nils-herrmann/clean_scirepeval huggingface dataset:
Needs, affect, and interactive products - Facets of user experience

Subsumed under the umbrella of User Experience (UX), practitioners and academics of Human–Computer Interaction look for ways to broaden their understanding of what constitutes ‘‘pleasurable experiences” with technology. The present study considered the fulfilment of universal psychological needs, such as competence, relatedness, popularity, stimulation, meaning, security, or autonomy, to be the major source of positive experience with interactive technologies. To explore this, we collected over 500 positive experiences with interactive products (e.g., mobile phones, computers). As expected, we found a clear relationship between need fulfilment and positive affect, with stimulation, relatedness, competence and popularity being especially salient needs. Experiences could be further categorized by the primary need they fulfil, with apparent qualitative differences among some of the categories in terms of the emotions involved. Need fulfilment was clearly linked to hedonic quality perceptions, but not as strongly to pragmatic quality (i.e., perceived usability), which supports the notion of hedonic quality as ‘‘motivator” and pragmatic quality as ‘‘hygiene factor.” Whether hedonic quality ratings reflected need fulfilment depended on the belief that the product was responsible for the experience (i.e., attribution).
Increasing the Capacity of Magnetic Induction Communication Using MIMO Coil-Array

Magnetic Induction (MI) is a promising solution for wireless communications in complex environments, such as oil reservoir, underground, indoor, and underwater. Due to the inherent electrically small size and simple structure of the magnetic coil antenna, it suffers from low radiation efficiency, extremely narrow bandwidth, and dramatic polarization loss. Recently, significant efforts have been made to increase MI communication's performance, for instance, the metamaterial- enhanced magnetic induction has been developed to increase the signal strength, the tri-directional coil antenna is used to overcome the polarization loss, among others. However, increasing the bandwidth of MI communication is still a great challenge. In this paper, we propose a MIMO antenna array with each antenna element having its own resonant frequency. Due to the strong coupling in the deep near field, the antenna elements mutually affect each other. Instead of eliminating this coupling, we rigorously derive a model to capture it and use metamaterial-inspired method to find the optimal configuration of the antenna array to achieve multiple bands. As a result, the overall bandwidth can be significantly increased. Meanwhile, the enhanced wireless channel can provide much better signal strength. Finally, we theoretically analyze the channel gain in comparison with the original MI to demonstrate the benefits of MI MIMO.
Understanding consumers' continuance intention towards mobile purchase: A theoretical framework and empirical study - A case of China

Although mobile purchase is convenient in the age of mobile commerce, many consumers still do not utilize mobile purchase to its full potential. From the mobile vendor’s perspective, retaining current customers and facilitating their continued purchase are crucial to create profitability and achieve a sustainable development. An understanding of the continuance intention towards mobile purchase can provide insights into mobile vendors’ marketing campaigns. Therefore, it is important to examine the determinants that impact continuance intentions of consumers for mobile purchase. Drawing upon information success model, flow theory and trust, this study proposed and empirically tested an integrated model to better understand the determinants of consumers’ continued intention to purchase on mobile sites. Empirical data from 462 users who had experience with mobile purchase were tested against the proposed research model by using structural equation modelling (SEM). The results indicated that information quality, and privacy and security concerns are the main factors affecting trust, whereas service quality is the main factor affecting flow. System quality, and privacy and security concerns affect satisfaction. Trust affects flow, which in turn affects satisfaction. These three factors together affect continued intention towards mobile purchase. The findings of this study provide several important implications for mobile commerce research and practice.
Automatic student plagiarism detection : future perspectives

The availability and use of computers in teaching has seen an increase in the rate of plagiarism among students because of the wide availability of electronic texts online. While computer tools that have appeared in recent years are capable of detecting simple forms of plagiarism, such as copy-paste, a number of recent research studies devoted to evaluation and comparison of plagiarism detection tools revealed that these contain limitations in detecting complex forms of plagiarism such as extensive paraphrasing and use of technical tricks, such as replacing original characters with similar-looking characters from foreign alphabets. This article investigates limitations in automatic detection of student plagiarism and proposes ways on how these issues could be tackled in future systems by applying various natural language processing and information retrieval technologies. A classification of types of plagiarism is presented, and an analysis is provided of the most promising technologies that have the potential of dealing with the limitations of current state-of-the-art systems. Furthermore, the article concludes with a discussion on legal and ethical issues related to the use of plagiarism detection software. The article, hence, provides a “roadmap” for developing the next generation of plagiarism detection systems.
Robust Trajectory Clustering for Motion Segmentation

