nils-herrmann/clean_scirepeval huggingface dataset:
Anomaly Detection Using Forecasting Methods ARIMA and HWDS

Understanding the normal operation of IP networks is a common step in building a solution for automatic detection of network anomalies. Toward this end, we analyze the usage of two different approaches: the AutoRegressive Integrated Moving Average (ARIMA) model and an improvement of the traditional Holt-winters method. We use both models for traffic characterization, called Digital Signature of Network Segment using Flow analysis (DSNSF), and volume anomaly or outliers detection. The DSNSFs obtained by the presented models are compared to the actual traffic of bits and packets of a real network environment and then subjected to specific evaluations in order to measure its accuracy. The presented models are capable of providing feedback through its predictive capabilities and hence provide an early warning system.
A corpus-based study of character and bigram frequencies in Chinese e-texts and its implications for Chinese language instruction

This paper describes the findings of a research project whose main objective is to compile a character frequency list based on a very large collection of Chinese texts collected from various online sources. As compared with several previous studies on Chinese character frequencies, this project uses a much larger corpus that not only covers more subject fields but also contains a better proportion of informative versus imaginative Modern Chinese texts. In addition, this project also computes two bigram frequency lists that can be used for compiling a list of most frequently used two-character words in Chinese.
Frame Buffer Compression without Color Information Loss

Due to the 60Hz or higher LCD refresh operations, display controller (DC) reads the pixels out from frame buffer at fixed rate. Accessing frame buffer consumes not only memory bandwidth, but power as well. Thus frame buffer compression (FBC) can contribute to alleviating both bandwidth and power consumption. A conceptual frame buffer compression model is proposed, and to the best of our knowledge, an arithmetic expression concerning the compression ratio and the read/update ratio of frame buffer is firstly presented, which reveals the correlation between frame buffer compression and target applications. Moreover, considering the linear access feature of frame buffer, we investigate a frame buffer compression without color information loss, named LFBC (Loss less Frame-Buffer Compression). LFBC defines new frame buffer compression data format, and employs run-length encoding (RLE) to implement the compression. For the applications suitable for frame buffer compression, LFBC reduces 50%90% bandwidth consumption and memory accesses caused by LCD refresh operations.
C - Reactive Protein Levels in Patients with Periodontal Disease and Normal Subjects

Although periodontitis is a chronic inflammatory disease but some factors of acute inflammation phase are involved in this disease among which is the C-Reactive protein (CRP). To minimize its effects, anti-inflammatory drugs or non-pharmacological approaches such as oral hygiene is recommended. CRP can also be used for the prediction and early detection of periodontal disease. The aim of the present study was the comparison of the amount of salivary C-Reactive protein (CRP) in healthy subjects and patients with periodontal disease. This case-control study was done on 90 patients referred to the Department of Periodontology of Babol Dentistry School. These subjects were divided into three groups of healthy (n = 30), gingivitis (n = 30), and chronic periodontitis (n = 30), based on Gingival Index (GI) and Clinical Attachment Loss (CAL) indices. 2ml saliva samples were collected from these people and clinical indicators including GI, CAL, Periodontal Pocket Depth (PPD), and Bleeding Index (BI) were assessed. ELISA method was used to evaluate the salivary CRP levels. Collected data were analyzed using SPSS statistical software by non-Parametric Kruskal-Wallis and Mann-Whitney test and Spearman correlation coefficient and P<0.05 was considered significant. The mean salivary CRP levels were 5332.62±5051.63pg/ml in periodontitis patients, 3545.41±3061.38pg/ml in gingivitis group and 3108.51±3574.47pg/ml in healthy subjects. The statistic analysis showed a significant difference in salivary CRP concentrations between the periodontitis patients and healthy subjects (P=0.045). The results indicate that there is a significant association between periodontitis and salivary CRP concentrations.
Cross cultural adaptation and validation of the Chinese Health Assessment Questionnaire for use in rheumatoid arthritis.

The Health Assessment Questionnaire - Disability Index (HAQ), used as a disability and outcome measurement in rheumatoid arthritis (RA), has been validated in several languages, but not in Chinese. Our aim was to validate the Chinese version of HAQ (Chinese-HAQ) to suit the needs of Chinese speaking patients with RA in an Asian setting.



The original HAQ was modified in the context of Chinese culture and translated into Chinese by 2 translators aware of the objective of the questionnaire. The Chinese HAQ was self-administered by 42 patients with RA during their routine followup visit and one week later.



