nils-herrmann/clean_scirepeval huggingface dataset:
Topical Clustering of Tweets

In the emerging field of micro-blogging and social communication services, users post millions of short messages every day. Keeping track of all the messages posted by your friends and the conversation as a whole can become tedious or even impossible. In this paper, we presented a study on automatically clustering and classifying Twitter messages, also known as “tweets”, into different categories, inspired by the approaches taken by news aggregating services like Google News. Our results suggest that the clusters produced by traditional unsupervised methods can often be incoherent from a topical perspective, but utilizing a supervised methodology that utilize the hash-tags as indicators of topics produce surprisingly good results. We also offer a discussion on temporal effects of our methodology and training set size considerations. Lastly, we describe a simple method of finding the most representative tweet in a cluster, and provide an analysis of the results.
A Comparative Study of Fuel-Cell–Battery, Fuel-Cell–Ultracapacitor, and Fuel-Cell–Battery–Ultracapacitor Vehicles

Although many researchers have investigated the use of different powertrain topologies, component sizes, and control strategies in fuel-cell vehicles, a detailed parametric study of the vehicle types must be conducted before a fair comparison of fuel-cell vehicle types can be performed. This paper compares the near-optimal configurations for three topologies of vehicles: fuel-cell-battery, fuel-cell-ultracapacitor, and fuel-cell-battery-ultracapacitor. The objective function includes performance, fuel economy, and powertrain cost. The vehicle models, including detailed dc/dc converter models, are programmed in Matlab/Simulink for the customized parametric study. A controller variable for each vehicle type is varied in the optimization.
Agent-Based Models of Strategies for the Emergence and Evolution of Grammatical Agreement

Grammatical agreement means that features associated with one linguistic unit (for example number or gender) become associated with another unit and then possibly overtly expressed, typically with morphological markers. It is one of the key mechanisms used in many languages to show that certain linguistic units within an utterance grammatically depend on each other. Agreement systems are puzzling because they can be highly complex in terms of what features they use and how they are expressed. Moreover, agreement systems have undergone considerable change in the historical evolution of languages. This article presents language game models with populations of agents in order to find out for what reasons and by what cultural processes and cognitive strategies agreement systems arise. It demonstrates that agreement systems are motivated by the need to minimize combinatorial search and semantic ambiguity, and it shows, for the first time, that once a population of agents adopts a strategy to invent, acquire and coordinate meaningful markers through social learning, linguistic self-organization leads to the spontaneous emergence and cultural transmission of an agreement system. The article also demonstrates how attested grammaticalization phenomena, such as phonetic reduction and conventionalized use of agreement markers, happens as a side effect of additional economizing principles, in particular minimization of articulatory effort and reduction of the marker inventory. More generally, the article illustrates a novel approach for studying how key features of human languages might emerge.
Musicians' and nonmusicians' short-term memory for verbal and musical sequences: comparing phonological similarity and pitch proximity.

Language-music comparative studies have highlighted the potential for shared resources or neural overlap in auditory short-term memory. However, there is a lack of behavioral methodologies for comparing verbal and musical serial recall. We developed a visual grid response that allowed both musicians and nonmusicians to perform serial recall of letter and tone sequences. The new method was used to compare the phonological similarity effect with the impact of an operationalized musical equivalent-pitch proximity. Over the course of three experiments, we found that short-term memory for tones had several similarities to verbal memory, including limited capacity and a significant effect of pitch proximity in nonmusicians. Despite being vulnerable to phonological similarity when recalling letters, however, musicians showed no effect of pitch proximity, a result that we suggest might reflect strategy differences. Overall, the findings support a limited degree of correspondence in the way that verbal and musical sounds are processed in auditory short-term memory.
Bringing Virtualization to the x86 Architecture with the Original VMware Workstation

