nils-herrmann/clean_scirepeval huggingface dataset:
The Evolution of the Web and Implications for an Incremental Crawler

In this paper we study how to build an effective incremental crawler. The crawler selectively and incrementally updates its index and/or local collection of web pages, instead of periodically refreshing the collection in batch mode. The incremental crawler can improve the “freshness” of the collection significantly and bring in new pages in a more timely manner. We first present results from an experiment conducted on more than half million web pages over 4 months, to estimate how web pages evolve over time. Based on these experimental results, we compare various design choices for an incremental crawler and discuss their trade-offs. We propose an architecture for the incremental crawler, which combines the best design choices.
Correlating cyber incident information to establish situational awareness in Critical Infrastructures

Protecting Critical Infrastructures (CIs) against contemporary cyber attacks has become a crucial as well as complex task. Modern attack campaigns, such as Advanced Persistent Threats (APTs), leverage weaknesses in the organization's business processes and exploit vulnerabilities of several systems to hit their target. Although their life-cycle can last for months, these campaigns typically go undetected until they achieve their goal. They usually aim at performing data exfiltration, cause service disruptions and can also undermine the safety of humans. Novel detection techniques and incident handling approaches are therefore required, to effectively protect CI's networks and timely react to this type of threats. Correlating large amounts of data, collected from a multitude of relevant sources, is necessary and sometimes required by national authorities to establish cyber situational awareness, and allow to promptly adopt suitable countermeasures in case of an attack. In this paper we propose three novel methods for security information correlation designed to discover relevant insights and support the establishment of cyber situational awareness.
Strategic Planning for Management Information Systems

Planning for the information systems in an organization generally has not been closely related to the overall strategic planning processes through which the organization prepares for its future. An M/S strategic planning process is conceptualized and illustrated as one which /inks the. organization’s "strategy set" tb an MIS "strategy set. " The literature of management information systems (MIS) concentrates largely on the nature and structure of MIS’s and on processes for designing and developing such systems. The idea of "planning for the MIS" is usually treated as either one of developing the need and the general design concept for such a system, or in the context of project planning for the MIS development effort. However, strategic planning for the informational needs of the organization is both feasible and necessary if the MIS is to support the basic purposes and goals of the organization. Indeed, one of the possible explanations  for the failure of many MIS’s i~-that they have been designed from the same "bottom up" point of view that characterized the development of the data processing systems of an earlier era. Such design approaches primarily reflect the pursuit of efficiency, such as through cost savings, rather than the pursuit of greater organizational  The modern view of an MIS as an organizational decision support system is inconsistent with the design/development approaches which are appropriate for data processing. The organization’s operating efficiency is but one aspect for consideration in management decision making. The achievement of greater organizational effectiveness is the paramount consideration in most of the management decisions which the MIS is to support; it also must be of paramount importance in the design of the MIS. There is an intrinsic linkage of the decisionsupporting MIS to the organization’s purpose, objectives, and strategy. While.this conclusion may appear to be straightforward, it has not been operationalized as a part of MIS design methodology. There are those who argue that the MIS designer cannot hope to get involved in such things as organizational missions, objectives, and strategies, since they are clearly beyond his domain of authority. This article describes an operationally feasible approach for identifying and utilizing the elements of the organization’s "strategy set" to plan for the MIS. Whether or not written state
Investigations of Early Nutrition Effects on Long-Term Health

Developmental programming resulting from maternal malnutrition can lead to an increased risk of metabolic disorders such as obesity, insulin resistance, type 2 diabetes and cardiovascular disorders in the offspring in later life. Furthermore, many conditions linked with developmental programming are also known to be associated with the aging process. This review summarizes the available evidence about the molecular mechanisms underlying these effects, with the potential to identify novel areas of therapeutic intervention. This could also lead to the discovery of new treatment options for improved patient outcomes.
Embodied Energy : a Case for Wood Construction

