nils-herrmann/clean_scirepeval huggingface dataset:
Emotional Intelligence and Academic Achievement among Students at the Higher Secondary Level

The present study aims to investigate emotional intelligence and academic achievement of students at the higher secondary level. Using random sampling technique 321 students, from the higher secondary level in different systems of education, namely, state, matriculation and central board schools are chosen. The Emotional Intelligence Scale (Hydes and others, 2002) has been used to assess the emotional intelligence and the marks scored in Science were taken from their half yearly performance. The data collected is subjected to statistical analysis, namely, mean, standard deviation, ‘t’test, ‘F’ratio, Karl Pearson’s Product Moment Correlation Co-efficient ‘r’. Results show a positive significant correlation between emotional intelligence and academic achievement among the students. Further the students belonging to the central board schools have a higher level of emotional intelligence compared to students in state board but did not differ with students in matriculation board schools at the higher secondary level. Similarly, students belonging to central board schools are found to perform better in academics compared to students in state and matriculation board schools at the higher secondary level.
Schema and ontology matching with COMA++

We demonstrate the schema and ontology matching tool COMA++. It extends our previous prototype COMA utilizing a composite approach to combine different match algorithms . COMA++ implements significant improvements and offers a comprehensive infrastructure to solve large real-world match problems. It comes with a graphical interface enabling a variety of user interactions. Using a generic data representation, COMA++ uniformly supports schemas and ontologies, e.g. the powerful standard languages W3C XML Schema and OWL. COMA++ includes new approaches for ontology matching, in particular the utilization of shared taxonomies. Furthermore, different match strategies can be applied including various forms of reusing previously determined match results and a so-called fragment-based match approach which decomposes a large match problem into smaller problems. Finally, COMA++ cannot only be used to solve match problems but also to comparatively evaluate the effectiveness of different match algorithms and strategies.
Detecting Word Ordering Errors in Chinese Sentences for Learning Chinese as a Foreign Language

Automatic detection of sentence errors is an important NLP task and is valuable to assist foreign language learners. In this paper, we investigate the problem of word ordering errors in Chinese sentences and propose classifiers to detect this type of errors. Word n-gram features in Google Chinese Web 5-gram corpus and ClueWeb09 corpus, and POS features in the Chinese POStagged ClueWeb09 corpus are adopted in the classifiers. The experimental results show that integrating syntactic features, web corpus features and perturbation features are useful for word ordering error detection, and the proposed classifier achieves 71.64% accuracy in the experimental datasets. 協助非中文母語學習者偵測中文句子語序錯誤 自動偵測句子錯誤是自然語言處理研究一項重要議題,對於協助外語學習者很有價值。在 這篇論文中,我們研究中文句子語序錯誤的問題,並提出分類器來偵測這種類型的錯誤。 在分類器中我們使用的特徵包括:Google 中文網路 5-gram 語料庫、與 ClueWeb09 語料庫 的中文詞彙 n-grams及中文詞性標注特徵。實驗結果顯示,整合語法特徵、網路語料庫特 徵、及擾動特徵對偵測中文語序錯誤有幫助。在實驗所用的資料集中,合併使用這些特徵 所得的分類器效能可達 71.64%。
Centrality and network flow

Centralitymeasures, or at least popular interpretations of thesemeasures,make implicit assumptions about the manner in which traffic flows through a network. For example, some measures count only geodesic paths, apparently assuming that whatever flows through the network only moves along the shortest possible paths. This paper lays out a typology of network flows based on two dimensions of variation, namely the kinds of trajectories that traffic may follow (geodesics, paths, trails, or walks) and the method of spread (broadcast, serial replication, or transfer). Measures of centrality are then matched to the kinds of flows that they are appropriate for. Simulations are used to examine the relationship between type of flow and the differential importance of nodes with respect to key measurements such as speed of reception of traffic and frequency of receiving traffic. It is shown that the off-the-shelf formulas for centrality measures are fully applicable only for the specific flow processes they are designed for, and that when they are applied to other flow processes they get the “wrong” answer. It is noted that the most commonly used centrality measures are not appropriate for most of the flows we are routinely interested in. A key claim made in this paper is that centrality measures can be regarded as generating expected values for certain kinds of node outcomes (such as speed and frequency of reception) given implicit models of how traffic flows, and that this provides a new and useful way of thinking about centrality.
THREE OMNI-DIRECTIONAL WHEELS CONTROL ON A MOBILE ROBOT

Traditional two wheels differential drive normally used on mobile robots have manoeuvrability limitations and take time to sort out. Most teams use two driving wheels (with one or two cast wheels), four driving wheels and even three driving wheels. A three wheel drive with omni-directional wheel has been tried with success, and was implemented on fast moving autonomous mobile robots. This paper deals with the mathematical kinematics description of such mobile platform, it describes the advantages and also the type of control used.
Bringing CUPID Indoor Positioning System to Practice

