nils-herrmann/clean_scirepeval huggingface dataset:
Data acquisition and control using Arduino-Android platform: Smart plug

In this work, we propose to develop a smart plug, an energy monitoring system that provides real time update of the energy consumption at the device level. The proposed device is using an arduino microcontroller board, an ENC28J60 ethernet module, and a current transformer sensor. The method of current sensing employed is non-invasive type. The User Interface of the device is to be developed in android and the data will be uploaded onto a server using the ethernet connection. The final output will be a smart plug that can monitor a remote device using arduino-android platform.
Adapting taggers to Twitter with not-so-distant supervision

We experiment with using different sources of distant supervision to guide unsupervised and semi-supervised adaptation of part-of-speech (POS) and named entity taggers (NER) to Twitter. We show that a particularly good source of not-so-distant supervision is linked websites. Specifically, with this source of supervision we are able to improve over the state-of-the-art for Twitter POS tagging (89.76% accuracy, 8% error reduction) and NER (F1=79.4%, 10% error reduction).
A structural approach to selection bias.

The term "selection bias" encompasses various biases in epidemiology. We describe examples of selection bias in case-control studies (eg, inappropriate selection of controls) and cohort studies (eg, informative censoring). We argue that the causal structure underlying the bias in each example is essentially the same: conditioning on a common effect of 2 variables, one of which is either exposure or a cause of exposure and the other is either the outcome or a cause of the outcome. This structure is shared by other biases (eg, adjustment for variables affected by prior exposure). A structural classification of bias distinguishes between biases resulting from conditioning on common effects ("selection bias") and those resulting from the existence of common causes of exposure and outcome ("confounding"). This classification also leads to a unified approach to adjust for selection bias.
Investor Overconfidence And Trading Volume : The Case Of An Emergent Market

It has been a challenge for financial economists to explain some stylized facts observed in securities markets, among them, high levels of trading volume. The most prominent explanation of excess volume is overconfidence. High market returns make investors overconfident and as a consequence, these investors trade more subsequently. The aim of our paper is to study the impact of the phenomenon of overconfidence on the trading volume and its role in the formation of the excess volume on the Tunisian stock market. Based on the work of Statman, Thorley and Vorkink (2006) and by using VAR models and impulse response functions, we find little evidence of the overconfidence hypothesis when we use volume (shares traded) as proxy of trading volume.
EtherNet / IP : Industrial Protocol White Paper

DeviceNetTM and ControlNetTM are two well-known industrial networks based on the CIP protocol (CIP = Control an Information Protocol). Both networks have been developed by Rockwell Automation, but are now owned and maintained by the two manufacturer's organizations ODVA (Open DeviceNet Vendors Association) and ControlNet International. ODVA and ControlNet International have recently introduced the newest member of this family – EtherNet/IP ("IP" stands for "Industrial Protocol"). This paper describes the techniques and mechanisms that are used to implement a fully consistent set of services and data objects on a TCP/UDP/IP based Ethernet® network.
Adaptive Distance Estimation and Localization in WSN using RSSI Measures

Localization is one of the most challenging and important issues in wireless sensor networks (WSNs), especially if cost-effective approaches are demanded. In this paper, we present intensively discuss and analyze approaches relying on the received signal strength indicator (RSSI). The advantage of employing the RSSI values is that no extra hardware (e.g. ultrasonic or infra-red) is needed for network-centric localization. We studied different factors that affect the measured RSSI values. Finally, we evaluate two methods to estimate the distance; the first approach is based on statistical methods. For the second one, we use an artificial neural network to estimate the distance.
Strategic development and SWOT analysis at the University of Warwick

SWOT analysis is an established method for assisting the formulation of strategy. An application to strategy formulation and its incorporation into the strategic development process at the University of Warwick is described. The application links SWOT analysis to resource-based planning, illustrates it as an iterative rather than a linear process and embeds it within the overall planning process. Lessons are drawn both for the University and for the strategy formulation process itself.
A review of feature selection methods on synthetic data

With the advent of high dimensionality, adequate identification of relevant features of the data has become indispensable in real-world scenarios. In this context, the importance of feature selection is beyond doubt and different methods have been developed. However, with such a vast body of algorithms available, choosing the adequate feature selection method is not an easy-to-solve question and it is necessary to check their effectiveness on different situations. Nevertheless, the assessment of relevant features is difficult in real datasets and so an interesting option is to use artificial data. In this paper, several synthetic datasets are employed for this purpose, aiming at reviewing the performance of feature selection methods in the presence of a crescent number or irrelevant features, noise in the data, redundancy and interaction between attributes, as well as a small ratio between number of samples and number of features. Seven filters, two embedded methods, and two wrappers are applied over eleven synthetic datasets, tested by four classifiers, so as to be able to choose a robust method, paving the way for its application to real datasets.
Author gender identification from text

