nils-herrmann/clean_scirepeval huggingface dataset:
A Neural Network Model for Low-Resource Universal Dependency Parsing

Accurate dependency parsing requires large treebanks, which are only available for a few languages. We propose a method that takes advantage of shared structure across languages to build a mature parser using less training data. We propose a model for learning a shared “universal” parser that operates over an interlingual continuous representation of language, along with language-specific mapping components. Compared with supervised learning, our methods give a consistent 8-10% improvement across several treebanks in low-resource simulations.
Investigation of MPPT for PV applications by mathematical model

This paper will discuss the PV system as whole by emulating load and PV power supply in the lab conditions. The paper discussion would be based on the obtained results for Monocrystalline silicon PV cell module. The important point of interest would be the voltage open circuit, short circuit current, maximum power. The lab has been done in the Standard Testing Conditions (STC) to ensure a fair comparison under different conditions. This paper confirmed that as temperature increased the voltage open circuit decreases as well as power decreases, and when insolation increases the short circuit current increases makes the maximum power increases. Per unit value investigating whether temperature or insolation has a bigger impact on Pmax, and for these experimental conditions, it has been found that insolation affects the system. As well as the paper will focus on building a SIMULINK/Matlab Module to simulate PV cells under different environmental conditions and help in designing future PV systems. The work will be compared with lab-emulated data to be validated and address the accuracy of the obtained results with the real life results. The paper will study the integration of DC-DC Boost converter with open loop control feedback circuit (Fixed Duty Ratio). In addition, the paper will discuss three different approaches of Maximum Power Point Tracking (MPPT) which are P&O, RCC and INC. The performance of each one will be investigated and verified.
Application of Artificial Intelligence to Real-Time Fault Detection in Permanent-Magnet Synchronous Machines

This paper discusses faults in rotating electrical machines in general and describes a fault detection technique using artificial neural network (ANN) which is an expert system to detect short-circuit fault currents in the stator windings of a permanent-magnet synchronous machine (PMSM). The experimental setup consists of PMSM coupled mechanically to a dc motor configured to run in torque mode. Particle swarm optimization is used to adjust the weights of the ANN. All simulations are carried out in MATLAB/SIMULINK environment. The technique is shown to be effective and can be applied to real-time fault detection.
EasyTicket: a ticket routing recommendation engine for enterprise problem resolution

Managing problem tickets is a key issue in IT service industry. A large service provider may handle thousands of problem tickets from its customers on a daily basis. The efficiency of processing these tickets highly depends on ticket routing—transferring problem tickets among expert groups in search of the right resolver to the ticket. Despite that many ticket management systems are available, ticket routing in these systems is still manually operated by support personnel. In this demo, we introduce EasyTicket, a ticket routing recommendation engine that helps automate this process. By mining ticket history data, we model an enterprise social network that represents the functional relationships among various expert groups in ticket routing. Based on this network, our system then provides routing recommendations to new tickets. Our experimental studies on 1.4 million real-world problem tickets show that on average, EasyTicket can improve the efficiency of ticket routing by 35%.
Support Vector Machines for Prediction of Futures Prices in Indian Stock Market

Machine learning methods are being used by several researchers for successfully predicting prices of financial instruments from the financial time series data of different markets. As the nature of markets in different regions are different, in this paper two machine learning techniques: Back Propagation Technique (BP) and Support Vector Machine Technique (SVM) have been used to predict futures prices traded in Indian stock market. The performances of these techniques are compared and it is observed that SVM provides better performance results as compared to BP technique. The implementation is carried out using MATLAB and SVM Tools (LS-SVM Tool Box).
On Limits of Wireless Communications in a Fading Environment when Using Multiple Antennas

This paper is motivated by the need for fundamental understanding of ultimate limits of bandwidth efficient delivery of higher bit-rates in digital wireless communications and to also begin to look into how these limits might be approached. We examine exploitation of multi-element array (MEA) technology, that is processing the spatial dimension (not just the time dimension) to improve wireless capacities in certain applications. Specifically, we present some basic information theory results that promise great advantages of using MEAs in wireless LANs and building to building wireless communication links. We explore the important case when the channel characteristic is not available at the transmitter but the receiver knows (tracks) the characteristic which is subject to Rayleigh fading. Fixing the overall transmitted power, we express the capacity offered by MEA technology and we see how the capacity scales with increasing SNR for a large but practical number, , of antenna elements at bothtransmitter and receiver. We investigate the case of independent Rayleigh faded paths between antenna elements and find that with high probability extraordinary capacity is available. Compared to the baseline n = 1 case, which by Shannon’s classical formula scales as one more bit/cycle for every 3 dB of signal-to-noise ratio (SNR) increase, remarkably with MEAs, the scaling is almost like n more bits/cycle for each 3 dB increase in SNR. To illustrate how great this capacity is, even for small n, take the cases n = 2, 4 and 16 at an average received SNR of 21 dB. For over 99% of the channels the capacity is about 7, 19 and 88 bits/cycle respectively, while if n = 1 there is only about 1.2 bit/cycle at the 99% level. For say a symbol rate equal to the channel bandwith, since it is the bits/symbol/dimension that is relevant for signal constellations, these higher capacities are not unreasonable. The 19 bits/cycle for n = 4 amounts to 4.75 bits/symbol/dimension while 88 bits/cycle for n = 16 amounts to 5.5 bits/symbol/dimension. Standard approaches such as selection and optimum combining are seen to be deficient when compared to what will ultimately be possible. New codecs need to be invented to realize a hefty portion of the great capacity promised.
Smart farming using Arduino and data mining

