nils-herrmann/clean_scirepeval huggingface dataset:
Target size study for one-handed thumb use on small touchscreen devices

This paper describes a two-phase study conducted to determine optimal target sizes for one-handed thumb use of mobile handheld devices equipped with a touch-sensitive screen. Similar studies have provided recommendations for target sizes when using a mobile device with two hands plus a stylus, and interacting with a desktop-sized display with an index finger, but never for thumbs when holding a small device in a single hand. The first phase explored the required target size for single-target (discrete) pointing tasks, such as activating buttons, radio buttons or checkboxes. The second phase investigated optimal sizes for widgets used for tasks that involve a sequence of taps (serial), such as text entry. Since holding a device in one hand constrains thumb movement, we varied target positions to determine if performance depended on screen location. The results showed that while speed generally improved as targets grew, there were no significant differences in error rate between target sizes =9.6 mm in discrete tasks and targets =7.7 mm in serial tasks. Along with subjective ratings and the findings on hit response variability, we found that target size of 9.2 mm for discrete tasks and targets of 9.6 mm for serial tasks should be sufficiently large for one-handed thumb use on touchscreen-based handhelds without degrading performance and preference.
High Speed Serial Link Transmitter for 10Gig Ethernet Applications

This paper presents a Current Mode Logic (CML) transmitter circuit that forms part of a Serializer/ Deserializer IP core used in a high speed I/O links targeted for 10+ Gbps Ethernet applications. The paper discusses the 3 tap FIR filter equalization implemented to minimize the effects of Inter Symbol interference (ISI) and attenuation of high speed signal content in the channel. The paper also discusses on the design optimization implemented using hybrid segmentation of driver segments which results in improved control on the step sizes variations, Differential Non Linearity (DNL) errors at segment boundaries over Process mismatch variations.
Implementation and Performance of the Simon and Speck Lightweight Block Ciphers on ASICs

Simon and Speck are families of lightweight block ciphers proposed in June 2013 by the US National Security Agency. Here we discuss ASIC implementations of these algorithms, presenting in some detail how one implements the smallest bit-serial versions of the algorithms. We also give area and throughput results for a variety of implementations—bit serial, iterated, and partially and fully pipelined. To the best of our knowledge, each version of Simon admits implementations with the smallest area of any comparable block cipher with a flexible key, and Speck is close behind: at the 64-bit block/128-bit key size, for example, both can be realized in under 1000 GE. More surprisingly, however, since they were intended for use on constrained platforms, Simon and Speck allow for extremely high efficiency and high-throughput implementations; each version of Simon, in particular, has the highest efficiency (throughput divided by area) of any comparably sized block cipher we’ve seen—lightweight or not.
JackHare: a framework for SQL to NoSQL translation using MapReduce

As data exploration has increased rapidly in recent years, the datastore and data processing are getting more and more attention in extracting important information. To find a scalable solution to process the large-scale data is a critical issue in either the relational database system or the emerging NoSQL database. With the inherent scalability and fault tolerance of Hadoop, MapReduce is attractive to process the massive data in parallel. Most of previous researches focus on developing the SQL or SQL-like queries translator with the Hadoop distributed file system. However, it could be difficult to update data frequently in such file system. Therefore, we need a flexible datastore as HBase not only to place the data over a scale-out storage system, but also to manipulate the changeable data in a transparent way. However, the HBase interface is not friendly enough for most users. A GUI composed of SQL client application and database connection to HBase will ease the learning curve. In this paper, we propose the JackHare framework with SQL query compiler, JDBC driver and a systematical method using MapReduce framework for processing the unstructured data in HBase. After importing the JDBC driver to a SQL client GUI, we can exploit the HBase as the underlying datastore to execute the ANSI-SQL queries. Experimental results show that our approaches can perform well with efficiency and scalability.
Scalable Predictive Analysis in Critically Ill Patients Using a Visual Open Data Analysis Platform

