nils-herrmann/clean_scirepeval huggingface dataset:
Deconstruct and Reconstruct: Using Topic Modeling on an Analytics Corpus

The question posed by the 2014 LAK Data Challenge is “What do analytics on learning analytics tell us?” The authors looked to take a two-pronged approach to this challenge. First, the authors wanted to use advanced analytical techniques on the corpus to make the “eat your own dog food” point. Since many of the EDM/LAK submissions explain advanced statistical or semantic analytic approaches, we wanted to utilize those same methods for our analysis. To that end, we used two natural language processing (NLP) tools to analyze the corpus of papers. First we used Latent Dirichlet allocation (LDA) to extract clusters of terms from the content. Second, we used Turbo Topics to convert the LDA output into phrases (bi-grams and tri-grams). The use of these NLP tools allowed us to execute the second part of our approach to the challenge. Once the corpus was aggregated as topics, we used Tableau to visually inspect the corpus for trends. In addition to standard descriptive visualizations, we were able to identify trends in the corpus topics from 2008 through 2013. Most interesting is that with both EDM and LAK, we noticed a trend of topic convergence after three years. Also, we were able to easily discern topic trends such as the increased popularity of "social" and "network," over the last three years, and the consistent appearance of ‘Cognitive Tutor’ related topics (e.g. intelligent tutoring, concept map). While these findings may not be unexpected, we believe that the ability to extract and visualize these outcomes is unique.
Classification of Imbalance Problem by MWMOTE and SSO

In this paper, a resampling ensemble algorithm is developed focused on the classification problems for imbalanced datasets. In this method, the small classes are oversampled and large classes are undersampled. The resampling scale is determined by the ratio of the minimum number of class and maximum number of class. Oversampling for “small” classes is done by MWMOTE technique and undersampling for “large” classes is performed according to SSO technique. Our aim is to reduce the time complexity as well as the enhancement of accuracy rate of classification result. Imbalanced classification, Resampling algorithm, SMOTE, MWMOTE, SSO. _________________________________________________________________________________________________________________
Berardinelli-Seip Congenital Lipodystrophy: Report of an Iranian Girl with a Novel Mutation of BSCL2 Gene

Congenital generalized lipodystrophies (CGLs) are very rare autosomal recessive disorders which have four types. Of the four CGL types, BSCL2 (Berardinelli–Seip Congenital lipodystrophy type 2) is the result of mutations in the BSCL2/seipin gene. BSCL2 that is the most severe lipodystrophic phenotype is characterized by generalized lipodystrophy, overgrowth, acanthosis nigricans, hepatomegaly, insulin resistance, and hyper-triglyceridemi. BSCL2 gene is responsible for encoding a protein called seipin. Seipin is responsible for production and accumulation of lipid droplets in the endoplasmic reticulum membranes and their storage inside the cells. Mutation in this gene disrupts this protein. The result is deficiency of lipid formation in the endoplasmic reticulum which causes CGL2 or BSCL2. We report a 4-year-old Iranian girl with typical findings of BSCL2. Molecular analysis of BSCL2 and BSCL1 genes by the sequencing method showed a novel homozygous mutation in the BSCL2 gene.
Optimization of Parallel I/O for Cannon's Algorithm Based on Lustre

Matrix multiplication is one of the most important operations in linear algebra, widely used in many fields of science and engineering. Cannon's algorithm is a classical distributed algorithm for matrix multiplication for two-dimensional meshes. Generally, MPI-IO is used for its I/O requirements. However it has been well documented that MPI-IO performs poorly in a Lustre file system environment. As the scale of matrix multiplication increased, this problem trends to be serious, becoming one key factor impacting performance of the program. In order to improve the performance of Collective I/O of Cannon's program, we proposed a new aggregation pattern (Stripe-continuous aggregation pattern), which fully considers the stripping mechanism and lock protocol of Lustre file system. The theoretical analysis and experimental results show that the pattern can make full use of the capacity of Lustre file system compared with the other patterns, and improve the I/O performance of the Cannon's program efficiently.
Coplanar waveguide to microstrip transition in thin polymer film for characterization and packaging of microwave photonic components