Due to occlusions and objects' non-rigid deformation in the scene, the obtained motion trajectories from common trackers may contain a number of missing or mis-associated entries. To cluster such corrupted point based trajectories into multiple motions is still a hard problem. In this paper, we present an approach that exploits temporal and spatial characteristics from tracked points to facilitate segmentation of incomplete and corrupted trajectories, thereby obtain highly robust results against severe data missing and noises. Our method first uses the Discrete Cosine Transform (DCT) bases as a temporal smoothness constraint on trajectory projection to ensure the validity of resulting components to repair pathological trajectories. Then, based on an observation that the trajectories of foreground and background in a scene may have different spatial distributions, we propose a two-stage clustering strategy that first performs foreground-background separation then segments remaining foreground trajectories. We show that, with this new clustering strategy, sequences with complex motions can be accurately segmented by even using a simple translational model. Finally, a series of experiments on Hopkins 155 dataset and Berkeley motion segmentation dataset show the advantage of our method over other state-of-the-art motion segmentation algorithms in terms of both effectiveness and robustness.
A Novel Variable Reluctance Resolver for HEV/EV Applications

In order to simplify the manufacturing process of variable reluctance (VR) resolvers for hybrid electric vehicle/electric vehicle (HEV/EV) applications, a novel VR resolver with nonoverlapping tooth-coil windings is proposed in this paper. A comparison of the winding configurations is first carried out between the existing and the proposed designs, followed by the description of the operating principle. Furthermore, the influence of actual application conditions is investigated by finite-element (FE) analyses, including operating speed and assembling eccentricity. In addition, identical stator and windings of the novel design can be employed in three resolvers of different rotor saliencies. The voltage difference among the three rotor combinations, as well as the detecting accuracy, is further investigated. Finally, prototypes are fabricated and tested to verify the analyses.
A novel directional routing scheme for flying ad-hoc networks

Current mobile ad-hoc routing protocols are not suitable for flying ad-hoc network, because of the dynamic environmental conditions, high mobility and terrain structures. In this paper, we propose a novel directional routing scheme for flying ad-hoc network using combined omnidirectional and directional transmissions. The proposed scheme features hybrid use of unicasting and geocasting routing using location and trajectory information. Moreover, our proposed scheme increases the route setup success rate and average path lifetime compared to conventional AODV protocol.
PGS: Parking Guidance System based on wireless sensor network

In this paper, we present PGS, a parking guidance system based on wireless sensor network(WSN) which guides a driver to an available parking lot. The system consists of a WSN-based VDS(vehicle detection sub-system) and a management sub-system. The WSN based VDS gathers information on the availability of each parking lot and the management sub-system processes the information and refines them and guides the driver to the available parking lot by controlling a VMS(Variable Messaging System). The paper describes the overall system architecture of PGS from the hardware platform to the application software in the view point of a WSN. We implemented the WSN based VDS of PGS and experimented on the system with several kinds of cars. The experimental results show that PGS succeeds in detecting various kinds of cars and the predicted battery life-time using measured current profiles is over 5 years.
A Low Phase Noise Quadrature Injection Locked Frequency Synthesizer for MM-Wave Applications

This paper proposes a 60 GHz quadrature PLL frequency synthesizer for the IEEE802.15.3c with wide tuning range and low phase noise. The synthesizer is constructed using a 20 GHz PLL that is coupled with a Quadrature Injection Locked Oscillator (QILO) as a frequency tripler to generate the 60 GHz signal. The 20 GHz PLL generates a signal with a phase noise that is lower than -105 dBc/Hz using tail feedback to improve the phase noise while having a 17% tuning range. The proposed 60 GHz QILO uses a combination of parallel and tail injection to enhance the locking range by improving the QILO injection efficiency at the moment of injection and has a 12% tuning range. Both the 20 GHz PLL and the QILO were fabricated as separate chips using a 65 nm CMOS process and measurement results show a phase noise that is less than -95 dBc/Hz@1 MHz at 60 GHz while consuming 80 mW from a 1.2 V supply. To the author's knowledge this phase noise is about 20 dB better than recently reported QPLLs and about 10 dB compared to differential PLLs operating at a similar frequency and at a similar offset.
Radar target detection and Doppler ambiguity resolution

A Continuous Wave (CW) radar with a linear Frequency Modulation (FM) and a very short chirp duration is considered in this paper. A single target range and Doppler frequency measurement consits of a sequence of L chirp signals with the same chirp duration. The maximum unambiguously measurable radial velocity in this case is limited by a minimum chirp duration. Therefore fast moving targets will cause ambiguous measurement results, that have to be resolved. The technique of resolving ambiguities on the basis of several subsequent measurements with different ambiguity interval lengths is well known as Chinese Remainder Theorem (CRT). It has been extensively described in literature . Two different signal processing approaches to apply this technique for the resolution of ambiguities are compared in this paper. These do not differ in terms of the ambiguity resolution itself, because they are based on the same principle. The detection performance however is different.
Human Brands : Investigating Antecedents to Consumers ' Strong Attachments to Celebrities

