness is a flexible resource allocation mechanism used in most datacenter schedulers. However, an increasing number of jobs have hard placement constraints, restricting the machines they can run on due to special hardware or software requirements. It is unclear how to define, and achieve, max-min fairness in the presence of such constraints. We propose Constrained Max-Min Fairness (CMMF), an extension to max-min fairness that supports placement constraints, and show that it is the only policy satisfying an important property that incentivizes users to pool resources. Optimally computing CMMF is challenging, but we show that a remarkably simple online scheduler, called Choosy, approximates the optimal scheduler well. Through experiments, analysis, and simulations, we show that Choosy on average differs 2% from the optimal CMMF allocation, and lets jobs achieve their fair share quickly.
Joint Chinese Word Segmentation and POS Tagging on Heterogeneous Annotated Corpora with Multiple Task Learning

Chinese word segmentation and part-ofspeech tagging (S&T) are fundamental steps for more advanced Chinese language processing tasks. Recently, it has attracted more and more research interests to exploit heterogeneous annotation corpora for Chinese S&T. In this paper, we propose a unified model for Chinese S&T with heterogeneous annotation corpora. We first automatically construct a loose and uncertain mapping between two representative heterogeneous corpora, Penn Chinese Treebank (CTB) and PKU’s People’s Daily (PPD). Then we regard the Chinese S&T with heterogeneous corpora as two “related” tasks and train our model on two heterogeneous corpora simultaneously. Experiments show that our method can boost the performances of both of the heterogeneous corpora by using the shared information, and achieves significant improvements over the state-of-the-art methods.
From Zero-Shot Learning to Conventional Supervised Classification: Unseen Visual Data Synthesis

Robust object recognition systems usually rely on powerful feature extraction mechanisms from a large number of real images. However, in many realistic applications, collecting sufficient images for ever-growing new classes is unattainable. In this paper, we propose a new Zero-shot learning (ZSL) framework that can synthesise visual features for unseen classes without acquiring real images. Using the proposed Unseen Visual Data Synthesis (UVDS) algorithm, semantic attributes are effectively utilised as an intermediate clue to synthesise unseen visual features at the training stage. Hereafter, ZSL recognition is converted into the conventional supervised problem, i.e. the synthesised visual features can be straightforwardly fed to typical classifiers such as SVM. On four benchmark datasets, we demonstrate the benefit of using synthesised unseen data. Extensive experimental results manifest that our proposed approach significantly improve the state-of-the-art results.
SemEval-2013 Task 11: Word Sense Induction and Disambiguation within an End-User Application

In this paper we describe our Semeval-2013 task on Word Sense Induction and Disambiguation within an end-user application, namely Web search result clustering and diversification. Given a target query, induction and disambiguation systems are requested to cluster and diversify the search results returned by a search engine for that query. The task enables the end-to-end evaluation and comparison of systems.
A Survey of Procedural Techniques for City Generation

The computer game industry requires a skilled workforce and this combined with the complexity of modern games, means that production costs are extremely high. One of the most time consuming aspects is the creation of game geometry, the virtual world which the players inhabit. Procedural techniques have been used within computer graphics to create natural textures, simulate special effects and generate complex natural models including trees and waterfalls. It is these procedural techniques that we intend to harness to generate geometry and textures suitable for a game situated in an urban environment. Procedural techniques can provide many benefits for computer graphics applications when the correct algorithm is used. An overview of several commonly used procedural techniques including fractals, L-systems, Perlin noise, tiling systems and cellular basis is provided. The function of each technique and the resulting output they create are discussed to better understand their characteristics, benefits and relevance to the city generation problem. City generation is the creation of an urban area which necessitates the creation of buildings, situated along streets and arranged in appropriate patterns. Some research has already taken place into recreating road network patterns and generating buildings that can vary in function and architectural style. We will study the main body of existing research into procedural city generation and provide an overview of their implementations and a critique of their functionality and results. Finally we present areas in which further research into the generation of cities is required and outline our research goals for city generation.
Information Security Maturity Model

To ensure security, it is important to build-in security in both the planning and the design phases and adapt a security architecture which makes sure that regular and security related tasks, are deployed correctly. Security requirements must be linked to the business goals. We identified four domains that affect security at an organization namely, organization governance, organizational culture, the architecture of the systems, and service management. In order to identify and explore the strength and weaknesses of particular organization’s security, a wide range model has been developed. This model is proposed as an information security maturity model (ISMM) and it is intended as a tool to evaluate the ability of organizations to meet the objectives of security.
From social media to public health surveillance: Word embedding based clustering method for twitter classification

Social media provide a low-cost alternative source for public health surveillance and health-related classification plays an important role to identify useful information. In this paper, we summarized the recent classification methods using social media in public health. These methods rely on bag-of-words (BOW) model and have difficulty grasping the semantic meaning of texts. Unlike these methods, we present a word embedding based clustering method. Word embedding is one of the strongest trends in Natural Language Processing (NLP) at this moment. It learns the optimal vectors from surrounding words and the vectors can represent the semantic information of words. A tweet can be represented as a few vectors and divided into clusters of similar words. According to similarity measures of all the clusters, the tweet can then be classified as related or unrelated to a topic (e.g., influenza). Our simulations show a good performance and the best accuracy achieved was 87.1%. Moreover, the proposed method is unsupervised. It does not require labor to label training data and can be readily extended to other classification problems or other diseases.
Hierarchical Adversarial Search Applied to Real-Time Strategy Games

