nils-herrmann/clean_scirepeval huggingface dataset:
Home Automation System ( HAS ) using Android for Mobile Phone

Automation of the surrounding environment of a modern human being allows increasing his wor k efficiency and comfort. There has been a significant developme nt in the area of an individual’s routine tasks and those can be automated. In the present times, we can find most o f the people clinging to their mobile phones and sm art devices throughout the day. Hence with the help of his comp anion – a mobile phone, some daily household tasks can be accomplished by personifying the use of the mobile phone. Analyzing the current smart phone market, novice mobile users are opting for Android based phones. It has b ecome a second name for a mobile phone in layman te rms. Home Automation System (HAS) has been designed for mobil e phones having Android platform to automate an 8 bit Bluetooth interfaced microcontroller which controls a number of home appliances like lights, fans, bulbs and man y more using on/off relay. This paper presents the automated app roach of controlling the devices in a household tha t could ease the tasks of using the traditional method of the switch . The most famous and efficient technology for shor t range wireless communicationBluetooth is used here to automate t he system. The HAS system for Android users is a st ep towards the ease of the tasks by controlling one to twenty four different appliances in any home environment.
Marr Revisited: 2D-3D Alignment via Surface Normal Prediction

We introduce an approach that leverages surface normal predictions, along with appearance cues, to retrieve 3D models for objects depicted in 2D still images from a large CAD object library. Critical to the success of our approach is the ability to recover accurate surface normals for objects in the depicted scene. We introduce a skip-network model built on the pre-trained Oxford VGG convolutional neural network (CNN) for surface normal prediction. Our model achieves state-of-the-art accuracy on the NYUv2 RGB-D dataset for surface normal prediction, and recovers fine object detail compared to previous methods. Furthermore, we develop a two-stream network over the input image and predicted surface normals that jointly learns pose and style for CAD model retrieval. When using the predicted surface normals, our two-stream network matches prior work using surface normals computed from RGB-D images on the task of pose prediction, and achieves state of the art when using RGB-D input. Finally, our two-stream network allows us to retrieve CAD models that better match the style and pose of a depicted object compared with baseline approaches.
Android anomaly detection system using machine learning classification

Android is one of the most popular open-source smartphone operating system and its access control permission mechanisms cannot detect any malware behavior. In this paper, new software behavior-based anomaly detection system is proposed to detect anomaly caused by malware. It works by analyzing anomalies on power consumption, battery temperature and network traffic data using machine learning classification algorithm. The result shows that this method can detect anomaly with 85.6% accuracy.
Prevalence of depression during pregnancy: systematic review.

Current estimates of the prevalence of depression during pregnancy vary widely. A more precise estimate is required to identify the level of disease burden and develop strategies for managing depressive disorders. The objective of this study was to estimate the prevalence of depression during pregnancy by trimester, as detected by validated screening instruments (ie, Beck Depression Inventory, Edinburgh Postnatal Depression Score) and structured interviews, and to compare the rates among instruments.



Observational studies and surveys were searched in MEDLINE from 1966, CINAHL from 1982, EMBASE from 1980, and HealthSTAR from 1975.



A validated study selection/data extraction form detailed acceptance criteria. Numbers and percentages of depressed patients, by weeks of gestation or trimester, were reported.


TABULATION, INTEGRATION, AND RESULTS
Two reviewers independently extracted data; a third party resolved disagreement. Two raters assessed quality by using a 12-point checklist. A random effects meta-analytic model produced point estimates and 95% confidence intervals (CIs). Heterogeneity was examined with the chi(2) test (no systematic bias detected). Funnel plots and Begg-Mazumdar test were used to assess publication bias (none found). Of 714 articles identified, 21 (19,284 patients) met the study criteria. Quality scores averaged 62%. Prevalence rates (95% CIs) were 7.4% (2.2, 12.6), 12.8% (10.7, 14.8), and 12.0% (7.4, 16.7) for the first, second, and third trimesters, respectively. Structured interviews found lower rates than the Beck Depression Inventory but not the Edinburgh Postnatal Depression Scale.



