nils-herrmann/clean_scirepeval huggingface dataset:
A cross model study of corporate financial distress prediction in Taiwan: Multiple discriminant analysis, logit, probit and neural networks models

In 2008, financial tsunami started to impair the economic development of many countries, including Taiwan. The prediction of financial crisis turns to be much more important and doubtlessly holds public attention when the world economy goes to depression. This study examined the predictive ability of the four most commonly used financial distress prediction models and thus constructed reliable failure prediction models for public industrial firms in Taiwan. Multiple discriminate analysis (MDA), logit, probit, and artificial neural networks (ANNs) methodology were employed to a dataset of matched sample of failed and non-failed Taiwan public industrial firms during 1998–2005. The final models are validated using within sample test and out-of-the-sample test, respectively. The results indicated that the probit, logit, and ANN models which used in this study achieve higher prediction accuracy and possess the ability of generalization. The probit model possesses the best and stable performance. However, if the data does not satisfy the assumptions of the statistical approach, then the ANN approach would demonstrate its advantage and achieve higher prediction accuracy. In addition, the models which used in this study achieve higher prediction accuracy and possess the ability of generalization than those of [Altman, Financial ratios—discriminant analysis and the prediction of corporate bankruptcy using capital market data,  (1968) 589–609, Ohlson, Financial ratios and the probability prediction of bankruptcy, Journal of Accounting Research 18 (1) (1980) 109–131, and Zmijewski, Methodological issues related to the estimation of financial distress prediction models, Journal of Accounting Research 22 (1984) 59–82]. In summary, the models used in this study can be used to assist investors, creditors, managers, auditors, and regulatory agencies in Taiwan to predict the probability of business failure. & 2009 Elsevier B.V. All rights reserved.
The Other Side of the Coin: User Experiences with Bitcoin Security and Privacy

We present the first large-scale survey to investigate how users experience the Bitcoin ecosystem in terms of security, privacy and anonymity. We surveyed 990 Bitcoin users to determine Bitcoin management strategies and identified how users deploy security measures to protect their keys and bitcoins. We found that about 46% of our participants use web-hosted solutions to manage at least some of their bitcoins, and about half of them use exclusively such solutions. We also found that many users do not use all security capabilities of their selected Bitcoin management tool and have significant misconceptions on how to remain anonymous and protect their privacy in the Bitcoin network. Also, 22% of our participants have already lost money due to security breaches or self-induced errors. To get a deeper understanding, we conducted qualitative interviews to explain some of the observed phenomena.
Does it matter which exercise? A randomized control trial of exercise for low back pain.

Multicentered randomized controlled trial.



To determine if previously validated low back pain (LBP) subgroups respond differently to contrasting exercise prescriptions.



The role of "patient-specific" exercises in managing LBP is controversial.



A total of 312 acute, subacute, and chronic patients, including LBP-only and sciatica, underwent a standardized mechanical assessment classifying them by their pain response, specifically eliciting either a "directional preference" (DP) (i.e., an immediate, lasting improvement in pain from performing either repeated lumbar flexion, extension, or sideglide/rotation tests), or no DP. Only DP subjects were randomized to: 1) directional exercises "matching" their preferred direction (DP), 2) exercises directionally "opposite" their DP, or 3) "nondirectional" exercises. Outcome measures included pain intensity, location, disability, medication use, degree of recovery, depression, and work interference.



A DP was elicited in 74% (230) of subjects. One third of both the opposite and non-directionally treated subjects withdrew within 2 weeks because of no improvement or worsening (no matched subject withdrew). Significantly greater improvements occurred in matched subjects compared with both other treatment groups in every outcome (P values <0.001), including a threefold decrease in medication use.



Consistent with prior evidence, a standardized mechanical assessment identified a large subgroup of LBP patients with a DP. Regardless of subjects' direction of preference, the response to contrasting exercise prescriptions was significantly different: exercises matching subjects' DP significantly and rapidly decreased pain and medication use and improved in all other outcomes. If repeatable, such subgroup validation has important implications for LBP management.
Spearman ’ s Rank Order Correlation Coefficient

• In this lesson, we will learn how to measure the coefficient of correlation for two sets of ranking. • The coefficient of correlation, r, measures the strength of association or correlation between two sets of data that can be measured. Sometimes, the data is not measurable but can only be ordered, as in ranking. • For example, two students can be asked to rank toast, cereals, and dim sum in terms of preference. They are asked to assign rank 1 to their favourite and rank 3 to the choice of breakfast that they like least. • We will use Spearman's Rank Order Correlation Coefficient to calculate the strength of association between the rankings produced by these two students.
Biosorption and me.