We report in this paper two types of broadband transitions between microstrip and coplanar lines on thin benzocyclobutene (BCB) polymer substrate. They are both via-free, using electromagnetic coupling between the bottom and top ground planes, which simplifies the manufacturing of components driven by microstrip electrodes. In the first ones, the bottom ground is not patterned, which makes them particularly suitable to on-wafer measurement of components under development with coplanar probes. An ultra-broad bandwidth of 68 GHz (from 1 GHz to 69 GHz) was achieved with 20-pm BCB. In the second ones, intended for connectorizing components on thin substrate with coplanar connectors, the bottom ground is patterned to match the narrow center conductor (54 μm) on thin substrate to the wide center conductor (127 μm) of the connector with a tapered section, achieving to a experimental bandwidth 13 GHz for the moment.
Applying Formal Methods to Networking: Theory, Techniques, and Applications

Despite its great importance, modern network infrastructure is remarkable for the lack of rigor in its engineering. The Internet, which began as a research experiment, was never designed to handle the users and applications it hosts today. The lack of formalization of the Internet architecture meant limited abstractions and modularity, particularly for the control and management planes, thus requiring for every new need a new protocol built from scratch. This led to an unwieldy ossified Internet architecture resistant to any attempts at formal verification and to an Internet culture where expediency and pragmatism are favored over formal correctness. Fortunately, recent work in the space of clean slate Internet design-in particular, the software defined networking (SDN) paradigm-offers the Internet community another chance to develop the right kind of architecture and abstractions. This has also led to a great resurgence in interest of applying formal methods to specification, verification, and synthesis of networking protocols and applications. In this paper, we present a self-contained tutorial of the formidable amount of work that has been done in formal methods and present a survey of its applications to networking.
Online Learning for Statistical Machine Translation

We present online learning techniques for statistical machine translation (SMT). The availability of large training data sets that grow constantly over time is becoming more and more frequent in the field of SMT—for example, in the context of translation agencies or the daily translation of government proceedings. When new knowledge is to be incorporated in the SMT models, the use of batch learning techniques require very time-consuming estimation processes over the whole training set that may take days or weeks to be executed. By means of the application of online learning, new training samples can be processed individually in real time. For this purpose, we define a state-of-the-art SMT model composed of a set of submodels, as well as a set of incremental update rules for each of these submodels. To test our techniques, we have studied two well-known SMT applications that can be used in translation agencies: post-editing and interactive machine translation. In both scenarios, the SMT system collaborates with the user to generate high-quality translations. These user-validated translations can be used to extend the SMT models by means of online learning. Empirical results in the two scenarios under consideration show the great impact of frequent updates in the system performance. The time cost of such updates was also measured, comparing the efficiency of a batch learning SMT system with that of an online learning system, showing that online learning is able to work in real time whereas the time cost of batch retraining soon becomes infeasible. Empirical results also showed that the performance of online learning is comparable to that of batch learning. Moreover, the proposed techniques were able to learn from previously estimated models or from scratch. We also propose two new measures to predict the effectiveness of online learning in SMT tasks. The translation system with online learning capabilities presented here is implemented in the open-source Thot toolkit for SMT.
Suicide by carbon dioxide.

Suicides by self-poisoning are common in all parts of the world. Among these intoxications, gases are rarely used, especially carbon dioxide (CO2). Very few cases of self-inflicted and deliberate carbon dioxide poisonings have been reported. This paper presents two uncommon suicides by carbon dioxide intoxication. In one case, a 53-year-old man tightly sealed a small bathroom and locked himself in it likely with dry ice. Warning notices were tagged to the door. In another case, a 48-year-old man working in a restaurant committed suicide by closing himself in a walk-in refrigerator and opening the stored carbon dioxide containers intended for the beverage dispensing equipment. The limited possibilities of proving lethal CO2 intoxications post-mortem necessitate a close cooperation of the involved parties during investigation. Only the synopsis of all findings permits a sound assessment regarding the manner and cause of death.
Indirect measures of gene flow and migration: FST not equal to 1/(4Nm + 1).

The difficulty of directly measuring gene flow has lead to the common use of indirect measures extrapolated from genetic frequency data. These measures are variants of FST, a standardized measure of the genetic variance among populations, and are used to solve for Nm, the number of migrants successfully entering a population per generation. Unfortunately, the mathematical model underlying this translation makes many biologically unrealistic assumptions; real populations are very likely to violate these assumptions, such that there is often limited quantitative information to be gained about dispersal from using gene frequency data. While studies of genetic structure per se are often worthwhile, and FST is an excellent measure of the extent of this population structure, it is rare that FST can be translated into an accurate estimate of Nm.
A network-driven methodology for sports ranking and prediction

