nils-herrmann/clean_scirepeval huggingface dataset:
Micro-opinion Sentiment Intensity Analysis and Summarization in Online Videos

There has been substantial progress in the field of text based sentiment analysis but little effort has been made to incorporate other modalities. Previous work in sentiment analysis has shown that using multimodal data yields to more accurate models of sentiment. Efforts have been made towards expressing sentiment as a spectrum of intensity rather than just positive or negative. Such models are useful not only for detection of positivity or negativity, but also giving out a score of how positive or negative a statement is. Based on the state of the art studies in sentiment analysis, prediction in terms of sentiment score is still far from accurate, even in large datasets [27]. Another challenge in sentiment analysis is dealing with small segments or micro opinions as they carry less context than large segments thus making analysis of the sentiment harder. This paper presents a Ph.D. thesis shaped towards comprehensive studies in multimodal micro-opinion sentiment intensity analysis.
m-Polar Fuzzy Sets: An Extension of Bipolar Fuzzy Sets

Recently, bipolar fuzzy sets have been studied and applied a bit enthusiastically and a bit increasingly. In this paper we prove that bipolar fuzzy sets and [0,1](2)-sets (which have been deeply studied) are actually cryptomorphic mathematical notions. Since researches or modelings on real world problems often involve multi-agent, multi-attribute, multi-object, multi-index, multi-polar information, uncertainty, or/and limit process, we put forward (or highlight) the notion of m-polar fuzzy set (actually, [0,1] (m)-set which can be seen as a generalization of bipolar fuzzy set, where m is an arbitrary ordinal number) and illustrate how many concepts have been defined based on bipolar fuzzy sets and many results which are related to these concepts can be generalized to the case of m-polar fuzzy sets. We also give examples to show how to apply m-polar fuzzy sets in real world problems.
Nonce Generation For The Digital Signature Standard

Digital Signature Algorithm (DSA) is an underlying algorithm to form a signature in the Digital Signature Standard (DSS). DSA uses a new random number (or nonce) each time a signature is generated for a message. In this paper, we present a Linear Congruential Generator (LCG) based approach to generate nonce for DSS. LCG has been shown to be insecure for nonce generation. If two message-signature pairs are known along with the parameters of the LCG used to generate the nonce then the private key in the signature scheme can be found, with high probability, by solving three congruences over different moduli. We use a comparison of the output of two LCGs to generate the nonces and show that our approach is secure. We also show that coupled multiple recursive generators which are similar to LCGs are also safe for nonce generation. Congruences can no longer be set up to solve for the private key. The advantage of LCG based schemes for pseudo-random number generation is their efficiency.
Substrate Integrated Metamaterial-Based Leaky-Wave Antenna With Improved Boresight Radiation Bandwidth

A substrate integrated metamaterial-based leaky-wave antenna is proposed to improve its boresight radiation bandwidth. The proposed leaky-wave antenna based on a composite right/left-handed substrate integrated waveguide consists of two leaky-wave radiator elements which are with different unit cells. The dual-element antenna prototype features boresight gain of 12.0 dBi with variation of 1.0 dB over the frequency range of 8.775-9.15 GHz or 4.2%. In addition, the antenna is able to offer a beam scanning from to with frequency from 8.25 GHz to 13.0 GHz.
E-WOM from e-commerce websites and social media: Which will consumers adopt?

The influence of user generated content on e-commerce websites and social media has been addressed in both practical and theoretical fields. Since most previous studies focus on either electronic word of mouth (eWOM) from e-commerce websites (EC-eWOM) or social media (SM-eWOM), little is known about the adoption process when consumers are presented EC-eWOM and SM-eWOM simultaneously. We focus on this problem by considering their adoption as an interactive process. It clarifies the mechanism of consumer’s adoption for those from the perspective of cognitive cost theory. A conceptual model is proposed about the relationship between the adoptions of the two types of eWOM. The empirical analysis shows that EC-eWOM’s usefulness and credibility positively influence the adoption of EC-eWOM, but negatively influence that of SM-eWOM. EC-eWOM adoption negatively impacts SM-eWOM adoption, and mediates the relationship between usefulness, credibility and SM-eWOM adoption. The moderating effects of consumers’ cognitive level and degree of involvement are also discussed. This paper further explains the adoption of the two types of eWOM based on the cognitive cost theory and enriches the theoretical research about eWOM in the context of social commerce. Implications for practice, as well as suggestions for future research, are also discussed.
Predicting Click-Through Rate Using Keyword Clusters

