nils-herrmann/clean_scirepeval huggingface dataset:
Improving homograph disambiguation with supervised machine learning

We describe a pre-existing rule-based homograph disambiguation system used for text-to-speech synthesis at Google, and compare it to a novel system which performs disambiguation using classifiers trained on a small amount of labeled data. An evaluation of these systems, using a new, freely available English data set, finds that hybrid systems (making use of both rules and machine learning) are significantly more accurate than either hand-written rules or machine learning alone. The evaluation also finds minimal performance degradation when the hybrid system is configured to run on limited-resource mobile devices rather than on production servers. The two best systems described here are used for homograph disambiguation on all US English text-to-speech traffic at Google.
Algebraic Laws for Nondeterminism and Concurrency

Since a nondeterministic and concurrent program may, in general, communicate repeatedly with its environment, its meaning cannot be presented naturally as an input/output function (as is often done in the denotational approach to semantics). In this paper, an alternative is put forth. First, a definition is given of what it is for two programs or program parts to be equivalent for all observers; then two program parts are said to be observation congruent if they are, in all program contexts, equivalent. The behavior of a program part, that is, its meaning, is defined to be its observation congruence class.
The paper demonstrates, for a sequence of simple languages expressing finite (terminating) behaviors, that in each case observation congruence can be axiomatized algebraically. Moreover, with the addition of recursion and another simple extension, the algebraic language described here becomes a calculus for writing and specifying concurrent programs and for proving their properties.
Brainport: an alternative input to the brain.

Brain Computer Interface (BCI) technology is one of the most rapidly developing areas of modern science; it has created numerous significant crossroads between Neuroscience and Computer Science. The goal of BCI technology is to provide a direct link between the human brain and a computerized environment. The objective of recent BCI approaches and applications have been designed to provide the information flow from the brain to the computerized periphery. The opposite or alternative direction of the flow of information (computer to brain interface, or CBI) remains almost undeveloped. The BrainPort is a CBI that offers a complementary technology designed to support a direct link from a computerized environment to the human brain - and to do so non-invasively. Currently, BrainPort research is pursuing two primary goals. One is the delivery of missing sensory information critical for normal human behavior through an additional artificial sensory channel around the damaged or malfunctioning natural sensory system. The other is to decrease the risk of sensory overload in human-machine interactions by providing a parallel and supplemental channel for information flow to the brain. In contrast, conventional CBI strategies (e.g., Virtual Reality), are usually designed to provide additional or substitution information through pre-existing sensory channels, and unintentionally aggravate the brain overload problem.
Individuality and Alignment in Generated Dialogues

It would be useful to enable dialogue agents to project, through linguistic means, their individuality or personality. Equally, each member of a pair of agents ought to adjust its language (to a greater or lesser extent) to match that of its interlocutor. We describe CRAG, which generates dialogues between pairs of agents, who are linguistically distinguishable, but able to align. CRAG-2 makes use of OPENCCG and an over-generation and ranking approach, guided by a set of language models covering both personality and alignment. We illustrate with examples of output, and briefly note results from user studies with the earlier CRAG-1, indicating how CRAG-2 will be further evaluated. Related work is discussed, along with current limitations and future directions.
Salty Secret: Let us secretly salt the secret

Using password is, perhaps, still the most versatile method of securing secret and confidential information, even though several recent studies have pointed out possibility of breaching it. A general trend of having different passwords for several user accounts of the same user (such as multiple email accounts, multiple social networking accounts, etc.) can barely overcome the possibility as users mostly prefer retaining similarity among own passwords, which results in the possibility of breaching almost all passwords once only one password gets breached. Consequently, several research studies attempted to strengthen passwords. However, none of the studies is yet to get wide popularity for not being able to achieve a delicate balance between strength of password and user friendliness. To achieve this goal, we present a new password based authentication system in this paper. The proposed system is based on intermixing between a fixed text (conventional part of a password) and a free random text (newly added) at different pre-defined indices having different per-defined lengths. The addition of the free random text adds an additional level of difficulty in breaching the password. We present different variants of our proposed system along with their possible attack models. We demonstrate strength of our proposed system through rigorous analytical formulation and numerical simulation. Besides, we confirm achieving a delicate balance between strength of the password and user friendliness through performing real user evaluation.
FPGA Implementations of the Round Two SHA-3 Candidates