Text is still the most prevalent Internet media type. Examples of this include popular social networking applications such as Twitter, Craigslist, Facebook, etc. Other web applications such as e-mail, blog, chat rooms, etc. are also mostly text based. A question we address in this paper that deals with text based Internet forensics is the following: given a short text document, can we identify if the author is a man or a woman? This question is motivated by recent events where people faked their gender on the Internet. Note that this is different from the authorship attribution problem. In this paper we investigate author gender identification for short length, multi-genre, content-free text, such as the ones found in many Internet applications. Fundamental questions we ask are: do men and women inherently use different classes of language styles? If this is true, what are good linguistic features that indicate gender? Based on research in human psychology, we propose 545 psycho-linguistic and gender-preferential cues along with stylometric features to build the feature space for this identification problem. Note that identifying the correct set of features that indicate gender is an open research problem. Three machine learning algorithms (support vector machine, Bayesian logistic regression and AdaBoost decision tree) are then designed for gender identification based on the proposed features. Extensive experiments on large text corpora (Reuters Corpus Volume 1 newsgroup data and Enron e-mail data) indicate an accuracy up to 85.1% in identifying the gender. Experiments also indicate that function words, word-based features and structural features are significant gender discriminators. a 2011
Walking in a Cube: Novel Metaphors for Safely Navigating Large Virtual Environments in Restricted Real Workspaces

Immersive spaces such as 4-sided displays with stereo viewing and high-quality tracking provide a very engaging and realistic virtual experience. However, walking is inherently limited by the restricted physical space, both due to the screens (limited translation) and the missing back screen (limited rotation). In this paper, we propose three novel locomotion techniques that have three concurrent goals: keep the user safe from reaching the translational and rotational boundaries; increase the amount of real walking and finally, provide a more enjoyable and ecological interaction paradigm compared to traditional controller-based approaches. We notably introduce the "Virtual Companion", which uses a small bird to guide the user through VEs larger than the physical space. We evaluate the three new techniques through a user study with travel-to-target and path following tasks. The study provides insight into the relative strengths of each new technique for the three aforementioned goals. Specifically, if speed and accuracy are paramount, traditional controller interfaces augmented with our novel warning techniques may be more appropriate; if physical walking is more important, two of our paradigms (extended Magic Barrier Tape and Constrained Wand) should be preferred; last, fun and ecological criteria would favor the Virtual Companion.
iBFS: Concurrent Breadth-First Search on GPUs

Breadth-First Search (BFS) is a key graph algorithm with many important applications. In this work, we focus on a special class of graph traversal algorithm - concurrent BFS - where multiple breadth-first traversals are performed simultaneously on the same graph. We have designed and developed a new approach called iBFS that is able to run i concurrent BFSes from i distinct source vertices, very efficiently on Graphics Processing Units (GPUs). iBFS consists of three novel designs. First, iBFS develops a single GPU kernel for joint traversal of concurrent BFS to take advantage of shared frontiers across different instances. Second, outdegree-based GroupBy rules enables iBFS to selectively run a group of BFS instances which further maximizes the frontier sharing within such a group. Third, iBFS brings additional performance benefit by utilizing highly optimized bitwise operations on GPUs, which allows a single GPU thread to inspect a vertex for concurrent BFS instances. The evaluation on a wide spectrum of graph benchmarks shows that iBFS on one GPU runs up to 30x faster than executing BFS instances sequentially, and on 112 GPUs achieves near linear speedup with the maximum performance of 57,267 billion traversed edges per second (TEPS).
Dynamic arm swinging in human walking.

Humans tend to swing their arms when they walk, a curious behaviour since the arms play no obvious role in bipedal gait. It might be costly to use muscles to swing the arms, and it is unclear whether potential benefits elsewhere in the body would justify such costs. To examine these costs and benefits, we developed a passive dynamic walking model with free-swinging arms. Even with no torques driving the arms or legs, the model produced walking gaits with arm swinging similar to humans. Passive gaits with arm phasing opposite to normal were also found, but these induced a much greater reaction moment from the ground, which could require muscular effort in humans. We therefore hypothesized that the reduction of this moment may explain the physiological benefit of arm swinging. Experimental measurements of humans (n = 10) showed that normal arm swinging required minimal shoulder torque, while volitionally holding the arms still required 12 per cent more metabolic energy. Among measures of gait mechanics, vertical ground reaction moment was most affected by arm swinging and increased by 63 per cent without it. Walking with opposite-to-normal arm phasing required minimal shoulder effort but magnified the ground reaction moment, causing metabolic rate to increase by 26 per cent. Passive dynamics appear to make arm swinging easy, while indirect benefits from reduced vertical moments make it worthwhile overall.
Resolving Referring Expressions in Conversational Dialogs for Natural User Interfaces