Click-through rate is a quantity of interest to online advertisers, search engine optimizers, and sponsored search providers alike. The rate at which users click on advertisements presented to them serves as both a metric for evaluating advertising effectiveness and a financial tool for cost and revenue projection. To predict future click-through rate, investigators typically use historical click information since it provides tangible examples of user behavior. In some cases plentiful historical data are available and this method provides a reliable estimate. More often, however, insufficient historical data exists and creative aggregation must fill the gap. In this work, we hypothesize that different terms have an inherently different likelihood of receiving a sponsored click. For example, the search terms “digital camera” and “brain structure” clearly express more and less shoppingoriented intent, respectively. We seek to estimate a termlevel click-through rate (CTR) reflecting these inherent differences. At times even aggregation to the term level leaves us with insufficient historical data for a confident estimate, so we also propose the use of clusters of related terms for less frequent, or even completely novel, terms. We reviewed other broad estimates of CTR (rank CTR and query-volume decile CTR) and compared them to the estimates computed using hierarchical clusters of related terms. We found that using historical data aggregated by cluster leads to more accurate estimates on average of term-level CTR for terms with little or no historical data.
ANALYZING EMPLOYEE ATTRITION USING DECISION TREE ALGORITHMS

Employee turnover is a serious concern in knowledge based organizations. When employees leave an organization, they carry with them invaluable tacit knowledge which is often the source of competitive advantage for the business. In order for an organization to continually have a higher competitive advantage over its competition, it should make it a duty to minimize employee attrition. This study identifies employee related attributes that contribute to the prediction of employees’ attrition in organizations. Three hundred and nine (309) complete records of employees of one of the Higher Institutions in Nigeria who worked in and left the institution between 1978 and 2006 were used for the study. The demographic and job related records of the employee were the main data which were used to classify the employee into some predefined attrition classes. Waikato Environment for Knowledge Analysis (WEKA) and See5 for Windows were used to generate decision tree models and rule-sets. The results of the decision tree models and rule-sets generated were then used for developing a a predictive model that was used to predict new cases of employee attrition. A framework for a software tool that can implement the rules generated in this study was also proposed.
Accuracy of dermoscopic criteria for the diagnosis of psoriasis, dermatitis, lichen planus and pityriasis rosea.

Dermoscopy is useful in evaluating skin tumours, but its applicability extends also to the field of inflammatory skin disorders. Plaque psoriasis (PP), dermatitis, lichen planus (LP) and pityriasis rosea (PR) are common inflammatory skin diseases, but little is currently known about their dermoscopic features.



To determine and compare the dermoscopic patterns associated with PP, dermatitis, LP and PR and to assess the validity of certain dermoscopic criteria in the diagnosis of PP.



Patients with PP, dermatitis, LP and PR were prospectively enrolled. The single most recently developed lesion was examined dermoscopically and histopathologically. Variables included vascular morphology, vascular arrangement, background colour, scale colour, scale distribution and presence of Wickham striae. Univariate and adjusted odds ratios were calculated. Discriminant functions were used to plot receiver-operator characteristic curves.



Eighty-three patients with PP and 86 patients with either dermatitis, LP or PR were included in the study. Dotted vessels in a regular arrangement over a light red background and white scales were highly predictive for the diagnosis of PP, whereas dermatitis more commonly showed yellow scales and dotted vessels in a patchy arrangement. PR was characterized by yellowish background, dotted vessels and peripheral scales; whitish lines (Wickham striae) were seen exclusively in LP.



PP, LP, PR and dermatitis show specific dermoscopic patterns that may aid their clinical diagnosis. Certain combinations of dermoscopic features can reliably predict the diagnosis of PP.
120W Ka Band Power Amplifier Utilizing GaN MMICs and Coaxial Waveguide Spatial Power Combining

The availability of increasingly higher power commercial microwave monolithic integrated circuit (MMIC) amplifiers enables the construction of solid state amplifiers achieving output powers and performance previously achievable only from traveling wave tube amplifiers (TWTAs). A high efficiency power amplifier incorporating an antipodal finline antenna array within a coaxial waveguide is investigated at Ka Band. The coaxial waveguide combiner structure is used to demonstrate a 120 Watt power amplifier from 27 to 31GHz by combining quantity (16), 10 Watt GaN MMIC devices; achieving typical PAE of 25% for the overall power amplifier assembly.
Sub-nanosecond avalanche transistor drivers for low impedance pulsed power applications

Ultra compact, short pulse, high voltage, high current pulsers are needed for a variety of non-linear electrical and optical applications. With a fast risetime and short pulse width, these drivers are capable of producing sub-nanosecond electrical and thus optical pulses by gain switching semiconductor laser diodes. Gain-switching of laser diodes requires a sub-nanosecond pulser capable of driving a low output impedance (5 /spl Omega/ or less). Optical pulses obtained had risetimes as fast as 20 ps. The designed pulsers also could be used for triggering photo-conductive semiconductor switches (PCSS), gating high speed optical imaging systems, and providing electrical and optical sources for fast transient sensor applications. Building on concepts from Lawrence Livermore National Laboratory, the development of pulsers based on solid state avalanche transistors was adapted to drive low impedances. As each successive stage is avalanched in the circuit, the amount of overvoltage increases, increasing the switching speed and improving the turn on time of the output pulse at the final stage. The output of the pulser is coupled into the load using a Blumlein configuration.
An End-to-End Deep Learning Architecture for Graph Classification