Rates of depression, especially during the second and third trimesters of pregnancy, are substantial. Clinical and economic studies to estimate maternal and fetal consequences are needed.
Deep hashing for compact binary codes learning

In this paper, we propose a new deep hashing (DH) approach to learn compact binary codes for large scale visual search. Unlike most existing binary codes learning methods which seek a single linear projection to map each sample into a binary vector, we develop a deep neural network to seek multiple hierarchical non-linear transformations to learn these binary codes, so that the nonlinear relationship of samples can be well exploited. Our model is learned under three constraints at the top layer of the deep network: 1) the loss between the original real-valued feature descriptor and the learned binary vector is minimized, 2) the binary codes distribute evenly on each bit, and 3) different bits are as independent as possible. To further improve the discriminative power of the learned binary codes, we extend DH into supervised DH (SDH) by including one discriminative term into the objective function of DH which simultaneously maximizes the inter-class variations and minimizes the intra-class variations of the learned binary codes. Experimental results show the superiority of the proposed approach over the state-of-the-arts.
Towards time-aware link prediction in evolving social networks

Prediction of links - both new as well as recurring - in a social network representing interactions between individuals is an important problem. In the recent years, there is significant interest in methods that use only the graph structure to make predictions. However, most of them consider a single snapshot of the network as the input, neglecting an important aspect of these social networks viz., their evolution over time.
 In this work, we investigate the value of incorporating the history information available on the interactions (or links) of the current social network state. Our results unequivocally show that time-stamps of past interactions significantly improve the prediction accuracy of new and recurrent links over rather sophisticated methods proposed recently. Furthermore, we introduce a novel testing method which reflects the application of link prediction better than previous approaches.
Detection of centerline crossing in abnormal driving using CapsNet

This paper presents the detection of centerline crossing in abnormal driving using a CapsNet. The benefit of the CapsNet is that the capsule contains all the data about the status of objects and recognizes objects as vectors; hence, these can be used to classify driving as normal or abnormal. The datasets use the Creative Commons Licenses from YouTube to obtain traffic accident footages and six time-flow images composed of data with our quantitative basis. A comparison of our proposed architecture with the CNN model showed that our method produces better results.
To See and Be Seen : Celebrity Practice on Twitter

Social media technologies let people connect by creating and sharing content. We examine the use of Twitter by famous people to conceptualize celebrity as a practice. On Twitter, celebrity is practiced through the appearance and performance of ‘backstage’ access. Celebrity practitioners reveal what appears to be personal information to create a sense of intimacy between participant and follower, publicly acknowledge fans, and use language and cultural references to create affiliations with followers. Interactions with other celebrity practitioners and personalities give the impression of candid, uncensored looks at the people behind the personas. But the indeterminate ‘authenticity’ of these performances appeals to some audiences, who enjoy the game playing intrinsic to gossip consumption. While celebrity practice is theoretically open to all, it is not an equalizer or democratizing discourse. Indeed, in order to successfully practice celebrity, fans must recognize the power differentials intrinsic to the relationship.
Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding

We marry two powerful ideas: deep representation learning for visual recognition and language understanding, and symbolic program execution for reasoning. Our neural-symbolic visual question answering (NS-VQA) system first recovers a structural scene representation from the image and a program trace from the question. It then executes the program on the scene representation to obtain an answer. Incorporating symbolic structure as prior knowledge offers three unique advantages. First, executing programs on a symbolic space is more robust to long program traces; our model can solve complex reasoning tasks better, achieving an accuracy of 99.8% on the CLEVR dataset. Second, the model is more dataand memory-efficient: it performs well after learning on a small number of training data; it can also encode an image into a compact representation, requiring less storage than existing methods for offline question answering. Third, symbolic program execution offers full transparency to the reasoning process; we are thus able to interpret and diagnose each execution step.
Conversational Exploratory Search via Interactive Storytelling

Conversational interfaces are likely to become more efficient, intuitive and engaging way for human-computer interaction than today’s text or touch-based interfaces. Current research efforts concerning conversational interfaces focus primarily on question answering functionality, thereby neglecting support for search activities beyond targeted information lookup. Users engage in exploratory search when they are unfamiliar with the domain of their goal, unsure about the ways to achieve their goals, or unsure about their goals in the first place. Exploratory search is often supported by approaches from information visualization. However, such approaches cannot be directly translated to the setting of conversational search. In this paper we investigate the affordances of interactive storytelling as a tool to enable exploratory search within the framework of a conversational interface. Interactive storytelling provides a way to navigate a document collection in the pace and order a user prefers. In our vision, interactive storytelling is to be coupled with a dialogue-based system that provides verbal explanations and responsive design. We discuss challenges and sketch the research agenda required to put this vision into life.
Collocated Intergenerational Console Gaming

