nils-herrmann/clean_scirepeval huggingface dataset:
Enabling Reliable, Asynchronous, and Bidirectional Communication in Sensor Networks over White Spaces

Low-Power Wide-Area Network (LPWAN) heralds a promising class of technology to overcome the range limits and scalability challenges in traditional wireless sensor networks. Recently proposed Sensor Network over White Spaces (SNOW) technology is particularly attractive due to the availability and advantages of TV spectrum in long-range communication. This paper proposes a new design of SNOW that is asynchronous, reliable, and robust. It represents the first highly scalable LPWAN over TV white spaces to support reliable, asynchronous, bi-directional, and concurrent communication between numerous sensors and a base station. This is achieved through a set of novel techniques. This new design of SNOW has an OFDM based physical layer that adopts robust modulation scheme and allows the base station using a single antenna-radio (1) to send different data to different nodes concurrently and (2) to receive concurrent transmissions made by the sensor nodes asynchronously. It has a lightweight MAC protocol that (1) efficiently implements per-transmission acknowledgments of the asynchronous transmissions by exploiting the adopted OFDM design; (2) combines CSMA/CA and location-aware spectrum allocation for mitigating hidden terminal effects, thus enhancing the flexibility of the nodes in transmitting asynchronously. Hardware experiments through deployments in three radio environments - in a large metropolitan city, in a rural area, and in an indoor environment - as well as large-scale simulations demonstrated that the new SNOW design drastically outperforms other LPWAN technologies in terms of scalability, energy, and latency.
Prosodic Units in Speech Production

Four experiments demonstrate effects of prosodic structure on speech production latencies. Experiments 1 to 3 exploit a modified version of the Sternberg et al. (1978, 1980) prepared speech production paradigm to look for evidence of the generation of prosodic structure during the final stages of sentence production. Experiment 1 provides evidence that prepared sentence production latency is a function of the number of phonological words that a sentence comprises when syntactic structure, number of lexical items, and number of syllables are held constant. Experiment 2 demonstrated that production latencies in Experiment 1 were indeed determined by prosodic structure rather than the number of content words that a sentence comprised. The phonological word effect was replicated in Experiment 3 using utterances with a different intonation pattern and phrasal structure. Finally, in Experiment 4, an on-line version of the sentence production task provides evidence for the phonological word as the preferred unit of articulation during the on-line production of continuous speech. Our findings are consistent with the hypothesis that the phonological word is a unit of processing during the phonological encoding of connected speech.
A Cloud-Based Knowledge Discovery System for Monitoring Fine-Grained Air Quality

Many developing countries are suffering from air pollution recently. Governments have built a few air quality monitoring stations in cities to inform people the concentration of air pollutants. Unfortunately, urban air quality is highly skewed in a city, depending on multiple complex factors, such as the meteorology, traffic volume, and land uses. Building more monitoring stations is very costly in terms of money, land uses, and human resources. As a result, people do not really know the fine-grained air quality of a location without a monitoring station. In this paper, we introduce a cloud-based knowledge discovery system that infers the real-time and fine-grained air quality information throughout a city based on the (historical and realtime) air quality data reported by existing monitor stations and a variety of data sources observed in the city, such as meteorology, traffic flow, human mobility, structure of road networks, and point of interests (POIs). The system also provides a mobile client, with which a user can monitor the air quality of multiple locations in a city (e.g. the current location, home and work places), and a web service that allows other applications to call the air quality of any location. The system has been evaluated based on the real data from 9 cities in China, including Beijing, Shanghai, Guanzhou, and Shenzhen, etc. The system is running on Microsoft Azure and the mobile client is publicly available in Window Phone App Store, entitled Urban Air. Our system gives a cost-efficient example for enabling a knowledge discovery prototype involving big data on the cloud.
A common role of insula in feelings, empathy and uncertainty

Although accumulating evidence highlights a crucial role of the insular cortex in feelings, empathy and processing uncertainty in the context of decision making, neuroscientific models of affective learning and decision making have mostly focused on structures such as the amygdala and the striatum. Here, we propose a unifying model in which insula cortex supports different levels of representation of current and predictive states allowing for error-based learning of both feeling states and uncertainty. This information is then integrated in a general subjective feeling state which is modulated by individual preferences such as risk aversion and contextual appraisal. Such mechanisms could facilitate affective learning and regulation of body homeostasis, and could also guide decision making in complex and uncertain environments.
On the assessment of usability testing methods for children

