nils-herrmann/clean_scirepeval huggingface dataset:
iACP: a sequence-based tool for identifying anticancer peptides

Cancer remains a major killer worldwide. Traditional methods of cancer treatment are expensive and have some deleterious side effects on normal cells. Fortunately, the discovery of anticancer peptides (ACPs) has paved a new way for cancer treatment. With the explosive growth of peptide sequences generated in the post genomic age, it is highly desired to develop computational methods for rapidly and effectively identifying ACPs, so as to speed up their application in treating cancer. Here we report a sequence-based predictor called iACP developed by the approach of optimizing the g-gap dipeptide components. It was demonstrated by rigorous cross-validations that the new predictor remarkably outperformed the existing predictors for the same purpose in both overall accuracy and stability. For the convenience of most experimental scientists, a publicly accessible web-server for iACP has been established at http://lin.uestc.edu.cn/server/iACP, by which users can easily obtain their desired results.
Pattern Recognition Through PerceptuallyImportant Points In Financial Time Series

Technical Analysis is a financial risk management practice that has been in use since the advent of the stock market and Pattern Recognition is an indivisible part of it. There has been a lot of research into pattern recognition in time series. Existing pattern recognition techniques lack dynamic extensibility. They do not provide any interfaces in order to include new patterns for recognition dynamically. This limits the operability of these techniques to a particular domain. This research devises a new technique for domain independent pattern recognition while giving sufficient speed and accuracy. This enables it to be used by critical Decision Support Systems for time series of different domains. The system emulates the human visual cognition process by implementing the concept of Perceptually Important Points Identification (PIPI). Perceptually Important Points (PIP) represents the minimal set of data points which are necessary to form a pattern. For dynamic inclusion of patterns a Pattern Definition Language (PDL) has been conceptualized for defining patterns in time series by using a declarative programming paradigm. This also results in domain independent pattern recognition without needing any modification.
Passive Rehabilitation Exercises with an Ankle Rehabilitation Prototype Based in a Robot Parallel Structure

In this paper the implementation of PID controllers for the development of passive rehabilitation exercises are presented. For which it is designed and built an ankle rehabilitation prototype based on a structure of parallel robot with a mechanism of the type 2-RRSP (two closed kinematic chains and consisting of joints: revolute-revolute-sphere in slot- fixed post with sphere). Free software is used to develop the computer programs associated with rehabilitation exercises. Regarding the laboratory testing stage, the results of passive exercises are reported, in these exercises path planning is included and PID control is used, for inversion-eversion, dorsal-plantar flexion and combined movements.
SGAN: An Alternative Training of Generative Adversarial Networks

The Generative Adversarial Networks (GANs) have demonstrated impressive performance for data synthesis, and are now used in a wide range of computer vision tasks. In spite of this success, they gained a reputation for being difficult to train, what results in a time-consuming and human-involved development process to use them. We consider an alternative training process, named SGAN, in which several adversarial "local" pairs of networks are trained independently so that a "global" supervising pair of networks can be trained against them. The goal is to train the global pair with the corresponding ensemble opponent for improved performances in terms of mode coverage. This approach aims at increasing the chances that learning will not stop for the global pair, preventing both to be trapped in an unsatisfactory local minimum, or to face oscillations often observed in practice. To guarantee the latter, the global pair never affects the local ones. The rules of SGAN training are thus as follows: the global generator and discriminator are trained using the local discriminators and generators, respectively, whereas the local networks are trained with their fixed local opponent. Experimental results on both toy and real-world problems demonstrate that this approach outperforms standard training in terms of better mitigating mode collapse, stability while converging and that it surprisingly, increases the convergence speed as well.
Memory Based Online Learning of Deep Representations from Video Streams

We present a novel online unsupervised method for face identity learning from video streams. The method exploits deep face descriptors together with a memory based learning mechanism that takes advantage of the temporal coherence of visual data. Specifically, we introduce a discriminative descriptor matching solution based on Reverse Nearest Neighbour and a forgetting strategy that detect redundant descriptors and discard them appropriately while time progresses. It is shown that the proposed learning procedure is asymptotically stable and can be effectively used in relevant applications like multiple face identification and tracking from unconstrained video streams. Experimental results show that the proposed method achieves comparable results in the task of multiple face tracking and better performance in face identification with offline approaches exploiting future information. Code will be publicly available.
Left–right coding of past and future in language: The mental timeline during sentence processing

