nils-herrmann/clean_scirepeval huggingface dataset:
Managing Dynamic Risks in Global IT Projects: Agile Risk-Management using the Principles of Service-Oriented Architecture

Risk management in global information technology (IT) projects is becoming a critical area of concern for practitioners. Global IT projects usually span multiple locations involving various culturally diverse groups that use multiple standards and technologies. These multiplicities cause dynamic risks through interactions among internal (i.e., people, process, and technology) and external elements (i.e., business and natural environments) of global IT projects. This study proposes an agile risk-management framework for global IT project settings. By analyzing the dynamic interactions among multiplicities (e.g., multi-locations, multi-cultures, multi-groups, and multi-interests) embedded in the project elements, we identify the dynamic risks threatening the success of a global IT project. Adopting the principles of service-oriented architecture (SOA), we further propose a set of agile management strategies for mitigating the dynamic risks. The mitigation strategies are conceptually validated. The proposed framework will help practitioners understand the potential risks in their global IT projects and resolve their complex situations when certain types of dynamic risks arise.
Information: theory, brain, and behavior.

In the 65 years since its formal specification, information theory has become an established statistical paradigm, providing powerful tools for quantifying probabilistic relationships. Behavior analysis has begun to adopt these tools as a novel means of measuring the interrelations between behavior, stimuli, and contingent outcomes. This approach holds great promise for making more precise determinations about the causes of behavior and the forms in which conditioning may be encoded by organisms. In addition to providing an introduction to the basics of information theory, we review some of the ways that information theory has informed the studies of Pavlovian conditioning, operant conditioning, and behavioral neuroscience. In addition to enriching each of these empirical domains, information theory has the potential to act as a common statistical framework by which results from different domains may be integrated, compared, and ultimately unified.
Surgical anatomy of the ligamentous attachments in the temple and periorbital regions.

This study documents the anatomy of the deep attachments of the superficial fasciae within the temporal and periorbital regions. A highly organized and consistent three-dimensional connective tissue framework supports the overlying skin and soft tissues in these areas. The regional nerves and vessels display constant and predictable relationships with both the fascial planes and their ligamentous attachments. Knowledge of these relationships allows the surgeon to use the tissue planes and soft-tissue ligaments as intraoperative landmarks for the vital neurovascular structures. This results in improved efficiency and safety for aesthetic procedures in these regions.
Mining User-Aware Rare Sequential Topic Patterns in Document Streams

Textual documents created and distributed on the Internet are ever changing in various forms. Most of existing works are devoted to topic modeling and the evolution of individual topics, while sequential relations of topics in successive documents published by a specific user are ignored. In this paper, in order to characterize and detect personalized and abnormal behaviors of Internet users, we propose Sequential Topic Patterns (STPs) and formulate the problem of mining User-aware Rare Sequential Topic Patterns (URSTPs) in document streams on the Internet. They are rare on the whole but relatively frequent for specific users, so can be applied in many real-life scenarios, such as real-time monitoring on abnormal user behaviors. We present a group of algorithms to solve this innovative mining problem through three phases: preprocessing to extract probabilistic topics and identify sessions for different users, generating all the STP candidates with (expected) support values for each user by pattern-growth, and selecting URSTPs by making user-aware rarity analysis on derived STPs. Experiments on both real (Twitter) and synthetic datasets show that our approach can indeed discover special users and interpretable URSTPs effectively and efficiently, which significantly reflect users' characteristics.
Human Motion Detection and Video Surveillance Using MATLAB

A human body detection algorithm based on the combination of moving information with shape information is proposed in the paper. Firstly, Eigen-object computed from three frames in the initial video sequences is used to detect the moving object. Secondly, the shape information of human body is used to classify human and other object. Furthermore, the occlusion between two objects during a short time is processed by using continues multiple frames. The advantages of the algorithm are accurately moving object detection, and the detection result doesn't effect by body pose. Moreover, as the shadow of moving object has been eliminated.
Sequence Symmetry Analysis and Disproportionality Analyses: What Percentage of Adverse Drug Reaction do they Signal?

