nils-herrmann/clean_scirepeval huggingface dataset:
IEMOCAP: interactive emotional dyadic motion capture database

Since emotions are expressed through a combination of verbal and non-verbal channels, a joint analysis of speech and gestures is required to understand expressive human communication. To facilitate such investigations, this paper describes a new corpus named the “interactive emotional dyadic motion capture database” (IEMOCAP), collected by the Speech Analysis and Interpretation Laboratory (SAIL) at the University of Southern California (USC). This database was recorded from ten actors in dyadic sessions with markers on the face, head, and hands, which provide detailed information about their facial expression and hand movements during scripted and spontaneous spoken communication scenarios. The actors performed selected emotional scripts and also improvised hypothetical scenarios designed to elicit specific types of emotions (happiness, anger, sadness, frustration and neutral state). The corpus contains approximately twelve hours of data. The detailed motion capture information, the interactive setting to elicit authentic emotions, and the size of the database make this corpus a valuable addition to the existing databases in the community for the study and modeling of multimodal and expressive human communication.
Detecting and Estimating Signals in Noisy Cable Structures, I: Neuronal Noise Sources

In recent theoretical approaches addressing the problem of neural coding, tools from statistical estimation and information theory have been applied to quantify the ability of neurons to transmit information through their spike outputs. These techniques, though fairly general, ignore the specific nature of neuronal processing in terms of its known biophysical properties. However, a systematic study of processing at various stages in a biophysically faithful model of a single neuron can identify the role of each stage in information transfer. Toward this end, we carry out a theoretical analysis of the information loss of a synaptic signal propagating along a linear, one-dimensional, weakly active cable due to neuronal noise sources along the way, using both a signal reconstruction and a signal detection paradigm. Here we begin such an analysis by quantitatively characterizing three sources of membrane noise: (1) thermal noise due to the passive membrane resistance, (2) noise due to stochastic openings and closings of voltage-gated membrane channels (Na+ and K+), and (3) noise due to random, background synaptic activity. Using analytical expressions for the power spectral densities of these noise sources, we compare their magnitudes in the case of a patch of membrane from a cortical pyramidal cell and explore their dependence on different biophysical parameters.
Word sense disambiguation as a traveling salesman problem

Word sense disambiguation (WSD) is a difficult problem in Computational Linguistics, mostly because of the use of a fixed sense inventory and the deep level of granularity. This paper formulates WSD as a variant of the traveling salesman problem (TSP) to maximize the overall semantic relatedness of the context to be disambiguated. Ant colony optimization, a robust nature-inspired algorithm, was used in a reinforcement learning manner to solve the formulated TSP. We propose a novel measure based on the Lesk algorithm and Vector Space Model to calculate semantic relatedness. Our approach to WSD is comparable to state-of-the-art knowledge-based and unsupervised methods for benchmark datasets. In addition, we show that the combination of knowledge-based methods is superior to the most frequent sense heuristic and significantly reduces the difference between knowledge-based and supervised methods. The proposed approach could be customized for other lexical disambiguation tasks, such as Lexical Substitution or Word Domain Disambiguation.
A Flexible Fabrication Approach Toward the Shape Engineering of Microscale Soft Pneumatic Actuators

The recent developments of soft robots have inspired a variety of applications that require compliance for safety and softness for biomimetics. With such prevalence and advantages of soft robots, researchers are diverting their interests toward applications like micromanipulation of biological tissue. However, the progress has been thwarted by the difficulty in producing soft robots in miniature scale. In this paper, we present a new kind of microscale soft pneumatic actuators (SPA) made from streamlined and standardized fabrication procedure with customizable bending modalities inspired by shape engineering. Preliminary mathematical models are given to interpret width-based shape engineering for customization and to compare the bending angle and radius of curvature measured from the characterization experiments. The fabricated SPA was tested on the sciatic nerve of a rat in order to show its future potentials in biomedical field. Ultimately, this paper will contribute to the diversification of SPA fabrication and customization, as well as biomedical applications that demand smaller dimensions and higher precision.
Big Data Security: Survey on Frameworks and Algorithms

Technology today has progressed to an extent wherein collection of data is possible for every granular aspect of a business, in real time. Electronic devices, power grids and modern software all generate huge volumes of data, which is in the form of petabytes, exabytes and zettabytes. It is important to secure existing big data environments due to increasing threats of breaches and leaks from confidential data and increased adoption of cloud technologies due to the ability of buying processing power and storage on-demand. This is exposing traditional and new data warehouses and repositories to the outside world and at the risk of being compromised to hackers and malicious outsiders and insiders. In this paper the current big data scenario has been summarized along with challenges faced and security issues that need attention. Also some existing approaches have been described to illustrate current and standard directions to solving the issues.
Behavioral Patterns of Older Adults in Assisted Living