The paper motivates the need to acquire methodological knowledge for involving children as test users in usability testing. It introduces a methodological framework for delineating comparative assessments of usability testing methods for children participants. This framework consists in three dimensions: (1) assessment criteria for usability testing methods, (2) characteristics describing usability testing methods and, finally, (3) characteristics of children that may impact upon the process and the result of usability testing. Two comparative studies are discussed in the context of this framework along with implications for future research.
The subspace Gaussian mixture model - A structured model for speech recognition

We describe a new approach to speech recognition, in which all Hidden Markov Model (HMM) states share the same Gaussian Mixture Model (GMM) structure with the same number of Gaussians in each state. The model is defined by vectors associated with each state with a dimension of, say, 50, together with a global mapping from this vector space to the space of parameters of the GMM. This model appears to give better results than a conventional model, and the extra structure offers many new opportunities for modeling innovations while maintaining compatibility with most standard techniques.
One Weird Trick to Stop Selfish Miners: Fresh Bitcoins, A Solution for the Honest Miner

A recent result in Bitcoin is the selfish mining strategy in which a selfish cartel withholds blocks they mine to gain an advantage. This strategy is both incentive-compatible and harmful to Bitcoin. In this paper we introduce a new defense against selfish mining that improves on the previous best result, we raise the threshold of mining power necessary to profitably selfishly mine from 25% to 32% under all propagation advantages. While the security of our system uses unforgeable timestamps, it is robust to their compromise. Additionally, we discuss the difficulty a mining conspiracy would face attempting to keep the compromise of our scheme secret and we analyze incentives for getting miners to adopt these changes.
A novel 3D ear reconstruction method using a single image

To achieve denser 3D ear model from less controlled 2D image, we explore a 3D Ear Morphable Model (3DEMM) for 3D ear reconstruction using a single 2D ear image. Considering the unique structure of ear, we propose a novel dense corresponding method. The proposed method can overcome the shortcoming of optical flow based method and achieve pixel level dense correspondences based on physiological features of ear without choosing a reference ear. Novel 3D ear shape can be recovered from a single ear image based on the proposed 3D ear morphable model. Extensive experimental results have shown that our proposed method can obtain denser 3D ear model with lower cost and higher efficiency than existing methods.
Holistic Planimetric prediction to Local Volumetric prediction for 3D Human Pose Estimation

We propose a novel approach to 3D human pose estimation from a single depth map. Recently, convolutional neural network (CNN) has become a powerful paradigm in computer vision. Many of computer vision tasks have benefited from CNNs, however, the conventional approach to directly regress 3D body joint locations from an image does not yield a noticeably improved performance. In contrast, we formulate the problem as estimating per-voxel likelihood of key body joints from a 3D occupancy grid. We argue that learning a mapping from volumetric input to volumetric output with 3D convolution consistently improves the accuracy when compared to learning a regression from depth map to 3D joint coordinates. We propose a two-stage approach to reduce the computational overhead caused by volumetric representation and 3D convolution: Holistic 2D prediction and Local 3D prediction. In the first stage, Planimetric Network (P-Net) estimates per-pixel likelihood for each body joint in the holistic 2D space. In the second stage, Volumetric Network (V-Net) estimates the per-voxel likelihood of each body joints in the local 3D space around the 2D estimations of the first stage, effectively reducing the computational cost. Our model outperforms existing methods by a large margin in publicly available datasets.
Robust channel estimation for OFDM systems with rapid dispersive fading channels

Orthogonal frequency-division multiplexing (OFDM) modulation is a promising technique for achieving the high bit rates required for a wireless multimedia service. Without channel estimation and tracking, OFDM systems have to use differential phase-shift keying (DPSK), which has a 3-dB signalto-noise ratio (SNR) loss compared with coherent phase-shift keying (PSK). To improve the performance of OFDM systems by using coherent PSK, we investigate robust channel estimation for OFDM systems. We derive a minimum mean-square-error (MMSE) channel estimator, which makes full use of the timeand frequency-domain correlations of the frequency response of time-varying dispersive fading channels. Since the channel statistics are usually unknown, we also analyze the mismatch of the estimator-to-channel statistics and propose a robust channel estimator that is insensitive to the channel statistics. The robust channel estimator can significantly improve the performance of OFDM systems in a rapid dispersive fading channel.
Enhanced telemetry for encrypted threat analytics

