nils-herrmann/clean_scirepeval huggingface dataset:
A Secure Two-Phase Data Deduplication Scheme

Data grows at the impressive rate of 50% per year, and 75% of the digital world is a copy! Although keeping multiple copies of data is necessary to guarantee their availability and long term durability, in many situations the amount of data redundancy is immoderate. By keeping a single copy of repeated data, data deduplication is considered as one of the most promising solutions to reduce the storage costs, and improve users experience by saving network bandwidth and reducing backup time. However, this solution must now solve many security issues to be completely satisfying. In this paper we target the attacks from malicious clients that are based on the manipulation of data identifiers and those based on backup time and network traffic observation. We present a deduplication scheme mixing an intraand an inter-user deduplication in order to build a storage system that is secure against the aforementioned type of attacks by controlling the correspondence between files and their identifiers, and making the inter-user deduplication unnoticeable to clients using deduplication proxies. Our method provides global storage space savings, per-client bandwidth network savings between clients and deduplication proxies, and global network bandwidth savings between deduplication proxies and the storage server. The evaluation of our solution compared to a classic system shows that the overhead introduced by our scheme is mostly due to data encryption which is necessary to ensure confidentiality.
What is the right way to represent document images?

In this article we study the problem of document image representation based on visual features. We propose a comprehensive experimental study that compares three types of visual document image representations: (1) traditional so-called shallow features, such as the RunLength and the Fisher-Vector descriptors, (2) deep features based on Convolutional Neural Networks, and (3) features extracted from hybrid architectures that take inspiration from the two previous ones. We evaluate these features in several tasks ( i.e. classification, clustering, and retrieval) and in different setups ( e.g. domain transfer) using several public and in-house datasets. Our results show that deep features generally outperform other types of features when there is no domain shift and the new task is closely related to the one used to train the model. However, when a large domain or task shift is present, the Fisher-Vector shallow features generalize better and often obtain the best results.
Review on dermatomycosis: pathogenesis and treatment

Dermatophytes, a group of keratinophilic fungi thriving on the keratin substrate are the etiological agents responsible for causing cutaneous infections. Dermatophytosis is currently treated with the commercially available topical and oral antifungal agents in spite of the existing side effects. Treatment of these cutaneous infections with secondary metabolites produced by marine microorganisms is considered as a novel approach. For many years these organisms have been explored with the view of developing antibacterial, antifungal, antiviral, anticancer and antiparasitic drugs. Exploring the unexplored aspect of actinobacteria for developing antidermatophytic drugs is a novel attempt which needs further investigation.
The internal and external responsiveness of Functional Assessment of Cancer Therapy-Prostate (FACT-P) and Short Form-12 Health Survey version 2 (SF-12 v2) in patients with prostate cancer

To examine the responsiveness of Functional Assessment of Cancer Therapy-Prostate (FACT-P) and Short Form-12 Health Survey version 2 (SF-12 v2) in prostate cancer patients because there is a lack of evidence to support their responsiveness in this patient population. One hundred sixty-eight subjects with prostate cancer were surveyed at baseline and at 6 months using the SF-12 v2 and FACT-P version 4. Internal responsiveness was assessed using paired t test and generalized estimating equation. External responsiveness was evaluated using receiver operating characteristic curve analysis. The internal responsiveness of the FACT-P and SF-12 v2 to detect positive change was satisfactory. The FACT-P and SF-12 v2 could not detect negative change. The FACT-P and the SF-12 v2 performed the best in distinguishing between improved general health and worsened general health. The FACT-P performed better in distinguishing between unchanged general health and worsened general health. The SF-12 v2 performed better in distinguishing between unchanged general health and improved general health. Positive change detected by these measures should be interpreted with caution as they might be too responsive to detect “noise,” which is not clinically significant. The ability of the FACT-P and the SF-12 v2 to detect negative change was disappointing. The internal and external responsiveness of the social well-being of the FACT-P cannot be supported, suggesting that it is not suitable to longitudinally monitor the social component of HRQOL in prostate cancer patients. The study suggested that generic and disease-specific measures should be used together to complement each other.
Changes in climate and land use have a larger direct impact than rising CO2 on global river runoff trends.