WiFi based indoor positioning has recently gained more attention due to the advent of the IEEE 802.11v standard, requirements by the FCC for E911 calls, and increased interest in location-based services. While there exist several indoor localization techniques, we find that these techniques tradeoff either accuracy, scalability, pervasiveness or cost -- all of which are important requirements for a truly deployable positioning solution. Wireless signal-strength based approaches suffer from location errors, whereas time-of-flight (ToF) based solutions provide good accuracy but are not scalable. Recent solutions address these issues by augmenting WiFi with either smartphone sensing or mobile crowdsourcing. However, they require tight coupling between WiFi infrastructure and a client device, or they can determine the client's location only if it is mobile. In this paper, we present CUPID2.0 which improved our previously proposed CUPID indoor positioning system to overcome these limitations. We achieve this by addressing the fundamental limitations in Time-of-Flight based localization and combining ToF with signal strength to address scalability. Experiments from $6$ cities using $40$ different mobile devices, comprising of more than $2.5$ million location fixes demonstrate feasibility. CUPID2.0 is currently under production, and we expect CUPID2.0 to ignite the wide adoption of WLAN-based positioning systems and their services.
Structure and motion estimation from rolling shutter video

The majority of consumer quality cameras sold today have CMOS sensors with rolling shutters. In a rolling-shutter camera, images are read out row by row, and thus each row is exposed during a different time interval. A rolling-shutter exposure causes geometric image distortions when either the camera or the scene is moving, and this causes state-of-the-art structure and motion algorithms to fail. We demonstrate a novel method for solving the structure and motion problem for rolling-shutter video. The method relies on exploiting the continuity of the camera motion, both between frames, and across a frame. We demonstrate the effectiveness of our method by controlled experiments on real video sequences. We show, both visually and quantitatively, that our method outperforms standard structure and motion, and is more accurate and efficient than a two-step approach, doing image rectification and structure and motion.
The developmental origins of a disposition toward empathy: Genetic and environmental contributions.

The authors investigated the development of a disposition toward empathy and its genetic and environmental origins. Young twins' (N = 409 pairs) cognitive (hypothesis testing) and affective (empathic concern) empathy and prosocial behavior in response to simulated pain by mothers and examiners were observed at multiple time points. Children's mean level of empathy and prosociality increased from 14 to 36 months. Positive concurrent and longitudinal correlations indicated that empathy was a relatively stable disposition, generalizing across ages, across its affective and cognitive components, and across mother and examiner. Multivariate genetic analyses showed that genetic effects increased, and that shared environmental effects decreased, with age. Genetic effects contributed to both change and continuity in children's empathy, whereas shared environmental effects contributed to stability and nonshared environmental effects contributed to change. Empathy was associated with prosocial behavior, and this relationship was mainly due to environmental effects.
Combined First and Second Order Total Variation Inpainting using Split Bregman

In this article we discuss the implementation of the combined first and second order total variation inpainting that was introduced by Papafitsoros and Schönlieb. We describe the algorithm we use (split Bregman) in detail, and we give some examples that indicate the difference between pure first and pure second order total variation inpainting. Source Code We provide a source code for the algorithm written in C and an online demonstration, accessible on the article web page http://dx.doi.org/10.5201/ipol.2013.40.
Font and Background Color Independent Text

We propose a novel method for binarization of color documents whereby the foreground text is output as black and the background as white regardless of the polarity of foreground-background shades. The method employs an edge-based connected component approach and automatically determines a threshold for each component. It has several advantages over existing binarization methods. Firstly, it can handle documents with multi-colored texts with different background shades. Secondly, the method is applicable to documents having text of widely varying sizes, usually not handled by local binarization methods. Thirdly, the method automatically computes the threshold for binarization and the logic for inverting the output from the image data and does not require any input parameter. The proposed method has been applied to a broad domain of target document types and environment and is found to have a good adaptability.
Comparison of Different POS Tagging Techniques ( n-gram , HMM and Brill ’ s tagger ) for Bangla

There are different approaches to the problem of assigning each word of a text with a parts-of-speech tag, which is known as Part-Of-Speech (POS) tagging. In this paper we compare the performance of a few POS tagging techniques for Bangla language, e.g. statistical approach (n-gram, HMM) and transformation based approach (Brill’s tagger). A supervised POS tagging approach requires a large amount of annotated training corpus to tag properly. At this initial stage of POS-tagging for Bangla, we have very limited resource of annotated corpus. We tried to see which technique maximizes the performance with this limited resource. We also checked the performance for English and tried to conclude how these techniques might perform if we can manage a substantial amount of annotated corpus.
Categorization and decision-making in a neurobiologically plausible spiking network using a STDP-like learning rule