The purpose of our article is to evaluate wood as a construction material in terms of the energy required for its construction and operation, compared to other types of construction materials. First, the role of construction and material manufacturing is evaluated within the full life cycle energy and CO2 emissions of a building, concluding that the issue of embodied energy justifi es the use of less energy intensive materials. Then the article reviews the literature dealing with the energy requirements of wood based construction, in order to establish whether the use of this natural, low density construction material is more energy effi cient than using brick, reinforced concrete and steel structures. According to our analysis, the vast majority of the studies found that the embodied energy is signifi cantly lower in wood based construction when compared to inorganic materials. According to several authors, wood construction could save much energy and signifi cantly reduce the emissions related to the building sector on the national level. Carbon sequestration, and the related mitigation of the global climate change effect, can be signifi cant if the share of durable wooden buildings can be increased in the market, using sustainably produced raw materials that are handled responsibly at the end of their lifetime. Some confl icting studies make important points concerning the heat storage, recycling and on-site labour demands related to these structures. These sources contribute to a deeper understanding of the issue, but do not alter the basic conclusions concerning the benefi ts of wood based construction. Some important aspects of wood extraction, manufacturing and construction that can help minimising the embodied energy of wood based structures are also discussed in the study.
Leveraging personal devices for stronger password authentication from untrusted computers

Internet authentication for popular end-user transactions, such as online banking and e-commerce, continues to be dominated by passwords entered through end-user personal computers (PCs). Most users continue to prefer (typically untrusted) PCs over smaller personal devices for actual transactions, due to usability features related to keyboard and screen size. However most such transactions and their existing underlying protocols are vulnerable to attacks including keylogging, phishing, and pharming, which can extract user identity and sensitive account information allowing account access. We propose a simple approach to counter such attacks, which cryptographically separates a user’s long-term secret input (typically low-entropy password) from the client PC. The latter continues to be used for most of the interaction and computations but has access only to temporary secrets, while the user’s long-term secret is input through an independent personal trusted device such as a cellphone which makes it available to the PC only after encryption under the intended far-end recipient’s public key. Our proposal is intended to safeguard passwords from the attacks mentioned above, as well as to provide transaction security to foil session hijacking. To facilitate a comparison to our proposal, we also provide a comprehensive survey of web authentication techniques that use an additional factor of authentication such as a cellphone, PDA (personal digital assistant) or hardware token; this survey may be of independent interest. A proof sketch of MP-Auth using the Protocol Composition Logic (PCL) is also provided.
Trunk muscle activation levels during eight stabilization exercises used in the functional kinetics concept: A controlled laboratory study.

To ensure accurate implementation of stabilization exercises in rehabilitation, physical therapists need to understand the muscle activation patterns of prescribed exercise.



Compare muscle activity during eight trunk and lumbar spine stabilization exercises of the Functional Kinetics concept by Klein-Vogelbach.



A controlled laboratory study with a single-group repeated-measures design was utilized to analyze surface electromyographic intensities of 14 female and 6 male young healthy participants performing eight exercises. Data were captured from the rectus abdominis, external/internal oblique and lumbar paraspinalis. The normalized muscle activation levels (maximum voluntary isometric contraction, MVIC) for three repetitions during each exercise and muscle were analyzed.



Side bridging (28 ± 20%MVIC) and advanced planking (29 ± 20%MVIC) reached the highest activity in the rectus abdominis. For external and internal oblique muscles, side bridging also showed the greatest activity of 99 ± 36%MVIC and 52 ± 25%MVIC, respectively. Apart from side bridging (52 ± 14%MVIC), the supine roll-out (31 ± 12%MVIC) and prone roll-out (31 ± 9%MVIC) showed the greatest activity for the paraspinalis. The advanced quadruped, seated back extension and flexion on chair/Swiss Ball, prone roll-out and advanced one-leg back bridging only yielded negligible muscle activities for the rectus abdominis (< 5%MVIC).



Based on the data obtained, recommendations for selective trunk muscle activation during eight stabilization exercises were established, which will guide physical therapists in the development of exercises tailored to the needs of their patients.
Cue strength as a moderator of the testing effect: the benefits of elaborative retrieval.

The current study explored the elaborative retrieval hypothesis as an explanation for the testing effect: the tendency for a memory test to enhance retention more than restudying. In particular, the retrieval process during testing may activate elaborative information related to the target response, thereby increasing the chances that activation of any of this information will facilitate later retrieval of the target. In a test of this view, participants learned cue-target pairs, which were strongly associated (e.g., Toast: Bread) or weakly associated (e.g., Basket: Bread), through either a cued recall test (Toast: _____) or a restudy opportunity (Toast: Bread). A final test requiring free recall of the targets revealed that tested items were retained better than restudied items, and although strong cues facilitated recall of tested items initially, items recalled from weak cues were retained better over time, such that this advantage was eliminated or reversed at the time of the final test. Restudied items were retained at similar rates on the final test regardless of the strength of the cue-target relationship. These results indicate that the activation of elaborative information-which would occur to a greater extent during testing than restudying--may be one mechanism that underlies the testing effect.
Label-aware Double Transfer Learning for Cross-Specialty Medical Named Entity Recognition