The significant worldwide increase in observed river runoff has been tentatively attributed to the stomatal "antitranspirant" response of plants to rising atmospheric CO(2) [Gedney N, Cox PM, Betts RA, Boucher O, Huntingford C, Stott PA (2006) . However, CO(2) also is a plant fertilizer. When allowing for the increase in foliage area that results from increasing atmospheric CO(2) levels in a global vegetation model, we find a decrease in global runoff from 1901 to 1999. This finding highlights the importance of vegetation structure feedback on the water balance of the land surface. Therefore, the elevated atmospheric CO(2) concentration does not explain the estimated increase in global runoff over the last century. In contrast, we find that changes in mean climate, as well as its variability, do contribute to the global runoff increase. Using historic land-use data, we show that land-use change plays an additional important role in controlling regional runoff values, particularly in the tropics. Land-use change has been strongest in tropical regions, and its contribution is substantially larger than that of climate change. On average, land-use change has increased global runoff by 0.08 mm/year(2) and accounts for approximately 50% of the reconstructed global runoff trend over the last century. Therefore, we emphasize the importance of land-cover change in forecasting future freshwater availability and climate.
Genes Encoding Enzymes Involved in Ethanol Metabolism

The effects of beverage alcohol (ethanol) on the body are determined largely by the rate at which it and its main breakdown product, acetaldehyde, are metabolized after consumption. The main metabolic pathway for ethanol involves the enzymes alcohol dehydrogenase (ADH) and aldehyde dehydrogenase (ALDH). Seven different ADHs and three different ALDHs that metabolize ethanol have been identified. The genes encoding these enzymes exist in different variants (i.e., alleles), many of which differ by a single DNA building block (i.e., single nucleotide polymorphisms [SNPs]). Some of these SNPs result in enzymes with altered kinetic properties. For example, certain ADH1B and ADH1C variants that are commonly found in East Asian populations lead to more rapid ethanol breakdown and acetaldehyde accumulation in the body. Because acetaldehyde has harmful effects on the body, people carrying these alleles are less likely to drink and have a lower risk of alcohol dependence. Likewise, an ALDH2 variant with reduced activity results in acetaldehyde buildup and also has a protective effect against alcoholism. In addition to affecting drinking behaviors and risk for alcoholism, ADH and ALDH alleles impact the risk for esophageal cancer.
Human-aided Multi-Entity Bayesian Networks Learning from Relational Data

An Artificial Intelligence (AI) system is an autonomous system which emulates human’s mental and physical activities such as Observe, Orient, Decide, and Act, called the OODA process. An AI system performing the OODA process requires a semantically rich representation to handle a complex real world situation and ability to reason under uncertainty about the situation. MultiEntity Bayesian Networks (MEBNs) combines First-Order Logic with Bayesian Networks for representing and reasoning about uncertainty in complex, knowledge-rich domains. MEBN goes beyond standard Bayesian networks to enable reasoning about an unknown number of entities interacting with each other in various types of relationships, a key requirement for the OODA process of an AI system. MEBN models have heretofore been constructed manually by a domain expert. However, manual MEBN modeling is labor-intensive and insufficiently agile. To address these problems, an efficient method is needed for MEBN modeling. One of the methods is to use machine learning to learn a MEBN model in whole or in part from data. In the era of Big Data, data-rich environments, characterized by uncertainty and complexity, have become ubiquitous. The larger the data sample is, the more accurate the results of the machine learning approach can be. Therefore, machine learning has potential to improve the quality of MEBN models as well as the effectiveness for MEBN modeling. In this research, we study a MEBN learning framework to develop a MEBN model from a combination of domain expert's knowledge and data. To evaluate the MEBN learning framework, we conduct an experiment to compare the MEBN learning framework and the existing manual MEBN modeling in terms of development efficiency.
Image Compression using DCT upon Various Quantization