Recent years have seen increasing interest in ranking elite athletes and teams in professional sports leagues, and in predicting the outcomes of games. In this work, we draw an analogy between this problem and one in the field of search engine optimization, namely, that of ranking webpages on the Internet. Motivated by the famous PageRank algorithm, our TeamRank methods define directed graphs of sports teams based on the observed outcomes of individual games, and use these networks to infer the importance of teams that determines their rankings. In evaluating these methods on data from recent seasons in the National Football League (NFL) and National Basketball Association (NBA), we find that they can predict the outcomes of games with up to 70% accuracy, and that they provide useful rankings of teams that cluster by league divisions. We also propose some extensions to TeamRank that consider overall team win records and shifts in momentum over time.
Bioavailability of Heavy Metals in Soil: Impact on Microbial Biodegradation of Organic Compounds and Possible Improvement Strategies

Co-contamination of the environment with toxic chlorinated organic and heavy metal pollutants is one of the major problems facing industrialized nations today. Heavy metals may inhibit biodegradation of chlorinated organics by interacting with enzymes directly involved in biodegradation or those involved in general metabolism. Predictions of metal toxicity effects on organic pollutant biodegradation in co-contaminated soil and water environments is difficult since heavy metals may be present in a variety of chemical and physical forms. Recent advances in bioremediation of co-contaminated environments have focussed on the use of metal-resistant bacteria (cell and gene bioaugmentation), treatment amendments, clay minerals and chelating agents to reduce bioavailable heavy metal concentrations. Phytoremediation has also shown promise as an emerging alternative clean-up technology for co-contaminated environments. However, despite various investigations, in both aerobic and anaerobic systems, demonstrating that metal toxicity hampers the biodegradation of the organic component, a paucity of information exists in this area of research. Therefore, in this review, we discuss the problems associated with the degradation of chlorinated organics in co-contaminated environments, owing to metal toxicity and shed light on possible improvement strategies for effective bioremediation of sites co-contaminated with chlorinated organic compounds and heavy metals.
Generating Regular Expressions from Natural Language Specifications: Are We There Yet?

Recent state-of-the-art approaches automatically generate regular expressions from natural language specifications. Given that these approaches use only synthetic data in both training datasets and validation/test datasets, a natural question arises: are these approaches effective to address various real-world situations? To explore this question, in this paper, we conduct a characteristic study on comparing two synthetic datasets used by the recent research and a real-world dataset collected from the Internet, and conduct an experimental study on applying a state-of-the-art approach on the real-world dataset. Our study results suggest the existence of distinct characteristics between the synthetic datasets and the real-world dataset, and the state-of-the-art approach (based on a model trained from a synthetic dataset) achieves extremely low effectiveness when evaluated on real-world data, much lower than the effectiveness when evaluated on the synthetic dataset. We also provide initial analysis on some of those challenging cases and discuss future directions.
Design of a Multiphase Hybrid Permanent Magnet Generator for Series Hybrid EV

Design of a multiphase hybrid permanent magnet (HPM) generator for series hybrid electric vehicle (SHEV) application is presented in this paper. The proposed hybrid excitation topology together with an integral passive rectifier replaces the permanent magnet (PM) machine and active power electronics converter in hybrid/electric vehicles, facilitating the control over constant PM flux-linkage. The HPM topology includes two rotor elements: a PM and a wound field (WF) rotor with a 30% split ratio, coupled on the same shaft in one machine housing. Both rotors share a nine-phase stator that results in higher output voltage and power density when compared to three-phase design. The HPM generator design is based on a 3-kW benchmark PM machine to ensure the feasibility and validity of design tools and procedures. The WF rotor is designed to realize the same pole shape and number as in the PM section and to obtain the same flux-density in the air-gap while minimizing the WF input energy. Having designed and analyzed the machine using equivalent magnetic circuit and finite element analysis, a laboratory prototype HPM generator is built and tested with the measurements compared to predicted results confirming the designed characteristics and machine performance. The paper also presents comprehensive machine loss and mass audits.
Parkinsons Disease Classification using Neural Network and Feature Selection

In this study, the Multi-Layer Perceptron (MLP)with Back-Propagation learning algorithm are used to classify to effective diagnosis Parkinsons disease(PD).It’s a challenging problem for medical community.Typically characterized by tremor, PD occurs due to the loss of dopamine in the brains thalamic region that results in involuntary or oscillatory movement in the body. A feature selection algorithm along with biomedical test values to diagnose Parkinson disease.Clinical diagnosis is done mostly by doctor’s expertise and experience.But still cases are reported of wrong diagnosis and treatment.Patients are asked to take number of tests for diagnosis.In many cases,not all the tests contribute towards effective diagnosis of a disease.Our work is to classify the presence of Parkinson disease with reduced number of attributes.Original,22 attributes are involved in classify.We use Information Gain to determine the attributes which reduced the number of attributes which is need to be taken from patients.The Artificial neural networks is used to classify the diagnosis of patients.Twenty-Two attributes are reduced to sixteen attributes.The accuracy is in training data set is 82.051% and in the validation data set is 83.333%. Data mining , classification , Parkinson disease , Artificial neural networks , Feature Selection ,
On Evaluating Adversarial Robustness