Understanding how the human brain is able to efficiently perceive and understand a visual scene is still a field of ongoing research. Although many studies have focused on the design and optimization of neural networks to solve visual recognition tasks, most of them either lack neurobiologically plausible learning rules or decision-making processes. Here we present a large-scale model of a hierarchical spiking neural network (SNN) that integrates a low-level memory encoding mechanism with a higher-level decision process to perform a visual classification task in real-time. The model consists of Izhikevich neurons and conductance-based synapses for realistic approximation of neuronal dynamics, a spike-timing-dependent plasticity (STDP) synaptic learning rule with additional synaptic dynamics for memory encoding, and an accumulator model for memory retrieval and categorization. The full network, which comprised 71,026 neurons and approximately 133 million synapses, ran in real-time on a single off-the-shelf graphics processing unit (GPU). The network was constructed on a publicly available SNN simulator that supports general-purpose neuromorphic computer chips. The network achieved 92% correct classifications on MNIST in 100 rounds of random sub-sampling, which is comparable to other SNN approaches and provides a conservative and reliable performance metric. Additionally, the model correctly predicted reaction times from psychophysical experiments. Because of the scalability of the approach and its neurobiological fidelity, the current model can be extended to an efficient neuromorphic implementation that supports more generalized object recognition and decision-making architectures found in the brain.
Large-Scale Cover Song Recognition Using the 2D Fourier Transform Magnitude

Large-scale cover song recognition involves calculating itemto-item similarities that can accommodate differences in timing and tempo, rendering simple Euclidean measures unsuitable. Expensive solutions such as dynamic time warping do not scale to million of instances, making them inappropriate for commercial-scale applications. In this work, we transform a beat-synchronous chroma matrix with a 2D Fourier transform and show that the resulting representation has properties that fit the cover song recognition task. We can also apply PCA to efficiently scale comparisons. We report the best results to date on the largest available dataset of around 18,000 cover songs amid one million tracks, giving a mean average precision of 3.0%.
Detecting misinformation in online social networks using cognitive psychology

The paper explores the use of concepts in cognitive psychology to evaluate the spread of misinformation, disinformation and propaganda in online social networks. Analysing online social networks to identify metrics to infer cues of deception will enable us to measure diffusion of misinformation. The cognitive process involved in the decision to spread information involves answering four main questions viz consistency of message, coherency of message, credibility of source and general acceptability of message. We have used the cues of deception to analyse these questions to obtain solutions for preventing the spread of misinformation. We have proposed an algorithm to effectively detect deliberate spread of false information which would enable users to make informed decisions while spreading information in social networks. The computationally efficient algorithm uses the collaborative filtering property of social networks to measure the credibility of sources of information as well as quality of news items. The validation of the proposed methodology has been done on the online social network `Twitter’.
Ground influence on the input impedance of transient dipole and bow-tie antennas

In this paper, the influence of a lossy ground on the input impedance of dipole and bow-tie antennas excited by a short pulse is investigated. It is shown that the ground influence on the input impedance of transient dipole and bow-tie antennas is significant only for elevations smaller than 1/5 of the wavelength that corresponds to the central frequency of the exciting pulse. Furthermore, a principal difference between the input impedance due to traveling-wave and standing-wave current distributions is pointed out.
Size Estimation of Cloud Migration Projects with Cloud Migration Point (CMP)

One major obstacle to enterprise adoption of cloud technologies has been the lack of visibility into migration effort and cost. In this paper, we present a methodology, called Cloud Migration Point (CMP), for estimating the size of cloud migration projects, by recasting a well-known software size estimation model called Function Point (FP) into the context of cloud migration. We empirically evaluate our CMP model by performing a cross-validation on six different small-scale cloud migration projects and show that our size estimation model can be used as a reliable predictor for effort estimation. Furthermore, we prove that our CMP model satisfies the fundamental properties of a software size measure.
SeCReT: Secure Channel between Rich Execution Environment and Trusted Execution Environment

ARM TrustZone, which provides a Trusted Execution Environment (TEE), normally plays a role in keeping security-sensitive resources safe. However, to properly control access to the resources, it is not enough to just isolate them from the Rich Execution Environment (REE). In addition to the isolation, secure communication should be guaranteed between security-critical resources in the TEE and legitimate REE processes that are permitted to use them. Even though there is a TEE security solution — namely, a kernel-integrity monitor — it aims to protect the REE kernel’s static regions, not to secure communication between the REE and TEE. We propose SeCReT to ameliorate this problem. SeCReT is a framework that builds a secure channel between the REE and TEE by enabling REE processes to use session keys in the REE that is regarded as unsafe region. SeCReT provides the session key to a requestor process only when the requestor’s code and control flow integrity are verified. To prevent the key from being exposed to an attacker who already compromised the REE kernel, SeCReT flushes the key from the memory every time the processor switches into kernel mode. In this paper, we present the design and implementation of SeCReT to show how it protects the key in the REE. Our prototype is implemented on Arndale board, which offers a Cortex-A15 dual-core processor with TrustZone as its security extension. We performed a security analysis by using a kernel rootkit and also ran LMBench microbenchmark to evaluate the performance overhead imposed by SeCReT.
The perception of animacy and intelligence based on a robot's embodiment

