nils-herrmann/clean_scirepeval huggingface dataset:
SEEG assistant: a 3DSlicer extension to support epilepsy surgery

In the evaluation of Stereo-Electroencephalography (SEEG) signals, the physicist’s workflow involves several operations, including determining the position of individual electrode contacts in terms of both relationship to grey or white matter and location in specific brain regions. These operations are (i) generally carried out manually by experts with limited computer support, (ii) hugely time consuming, and (iii) often inaccurate, incomplete, and prone to errors. In this paper we present SEEG Assistant, a set of tools integrated in a single 3DSlicer extension, which aims to assist neurosurgeons in the analysis of post-implant structural data and hence aid the neurophysiologist in the interpretation of SEEG data. SEEG Assistant consists of (i) a module to localize the electrode contact positions using imaging data from a thresholded post-implant CT, (ii) a module to determine the most probable cerebral location of the recorded activity, and (iii) a module to compute the Grey Matter Proximity Index, i.e. the distance of each contact from the cerebral cortex, in order to discriminate between white and grey matter location of contacts. Finally, exploiting 3DSlicer capabilities, SEEG Assistant offers a Graphical User Interface that simplifies the interaction between the user and the tools. SEEG Assistant has been tested on 40 patients segmenting 555 electrodes, and it has been used to identify the neuroanatomical loci and to compute the distance to the nearest cerebral cortex for 9626 contacts. We also performed manual segmentation and compared the results between the proposed tool and gold-standard clinical practice. As a result, the use of SEEG Assistant decreases the post implant processing time by more than 2 orders of magnitude, improves the quality of results and decreases, if not eliminates, errors in post implant processing. The SEEG Assistant Framework for the first time supports physicists by providing a set of open-source tools for post-implant processing of SEEG data. Furthermore, SEEG Assistant has been integrated into 3D Slicer, a software platform for the analysis and visualization of medical images, overcoming limitations of command-line tools.
Gated Word-Character Recurrent Language Model

We introduce a recurrent neural network language model (RNN-LM) with long shortterm memory (LSTM) units that utilizes both character-level and word-level inputs. Our model has a gate that adaptively finds the optimal mixture of the character-level and wordlevel inputs. The gate creates the final vector representation of a word by combining two distinct representations of the word. The character-level inputs are converted into vector representations of words using a bidirectional LSTM. The word-level inputs are projected into another high-dimensional space by a word lookup table. The final vector representations of words are used in the LSTM language model which predicts the next word given all the preceding words. Our model with the gating mechanism effectively utilizes the character-level inputs for rare and out-ofvocabulary words and outperforms word-level language models on several English corpora.
Building Enterprise Systems Infrastructure Flexibility as Enabler of Organisational Agility: Empirical Evidence

Enterprise systems (ES) that capture the most advanced developments of information technology are becoming common fixtures in most organisations. However, how ES affect organizational agility (OA) has been less researched and the existing research remains equivocal. From the perspective that ES can positively contribute to OA, this research via theory-based model development and rigorous empirical investigation of the proposed model, has bridged significant research gaps and provided empirical evidence for, and insights into, the effect of ES on OA. The empirical results based on data collected from 179 large organizations in Australia and New Zealand which have implemented and used ES for at least one year show that organisations can achieve agility out of their ES in two ways: by developing ES technical competences to build ES-enabled capabilities that digitise their key sensing and responding processes; and when ES-enabled sensing and responding capabilities are aligned in relatively turbulent environment.
English as a Formal Specification Language

PENG is a computer-processable controlled natural language designed for writing unambiguous and precise specifications. PENG covers a strict subset of standard English and is precisely defined by a controlled grammar and a controlled lexicon. In contrast to other controlled languages, the author does not need to know the grammatical restrictions explicitly. ECOLE, a look-ahead text editor, indicates the restrictions while the specification is written. The controlled lexicon contains domain-specific content words that can be defined by the author on the fly and predefined function words. Specifications written in PENG can be deterministically translated into discourse representations structures to cope with anaphora and presuppositions and also into first-order predicate logic. To test the formal properties of PENG, we reformulated Schubert’s steamroller puzzle in PENG, translated the resulting specification via discourse representation structures into first-order predicate logic with equality, and proved the steamroller’s conclusion with OTTER, a standard theorem prover.
Gait feature extraction in Parkinson's disease using low-cost accelerometers