In this paper, we examine at-home activity rhythms and present a dozen of behavioral patterns obtained from an activity monitoring pilot study of 22 residents in an assisted living setting with four case studies. Established behavioral patterns have been captured using custom software based on a statistical predictive algorithm that models circadian activity rhythms (CARs) and their deviations. The CAR was statistically estimated based on the average amount of time a resident spent in each room within their assisted living apartment, and also on the activity level given by the average n.umber of motion events per room. A validated in-home monitoring system (IMS) recorded the monitored resident's movement data and established the occupancy period and activity level for each room. Using these data, residents' circadian behaviors were extracted, deviations indicating anomalies were detected, and the latter were correlated to activity reports generated by the IMS as well as notes of the facility's professional caregivers on the monitored residents. The system could be used to detect deviations in activity patterns and to warn caregivers of such deviations, which could reflect changes in health status, thus providing caregivers with the opportunity to apply standard of care diagnostics and to intervene in a timely manner.
Integrating Reactive and Scripted Behaviors in a Life-Like Presentation Agent

1.  Animated agents based either on real video, cartoon-style drawings or even model-based 3D graphics offer great promise for computer-based presentations as they make presentations more lively and appealing and allow for the emulation of conversation styles known from human-human communication. In this paper, we describe a life-like interface agent which presents multimedia material to the user following the directives of a script. The overall behavior of the presentation agent is partly determined by such a script, and partly by the agent's self-behavior. In our approach, the agent's behavior is defined in a declarative specification language. Behavior specifications are used to automatically generate a control module for an agent display system. The first part of the paper describes the generation process which involves AI planning and a two-step compilation. Since the manual creation of presentation scripts is tedious and error-prone, we also address the automated generation of presentation scripts which may be forwarded to the interface agent. The second part of the paper presents an approach for multimedia presentation design which combines hierarchical planning with temporal reasoning.
The Gut Microbiota and Autism Spectrum Disorders

Gastrointestinal (GI) symptoms are a common comorbidity in patients with autism spectrum disorder (ASD), but the underlying mechanisms are unknown. Many studies have shown alterations in the composition of the fecal flora and metabolic products of the gut microbiome in patients with ASD. The gut microbiota influences brain development and behaviors through the neuroendocrine, neuroimmune and autonomic nervous systems. In addition, an abnormal gut microbiota is associated with several diseases, such as inflammatory bowel disease (IBD), ASD and mood disorders. Here, we review the bidirectional interactions between the central nervous system and the gastrointestinal tract (brain-gut axis) and the role of the gut microbiota in the central nervous system (CNS) and ASD. Microbiome-mediated therapies might be a safe and effective treatment for ASD.
Meta-analysis of correlations among usability measures

Understanding the relation between usability measures seems crucial to deepen our conception of usability and to select the right measures for usability studies. We present a meta-analysis of correlations among usability measures calculated from the raw data of 73 studies. Correlations are generally low: effectiveness measures (e.g., errors) and efficiency measures (e.g., time) have a correlation of .247 ± .059 (Pearson's product-moment correlation with 95% confidence interval), efficiency and satisfaction (e.g., preference) one of .196 ± .064, and effectiveness and satisfaction one of .164 ± .062. Changes in task complexity do not influence these correlations, but use of more complex measures attenuates them. Standard questionnaires for measuring satisfaction appear more reliable than homegrown ones. Measures of users' perceptions of phenomena are generally not correlated with objective measures of the phenomena. Implications for how to measure usability are drawn and common models of usability are criticized.
Millimeter wave microstrip patch antenna for 5G mobile communication

This paper presents a micro strip patch antenna for future 5G-communication technology at centered frequency 38GHz and 54GHz having bandwidth 1.94GHz and 2GHz respectively with low cost substrate and small size patch best suited for miniaturized devices. It consists of Rogers RT5880 (lossy) substrate with dielectric constant 2.2 with loss tangent of 0.0013 and standard thickness 0.508mm, PEC patch and PEC ground. Substrate of dimensions 6mm×6.25mm and patch with dimensions 2mm×2mm is used. Microstrip-line feeding technique is used. Array having 4 elements with 4mm spacing has been proposed to achieve 12 dB gain for mobile data Applications on millimeter wave frequencies at 38.6GHz, 47.7GHz and 54.3GHz having bandwidth 3.5GHz, 2.5GHz and 1.3GHz respectively with Tapered line feeding. Overall size of antenna is 6mm×6.25mm×0.578mm. The proposed Antenna design is simulated on CST Microwave Studio.
BEDTools: a flexible suite of utilities for comparing genomic features