Neural networks are typically designed to deal with data in tensor forms. In this paper, we propose a novel neural network architecture accepting graphs of arbitrary structure. Given a dataset containing graphs in the form of (G, y) where G is a graph and y is its class, we aim to develop neural networks that read the graphs directly and learn a classification function. There are two main challenges: 1) how to extract useful features characterizing the rich information encoded in a graph for classification purpose, and 2) how to sequentially read a graph in a meaningful and consistent order. To address the first challenge, we design a localized graph convolution model and show its connection with two graph kernels. To address the second challenge, we design a novel SortPooling layer which sorts graph vertices in a consistent order so that traditional neural networks can be trained on the graphs. Experiments on benchmark graph classification datasets demonstrate that the proposed architecture achieves highly competitive performance with state-of-the-art graph kernels and other graph neural network methods. Moreover, the architecture allows end-to-end gradient-based training with original graphs, without the need to first transform graphs into vectors.
Construction and evaluation of event graphs

Events play an important role in natural language processing and information retrieval due to numerous event-oriented texts and information needs. Many natural language processing and information retrieval applications could benefit from a structured event-oriented document representation. In this paper, we propose event graphs as a novel way of structuring eventbased information from text. Nodes in event graphs represent the individual mentions of events, whereas edges represent the temporal and coreference relations between mentions. Contrary to previous natural language processing research, which has mainly focused on individual event extraction tasks, we describe a complete end-to-end system for event graph extraction from text. Our system is a three-stage pipeline that performs anchor extraction, argument extraction, and relation extraction (temporal relation extraction and event coreference resolution), each at a performance level comparable with the state of the art. We present EvExtra, a large newspaper corpus annotated with event mentions and event graphs, on which we train and evaluate our models. To measure the overall quality of the constructed event graphs, we propose two metrics based on the tensor product between automatically and manually constructed graphs. Finally, we evaluate the overall quality of event graphs with the proposed evaluation metrics and perform a headroom analysis of the system.
Beyond NomBank: A Study of Implicit Arguments for Nominal Predicates

Despite its substantial coverage, NomBank does not account for all withinsentence arguments and ignores extrasentential arguments altogether. These arguments, which we call implicit, are important to semantic processing, and their recovery could potentially benefit many NLP applications. We present a study of implicit arguments for a select group of frequent nominal predicates. We show that implicit arguments are pervasive for these predicates, adding 65% to the coverage of NomBank. We demonstrate the feasibility of recovering implicit arguments with a supervised classification model. Our results and analyses provide a baseline for future work on this emerging task.
Using interpolation to improve path planning: The Field D* algorithm

We present an interpolation-based planning and replanning algorithm for generating low-cost paths through uniform and nonuniform resolution grids. Most grid-based path planners use discrete state transitions that artificially constrain an agent’s motion to a small set of possible headings e.g., 0, /4, /2, etc. . As a result, even “optimal” gridbased planners produce unnatural, suboptimal paths. Our approach uses linear interpolation during planning to calculate accurate path cost estimates for arbitrary positions within each grid cell and produce paths with a range of continuous headings. Consequently, it is particularly well suited to planning low-cost trajectories for mobile robots. In this paper, we introduce a version of the algorithm for uniform resolution grids and a version for nonuniform resolution grids. Together, these approaches address two of the most significant shortcomings of grid-based path planning: the quality of the paths produced and the memory and computational requirements of planning over grids. We demonstrate our approaches on a number of example planning problems, compare them to related algorithms, and present several implementations on real robotic systems.
Cross-Subject EEG Feature Selection for Emotion Recognition Using Transfer Recursive Feature Elimination

Using machine-learning methodologies to analyze EEG signals becomes increasingly attractive for recognizing human emotions because of the objectivity of physiological data and the capability of the learning principles on modeling emotion classifiers from heterogeneous features. However, the conventional subject-specific classifiers may induce additional burdens to each subject for preparing multiple-session EEG data as training sets. To this end, we developed a new EEG feature selection approach, transfer recursive feature elimination (T-RFE), to determine a set of the most robust EEG indicators with stable geometrical distribution across a group of training subjects and a specific testing subject. A validating set is introduced to independently determine the optimal hyper-parameter and the feature ranking of the T-RFE model aiming at controlling the overfitting. The effectiveness of the T-RFE algorithm for such cross-subject emotion classification paradigm has been validated by DEAP database. With a linear least square support vector machine classifier implemented, the performance of the T-RFE is compared against several conventional feature selection schemes and the statistical significant improvement has been found. The classification rate and F-score achieve 0.7867, 0.7526, 0.7875, and 0.8077 for arousal and valence dimensions, respectively, and outperform several recent reported works on the same database. In the end, the T-RFE based classifier is compared against two subject-generic classifiers in the literature. The investigation of the computational time for all classifiers indicates the accuracy improvement of the T-RFE is at the cost of the longer training time.
NASA's L-Band Digital Beamforming Synthetic Aperture Radar