Correctly evaluating defenses against adversarial examples has proven to be extremely difficult. Despite the significant amount of recent work attempting to design defenses that withstand adaptive attacks, few have succeeded; most papers that propose defenses are quickly shown to be incorrect. We believe a large contributing factor is the difficulty of performing security evaluations. In this paper, we discuss the methodological foundations, review commonly accepted best practices, and suggest new methods for evaluating defenses to adversarial examples. We hope that both researchers developing defenses as well as readers and reviewers who wish to understand the completeness of an evaluation consider our advice in order to avoid common pitfalls.
Techniques in hacking and simulating a modem automotive controller area network

This research will demonstrate hacking techniques on the modern automotive network and describe the design and implementation of a benchtop simulator. In currently-produced vehicles, the primary network is based on the Controller Area Network (CAN) bus described in the ISO 11898 family of protocols. The CAN bus performs well in the electronically noisy environment found in the modern automobile. While the CAN bus is ideal for the exchange of information in this environment, when the protocol was designed security was not a priority due to the presumed isolation of the network. That assumption has been invalidated by recent, well-publicized attacks where hackers were able to remotely control an automobile, leading to a product recall that affected more than a million vehicles. The automobile has a multitude of electronic control units (ECUs) which are interconnected with the CAN bus to control the various systems which include the infotainment, light, and engine systems. The CAN bus allows the ECUs to share information along a common bus which has led to improvements in fuel and emission efficiency, but has also introduced vulnerabilities by giving access on the same network to cyber-physical systems (CPS). These CPS systems include the anti-lock braking systems (ABS) and on late model vehicles the ability to turn the steering wheel and control the accelerator. Testing functionality on an operational vehicle can be dangerous and place others in harm's way, but simulating the vehicle network and functionality of the ECUs on a bench-top system provides a safe way to test for vulnerabilities and to test possible security solutions to prevent CPS access over the CAN bus network. This paper will describe current research on the automotive network, provide techniques in capturing network traffic for playback, and demonstrate the design and implementation of a benchtop system for continued research on the CAN bus.
ATRX, IDH1-R132H and Ki-67 immunohistochemistry as a classification scheme for astrocytic tumors

Recurrence and progression to higher grade lesions are key biological events and characteristic behaviors in the evolution process of glioma. Malignant astrocytic tumors such as glioblastoma (GBM) are the most lethal intracranial tumors. However, the clinical practicability and significance of molecular parameters for the diagnostic and prognostic prediction of astrocytic tumors is still limited. In this study, we detected ATRX, IDH1-R132H and Ki-67 by immunohistochemistry and observed the association of IDH1-R132H with ATRX and Ki-67 expression. There was a strong association between ATRX loss and IDH1-R132H (p<0.0001). However, Ki-67 high expression restricted in the tumors with IDH1-R132H negative (p=0.0129). Patients with IDH1-R132H positive or ATRX loss astrocytic tumors had a longer progressive- free survival (p<0.0001, p=0.0044, respectively). High Ki-67 expression was associated with shorter PFS in patients with astrocytic tumors (p=0.002). Then we characterized three prognostic subgroups of astrocytic tumors (referred to as A1, A2 and A3). The new model demonstrated a remarkable separation of the progression interval in the three molecular subgroups and the distribution of patients' age in the A1-A2-A3 model was also significant different. This model will aid predicting the overall survival and progressive time of astrocytic tumors' patients.
Wearable Sensor-Based Hand Gesture and Daily Activity Recognition for Robot-Assisted Living

In this paper, we address natural human-robot interaction (HRI) in a smart assisted living (SAIL) system for the elderly and the disabled. Two common HRI problems are studied: hand gesture recognition and daily activity recognition. For hand gesture recognition, we implemented a neural network for gesture spotting and a hierarchical hidden Markov model for context-based recognition. For daily activity recognition, a multisensor fusion scheme is developed to process motion data collected from the foot and the waist of a human subject. Experiments using a prototype wearable sensor system show the effectiveness and accuracy of our algorithms.