The test-retest reliability assessed using Spearman's correlation coefficient was 0.84. Between dimensions measured in the HAQ, the highest test-retest reliability was observed for walking (Spearman correlation coefficient rs=0.80) and the lowest was for eating (rs=0.54). The internal consistency of the scale using Cronbach's alpha was high at 0.86. In terms of criterion validity, the Chinese-HAQ score was found to correlate well with American College of Rheumatology functional status (rs=0.501, p=0.01). The Chinese-HAQ scores also correlated well with markers of disease activity such as patient's perception of pain measured on a visual analog scale (rs=0.55, p < 0.001), grip strength in mm Hg (rs=-0.55. p < 0.001 ), and physician's assessment of disease activity (rs=0.59, p < 0.001).



The Chinese HAQ is a reliable and valid instrument for studies measuring disability of patients with RA in Singapore.
Principal angles between subspaces and their tangents I

Principal angles between subspaces (PABS) (also called canonical angles) serve as a classical tool in mathematics, statistics, and applications, e.g., data mining. Traditionally, PABS are introduced and used via their cosines. The tangents of PABS have attracted relatively less attention, but are important for analysis of convergence of subspace iterations for eigenvalue problems. We explicitly construct matrices, such that their singular values are equal to the tangents of PABS, using several approaches: orthonormal and nonorthonormal bases for subspaces, and orthogonal projectors. Cornell University Library This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved.
Advancing therapeutic applications of synthetic gene circuits.

Synthetic biology aims to introduce new sense-and-respond capabilities into living cells, which would enable novel therapeutic strategies. The development of regulatory elements, molecular computing devices, and effector screening technologies has enabled researchers to design synthetic gene circuits in many organisms, including mammalian cells. Engineered gene networks, such as closed-loop circuits or Boolean logic gate circuits, can be used to program cells to perform specific functions with spatiotemporal control and restoration of homeostasis in response to the extracellular environment and intracellular signaling. In addition, genetically modified microbes can be designed as local delivery of therapeutic molecules. In this review, we will discuss recent advances in therapeutic applications of synthetic gene circuits, as well as challenges and future opportunities for biomedicine.
Coupled Inductors in Interleaved Multiphase Three-Level DC–DC Converter for High-Power Applications

This paper investigates and evaluates coupled inductors (CIs) in the interleaved multiphase three-level dc-dc converter. If non-CIs are used in the multiphase three-level dc-dc converter, interleaving operation of the converter will increase inductor current ripple, although the overall output current ripple and common-mode (CM) voltage will become smaller. To reduce inductor current ripple, inverse-CIs are employed. The current ripple in the CI is analyzed in detail. The benefits of the three-level dc-dc converter with CIs under interleaving operation are evaluated. By adding CIs and working under interleaving operation, smaller inductor current ripple, smaller overall output current ripple, and smaller CM voltage can be achieved simultaneously compared with the noninterleaving case. The analysis results are verified by simulations and 10 kW scale-down experiments.
A Host-Based Intrusion Detection and Mitigation Framework for Smart Home IoT Using OpenFlow

Smart devices are gaining popularity in our homes with the promise to make our lives easier and more comfortable. However, the increased deployment of such smart devices brings an increase in potential security risks. In this work, we propose an intrusion detection and mitigation framework, called IoT-IDM, to provide a network-level protection for smart devices deployed in home environments. IoT-IDM monitors the network activities of intended smart devices within the home and investigates whether there is any suspicious or malicious activity. Once an intrusion is detected, it is also capable of blocking the intruder in accessing the victim device on the fly. The modular design of IoT-IDM gives its users the flexibility to employ customized machine learning techniques for detection based on learned signature patterns of known attacks. Software-defined networking technology and its enabling communication protocol, OpenFlow, are used to realise this framework. Finally, a prototype of IoT-IDM is developed and the applicability and efficiency of proposed framework demonstrated through a real IoT device: a smart light bulb.
Detection of chewing from piezoelectric film sensor signals using ensemble classifiers

Selection and use of pattern recognition algorithms is application dependent. In this work, we explored the use of several ensembles of weak classifiers to classify signals captured from a wearable sensor system to detect food intake based on chewing. Three sensor signals (Piezoelectric sensor, accelerometer, and hand to mouth gesture) were collected from 12 subjects in free-living conditions for 24 hrs. Sensor signals were divided into 10 seconds epochs and for each epoch combination of time and frequency domain features were computed. In this work, we present a comparison of three different ensemble techniques: boosting (AdaBoost), bootstrap aggregation (bagging) and stacking, each trained with 3 different weak classifiers (Decision Trees, Linear Discriminant Analysis (LDA) and Logistic Regression). Type of feature normalization used can also impact the classification results. For each ensemble method, three feature normalization techniques: (no-normalization, z-score normalization, and minmax normalization) were tested. A 12 fold cross-validation scheme was used to evaluate the performance of each model where the performance was evaluated in terms of precision, recall, and accuracy. Best results achieved here show an improvement of about 4% over our previous algorithms.
Self-adaptive attribute weighting for Naive Bayes classification

