nils-herrmann/clean_scirepeval huggingface dataset:
Antecedents and consequences of mobile phone usability: Linking simplicity and interactivity to satisfaction, trust, and brand loyalty

Usability is a central issue for mobile phone design and service because users need to access various functionalities via limited user interfaces (UI) often while they are on the road. In this paper, we propose simplicity and interactivity as the key determinants of mobile phone usability and assess their significance in an empirical setting. Furthermore, we examine the effects of mobile phone usability on user satisfaction, trust, and brand loyalty and provide a holistic view of the causal relationships between the proposed UI features and important organizational variables for building and maintaining long-term customer relationships. The study was conducted using survey questionnaire data collected from 310 mobile phone users in South Korea. The findings of the study confirm that simplicity and interactivity are two significant determinants of mobile phone usability and that interface simplicity is an important precondition for positive interactivity and usability experience. Our findings also show that usability is a distal determinant of brand loyalty, exerting its influence indirectly through the mediators of satisfaction and trust. We discuss the implications of the study findings for usability research and UI design.  Corresponding author at: Seoul National University, 8203A, Bd. 200, Gwanangno 599, Gwanak-gu, Seoul 151-921, Republic of Korea. Tel.: +82 2 880 4722; fax: +82 2 873 5080. E-mail addresses: dongwon.lee@rhsmith.umd.edu (D. Lee), moonj@snu.ac.kr (J. Moon), yongjkim@sogang.ac.kr (Y.J. Kim), munyi@kaist.ac.kr (M.Y. Yi).
Bankruptcy prediction using neural networks

Prediction of firm bankruptcies have been extensively studied in accounting, as all stakeholders in a firm have a vested interest in monitoring its financial performance. This paper presents an exploratory study which compares the predictive capabilities for firm bankruptcy of neural networks and classical multivariate discriminant analysis. The predictive accuracy of the two techniques is presented within a comprehensive, statistically sound framework, indicating the value added to the forecasting problem by each technique. The study indicates that neural networks perform significantly better than discriminant analysis at predicting firm bankruptcies. Implications of our results for the accounting professional, neural networks researcher and decision support system builders are highlighted.
Tools to support systematic reviews in software engineering: a cross-domain survey using semi-structured interviews

A number of software tools are being developed to support systematic reviewers within the software engineering domain. However, at present, we are not sure which aspects of the review process can most usefully be supported by such tools or what characteristics of the tools are most important to reviewers.  The aim of the study is to explore the scope and practice of tool support for systematic reviewers in other disciplines.  Researchers with experience of performing systematic reviews in Healthcare and the Social Sciences were surveyed. Qualitative data was collected through semi-structured interviews and data analysis followed an inductive approach.  13 interviews were carried out. 21 software tools categorised into one of seven types were identified. Reference managers were the most commonly mentioned tools. Features considered particularly important by participants were support for multiple users, support for data extraction and support for tool maintenance. The features and importance levels identified by participants were compared with those proposed for tools to support systematic reviews in software engineering.  Many problems faced by systematic reviewers in other disciplines are similar to those faced in software engineering. There is general consensus across domains that improved tools are needed.
Text-Adaptive Generative Adversarial Networks: Manipulating Images with Natural Language

This paper addresses the problem of manipulating images using natural language description. Our task aims to semantically modify visual attributes of an object in an image according to the text describing the new visual appearance. Although existing methods synthesize images having new attributes, they do not fully preserve textirrelevant contents of the original image. In this paper, we propose the text-adaptive generative adversarial network (TAGAN) to generate semantically manipulated images while preserving text-irrelevant contents. The key to our method is the text-adaptive discriminator that creates word-level local discriminators according to input text to classify fine-grained attributes independently. With this discriminator, the generator learns to generate images where only regions that correspond to the given text are modified. Experimental results show that our method outperforms existing methods on CUB and Oxford-102 datasets, and our results were mostly preferred on a user study. Extensive analysis shows that our method is able to effectively disentangle visual attributes and produce pleasing outputs.
Adverse Drug Event Detection in Tweets with Semi-Supervised Convolutional Neural Networks