Biosorption has been defined as the property of certain biomolecules (or types of biomass) to bind and concentrate selected ions or other molecules from aqueous solutions. As opposed to a much more complex phenomenon of bioaccumulation based on active metabolic transport, biosorption by dead biomass (or by some molecules and/or their active groups) is passive and based mainly on the "affinity" between the (bio-)sorbent and sorbate. A personal overview of the field and its origins is given here, focusing on R&D reasoning and know-how that is not normally published in the scientific literature. While biosorption of heavy metals has become a popular environmentally driven research topic, it represents only one particular type of a concentration-removal aspect of the sorption process. The methodology of studying biosorption is based on an interdisciplinary approach to it, whereby the phenomenon can be studied, examined and analyzed from different angles and perspectives-by chemists, (micro-)biologists as well as (process) engineers. A pragmatic science approach directs us towards the ultimate application of the phenomenon when reasonably well understood. Considering the variety of parameters affecting the biosorption performance, we have to avoid the endless empirical and, indeed, alchemistic approach to elucidating and optimizing the phenomenon-and this is where the power of computers becomes most useful. This is all still in the domain of science-or "directed curiosity". When the knowledge of biosorption is adequate, it is time to use it-applications of certain types of biosorption are on the horizon, inviting the "new technology" enterprise ventures and presenting new and quite different challenges.
Knowledge Management and Organizational Performance: An Exploratory Survey

Ninety organizations were surveyed in an exploratory investigation of the organizational impact of knowledge management (KM). A search of the literature revealed 12 KM practices. Results indicated that these KM practices were directly related to organizational performance which, in turn, was directly related to financial performance. In addition, a different set of KM practices were associated with specific value disciplines (i.e., customer intimacy, product development and operational excellence). Interestingly, a significant gap exists between the KM practices that firms believe to be important and those that turned out to be directly related to organizational performance. The implications of this study are significant for both practitioners and academics. Suggestions are offered for future work in this area.
Designing a GDPR-Compliant and Usable Privacy Dashboard

The role of personal data gained significance across all business domains in past decades. Despite strict legal restrictions that processing personal data is subject to, users tend to respond to the extensive collection of data by service providers with distrust. Legal battles between data subjects and processors emphasized the need of adaptations by the current law to face today’s challenges. The European Union has taken action by introducing the General Data Protection Regulation (GDPR), which was adopted in April 2016 and will inure in May 2018. The GDPR extends existing data privacy rights of EU citizens and simultaneously puts pressure on controllers and processors by defining high penalties in case of non-compliance. Uncertainties remain to which extent controllers and processors need to adjust their existing technologies in order to conform to the new law. This work designs, implements, and evaluates a privacy dashboard for data subjects intending to enable and ease the execution of data privacy rights granted by the GDPR.
Computer-Supported Cooperative Crime ( Short Paper )

This work addresses fundamental questions about the nature of cybercriminal organization. We investigate the organization of three underground forums: BlackhatWorld, Carders and L33tCrew to understand the nature of distinct communities within a forum, the structure of organization and the impact of enforcement, in particular banning members, on the structure of these forums. We find that each forum is divided into separate competing communities. Smaller communities are limited to 100-230 members, have a two-tiered hierarchy akin to a gang, and focus on a subset of cybercrime activities. Larger communities may have thousands of members and a complex organization with a distributed multi-tiered hierarchy more akin to a mob; such communities also have a more diverse cybercrime portfolio compared to smaller cohorts. Finally, despite differences in size and cybercrime portfolios, members on a single forum have similar operational practices, for example, they use the same electronic currency.
Assessment of exposure to radio frequency electromagnetic fields from smart utility meters in GB; part II) numerical assessment of induced SAR within the human body.

Human body exposure to radiofrequency electromagnetic waves emitted from smart meters was assessed using various exposure configurations. Specific energy absorption rate distributions were determined using three anatomically realistic human models. Each model was assigned with age- and frequency-dependent dielectric properties representing a collection of age groups. Generalized exposure conditions involving standing and sleeping postures were assessed for a home area network operating at 868 and 2,450 MHz. The smart meter antenna was fed with 1 W power input which is an overestimation of what real devices typically emit (15 mW max limit). The highest observed whole body specific energy absorption rate value was 1.87 mW kg-1 , within the child model at a distance of 15 cm from a 2,450 MHz device. The higher values were attributed to differences in dimension and dielectric properties within the model. Specific absorption rate (SAR) values were also estimated based on power density levels derived from electric field strength measurements made at various distances from smart meter devices. All the calculated SAR values were found to be very small in comparison to International Commission on Non-Ionizing Radiation Protection limits for public exposure. Bioelectromagnetics.  2018.
A SDN-IoT Architecture with NFV Implementation