Naive Bayes (NB) is a popular machine learning tool for classification, due to its simplicity, high computational efficiency, and good classification accuracy, especially for high dimensional data such as texts. In reality, the pronounced advantage of NB is often challenged by the strong conditional independence assumption between attributes, which may deteriorate the classification performance. Accordingly, numerous efforts have been made to improve NB, by using approaches such as structure extension, attribute selection, attribute weighting, instance weighting, local learning and so on. In this paper, we propose a new Artificial Immune System (AIS) based self-adaptive attribute weighting method for Naive Bayes classification. The proposed method, namely AISWNB, uses immunity theory in artificial immune systems to search optimal attribute weight values, where self-adjusted weight values will alleviate the conditional independence assumption and help calculate the conditional probability in an accurate way. One noticeable advantage of AISWNB is that the unique immune system based evolutionary computation process, including initialization, clone, section, and mutation, ensures that AISWNB can adjust itself to the data without explicit specification of functional or distributional forms of the underlying model. As a result, AISWNB can obtain good attribute weight values during the learning process. Experiments and comparisons on 36 machine learning benchmark data sets and six image classification data sets demonstrate that AISWNB significantly outperforms its peers in classification accuracy, class probability estimation, and class ranking performance.
Weighted dynamic time warping for time series classification

Dynamic time warping (DTW), which finds the minimum path by providing non-linear alignments between two time series, has been widely used as a distance measure for time series classification and clustering. However, DTW does not account for the relative importance regarding the phase difference between a reference point and a testing point. This may lead to misclassification especially in applications where the shape similarity between two sequences is a major consideration for an accurate recognition. Therefore, we propose a novel distance measure, called a weighted DTW (WDTW), which is a penaltybased DTW. Our approach penalizes points with higher phase difference between a reference point and a testing point in order to prevent minimum distance distortion caused by outliers. The rationale underlying the proposed distance measure is demonstrated with some illustrative examples. A new weight function, called the modified logistic weight function (MLWF), is also proposed to systematically assign weights as a function of the phase difference between a reference point and a testing point. By applying different weights to adjacent points, the proposed algorithm can enhance the detection of similarity between two time series. We show that some popular distance measures such as DTW and Euclidean distance are special cases of our proposed WDTW measure. We extend the proposed idea to other variants of DTW such as derivative dynamic time warping (DDTW) and propose the weighted version of DDTW. We have compared the performances of our proposed procedures with other popular approaches using public data sets available through the UCR Time Series Data Mining Archive for both time series classification and clustering problems. The experimental results indicate that the proposed approaches can achieve improved accuracy for time series classification and clustering problems. & 2011
Information Technology Diffusion: A Review of Empirical Research

Innovation diffusion theory provides a useful perspective on one of the most persistently challenging topics in the IT field, namely, how to improve technology assessment, adoption and implementation. For this reason, diffusion is growing in popularity as a reference theory for empirical studies of information technology adoption and diffusion, although no comprehensive review of this body of work has been published to date. This paper presents the results of a critical review of eighteen empirical studies published during the period 1981-1991. Conclusive results were most likely when the adoption context closely matched the contexts in which classical diffusion theory was developed (for example, individual adoption of personal-use technologies) or when researchers extended diffusion theory to account for new factors specific to the IT adoption context under study. Based on classical diffusion theory and other recent conceptual work, a framework is developed to guide future research in IT diffusion. The framework maps two classes of technology (ones that conform closely to classical diffusion assumptions versus ones that do no0 against locus of adoption (individual versus organizational), resulting in four IT adoption contexts. For each adoption context, variables impacting adoption and diffusion are identified. Additionally, directions for future research are discussed.
Spatial Filtering Based on Canonical Correlation Analysis for Classification of Evoked or Event-Related Potentials in EEG Data