With the accumulation of large amounts of health related data, predictive analytics could stimulate the transformation of reactive medicine towards Predictive, Preventive and Personalized (PPPM) Medicine, ultimately affecting both cost and quality of care. However, high-dimensionality and high-complexity of the data involved, prevents data-driven methods from easy translation into clinically relevant models. Additionally, the application of cutting edge predictive methods and data manipulation require substantial programming skills, limiting its direct exploitation by medical domain experts. This leaves a gap between potential and actual data usage. In this study, the authors address this problem by focusing on open, visual environments, suited to be applied by the medical community. Moreover, we review code free applications of big data technologies. As a showcase, a framework was developed for the meaningful use of data from critical care patients by integrating the MIMIC-II database in a data mining environment (RapidMiner) supporting scalable predictive analytics using visual tools (RapidMiner's Radoop extension). Guided by the CRoss-Industry Standard Process for Data Mining (CRISP-DM), the ETL process (Extract, Transform, Load) was initiated by retrieving data from the MIMIC-II tables of interest. As use case, correlation of platelet count and ICU survival was quantitatively assessed. Using visual tools for ETL on Hadoop and predictive modeling in RapidMiner, we developed robust processes for automatic building, parameter optimization and evaluation of various predictive models, under different feature selection schemes. Because these processes can be easily adopted in other projects, this environment is attractive for scalable predictive analytics in health research.
A New Pattern Recognition Method for Detection and Localization of Myocardial Infarction Using T-Wave Integral and Total Integral as Extracted Features from One Cycle of ECG Signal

In this paper we used two new features i.e. T-wave integral and total integral as extracted feature from one cycle of normal and patient ECG signals to detection and localization of myocardial infarction (MI) in left ventricle of heart. In our previous work we used some features of body surface potential map data for this aim. But we know the standard ECG is more popular, so we focused our detection and localization of MI on standard ECG. We use the T-wave integral because this feature is important impression of T-wave in MI. The second feature in this research is total integral of one ECG cycle, because we believe that the MI affects the morphology of the ECG signal which leads to total integral changes. We used some pattern recognition method such as Artificial Neural Network (ANN) to detect and localize the MI, because this method has very good accuracy for classification of normal signal and abnormal signal. We used one type of Radial Basis Function (RBF) that called Probabilistic Neural Network (PNN) because of its nonlinearity property, and used other classifier such as k-Nearest Neighbors (KNN), Multilayer Perceptron (MLP) and Naive Bayes Classification. We used PhysioNet database as our training and test data. We reached over 76% for accuracy in test data for localization and over 94% for detection of MI. Main advantages of our method are simplicity and its good accuracy. Also we can improve the accuracy of classification by adding more features in this method. A simple method based on using only two features which were extracted from standard ECG is presented and has good accuracy in MI localization.
Monitoring in vivo hypogastric nerve activity during bladder filling in canines.

transection of the lumbosacral spinal roots innervating the bladder as well as the hypogastric nerves.  The residual, low amplitude evoked contraction during L2 spinal root stimulation is likely due to low number of direct projections from the L2 ventral horn to the bladder.1  Recording results suggests hypogastric efferent fibers mainly contribute to bladder storage   These refined electroneurogram recording methods may be suitable by monitoring sensory and motor activity in the transferred nerves after bladder reinnervation. Ekta Tiwari, Mary F. Barbe Michel A. Lemay, Danielle M. Salvadeo, Matthew M. Wood, Michael Mazzei, Luke V. Musser, Zdenka J. Delalic, Alan S. Braverman, and Michael R. Ruggieri, Sr.
ClusterViz: A Cytoscape APP for Cluster Analysis of Biological Network