This article explores recent advances in self-determination research to address why consumers develop strong attachments to "human brands," a term that refers to any well-known persona who is the subject of marketing communications efforts. Study 1 uses a survey that is analyzed with structural equation modeling. Study 2 is qualitative and offers corroborating evidence for the proposed theoretical model. Study 3 extends the model with a more naturalistic sample and tests several alternative hypotheses using hierarchical regression. The results suggest that when a human brand enhances a person's feelings of autonomy and relatedness and does not suppress feelings of competence, the person is likely to become more strongly attached to it. This article documents that strong attachments are predictive of satisfied, trusting, and committed relationships and proposes that attachment strength may be a parsimonious proxy for consumer-brand relationship strength. The results imply that benefits would accrue to organizations such as entertainment firms and political parties that establish direct and routine interaction between human brands and consumers, that human brands to which consumers are attached offer significant potential as endorsers, and that organizations should address how to make the human brands they manage more authentic.
Dynamic analysis and control of lego mindstorms NXT bicycle

The paper is inspired from a challenging experiment conducted with undergraduate students for bachelor project within Automatic Control and Computer Engineering Faculty, Iaşi. A bicycle type system was implemented, analyzed and some control strategies were implemented and tested in order to self-stabilize the system. The bicycle study is enjoyable both to undergraduate students and researchers because it uses a “hi-tech” relevant concrete example. This work encompasses and uses a full range of system-theoretic aspects which covers knowledge from analytical, computational and experimental point of view. The dynamic description of the bicycle is done using a set of dynamic equations that describes the location and orientation of the body, considering four rigid subsystems that form the bicycle. The control issues were conducted in order to follow a desired forward speed using classical PI controller, followed by the LQR approach for steering angle.
Security/privacy of wearable fitness tracking IoT devices

As wearable fitness trackers gain widespread acceptance among the general population, there is a concomitant need to ensure that associated privacy and security vulnerabilities are kept to a minimum. We discuss potential vulnerabilities of these trackers, in general, and specific vulnerabilities in one such tracker - Fitbit - identified by Rahman et al. (2013) who then proposed means to address identified vulnerabilities. However, the `fix' has its own vulnerabilities. We discuss possible means to alleviate related issues.
FLUX: A Logic Programming Method for Reasoning Agents

FLUX is a programming method for the design of agents that reason logically about their actions and sensor information in the presence of incomplete knowledge. The core of FLUX is a system of Constraint Handling Rules, which enables agents to maintain an internal model of their environment by which they control their own behavior. The general action representation formalism of the uent calculus provides the formal semantics for the constraint solver. FLUX exhibits excellent computational behavior due to both a carefully restricted expressiveness and the inference paradigm of progression.
HEPeak: an HMM-based exome peak-finding package for RNA epigenome sequencing data

Methylated RNA Immunoprecipatation combined with RNA sequencing (MeRIP-seq) is revolutionizing the de novo study of RNA epigenomics at a higher resolution. However, this new technology poses unique bioinformatics problems that call for novel and sophisticated statistical computational solutions, aiming at identifying and characterizing transcriptome-wide methyltranscriptome. We developed HEP, a Hidden Markov Model (HMM)-based Exome Peak-finding algorithm for predicting transcriptome methylation sites using MeRIP-seq data. In contrast to exomePeak, our previously developed MeRIP-seq peak calling algorithm, HEPeak models the correlation between continuous bins in an m6A peak region and it is a model-based approach, which admits rigorous statistical inference. HEPeak was evaluated on a simulated MeRIP-seq dataset and achieved higher sensitivity and specificity than exomePeak. HEPeak was also applied to real MeRIP-seq datasets from human HEK293T cell line and mouse midbrain cells and was shown to be able to recapitulate known m6A distribution in transcripts and identify novel m6A sites in long non-coding RNAs. In this paper, a novel HMM-based peak calling algorithm, HEPeak, was developed for peak calling for MeRIP-seq data. HEPeak is written in R and is publicly available.
Finite Automata and Their Decision Problems

Finite automata are considered in this paper a s instruments for classifying finite tapes. Each onetape automaton defines a set of tapes, a two-tape automaton defines a set of pairs of tapes, et cetera. The structure of the defined sets is studied. Various generalizations of the notion of an automaton are introduced and their relation to the classical automata is determined. Some decision problems concerning automata are shown to be solvable by effective algorithms; others turn out to be unsolvable by algorithms.
Finding Structural Similarity in Time Series Data Using Bag-of-Patterns Representation