The clinical hallmarks of Parkinson's disease (PD) are movement poverty and slowness (i.e. bradykinesia), muscle rigidity, limb tremor or gait disturbances. Parkinson's gait includes slowness, shuffling, short steps, freezing of gait (FoG) and/or asymmetries in gait. There are currently no validated clinical instruments or device that allow a full characterization of gait disturbances in PD. As a step towards this goal, a four accelerometer-based system is proposed to increase the number of parameters that can be extracted to characterize parkinsonian gait disturbances such as FoG or gait asymmetries. After developing the hardware, an algorithm has been developed, that automatically epoched the signals on a stride-by-stride basis and quantified, among others, the gait velocity, the stride time, the stance and swing phases, the single and double support phases or the maximum acceleration at toe-off, as validated by visual inspection of video recordings during the task. The results obtained in a PD patient and a healthy volunteer are presented. The FoG detection will be improved using time-frequency analysis and the system is about to be validated with a state-of-the-art 3D movement analysis system.
A survey on expert finding techniques

Finding experts in specified areas is an important task and has attracted much attention in the information retrieval community. Research on this topic has made significant progress in the past few decades and various techniques have been proposed. In this survey, we review the state-of-the-art methods in expert finding and summarize these methods into different categories based on their underlying algorithms and models. We also introduce the most widely used data collection for evaluating expert finding systems, and discuss future research directions.
MCEIL: An Improved Scoring Function for Overlapping Community Detection using Seed Expansion Methods

Community detection is one of the most well known problems in complex network analysis. In real-world networks, communities often overlap. Various approaches have been proposed in the literature to detect overlapping communities in networks. Local Expansion and optimization approaches have gained popularity due to their scalability and robustness. In a method based on local expansion, the seeding strategy and scoring function employed are crucial to the performance of the algorithm.
 In this paper, a scoring function called CEIL score is used with ground-truth seeds in local expansion and optimization algorithm. Using CEIL score has significantly improved performance of the algorithms with respect to evaluation metrics NMI and F1 score. However, CEIL has lower coverage than conductance. An extension to CEIL score, called MCEIL score is proposed. Using MCEIL score returns communities with coverage as high as conductance, and NMI and F1 scores higher than conductance on different kinds of datasets.
 Experiments on datasets of different types with different seeding strategies show that the improvements in NMI and F1 score obtained by MCEIL score are substantial.
Automated Accessibility Testing of Mobile Apps

It is important to make mobile apps accessible, so as not to exclude users with common disabilities such as blindness, low vision, or color blindness. Even when developers are aware of these accessibility needs, the lack of tool support makes the development and assessment of accessible apps challenging. Some accessibility properties can be checked statically, but user interface widgets are often created dynamically and are not amenable to static checking. Some accessibility checking frameworks analyze accessibility properties at runtime, but have to rely on existing thorough test suites. In this paper, we introduce the idea of using automated test generation to explore the accessibility of mobile apps. We present the MATE tool (Mobile Accessibility Testing), which automatically explores apps while applying different checks for accessibility issues related to visual impairment. For each issue, MATE generates a detailed report that supports the developer in fixing the issue. Experiments on a sample of 73 apps demonstrate that MATE detects more basic accessibility problems than static analysis, and many additional types of accessibility problems that cannot be detected statically at all. Comparison with existing accessibility testing frameworks demonstrates that the independence of an existing test suite leads to the identification of many more accessibility problems. Even when enabling Android's assistive features like contrast enhancement, MATE can still find many accessibility issues.
Symbolic Representation Based on Temporal Order Information for Time Series Classification

In the last decade symbolic representations approaches have been proposed for knowledge discovery in time series. However, the conventional symbolic methods ignore the temporal order of symbols, so this core feature of time series is lost. In this paper, to treat this problem we present a symbolic representation method to incorporate the temporal information in the symbols. The proposed method was evaluated on a decision tree classification using the Symbolic Aggregate Approximation and Equal Fixed Values Discretization approaches applied to 45 time series datasets that includes artificial and real-world data. The experimental results demonstrate the method effectiveness to improve the classification accuracy and the decision tree size for most datasets while preserving the temporal order information into symbolic representations.
Optimal Operation of Distribution Feeders in Smart Grids

This paper presents a generic and comprehensive distribution optimal power flow (DOPF) model that can be used by local distribution companies (LDCs) to integrate their distribution system feeders into a Smart Grid. The proposed three-phase DOPF framework incorporates detailed modeling of distribution system components and considers various operating objectives. Phase specific and voltage dependent modeling of customer loads in the three-phase DOPF model allows LDC operators to determine realistic operating strategies that can improve the overall feeder efficiency. The proposed distribution system operation objective is based on the minimization of the energy drawn from the substation while seeking to minimize the number of switching operations of load tap changers and capacitors. A novel method for solving the three-phase DOPF model by transforming the mixed-integer nonlinear programming problem to a nonlinear programming problem is proposed which reduces the computational burden and facilitates its practical implementation and application. Two practical case studies, including a real distribution feeder test case, are presented to demonstrate the features of the proposed methodology. The results illustrate the benefits of the proposed DOPF in terms of reducing energy losses while limiting the number of switching operations.
Branches of evolutionary algorithms and their effectiveness for clustering records