The metaphoric mapping theory suggests that abstract concepts, like time, are represented in terms of concrete dimensions such as space. This theory receives support from several lines of research ranging from psychophysics to linguistics and cultural studies; especially strong support comes from recent response time studies. These studies have reported congruency effects between the dimensions of time and space indicating that time evokes spatial representations that may facilitate or impede responses to words with a temporal connotation. The present paper reports the results of three linguistic experiments that examined this congruency effect when participants processed past- and future-related sentences. Response time was shorter when past-related sentences required a left-hand response and future-related sentences a right-hand response than when this mapping of time onto response hand was reversed (Experiment 1). This result suggests that participants can form time-space associations during the processing of sentences and thus this result is consistent with the view that time is mentally represented from left to right. The activation of these time-space associations, however, appears to be non-automatic as shown by the results of Experiments 2 and 3 when participants were asked to perform a non-temporal meaning discrimination task.
Security Engineering: a Guide to Building Dependable Distributed Systems Banking and Bookkeeping

Computers are not (yet?) capable of being reasonable any more than is a Second Lieutenant. Against stupidity, the Gods themselves contend in vain. Banking systems include the back-end bookkeeping systems that record customers' account details and transaction processing systems such as cash machine networks and high-value interbank money transfer systems that feed them with data. They are important for a number of reasons. First, bookkeeping was for many years the main business of the computer industry, and banking was its most intensive area of application. Personal applications such as Netscape and Powerpoint might now run on more machines, but accounting is still the critical application for the average business. So the protection of bookkeeping systems is of great practical importance. It also gives us a well-understood model of protection in which confidentiality plays almost no role, but where the integrity of records (and their immutability once made) is of paramount importance. Second, transaction processing systems—whether for small debits such as $50 cash machine withdrawals or multimillion-dollar wire transfers—were the applications that launched commercial cryptography. Banking applications drove the development not just of encryption algorithms and protocols, but also of the supporting technologies, such as tamper-resistant cryptographic processors. These processors provide an important and interesting example of a trusted computing base that is quite different from
Extractable Witness Encryption and Timed-Release Encryption from Bitcoin

We propose a new witness encryption based on Subset-Sum which achieves extractable security without relying on obfuscation and is more efficient than the existing ones. Our witness encryption employs multilinear maps of arbitrary order and it is independent of the implementations of multilinear maps. As an application, we construct a new timed-release encryption based on the Bitcoin protocol and extractable witness encryption. The novelty of our scheme is that the decryption key will be automatically revealed in the bitcoin block-chain when the block-chain reaches a certain length.
Factors affecting sustainable adoption of e-health technology in developing countries: an exploratory survey of Nigerian hospitals from the perspective of healthcare professionals

E-health technology applications are essential tools of modern information technology that improve quality of healthcare delivery in hospitals of both developed and developing countries. However, despite its positive benefits, studies indicate that the rate of the e-health adoption in some developing countries is either low or underutilized. This is due in part, to barriers such as resistance from healthcare professionals, poor infrastructure, and low technical expertise among others.



The aim of this study is to investigate, identify and analyze the underlying factors that affect healthcare professionals decision to adopt and use e-health technology applications in developing countries, with particular reference to hospitals in Nigeria.



The study used a cross sectional approach in the form of a close-ended questionnaire to collect quantitative data from a sample of 465 healthcare professionals randomly selected from 15 hospitals in Nigeria. We used the modified Technology Acceptance Model (TAM) as the dependent variable and external factors as independent variables. The collected data was then analyzed using SPSS statistical analysis such as frequency test, reliability analysis, and correlation coefficient analysis.



The results obtained, which correspond with findings from other researches published, indicate that perceived usefulness, belief, willingness, as well as attitude of healthcare professionals have significant influence on their intention to adopt and use the e-health technology applications. Other strategic factors identified include low literacy level and experience in using the e-health technology applications, lack of motivation, poor organizational and management policies.



The study contributes to the literature by pinpointing significant areas where findings can positively affect, or be found useful by, healthcare policy decision makers in Nigeria and other developing countries. This can help them understand their areas of priorities and weaknesses when planning for e-health technology adoption and implementation.
Rethinking Vehicular Communications: Merging VANET with cloud computing