Post-marketing surveillance systems rely on spontaneous reporting databases maintained by health regulators to identify safety issues arising from medicines once they are marketed. Quantitative safety signal detection methods such as Proportional Reporting ratio (PRR), Reporting Odds Ratio (ROR), Bayesian Confidence Propagation Neural Network (BCPNN), and empirical Bayesian technique are applied to spontaneous reporting data to identify safety signals [1-3]. These methods have been adopted as standard quantitative methods by many pharmaco-surveillance centres to screen for safety signals of medicines [2-5]. Studies have validated these methods and showed that the methods have low to moderate sensitivity to detect adverse drug reaction (ADR) signals, ranging between 28% to 56%, while the specificity of the methods ranged from 82% to 95% [6-8].
Learning To Count Objects in Images

We propose a new supervised learning framework for visual object counting tasks, such as estimating the number of cells in a microscopic image or the number of humans in surveillance video frames. We focus on the practically-attractive case when the training images are annotated with dots (one dot per object). Our goal is to accurately estimate the count. However, we evade the hard task of learning to detect and localize individual object instances. Instead, we cast the problem as that of estimating an image density whose integral over any image region gives the count of objects within that region. Learning to infer such density can be formulated as a minimization of a regularized risk quadratic cost function. We introduce a new loss function, which is well-suited for such learning, and at the same time can be computed efficiently via a maximum subarray algorithm. The learning can then be posed as a convex quadratic program solvable with cutting-plane optimization. The proposed framework is very flexible as it can accept any domain-specific visual features. Once trained, our system provides accurate object counts and requires a very small time overhead over the feature extraction step, making it a good candidate for applications involving real-time processing or dealing with huge amount of visual data.
Challenges in Internet Addiction Disorder: Is a Diagnosis Feasible or Not?

An important international discussion began because of some pioneer studies carried out by Young (a) on the internet addiction disorder (IAD). In the fifth and most recent version of the Diagnostic, and Statistical Manual of Mental Disorders (DSM) there is no mention of this disorder and among researchers there are basically two opposite positions. Those who are in favor of a specific diagnosis and those who are claiming the importance of specific criteria characterizing this behavior and the precise role it has in the patient's life. The aim of the present paper is to answer the question whether it is possible or not to formulate diagnoses of internet-related disorders. We revised literature on the history of diagnostic criteria, on neurocognitive evidence, on the topic debate and on IAD instrumental measures. We found that the disorder was not univocally defined and that the construct was somehow too broad and generic to be explicative for a diagnosis. Indeed, the models are borrowed from other addiction pathologies and they are often formulated before the development of internet as intended in current society. In conclusion, we think we need a more innovative, integrated and comprehensive model for an IAD diagnosis.
A 6.05-Mb/mm2 16-nm FinFET double pumping 1W1R 2-port SRAM with 313 ps read access time

High-density and low-leakage 1W1R 2-port (2P) SRAM is realized by 6T 1-port SRAM bitcell with double pumping internal clock in 16 nm FinFET technology. Proposed clock generator with address latch circuit enables robust timing design without sever setup/hold margin. We designed a 256 kb 1W1R 2P SRAM macro which achieves the highest density of 6.05 Mb/mm2. Measured data shows that a 313 ps of read-access-time is observed at 0.8 V. Standby leakage power in resume standby (RS) mode is reduced by 79% compared to the conventional dual-port SRAM without RS.
Combining Syntax and Semantics for Automatic Extractive Single-Document Summarization

The goal of automated summarization is to tackle the “information overload” problem by extracting and perhaps compressing the most important content of a document. Due to the difficulty that singledocument summarization has in beating a standard baseline, especially for news articles, most efforts are currently focused on multi-document summarization. The goal of this study is to reconsider the importance of single-document summarization by introducing a new approach and its implementation. This approach essentially combines syntactic, semantic, and statistical methodologies, and reflects psychological findings that pinpoint specific selection patterns as humans construct summaries. Successful summary evaluation results and baseline out-performance are demonstrated when our system is executed on two separate datasets: the Document Understanding Conference (DUC) 2002 data set and a scientific magazine article set. These results have implications not only for extractive and abstractive single-document summarization, but could also be leveraged in multi-document summarization.
Towards segment-based recognition of argumentation structure in short texts

Despite recent advances in discourse parsing and causality detection, the automatic recognition of argumentation structure of authentic texts is still a very challenging task. To approach this problem, we collected a small corpus of German microtexts in a text generation experiment, resulting in texts that are authentic but of controlled linguistic and rhetoric complexity. We show that trained annotators can determine the argumentation structure on these microtexts reliably. We experiment with different machine learning approaches for automatic argumentation structure recognition on various levels of granularity of the scheme. Given the complex nature of such a discourse understanding tasks, the first results presented here are promising, but invite for further investigation.
Bringing Identity Theory into Environmental Sociology

