nils-herrmann/clean_scirepeval huggingface dataset:
A compositional approach to statecharts semantics

Statecharts is a visual language for specifying reactive system behavior. The formalism extends traditional finite-state machines with notions of hierarchy and concurrency, and it is used in many popular software design notations. A large part of the appeal of Statecharts derives from its basis in state machines, with their intuitive operational interpretation. The classical semantics of Statecharts, however, suffers from a serious defect; it is not compositional, meaning that the behavior of system descriptions cannot be inferred from the behavior of their subsystems. Compositionality is a prerequisite for exploiting the modular structure of Statecharts for simulation, verification, and code generation, and it also provides the necessary foundation for reusability.
This paper suggests a new compositional approach to formalizing Statecharts semantics as flattened labeled transition systems in which transitions represent system steps. The approach builds on ideas developed for timed process calculi and employs structural operational rules to define the transitions of a Statecharts expression in terms of the transitions of its subexpressions. It is first presented for a simple dialect of Statecharts, with respect to a variant of Pnueli and Shalev's semantics, and is illustrated by means of a small example. To demonstrate its flexibility, the proposed approach is then extended to deal with practically useful features available in many Statecharts variants, namely state references, history states, and priority concepts along state hierarchies.
Synthetic Fingerprint-Database Generation

This work complements our previous efforts in generating realistic fingerprint images for test purposes. The main variability which characterizes the acquisition of a fingerprint through an on-line sensor is modeled and a sequence of steps is defined to derive a series of impressions from the same master-fingerprint. This allows large fingerprint databases to be randomly generated according to some given parameters. The experimental results validate our technique and prove that it can be very useful for performance evaluation, learning and testing in fingerprint-based systems.
Trusting Humans and Avatars: Behavioral and Neural Evidence

Over the past decade, information technology has dramatically changed the context in which economic transactions take place. Increasingly, transactions are computer-mediated, so that, relative to humanhuman interactions, human-computer interactions are gaining in relevance. Computer-mediated transactions, and in particular those related to the Internet, increase perceptions of uncertainty. Therefore, trust becomes a crucial factor in the reduction of these perceptions. To investigate this important construct, we studied individual trust behavior and the underlying brain mechanisms through a multi-round trust game. Participants acted in the role of an investor, playing against both humans and avatars. The behavioral results show that participants trusted avatars to a similar degree as they trusted humans. Participants also revealed similarity in learning an interaction partner’s trustworthiness, independent of whether the partner was human or avatar. However, the neuroimaging findings revealed differential responses within the brain network that is associated with theory of mind (mentalizing) depending on the interaction partner. Based on these results, the major conclusion of our study is that, in a situation of a computer with human-like characteristics (avatar), trust behavior in human-computer interaction resembles that of human-human interaction. On a deeper neurobiological level, our study reveals that thinking about an interaction partner’s trustworthiness activates the mentalizing network more strongly if the trustee is a human rather than an avatar. We discuss implications of these findings for future research.
A Phrase-Based Alignment Model for Natural Language Inference

The alignment problem—establishing links between corresponding phrases in two related sentences—is as important in natural language inference (NLI) as it is in machine translation (MT). But the tools and techniques of MT alignment do not readily transfer to NLI, where one cannot assume semantic equivalence, and for which large volumes of bitext are lacking. We present a new NLI aligner, the MANLI system, designed to address these challenges. It uses a phrase-based alignment representation, exploits external lexical resources, and capitalizes on a new set of supervised training data. We compare the performance of MANLI to existing NLI and MT aligners on an NLI alignment task over the well-known Recognizing Textual Entailment data. We show that MANLI significantly outperforms existing aligners, achieving gains of 6.2% in F1 over a representative NLI aligner and 10.5% over GIZA++.
The Ritvo Autism Asperger Diagnostic Scale-Revised (RAADS-R): A Scale to Assist the Diagnosis of Autism Spectrum Disorder in Adults: An International Validation Study

The Ritvo Autism Asperger Diagnostic Scale-Revised (RAADS-R) is a valid and reliable instrument to assist the diagnosis of adults with Autism Spectrum Disorders (ASD). The 80-question scale was administered to 779 subjects (201 ASD and 578 comparisons). All ASD subjects met inclusion criteria: DSM-IV-TR, ADI/ADOS diagnoses and standardized IQ testing. Mean scores for each of the questions and total mean ASD vs. the comparison groups' scores were significantly different (p < .0001). Concurrent validity with Constantino Social Responsiveness Scale-Adult = 95.59%. Sensitivity = 97%, specificity = 100%, test-retest reliability r = .987. Cronbach alpha coefficients for the subscales and 4 derived factors were good. We conclude that the RAADS-R is a useful adjunct diagnostic tool for adults with ASD.
Alzheimer's disease: etiologies, pathophysiology, cognitive reserve, and treatment opportunities.