Current Adverse Drug Events (ADE) surveillance systems are often associated with a sizable time lag before such events are published. Online social media such as Twitter could describe adverse drug events in real-time, prior to official reporting. Deep learning has significantly improved text classification performance in recent years and can potentially enhance ADE classification in tweets. However, these models typically require large corpora with human expert-derived labels, and such resources are very expensive to generate and are hardly available. Semi-supervised deep learning models, which offer a plausible alternative to fully supervised models, involve the use of a small set of labeled data and a relatively larger collection of unlabeled data for training. Traditionally, these models are trained on labeled and unlabeled data from similar topics or domains. In reality, millions of tweets generated daily often focus on disparate topics, and this could present a challenge for building deep learning models for ADE classification with random Twitter stream as unlabeled training data. In this work, we build several semi-supervised convolutional neural network (CNN) models for ADE classification in tweets, specifically leveraging different types of unlabeled data in developing the models to address the problem. We demonstrate that, with the selective use of a variety of unlabeled data, our semi-supervised CNN models outperform a strong state-ofthe-art supervised classification model by +9.9% F1-score. We evaluated our models on the Twitter data set used in the PSB 2016 Social Media Shared Task. Our results present the new state-of-the-art for this data set.
2-D antenna array geometries for MIMO radar imaging by Digital Beamforming

In general, two-dimensional radar imaging applications, which require angular information in azimuth and elevation, use full planar antenna arrays. If orthogonally arranged transmit and receive arrays are used instead, the angular resolution equals that of a radar system using only a single antenna on the transmit and a full antenna array on the receiver side or vice-versa. This paper evaluates the influence of different antenna array configurations on the angular resolution capability by means of a 24GHz Digital Beamforming radar system. Besides the so-called inverted T-shaped antenna arrangement, various array geometries are discussed. Compared to the full planar antenna array the introduced geometries provide better angular resolution, while allowing for a highly reduced number of antennas, saving space and cost. Each presented antenna array is realized in hardware and the angular resolution capability is evaluated by means of a 24 GHz Digital Beamforming radar system.
Use of Network Latency Profiling and Redundancy for Cloud Server Selection

As servers are placed in diverse locations in networked services today, it becomes vital to direct a client's request to the best server(s) to achieve both high performance and reliability. In this distributed setting, non-negligible latency and server availability become two major concerns, especially for highly-interactive applications. Profiling latencies and sending redundant data have been investigated as solutions to these issues. The notion of a cloudlet in mobile-cloud computing is also relevant in this context, as the cloudlet can supply these solution approaches on behalf of the mobile. In this paper, we investigate the effects of profiling and redundancy on latency when a client has a choice of multiple servers to connect to, using measurements from real experiments and simulations. We devise and test different server selection and data partitioning strategies in terms of profiling and redundancy. Our key findings are summarized as follows. First, intelligent server selection algorithms help find the optimal group of servers that minimize latency with profiling. Second, we can achieve good performance with relatively simple approaches using redundancy. Our analysis of profiling and redundancy provides insight to help designers determine how many servers and which servers to select to reduce latency.
Network attack detection at flow level

In this paper, we propose a new method for detecting unauthorized network intrusions, based on a traffic flow model and Cisco NetFlow protocol application. The method developed allows us not only to detect the most common types of network attack (DDoS and port scanning), but also to make a list of trespassers’ IP-addresses. Therefore, this method can be applied in intrusion detection systems, and in those systems which lock these IP-addresses.
The neurobiology of Meditation and its clinical effectiveness in psychiatric disorders

This paper reviews the evidence for changes of Meditation on body and brain physiology and for clinical effectiveness in disorders of psychiatry. The aim of Meditation is to reduce or eliminate irrelevant thought processes through training of internalised attention, thought to lead to physical and mental relaxation, stress reduction, psycho-emotional stability and enhanced concentration. Physiological evidence shows a reduction with Meditation of stress-related autonomic and endocrine measures, while neuroimaging studies demonstrate the functional up-regulation of brain regions of affect regulation and attention control. Clinical studies show some evidence for the effectiveness of Meditation in disorders of affect, anxiety and attention. The combined evidence from neurobiological and clinical studies seems promising. However, a more thorough understanding of the neurobiological mechanisms of action and clinical effectiveness of the different Meditative practices is needed before Meditative practices can be leveraged in the prevention and intervention of mental illness.
Empirical Study of Unsupervised Chinese Word Segmentation Methods for SMT on Large-scale Corpora