Discrete cosine transform (DCT) is a widely compression technique for converting an image into elementary frequency components. However, level of quality and compression is desired, scalar multiples of the JPEG standard quantization may be used. In this paper, DCT method was applied to compress image under various level of quality. Different quantization matrices of DCT’s coefficients are used to improve level of quality and compression ratio of JPEG image.
CoCo: Collaboration Coach for Understanding Team Dynamics during Video Conferencing

We present and discuss a fully-automated collaboration system, CoCo, that allows multiple participants to video chat and receive feedback through custom video conferencing software. After a conferencing session, a virtual feedback assistant provides insights on the conversation to participants. CoCo automatically pulls audial and visual data during conversations and analyzes the extracted streams for affective features, including smiles, engagement, attention, as well as speech overlap and turn-taking. We validated CoCo with 39 participants split into 10 groups. Participants played two back-to-back team-building games, Lost at Sea and Survival on the Moon, with the system providing feedback between the two. With feedback, we found a statistically significant change in balanced participation---that is, everyone spoke for an equal amount of time. There was also statistically significant improvement in participants' self-evaluations of conversational skills awareness, including how often they let others speak, as well as of teammates' conversational skills. The entire framework is available at https://github.com/ROC-HCI/CollaborationCoach_PostFeedback.
A literature review of wind forecasting technology in the world

Large intermittent generations have grown the influence on the grid security, system operation, and market economics. Although wind energy may not be dispatched, the cost impacts of wind can be substantially reduced if the wind energy can be scheduled using accurate wind forecasting. In other words, the improvement of the performance of wind power forecasting tool has significant technology and economic impact on the system operation with increased wind power penetration. Forecasting has been a vital part of business planning in today's competitive environment, especially in areas characterized by a high concentration of wind generation and a limited capacity of network. The target of this paper is to present a critical literature review and an up-to-date bibliography on wind forecasting technologies over the world. Various forecasting aspects concerning the wind speed and power have been highlighted. These technologies based on numeric weather prediction (NWP) methods, statistical methods, methods based upon artificial neural networks (ANNs), and hybrid forecasting approaches will be discussed. Furthermore, the difference between wind speed and power forecasting, the lead time of forecasting, and the further research will also be discussed in this paper.
Vision Based UAV Attitude Estimation: Progress and Insights

Unmanned aerial vehicles (UAVs) are increasingly replacing manned systems in situations that are dangerous, remote, or difficult for manned aircraft to access. Its control tasks are empowered by computer vision technology. Visual sensors are robustly used for stabilization as primary or at least secondary sensors. Hence, UAV stabilization by attitude estimation from visual sensors is a very active research area. Vision based techniques are proving their effectiveness and robustness in handling this problem. In this work a comprehensive review of UAV vision based attitude estimation approaches is covered, starting from horizon based methods and passing by vanishing points, optical flow, and stereoscopic based techniques. A novel segmentation approach for UAV attitude estimation based on polarization is proposed. Our future insightes for attitude estimation from uncalibrated catadioptric sensors are also discussed.
Security and Privacy in m-Learning and Beyond : Challenges and State-ofthe-art Georgios Kambourakis Laboratory of Information and Communications Systems Security

Mobile learning is constantly evolving, following the shift of mobile technologies from laptops to handheld devices and smartphones. Indeed, the opportunities for innovation in this area are numerous and constantly under the focus of all the parties involved, ranging from traditional schools and universities to individual learners. However, mobile technology brings along increased threats to system and data security and privacy, given the fact that learners and educators are mobile, and in most cases, permitted to use their own mobile devices to access resources and services. After identifying the challenges, this paper provides a comprehensive review and classification of the state-of-the-art research on security and privacy in the m-learning realm and beyond. As far as we are aware of, this is the first time an exhaustive and detailed survey of this kind is attempted.
HG-DAgger: Interactive Imitation Learning with Human Experts