We study the problem of named entity recognition (NER) from electronic medical records, which is one of the most fundamental and critical problems for medical text mining. Medical records which are written by clinicians from different specialties usually contain quite different terminologies and writing styles. The difference of specialties and the cost of human annotation makes it particularly difficult to train a universal medical NER system. In this paper, we propose a labelaware double transfer learning framework (LaDTL) for cross-specialty NER, so that a medical NER system designed for one specialty could be conveniently applied to another one with minimal annotation efforts. The transferability is guaranteed by two components: (i) we propose label-aware MMD for feature representation transfer, and (ii) we perform parameter transfer with a theoretical upper bound which is also label aware. We conduct extensive experiments on 12 cross-specialty NER tasks. The experimental results demonstrate that La-DTL provides consistent accuracy improvement over strong baselines. Besides, the promising experimental results on non-medical NER scenarios indicate that LaDTL is potential to be seamlessly adapted to a wide range of NER tasks.
Unsupervised Video Object Segmentation for Deep Reinforcement Learning

• Current deep RL techniques require large amounts of data to find a good policy • Once found, the policy remains a black box to practitioners • Practitioners cannot verify that the policy is making decisions based on reasonable information • MOREL (Motion-Oriented REinforcement Learning) automatically detects moving objects and uses the relevant information for action selection • We gather a dataset using a uniform random policy • Train a network without supervision to capture a structured representation of motion between frames • Network predicts object masks, object motion, and camera motion to warp one frame into the next Introduction Learning to Segment Moving Objects Experiments Visualization
A Comparative Study of Word Embeddings for Reading Comprehension

The focus of past machine learning research for Reading Comprehension tasks has been primarily on the design of novel deep learning architectures. Here we show that seemingly minor choices made on (1) the use of pre-trained word embeddings, and (2) the representation of outof-vocabulary tokens at test time, can turn out to have a larger impact than architectural choices on the final performance. We systematically explore several options for these choices, and provide recommendations to researchers working in this area.
The quantitative measurement of organizational culture in health care: a review of the available instruments.

To review the quantitative instruments available to health service researchers who want to measure culture and cultural change.



A literature search was conducted using Medline, Cinahl, Helmis, Psychlit, Dhdata, and the database of the King's Fund in London for articles published up to June 2001, using the phrase "organizational culture." In addition, all citations and the gray literature were reviewed and advice was sought from experts in the field to identify instruments not found on the electronic databases. The search focused on instruments used to quantify culture with a track record, or potential for use, in health care settings.



For each instrument we examined the cultural dimensions addressed, the number of items for each questionnaire, the measurement scale adopted, examples of studies that had used the tool, the scientific properties of the instrument, and its strengths and limitations.



Thirteen instruments were found that satisfied our inclusion criteria, of which nine have a track record in studies involving health care organizations. The instruments varied considerably in terms of their grounding in theory, format, length, scope, and scientific properties.



A range of instruments with differing characteristics are available to researchers interested in organizational culture, all of which have limitations in terms of their scope, ease of use, or scientific properties. The choice of instrument should be determined by how organizational culture is conceptualized by the research team, the purpose of the investigation, intended use of the results, and availability of resources.
ADef: an Iterative Algorithm to Construct Adversarial Deformations

While deep neural networks have proven to be a powerful tool for many recognition and classification tasks, their stability properties are still not well understood. In the past, image classifiers have been shown to be vulnerable to so-called adversarial attacks, which are created by additively perturbing the correctly classified image. In this paper, we propose the ADef algorithm to construct a different kind of adversarial attack created by iteratively applying small deformations to the image, found through a gradient descent step. We demonstrate our results on MNIST with convolutional neural networks and on ImageNet with Inception-v3 and ResNet-101.
GPLAG: detection of software plagiarism by program dependence graph analysis