This article describes the historical context, technical challenges, and main implementation techniques used by VMware Workstation to bring virtualization to the x86 architecture in 1999. Although virtual machine monitors (VMMs) had been around for decades, they were traditionally designed as part of monolithic, single-vendor architectures with explicit support for virtualization. In contrast, the x86 architecture lacked virtualization support, and the industry around it had disaggregated into an ecosystem, with different vendors controlling the computers, CPUs, peripherals, operating systems, and applications, none of them asking for virtualization. We chose to build our solution independently of these vendors.
 As a result, VMware Workstation had to deal with new challenges associated with (i) the lack of virtualization support in the x86 architecture, (ii) the daunting complexity of the architecture itself, (iii) the need to support a broad combination of peripherals, and (iv) the need to offer a simple user experience within existing environments. These new challenges led us to a novel combination of well-known virtualization techniques, techniques from other domains, and new techniques.
 VMware Workstation combined a hosted architecture with a VMM. The hosted architecture enabled a simple user experience and offered broad hardware compatibility. Rather than exposing I/O diversity to the virtual machines, VMware Workstation also relied on software emulation of I/O devices. The VMM combined a trap-and-emulate direct execution engine with a system-level dynamic binary translator to efficiently virtualize the x86 architecture and support most commodity operating systems. By relying on x86 hardware segmentation as a protection mechanism, the binary translator could execute translated code at near hardware speeds. The binary translator also relied on partial evaluation and adaptive retranslation to reduce the overall overheads of virtualization.
 Written with the benefit of hindsight, this article shares the key lessons we learned from building the original system and from its later evolution.
Generative Adversarial Imitation from Observation

Imitation from observation (IfO) is the problem of learning directly from state-only demonstrations without having access to the demonstrator’s actions. The lack of action information both distinguishes IfO from most of the literature in imitation learning, and also sets it apart as a method that may enable agents to learn from a large set of previously inapplicable resources such as internet videos. In this paper, we propose both a general framework for IfO approaches and also a new IfO approach based on generative adversarial networks called generative adversarial imitation from observation (GAIfO). We conduct experiments in two different settings: (1) when demonstrations consist of low-dimensional, manually-defined state features, and (2) when demonstrations consist of high-dimensional, raw visual data. We demonstrate that our approach performs comparably to classical imitation learning approaches (which have access to the demonstrator’s actions) and significantly outperforms existing imitation from observation methods in highdimensional simulation environments.
Antenna Array Design for Multi-Gbps mmWave Mobile Broadband Communication

Almost all cellular mobile communications including first generation analog systems, second generation digital systems, third generation WCDMA, and fourth generation OFDMA systems use Ultra High Frequency (UHF) band of radio spectrum with frequencies in the range of 300MHz-3GHz. This band of spectrum is becoming increasingly crowded due to spectacular growth in mobile data and other related services. More recently, there have been proposals to explore mmWave spectrum (3-300GHz) for commercial mobile applications due to its unique advantages such as spectrum availability and small component sizes. In this paper, we discuss system design aspects such as antenna array design, base station and mobile station requirements. We also provide system performance and SINR geometry results to demonstrate the feasibility of an outdoor mmWave mobile broadband communication system. We note that with adaptive antenna array beamforming, multi-Gbps data rates can be supported for mobile cellular deployments.
Behavioral manifestations of autism in the first year of life

In the interest of more systematically documenting the early signs of autism, and of testing specific hypotheses regarding their underlying neurodevelopmental substrates, we have initiated a longitudinal study of high-risk infants, all of whom have an older sibling diagnosed with an autistic spectrum disorder. Our sample currently includes 150 infant siblings, including 65 who have been followed to age 24 months, who are the focus of this paper. We have also followed a comparison group of low-risk infants. Our measures include a novel observational scale (the first, to our knowledge, that is designed to assess autism-specific behavior in infants), a computerized visual orienting task, and standardized measures of temperament, cognitive and language development. Our preliminary results indicate that by 12 months of age, siblings who are later diagnosed with autism may be distinguished from other siblings and low-risk controls on the basis of: (1) several specific behavioral markers, including atypicalities in eye contact, visual tracking, disengagement of visual attention, orienting to name, imitation, social smiling, reactivity, social interest and affect, and sensory-oriented behaviors; (2) prolonged latency to disengage visual attention; (3) a characteristic pattern of early temperament, with marked passivity and decreased activity level at 6 months, followed by extreme distress reactions, a tendency to fixate on particular objects in the environment, and decreased expression of positive affect by 12 months; and (4) delayed expressive and receptive language. We discuss these findings in the context of various neural networks thought to underlie neurodevelopmental abnormalities in autism, including poor visual orienting. Over time, as we are able to prospectively study larger numbers and to examine interrelationships among both early-developing behaviors and biological indices of interest, we hope this work will advance current understanding of the neurodevelopmental origins of autism.
Exploring the Realization of Irony in Twitter Data

