nils-herrmann/clean_scirepeval huggingface dataset:
Heat-induced post-mortem defect of the skull simulating an exit gunshot wound of the calvarium.

A severely burned body was found in a burnt-out apartment following a house fire. At the death scene, police investigators noted a defect at the back of the head of the deceased. This defect perforated the occipital bone and showed external bevelling. To clarify whether this skull wound corresponded to a gunshot exit wound and for identification purposes, a medico-legal autopsy was ordered. External examination of the body showed intense charring with skin burned away and musculature exposed, fourth degree burns of the remaining skin and heat flexures of the limbs as well as heat-related rupture of both the abdominal cavity and chest wall . An oval defect of the occipital bone with cratering of the external table of the cranium was found . The defect measured 2 cm in diameter, was sharp-edged and the excavation of the outer table of the cranium showed no charring in contrast to the surrounding parts of the occipital bone . Apart from this finding, radiology of the body before autopsy was unremarkable. At autopsy, gross sections of the mucosa of the trachea and main bronchi were covered by a thick layer of soot with soot particles also present within the esophageus and the stomach. The brain showed a reduced volume, flattening of gyri and obliteration of sulci but no signs of trauma. Except for the defect of the occipital bone, no other signs of ante-mortem or post-mortem trauma could be detected. The level of carboxyhemoglobin in heart blood was 65% and the blood alcohol level was 285 mg/dL.
On the Visualization of Hierarchical Relations and Tree Structures with TagSpheres

Tag clouds are widely applied, popular visualization techniques as they illustrate summaries of textual data in an intuitive, lucid manner. Many layout algorithms for tag clouds have been developed in the recent years, but none of these approaches is designed to reflect the notion of hierarchical distance. For that purpose, we introduce a novel tag cloud layout called TagSpheres. By arranging tags on various hierarchy levels and applying appropriate colors, the importance of individual tags to the observed topic gets assessable. To explore relationships among various hierarchy levels, we aim to place related tags closely. Various usage scenarios from the digital humanities, sports, aviation and natural disaster management point out the benefit of TagSpheres for different domains. In addition, we highlight that TagSpheres is also a novel layout approach for tree structures.
Image Enhancement in Encrypted Domain over Cloud

Cloud-based multimedia systems are becoming increasingly common. These systems offer not only storage facility, but also high-end computing infrastructure which can be used to process data for various analysis tasks ranging from low-level data quality enhancement to high-level activity and behavior identification operations. However, cloud data centers, being third party servers, are often prone to information leakage, raising security and privacy concerns. In this article, we present a Shamir's secret sharing based method to enhance the quality of encrypted image data over cloud. Using the proposed method we show that several image enhancement operations such as noise removal, antialiasing, edge and contrast enhancement, and dehazing can be performed in encrypted domain with near-zero loss in accuracy and minimal computation and data overhead. Moreover, the proposed method is proven to be information theoretically secure.
Real-time Incremental Speech-to-Speech Translation of Dialogs

In a conventional telephone conversation between two speakers of the same language, the interaction is real-time and the speakers process the information stream incrementally. In this work, we address the problem of incremental speech-to-speech translation (S2S) that enables cross-lingual communication between two remote participants over a telephone. We investigate the problem in a novel real-time Session Initiation Protocol (SIP) based S2S framework. The speech translation is performed incrementally based on generation of partial hypotheses from speech recognition. We describe the statistical models comprising the S2S system and the SIP architecture for enabling real-time two-way cross-lingual dialog. We present dialog experiments performed in this framework and study the tradeoff in accuracy versus latency in incremental speech translation. Experimental results demonstrate that high quality translations can be generated with the incremental approach with approximately half the latency associated with nonincremental approach.
Implementation of ZigBee-GSM based home security monitoring and remote control system

Home security and control is one of the basic needs of mankind from early days. But today it has to be updated with the rapidly changing technology to ensure vast coverage, remote control, reliability, and real time operation. Deploying wireless technologies for security and control in home automation systems offers attractive benefits along with user friendly interface. In this paper, implementation of a novel security and control system for home automation is presented. The proposed system consists of a control console interfaced with different sensors using ZigBee. Suspected activities are conveyed to remote user through SMS (Short Message Service) or Call using GSM (Global System for Mobile communication) technology. Upon reply, the remote user can control his premises again through GSM-ZigBee combination. Besides, traditional burglar alarm enhances security in case of no acknowledgment from remote user. This system offers a low cost, low power consumption and user friendly way of a reliable portable monitoring and control of the secured environment. Using the concept of serial communication and mobile phone AT-commands (Attention Telephone/Terminal commands), the software is programmed using C-language. The design has been implemented in the hardware using ZigBee EM357 module, Atmega128 MCU (microcontroller unit) and Sony Ericsson T290i mobile phone set.
A Compact, Low-Profile Metasurface-Enabled Antenna for Wearable Medical Body-Area Network Devices