The second round of the NIST-run public competition is underway to find a new hash algorithm(s) for inclusion in the NIST Secure Hash Standard (SHA-3). This paper presents the full implementations of all of the second round candidates in hardware with all of their variants. In order to determine their computational efficiency, an important aspect in NIST's round two evaluation criteria, this paper gives an area/speed comparison of each design both with and without a hardware interface, thereby giving an overall impression of their performance in resource constrained and resource abundant environments. The implementation results are provided for a Virtex-5 FPGA device. The efficiency of the architectures for the hash functions are compared in terms of throughput per unit area.
Watching together: integrating text chat with video

Watching video online is becoming increasingly popular, and new video streaming technologies have the potential to transform video watching from a passive, isolating experience into an active, socially engaging experience. However, the viability of an active social experience is unclear: both chatting and watching video require attention, and may interfere with one another and detract from the experience. In this paper, we empirically examine the activity of chatting while watching video online. We examine how groups of friends and strangers interact, and find that chat has a positive influence on social relationships, and people chat despite being distracted. We discuss the benefits and opportunities provided by mixing chat and video, uncover some of the attentional and social challenges inherent in this combination of media, and provide guidance for structuring the viewing experience.
Young adults' experiences of seeking online information about diabetes and mental health in the age of social media

The Internet is a primary source of health information for many. Since the widespread adoption of social media, user-generated health-related content has proliferated, particularly around long-term health issues such as diabetes and common mental health disorders (CMHDs).



To explore perceptions and experiences of engaging with health information online in a sample of young adults familiar with social media environments and variously engaged in consuming user-generated content.



Forty semi-structured interviews were conducted with young adults, aged 18-30, with experience of diabetes or CMHDs. Data were analysed following a thematic networks approach to explore key themes around online information-seeking and content consumption practices.



Although participants primarily discussed well-rehearsed approaches to health information-seeking online, particularly reliance on search engines, their accounts also reflected active engagement with health-related content on social media sites. Navigating between professionally produced websites and user-generated content, many of the young adults seemed to appreciate different forms of health knowledge emanating from varied sources. Participants described negotiating health content based on social media practices and features and assessing content heuristically. Some also discussed habitual consumption of content related to their condition as integrated into their everyday social media use.



Technologies such as Facebook, Twitter and YouTube offer opportunities to consume and assess content which users deem relevant and useful. As users and organizations continue to colonize social media platforms, opportunities are increasing for health communication and intervention. However, how such innovations are adopted is dependent on their alignment with users' expectations and consumption practices.
Visual Analytics: Scope and Challenges

In today’s applications data is produced at unprecedented rates. While the capacity to collect and store new data rapidly grows, the ability to analyze these data volumes increases at much lower rates. This gap leads to new challenges in the analysis process, since analysts, decision makers, engineers, or emergency response teams depend on information hidden in the data. The emerging field of visual analytics focuses on handling these massive, heterogenous, and dynamic volumes of information by integrating human judgement by means of visual representations and interaction techniques in the analysis process. Furthermore, it is the combination of related research areas including visualization, data mining, and statistics that turns visual analytics into a promising field of research. This paper aims at providing an overview of visual analytics, its scope and concepts, addresses the most important research challenges and presents use cases from a wide variety of application scenarios.
Competitive intelligence: construct exploration, validation and equivalence

Little empirical research has been conducted on competitive intelligence (CI). This paper aims to contribute to the quantitative strand of the CI literature by exploring and validating the theoretical constructs of the CI process.  Data from 601 questionnaires filled out by South African and Flemish exporters were subjected to exploratory factor analysis and construct equivalence analysis between the sub-samples.  The results showed that the CI process consists of three constructs, while the context in which CI takes place consists of four constructs. This agrees to some extent with the literature. When verifying the constructs for both cultures it was found that all but one CI context construct can be viewed as equivalent in both groups. Bias analysis identified one item in the questionnaire that was biased. Via regression analysis it was also indicated that the context in which CI takes place influences the CI process to a large extent. The research identified size as an important influencing factor in a business’ CI process.  Businesses involved in CI should take note that an improvement in their formal infrastructure, employee involvement and internal information processes could enhance their CI capability.  This paper contributes towards the formalising of the constructs of competitive intelligence.
Comprehensive Optimization Model for Sizing and Siting of DG Units, EV Charging Stations, and Energy Storage Systems