Imitation learning has proven to be useful for many real-world problems, but approaches such as behavioral cloning suffer from data mismatch and compounding error issues. One attempt to address these limitations is the DAGGER algorithm, which uses the state distribution induced by the novice to sample corrective actions from the expert. Such sampling schemes, however, require the expert to provide action labels without being fully in control of the system. This can decrease safety and, when using humans as experts, is likely to degrade the quality of the collected labels due to perceived actuator lag. In this work, we propose HG-DAGGER, a variant of DAGGER that is more suitable for interactive imitation learning from human experts in real-world systems. In addition to training a novice policy, HG-DAGGER also learns a safety threshold for a model-uncertainty-based risk metric that can be used to predict the performance of the fully trained novice in different regions of the state space. We evaluate our method on both a simulated and real-world autonomous driving task, and demonstrate improved performance over both DAGGER and behavioral cloning.
N6-Adenosine Methylation in MiRNAs

Methylation of N6-adenosine (m6A) has been observed in many different classes of RNA, but its prevalence in microRNAs (miRNAs) has not yet been studied. Here we show that a knockdown of the m6A demethylase FTO affects the steady-state levels of several miRNAs. Moreover, RNA immunoprecipitation with an anti-m6A-antibody followed by RNA-seq revealed that a significant fraction of miRNAs contains m6A. By motif searches we have discovered consensus sequences discriminating between methylated and unmethylated miRNAs. The epigenetic modification of an epigenetic modifier as described here adds a new layer to the complexity of the posttranscriptional regulation of gene expression.
BUSINESS ETHICS WITHOUT STAKEHOLDERS

One of the most influential ideas in the field of business ethics has been the suggestion that ethical conduct in a business context should be analyzed in terms of a set of fiduciary obligations toward various “stakeholder” groups. Moral problems, according to this view, involve reconciling such obligations in cases where stakeholder groups have conflicting interests. The question posed in this paper is whether the stakeholder paradigm represents the most fruitful way of articulating the moral problems that arise in business. By way of contrast, I outline two other possible approaches to business ethics: one, a more minimal conception, anchored in the notion of a fiduciary obligation toward shareholders; and the other, a broader conception, focused on the concept of market failure. I then argue that the latter offers a more satisfactory framework for the articulation of the social responsibilities
Information Retrieval and the Philosophy of Language

This discussion takes the position that information retrieval systems are fundamentally linguistic in nature in essence, the languages of document representation and searching are dialects of natural language. Because of this, the discipline of the Philosophy of Language should have some bearing on the problems of document representation and search query formulation. The philosophies of Austin, Searle, Grice and Wittgenstein are briefly examined and their relevance to information retrieval theory is discussed.
Programming Languages and Artificial General Intelligence

Despite the fact that there are thousands of programming languages existing there is a huge controversy about what language is better to solve a particular problem. In this paper we discuss requirements for programming language with respect to AGI research. In this article new language will be presented. Unconventional features (e.g. probabilistic programming and partial evaluation) are discussed as important parts of language design and implementation. Besides, we consider possible applications to particular problems related to AGI. Language interpreter for Lisp-like probabilistic mixed paradigm programming language is implemented in Haskell.
Recognizing Mentions of Adverse Drug Reaction in Social Media Using Knowledge-Infused Recurrent Models

Recognizing mentions of Adverse Drug Reactions (ADR) in social media is challenging: ADR mentions are contextdependent and include long, varied and unconventional descriptions as compared to more formal medical symptom terminology. We use the CADEC corpus to train a recurrent neural network (RNN) transducer, integrated with knowledge graph embeddings of DBpedia, and show the resulting model to be highly accurate (93.4 F1). Furthermore, even when lacking high quality expert annotations, we show that by employing an active learning technique and using purpose built annotation tools, we can train the RNN to perform well (83.9 F1).
Reliability of a quantitative clinical posture assessment tool among persons with idiopathic scoliosis.

