nils-herrmann/clean_scirepeval huggingface dataset:
Malaria in India: Challenges and opportunities

India contributes about 70% of malaria in the South East Asian Region of WHO. Although annually India reports about two million cases and 1000 deaths attributable to malaria, there is an increasing trend in the proportion of Plasmodium falciparum as the agent. There exists heterogeneity and variability in the risk of malaria transmission between and within the states of the country as many ecotypes/paradigms of malaria have been recognized. The pattern of clinical presentation of severe malaria has also changed and while multi-organ failure is more frequently observed in falciparum malaria, there are reports of vivax malaria presenting with severe manifestations. The high burden populations are ethnic tribes living in the forested pockets of the states like Orissa, Jharkhand, Madhya Pradesh, Chhattisgarh and the North Eastern states which contribute bulk of morbidity and mortality due to malaria in the country. Drug resistance, insecticide resistance, lack of knowledge of actual disease burden along with new paradigms of malaria pose a challenge for malaria control in the country. Considering the existing gaps in reported and estimated morbidity and mortality, need for estimation of true burden of malaria has been stressed. Administrative, financial, technical and operational challenges faced by the national programme have been elucidated. Approaches and priorities that may be helpful in tackling serious issues confronting malaria programme have been outlined.
Network security analysis SCADA system automation on industrial process

Supervisory Control and Data Acquisition (SCADA) is a unit control system that has been used almost in various industries around the world in terms of process automation. This system delineates the real infrastructure and provides ease in operations and monitoring, but this system has vulnerability in the security aspects of data communications connected between SCADA support devices. This can have a major impact on industry and the economy. This research was conducted by designing and building SCADA infrastructure and analyzing vulnerability threats to SCADA network security. This research penetrates SCADA network using Kali Linux and data traffic analysis on SCADA network using Wireshark. From the results of Wireshark analysis got the attacker with User Anonymous. Analysis performed with normal and abnormal data traffic conditions. The result of this research is penetration of SCADA network using Kali Linux, which is used to attack and make data traffic between Programable Logic Controller (PLC) with Human Machine Interface (HMI) becomes solid, and result from penetration testing, SCADA system become down due of data traffic on a dense network, thereby indicating that SCADA networks are vulnerable to malware threats and attacks, the results of this study are recommendations and network security strategy SCADA system.
Generalizing Pooling Functions in CNNs: Mixed, Gated, and Tree

In this paper, we seek to improve deep neural networks by generalizing the pooling operations that play a central role in the current architectures. We pursue a careful exploration of approaches to allow pooling to learn and to adapt to complex and variable patterns. The two primary directions lie in: (1) learning a pooling function via (two strategies of) combining of max and average pooling, and (2) learning a pooling function in the form of a tree-structured fusion of pooling filters that are themselves learned. In our experiments every generalized pooling operation we explore improves performance when used in place of average or max pooling. We experimentally demonstrate that the proposed pooling operations provide a boost in invariance properties relative to conventional pooling and set the state of the art on several widely adopted benchmark datasets. These benefits come with only a light increase in computational overhead during training (ranging from additional 5 to 15 percent in time complexity) and a very modest increase in the number of model parameters (e.g., additional 1, 9, and 27 parameters for mixed, gated, and 2-level tree pooling operators, respectively). To gain more insights about our proposed pooling methods, we also visualize the learned pooling masks and the embeddings of the internal feature responses for different pooling operations. Our proposed pooling operations are easy to implement and can be applied within various deep neural network architectures.
To Make a Robot Secure: An Experimental Analysis of Cyber Security Threats Against Teleoperated Surgical Robots

Teleoperated robots are playing an increasingly important role in military actions and medical services. In the future, remotely operated surgical robots will likely be used in more scenarios such as battlefields and emergency response. But rapidly growing applications of teleoperated surgery raise the question; what if the computer systems for these robots are attacked, taken over and even turned into weapons? Our work seeks to answer this question by systematically analyzing possible cyber security attacks against Raven II R ©, an advanced teleoperated robotic surgery system. We identify a slew of possible cyber security threats, and experimentally evaluate their scopes and impacts. We demonstrate the ability to maliciously control a wide range of robots functions, and even to completely ignore or override command inputs from the surgeon. We further find that it is possible to abuse the robot’s existing emergency stop (E-stop) mechanism to execute efficient (single packet) attacks. We then consider steps to mitigate these identified attacks, and experimentally evaluate the feasibility of applying the existing security solutions against these threats. The broader goal of our paper, however, is to raise awareness and increase understanding of these emerging threats. We anticipate that the majority of attacks against telerobotic surgery will also be relevant to other teleoperated robotic and co-robotic systems.
Generation of circularly polarized conical beam pattern using (3, 8) torus knot antenna