Cluster analysis of biological networks is one of the most important approaches for identifying functional modules and predicting protein functions. Furthermore, visualization of clustering results is crucial to uncover the structure of biological networks. In this paper, ClusterViz, an APP of Cytoscape 3 for cluster analysis and visualization, has been developed. In order to reduce complexity and enable extendibility for ClusterViz, we designed the architecture of ClusterViz based on the framework of Open Services Gateway Initiative. According to the architecture, the implementation of ClusterViz is partitioned into three modules including interface of ClusterViz, clustering algorithms and visualization and export. ClusterViz fascinates the comparison of the results of different algorithms to do further related analysis. Three commonly used clustering algorithms, FAG-EC, EAGLE and MCODE, are included in the current version. Due to adopting the abstract interface of algorithms in module of the clustering algorithms, more clustering algorithms can be included for the future use. To illustrate usability of ClusterViz, we provided three examples with detailed steps from the important scientific articles, which show that our tool has helped several research teams do their research work on the mechanism of the biological networks.
STM32 implementation of state observer of the longitudinal movement of an aircraft

This paper presents an estimation of the longitudinal movement of an aircraft using the STM32 microcontroller F1 Family. The focus of this paper is on developing code to implement the famous Luenberger Observer and using the different devices existing in STM32 F1 micro-controllers. The suggested Luenberger observer was achieved using the Keil development tools designed for devices microcontrollers based on the ARM processor and labor with C / C ++ language. The Characteristics that show variations in time of the state variables and step responses prove that the identification of the longitudinal movement of an aircraft were performed with minor errors in the right conditions. These results lead to easily develop predictive algorithms for programmable hardware in the industry.
Double LDA: A Sentiment Analysis Model Based on Topic Model

The sentiment mining is a fast growing topic of both academic research and commercial applications, especially with the widespread of short-text applications on the Web. A fundamental problem that confronts sentiment mining is the automatics and correctness of mined sentiment. This paper proposes an DLDA (Double Latent Dirichlet Allocation) model to analyze sentiment for short-texts based on topic model. Central to DLDA is to add sentiment to topic model and consider sentiment as equal to topic, but independent of topic. DLDA is actually two methods DLDA I and its improvement DLDA II. Compared to the single topic-word LDA, the double LDA I, i.e., DLDA I designs another sentiment-word LDA. Both LDAs are independent of each other, but they combine to influence the selected words in short-texts. DLDA II is an improvement of DLDA I. It employs entropy formula to assign weights of words in the Gibbs sampling based on the ideas that words with stronger sentiment orientation should be assigned with higher weights. Experiments show that compared with other traditional topic methods, both DLDA I and II can achieve higher accuracy with less manual needs.
Boosting Transition-based AMR Parsing with Refined Actions and Auxiliary Analyzers

We report improved AMR parsing results by adding a new action to a transitionbased AMR parser to infer abstract concepts and by incorporating richer features produced by auxiliary analyzers such as a semantic role labeler and a coreference resolver. We report final AMR parsing results that show an improvement of 7% absolute in F1 score over the best previously reported result. Our parser is available at: https://github.com/ Juicechuan/AMRParsing
Designing A 48 V to 24 V DC-DC converter for vehicle application using a resonant switched capacitor converter topology

Electromagnetic elements such as inductance are not used in switched-capacitor converters to convert electrical power. In contrast, capacitors are used for storing and transforming the electrical power in these new topologies. Lower volume, higher power density, and more integration ability are the most important features of these kinds of converters. In this paper, the most important switched-capacitor converters topologies, which have been developed in the last decade as new topologies in power electronics, are introduced, analyzed, and compared with each other, in brief. Finally, a 100 watt double-phase half-mode resonant converter is simulated to convert 48V dc to 24 V dc for light weight electrical vehicle applications. Low output voltage ripple (0.4%), and soft switching for all power diodes and switches are achieved under the worst-case conditions.
ReHAR: Robust and Efficient Human Activity Recognition