Cynthia Van Hee, Els Lefever and Véronique Hoste LT3, Language and Translation Technology Team Ghent University Groot-Brittanniëlaan 45, 9000 Ghent, Belgium cynthia.vanhee, els.lefever, veronique.hoste@ugent.be  Handling figurative language like irony is currently a challenging task in natural language processing. Since irony is commonly used in user-generated content, its presence can significantly undermine accurate analysis of opinions and sentiment in such texts. Understanding irony is therefore important if we want to push the state-of-the-art in tasks such as sentiment analysis. In this research, we present the construction of a Twitter dataset for two languages, being English and Dutch, and the development of new guidelines for the annotation of verbal irony in social media texts. Furthermore, we present some statistics on the annotated corpora, from which we can conclude that the detection of contrasting evaluations might be a good indicator for recognizing irony.
A True 0.4-V Delta–Sigma Modulator Using a Mixed DDA Integrator Without Clock Boosted Switches

This brief proposes a delta-sigma modulator that operates at extremely low voltage without using a clock boosting technique. To maintain the advantages of a discrete-time integrator in oversampled data converters, a mixed differential difference amplifier (DDA) integrator is developed that removes the input sampling switch in a switched-capacitor integrator. Conventionally, many low-voltage delta-sigma modulators have used high-voltage generating circuits to boost the clock voltage levels. A mixed DDA integrator with both a switched-resistor and a switched-capacitor technique is developed to implement a discrete-time integrator without clock boosted switches. The proposed mixed DDA integrator is demonstrated by a third-order delta-sigma modulator with a feedforward topology. The fabricated modulator shows a 68-dB signal-to-noise-plus-distortion ratio for a 20-kHz signal bandwidth with an oversampling ratio of 80. The chip consumes 140 μW of power at a true 0.4-V power supply, which is the lowest voltage without a clock boosting technique among the state-of-the-art modulators in this signal band.
Time After Time : The Psychological Reality of the Ego-and Time-Reference-Point Distinction in Metaphorical Construals of Time

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Adaptive Coded Modulation for Fading Channels

We apply coset codes to adaptive modulation in fading channels. Adaptive modulation is a powerful technique to improve the energy efficiency and increase the data rate over a fading channel. Coset codes are a natural choice to use with adaptive modulation since the channel coding and modulation designs are separable. Therefore, trellis and lattice codes designed for additive white Gaussian noise (AWGN) channels can be superimposed on adaptive modulation for fading channels, with the same approximate coding gains. We first describe the methodology for combining coset codes with a general class of adaptive modulation techniques. We then apply this methodology to a spectrally efficient adaptive M -ary quadrature amplitude modulation (MQAM) to obtain trellis-coded adaptive MQAM. We present analytical and simulation results for this design which show an effective coding gain of 3 dB relative to uncoded adaptive MQAM for a simple four-state trellis code, and an effective 3.6-dB coding gain for an eight-state trellis code. More complex trellis codes are shown to achieve higher gains. We also compare the performance of trellis-coded adaptive MQAM to that of coded modulation with built-in time diversity and fixed-rate modulation. The adaptive method exhibits a power savings of up to 20 dB.
Be Wary of the Economics of "Serverless" Cloud Computing

One of the latest developments in cloud computing is usually called "serverless" computing, despite the fact that servers are still where processing takes place. The economic benefits of serverless computing heavily depend on the execution behavior and volumes of the application workload. Serverless has the potential to be a great abstraction offering economic advantages for simple workflows, however it's important to model the economic impact of your architecture and operation choices.
Contextual spoken language understanding using recurrent neural networks

We present a contextual spoken language understanding (contextual SLU) method using Recurrent Neural Networks (RNNs). Previous work has shown that context information, specifically the previously estimated domain assignment, is helpful for domain identification. We further show that other context information such as the previously estimated intent and slot labels are useful for both intent classification and slot filling tasks in SLU. We propose a step-n-gram model to extract sentence-level features from RNNs, which extract sequential features. The step-n-gram model is used together with a stack of Convolution Networks for training domain/intent classification. Our method therefore exploits possible correlations among domain/intent classification and slot filling and incorporates context information from the past predictions of domain/intent and slots. The proposed method obtains new state-of-the-art results on ATIS and improved performances over baseline techniques such as conditional random fields (CRFs) on a large context-sensitive SLU dataset.
Color gamut transform pairs