The sizing and siting of renewable resources-based distributed generation (DG) units has been a topic of growing interest, especially during the last decade due to the increasing interest in renewable energy systems and the possible impacts of their volatility on distribution system operation. This paper goes beyond the existing literature by presenting a comprehensive optimization model for the sizing and siting of different renewable resources-based DG units, electric vehicle charging stations, and energy storage systems within the distribution system. The proposed optimization model is formulated as a second order conic programming problem, considering also the time-varying nature of DG generation and load consumption, in contrast with the majority of the relevant studies that have been based on static values.
Scale-Stack Bar Charts

It is difficult to create appropriate bar charts for data that cover large value ranges. The usual approach for these cases employs a logarithmic scale, which, however, suffers from issues inherent to its non-linear mapping: for example, a quantitative comparison of different values is difficult. We present a new approach for bar charts that combines the advantages of linear and logarithmic scales, while avoiding their drawbacks. Our scale-stack bar charts use multiple scales to cover a large value range, while the linear mapping within each scale preserves the ability to visually compare quantitative ratios. Scale-stack bar charts can be used for the same applications as classic bar charts; in particular, they can readily handle stacked bar representations and negative values. Our visualization technique is demonstrated with results for three different application areas and is assessed by an expert review and a quantitative user study confirming advantages of our technique for quantitative comparisons.
From Non-Functional Requirements to Design through Patterns

Design patterns aid in documenting and communicating proven design solutions to recurring problems. They describe not only how to solve design problems, but also why a solution is chosen over others and what trade-offs are made. Non-functional requirements (NFRs) are pervasive in descriptions of design patterns. They play a crucial role in understanding the problem being addressed, the trade-offs discussed, and the design solution proposed. However, since design patterns are mostly expressed as informal text, the structure of the design reasoning is not systematically organised. This paper proposes a systematic treatment of NFRs in descriptions of patterns and when applying patterns during design. The approach organises, analyses and refines non-functional requirements, and provides guidance and reasoning support when applying patterns during the design of a software system. Three design patterns taken from the literature are used to illustrate this approach.
Authoring Behaviors for Games using Learning from Demonstration

Behavior authoring for computer games involves writing behaviors in a programming language. This method is cumbersome and requires a lot of programming effort to author the behavior sets. Further this approach restricts the behavior set authoring to people who are experts in programming. This paper will describe our approach to design a system that will allow a user to demonstrate behaviors to the system, which the system will use to learn behavior sets for a game domain. With learning from demonstration, we aim at removing the requirement that the user has to be an expert in programming, and only require him to be an expert in the game. The approach has been integrated in a easy to use visual interface and instantiated for two domains, one a real time strategy game and another an interactive drama.
The Effect of Metrics and Workloads on the Evaluation of Computer Systems

Metrics ought to be objective, as they are the judge of perfor mance. Workloads ought to be representative, so that evaluations will lead to applicabl e results. But sometimes metrics and workloads collude to taint the performance evaluation proc ess, leading to results of dubious merit. We use a case study dealing with parallel job scheduli ng to exemplify these issues. An analysis of interactions among the metrics, the workload s, nd the systems being studied reveals that such interactions may dominate the evaluation results. Moreover, in some cases factors that were originally thought to be minor and ignorab le are actually very important, and may overshadow the differences between the different syste ms. It is therefore recommended that multiple workloads and metrics be used in performance e valuation studies, and that the causes of inconsistent results be studied thoroughly.
Two case studies of open source software development: Apache and Mozilla

According to its proponents, open source style software development has the capacity to compete successfully, and perhaps in many cases displace, traditional commercial development methods. In order to begin investigating such claims, we examine data from two major open source projects, the Apache web server and the Mozilla browser. By using email archives of source code change history and problem reports we quantify aspects of developer participation, core team size, code ownership, productivity, defect density, and problem resolution intervals for these OSS projects. We develop several hypotheses by comparing the Apache project with several commercial projects. We then test and refine several of these hypotheses, based on an analysis of Mozilla data. We conclude with thoughts about the prospects for high-performance commercial/open source process hybrids.
Advanced zinc-air batteries based on high-performance hybrid electrocatalysts.