It is anticipated that SDN coupled with NFV and cloud computing, will become a critical enabling technology to radically revolutionize the way network operators will architect and monetize their infrastructure. On the other hand, the Internet of Things (IoT) is transforming the interaction between cyberspace and the physical space with a tremendous impact on everyday life. The effectiveness of these technologies will require new methodological and engineering approaches due to the impressive scale of the problem and the new challenging requests in terms of performance, security and reliability. This paper presents a simple and general SDN-IoT architecture with NFV implementation with specific choices on where and how to adopt SDN and NFV approaches to address the new challenges of the Internet of Things. The architecture will accelerate innovations in the IoT sector, thanks to its flexibility opening new perspectives for fast deployment of software-enabled worldwide services. The paper also look at the business perspective by considering SDN and NFV as enablers of new added value services on top to the existing infrastructure providing more opportunities for revenues leveraging fast deployed services in the value chain.
Docker container security via heuristics-based multilateral security-conceptual and pragmatic study

In this paper we attempt to provide unified security and privacy multilateral security architecture for cloud services stack, using key latest technology via LxC in general and Docker containers in specific. Docker is one of the first to use LxC at such a large scale, but LxC is not a very new concept, it is around for quite some time. Docker container service is a key Platform as a Service (PaaS) based engine. Unified layered cloud computing multilateral security implementation addresses the potential risks associated with cloud computing environment. It also specifies when, at what time, where, and how to apply security controls. Unified Design includes the structured connection between the components of cloud ecosystem for basket and bucket of unified cloud-based hybrid services using micro service oriented venture architectural services realized in terms of PAAS using Docker container as a service. We surveyed various industry/academic professionals and based on our experience we tried to understand the challenges they faced while using the cloud system. We tried to understand the security challenges, incidents, story pieces, accidents, experience they faced while designing, implementing and using the cloud services/resources virtually via online. This work helps to assess the security design and architecture quality using multilateral security framework for Docker container. We try to present the architecture using OSI/TCP/IP stack model with reference to Cloud service stack model and deployment stack model and try to propose the balanced multilateral security prototype with reference to Docker Container security architecture.
Deep Joint Entity Disambiguation with Local Neural Attention

We propose a novel deep learning model for joint document-level entity disambiguation, which leverages learned neural representations. Key components are entity embeddings, a neural attention mechanism over local context windows, and a differentiable joint inference stage for disambiguation. Our approach thereby combines benefits of deep learning with more traditional approaches such as graphical models and probabilistic mention-entity maps. Extensive experiments show that we are able to obtain competitive or stateof-the-art accuracy at moderate computational costs.
Untangling Blockchain: A Data Processing View of Blockchain Systems

Blockchain technologies are gaining massive momentum in the last few years. Blockchains are distributed ledgers that enable parties who do not fully trust each other to maintain a set of global states. The parties agree on the existence, values, and histories of the states. As the technology landscape is expanding rapidly, it is both important and challenging to have a firm grasp of what the core technologies have to offer, especially with respect to their data processing capabilities. In this paper, we first survey the state of the art, focusing on private blockchains (in which parties are authenticated). We analyze both in-production and research systems in four dimensions: distributed ledger, cryptography, consensus protocol, and smart contract. We then present BLOCKBENCH, a benchmarking framework for understanding performance of private blockchains against data processing workloads. We conduct a comprehensive evaluation of three major blockchain systems based on BLOCKBENCH, namely Ethereum, Parity, and Hyperledger Fabric. The results demonstrate several trade-offs in the design space, as well as big performance gaps between blockchain and database systems. Drawing from design principles of database systems, we discuss several research directions for bringing blockchain performance closer to the realm of databases.
Effect of different biofloc system on water quality,biofloc composition and growth performance inLitopenaeus vannamei (Boone, 1931)