Testing for correlations between different sets of genomic features is a fundamental task in genomics research. However, searching for overlaps between features with existing web-based methods is complicated by the massive datasets that are routinely produced with current sequencing technologies. Fast and flexible tools are therefore required to ask complex questions of these data in an efficient manner.



This article introduces a new software suite for the comparison, manipulation and annotation of genomic features in Browser Extensible Data (BED) and General Feature Format (GFF) format. BEDTools also supports the comparison of sequence alignments in BAM format to both BED and GFF features. The tools are extremely efficient and allow the user to compare large datasets (e.g. next-generation sequencing data) with both public and custom genome annotation tracks. BEDTools can be combined with one another as well as with standard UNIX commands, thus facilitating routine genomics tasks as well as pipelines that can quickly answer intricate questions of large genomic datasets.



BEDTools was written in C++. Source code and a comprehensive user manual are freely available at http://code.google.com/p/bedtools



aaronquinlan@gmail.com; 
Supplementary data are available at Bioinformatics online.
Identifying bilingual Multi-Word Expressions for Statistical Machine Translation

MultiWord Expressions (MWEs) repesent a key issue for numerous applications in Natural Language Processing (NLP) especially for Machine Translation (MT). In this paper, we describe a strategy for detecting translation pairs of MWEs in a French-English parallel corpus. In addition we introduce three methods aiming to integrate extracted bilingual MWES in MOSES, a phrase based Statistical Machine Translation (SMT) system. We experimentally show that these textual units can improve translation quality.
IOT Based Air And Sound Pollution Monitoring System

The growing air and sound pollution is one of the serious issues these days. This large amount of increasing pollution has made human life prone to large number of diseases. Therefore, it has now become necessary to control the pollution to ensure healthy livelihood and better future. The Air and Sound Pollution Monitoring device can be accessed by the authorities and the common people belonging to the area. The device will be installed through a mobile application which will show the live updates of the pollution level of the area. This device is also capable of detecting the fire in its area and notify the same to the fire brigade authorities so that they could take necessary actions accordingly, and also the mobile applications will be installed in the fire brigades itself so that if a fire is taking place nearby, it could be controlled in time to reduce loss of people and property. This system works on the methods of IOT which is a rising technology based on the fusion of electronics and computer science. The embedded sensors in the system help to detect major air polluting gases such as CO2, SO2 and CO and level of sound pollution. The concept of IOT helps to access data from remote locations and save it in database so that we don’t need to actually be present in that area.
Examining micro-payments for participatory sensing data collections

The rapid adoption of mobile devices that are able to capture and transmit a wide variety of sensing modalities (media and location) has enabled a new data collection paradigm - participatory sensing. Participatory sensing initiatives organize individuals to gather sensed information using mobile devices through cooperative data collection. A major factor in the success of these data collection projects is sustained, high quality participation. However, since data capture requires a time and energy commitment from individuals, incentives are often introduced to motivate participants. In this work, we investigate the use of micro-payments as an incentive model. We define a set of metrics that can be used to evaluate the effectiveness of incentives and report on findings from a pilot study using various micro-payment schemes in a university campus sustainability initiative.
A viability theory for digital businesses: Exploring the evolutionary changes of revenue mechanisms to support managerial decisions

The ongoing integration of Information Technology (IT) into various areas of our lives has led to a plethora of digital products and services. To survive competition in the long run, these offerings not only have to keep up with constant technological developments, but also have to adapt from a business point of view. Managers of these digital businesses have to especially focus on the design and evolution of their business’ revenue mechanisms to ensure the viability of their offerings. The related decisions are not trivial, as managers have to be aware of the relevant contextual factors and have to react quickly to changes in the environment. This paper proposes a viability theory for digital businesses described by 17 propositions that may guide managers in the design of revenue mechanisms and thereby support the evolution as well as the viability of a digital business.
Unikernels: library operating systems for the cloud