Designing a scheme that can achieve a good performance in predicting single person activities and group activities is a challenging task. In this paper, we propose a novel robust and efficient human activity recognition scheme called ReHAR, which can be used to handle single person activities and group activities prediction. First, we generate an optical flow image for each video frame. Then, both video frames and their corresponding optical flow images are fed into a Single Frame Representation Model to generate representations. Finally, an LSTM is used to predict the final activities based on the generated representations. The whole model is trained end-to-end to allow meaningful representations to be generated for the final activity recognition. We evaluate ReHAR using two well-known datasets: the NCAA Basketball Dataset and the UCFSports Action Dataset. The experimental results show that the proposed ReHAR achieves a higher activity recognition accuracy with an order of magnitude shorter computation time compared to the state-of-the-art methods.
Sputum smear negative pulmonary tuberculosis: sensitivity and specificity of diagnostic algorithm

The diagnosis of pulmonary tuberculosis in patients with Human Immunodeficiency Virus (HIV) is complicated by the increased presence of sputum smear negative tuberculosis. Diagnosis of smear negative pulmonary tuberculosis is made by an algorithm recommended by the National Tuberculosis and Leprosy Programme that uses symptoms, signs and laboratory results. The objective of this study is to determine the sensitivity and specificity of the tuberculosis treatment algorithm used for the diagnosis of sputum smear negative pulmonary tuberculosis. A cross-section study with prospective enrollment of patients was conducted in Dar-es-Salaam Tanzania. For patients with sputum smear negative, sputum was sent for culture. All consenting recruited patients were counseled and tested for HIV. Patients were evaluated using the National Tuberculosis and Leprosy Programme guidelines and those fulfilling the criteria of having active pulmonary tuberculosis were started on anti tuberculosis therapy. Remaining patients were provided appropriate therapy. A chest X-ray, mantoux test, and Full Blood Picture were done for each patient. The sensitivity and specificity of the recommended algorithm was calculated. Predictors of sputum culture positive were determined using multivariate analysis. During the study, 467 subjects were enrolled. Of those, 318 (68.1%) were HIV positive, 127 (27.2%) had sputum culture positive for Mycobacteria Tuberculosis, of whom 66 (51.9%) were correctly treated with anti-Tuberculosis drugs and 61 (48.1%) were missed and did not get anti-Tuberculosis drugs. Of the 286 subjects with sputum culture negative, 107 (37.4%) were incorrectly treated with anti-Tuberculosis drugs. The diagnostic algorithm for smear negative pulmonary tuberculosis had a sensitivity and specificity of 38.1% and 74.5% respectively. The presence of a dry cough, a high respiratory rate, a low eosinophil count, a mixed type of anaemia and presence of a cavity were found to be predictive of smear negative but culture positive pulmonary tuberculosis. The current practices of establishing pulmonary tuberculosis diagnosis are not sensitive and specific enough to establish the diagnosis of Acid Fast Bacilli smear negative pulmonary tuberculosis and over treat people with no pulmonary tuberculosis.
Environmental and pest analysis : An approach to external business environment Dr

Environmental management, a term encompassing environmental planning, protection, monitoring, assessment, research, education, conservation and sustainable use of resources, is now accepted as a major guiding factor for sustainable development at the regional and national level. It is now being increasingly recognized that environmental factors and ecological imperatives must be in built to the total planning process if the long-term goal of making industrial development sustainable is to be achieved. Here we will try to define and discuss the role of Environmental Analysis in the strategic management process of organization. The present complex world require as far as is feasible, it consider impact of important factors related to organizations in strategic planning. The strategic planning of business includes all functional subdivisions and forwards them in a united direction. One of these subsystems is human resource management. Strategic human resource management comes after the strategic planning, and followed by strategic human resource planning as a major activity in all the industries. In strategic planning, it can use different analytical methods and techniques that one of them is PEST analysis. This paper introduces how to apply it in a new manner.
OpenEDGAR: Open Source Software for SEC EDGAR Analysis