The current scenario in India depicts a steady decrease of agriculture contribution to the Indian GDP. The reasons for this condition are mainly - the current erratic weather condition and crop loss. New technologies and advanced fertilizers have not penetrated through the corners of India where majority of the farmers reside. Through this paper, we introduce a concept for smart farming which utilizes wireless sensor web technology for moisture detection in the soil in conjunction with a smart phone application which plays a vital role in helping farmers. We introduce Arduino based automatic plant watering system and android application which will help to control Arduino via internet. Also, this android application provides farmers with agricultural related information such as costs of seeds, moisture level required, type of soil needed, weather forecast, fertilizers and pesticides to be used.
Toward Enabling Automated Cognition and Decision-Making in Complex Cyber-Physical Systems

This article presents a framework for empowering automated cognition and decision making in complex cyber-physical systems (CPS). The key idea is for each cyber-physical component in the system to be able to construct from observations and actions a reduced model of its behavior and respond to external stimuli in the form of a (timed) probabilistic automaton such as a semi-Markov decision process. The reduced order behavioral models of various cyber-physical components can then be integrated into an optimization and decision-making module to determine the best actions for the components under different operating scenarios and cost / payoff functions by solving a bounded-rationality global decision-making
Active Clamp Sepic Converter with Power Factor Correction

A soft switching sepic converter with active clamp scheme is presented in this paper to achieve zero voltage switching (ZVS) on the power switches. The active clamp topology is used in the circuit to limit the voltage stresses of power semiconductors. When main switch is turned off, the positive resonant inductor current flows through the anti-parallel diode of auxiliary switch so that the auxiliary switch can be turned on at ZVS. When auxiliary switch is turned off, the negative resonant inductor current will discharge the output capacitor of main switch so that the ZVS turn-on of main switch can be achieved. Finally, experiments based on a laboratory prototype rated at 300 W are presented to verify the effectiveness of the proposed converter.
Low Power High-Efficiency Shift Register Using Implicit Pulse-Triggered Flip-Flop in 130 nm CMOS Process for a Cryptographic RFID Tag

The shift register is a type of sequential logic circuit which is mostly used for storing digital data or the transferring of data in the form of binary numbers in radio frequency identification (RFID) applications to improve the security of the system. A power-efficient shift register utilizing a new flip-flop with an implicit pulse-triggered structure is presented in this article. The proposed flip-flop has features of high performance and low power. It is composed of a sampling circuit implemented by five transistors, a C-element for rise and fall paths, and a keeper stage. The speed is enhanced by executing four clocked transistors together with a transition condition technique. The simulation result confirms that the proposed topology consumes the lowest amounts of power of 30.1997 and 22.7071 nW for parallel in –parallel out (PIPO) and serial in –serial out (SISO) shift register respectively covering 22 μm2 chip area. The overall design consist of only 16 transistors and is simulated in 130 nm complementary-metal-oxide-semiconductor (CMOS) technology with a 1.2 V power supply.
Influence of music and its genres on respiratory rate and pupil diameter variations in cats under general anaesthesia: contribution to promoting patient safety.

The aims of the study were to recognise if there is any auditory sensory stimuli processing in cats under general anaesthesia, and to evaluate changes in respiratory rate (RR) and pupillary diameter (PD) in anaesthetised patients exposed to different music genres, while relating this to the depth of anaesthesia.



A sample of 12 cats submitted for elective ovariohysterectomy was exposed to 2 min excerpts of three different music genres (classical [CM], pop [PM] and heavy metal [HM]) at three points during surgery (T1 = coeliotomy; T2 = ligature placement and transection of the ovarian pedicle; T3 = ligature placement and transection of the uterine body). A multiparametric medical monitor was used to measure the RR, and a digital calliper was used for PD measurement. Music was delivered through headphones, which fully covered the patient's ears. P values   <0.05 were considered to be statistically significant.   