Alzheimer's disease (AD) can be diagnosed with a considerable degree of accuracy. In some centers, clinical diagnosis predicts the autopsy diagnosis with 90% certainty in series reported from academic centers. The characteristic histopathologic changes at autopsy include neurofibrillary tangles, neuritic plaques, neuronal loss, and amyloid angiopathy. Mutations on chromosomes 21, 14, and 1 cause familial AD. Risk factors for AD include advanced age, lower intelligence, small head size, and history of head trauma; female gender may confer additional risks. Susceptibility genes do not cause the disease by themselves but, in combination with other genes or epigenetic factors, modulate the age of onset and increase the probability of developing AD. Among several putative susceptibility genes (on chromosomes 19, 12, and 6), the role of apolipoprotein E (ApoE) on chromosome 19 has been repeatedly confirmed. Protective factors include ApoE-2 genotype, history of estrogen replacement therapy in postmenopausal women, higher educational level, and history of use of nonsteroidal anti-inflammatory agents. The most proximal brain events associated with the clinical expression of dementia are progressive neuronal dysfunction and loss of neurons in specific regions of the brain. Although the cascade of antecedent events leading to the final common path of neurodegeneration must be determined in greater detail, the accumulation of stable amyloid is increasingly widely accepted as a central pathogenetic event. All mutations known to cause AD increase the production of beta-amyloid peptide. This protein is derived from amyloid precursor protein and, when aggregated in a beta-pleated sheet configuration, is neurotoxic and forms the core of neuritic plaques. Nerve cell loss in selected nuclei leads to neurochemical deficiencies, and the combination of neuronal loss and neurotransmitter deficits leads to the appearance of the dementia syndrome. The destructive aspects include neurochemical deficits that disrupt cell-to-cell communications, abnormal synthesis and accumulation of cytoskeletal proteins (e.g., tau), loss of synapses, pruning of dendrites, damage through oxidative metabolism, and cell death. The concepts of cognitive reserve and symptom thresholds may explain the effects of education, intelligence, and brain size on the occurrence and timing of AD symptoms. Advances in understanding the pathogenetic cascade of events that characterize AD provide a framework for early detection and therapeutic interventions, including transmitter replacement therapies, antioxidants, anti-inflammatory agents, estrogens, nerve growth factor, and drugs that prevent amyloid formation in the brain.
Angelic Semantics for High-Level Actions

High-level actions (HLAs) lie at the heart of hierarchical planning. Typically, an HLA admits multiple refinements into primitive action sequences. Correct descriptions of the effects of HLAs may be essential to their effective use, yet the literature is mostly silent. We propose an angelic semantics for HLAs, the key concept of which is the set of states reachable by some refinement of a high-level plan, representing uncertainty that will ultimately be resolved in the planning agent’s own best interest. We describe upper and lower approximations to these reachable sets, and show that the resulting definition of a high-level solution automatically satisfies the upward and downward refinement properties. We define a STRIPS-like notation for such descriptions. A sound and complete hierarchical planning algorithm is given and its computational benefits are demonstrated.
Modeling and Sliding Mode Control of Dc-Dc Buck-Boost Converter

475 Abstract— In this paper a dc-dc buck-boost converter is modeled and controlled using sliding mode technique. First the buck-boost converter is modeled and dynamic equations describing the converter are derived and sliding mode controller is designed. The robustness of the converter system is tested against step load changes and input voltage variations. Matlab/Simulink is used for the simulations. The simulation results are presented..
Implementation of health-care monitoring system using Raspberry Pi

Devotion towards own body is one of the important factor considered in this era. The equipments which provide results at run time and also accuracy maintained are provided by the electronic engineers. With the help of new technology of Raspberry Pi, health care system can be monitored. In this type of technology same area network is shared by multiple users which helps in monitoring. Wireless communication is done through Wi-Fi which provides flexibility and extendibility. In this paper basic parameters like body temperature is monitored and is transferred on webpage to make it locally visible for users.
Measurement of Safe Driving Distance Based on Stereo Vision

The measurement of safe driving distance based on stereo vision is proposed. The model of camera imaging is established using traditional camera calibration method firstly. Secondly, the projection matrix is deduced according to camera's internal and external parameter and used to calibrate the camera. The method of camera calibration based on two-dimensional target plane is adopted. Then the distortion parameters are calculated when the nonlinear geometric model of camera imaging is built. Moreover, the camera's internal and external parameters are optimized on the basis of the projection error' least squares criterion so that the un-distortion image can be obtained. The matching is done between the left image and the right image corresponding to angular point. The parallax error and the distance between the target vehicle and the camera can be calculated. The experimental results show that the measurement scheme is an effective one in a security vehicles spacing survey. The proposed system is convenient for driver to control in time and precisely. It is able to increase the security in intelligent transportation vehicles.
Analysis of DNN approaches to speaker identification