Robots exhibit life-like behavior by performing intelligent actions. To enhance human-robot interaction it is necessary to investigate and understand how end-users perceive such animate behavior. In this paper, we report an experiment to investigate how people perceived different robot embodiments in terms of animacy and intelligence. iCat and Robovie II were used as the two embodiments in this experiment. We conducted a between-subject experiment where robot type was the independent variable, and perceived animacy and intelligence of the robot were the dependent variables. Our findings suggest that a robots perceived intelligence is significantly correlated with animacy. The correlation between the intelligence and the animacy of a robot was observed to be stronger in the case of the iCat embodiment. Our results also indicate that the more animated the face of the robot, the more likely it is to attract the attention of a user. We also discuss the possible and probable explanations of the results obtained.
Abstraction of 2D shapes in terms of parts

Abstraction in imagery results from the strategic simplification and elimination of detail to clarify the visual structure of the depicted shape. It is a mainstay of artistic practice and an important ingredient of effective visual communication. We develop a computational method for the abstract depiction of 2D shapes. Our approach works by organizing the shape into parts using a new synthesis of holistic features of the part shape, local features of the shape boundary, and global aspects of shape organization. Our abstractions are new shapes with fewer and clearer parts.
Wireless Implantable Blood Pressure Sensing Microsystem Design for Monitoring of Small Laboratory Animals

A design of a novel wireless implantable blood pressure sensing microsystem for advanced biological research is presented. The system employs a miniature instrumented elastic cuff, wrapped around a blood vessel, for small laboratory animal real-time blood pressure monitoring. The elastic cuff is made of biocompatible soft silicone material by a molding process and is filled by insulating silicone oil with an immersed MEMS capacitive pressure sensor interfaced with low-power integrated electronic system. This technique avoids vessel penetration and substantially minimizes vessel restriction due to the soft cuff elasticity, and is thus attractive for long-term implant. The MEMS pressure sensor detects the coupled blood pressure waveform caused by the vessel expansion and contraction, followed by amplification, 11-bit digitization, and wireless FSK data transmission to an external receiver. The integrated electronics are designed with capability of receiving RF power from an external power source and converting the RF signal to a stable 2 V DC supply in an adaptive manner to power the overall implant system, thus enabling the realization of stand-alone batteryless implant microsystem. The electronics are fabricated in a 1.5 μm CMOS process and occupy an area of 2 mm × 2 mm. The prototype monitoring cuff is wrapped around the right carotid artery of a laboratory rat to measure real-time blood pressure waveform. The measured in vivo blood waveform is compared with a reference waveform recorded simultaneously using a commercial catheter-tip transducer inserted into the left carotid artery. The two measured waveforms are closely matched with a constant scaling factor. The ASIC is interfaced with a 5-mm-diameter RF powering coil with four miniature surface-mounted components (one inductor and three capacitors) over a thin flexible substrate by bond wires, followed by silicone coating and packaging with the prototype blood pressure monitoring cuff. The overall system achieves a measured average sensitivity of 7 LSB/ mmHg, a nonlinearity less than 2.5% of full scale, and a hysteresis less than 1% of full scale. From noise characterization, a blood vessel pressure change sensing resolution 328 of 1 mmHg can be expected. The system weighs 330 mg, representing an order of magnitude mass reduction compared with state-of-the-art commercial technology.
Link Prediction in Heterogeneous Networks : Influence and Time Matters

Link prediction is an important task in network analysis, benefiting researchers and organizations in a variety of fields. Many networks in the real world, for example social networks, are heterogeneous, having multiple types of links and complex dependence structures. Link prediction in such networks must model the influence propagating between heterogeneous relationships to achieve better link prediction performance than in homogeneous networks. In this paper, we introduce MultiRelational Influence Propagation (MRIP), a novel probabilistic method for heterogeneous networks. We demonstrate that MRIP is useful for predicting links in sparse networks, which present a significant challenge due to the severe disproportion of the number of potential links to the number of real formed links. We also explore the impact of time in the process of link formation. We use of the temporal-related features by carefully investigating the issues of feasibility and generality. In accordance with our work in unsupervised learning, we further design an appropriate supervised approach in heterogeneous networks. Our experiments on co-authorship prediction demonstrate the effectiveness of our approach.