Despite the surge in Vehicular Ad Hoc NETwork (VANET) research, future high-end vehicles are expected to under-utilize the on-board computation, communication, and storage resources. Olariu et al. envisioned the next paradigm shift from conventional VANET to Vehicular Cloud Computing (VCC) by merging VANET with cloud computing. But to date, in the literature, there is no solid architecture for cloud computing from VANET standpoint. In this paper, we put forth the taxonomy of VANET based cloud computing. It is, to the best of our knowledge, the first effort to define VANET Cloud architecture. Additionally we divide VANET clouds into three architectural frameworks named Vehicular Clouds (VC), Vehicles using Clouds (VuC), and Hybrid Vehicular Clouds (HVC). We also outline the unique security and privacy issues and research challenges in VANET clouds.
A hybrid system for malware detection on big data

In recent years, the increasing diffusion of malicious software has encouraged the adoption of advanced machine learning algorithms to timely detect new threats. A cloud-based approach allows to exploit the big data produced by client agents to train such algorithms, but on the other hand, poses severe challenges on their scalability and performance. We propose a hybrid cloud-based malware detection system in which static and dynamic analyses are combined in order to find a good trade-off between response time and detection accuracy. Our system performs a continuous learning process of its models, based on deep networks, by exploiting the growing amount of data provided by clients. The preliminary experimental evaluation confirms the suitability of the approach proposed here.
Primary infertility after genital mutilation in girlhood in Sudan: a case-control study

In theory, infections that arise after female genital mutilation (FGM) in childhood might ascend to the internal genitalia, causing inflammation and scarring and subsequent tubal-factor infertility. Our aim was to investigate this possible association between FGM and primary infertility.



We did a hospital-based case-control study in Khartoum, Sudan, to which we enrolled women (n=99) with primary infertility not caused by hormonal or iatrogenic factors (previous abdominal surgery), or the result of male-factor infertility. These women underwent diagnostic laparoscopy. Our controls were primigravidae women (n=180) recruited from antenatal care. We used exact conditional logistic regression, stratifying for age and controlling for socioeconomic status, level of education, gonorrhoea, and chlamydia, to compare these groups with respect to FGM.



Of the 99 infertile women examined, 48 had adnexal pathology indicative of previous inflammation. After controlling for covariates, these women had a significantly higher risk than controls of having undergone the most extensive form of FGM, involving the labia majora (odds ratio 4.69, 95% CI 1.49-19.7). Among women with primary infertility, both those with tubal pathology and those with normal laparoscopy findings were at a higher risk than controls of extensive FGM, both with borderline significance (p=0.054 and p=0.055, respectively). The anatomical extent of FGM, rather than whether or not the vulva had been sutured or closed, was associated with primary infertility.



Our findings indicate a positive association between the anatomical extent of FGM and primary infertility. Laparoscopic postinflammatory adnexal changes are not the only explanation for this association, since cases without such pathology were also affected. The association between FGM and primary infertility is highly relevant for preventive work against this ancient practice.
Open Design and Local Manufacturing of Small Wind Turbines: Case Studies in Ethiopia and Nepal

During the last decade, the local manufacturing of small wind turbines is becoming an increasingly common approach in rural electrification applications, especially among international networks of renewable energy practitioners. As the number of locally manufactured small wind turbine installations is increasing on all continents, supply chain issues, material shortages or design issues in custom applications are becoming evident. In this paper the development of a series of open access design and analysis tools is presented, which allows the local manufacturer to redesign the small wind turbine according to available materials. In the two case studies, the design tools are used in the field by practitioners in order to overcome supply chain issues during the implementation and design phases of rural electrification projects in Ethiopia and Nepal.
Understanding Blind People's Experiences with Computer-Generated Captions of Social Media Images

Research advancements allow computational systems to automatically caption social media images. Often, these captions are evaluated with sighted humans using the image as a reference. Here, we explore how blind and visually impaired people experience these captions in two studies about social media images. Using a contextual inquiry approach (n=6 blind/visually impaired), we found that blind people place a lot of trust in automatically generated captions, filling in details to resolve differences between an image's context and an incongruent caption. We built on this in-person study with a second, larger online experiment (n=100 blind/visually impaired) to investigate the role of phrasing in encouraging trust or skepticism in captions. We found that captions emphasizing the probability of error, rather than correctness, encouraged people to attribute incongruence to an incorrect caption, rather than missing details. Where existing research has focused on encouraging trust in intelligent systems, we conclude by challenging this assumption and consider the benefits of encouraging appropriate skepticism.
Hierarchical k-nearest neighbours classification and binary differential evolution for fault diagnostics of automotive bearings operating under variable conditions

Electric traction motors in automotive applications work in operational conditions characterized by variable load, rotational speed and other external conditions: this complicates the task of diagnosing bearing defects. The objective of the present work is the development of a diagnostic system for detecting the onset of degradation, isolating the degrading bearing, classifying the type of defect. The developed diagnostic system is based on an hierarchical structure of K-Nearest Neighbours classifiers. The selection of the features from the measured vibrational signals to be used in input by the bearing diagnostic system is done by a wrapper approach based on a Multi-Objective (MO) optimization that integrates a Binary Differential Evolution (BDE) algorithm with the K-Nearest Neighbour (KNN) classifiers. The developed approach is applied to an experimental dataset. The satisfactory diagnostic performances obtain show the capability of the method, independently from the bearings operational conditions.
Proteolytic enzymes: a new treatment strategy for prosthetic infections?