Real-Time Strategy (RTS) video games have proven to be a very challenging application area for artificial intelligence research. Existing AI solutions are limited by vast state and action spaces and real-time constraints. Most implementations efficiently tackle various tactical or strategic sub-problems, but there is no single algorithm fast enough to be successfully applied to big problem sets (such as a complete instance of the StarCraft RTS game). This paper presents a hierarchical adversarial search framework which more closely models the human way of thinking — much like the chain of command employed by the military. Each level implements a different abstraction — from deciding how to win the game at the topion — from deciding how to win the game at the top of the hierarchy to individual unit orders at the bottom. We apply a 3-layer version of our model to SparCraft — a StarCraft combat simulator — and show that it outperforms state of the art algorithms such as Alpha-Beta, UCT, and Portfolio Search in large combat scenarios featuring multiple bases and up to 72 mobile units per player under real-time constraints of 40 ms per search episode.
Sensor Cloud: A Cloud of Virtual Sensors

Newer models for interacting with wireless sensors such as Internet of Things and Sensor Cloud aim to overcome restricted resources and efficiency. The Missouri S&T (science and technology) sensor cloud enables different networks, spread in a huge geographical area, to connect together and be employed simultaneously by multiple users on demand. Virtual sensors, which are at the core of this sensor cloud architecture, assist in creating a multiuser environment on top of resource-constrained physical wireless sensors and can help in supporting multiple applications.
Harmonic radar transceiver design: miniature tags for insect tracking

The design and operation along with verifying measurements of a harmonic radar transceiver, or tag, developed for insect tracking are presented. A short length of wire formed the antenna while a beam lead Schottky diode across a resonant loop formed the frequency doubler circuit yielding a total tag mass of less than 3 mg. Simulators using the method-of-moments for the antenna, finite-integral time-domain for the loop, and harmonic balance for the nonlinear diode element were used to predict and optimize the transceiver performance. This performance is compared to the ideal case and to measurements performed using a pulsed magnetron source within an anechoic chamber. A method for analysis of the tag is presented and used to optimize the design by creating the largest possible return signal at the second harmonic frequency for a particular incident power density. These methods were verified through measurement of tags both in isolation and mounted on insects. For excitation at 9.41 GHz the optimum tag in isolation had an antenna length of 12 mm with a loop diameter of 1 mm which yielded a harmonic cross-section of 40 mm/sup 2/. For tags mounted on Colorado potato beetles, optimum performance was achieved with an 8 mm dipole fed 2 mm from the beetle attached end. A theory is developed that describes harmonic radar in a fashion similar to the conventional radar range equation but with harmonic cross-section replacing the conventional radar cross-section. This method provides a straightforward description of harmonic radar system performance as well as provides a means to describe harmonic radar tag performance.
A Nested Relational Approach to Processing SQL Subqueries

One of the most powerful features of SQL is the use of nested queries. Most research work on the optimization of nested queries focuses on aggregate subqueries. However, the solutions proposed for non-aggregate subqueries are still limited, especially for queries having multiple subqueries and null values. In this paper, we show that existing approaches to queries containing non-aggregate subqueries proposed in the literature (including rewrites) are not adequate. We then propose a new efficient approach, the nested relational approach, based on the nested relational algebra. Our approach directly unnests non-aggregate subqueries using hash joins, and treats all subqueries in a uniform manner, being able to deal with nested queries of any type and any level. We report on experimental work that confirms that existing approaches have difficulties dealing with non-aggregate subqueries, and that our approach offers better performance. We also discuss some possibilities for algebraic optimization and the issue of integrating our approach in a relational database system.
Intimal plus medial thickness of the arterial wall: a direct measurement with ultrasound imaging.

A study in vitro of specimens of human aortic and common carotid arteries was carried out to determine the feasibility of direct measurement (i.e., not from residual lumen) of arterial wall thickness with B mode real-time imaging. Measurements in vivo by the same technique were also obtained from common carotid arteries of 10 young normal male subjects. Aortic samples were classified as class A (relatively normal) or class B (with one or more atherosclerotic plaques). In all class A and 85% of class B arterial samples a characteristic B mode image composed of two parallel echogenic lines separated by a hypoechoic space was found. The distance between the two lines (B mode image of intimal + medial thickness) was measured and correlated with the thickness of different combinations of tunicae evaluated by gross and microscopic examination. On the basis of these findings and the results of dissection experiments on the intima and adventitia we concluded that results of B mode imaging of intimal + medial thickness did not differ significantly from the intimal + medial thickness measured on pathologic examination. With respect to the accuracy of measurements obtained by B mode imaging as compared with pathologic findings, we found an error of less than 20% for measurements in 77% of normal and pathologic aortic walls. In addition, no significant difference was found between B mode-determined intimal + medial thickness in the common carotid arteries evaluated in vitro and that determined by this method in vivo in young subjects, indicating that B mode imaging represents a useful approach for the measurement of intimal + medial thickness of human arteries in vivo.
Identifying table boundaries in digital documents via sparse line detection

Most prior work on information extraction has focused on extracting information from text in digital documents. However, often, the most important information being reported in an article is presented in tabular form in a digital document. If the data reported in tables can be extracted and stored in a database, the data can be queried and joined with other data using database management systems. In order to prepare the data source for table search, accurately detecting the table boundary plays a crucial role for the later table structure decomposition. Table boundary detection and content extraction is a challenging problem because tabular formats are not standardized across all documents. In this paper, we propose a simple but effective preprocessing method to improve the table boundary detection performance by considering the sparse-line property of table rows. Our method easily simplifies the table boundary detection problem into the sparse line analysis problem with much less noise. We design eight line label types and apply two machine learning techniques, Conditional Random Field (CRF) and Support Vector Machines (SVM), on the table boundary detection field. The experimental results not only compare the performances between the machine learning methods and the heuristics-based method, but also demonstrate the effectiveness of the sparse line analysis in the table boundary detection.