For more than one decade, time series similarity search has been given a great deal of attention by data mining researchers. As a result, many time series representations and distance measures have been proposed. However, most existing work on time series similarity search focuses on finding shape-based similarity. While some of the existing approaches work well for short time series data, they typically fail to produce satisfactory results when the sequence is long. For long sequences, it is more appropriate to consider the similarity based on the higher-level structures. In this work, we present a histogram-based representation for time series data, similar to the “bag of words” approach that is widely accepted by the text mining and information retrieval communities. We show that our approach outperforms the existing methods in clustering, classification, and anomaly detection on several real datasets.
Using temporal IDF for efficient novelty detection in text streams

Novelty detection in text streams is a challenging task that emerges in quite a few different scenarios, ranging from email thread filtering to RSS news feed recommendation on a smartphone. An efficient novelty detection algorithm can save the user a great deal of time and resources when browsing through relevant yet usually previously-seen content. Most of the recent research on detection of novel documents in text streams has been building upon either geometric distances or distributional similarities, with the former typically performing better but being much slower due to the need of comparing an incoming document with all the previously-seen ones. In this paper, we propose a new approach to novelty detection in text streams. We describe a resource-aware mechanism that is able to handle massive text streams such as the ones present today thanks to the burst of social media and the emergence of the Web as the main source of information. We capitalize on the historical Inverse Document Frequency (IDF) that was known for capturing well term specificity and we show that it can be used successfully at the document level as a measure of document novelty. This enables us to avoid similarity comparisons with previous documents in the text stream, thus scaling better and leading to faster execution times. Moreover, as the collection of documents evolves over time, we use a temporal variant of IDF not only to maintain an efficient representation of what has already been seen but also to decay the document frequencies as the time goes by. We evaluate the performance of the proposed approach on a real-world news articles dataset created for this task. We examine an exhaustive number of variants of the model and compare them to several commonly used baselines that rely on geometric distances. The results show that the proposed method outperforms all of the baselines while managing to operate efficiently in terms of time complexity and memory usage, which are of great importance in a mobile setting scenario.
Missing value estimation methods for DNA microarrays

Gene expression microarray experiments can generate data sets with multiple missing expression values. Unfortunately, many algorithms for gene expression analysis require a complete matrix of gene array values as input. For example, methods such as hierarchical clustering and K-means clustering are not robust to missing data, and may lose effectiveness even with a few missing values. Methods for imputing missing data are needed, therefore, to minimize the effect of incomplete data sets on analyses, and to increase the range of data sets to which these algorithms can be applied. In this report, we investigate automated methods for estimating missing data.



We present a comparative study of several methods for the estimation of missing values in gene microarray data. We implemented and evaluated three methods: a Singular Value Decomposition (SVD) based method (SVDimpute), weighted K-nearest neighbors (KNNimpute), and row average. We evaluated the methods using a variety of parameter settings and over different real data sets, and assessed the robustness of the imputation methods to the amount of missing data over the range of 1--20% missing values. We show that KNNimpute appears to provide a more robust and sensitive method for missing value estimation than SVDimpute, and both SVDimpute and KNNimpute surpass the commonly used row average method (as well as filling missing values with zeros). We report results of the comparative experiments and provide recommendations and tools for accurate estimation of missing microarray data under a variety of conditions.
Image analysis of blood microscopic images for acute leukemia detection

Acute lymphoblastic leukemia (ALL) is an serious hematological neoplasia of childhood which is characterized by abnormal growth and development of immature white blood cells (lymphoblasts). ALL makes around 80% of childhood leukemia and it mostly occur in the age group of 3–7. The nonspecific nature of the signs and symptoms of ALL often leads to wrong diagnosis. Diagnostic confusion is also posed due to imitation of similar signs by other disorders. Careful microscopic examination of stained blood smear or bone marrow aspirate is the only way to effective diagnosis of leukemia. Techniques such as fluorescence in situ hybridization (FISH), immunophenotyping, cytogenetic analysis and cytochemistry are also employed for specific leukemia detection. The need for automation of leukemia detection arises since the above specific tests are time consuming and costly. Morphological analysis of blood slides are influenced by factors such as hematologists experience and tiredness, resulting in non standardized reports. A low cost and efficient solution is to use image analysis for quantitative examination of stained blood microscopic images for leukemia detection. A fuzzy clustering based two stage color segmentation strategy is employed for segregating leukocytes or white blood cells (WBC) from other blood components. Discriminative features i.e. nucleus shape, texture are used for final detection of leukemia. In the present paper two novel shape features i.e., Hausdorff Dimension and contour signature is implemented for classifying a lymphocytic cell nucleus. Support Vector Machine (SVM) is employed for classification. A total of 108 blood smear images were considered for feature extraction and final performance evaluation is validated with the results of a hematologist.