We propose a compact conformal wearable antenna that operates in the 2.36-2.4 GHz medical body-area network band. The antenna is enabled by placing a highly truncated metasurface, consisting of only a two by two array of I-shaped elements, underneath a planar monopole. In contrast to previously reported artificial magnetic conducting ground plane backed antenna designs, here the metasurface acts not only as a ground plane for isolation, but also as the main radiator. An antenna prototype was fabricated and tested, showing a strong agreement between simulation and measurement. Comparing to previously proposed wearable antennas, the demonstrated antenna has a compact form factor of 0.5 λ0 ×0.3 λ0 ×0.028 λ0, all while achieving a 5.5% impedance bandwidth, a gain of 6.2 dBi, and a front-to-back ratio higher than 23 dB. Further numerical and experimental investigations reveal that the performance of the antenna is extraordinarily robust to both structural deformation and human body loading, far superior to both planar monopoles and microstrip patch antennas. Additionally, the introduced metal backed metasurface enables a 95.3% reduction in the specific absorption rate, making such an antenna a prime candidate for incorporation into various wearable devices.
Another Stemmer

In natural language processing, conflation is the process of merging or lumping together nonidentical words which refer to the same principal concept. This can relate both to words which are entirely different in form (e.g., "group" and "collection"), and to words which share some common root (e.g., "group", "grouping", "subgroups"). In the former case the words can only be mapped by referring to a dictionary or thesaurus, but in the latter case use can be made of the orthographic similarities between the forms. One popular approach is to remove affixes from the input words, thus reducing them to a stem; if this could be done correctly, all the variant forms of a word would be converted to the same standard form. Since the process is aimed at mapping for retrieval purposes, the stem need not be a linguistically correct lemma or root (see also Frakes 1982).
Raman spectrum of graphene and graphene layers.

Graphene is the two-dimensional building block for carbon allotropes of every other dimensionality. We show that its electronic structure is captured in its Raman spectrum that clearly evolves with the number of layers. The D peak second order changes in shape, width, and position for an increasing number of layers, reflecting the change in the electron bands via a double resonant Raman process. The G peak slightly down-shifts. This allows unambiguous, high-throughput, nondestructive identification of graphene layers, which is critically lacking in this emerging research area.
Multi-Criteria Recommender Systems

This chapter aims to provide an overview of the class of multi-criteria recommender systems. First, it defines the recommendation problem as a multi-criteria decision making (MCDM) problem, and reviews MCDM methods and techniques that can support the implementation of multi-criteria recommenders. Then, it focuses on the category of multi-criteria rating recommenders – techniques that provide recommendations by modelling a user’s utility for an item as a vector of ratings along several criteria. A review of current algorithms that use multicriteria ratings for calculating predictions and generating recommendations is provided. Finally, the chapter concludes with a discussion on open issues and future challenges for the class of multi-criteria rating recommenders.
A Comparison of Waveform Fractal Dimension Algorithms

The fractal dimension of a waveform represents a powerful tool for transient detection. In particular, in analysis of electroencephalograms and electrocardiograms, this feature has been used to identify and distinguish specific states of physiologic function. A variety of algorithms are available for the computation of fractal dimension. In this study, the most common methods of estimating the fractal dimension of biomedical signals directly in the time domain (considering the time series as a geometric object) are analyzed and compared. The analysis is performed over both synthetic data and intracranial electroencephalogram data recorded during presurgical evaluation of individuals with epileptic seizures. The advantages and drawbacks of each technique are highlighted. The effects of window size, number of overlapping points, and signal-to-noise ratio are evaluated for each method. This study demonstrates that a careful selection of fractal dimension algorithm is required for specific applications.
Antimicrobial agents from plants: antibacterial activity of plant volatile oils.

The volatile oils of black pepper [Piper nigrum L. (Piperaceae)], clove [Syzygium aromaticum (L.) Merr. & Perry (Myrtaceae)], geranium [Pelargonium graveolens L'Herit (Geraniaceae)], nutmeg [Myristica fragrans Houtt. (Myristicaceae), oregano [Origanum vulgare ssp. hirtum (Link) Letsw.  and thyme [Thymus vulgaris L. (Lamiaceae)] were assessed for antibacterial activity against 25 different genera of bacteria. These included animal and plant pathogens, food poisoning and spoilage bacteria. The volatile oils exhibited considerable inhibitory effects against all the organisms under test while their major components demonstrated various degrees of growth inhibition.
PLANT LEAF RECOGNITION