Primary and rechargeable Zn-air batteries could be ideal energy storage devices with high energy and power density, high safety and economic viability. Active and durable electrocatalysts on the cathode side are required to catalyse oxygen reduction reaction during discharge and oxygen evolution reaction during charge for rechargeable batteries. Here we developed advanced primary and rechargeable Zn-air batteries with novel CoO/carbon nanotube hybrid oxygen reduction catalyst and Ni-Fe-layered double hydroxide oxygen evolution catalyst for the cathode. These catalysts exhibited higher catalytic activity and durability in concentrated alkaline electrolytes than precious metal Pt and Ir catalysts. The resulting primary Zn-air battery showed high discharge peak power density ~265 mW cm(-2), current density ~200 mA cm(-2) at 1 V and energy density >700 Wh kg(-1). Rechargeable Zn-air batteries in a tri-electrode configuration exhibited an unprecedented small charge-discharge voltage polarization of ~0.70 V at 20 mA cm(-2), high reversibility and stability over long charge and discharge cycles.
Learning Common Representation from RGB and Depth Images

We propose a new deep learning architecture for the tasks of semantic segmentation and depth prediction from RGBD images. We revise the state of art based on the RGB and depth feature fusion, where both modalities are assumed to be available at train and test time. We propose a new architecture where the feature fusion is replaced with a common deep representation. Combined with an encoder-decoder type of the network, the architecture can jointly learn models for semantic segmentation and depth estimation based on their common representation. This representation, inspired by multi-view learning, offers several important advantages, such as using one modality available at test time to reconstruct the missing modality. In the RGB-D case, this enables the cross-modality scenarios, such as using depth data for semantically segmentation and the RGB images for depth estimation. We demonstrate the effectiveness of the proposed network on two publicly available RGB-D datasets. The experimental results show that the proposed method works well in both semantic segmentation and depth estimation tasks.
Screen space spherical harmonics occlusion (S3HO) sampling

One objective of global illumination (GI) models is to solve the lighting integral of the upper hemisphere at a surface point. One particular effect of GI that has become very popular in the past years is Ambient Occlusion (AO), where, independently of the lighting, a coverage factor that indicates the amount of occlusion induced by its local neighboring geometry is calculated for every surface point. Screen Space Ambient Occlusion (SSAO) algorithms allow for the approximation of this occlusion factor in real time. Ritschel et al. [2009] proposed Screen Space Directional Occlusion (SSDO), combining the calculation of occlusion and lighting by simultaneously accumulating the light of unoccluded directions when sampling the occlusion. Contrary to AO this results in colored shadows simulating directional lighting effects. Green [2003] outlined techniques based on Spherical Harmonics (SH) that approximate global lighting models with the basic principle of representing both, the incoming radiance field and the occlusion, as functions defined on the sphere that can be efficiently represented in the spherical Fourier domain. Calculating the spherical occlusion function, however, remains computationally expensive since ray tracing methods have to be applied.
Concept drift detection for streaming data

Common statistical prediction models often require and assume stationarity in the data. However, in many practical applications, changes in the relationship of the response and predictor variables are regularly observed over time, resulting in the deterioration of the predictive performance of these models. This paper presents Linear Four Rates (LFR), a framework for detecting these concept drifts and subsequently identifying the data points that belong to the new concept (for relearning the model). Unlike conventional concept drift detection approaches, LFR can be applied to both batch and stream data; is not limited by the distribution properties of the response variable (e.g., datasets with imbalanced labels); is independent of the underlying statistical-model; and uses user-specified parameters that are intuitively comprehensible. The performance of LFR is compared to benchmark approaches using both simulated and commonly used public datasets that span the gamut of concept drift types. The results show LFR significantly outperforms benchmark approaches in terms of recall, accuracy and delay in detection of concept drifts across datasets.
Facebook, Twitter and Google Plus for Breaking News: Is There a Winner?

Twitter is widely seen as being the go to place for breaking news. Recently however, competing Social Media have begun to carry news. Here we examine how Facebook, Google Plus and Twitter report on breaking news. We consider coverage (whether news events are reported) and latency (the time when they are reported). Using data drawn from three weeks in December 2013, we identify 29 major news events, ranging from celebrity deaths, plague outbreaks to sports events. We find that all media carry the same major events, but Twitter continues to be the preferred medium for breaking news, almost consistently leading Facebook or Google Plus. Facebook and Google Plus largely repost newswire stories and their main research value is that they conveniently package multitple sources of information together.