Classification of evoked or event-related potentials is an important prerequisite for many types of brain-computer interfaces (BCIs). To increase classification accuracy, spatial filters are used to improve the signal-to-noise ratio of the brain signals and thereby facilitate the detection and classification of evoked or event-related potentials. While canonical correlation analysis (CCA) has previously been used to construct spatial filters that increase classification accuracy for BCIs based on visual evoked potentials, we show in this paper, how CCA can also be used for spatial filtering of event-related potentials like P300. We also evaluate the use of CCA for spatial filtering on other data with evoked and event-related potentials and show that CCA performs consistently better than other standard spatial filtering methods.
Can You See the Real Me ? Activation and Expression of the “ True Self ” on the Internet

Those who feel better able to express their “true selves” in Internet rather than face-to-face interaction settings are more likely to form close relationships with people met on the Internet (McKenna, Green, & Gleason, this issue). Building on these correlational findings from survey data, we conducted three laboratory experiments to directly test the hypothesized causal role of differential self-expression in Internet relationship formation. Experiments 1 and 2, using a reaction time task, found that for university undergraduates, the true-self concept is more accessible in memory during Internet interactions, and the actual self more accessible during face-to-face interactions. Experiment 3 confirmed that people randomly assigned to interact over the Internet (vs. face to face) were better able to express their true-self qualities to their partners.
A Powerful Finite Control Set-Model Predictive Control Algorithm for Quasi Z-Source Inverter

Today, a predictive controller becomes one of the state of the art in power electronics control techniques. The performance of this powerful control approach will be pushed forward by simplifying the main control criterion and objective function, and decreasing the number of calculations per sampling time. Recently, predictive control has been incorporated in the Z-source inverter (ZSI) family. For example, in quasi ZSI, the inverter capacitor voltage, inductor current, and output load currents are controlled to their setting points through deciding the required state; active or shoot through. The proposed algorithm reduces the number of calculations, where it decides the shoot-through (ST) case without checking the other possible states. The ST case is roughly optimized every two sampling periods. Through the proposed strategy, about 50% improvement in the computational power has been achieved as compared with the previous algorithm. Also, the objective function for the proposed algorithm consists of one weighting factor for the capacitor voltage without involving the inductor current term in the main objective function. The proposed algorithm is investigated with the simulation results based on MATLAB/SIMULINK software. A prototype of qZSI is constructed in the laboratory to obtain the experimental results using the Digital Signal Processor F28335.
Research on Operational Risk Management Framework for Commercial Banks in Internet World - Based on McKinsey 7S Model

Operational risk has become major field of research on risk management in global banking. The spirit of open, cooperation, equality and globalization advocated by Internet has changed the business of commercial banks completely and largered their operational risk several times. Based on introducing the definition and characteristics of operational risk, this paper analyses the development trend of operational risk and proposes the operational risk management framework for commercial banks based on McKinsey 7S Model in Internet World.
Q-Adjectives and the Semantics of Quantity

The adjectives of quantity (Q-adjectives) many, few, much and little stand out from other quantity expressions on account of their syntactic flexibility, occurring in positions that could be called quantificational (many students attended), predicative (John’s friends were many), attributive (the many students), differential (much more than a liter) and adverbial (slept too much). This broad distribution poses a challenge for the two leading theories of this class, which treat them as either quantifying determiners or predicates over individuals. This paper develops an analysis of Q-adjectives as gradable predicates of sets of degrees or (equivalently) gradable quantifiers over degrees. It is shown that this proposal allows a unified analysis of these items across the positions in which they occur, while also overcoming several issues facing competing accounts, among others the divergences between Q-adjectives and ‘ordinary’ adjectives, the operator-like behavior of few and little, and the use of much as a dummy element. Overall the findings point to the central role of degrees in the semantics of quantity.
Hierarchical binary CNNs for landmark localization with limited resources

Our goal is to design architectures that retain the groundbreaking performance of Convolutional Neural Networks (CNNs) for landmark localization and at the same time are lightweight, compact and suitable for applications with limited computational resources. To this end, we make the following contributions: (a) we are the first to study the effect of neural network binarization on localization tasks, namely human pose estimation and face alignment. We exhaustively evaluate various design choices, identify performance bottlenecks, and more importantly propose multiple orthogonal ways to boost performance. (b) Based on our analysis, we propose a novel hierarchical, parallel and multi-scale residual architecture that yields large performance improvement over the standard bottleneck block while having the same number of parameters, thus bridging the gap between the original network and its binarized counterpart. (c) We perform a large number of ablation studies that shed light on the properties and the performance of the proposed block. (d) We present results for experiments on the most challenging datasets for human pose estimation and face alignment, reporting in many cases state-of-the-art performance.  We further provide additional results for the problem of facial part segmentation. Code can be downloaded from https://www.adrianbulat.com/binary-cnn-landmarks.