Statistically significant differences between stimuli conditions for all surgical points were obtained for RR (T1, P = 0.03; T2, P = 0.00; T3, P = 0.00) and for PD (T1, P = 0.03; T2, P = 0.04; T3, P = 0.00). Most individuals exhibited lower values for RR and PD when exposed to CM, intermediate values to PM and higher values to HM.



The results suggest that cats under general anaesthesia are likely to perform auditory sensory stimuli processing. The exposure to music induces RR and PD variations modulated by the genre of music and is associated with autonomic nervous system activity. The use of music in the surgical theatre may contribute to allowing a reduced anaesthetic dose, minimising undesirable side effects and thus promoting patient safety.
Real time personalized search on social networks

Internet users are shifting from searching on traditional media to social network platforms (SNPs) to retrieve up-to-date and valuable information. SNPs have two unique characteristics: frequent content update and small world phenomenon. However, existing works are not able to support these two features simultaneously. To address this problem, we develop a general framework to enable real time personalized top-k query. Our framework is based on a general ranking function that incorporates time freshness, social relevance and textual similarity. To ensure efficient update and query processing, there are two key challenges. The first is to design an index structure that is update-friendly while supporting instant query processing. The second is to efficiently compute the social relevance in a complex graph. To address these challenges, we first design a novel 3D cube inverted index to support efficient pruning on the three dimensions simultaneously. Then we devise a cube based threshold algorithm to retrieve the top-k results, and propose several pruning techniques to optimize the social distance computation, whose cost dominates the query processing. Furthermore, we optimize the 3D index via a hierarchical partition method to enhance our pruning on the social dimension. Extensive experimental results on two real world large datasets demonstrate the efficiency and the robustness of our proposed solution.
Energy Management System of Fuel-Cell-Battery Hybrid Tramway

This paper describes the configuration, modeling, and control of a fuel cell (FC)-battery-powered hybrid system for the Metro Centro tramway in Seville, Spain. The proposed hybrid system presents a polymer electrolyte membrane FC as the primary energy source and a nickel-metal hydride cell battery as the secondary energy source, which supplements the output of the FC during tramway acceleration or whenever else needed and cruise and for energy recovery during braking. The tramway presents a traction system which is composed of four traction induction motor drives. The hybrid system also supplies the power for the auxiliary services. The power conditioning system is composed of two converters: 1) a boost-type unidirectional dc/dc converter for the FC and 2) a boost-type bidirectional dc/dc converter for the battery. The energy management system (EMS) of the hybrid tramway determines the reference signals for the electric motor drives, FC, and power converters in order to regulate accurately the power from the two electrical sources. EMS also determines the reference signal for energy dissipation in braking chopper when required during regenerative braking. In this paper, the proposed hybrid system is evaluated for the real driving cycle of the tramway. The results demonstrate the hybrid system capability to meet appropriate driving cycle.
Visual stimuli activate auditory cortex in the deaf

Previous brain imaging studies have demonstrated responses to tactile and auditory stimuli in visual cortex of blind subjects, suggesting that removal of one sensory modality leads to neural reorganization of the remaining modalities. To investigate whether similar 'cross-modal' plasticity occurs in human auditory cortex, we used functional magnetic resonance imaging (fMRI) to measure visually evoked activity in auditory areas of both early-deafened and hearing individuals. Here we find that deaf subjects exhibit activation in a region of the right auditory cortex, corresponding to Brodmann's areas 42 and 22, as well as in area 41 (primary auditory cortex), demonstrating that early deafness results in the processing of visual stimuli in auditory cortex.
Social Media: The Good, the Bad, and the Ugly

Companies when strategizing are looking for innovative ways to have a competitive advantage over their competitors. One way in which they compete is by the adoption of social media. Social media has evolved over the years and as a result, new concepts and applications are developed which promises to provide business value to a company. However, despite the usefulness of social media, many businesses fail to reap its full benefits. The current literature shows evidence of lack of strategically designed process for companies to successfully implement social media. The purpose of this study is to suggest a framework which provides the necessary alignment between social media goals with business objectives. From the literature review, a social media strategy framework was derived to offer an effective step by step approach to the development and imple‐ mentation of social media goals aligned with a company’s business objectives. The contribution to this study is the development of a social media strategy framework that can be used by organisations for business value.
Homography-based 2D Visual Tracking and Servoing