OpenEDGAR is an open source Python framework designed to rapidly construct research databases based on the Electronic Data Gathering, Analysis, and Retrieval (EDGAR) system operated by the US Securities and Exchange Commission (SEC). OpenEDGAR is built on the Django application framework, supports distributed compute across one or more servers, and includes functionality to (i) retrieve and parse index and filing data from EDGAR, (ii) build tables for key metadata like form type and filer, (iii) retrieve, parse, and update CIK to ticker and industry mappings, (iv) extract content and metadata from filing documents, and (v) search filing document contents. OpenEDGAR is designed for use in both academic research and industrial applications, and is distributed under MIT License at https://github.com/LexPredict/openedgar.
C-Ku band ultra broadband GaN MMIC amplifier with 20W output power

This paper reports on a Gallium Nitride High Electron Mobility Transistor (GaN HEMT) Monolithic Microwave Integrated Circuit (MMIC) high power amplifier (HPA), which features high power and high gain over C-Ku band 115 % relative bandwidth. A C-Ku band (6 ∼ 18 GHz) GaN HEMT MMIC amplifier was manufactured and measured. The circuit dimension is 4.8 mm by 4 mm. The fabricated MMIC HPA derived an averaged output power of 20 W with averaged power gain of 9.6 dB over C-Ku band. The output power is state-of-the-art output power for GaN HEMT MMIC amplifiers with more than 100 % relative bandwidth and up to Ku band operation frequency.
Information fusion in continuous assurance

We extend continuous assurance research by proposing a novel continuous assurance architecture grounded in information fusion research. Existing continuous assurance architectures focus primarily on methods of monitoring assurance clients’ systems to detect anomalous activities and have not addressed the question of how to process the detected anomalies. Consequently, actual implementations of these systems typically detect a large number of anomalies, with the resulting information overload leading to suboptimal decision making due to human information processing limitations. The proposed architecture addresses these issues by performing anomaly detection, aggregation and evaluation. Within the proposed architecture, artifacts developed in prior continuous assurance, ontology, and artificial intelligence research are used to perform the detection, aggregation and evaluation information fusion tasks. The architecture contributes to the academic continuous assurance literature and has implications for practitioners involved in the development of more robust and useful continuous assurance systems.
CAT-SLAM: probabilistic localisation and mapping using a continuous appearance-based trajectory

This paper describes a new system, dubbed Continuous Appearance-based Trajectory SLAM (CAT-SLAM), which augments sequential appearance-based place recognition with local metric pose filtering to improve the frequency and reliability of appearance based loop closure. As in other approaches to appearance-based mapping, loop closure is performed without calculating global feature geometry or performing 3D map construction. Loop closure filtering uses a probabilistic distribution of possible loop closures along the robot’s previous trajectory, which is represented by a linked list of previously visited locations linked by odometric information. Sequential appearance-based place recognition and local metric pose filtering are evaluated simultaneously using a Rao-Blackwellised particle filter, which weights particles based on appearance matching over sequential frames and the similarity of robot motion along the trajectory. The particle filter explicitly models both the likelihood of revisiting previous locations and exploring new locations. A modified resampling scheme counters particle deprivation and allows loop closure updates to be performed in constant time for a given environment. We compare the performance of CAT-SLAM to FAB-MAP (a state-of-the-art appearance-only SLAM algorithm) using multiple real-world datasets, demonstrating an increase in the number of correct loop closures detected by CAT-SLAM.
Phase calibrated ring oscillator PUF design and implementation on FPGAs

A ring oscillator physical unclonable function (RO PUF) is an application-constrained hardware security primitive that can be used for authentication and key generation. PUFs depend on variability during the fabrication process to produce random outputs that are nevertheless stable across multiple measurements. Unfortunately, RO PUFs are known to be unstable especially when implemented on an Field Programmable Gate Array (FPGA). In this work, we comprehensively evaluate the RO PUF's stability on FPGAs, and we propose a phase calibration process to improve the stability of RO PUFs. The results show that the bit errors in our PUFs are reduced to less than 1%.