We present results from a multi-generational study of collocated group console gaming. We examine the intergenerational gaming practices of four generations of gamers, from ages 3 to 83 and, in particular, the roles that gamers of different generations take on when playing together in groups. Our findings highlight the extent to which existing gaming technologies are amenable to interactions within collocated intergenerational groups and the broader set of roles that have emerged in these computer-mediated interactions than have previously been documented by studies of more traditional collocated, intergenerational interactions. We articulate attributes of the games that encourage intergenerational interaction.
Fast free-form deformation using graphics processing units

A large number of algorithms have been developed to perform non-rigid registration and it is a tool commonly used in medical image analysis. The free-form deformation algorithm is a well-established technique, but is extremely time consuming. In this paper we present a parallel-friendly formulation of the algorithm suitable for graphics processing unit execution. Using our approach we perform registration of T1-weighted MR images in less than 1 min and show the same level of accuracy as a classical serial implementation when performing segmentation propagation. This technology could be of significant utility in time-critical applications such as image-guided interventions, or in the processing of large data sets.
E-Commerce Systems Success: An Attempt to Extend and Respecify the Delone and MaClean Model of IS Success

E-commerce success is an issue that has attracted the interest of many pundits. There is a general scarcity of models and frameworks for evaluating e-commerce success. Whether traditional information systems success models can be extended to investigating e-commerce success is yet to be investigated. This paper proposes a partial extension and respecification of the Delone and Maclean model of IS success to e-commerce systems. Customer Ecommerce Satisfaction (CES) is proposed as a dependent variable to e-commerce success and its relationships with e-commerce system quality, content quality, use, trust and support are defined and discussed. Further research into developing, validating and empirically testing the model is proposed.
UKP: Computing Semantic Textual Similarity by Combining Multiple Content Similarity Measures

We present the UKP system which performed best in the Semantic Textual Similarity (STS) task at SemEval-2012 in two out of three metrics. It uses a simple log-linear regression model, trained on the training data, to combine multiple text similarity measures of varying complexity. These range from simple character and word n-grams and common subsequences to complex features such as Explicit Semantic Analysis vector comparisons and aggregation of word similarity based on lexical-semantic resources. Further, we employ a lexical substitution system and statistical machine translation to add additional lexemes, which alleviates lexical gaps. Our final models, one per dataset, consist of a log-linear combination of about 20 features, out of the possible 300+ features implemented.
Possessives and relational nouns

This article concentrates on nominal possessives (John’s friend) rather than on verbal possessives (John has a friend). In John’s friend, John is the possessor, and friend describes the entity possessed (the possessee). Nominal possessives constitute a major construction type in the languages of the world. In contrast with a sortal noun (e.g., person), friend is a (two-place) relational noun: a person counts as a friend only in virtue of standing in a particular relationship with another individual. Relational nouns are an important element in the study of possessives because the content of a possessive typically, perhaps characteristically, depends on the content of a relational nominal. Possessives provide particularly compelling support for type shifting as a general principle of syntactic and semantic composition. Possessives also inform debates involving definiteness, binding, and a number of other semantic phenomena. Some bibliographic notes: Partee 1997, an influential analysis of the possessive, first circulated in manuscript form around 1983, though by 1990 my attempts to get hold of a copy were not successful. My 1991 dissertation, published in 1995, provides a general introduction to nominal possessives and relational nouns. Taylor 1996 and Heine 1997 are book-length treatments in the Cognitive Grammar tradition. There is a literature in French, discussed in Dobrovie-Sorin 2000a with special attention to the contributions of Milner. Peters and Westerst̊al 2006: chapter 7 covers much of the same ground as this article from a different point of view, and Coene and d’Hulst 2003 and Kim et al. 2004 contain a number of studies discussing the syntax and semantics of possessives. Some works specific to possessives are available at semanticsarchive.net/links.html, notably bibliographies and other resources compiled by Yury Lander and by Barbara Partee.
Teddy: a sketching interface for 3D freeform design

We present a sketching interface for quickly and easily designing freeform models such as stuffed animals and other rotund objects. The user draws several 2D freeform strokes interactively on the screen and the system automatically constructs plausible 3D polygonal surfaces. Our system supports several modeling operations, including the operation to construct a 3D polygonal surface from a 2D silhouette drawn by the user: it inflates the region surrounded by the silhouette making wide areas fat, and narrow areas thin. Teddy, our prototype system, is implemented as a Java#8482; program, and the mesh construction is done in real-time on a standard PC. Our informal user study showed that a first-time user typically masters the operations within 10 minutes, and can construct interesting 3D models within minutes.
SmartGPA: how smartphones can assess and predict academic performance of college students