In an effort to explain pro-environmental behavior, environmental sociologists often study environmental attitudes. While much of this work is atheoretical, the focus on attitudes suggests that researchers are implicitly drawing upon attitude theory in psychology. The present research brings sociological theory to environmental sociology by drawing on identity theory to understand environmentally responsive behavior. We develop an environment identity model of environmental behavior that includes not only the meanings of the environment identity, but also the prominence and salience of the environment identity and commitment to the environment identity. We examine the identity process as it relates to behavior, though not to the exclusion of examining the effects of environmental attitudes. The findings reveal that individual agency is important in influencing environmentally responsive behavior, but this agency is largely through identity processes, rather than attitude processes. This provides an important theoretical and empirical advance over earlier work in environmental sociology.
High-Precision Sensorless Drive for High-Speed BLDC Motors Based on the Virtual Third Harmonic Back-EMF

In order to improve the performance of the high-speed brushless direct current motor drives, a novel high-precision sensorless drive has been developed. It is well known that the inevitable voltage pulses, which are generated during the commutation periods, will impact the rotor position detecting accuracy, and further impact the performance of the overall sensorless drive, especially in the higher speed range or under the heavier load conditions. For this reason, the active compensation method based on the virtual third harmonic back electromotive force incorporating the SFF-SOGI-PLL (synchronic-frequency filter incorporating the second-order generalized integrator based phase-locked loop) is proposed to precise detect the commutation points for sensorless drive. An experimental driveline system used for testing the electrical performance of the developed magnetically suspended motor is built. The mathematical analysis and the comparable experimental results have been shown to validate the effectiveness of the proposed sensorless drive algorithm.
Characteristics of AMI using DLMS/COSEM and IEEE 802.15.4g multi-hop wireless communication

IEC 62056 for upper-layer protocols and IEEE 802.15.4g for communication infrastructure are promising means of advanced metering infrastructure (AMI) in Japan. However, since the characteristics of a communication system based on these combined technologies have yet to be identified, this paper gives the communication failure rates and latency acquired by calculations. In addition, the calculation results suggest some adequate AMI configurations, and show its extensibility in consideration of the usage environment.
User's Action and Decision Making of Retweet Messages towards Reducing Misinformation Spread during Disaster

The online social media such as Facebook, Twitter and YouTube has been used extensively during disaster and emergency situation. Despite the advantages offered by these services on supplying information in vague situation by citizen, we raised the issue of spreading misinformation on Twitter by using retweets. Accordingly, in this study, we conduct a user survey (n = 133) to investigate what is the user’s action towards spread message in Twitter, and why user decide to perform retweet on the spread message. As the result of the factor analyses, we extracted 3 factors on user’s action towards spread message which are: 1) Desire to spread the retweet messages as it is considered important, 2) Mark the retweet messages as favorite using Twitter “Favorite” function, and 3) Search for further information about the content of the retweet messages. Then, we further analyze why user decides to perform retweet. The results reveal that user has desire to spread the message which they think is important and the reason why they retweet it is because of the need to retweet, interesting tweet content and the tweet user. The results presented in this paper provide an understanding on user behavior of information diffusion, with the aim to reduce the spread of misinformation using Twitter during emergency situation.
Biochemistry of Statins.

Cardiovascular disease (CVD) is the leading cause of morbidity and mortality worldwide. Elevated blood lipids may be a major risk factor for CVD. Due to consistent and robust association of higher low-density lipoprotein (LDL)-cholesterol levels with CVD across experimental and epidemiologic studies, therapeutic strategies to decrease risk have focused on LDL-cholesterol reduction as the primary goal. Current medication options for lipid-lowering therapy include statins, bile acid sequestrants, a cholesterol-absorption inhibitor, fibrates, nicotinic acid, and omega-3 fatty acids, which all have various mechanisms of action and pharmacokinetic properties. The most widely prescribed lipid-lowering agents are the HMG-CoA reductase inhibitors, or statins. Since their introduction in the 1980s, statins have emerged as the one of the best-selling medication classes to date, with numerous trials demonstrating powerful efficacy in preventing cardiovascular outcomes (Kapur and Musunuru, 2008 ). The statins are commonly used in the treatment of hypercholesterolemia and mixed hyperlipidemia. This chapter focuses on the biochemistry of statins including their structures, pharmacokinetics, and mechanism of actions as well as the potential adverse reactions linked to their clinical uses.
Open-Domain Audio-Visual Speech Recognition: A Deep Learning Approach