We present unikernels, a new approach to deploying cloud services via applications written in high-level source code. Unikernels are single-purpose appliances that are compile-time specialised into standalone kernels, and sealed against modification when deployed to a cloud platform. In return they offer significant reduction in image sizes, improved efficiency and security, and should reduce operational costs. Our Mirage prototype compiles OCaml code into unikernels that run on commodity clouds and offer an order of magnitude reduction in code size without significant performance penalty. The architecture combines static type-safety with a single address-space layout that can be made immutable via a hypervisor extension. Mirage contributes a suite of type-safe protocol libraries, and our results demonstrate that the hypervisor is a platform that overcomes the hardware compatibility issues that have made past library operating systems impractical to deploy in the real-world.
Analysis of political discourse on twitter in the context of the 2016 US presidential elections

Social media now plays a pivotal role in electoral campaigns. Rapid dissemination of information through platforms such as Twitter has enabled politicians to broadcast their message to a wide audience. In this paper, we investigated the sentiment of tweets by the two main presidential candidates, Hillary Clinton and Donald Trump, along with almost 2.9 million tweets by Twitter users during the 2016 US Presidential Elections. We analyzed these short texts to evaluate how accurately Twitter represented the public opinion and real world events of significance related with the elections. We also analyzed the behavior of over a million distinct Twitter users to identify whether the platform was used to share original opinions and to interact with other users or whether few opinions were repeated over and over again with little inter-user dialogue. Finally, we wanted to assess the sentiment of tweets by both candidates and their impact on the election related discourse on Twitter. Some of our findings included the discovery that little original content was created by users and Twitter was primarily used for rebroadcasting already present opinions in the form of retweets with little communication between users. Also of significance was the finding that sentiment and topics expressed on Twitter can be a good proxy of public opinion and important election related events. Moreover, we found that Donald Trump offered a more optimistic and positive campaign message than Hillary Clinton and enjoyed better sentiment when mentioned in messages by Twitter users.
Self-Fulfilling Prophesies , Placebo Effects , and the Social – Psychological Creation of Reality

In this essay, we pull together foundational research from the psychological, sociological, and medical sciences to illuminate the undeniable influence of the psychosocial context in constructing objective reality. From psychology, we review the growing body of research on how beliefs and expectations about common experiences (e.g., nutrition, stress, and aging) can fundamentally alter the impact of those experiences. From sociology, we review the role of social influence in constructing the quality and impact of cultural products and experiences. And from medicine, we review the neurological and physiological underpinnings of the placebo effect, a powerful demonstration of expectation and social context to produce physiological changes in the body. As we align evidence from these related—although currently disconnected—fields, we uncover important limitations from within each field of study andportray howan integrative approach can offer amore rich and comprehensive understanding of the phenomena underlying the social–psychological creation of reality. Combining foundational research with the interdisciplinary findings from our laboratory, we explore how psychological and social contexts can fundamentally alter the psychological, behavioral, and physiological effects of one of the most common human experiences: drinking water. To conclude, we present a series of questions and suggestions to assist and inspire further interdisciplinary collaboration.We offer a pathway for researchers to more frequently acknowledge, more thoroughly understand, andmore effectively utilize the power of psychosocial influence to effect positive change in a number of disciplines includingmarketing,medicine, andpublic health.
Clustering Based URL Normalization Technique for Web Mining

URL (Uniform Resource Locator) normalization is an important activity in web mining. Web data can be retrieved in smoother way using effective URL normalization technique. URL normalization also reduces lot of calculations in web mining activities. A web mining technique for URL normalization is proposed in this paper. The proposed technique is based on content, structure and semantic similarity and web page redirection and forwarding similarity of the given set of URLs. Web page redirection and forward graphs can be used to measure the similarities between the URL’s and can also be used for URL clusters. The URL clusters can be used for URL normalization. A data structure is also suggested to store the forward and redirect URL information.
An Ontology-based Context-aware System for Smart Homes: E-care@home

Smart home environments have a significant potential to provide for long-term monitoring of users with special needs in order to promote the possibility to age at home. Such environments are typically equipped with a number of heterogeneous sensors that monitor both health and environmental parameters. This paper presents a framework called E-care@home, consisting of an IoT infrastructure, which provides information with an unambiguous, shared meaning across IoT devices, end-users, relatives, health and care professionals and organizations. We focus on integrating measurements gathered from heterogeneous sources by using ontologies in order to enable semantic interpretation of events and context awareness. Activities are deduced using an incremental answer set solver for stream reasoning. The paper demonstrates the proposed framework using an instantiation of a smart environment that is able to perform context recognition based on the activities and the events occurring in the home.