Digital control of color television monitors—in particular, via frame buffers—has added precise control of a large subset of human colorspace to the capabilities of computer graphics. This subset is the gamut of colors spanned by the red, green, and blue (RGB) electron guns exciting their respective phosphors. It is called the RGB monitor gamut. Full-blown color theory is a quite complex subject involving physics, psychology, and physiology, but restriction to the RGB monitor gamut simplifies matters substantially. It is linear, for example, and admits to familiar spatial representations. This paper presents a set of alternative models of the RGB monitor gamut based on the perceptual variables hue (H), saturation (S), and value (V) or brightness (L). Algorithms for transforming between these models are derived. Particular emphasis is placed on an RGB to HSV non-trigonometric pair of transforms which have been used successfully for about four years in frame buffer painting programs. These are fast, accurate, and adequate in many applications. Computationally more difficult transform pairs are sometimes necessary, however. Guidelines for choosing among the models are provided. Psychophysical corrections are described within the context of the definitions established by the NTSC (National Television Standards Committee).
Stash: Have your scratchpad and cache it too

Heterogeneous systems employ specialization for energy efficiency. Since data movement is expected to be a dominant consumer of energy, these systems employ specialized memories (e.g., scratchpads and FIFOs) for better efficiency for targeted data. These memory structures, however, tend to exist in local address spaces, incurring significant performance and energy penalties due to inefficient data movement between the global and private spaces. We propose an efficient heterogeneous memory system where specialized memory components are tightly coupled in a unified and coherent address space. This paper applies these ideas to a system with CPUs and GPUs with scratchpads and caches.
 We introduce a new memory organization, stash, that combines the benefits of caches and scratchpads without incurring their downsides. Like a scratchpad, the stash is directly addressed (without tags and TLB accesses) and provides compact storage. Like a cache, the stash is globally addressable and visible, providing implicit data movement and increased data reuse. We show that the stash provides better performance and energy than a cache and a scratchpad, while enabling new use cases for heterogeneous systems. For 4 microbenchmarks, which exploit new use cases (e.g., reuse across GPU compute kernels), compared to scratchpads and caches, the stash reduces execution cycles by an average of 27% and 13% respectively and energy by an average of 53% and 35%. For 7 current GPU applications, which are not designed to exploit the new features of the stash, compared to scratchpads and caches, the stash reduces cycles by 10% and 12% on average (max 22% and 31%) respectively, and energy by 16% and 32% on average (max 30% and 51%).
Assessing and moving on from the dominant project management discourse in the light of project overruns

There has been much prescriptive work in project management, exemplified in various "Bodies of Knowledge". However, experience shows some projects overspending considerably. Recently, systemic modeling research into the behavior of large projects explains project oversponds by "systemic" effects and the (sometimes counterintuitive) effect of management actions. However, while this work is becoming more widely known, embedding the lessons in project-management practice is not straightforward. The current prescriptive dominant discourse of project management contains implicit underlying assumptions with which the systemic modeling work clashes, indeed showing how conventional methods can exacerbate rather than alleviate project problems. Exploration of this modeling suggests that for projects that are complex, uncertain, and time-limited, conventional methods might be inappropriate, and aspects of newer methodologies in which the project "emerges" rather than being fully preplanned might be more appropriate. Some of the current literature on project-classification schemes also suggests similar parameters, without the rationale that the systemic modeling provides, thus providing useful backup to this analysis. The eventual aim of this line of work is to enable project managers to choose effective ways to manage projects based on understanding and model-based theory.
Ultrasonic Doppler Sensor for Voice Activity Detection

This letter describes a robust voice activity detector using an ultrasonic Doppler sonar device. An ultrasonic beam is incident on the talker's face. Facial movements result in Doppler frequency shifts in the reflected signal that are sensed by an ultrasonic sensor. Speech-related facial movements result in identifiable patterns in the spectrum of the received signal that can be used to identify speech activity. These sensors are not affected by even high levels of ambient audio noise. Unlike most other non-acoustic sensors, the device need not be taped to a talker. A simple yet robust method of extracting the voice activity information from the ultrasonic Doppler signal is developed and presented in this letter. The algorithm is seen to be very effective and robust to noise, and it can be implemented in real time.