Unsupervised word segmentation (UWS) can provide domain-adaptive segmentation for statistical machine translation (SMT) without annotated data, and bilingual UWS can even optimize segmentation for alignment. Monolingual UWS approaches of explicitly modeling the probabilities of words through Dirichlet process (DP) models or Pitman-Yor process (PYP) models have achieved high accuracy, but their bilingual counterparts have only been carried out on small corpora such as basic travel expression corpus (BTEC) due to the computational complexity. This paper proposes an efficient unified PYP-based monolingual and bilingual UWS method. Experimental results show that the proposed method is comparable to supervised segmenters on the in-domain NIST OpenMT corpus, and yields a 0.96 BLEU relative increase on NTCIR PatentMT corpus which is out-of-domain.
On the effects of using word2vec representations in neural networks for dialogue act recognition

Dialogue act recognition is an important component of a large number of natural language processing pipelines. Many research works have been carried out in this area, but relatively few investigate deep neural networks and word embeddings. This is surprising, given that both of these techniques have proven exceptionally good in most other language-related domains. We propose in this work a new deep neural network that explores recurrent models to capture word sequences within sentences, and further study the impact of pretrained word embeddings. We validate this model on three languages: English, French and Czech. The performance of the proposed approach is consistent across these languages and it is comparable to the state-of-the-art results in English. More importantly, we confirm that deep neural networks indeed outperform a Maximum Entropy classifier, which was expected. However, and this is more surprising, we also found that standard word2vec embeddings do not seem to bring valuable information for this task and the proposed model, whatever the size of the training corpus is. We thus further analyse the resulting embeddings and conclude that a possible explanation may be related to the mismatch between the type of lexical-semantic information captured by the word2vec embeddings, and the kind of relations Preprint submitted to Computer Speech and Language August 20, 2017 between words that is the most useful for the dialogue act recognition task.
Knowledge Extraction from Structured Engineering Drawings

As a typical type of structured documents, table drawings are widely used in engineering fields. Knowledge extraction of such structured documents plays an important role in automatic interpretation systems. In this paper, we propose a new knowledge extraction method based on automatically analyzing drawing layout and extracting physical or logical structures from the given engineering table drawings. Then based on the automatic interpretation results, we further propose normalization method to integrate varied types of engineering tables with other engineering drawings and extract implied domain knowledge.
Omni-drive Robot Motion on Curved Paths: The Fastest Path between Two Points Is Not a Straight-Line

Omni-drive systems operate by having individual wheels apply torque in one direction in the same way as a regular wheel, but are able to slide freely in another direction (often perpendicular to the torque vector). The key advantage of omni-drive systems is that translational and rotational motion are decoupled for simple motion. However, in considering the fastest possible motion this is not necessarily the case. In this paper, we review all the current popular designs of omni-drive transport systems, and compare them in terms of practical and theoretical considerations. We then present a kinematic analysis that applies to two major omni-drive robot vehicles classes, for any number of wheels. Finally, we show that for three-wheeled omni-drive transport systems and certain ranges of trajectories and starting conditions, a curved path can be traversed faster than a straight-line path, we confirm this result experimentally.
Recognizing Human Activities from Raw Accelerometer Data Using Deep Neural Networks

Activity recognition from wearable sensor data has been researched for many years. Previous works usually extracted features manually, which were hand-designed by the researchers, and then were fed into the classifiers as the inputs. Due to the blindness of manually extracted features, it was hard to choose suitable features for the specific classification task. Besides, this heuristic method for feature extraction could not generalize across different application domains, because different application domains needed to extract different features for classification. There was also work that used auto-encoders to learn features automatically and then fed the features into the K-nearest neighbor classifier. However, these features were learned in an unsupervised manner without using the information of the labels, thus might not be related to the specific classification task. In this paper, we recommend deep neural networks (DNNs) for activity recognition, which can automatically learn suitable features. DNNs overcome the blindness of hand-designed features and make use of the precious label information to improve activity recognition performance. We did experiments on three publicly available datasets for activity recognition and compared deep neural networks with traditional methods, including those that extracted features manually and auto-encoders followed by a K-nearest neighbor classifier. The results showed that deep neural networks could generalize across different application domains and got higher accuracy than traditional methods.
Inside the adaptive enterprise: an information technology capabilities perspective on business process agility

Recent innovations in utility computing, web services, and service-oriented architectures, combined with a growing array of IT skills, have improved firms’ ability to be more agile in responding to change. Using the resource-based view of the firm, research suggests that IT resources, in isolation, are unlikely to yield superior performance and so as firms try to boost their agility, the question becomes how to configure IT resources to prepare for, or react to, change. In this paper, we posit that managerial IT capabilities based on IT-business partnerships, strategic planning, and ex-post IT project analysis lead to the development of technical IT capabilities associated with a flexible IT infrastructure which in turn drives agility or a firm’s ability to react to changes in its products or markets. Using data from matched surveys of IT and business executives in 241 firms, we find that managerial and technical capabilities affect agility. In further testing our model for firms in stable settings, we find that technical IT capabilities are more important for agility than managerial IT capabilities, whereas in volatile settings, the opposite is true. Accordingly, for firms in volatile markets, effective models of IT governance are of vital importance for agility or adaptiveness.
Model-Driven Approach for Body Area Network Application Development