Clustering is a process that aims to group the similar records in one cluster and dissimilar records in different clusters. K-means is one of the most popular and well-known clustering technique for its simplicity and light weight. However, the main drawback of K-means clustering technique is that it requires a user (data miner) to estimate the number of clusters in advance. Another limitation of K-means is that it has a tendency to get stuck at local optima. In order to overcome these limitations many evolutionary algorithm based clustering techniques have been proposed since the 1990s and applied to various fields. In this paper, we present an up-to-date review of some major evolutionary algorithm based clustering techniques for the last twenty (20) years (1995-2015). A total of 63 ranked (i.e. based on citation reports and JCR/CORE rank) evolutionary algorithm based clustering approaches are reviewed. Maximum of the techniques do not require any user to define the number of clusters in advance. We present the limitations and advantages of some evolutionary algorithm based clustering techniques. We also present a thorough discussion and future research directions of evolutionary algorithm based clustering techniques.
Human auditory steady state responses to binaural and monaural beats

Binaural beat sensations depend upon a central combination of two different temporally encoded tones, separately presented to the two ears. We tested the feasibility to record an auditory steady state evoked response (ASSR) at the binaural beat frequency in order to find a measure for temporal coding of sound in the human EEG.



We stimulated each ear with a distinct tone, both differing in frequency by 40Hz, to record a binaural beat ASSR. As control, we evoked a beat ASSR in response to both tones in the same ear. We band-pass filtered the EEG at 40Hz, averaged with respect to stimulus onset and compared ASSR amplitudes and phases, extracted from a sinusoidal non-linear regression fit to a 40Hz period average.



A 40Hz binaural beat ASSR was evoked at a low mean stimulus frequency (400Hz) but became undetectable beyond 3kHz. Its amplitude was smaller than that of the acoustic beat ASSR, which was evoked at low and high frequencies. Both ASSR types had maxima at fronto-central leads and displayed a fronto-occipital phase delay of several ms.



The dependence of the 40Hz binaural beat ASSR on stimuli at low, temporally coded tone frequencies suggests that it may objectively assess temporal sound coding ability. The phase shift across the electrode array is evidence for more than one origin of the 40Hz oscillations.



The binaural beat ASSR is an evoked response, with novel diagnostic potential, to a signal that is not present in the stimulus, but generated within the brain.
Quantitative Analysis of Human-Model Agreement in Visual Saliency Modeling: A Comparative Study

Visual attention is a process that enables biological and machine vision systems to select the most relevant regions from a scene. Relevance is determined by two components: 1) top-down factors driven by task and 2) bottom-up factors that highlight image regions that are different from their surroundings. The latter are often referred to as “visual saliency.” Modeling bottom-up visual saliency has been the subject of numerous research efforts during the past 20 years, with many successful applications in computer vision and robotics. Available models have been tested with different datasets (e.g., synthetic psychological search arrays, natural images or videos) using different evaluation scores (e.g., search slopes, comparison to human eye tracking) and parameter settings. This has made direct comparison of models difficult. Here, we perform an exhaustive comparison of 35 state-of-the-art saliency models over 54 challenging synthetic patterns, three natural image datasets, and two video datasets, using three evaluation scores. We find that although model rankings vary, some models consistently perform better. Analysis of datasets reveals that existing datasets are highly center-biased, which influences some of the evaluation scores. Computational complexity analysis shows that some models are very fast, yet yield competitive eye movement prediction accuracy. Different models often have common easy/difficult stimuli. Furthermore, several concerns in visual saliency modeling, eye movement datasets, and evaluation scores are discussed and insights for future work are provided. Our study allows one to assess the state-of-the-art, helps to organizing this rapidly growing field, and sets a unified comparison framework for gauging future efforts, similar to the PASCAL VOC challenge in the object recognition and detection domains.
The Role of Artificial Intelligence Technologies in Crisis Response

Crisis response poses many of the most difficult information technology in crisis management. It requires information and communication-intensive efforts, utilized for reducing uncertainty, calculating and comparing costs and benefits, and managing resources in a fashion beyond those regularly available to handle routine problems. In this paper, we explore the benefits of artificial intelligence technologies in crisis response. This paper discusses the role of artificial intelligence technologies; namely, robotics, ontology and semantic web, and multi-agent systems in crisis response.
Common mode noise modeling and analysis of dual boost PFC circuit