This paper proposes an automated system for recognizing plant species based on leaf images. Plant leaf images corresponding to three plant types, are analyzed using three different shape modelling techniques, the first two based on the Moments-Invariant (M-I) model and the Centroid-Radii (C-R) model and the third based on a proposed technique of Binary-Superposition (B-S). For the M-I model the first four central normalized moments have been considered. For the C-R model an edge detector has been used to identify the boundary of the leaf shape and 36 radii at 10 degree angular separation have been used to build the shape vector. The proposed approach consists of comparing binary versions of the leaf images through superposition and using the sum of non-zero pixel values of the resultant as the feature vector. The data set for experimentations consists of 180 images divided into training and testing sets and comparison between them is done using Manhattan, Euclidean and intersection norms. Accuracies obtained using the proposed technique is seen to be an improvement over the M-I and C-R based techniques, and comparable to the best figures reported in extant literature.
Trend Recalling Algorithm for Automated Online Trading in Stock Market

Unlike financial forecasting, a type of mechanical trading technique called Trend Following (TF) doesn’t predict any market movement; instead it identifies a trend at early time of the day, and trades automatically afterwards by a pre-defined strategy regardless of the moving market directions during run time. Trend following trading has a long and successful history among speculators. The traditional TF trading method is by human judgment in setting the rules (aka the strategy). Subsequently the TF strategy is executed in pure objective operational manner. Finding the correct strategy at the beginning is crucial in TF. This usually involves human intervention in first identifying a trend, and configuring when to place an order and close it out, when certain conditions are met. In this paper, we presented a new type of TF, namely Trend Recalling algorithm that operates in a totally automated manner. It works by partially matching the current trend with one of the proven successful patterns from the past. Our experiments based on real stock market data show that this algorithm has an edge over the other trend following methods in profitability. The new algorithm is also compared to time-series forecasting type of stock trading, and it can even outperform the best forecasting type in a
Big data framework for students' academic performance prediction: A systematic literature review

Big Data is becoming an integral part of education system worldwide, bringing in so much of prediction potential and therefore opportunities to improve learning and teaching methodologies. In fact, it has become the digital policy instrument for policy makers to make strategic decisions supported by big data analytics.  to establish a general overview and background in establishing gaps that need to be addressed in big data analytics for education landscape. A total of 59 research papers from 473 papers were chosen and analyzed. There is an increased trend in incorporating big data in education however, it is not sufficient and need more research in this area particularly predictive models and frameworks for educational settings.
A meta-analysis of human-system interfaces in unmanned aerial vehicle (UAV) swarm management.

A meta-analysis was conducted to systematically evaluate the current state of research on human-system interfaces for users controlling semi-autonomous swarms composed of groups of drones or unmanned aerial vehicles (UAVs). UAV swarms pose several human factors challenges, such as high cognitive demands, non-intuitive behavior, and serious consequences for errors. This article presents findings from a meta-analysis of 27 UAV swarm management papers focused on the human-system interface and human factors concerns, providing an overview of the advantages, challenges, and limitations of current UAV management interfaces, as well as information on how these interfaces are currently evaluated. In general allowing user and mission-specific customization to user interfaces and raising the swarm's level of autonomy to reduce operator cognitive workload are beneficial and improve situation awareness (SA). It is clear more research is needed in this rapidly evolving field.
Handling class imbalance in customer churn prediction

0957-4174/$ see front matter 2008  A doi:10.1016/j.eswa.2008.05.027 * Corresponding author.  http://www.crm.UGent.be (D. Van den Poel). Customer churn is often a rare event in service industries, but of great interest and great value. Until recently, however, class imbalance has not received much attention in the context of data mining [Weiss, G. M. (2004). Mining with rarity: A unifying framework.  Explorations, 6 (1), 7–19]. In this study, we investigate how we can better handle class imbalance in churn prediction. Using more appropriate evaluation metrics (AUC, lift), we investigated the increase in performance of sampling (both random and advanced under-sampling) and two specific modelling techniques (gradient boosting and weighted random forests) compared to some standard modelling techniques. AUC and lift prove to be good evaluation metrics. AUC does not depend on a threshold, and is therefore a better overall evaluation metric compared to accuracy. Lift is very much related to accuracy, but has the advantage of being well used in marketing practice [Ling, C., & Li, C. (1998). Data mining for direct marketing problems and solutions. In Proceedings of the fourth international conference on knowledge discovery and data mining (KDD-98). New York, NY: AAAI Press]. Results show that under-sampling can lead to improved prediction accuracy, especially when evaluated with AUC. Unlike Ling and Li [Ling, C., & Li, C. (1998). Data mining for direct marketing problems and solutions. In Proceedings of the fourth international conference on knowledge discovery and data mining (KDD98). New York, NY: AAAI Press], we find that there is no need to under-sample so that there are as many churners in your training set as non churners. Results show no increase in predictive performance when using the advanced sampling technique CUBE in this study. This is in line with findings of Japkowicz [Japkowicz, N. (2000). The class imbalance problem: significance and strategies. In Proceedings of the 2000 international conference on artificial intelligence (IC-AI’2000): Special track on inductive learning, Las Vegas, Nevada], who noted that using sophisticated sampling techniques did not give any clear advantage. Weighted random forests, as a cost-sensitive learner, performs significantly better compared to random forests, and is therefore advised. It should, however always be compared to logistic regression. Boosting is a very robust classifier, but never outperforms any other technique.
The Air Traffic Flow Management Problem: An Integer Optimization Approach