This work studies the usage of the Deep Neural Network (DNN) Bottleneck (BN) features together with the traditional MFCC features in the task of i-vector-based speaker recognition. We decouple the sufficient statistics extraction by using separate GMM models for frame alignment, and for statistics normalization and we analyze the usage of BN and MFCC features (and their concatenation) in the two stages. We also show the effect of using full-covariance GMM models, and, as a contrast, we compare the result to the recent DNN-alignment approach. On the NIST SRE2010, telephone condition, we show 60% relative gain over the traditional MFCC baseline for EER (and similar for the NIST DCF metrics), resulting in 0.94% EER.
An Adaptable Surface Parameterization Method

Parameterizations of triangulated surfaces are used in an increasing number of mesh processing applications for various purposes. Although demands vary, they are often required to preserve the surface metric and thus minimize angle, area and length deformation. However, most of the existing techniques primarily target at angle preservation while disregarding global area deformation. In this paper an energy functional is proposed, that quantifies angle and global area deformations simultaneously, while the relative importance between angle and area preservation can be controlled by the user through a parameter. We show how this parameter can be chosen to obtain parameterizations, that are optimized for an uniform sampling of the surface of a model. Maps obtained by minimizing this energy are well suited for applications that desire an uniform surface sampling, like re-meshing or mapping regularly patterned textures. Besides being invariant under rotation and translation of the domain, the energy is designed to prevent face flips during minimization and does not require a fixed boundary in the parameter domain. Although the energy is nonlinear, we show how it can be minimized efficiently using non-linear conjugate gradient methods in a hierarchical optimization framework and prove the convergence of the algorithm. The ability to control the tradeoff between the degree of angle and global area preservation is demonstrated for several models of varying complexity.
Disease named entity recognition by combining conditional random fields and bidirectional recurrent neural networks

The recognition of disease and chemical named entities in scientific articles is a very important subtask in information extraction in the biomedical domain. Due to the diversity and complexity of disease names, the recognition of named entities of diseases is rather tougher than those of chemical names. Although there are some remarkable chemical named entity recognition systems available online such as ChemSpot and tmChem, the publicly available recognition systems of disease named entities are rare. This article presents a system for disease named entity recognition (DNER) and normalization. First, two separate DNER models are developed. One is based on conditional random fields model with a rule-based post-processing module. The other one is based on the bidirectional recurrent neural networks. Then the named entities recognized by each of the DNER model are fed into a support vector machine classifier for combining results. Finally, each recognized disease named entity is normalized to a medical subject heading disease name by using a vector space model based method. Experimental results show that using 1000 PubMed abstracts for training, our proposed system achieves an F1-measure of 0.8428 at the mention level and 0.7804 at the concept level, respectively, on the testing data of the chemical-disease relation task in BioCreative V.Database URL: http://219.223.252.210:8080/SS/cdr.html.
Customer Relationship Management : Strategies And Practices In Selected Banks Of Pakistan

As economic globalization intensifies competition and creates a climate of constant change, winning and keeping customers has never been more important. Nowadays, Banks have realized that customer relationships are a very important factor for their success. Customer relationship management (CRM) is a strategy that can help them to build long-lasting relationships with their customers and increase their revenues and profits. CRM in the banking sector is of greater importance. The aim of this study is to explore and analyze the strategic implementation of CRM in selected banks of Pakistan, identify the benefits, the problems, as well as the success and failure factors of the implementation and develop a better understanding of CRM impact on banking competitiveness as well as provide a greater understanding of what constitutes good CRM practices. In this study, CMAT (Customer Management Assessment Tool) model is used which encompasses all the essential elements of practical customer relationship management. Data is collected through questionnaires from the three major banks (HBL, MCB, and Citibank) of Pakistan. The evidence supports that CRM is gradually being practiced in studied banks; however the true spirit of CRM is still needed to be on the active agenda of the banking sector in Pakistan. This study contributes to the financial services literature as it is one of the very few that have examined CRM applications, a comparatively new technology, in the Pakistani banking sector, where very limited research has taken place on the implementation of CRM.
Distributed LDA-based Topic Modeling and Topic Agglomeration in a Latent Space

We describe the methodology that we followed to automatically extract topics corresponding to known events provided by the SNOW 2014 challenge in the context of the SocialSensor project. A data crawling tool and selected filtering terms were provided to all the teams. The crawled data was to be divided in 96 (15-minute) timeslots spanning a 24 hour period and participants were asked to produce a fixed number of topics for the selected timeslots. Our preliminary results are obtained using a methodology that pulls strengths from several machine learning techniques, including Latent Dirichlet Allocation (LDA) for topic modeling and Non-negative Matrix Factorization (NMF) for automated hashtag annotation and for mapping the topics into a latent space where they become less fragmented and can be better related with one another. In addition, we obtain improved topic quality when Copyright c © by the paper’s authors. Copying permitted only for private and academic purposes. In: S. Papadopoulos, D. Corney, L. Aiello (eds.): 2014, published at http://ceur-ws.org sentiment detection is performed to partition the tweets based on polarity, prior to topic modeling.
Implementation and Evaluation of On-Chip Network Architectures