To achieve high efficiency PFC front stage in switching mode power supply (SMPS), dual boost PFC (DBPFC) topology shows superior characteristics compared with traditional boost PFC, but it by nature brings higher EMI noise, especially common mode (CM) noise. This paper deals with the modeling and analysis of DBPFC CM noise based on and compared with boost PFC, noise propagation equivalent circuits of both topologies are deduced, and theoretical analysis illustrates the difference. Experiments are performed to validate the EMI model and analysis.
The Analysis of Android Malware Behaviors

Currently the intelligent terminal based on the Android has occupied most of the market, and the number of malware aiming at Android platform is also increasing. The problems of security threats and privacy disclosure caused by malicious behaviors are becoming more serious. How to make the security assessments and metrics effectively for the security of application has become a research hotspot in recent years. In this paper, we use static behavioral analysis approach, the thesis analyzes Android malware, summarizes its malicious behaviors and its ways of stealing private data, and puts forward the methods of detection and prevention.
k-Nearest Neighbors Algorithm in Profiling Power Analysis Attacks

Power analysis presents the typical example of successful attacks against trusted cryptographic devices such as RFID (Radio-Frequency IDentifications) and contact smart cards. In recent years, the cryptographic community has explored new approaches in power analysis based on machine learning models such as Support Vector Machine (SVM), RF (Random Forest) and Multi-Layer Perceptron (MLP). In this paper, we made an extensive comparison of machine learning algorithms in the power analysis. For this purpose, we implemented a verification program that always chooses the optimal settings of individual machine learning models in order to obtain the best classification accuracy. In our research, we used three datasets, the first contains the power traces of an unprotected AES (Advanced Encryption Standard) implementation. The second and third datasets are created independently from public available power traces corresponding to a masked AES implementation (DPA Contest v4). The obtained results revealed some interesting facts, namely, an elementary k-NN (k-Nearest Neighbors) algorithm, which has not been commonly used in power analysis yet, shows great application potential in practice.
Procedural Map Generation for a Rts Game

Procedural content generation (PCG) is the programmatic generation of game content using a random or pseudo-random process that results in an unpredictable range of possible game play spaces. This methodology brings many advantages to game developers, such as reduced memory consumption. In this paper we introduce a procedural map generator for a real-time strategy (RTS) game. The main component of this generator is a genetic algorithm devoted to create and evolve balanced maps, i.e. maps where no player has any map related advantage with respect to other players. The selected RTS game is called Planet Wars and it was used in the Google AI Challenge 2010. It is a space conquest game whose objective is to take over all the planets on the
Penile epidermal inclusion cyst.

We report a case of epidermal inclusion cyst of penis in a five-year-old boy, who had presented to the outpatient department of our hospital. Epidermal inclusion cysts are benign lesions that can develop in any part of the body. However, the finding of an epidermal inclusion cyst in the penis is rare. The child was operated and discharged uneventfully. The objective of reporting this case is to highlight the rare possibility of an inclusion cyst arising from penis as a late complication of circumcision.
A Quantitative Comparison of Calibration Methods for RGB-D Sensors Using Different Technologies

RGB-D (Red Green Blue and Depth) sensors are devices that can provide color and depth information from a scene at the same time. Recently, they have been widely used in many solutions due to their commercial growth from the entertainment market to many diverse areas (e.g., robotics, CAD, etc.). In the research community, these devices have had good uptake due to their acceptable levelofaccuracyformanyapplicationsandtheirlowcost,butinsomecases,theyworkatthelimitof their sensitivity, near to the minimum feature size that can be perceived. For this reason, calibration processes are critical in order to increase their accuracy and enable them to meet the requirements of such kinds of applications. To the best of our knowledge, there is not a comparative study of calibration algorithms evaluating its results in multiple RGB-D sensors. Speciﬁcally, in this paper, a comparison of the three most used calibration methods have been applied to three different RGB-D sensors based on structured light and time-of-ﬂight. The comparison of methods has been carried out by a set of experiments to evaluate the accuracy of depth measurements. Additionally, an object reconstruction application has been used as example of an application for which the sensor works at the limit of its sensitivity. The obtained results of reconstruction have been evaluated through visual inspection and quantitative measurements.
Surge capability of 1.2kV SiC diodes with high-temperature implantation

This paper presents a high-temperature implanted 4H-SiC JBS diode with improved surge capability. The fabrication of the P+ region is implemented with 500 °C implantation. It was found that Ti can form ohmic contact on high temperature implanted P+ region without any additional annealing. This could simplify the ohmic contact process for MPS fabrication. In this work, a wide transition P+ region between cell and termination is designed, which can alleviate snapback phenomenon and improve the surge capability.