A novel circularly polarized knot antenna with the conical beam radiation pattern is presented. It consists of a (3, 8) torus knot as a radiator which is excited by a monopole probe. Measured results show that the antenna has an impedance bandwidth of 39.6% covering the frequency range 3.54 GHz to 5.29 GHz and usable axial ratio bandwidth of 26.8%.  torus knot antenna is mechanically simple to fabricate using 3D printing technology. The proposed prototype is suitable for mounting on vehicles to facilitate communication with geostationary satellites.
School-based extracurricular activity involvement and adolescent self-esteem: a growth-curve analysis.

Research on adolescent self-esteem indicates that adolescence is a time in which individuals experience important changes in their physical, cognitive, and social identities. Prior research suggests that there is a positive relationship between an adolescent's participation in structured extracurricular activities and well-being in a variety of domains, and some research indicates that these relationships may be dependent on the type of activities in which adolescents participate. Building on previous research, a growth-curve analysis was utilized to examine self-esteem trajectories from adolescence (age 14) to young adulthood (age 26). Using 3 waves of data from National Longitudinal Study of Adolescent Health (n = 5,399; 47.8% male), the analysis estimated a hierarchical growth-curve model emphasizing the effects of age and type of school-based extracurricular activity portfolio, including sports and school clubs, on self-esteem. The results indicated that age had a linear relationship with self-esteem over time. Changes in both the initial level of self-esteem and the growth of self-esteem over time were significantly influenced by the type of extracurricular activity portfolio. The findings were consistent across race and sex. The results support the utility of examining the longitudinal impact of portfolio type on well-being outcomes.
Feature-based Malicious URL and Attack Type Detection Using Multi-class Classification

Nowadays, malicious URLs are the common threat to the businesses, social networks, net-banking. Existing approaches have focused on binary detection i.e., either the URL is malicious or benign. Very few literature is found which focused on the detection of malicious URLs and their attack types. Hence, it becomes necessary to know the attack type and adopt an effective countermeasure. This paper proposes a methodology to detect malicious URLs and the type of attacks based on multi-class classification. In this work, we propose 42 new features of spam, phishing and malware URLs. These features are not considered in the earlier studies for malicious URLs detection and attack types identification. Binary and multi-class dataset is constructed using 49935 malicious and benign URLs. It consists of 26041 benign and 23894 malicious URLs containing 11297 malware, 8976 phishing and 3621 spam URLs. To evaluate the proposed approach, the state-of-the-art supervised batch and online machine learning classifiers are used. Experiments are performed on the binary and multi-class dataset using the aforementioned machine learning classifiers. It is found that, confidence weighted learning classifier achieves the best 98.44% average detection accuracy with 1.56% error-rate in the multi-class setting and 99.86% detection accuracy with negligible error-rate of 0.14% in binary setting using our proposed URL features.
DRAMA: Exploiting DRAM Addressing for Cross-CPU Attacks

In cloud computing environments, multiple tenants are often co-located on the same multi-processor system. Thus, preventing information leakage between tenants is crucial. While the hypervisor enforces software isolation, shared hardware, such as the CPU cache or memory bus, can leak sensitive information. For security reasons, shared memory between tenants is typically disabled. Furthermore, tenants often do not share a physical CPU. In this setting, cache attacks do not work and only a slow cross-CPU covert channel over the memory bus is known. In contrast, we demonstrate a high-speed covert channel as well as the first side-channel attack working across processors and without any shared memory. To build these attacks, we use the undocumented DRAM address mappings. We present two methods to reverse engineer the mapping of memory addresses to DRAM channels, ranks, and banks. One uses physical probing of the memory bus, the other runs entirely in software and is fully automated. Using this mapping, we introduce DRAMA attacks, a novel class of attacks that exploit the DRAM row buffer that is shared, even in multi-processor systems. Thus, our attacks work in the most restrictive environments. First, we build a covert channel with a capacity of up to 2 Mbps, which is three to four orders of magnitude faster than memory-bus-based channels. Second, we build a side-channel template attack that can automatically locate and monitor memory accesses. Third, we show how using the DRAM mappings improves existing attacks and in particular enables practical Rowhammer attacks on DDR4.
Pseudo-random number generators for Monte Carlo simulations on ATI Graphics Processing Units