In this paper, we present a new Integer Program (IP) for the Air Traffic Flow Management (ATFM) problem. The model we propose provides a complete representation of all the phases of each flights, i.e., the phase of taking-off, of cruising and of landing; suggesting all the actions to be implemented to achieve the goal of safe, efficient, and expeditious aircraft movement. The distinctive feature of the model is that it allows rerouting decisions. These decisions are formulated by means of “local” conditions, which allow us to represent such decisions in a very compact way by only introducing new constraints. Moreover, to strengthen the polyhedral structure of the underlying relaxation, we also present three classes of valid inequalities. We report short computational times (less than 15 minutes) on instances of the size of the US air traffic control system that make it realistic that our approach can be used as the main engine of managing air traffic in the US.
RFID based attendance system

Most educational institutions' administrators are concerned about student irregular attendance. Truancies can affect student overall academic performance. The conventional method of taking attendance by calling names or signing on paper is very time consuming and insecure, hence inefficient. Radio Frequency Identification (RFID) based attendance system is one of the solutions to address this problem. This system can be used to take attendance for student in school, college, and university. It also can be used to take attendance for workers in working places. Its ability to uniquely identify each person based on their RFID tag type of ID card make the process of taking the attendance easier, faster and secure as compared to conventional method. Students or workers only need to place their ID card on the reader and their attendance will be taken immediately. With real time clock capability of the system, attendance taken will be more accurate since the time for the attendance taken will be recorded. The system can be connected to the computer through RS232 or Universal Serial Bus (USB) port and store the attendance taken inside database. An alternative way of viewing the recorded attendance is by using HyperTerminal software. A prototype of the system has been successfully fabricated.
Long non-coding RNAs: insights into functions

In mammals and other eukaryotes most of the genome is transcribed in a developmentally regulated manner to produce large numbers of long non-coding RNAs (ncRNAs). Here we review the rapidly advancing field of long ncRNAs, describing their conservation, their organization in the genome and their roles in gene regulation. We also consider the medical implications, and the emerging recognition that any transcript, regardless of coding potential, can have an intrinsic function as an RNA.
A randomised controlled trial of adjunctive yoga and adjunctive physical exercise training for cognitive dysfunction in schizophrenia.

Yoga and physical exercise have been used as adjunctive intervention for cognitive dysfunction in schizophrenia (SZ), but controlled comparisons are lacking.  A single-blind randomised controlled trial was designed to evaluate whether yoga training or physical exercise training enhance cognitive functions in SZ, based on a prior pilot study.



Consenting, clinically stable, adult outpatients with SZ (n=286) completed baseline assessments and were randomised to treatment as usual (TAU), supervised yoga training with TAU (YT) or supervised physical exercise training with TAU (PE). Based on the pilot study, the primary outcome measure was speed index for the cognitive domain of 'attention' in the Penn computerised neurocognitive battery. Using mixed models and contrasts, cognitive functions at baseline, 21 days (end of training), 3 and 6 months post-training were evaluated with intention-to-treat paradigm.



Speed index of attention domain in the YT group showed greater improvement than PE at 6 months follow-up (p<0.036, effect size 0.51). In the PE group, 'accuracy index of attention domain showed greater improvement than TAU alone at 6-month follow-up (p<0.025, effect size 0.61). For several other cognitive domains, significant improvements were observed with YT or PE compared with TAU alone (p<0.05, effect sizes 0.30-1.97).



Both YT and PE improved attention and additional cognitive domains well past the training period, supporting our prior reported beneficial effect of YT on speed index of attention domain. As adjuncts, YT or PE can benefit individuals with SZ.
Service Management and IT Governance : Review , Comparative Analysis and their Impact on Utility Computing

Over the years, IT has become the backbone of businesses to the point where it would be impossible for many to function (let alone succeed) without it. As a result of its increasing role in the enterprise, the IT function is changing, morphing from a technology provider into a strategic partner. Concurrent to these changes, the IT infrastructure is moving towards a centralized, highly adaptive utility model. In this paper, we review the different open and industrial frameworks that support IT organizations in this transition and explore their impact on the next generation of IT infrastructure.