Traditional flow monitoring provides a high-level view of network communications by reporting the addresses, ports, and byte and packet counts of a flow. This data is valuable, but it gives little insight into the actual content or context of a flow. To obtain this missing insight, we investigated intra-flow data, that is, information about events that occur inside of a flow that can be conveniently collected, stored, and analyzed within a flow monitoring framework. The focus of our work is on new types of data that are independent of protocol details, such as the lengths and arrival times of messages within a flow. These data elements have the attractive property that they apply equally well to both encrypted and unencrypted flows. Protocol-aware telemetry, specifically TLS-aware telemetry, is also analyzed. In this paper, we explore the benefits of enhanced telemetry, desirable properties of new intra-flow data features with respect to a flow monitoring system, and how best to use machine learning classifiers that operate on this data. We provide results on millions of flows processed by our open source program. Finally, we show that leveraging appropriate data features and simple machine learning models can successfully identify threats in encrypted network traffic.
A comprehensive performance analysis of HSA and OpenCL 2.0

Heterogeneous systems, that marry CPUs and GPUs together in a range of configurations, are quickly becoming the design paradigm for today's platforms because of their impressive parallel processing capabilities. However, in many existing heterogeneous systems, the GPU is only treated as an accelerator by the CPU, working as a slave to the CPU master. But recently we are starting to see the introduction of a new class of devices and changes to the system runtime model, which enable accelerators to be treated as first-class computing devices. To support programmability and efficiency of heterogeneous programming, the HSA foundation introduced the Heterogeneous System Architecture (HSA), which defines a platform and runtime architecture that provides rich support for OpenCL 2.0 features including shared virtual memory, dynamic parallelism, and improved atomic operations. In this paper, we provide the first comprehensive study of OpenCL 2.0 and HSA 1.0 execution, considering OpenCL 1.2 as the baseline. For workloads, we develop a suite of OpenCL micro-benchmarks designed to highlight the features of these emerging standards and also utilize real-world applications to better understand their impact at an application level. To fully exercise the new features provided by the HSA model, we experiment with a producer-consumer algorithm and persistent kernels. We find that by using HSA signals, we can remove 92% of the overhead due to synchronous kernel launches. In our real-world applications, the OpenCL 2.0 runtime achieves up to a 1.2X speedup, while the HSA 1.0 runtime achieves a 2.7X speedup over OpenCL 1.2.
Segmented Architecture for Successive Approximation Analog-to-Digital Converters

In this paper, the structure of a binary-weighted capacitive digital-to-analog converter (DAC) in a successive-approximation analog-to-digital converter (SA-ADC) is modified to a unary or segmented configuration to reduce the power consumption and improve the static linearity performance. In order to be able to choose the optimum value of the segmentation degree (i.e., the number of unary bits), the power consumption and the static linearity behavior of the segmented architecture as functions of the segmentation degree are analyzed. Circuit-level simulation results are presented to show the accuracy of the proposed equations. It is shown that for moderate and high-resolution ADCs, a segmentation degree of four or five bits is the optimum choice from the power-consumption viewpoint. Simulation results of a 1 V, 10-bit, 100-kS/s SA-ADC show that the power consumption of the entire capacitive DAC and the digital circuit of the segmented implementation with a segmentation degree of 4 is 30% less than the conventional design while the standard deviation of the differential nonlinearity is reduced by a factor of 2√(2).
Glowworm swarm optimization for simultaneous capture of multiple local optima of multimodal functions

This paper presents glowworm swarm optimization (GSO), a novel algorithm for the simultaneous computation of multiple optima of multimodal functions. The algorithm shares a few features with some better known swarm intelligence based optimization algorithms, such as ant colony optimization and particle swarm optimization, but with several significant differences. The agents in GSO are thought of as glowworms that carry a luminescence quantity called luciferin along with them. The glowworms encode the fitness of their current locations, evaluated using the objective function, into a luciferin value that they broadcast to their neighbors. The glowworm identifies its neighbors and computes its movements by exploiting an adaptive neighborhood, which is bounded above by its sensor range. Each glowworm selects, using a probabilistic mechanism, a neighbor that has a luciferin value higher than its own and moves toward it. These movements—based only on local information and selective neighbor interactions—enable the swarm of glowworms to partition into disjoint subgroups that converge on multiple optima of a given multimodal function. We provide some theoretical results related to the luciferin update mechanism in order to prove the bounded nature and convergence of luciferin levels of the glowworms. Experimental results demonstrate the efficacy of the proposed glowworm based algorithm in capturing multiple optima of a series of standard multimodal test functions and more complex ones, such as stair-case and multiple-plateau functions. We also report the results of tests in higher dimensional spaces with a large number of peaks. We address the parameter selection problem by conducting experiments to show that only two parameters need to be selected by the user. Finally, we provide some comparisons of GSO with PSO and an experimental comparison with Niche-PSO, a PSO variant that is designed for the simultaneous computation of multiple optima.
Modeling and Understanding Visual Attributes of Mental Health Disclosures in Social Media