This paper introduces the sensor-networked IoT model as a prototype to support the design of Body Area Network (BAN) applications for healthcare. Using the model, we analyze the synergistic effect of the functional requirements (data collection from the human body and transferring it to the top level) and non-functional requirements (trade-offs between energy-security-environmental factors, treated as Quality-of-Service (QoS)). We use feature models to represent the requirements at the earliest stage for the analysis and describe a model-driven methodology to design the possible BAN applications. Firstly, we specify the requirements as the problem domain (PD) variability model for the BAN applications. Next, we introduce the generative technology (meta-programming as the solution domain (SD)) and the mapping procedure to map the PD feature-based variability model onto the SD feature model. Finally, we create an executable meta-specification that represents the BAN functionality to describe the variability of the problem domain though transformations. The meta-specification (along with the meta-language processor) is a software generator for multiple BAN-oriented applications. We validate the methodology with experiments and a case study to generate a family of programs for the BAN sensor controllers. This enables to obtain the adequate measure of QoS efficiently through the interactive adjustment of the meta-parameter values and re-generation process for the concrete BAN application.
A 25-kHz 3rd-order continuous-time Delta-Sigma modulator using tri-level quantizer

This paper presents a 3rd order continuous-time Delta-Sigma modulator with a tri-level quantizer, which provides 3-dB reduction of quantization noise without dynamic element matching (DEM). The tri-level DAC linearity is analyzed and it shows that a highly linear tri-level DAC can be realized in fully-differential active-RC Delta-Sigma modulator. The performance of the tri-level continuous-time Delta-Sigma modulator has been verified through simulations using a standard 0.18-μm CMOS process. It achieves 81-dB SNDR at 3.2-MS/s sampling rate and consumes 1.14-μW power with ideal amplifier.
Goubau-Line Leaky-Wave Antenna for Wide-Angle Beam Scanning From Backfire to Endfire

A Goubau-line leaky-wave antenna (LWA) with a large scanning angle is presented in this letter. In contrast to the conventional Goubau-line leaky wave with a small scanning angle range, this letter employed a periodically bending Goubau line, which not only brings in a periodic perturbation for leaky-wave radiation, but also enhances the scanning range due to the increased delay for each line element. The simulation and experimental results show that the proposed LWA provides $90\%$ radiation efficiency and 7–10 dBi radiation gain from backfire to endfire through broadside as frequency changes. The proposed antenna features good radiation performance and has a compact and low-profile configuration.
Stemming Algorithms: A Comparative Study and their Analysis

Stemming is an approach used to reduce a word to its stem or root form and is used widely in information retrieval tasks to increase the recall rate and give us most relevant results. There are number of ways to perform stemming ranging from manual to automatic methods, from language specific to language independent each having its own advantage over the other. This paper represents a comparative study of various available stemming alternatives widely used to enhance the effectiveness and efficiency of information retrieval.
DeepPointSet : A Point Set Generation Network for 3 D Object Reconstruction from a Single Image Supplementary Material

We conducted human study to provide reference to our current CD and EMD values reported on the rendered dataset. We provided the human subject with a GUI tool to create a triangular mesh from the image. The tool (see Fig 1) enables the user to edit the mesh in 3D and to align the modeled object back to the input image. In total 16 models are created from the input images of our validation set. N = 1024 points are sampled from each model.
TReMAP: Automatic 3D Neuron Reconstruction Based on Tracing, Reverse Mapping and Assembling of 2D Projections

Efficient and accurate digital reconstruction of neurons from large-scale 3D microscopic images remains a challenge in neuroscience. We propose a new automatic 3D neuron reconstruction algorithm, TReMAP, which utilizes 3D Virtual Finger (a reverse-mapping technique) to detect 3D neuron structures based on tracing results on 2D projection planes. Our fully automatic tracing strategy achieves close performance with the state-of-the-art neuron tracing algorithms, with the crucial advantage of efficient computation (much less memory consumption and parallel computation) for large-scale images.