Many cognitive, behavioral, and environmental factors impact student learning during college. The SmartGPA study uses passive sensing data and self-reports from students' smartphones to understand individual behavioral differences between high and low performers during a single 10-week term. We propose new methods for better understanding study (e.g., study duration) and social (e.g., partying) behavior of a group of undergraduates. We show that there are a number of important behavioral factors automatically inferred from smartphones that significantly correlate with term and cumulative GPA, including time series analysis of activity, conversational interaction, mobility, class attendance, studying, and partying. We propose a simple model based on linear regression with lasso regularization that can accurately predict cumulative GPA. The predicted GPA strongly correlates with the ground truth from students' transcripts (r = 0:81 and p < 0:001) and predicts GPA within ±0:179 of the reported grades. Our results open the way for novel interventions to improve academic performance.
Investigation of Indian English Speech Recognition using CMU Sphinx

In the recent years, research on speech recognition has given much diligence to the automatic transcription of speech data such as broadcast news (BN), medical transcription, etc. Large Vocabulary Continuous Speech Recognition (LVCSR) systems have been developed successfully for Englishes (American English (AE), British English (BE), etc.) and other languages but in case of Indian English (IE), it is still at infancy stage. IE is one of the varieties of English spoken in Indian subcontinent and is largely different from the English spoken in other parts of the world. In this paper, we have presented our work on LVCSR of IE video lectures. The speech data contains video lectures on various engineering subjects given by the experts from all over India as part of the NPTEL project which comprises of 23 hours. We have used CMU Sphinx for training and decoding in our large vocabulary continuous speech recognition experiments. The results analysis instantiate that building IE acoustic model for IE speech recognition is essential due to the fact that it has given 34% less average word error rate (WER) than HUB-4 acoustic models. The average WER before and after adaptation of IE acoustic model is 38% and 31% respectively. Even though, our IE acoustic model is trained with limited training data and the corpora used for building the language models do not mimic the spoken language, the results are promising and comparable to the results reported for AE lecture recognition in the literature.
From Agents to Organizations: An Organizational View of Multi-agent Systems

While multi-agent systems seem to provide a good basis for building complex software systems, this paper points out some of the drawbacks of classical “agent centered” multi-agent systems. To resolve these difficulties we claim that organization centered multi-agent system, or OCMAS for short, may be used. We propose a set of general principles from which true OCMAS may be designed. One of these principles is not to assume anything about the cognitive capabilities of agents. In order to show how OCMAS models may be designed, we propose a very concise and minimal OCMAS model called AGR, for Agent/Group/Role. We propose a set of notations and a methodological framework to help the designer to build MAS using AGR. We then show that it is possible to design multi-agent systems using only OCMAS models.
Trust and Privacy Concern Within Social Networking Sites: A Comparison of Facebook and MySpace

It is not well understood how privacy concern and trust influence social interactions within social networking sites. An online survey of two popular social networking sites, Facebook and MySpace, compared perceptions of trust and privacy concern, along with willingness to share information and develop new relationships. Members of both sites reported similar levels of privacy concern. Facebook members expressed significantly greater trust in both Facebook and its members, and were more willing to share identifying information. Even so, MySpace members reported significantly more experience using the site to meet new people. These results suggest that in online interaction, trust is not as necessary in the building of new relationships as it is in face to face encounters. They also show that in an online site, the existence of trust and the willingness to share information do not automatically translate into new social interaction. This study demonstrates online relationships can develop in sites where perceived trust and privacy safeguards are weak.
Fundamentals on Wireless Communication

The past decade has seen many advances in physical-layer wireless communication theory and their implementation in wireless systems. This textbook takes a unified view of the fundamentals of wireless communication and explains the web of concepts underpinning these advances at a level accessible to an audience with a basic background in probability and digital communication. Topics covered include MIMO (multiple input multiple output) communication, space-time coding, opportunistic communication, OFDM and CDMA. The concepts are illustrated using many examples from wireless systems such as GSM, IS-95 (CDMA), IS-856 (1× EV-DO), Flash OFDM and ArrayComm SDMA systems. Particular emphasis is placed on the interplay between concepts and their implementation in systems. An abundant supply of exercises and figures reinforce the material in the text. This book is intended for use on graduate courses in electrical and computer engineering and will also be of great interest to practicing engineers.