Among the different mechanisms of bacterial resistance to antimicrobial agents that have been studied, biofilm formation is one of the most widespread. This mechanism is frequently the cause of failure in the treatment of prosthetic device infections, and several attempts have been made to develop molecules and protocols that are able to inhibit biofilm-embedded bacteria. We present data suggesting the possibility that proteolytic enzymes could significantly enhance the activities of antibiotics against biofilms. Antibiotic susceptibility tests on both planktonic and sessile cultures, studies on the dynamics of colonization of 10 biofilm-forming isolates, and then bioluminescence and scanning electron microscopy under seven different experimental conditions showed that serratiopeptidase greatly enhances the activity of ofloxacin on sessile cultures and can inhibit biofilm formation.
Information Flow Management of Vendor-Managed Inventory System in Automobile Parts Inbound Logistics Based on Internet of Things

Reducing inventory levels is a major supply chain management challenge in automobile industries. With the development of information technology new cooperative supply chain contracts emerge such as Vendor-Managed Inventory (VMI). This research aims to look at the literature of information management of VMI and the Internet of Things, then analyzes information flow model of VMI system. The paper analyzes information flow management of VMI system in automobile parts inbound logistics based on the environment of Internet of Things.
Pedicle screw instrumentation in adolescent idiopathic scoliosis (AIS)

Pedicle screw instrumentation in AIS has advantages of rigid fixation, improved deformity correction and a shorter fusion, but needs an exacting technique. The author has been using the K-wire method with intraoperative single PA and lateral radiographs, because it is safe, accurate and fast. Pedicle screws are inserted in every segment on the correction side (thoracic concave) and every 2–3 on the supportive side (thoracic convex). After an over-bent rod is inserted on the corrective side, the rod is rotated 90° counterclockwise. This maneuver corrects the coronal and sagittal curves. Then the vertebra is derotated by direct vertebral rotation (DVR) correcting the rotational deformity. The direction of DVR should be opposite to that of the vertebral rotation. A rigid rod has to be used to prevent the rod from straightening out during the rod derotation and DVR. The ideal classification of AIS should address all curve patterns, predicts accurate fusion extent and have good inter/intraobserver reliability. The Suk classification matches the ideal classification is simple and memorable, and has only four structural curve patterns; single thoracic, double thoracic, double major and thoracolumbar/lumbar. Each curve has two types, A and B. When using pedicle screws in thoracic AIS, curves are usually fused from upper neutral to lower neutral vertebra. Identification of the end vertebra and the neutral vertebra is important in deciding the fusion levels and the direction of DVR. In lumbar AIS, fusion is performed from upper neutral vertebra to L3 or L4 depending on its curve types. Rod derotation and DVR using pedicle screw instrumentation give true three dimensional deformity correction in the treatment of AIS. Suk classification with these methods predicts exact fusion extent and is easy to understand and remember.
Spinal muscular atrophy: a timely review.

Spinal muscular atrophy (SMA) is a neurodegenerative disease characterized by loss of motor neurons in the anterior horn of the spinal cord and resultant weakness. The most common form of SMA, accounting for 95% of cases, is autosomal recessive proximal SMA associated with mutations in the survival of motor neurons (SMN1) gene. Relentless progress during the past 15 years in the understanding of the molecular genetics and pathophysiology of SMA has resulted in a unique opportunity for rational, effective therapeutic trials. The goal of SMA therapy is to increase the expression levels of the SMN protein in the correct cells at the right time. With this target in sight, investigators can now effectively screen potential therapies in vitro, test them in accurate, reliable animal models, move promising agents forward to clinical trials, and accurately diagnose patients at an early or presymptomatic stage of disease. A major challenge for the SMA community will be to prioritize and develop the most promising therapies in an efficient, timely, and safe manner with the guidance of the appropriate regulatory agencies. This review will take a historical perspective to highlight important milestones on the road to developing effective therapies for SMA.