Basic uniform pseudo-random number generators are implemented on ATI Graphics Processing Units (GPU). The performance results of the realized generators (multiplicative linear congruential (GGL), XOR-shift (XOR128), RANECU, RANMAR, RANLUX and Mersenne Twister (MT19937)) on CPU and GPU are discussed. The obtained speed-up factor is hundreds of times in comparison with CPU. RANLUX generator is found to be the most appropriate for using on GPU in Monte Carlo simulations. The brief review of the pseudorandom number generators used in modern software packages for Monte Carlo simulations in high-energy physics is present.
Deep learning with convolutional neural networks for brain mapping and decoding of movement-related information from the human EEG

Deep learning with convolutional neural networks (deep ConvNets) has revolutionized computer vision through end-to-end learning, i.e. learning from the raw data. Now, there is increasing interest in using deep ConvNets for end-to-end EEG analysis. However, little is known about many important aspects of how to design and train ConvNets for end-to-end EEG decoding, and there is still a lack of techniques to visualize the informative EEG features the ConvNets learn. Here, we studied deep ConvNets with a range of different architectures, designed for decoding imagined or executed movements from raw EEG. Our results show that recent advances from the machine learning field, including batch normalization and exponential linear units, together with a cropped training strategy, boosted the deep ConvNets decoding performance, reaching or surpassing that of the widely-used filter bank common spatial patterns (FBCSP) decoding algorithm. While FBCSP is designed to use spectral power modulations, the features used by ConvNets are not fixed a priori. Our novel methods for visualizing the learned features demonstrated that ConvNets indeed learned to use spectral power modulations in the alpha, beta and high gamma frequencies. These methods also proved useful as a technique for spatially mapping the learned features, revealing the topography of the causal contributions of features in different frequency bands to decoding the movement classes. Our study thus shows how to design and train ConvNets to decode movement-related information from the raw EEG without handcrafted features and highlights the potential of deep ConvNets combined with advanced visualization techniques for EEG-based brain mapping. Tonio Ball
Dual-Band Low-Profile Crossed Asymmetric Dipole Antenna on Dual-Band AMC Surface

A dual-band, low-profile, circularly polarized antenna on an artificial magnetic conductor (AMC) is introduced in this letter. The antenna employs a single feed and two crossed asymmetric dipoles as the primary radiating elements. In order to achieve a low profile and broadband characteristics in terms of impedance matching and 3-dB axial-ratio (AR) bandwidths at both bands, a dual-band AMC is utilized as a reflector of the antenna. The AMC structure utilizes four T-shaped slits in a unit-cell patch to significantly reduce its first and second resonant frequency ratio, and consequently its first and second resonances are easily adjusted for the desired operating frequencies. For performance verification, an antenna prototype was fabricated with an overall 2.4-GHz frequency size of 0.576λ0 × 0.576λ0 × 0.088λ0 (72 × 72 × 11 mm3). The measurements resulted in impedance bandwidths of 2.20-2.60 and 4.90-5.50 GHz for 10 dB and 3-dB AR bandwidths of 2.30-2.50 and 5.05-5.35 GHz. Additionally, the antenna yields right-hand circular polarization and high antenna efficiency at both bands.
Impact of a new ultrafast CZT SPECT camera for myocardial perfusion imaging: fewer equivocal results and lower radiation dose