The Digital Beamforming Synthetic Aperture Radar (DBSAR) is a state-of-the-art L-band radar that employs advanced radar technology and a customized data acquisition and real-time processor in order to enable multimode measurement techniques in a single radar platform. DBSAR serves as a test bed for the development, implementation, and testing of digital beamforming radar techniques applicable to Earth science and planetary measurements. DBSAR flew its first field campaign on board the National Aeronautics and Space Administration P3 aircraft in October 2008, demonstrating enabling techniques for scatterometry, synthetic aperture, and altimetry.
AROMA: Abstract Representation of Presence Supporting Mutual Awareness

The AROMA project is exploring the kind of awareness that people effortless are able to maintain about other beings who are located physically close. We are designing technology that attempts to mediate a similar kind of awareness among people who are geographically dispersed but want to stay better in touch. AROMA technology can be thought of as a stand-alone communication device or -more likely -an augmentation of existing technologies such as the telephone or full-blown media spaces. Our approach differs from other recent designs for awareness (a) by choosing pure abstract representations on the display site, (b) by possibly remapping the signal across media between capture and display, and, finally, (c) by explicitly extending the application domain to include more than the working life, to embrace social interaction in general. We are building a series of prototypes to learn if abstract representation of activity data does indeed convey a sense of remote presence and does so in a sutTiciently subdued manner to allow the user to concentrate on his or her main activity. We have done some initial testing of the technical feasibility of our designs. What still remains is an extensive effort of designing a symbolic language of remote presence, done in parallel with studies of how people will connect and communicate through such a language as they live with the AROMA system.
Representation of three-dimensional space in the hippocampus of flying bats.

Many animals, on air, water, or land, navigate in three-dimensional (3D) environments, yet it remains unclear how brain circuits encode the animal's 3D position. We recorded single neurons in freely flying bats, using a wireless neural-telemetry system, and studied how hippocampal place cells encode 3D volumetric space during flight. Individual place cells were active in confined 3D volumes, and in >90% of the neurons, all three axes were encoded with similar resolution. The 3D place fields from different neurons spanned different locations and collectively represented uniformly the available space in the room. Theta rhythmicity was absent in the firing patterns of 3D place cells. These results suggest that the bat hippocampus represents 3D volumetric space by a uniform and nearly isotropic rate code.
Pulse Induction Metal Detector Using Sample and Hold Method

This paper presents the Pulse Induction Metal Detector Using Sample and Hold Method. The proposed method is to measure output voltage from search coil. The system consists of the pulse generator circuit, amplifier circuit, integrator circuit, three mono-stable circuits, sample and hold circuit, comparator circuit and display circuit. The voltage output signal of the proposed technique is proportional to size, shape and type of metal target. The metal target with differential size was used in our experiment as an illustrative case study. Experimental results show that the proposed method can detect metal with satisfied sensitivity and good linearity.
From Fly-by-Wire to Drive-by-Wire : Safety implications of automation in vehicles

The purpose of this paper is to critically review the current trend in automobile engineering toward automation of many of the functions previously performed by the driver. Working on the assumption that automation in aviation represents the basic model for driver automation, the costs and benefits of automation in aviation are explored as a means of establishing where automation of drivers tasks are likely to yield benefits. It is concluded that there are areas where automation can provide benefits to the driver, but there are other areas where this is unlikely to be the case. Automation per se does not guarantee success, and therefore it becomes vital to involve Human Factors into design to identify where automation of driver functions can be allocated with a beneficial outcome for driving performance.
Implicit ReasoNet: Modeling Large-Scale Structured Relationships with Shared Memory

Recent studies on knowledge base completion, the task of recovering missing relationships based on recorded relations, demonstrate the importance of learning embeddings from multi-step relations. However, due to the size of knowledge bases, learning multi-step relations directly on top of observed instances could be costly. In this paper, we propose Implicit ReasoNets (IRNs), which is designed to perform large-scale inference implicitly through a search controller and shared memory. Unlike previous work, IRNs use training data to learn to perform multi-step inference through the shared memory, which is also jointly updated during training. While the inference procedure is not operating on top of observed instances for IRNs, our proposed model outperforms all previous approaches on the popular FB15k benchmark by more than 5.7%.