Driven by the need for higher bandwidth and complexity reduction, off-chip interconnect has evolved from proprietary busses to networked architectures. A similar evolution is occurring in on-chip interconnect. This paper presents the design, implementation and evaluation of one such on-chip network, the TRIPS OCN. The OCN is a wormhole routed, 4x10, 2D mesh network with four virtual channels. It provides a high bandwidth, low latency interconnect between the TRIPS processors, L2 cache banks and I/O units. We discuss the tradeoffs made in the design of the OCN, in particular why area and complexity were traded off against latency. We then evaluate the OCN using synthetic as well as realistic loads. We found that synthetic benchmarks do not provide sufficient indication of the behavior of realistic loads on this network. Finally, we examine the effect of link bandwidth and router FIFO depth on overall performance.
Information-theoretic Model Identification and Policy Search using Physics Engines with Application to Robotic Manipulation

We consider the problem of a robot learning the mechanical properties of objects through physical interaction with the object, and introduce a practical, data-efficient approach for identifying the motion models of these objects. The proposed method utilizes a physics engine, where the robot seeks to identify the inertial and friction parameters of the object by simulating its motion under different values of the parameters and identifying those that result in a simulation which matches the observed real motions. The problem is solved in a Bayesian optimization framework. The same framework is used for both identifying the model of an object online and searching for a policy that would minimize a given cost function according to the identified model. Experimental results both in simulation and using a real robot indicate that the proposed method outperforms state-of-the-art model-free reinforcement learning approaches.
Towards context-sensitive domain of Islamic knowledge ontology extraction

Ontology is one of the essential topics in the scope of an important area of current computer science and Semantic Web. Ontologies present well defined, straightforward and standardized form of the repositories (vast and reliable knowledge) where it can be interoperable and machine understandable. There are many possible utilization of ontologies from automatic annotation of web resources to domain representation and reasoning task. Ontology is an effective conceptualism used for the semantic web. However there is none of the research try to construct an ontology from Islamic knowledge which consist of Holy Quran, Hadiths and etc. Therefore as a first stage, in this paper we try to propose a simple methodology in order to extract a concept based on Al-Quran. Finally, we discuss about the experiment that have been conducted.
DECISION TREE BASED FEATURE SELECTION AND MULTILAYER PERCEPTRON FOR SENTIMENT ANALYSIS

Sentiment analysis plays a big role in brand and product positioning, consumer attitude detection, market research and customer relationship management. Essential part of information-gathering for market research is to find the opinion of people about the product. With availability and popularity of like online review sites and personal blogs, more chances and challenges arise as people now can, and do use information technologies to understand others opinions. In this paper, a Multi-Layer Perceptron (MLP) is used to classify the features extracted from the movie reviews. A Decision Tree-based Feature Ranking is proposed for feature selection. The ranking is based on Manhattan Hierarchical Cluster Criterion In the proposed feature selection; a decision tree induction selects relevant features. Decision tree induction constructs a tree structure with internal nodes denoting an attribute test with the branch representing test outcome and external node denotes class prediction. In this paper, a hybrid algorithm based on Differential Evolution (DE) and Genetic Algorithm (GA) for weight optimization algorithm to optimize MLPNN is proposed. IMDb dataset is used to evaluate the proposed method. Experimental results showed that the MLP with proposed feature selection improves the performance of MLP significantly by 3.96% to 6.56%. Classification accuracy of 81.25% was achieved when 70 or 90 features were selected.
Mimo for millimeter-wave wireless communications: beamforming, spatial multiplexing, or both?

The use of mmWave frequencies for wireless communications offers channel bandwidths far greater than previously available, while enabling dozens or even hundreds of antenna elements to be used at the user equipment, base stations, and access points. To date, MIMO techniques, such as spatial multiplexing, beamforming, and diversity, have been widely deployed in lower-frequency systems such as IEEE 802.11n/ac (wireless local area networks) and 3GPP LTE 4G cellphone standards. Given the tiny wavelengths associated with mmWave, coupled with differences in the propagation and antennas used, it is unclear how well spatial multiplexing with multiple streams will be suited to future mmWave mobile communications. This tutorial explores the fundamental issues involved in selecting the best communications approaches for mmWave frequencies, and provides insights, challenges, and appropriate uses of each MIMO technique based on early knowledge of the mmWave propagation environment.