Unlike traditional over-the-phone spoken dialog systems (SDSs), modern dialog systems tend to have visual rendering on the device screen as an additional modality to communicate the system’s response to the user. Visual display of the system’s response not only changes human behavior when interacting with devices, but also creates new research areas in SDSs. Onscreen item identification and resolution in utterances is one critical problem to achieve a natural and accurate humanmachine communication. We pose the problem as a classification task to correctly identify intended on-screen item(s) from user utterances. Using syntactic, semantic as well as context features from the display screen, our model can resolve different types of referring expressions with up to 90% accuracy. In the experiments we also show that the proposed model is robust to domain and screen layout changes.
Planning for Conjunctive Goals

The problem of achieving COnlUnCtlve goals has been central to domain-independent planning research, the nonhnear constraint-posting approach has been most successful Previous planners of this type have been comphcated, heurtstw, and ill-defined 1 have combmed and dtstdled the state of the art into a simple, precise, Implemented algorithm (TWEAK) which I have proved correct and complete 1 analyze previous work on domam-mdependent conlunctwe plannmg; tn retrospect tt becomes clear that all conluncttve planners, hnear and nonhnear, work the same way The efficiency and correctness of these planners depends on the traditional add/ delete-hst representation for actions, which drastically limits their usefulness I present theorems that suggest that efficient general purpose planning with more expressive action representations ts impossible, and suggest ways to avoid this problem
Automated Correction for Syntax Errors in Programming Assignments using Recurrent Neural Networks

We present a technique for providing feedback on syntax errors that uses Recurrent neural networks (RNNs) to model syntactically valid token sequences. Syntax errors constitute one of the largest classes of errors (34%) in our dataset of student submissions obtained from a MOOC course on edX. For a given programming assignment, we first learn an RNN to model all valid token sequences using the set of syntactically correct submissions. Then, for a student submission with syntax errors, we query the learnt RNN model with the prefix token sequence to predict token sequences that can fix the error by either replacing or inserting the predicted token sequence at the error location. We evaluate our technique on over 14, 000 student submissions with syntax errors.
Human activity recognition using smartphone sensors with two-stage continuous hidden Markov models

Recognizing human activities from temporal streams of sensory data observations is a very important task on a wide variety of applications in context recognition. Especially for time-series sensory data, a method that takes into account the inherent sequential characteristics of the data is needed. Moreover, activities are hierarchical in nature, in as much that complex activities can be decomposed to a number of simpler ones. In this paper, we propose a two-stage continuous hidden Markov model (CHMM) approach for the task of activity recognition using accelerometer and gyroscope sensory data gathered from a smartphone. The proposed method consists of first-level CHMMs for coarse classification, which separates stationary and moving activities, and second-level CHMMs for fine classification, which classifies the data into their corresponding activity classes. Random Forests (RF) variable importance measures are exploited to determine the optimal feature subsets for both coarse and fine classification. Experiments show that with the use of a significantly reduced number of features, the proposed method shows competitive performance in comparison to other classification algorithms, achieving an over-all accuracy of 91.76%.
Software Defined Wireless Networks: Unbridling SDNs

The software defined networking (SDN) paradigm promises to dramatically simplify network configuration and resource management. Such features are extremely valuable to network operators and therefore, the industrial (besides the academic) research and development community is paying increasing attention to SDN. Although wireless equipment manufacturers are increasing their involvement in SDN-related activities, to date there is not a clear and comprehensive understanding of what are the opportunities offered by SDN in most common networking scenarios involving wireless infrastructureless communications and how SDN concepts should be adapted to suit the characteristics of wireless and mobile communications. This paper is a first attempt to fill this gap as it aims at analyzing how SDN can be beneficial in wireless infrastructureless networking environments with special emphasis on wireless personal area networks (WPAN). Furthermore, a possible approach (called SDWN) for such environments is presented and some design guidelines are provided.
Trilinear Tensor: The Fundamental Construct of Multiple-view Geometry and Its Applications