Content shared on social media platforms has been identified to be valuable in gaining insights into people's mental health experiences. Although there has been widespread adoption of photo-sharing platforms such as Instagram in recent years, the role of visual imagery as a mechanism of self-disclosure is less understood. We study the nature of visual attributes manifested in images relating to mental health disclosures on Instagram. Employing computer vision techniques on a corpus of thousands of posts, we extract and examine three visual attributes: visual features (e.g., color), themes, and emotions in images. Our findings indicate the use of imagery for unique self-disclosure needs, quantitatively and qualitatively distinct from those shared via the textual modality: expressions of emotional distress, calls for help, and explicit display of vulnerability. We discuss the relationship of our findings to literature in visual sociology, in mental health self disclosure, and implications for the design of health interventions.
Enhanced Dual-Band Ambient RF Energy Harvesting With Ultra-Wide Power Range

This letter presents a novel dual-band rectifier with extended power range (EPR) and an optimal incident RF power strategy in the settings where the available RF energy fluctuates considerably. It maintains high power conversion efficiency (PCE) in an ultra-wide input power range by adopting a pHEMT in the proposed topology. Simultaneous RF power incident mode is proposed and preferred to the traditional independent mode for multi-band harvesting. Measured results show that more than 30% PCE is obtained with input power ranging from -15 dBm to 20 dBm and peak PCE of 60% is maintained from 5 to 15 dBm. Positive power gain is achieved from -20 dBm to more than 10 dBm. Investigation about the effect of RF power incident ratio on dual-band harvesting's performance is presented and it provides a good reference for future multi-band harvesting system design.
Traffic Morphing: An Efficient Defense Against Statistical Traffic Analysis

Recent work has shown that properties of network traffic that remain observable after encryption, namely packet sizes and timing, can reveal surprising information about the traffic’s contents (e.g., the language of a VoIP call [29], passwords in secure shell logins [20], or even web browsing habits [21, 14]). While there are some legitimate uses for encrypted traffic analysis, these techniques also raise important questions about the privacy of encrypted communications. A common tactic for mitigating such threats is to pad packets to uniform sizes or to send packets at fixed timing intervals; however, this approach is often inefficient. In this paper, we propose a novel method for thwarting statistical traffic analysis algorithms by optimally morphing one class of traffic to look like another class. Through the use of convex optimization techniques, we show how to optimally modify packets in real-time to reduce the accuracy of a variety of traffic classifiers while incurring much less overhead than padding. Our evaluation of this technique against two published traffic classifiers for VoIP [29] and web traffic [14] shows that morphing works well on a wide range of network data—in some cases, simultaneously providing better privacy and lower overhead than naı̈ve
Kello Depa Depa Warmth and Competence as Universal Dimensions of Social Perception : The Stereotype Content Model and the BIAS Map

The stereotype content model (SCM) defines two fundamental dimensions of social perception, warmth and competence, predicted respectively by perceived competition and status. Combinations of warmth and competence generate distinct emotions of admiration, contempt, envy, and pity. From these intergroup emotions and stereotypes, the behavior from intergroup affect and stereotypes (BIAS) map predicts distinct behaviors: active and passive, facilitative and harmful. After defining warmth/communion and competence/agency, the chapter integrates converging work documenting the centrality of these dimensions in interpersonal as well as intergroup perception. Structural origins of warmth and competence perceptions result from competitors judged as not warm, and allies judged as warm; high status confers competence and low status incompetence. Warmth and competence judgments support systematic patterns of cognitive, emotional, and behavioral reactions, including ambivalent prejudices. Past views of prejudice as a univalent antipathy have obscured the unique responses toward groups stereotyped as competent but not warm or warm but not competent. Finally, the chapter addresses unresolved issues and future research directions.
Doing better but feeling worse. Looking for the "best" job undermines satisfaction.

Expanding upon Simon's (1955) seminal theory, this investigation compared the choice-making strategies of maximizers and satisficers, finding that maximizing tendencies, although positively correlated with objectively better decision outcomes, are also associated with more negative subjective evaluations of these decision outcomes. Specifically, in the fall of their final year in school, students were administered a scale that measured maximizing tendencies and were then followed over the course of the year as they searched for jobs. Students with high maximizing tendencies secured jobs with 20% higher starting salaries than did students with low maximizing tendencies. However, maximizers were less satisfied than satisficers with the jobs they obtained, and experienced more negative affect throughout the job-search process. These effects were mediated by maximizers' greater reliance on external sources of information and their fixation on realized and unrealized options during the search and selection process.