The experiment was conducted with three biofloc treatments and one control in triplicate in 500 L capacity indoor tanks. Biofloc tanks, filled with 350 L of water, were fed with sugarcane molasses (BFTS), tapioca flour (BFTT), wheat flour (BFTW) and clean water as control without biofloc and allowed to stand for 30 days. The postlarvae of Litopenaeus vannamei (Boone, 1931) with an Average body weight of 0.15 0.02 g were stocked at the rate of 130 PL m 2 and cultured for a period of 60 days fed with pelleted feed at the rate of 1.5% of biomass. The total suspended solids (TSS) level was maintained at around 500 mg L 1 in BFT tanks. The addition of carbohydrate significantly reduced the total ammoniaN (TAN), nitrite-N and nitrate-N in water and it significantly increased the total heterotrophic bacteria (THB) population in the biofloc treatments. There was a significant difference in the final average body weight (8.49 0.09 g) in the wheat flour treatment (BFTW) than those treatment and control group of the shrimp. Survival of the shrimps was not affected by the treatments and ranged between 82.02% and 90.3%. The proximate and chemical composition of biofloc and proximate composition of the shrimp was significantly different between the biofloc treatments and control. Tintinids, ciliates, copepods, cyanobacteria and nematodes were identified in all the biofloc treatments, nematodes being the most dominant group of organisms in the biofloc. It could be concluded that the use of wheat flour (BFTW) effectively enhanced the biofloc production and contributed towards better water quality which resulted in higher production of shrimp.
Problem Set # 3 Solutions 1 CS 229 , Autumn 2014 Problem Set # 3 Solutions : Theory & Unsupervised learning

Notes: (1) These questions require thought, but do not require long answers. Please be as concise as possible. (2) If you have a question about this homework, we encourage you to post your question on our Piazza forum, at https://piazza.com/stanford/autumn2016/cs229. (3) If you missed the first lecture or are unfamiliar with the collaboration or honor code policy, please read the policy on Handout #1 (available from the course website) before starting work. (4) For problems that require programming, please include in your submission a printout of your code (with comments) and any figures that you are asked to plot. If you are skipping a question, please include it on your PDF/photo, but leave the question blank and tag it appropriately on Gradescope. This includes extra credit problems. If you are scanning your document by cellphone, please check the Piazza forum for recommended cellphone scanning apps and best practices. 1.  The country of Elbania is organized into states, and there are only two candidates running in this election: One from the Elbanian Democratic party, and another from the Labor Party of Elbania. The plan for making our electorial predictions is as follows: We'll sample m voters from each state, and ask whether they're voting democrat. We'll then publish, for each state, the estimated fraction of democrat voters. In this problem, we'll work out how many voters we need to sample in order to ensure that we get good predictions with high probability. One reasonable goal might be to set m large enough that, with high probability, we obtain uniformly accurate estimates of the fraction of democrat voters in every state. But this might require surveying very many people, which would be prohibitively expensive. So, we're instead going to demand only a slightly lower degree of accuracy. Specifically, we'll say that our prediction for a state is " highly inaccurate " if the estimated fraction of democrat voters differs from the actual fraction of democrat voters within that state by more than a tolerance factor γ. CNN knows that their viewers will tolerate some small number of states' estimates being highly inaccurate; however, their credibility would be damaged if they reported highly inaccurate estimates for too many states. So, rather than …
Article : A comparison of flood and earthquake vulnerability assessment indicators

In a cross-disciplinary study, we carried out an extensive literature review to increase understanding of vulnerability indicators used in the disciplines of earthquakeand flood vulnerability assessments. We provide insights into potential improvements in both fields by identifying and comparing quantitative vulnerability indicators grouped into physical and social categories. Next, a selection of indexand curve-based vulnerability models that use these indicators are described, comparing several characteristics such as temporal and spatial aspects. Earthquake vulnerability methods traditionally have a strong focus on object-based physical attributes used in vulnerability curve-based models, while flood vulnerability studies focus more on indicators applied to aggregated land-use classes in curve-based models. In assessing the differences and similarities between indicators used in earthquake and flood vulnerability models, we only include models that separately assess either of the two hazard types. Flood vulnerability studies could be improved using approaches from earthquake studies, such as developing object-based physical vulnerability curve assessments and incorporating time-of-the-day-based building occupation patterns. Likewise, earthquake assessments could learn from flood studies by refining their selection of social vulnerability indicators. Based on the lessons obtained in this study, we recommend future studies for exploring risk assessment methodologies across different hazard types.
Modeling Skip-Grams for Event Detection with Convolutional Neural Networks

Convolutional neural networks (CNN) have achieved the top performance for event detection due to their capacity to induce the underlying structures of the k-grams in the sentences. However, the current CNN-based event detectors only model the consecutive k-grams and ignore the non-consecutive kgrams that might involve important structures for event detection. In this work, we propose to improve the current CNN models for ED by introducing the non-consecutive convolution. Our systematic evaluation on both the general setting and the domain adaptation setting demonstrates the effectiveness of the nonconsecutive CNN model, leading to the significant performance improvement over the current state-of-the-art systems.