The topic of representation, recovery and manipulation of three-dimensional (3D) scenes from two-dimensional (2D) images thereof, provides a fertile ground for both intellectual theoretically inclined questions related to the algebra and geometry of the problem and to practical applications such as Visual Recognition, Animation and View Synthesis, recovery of scene structure and camera ego-motion, object detection and tracking, multi-sensor alignment, etc. The basic materials have been known since the turn of the century, but the full scope of the problem has been under intensive study since 1992, rst on the algebra of two views and then on the algebra of multiple views leading to a relatively mature understanding of what is known as \multilinear matching constraints", and the \trilinear tensor" of three or more views. The purpose of this paper is, rst and foremost, to provide a coherent framework for expressing the ideas behind the analysis of multiple views. Secondly, to integrate the various incremental results that have appeared on the subject into one coherent manuscript.
Quaternion Recurrent Neural Networks

Recurrent neural networks (RNNs) are powerful architectures to model sequential data, due to their capability to learn short and long-term dependencies between the basic elements of a sequence. Nonetheless, popular tasks such as speech or images recognition, involve multi-dimensional input features that are characterized by strong internal dependencies between the dimensions of the input vector. We propose a novel quaternion recurrent neural network (QRNN), alongside with a quaternion long-short term memory neural network (QLSTM), that take into account both the external relations and these internal structural dependencies with the quaternion algebra. Similarly to capsules, quaternions allow the QRNN to code internal dependencies by composing and processing multidimensional features as single entities, while the recurrent operation reveals correlations between the elements composing the sequence. We show that both QRNN and QLSTM achieve better performances than RNN and LSTM in a realistic application of automatic speech recognition. Finally, we show that QRNN and QLSTM reduce by a maximum factor of 3.3x the number of free parameters needed, compared to real-valued RNNs and LSTMs to reach better results, leading to a more compact representation of the relevant information.
Biodiesel production from heterotrophic microalgal oil.

The present study introduced an integrated method for the production of biodiesel from microalgal oil. Heterotrophic growth of Chlorella protothecoides resulted in the accumulation of high lipid content (55%) in cells. Large amount of microalgal oil was efficiently extracted from these heterotrophic cells by using n-hexane. Biodiesel comparable to conventional diesel was obtained from heterotrophic microalgal oil by acidic transesterification. The best process combination was 100% catalyst quantity (based on oil weight) with 56:1 molar ratio of methanol to oil at temperature of 30 degrees C, which reduced product specific gravity from an initial value of 0.912 to a final value of 0.8637 in about 4h of reaction time. The results suggested that the new process, which combined bioengineering and transesterification, was a feasible and effective method for the production of high quality biodiesel from microalgal oil.
What’s So Different about Blockchain? — Blockchain is a Probabilistic State Machine

Blockchain is a distributed timestamp server technology introduced for realization of Bitcoin, a digital cash system. It has been attracting much attention especially in the areas of financial and legal applications. But such applications would fail if they are designed without knowledge of the fundamental differences in blockchain from existing technology. We show that blockchain is a probabilistic state machine in which participants can never commit on decisions, we also show that this probabilistic nature is necessarily deduced from the condition where the number of participants remains unknown. This work provides useful abstractions to think about blockchain, and raises discussion for promoting the better use of the technology.
Emotional intelligence and social interaction.

Two studies found positive relationships between the ability to manage emotions and the quality of social interactions, supporting the predictive and incremental validity of an ability measure of emotional intelligence, the Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT). In a sample of 118 American college students (Study 1), higher scores on the managing emotions subscale of the MSCEIT were positively related to the quality of interactions with friends, evaluated separately by participants and two friends. In a diary study of social interaction with 103 German college students (Study 2), managing emotions scores were positively related to the perceived quality of interactions with opposite sex individuals. Scores on this subscale were also positively related to perceived success in impression management in social interactions with individuals of the opposite sex. In both studies, the main findings remained statistically significant after controlling for Big Five personality traits.
Comparisons of 6LoWPAN Implementations on Wireless Sensor Networks

This paper introduces our work on the communication stack of wireless sensor networks. We present the IPv6 approach for wireless sensor networks called 6LoWPAN in its IETF charter. We then compare the different implementations of 6LoWPAN subsets for several sensor nodes platforms. We present our approach for the 6LoWPAN implementation which aims to preserve the advantages of modularity while keeping a small memory footprint and a good efficiency.
Amazon dynamoDB: a seamlessly scalable non-relational database service

Reliability and scalability of an application is dependent on how its application state is managed. To run applications at massive scale requires one to operate datastores that can scale to operate seamlessly across thousands of servers and can deal with various failure modes such as server failures, datacenter failures and network partitions. The goal of Amazon DynamoDB is to eliminate this complexity and operational overhead for our customers by offering a seamlessly scalable database service. In this talk, I will talk about how developers can build applications on DynamoDB without having to deal with the complexity of operating a large scale database.