Automatic speech recognition (ASR) on video data naturally has access to two modalities: audio and video. In previous work, audio-visual ASR, which leverages visual features to help ASR, has been explored on restricted domains of videos. This paper aims to extend this idea to open-domain videos, for example videos uploaded to YouTube. We achieve this by adopting a unified deep learning approach. First, for the visual features, we propose to apply segment(utterance-) level features, instead of highly restrictive frame-level features. These visual features are extracted using deep learning architectures which have been pre-trained on computer vision tasks, e.g., object recognition and scene labeling. Second, the visual features are incorporated into ASR under deep learning based acoustic modeling. In addition to simple feature concatenation, we also apply an adaptive training framework to incorporate visual features in a more flexible way. On a challenging video transcribing task, audio-visual ASR using our proposed approach gets notable improvements in terms of word error rates (WERs), compared to ASR merely using speech features.
Effects of total flavonoids from Drynariae Rhizoma prevent bone loss in vivo and in vitro

Estrogen deficiency is one of the major causes of osteoporosis in postmenopausal women. Drynariae Rhizoma is a widely used traditional Chinese medicine for the treatment of bone diseases. In this study, we investigated the therapeutic effects of the total Drynariae Rhizoma flavonoids (DRTF) on estrogen deficiency-induced bone loss using an ovariectomized rat model and osteoblast-like MC3T3-E1 cells. Our results indicated that DRTF produced osteo-protective effects on the ovariectomized rats in terms of bone loss reduction, including decreased levels of bone turnover markers, enhanced biomechanical femur strength and trabecular bone microarchitecture deterioration prevention. In vitro experiments revealed that the actions of DRTF on regulating osteoblastic activities were mediated by the estrogen receptor (ER) dependent pathway. Our data also demonstrated that DRTF inhibited osteoclastogenesis via up-regulating osteoprotegrin (OPG), as well as down-regulating receptor activator of NF-κB ligand (RANKL) expression. In conclusion, this study indicated that DRTF treatment effectively suppressed bone mass loss in an ovariectomized rat model, and in vitro evidence suggested that the effects were exerted through actions on both osteoblasts and osteoclasts.
The Use of Degenerate Primers in qPCR Analysis of Functional Genes Can Cause Dramatic Quantification Bias as Revealed by Investigation of nifH Primer Performance

The measurement of functional gene abundance in diverse microbial communities often employs quantitative PCR (qPCR) with highly degenerate oligonucleotide primers. While degenerate PCR primers have been demonstrated to cause template-specific bias in PCR applications, the effect of such bias on qPCR has been less well explored. We used a set of diverse, full-length nifH gene standards to test the performance of several universal nifH primer sets in qPCR. We found significant template-specific bias in all but the PolF/PolR primer set. Template-specific bias caused more than 1000-fold mis-estimation of nifH gene copy number for three of the primer sets and one primer set resulted in more than 10,000-fold mis-estimation. Furthermore, such template-specific bias will cause qPCR estimates to vary in response to beta-diversity, thereby causing mis-estimation of changes in gene copy number. A reduction in bias was achieved by increasing the primer concentration. We conclude that degenerate primers should be evaluated across a range of templates, annealing temperatures, and primer concentrations to evaluate the potential for template-specific bias prior to their use in qPCR.
Exploiting Low-Rank Structure from Latent Domains for Domain Generalization

In this paper, we propose a new approach for domain generalization by exploiting the low-rank structure from multiple latent source domains. Motivated by the recent work on exemplar-SVMs, we aim to train a set of exemplar classifiers with each classifier learnt by using only one positive training sample and all negative training samples. While positive samples may come from multiple latent domains, for the positive samples within the same latent domain, their likelihoods from each exemplar classifier are expected to be similar to each other. Based on this assumption, we formulate a new optimization problem by introducing the nuclear-norm based regularizer on the likelihood matrix to the objective function of exemplar-SVMs. We further extend Domain Adaptation Machine (DAM) to learn an optimal target classifier for domain adaptation. The comprehensive experiments for object recognition and action recognition demonstrate the effectiveness of our approach for domain generalization and domain adaptation.
BlueWallet : The Secure Bitcoin

With the increasing popularity of Bitcoin, a digital decentralized currency and payment system, the number of malicious third parties attempting to steal bitcoins has grown substantially. Attackers have stolen bitcoins worth millions of dollars from victims by using malware to gain access to the private keys stored on the victims’ computers or smart phones. In order to protect the Bitcoin private keys, we propose the use of a hardware token for the authorization of transactions. We created a proof-of-concept Bitcoin hardware token: BlueWallet. The device communicates using Bluetooth Low Energy and is able to securely sign Bitcoin transactions. The device can also be used as an electronic wallet in combination with a point of sale and serves as an alternative to cash and credit cards.