The objective of this paper is to propose a new homography-based approach to image-based visual tracking and servoing. The visual tracking algorithm proposed in the paper is based on a new efficient second-order minimization method. Theoretical analysis and comparative experiments with other tracking approaches show that the proposed method has a higher convergence rate than standard first-order minimization techniques. Therefore, it is well adapted to real-time robotic applications. The output of the visual tracking is a homography linking the current and the reference image of a planar target. Using the homography, a task function isomorphic to the camera pose has been designed. A new image-based control law is proposed which does not need any measure of the 3D structure of the observed target (e.g. the normal to the plane). The theoretical proof of the existence of the isomorphism between the task function and the camera pose and the theoretical proof of the stability of the control law are provided. The experimental results, obtained with a 6 d.o.f. robot, show the advantages of the proposed method with respect to the existing approaches. visual tracking, visual servoing, efficient second-order minimization, homography-based control law
Design, Implementation, and Performance Evaluation of a Flexible Low-Latency Nanowatt Wake-Up Radio Receiver

Wireless sensor networks (WSNs) have received significant attention in recent years and have found a wide range of applications, including structural and environmental monitoring, mobile health, home automation, Internet of Things, and others. As these systems are generally battery operated, major research efforts focus on reducing power consumption, especially for communication, as the radio transceiver is one of the most power-hungry components of a WSN. Moreover, with the advent of energy-neutral systems, the emphasis has shifted toward research in microwatt (or even nanowatt) communication protocols or systems. A significant number of wake-up radio receiver (WUR) architectures have been proposed to reduce the communication power of WSN nodes. In this work, we present an optimized ultra-low power (nanowatt) wake-up receiver for use in WSNs, designed with low-cost off-the-shelf components. The wake-up receiver achieves power consumption of 152 nW (with -32 dBm sensitivity), sensitivity up to -55 dBm (with maximum power of 1,2 μW), latency from 8 μs, tunable frequency, and short commands communication. In addition, a low power solution, which includes addressing capability directly in the wake-up receiver, is proposed. Experimental results and simulations demonstrate low power consumption, functionality, and benefits of the design optimization compared with other solutions, as well as the benefits of addressing false positive (FP) outcomes reduction.
Axe UX: Exploring long-term user experience with iScale and AttrakDiff

Positive user experience (UX) is an important goal in product design. Positive long-term UX is believed to improve customer loyalty, therefore being vital for continuous commercial success. Most UX research investigates momentary or short-term UX although the relationship between the user and the product evolves over time. There is a need to develop methods for measuring long-term UX and evaluate their feasibility in different product contexts. In this explorative study, 18 customers reported their experiences during the first three months of use of a non-interactive design tool, an axe. UX was evaluated with retrospective iScale tool and monthly repeated AttrakDiff questionnaire. iScale demonstrated the long-term trend of the attractiveness of the product well and provided information about the causes of the change in the experience. The AttrakDiff questionnaire was a good indicator of attractiveness during a longer period of time and is also well suited to remote studies.
Detecting kernel-level rootkits through binary analysis

A rootkit is a collection of tools used by intruders to keep the legitimate users and administrators of a compromised machine unaware of their presence. Originally, root-kits mainly included modified versions of system auditing programs (e.g., ps or netstat on a Unix system). However, for operating systems that support loadable kernel modules (e.g., Linux and Solaris), a new type of rootkit has recently emerged. These rootkits are implemented as kernel modules, and they do not require modification of user-space binaries to conceal malicious activity. Instead, these rootkits operate within the kernel, modifying critical data structures such as the system call table or the list of currently-loaded kernel modules. This paper presents a technique that exploits binary analysis to ascertain, at load time, if a module's behavior resembles the behavior of a rootkit. Through this method, it is possible to provide additional protection against this type of malicious modification of the kernel. Our technique relies on an abstract model of module behavior that is not affected by small changes in the binary image of the module. Therefore, the technique is resistant to attempts to conceal the malicious nature of a kernel module.
Colostrum – its Composition, Benefits as a Nutraceutical – A Review.

Colostrumis ‘the first milk secreted at the time of parturition, differing from the milk secreted later, by containing more lactalbumin and lactoprotein, and also being rich in antibodies that confer passive immunity to the newborn, also called “foremilk”. Human Colostrum (HC) and Bovine Colostrum (BC) are rich in protein, immunoglobulin, lactoferrin and growth factors. Recent studies suggest that colostrum components, immunoglobulin and growth factor benefits physically active personandin treatment of autoimmune disorders. It is used for the treatment of a wide variety of gastrointestinal conditions, including non-steroidal anti-inflammatory drug–induced gut injury, H pylori infection, immune deficiency related diarrhea as well as infective diarrhea. This review explores the current knowledge on the beneficial effect of colostrum supplementation in the above condition.
Minimally Supervised Number Normalization

We propose two models for verbalizing numbers, a key component in speech recognition and synthesis systems. The first model uses an end-to-end recurrent neural network. The second model, drawing inspiration from the linguistics literature, uses finite-state transducers constructed with a minimal amount of training data. While both models achieve near-perfect performance, the latter model can be trained using several orders of magnitude less data than the former, making it particularly useful for low-resource languages.