To determine overall, test-retest and inter-rater reliability of posture indices among persons with idiopathic scoliosis.



A reliability study using two raters and two test sessions.



Tertiary care paediatric centre.



Seventy participants aged between 10 and 20 years with different types of idiopathic scoliosis (Cobb angle 15 to 60°) were recruited from the scoliosis clinic.



Based on the XY co-ordinates of natural reference points (e.g., eyes) as well as markers placed on several anatomical landmarks, 32 angular and linear posture indices taken from digital photographs in the standing position were calculated from a specially developed software program. Generalisability theory served to estimate the reliability and standard error of measurement (SEM) for the overall, test-retest and inter-rater designs. Bland and Altman's method was also used to document agreement between sessions and raters.



In the random design, dependability coefficients demonstrated a moderate level of reliability for six posture indices (ϕ=0.51 to 0.72) and a good level of reliability for 26 posture indices out of 32 (ϕ≥0.79). Error attributable to marker placement was negligible for most indices. Limits of agreement and SEM values were larger for shoulder protraction, trunk list, Q angle, cervical lordosis and scoliosis angles. The most reproducible indices were waist angles and knee valgus and varus.



Posture can be assessed in a global fashion from photographs in persons with idiopathic scoliosis. Despite the good reliability of marker placement, other studies are needed to minimise measurement errors in order to provide a suitable tool for monitoring change in posture over time.
NFC-based Mobile Payment Protocol with User Anonymity

Following the developments of wireless and mobile communication technologies, mobile-commerce (M-commerce) has become more and more popular. However, most of the existing M-commerce protocols do not consider the user anonymity during transactions. This means that it is possible to trace the identity of a payer from a M-commerce transaction. Luo et al. in 2014 proposed an NFC-based anonymous mobile payment protocol. It used an NFC-enabled smartphone and combined a built-in secure element (SE) as a trusted execution environment to build an anonymous mobile payment service. But their scheme has several problems and cannot be functional in practice. In this paper, we introduce a new NFC-based anonymous mobile payment protocol. Our scheme has the following features:(1) Anonymity. It prevents the disclosure of user's identity by using virtual identities instead of real identity during the transmission. (2) Efficiency. Confidentiality is achieved by symmetric key cryptography instead of public key cryptography so as to increase the performance. (3) Convenience. The protocol is based on NFC and is EMV compatible. (4) Security. All the transaction is either encrypted or signed by the sender so the confidentiality and authenticity are preserved.
Diagnosis and management of granuloma annulare.

Granuloma annulare is a benign, asymptomatic, self-limited papular eruption found in patients of all ages. The primary skin lesion usually is grouped papules in an enlarging annular shape, with color ranging from flesh-colored to erythematous. The two most common types of granuloma annulare are localized, which typically is found on the lateral or dorsal surfaces of the hands and feet; and disseminated, which is widespread. Localized disease generally is self-limited and resolves within one to two years, whereas disseminated disease lasts longer. Because localized granuloma annulare is self-limited, no treatment other than reassurance may be necessary. There are no well-designed randomized controlled trials of the treatment of granuloma annulare. Treatment recommendations are based on the pathophysiology of the disease, expert opinion, and case reports only. Liquid nitrogen, injected steroids, or topical steroids under occlusion have been recommended for treatment of localized disease. Disseminated granuloma annulare may be treated with one of several systemic therapies such as dapsone, retinoids, niacinamide, antimalarials, psoralen plus ultraviolet A therapy, fumaric acid esters, tacrolimus, and pimecrolimus. Consultation with a dermatologist is recommended because of the possible toxicities of these agents.