Along with the blossom of open source projects comes the convenience for software plagiarism. A company, if less self-disciplined, may be tempted to plagiarize some open source projects for its own products. Although current plagiarism detection tools appear sufficient for academic use, they are nevertheless short for fighting against serious plagiarists. For example, disguises like statement reordering and code insertion can effectively confuse these tools. In this paper, we develop a new plagiarism detection tool, called GPLAG, which detects plagiarism by mining program dependence graphs (PDGs). A PDG is a graphic representation of the data and control dependencies within a procedure. Because PDGs are nearly invariant during plagiarism, GPLAG is more effective than state-of-the-art tools for plagiarism detection. In order to make GPLAG scalable to large programs, a statistical lossy filter is proposed to prune the plagiarism search space. Experiment study shows that GPLAG is both effective and efficient: It detects plagiarism that easily slips over existing tools, and it usually takes a few seconds to find (simulated) plagiarism in programs having thousands of lines of code.
Link Prediction Adversarial Attack

Deep neural network has shown remarkable performance in solving computer vision and some graph evolved tasks, such as node classification and link prediction. However, the vulnerability of deep model has also been revealed by carefully designed adversarial examples generated by various adversarial attack methods. With the wider application of deep model in complex network analysis, in this paper we define and formulate the link prediction adversarial attack problem and put forward a novel iterative gradient attack (IGA) based on the gradient information in trained graph auto-encoder (GAE). To our best knowledge, it is the first time link prediction adversarial attack problem is defined and attack method is brought up. Not surprisingly, GAE was easily fooled by adversarial network with only a few links perturbed on the clean network. By conducting comprehensive experiments on different real-world data sets, we can conclude that most deep model based and other state-of-art link prediction algorithms cannot escape the adversarial attack just like GAE. We can benefit the attack as an efficient privacy protection tool from link prediction unknown violation, on the other hand, link prediction attack can be a robustness evaluation metric for current link prediction algorithm in attack defensibility.
Wideband dual-polarized magneto-electric miniaturization using capacitive loading

In this paper, an original method to reduce the height of a dual-polarized unidirectional wideband antenna based on two crossed magneto-electric dipoles is proposed. The miniaturization technique consists in adding a capacitive load between vertical plates. The height of the radiating element is reduced to 0.1λ0, where λ0 is the wavelength at the lowest operation frequency for a Standing Wave Ratio (SWR) <2.5, which corresponds to a reduction factor of 37.5%. The measured input impedance bandwidth is 64% from 1.6 GHz to 3.1 GHz with a SWR <2.5.
Category Enhanced Word Embedding

Distributed word representations have been demonstrated to be effective in capturing semantic and syntactic regularities. Unsupervised representation learning from large unlabeled corpora can learn similar representations for those words that present similar cooccurrence statistics. Besides local occurrence statistics, global topical information is also important knowledge that may help discriminate a word from another. In this paper, we incorporate category information of documents in the learning of word representations and to learn the proposed models in a documentwise manner. Our models outperform several state-of-the-art models in word analogy and word similarity tasks. Moreover, we evaluate the learned word vectors on sentiment analysis and text classification tasks, which shows the superiority of our learned word vectors. We also learn high-quality category embeddings that reflect topical meanings.
Recycling Waste Thermoplastic for Making Lightweight Bricks

Plastics are key resources in circular economy and recycling after the end of useful life with economic value creation and minimal damage to environment is the key to their sustainable management. Studies in a large stream of researches have explored impregnating waste plastics in concrete and reported encouraging results with multiple benefits. The present study makes a critical review of some of these findings and gleans some common useful trends in the properties reported in these studies. The study also presents results of experimental work on bricks made of: non-recyclable waste thermoplastic granules constituting 0 to 10% by weight, fly ash 15%, cement 15% and sand making up the remainder. The bricks were cured under water for 28 days and baked at temperature ranging from 90 o C to 110 o C for 2 hours. The key characteristics of these bricks are found to be lightweight, porous, of low thermal conductivity, and of appreciable mechanical strengths. Though such bricks hold promise, no similar study appears to have been reported so far. Unlike other processes of making porous bricks, which usually involve incineration to burn combustible materials in order to form pores with implication of high carbon emission, the proposed process is non-destructive in that the bricks are merely baked at low temperature, sufficient to melt the waste plastic that gets diffused within the body of the bricks. The compressive strengths after addition of waste plastic to the extent of 10% by weight is about 17MPa that is in conformity with the minimum specified in the ASTM standards. The bricks are likely to add energy efficiency in buildings and help create economic value to manufacturers, thereby, encouraging the ecosystem of plastic waste management involving all actors in the value chain. A mathematical model is developed to predict compressive strength of bricks at varying plastic contents. The study introduces a new strand of research on sustainable thermoplastic waste management.