The new ultrafast cardiac single photon emission computed tomography (SPECT) cameras with cadmium-zinc-telluride (CZT)-based detectors are faster and produce higher quality images as compared to conventional SPECT cameras. We assessed the need for additional imaging, total imaging time, tracer dose and 1-year outcome between patients scanned with the CZT camera and a conventional SPECT camera. A total of 456 consecutive stable patients without known coronary artery disease underwent myocardial perfusion imaging on a hybrid SPECT/CT (64-slice) scanner using either conventional (n = 225) or CZT SPECT (n = 231). All patients started with low-dose stress imaging, combined with coronary calcium scoring. Rest imaging was only done when initial stress SPECT testing was equivocal or abnormal. Coronary CT angiography was subsequently performed in cases of ischaemic or equivocal SPECT findings. Furthermore, 1-year clinical follow-up was obtained with regard to coronary revascularization, nonfatal myocardial infarction or death. Baseline characteristics were comparable between the two groups. With the CZT camera, the need for rest imaging (35 vs 56%, p < 0.001) and additional coronary CT angiography (20 vs 28%, p = 0.025) was significantly lower as compared with the conventional camera. This resulted in a lower mean total administered isotope dose per patient (658 ± 390 vs 840 ± 421 MBq, p < 0.001) and shorter imaging time (6.39 ± 1.91 vs 20.40 ± 7.46 min, p < 0.001) with the CZT camera. After 1 year, clinical outcome was comparable between the two groups. As compared to images on a conventional SPECT camera, stress myocardial perfusion images acquired on a CZT camera are more frequently interpreted as normal with identical clinical outcome after 1-year follow-up. This lowers the need for additional testing, results in lower mean radiation dose and shortens imaging time.
Fall Detection and Prevention System for Elderly People using MEMS Sensors and IOT

Falls are leading cause of injury and death for elderly people. T herefore it is necessary to design a proper fall prevention system to prevent falls at old age The use of MEMS sensor drastically reduces the size of the system which enables the module to be developed as a wearable suite. A special alert notification regarding the fall is activated using twitter. The state of the person can be viewed every 30sec and is well suited for monitoring aged persons. On a typical fall motion the device releases the compressed air module which is to be designed and alarms the concerned.
Work environment factors other than staffing associated with nurses' ratings of patient care quality.

The impact of registered nurse (RN) staffing on patient care quality has been extensively studied. Identifying additional modifiable work environment factors linked to patient care quality is critical as the projected shortage of approximately 250,000 RNs over the next 15 years will limit institutions' ability to rely on RN staffing alone to ensure high-quality care.



We examined the association between RNs' ratings of patient care quality and several novel work environment factors adjusting for the effects of two staffing variables: reported patient-to-RN ratios and ratings of staffing adequacy.



We used a cross-sectional, correlational design and a mailed survey to collect data in 2009 from a national sample of RNs (n = 1,439) in the United States. A multivariate logistic regression was used to analyze the data.



Workgroup cohesion, nurse-physician relations, procedural justice, organizational constraints, and physical work environment were associated with RNs' ratings of quality, adjusting for staffing. Furthermore, employment in a Magnet hospital and job satisfaction were positively related to ratings of quality, whereas supervisory support was not.



Our evidence demonstrates the importance of considering RN work environment factors other than staffing when planning improvements in patient care quality. Health care managers can use the results of our study to strategically allocate resources toward work environment factors that have the potential to improve quality of care.
An Empirical Study of Consumer Perceptions and Comprehension of Web Site Privacy Policies

The U.S. legislation at both the federal and state levels mandates certain organizations to inform customers about information uses and disclosures. Such disclosures are typically accomplished through privacy policies, both online and offline. Unfortunately, the policies are not easy to comprehend, and, as a result, online consumers frequently do not read the policies provided at healthcare Web sites. Because these policies are often required by law, they should be clear so that consumers are likely to read them and to ensure that consumers can comprehend these policies. This, in turn, may increase consumer trust and encourage consumers to feel more comfortable when interacting with online organizations. In this paper, we present results of an empirical study, involving 993 Internet users, which compared various ways to present privacy policy information to online consumers. Our findings suggest that users perceive typical, paragraph-form policies to be more secure than other forms of policy representation, yet user comprehension of such paragraph-form policies is poor as compared to other policy representations. The results of this study can help managers create more trustworthy policies, aid compliance officers in detecting deceptive organizations, and serve legislative bodies by providing tangible evidence as to the ineffectiveness of current privacy policies.
The Theta-Gamma Neural Code

Theta and gamma frequency oscillations occur in the same brain regions and interact with each other, a process called cross-frequency coupling. Here, we review evidence for the following hypothesis: that the dual oscillations form a code for representing multiple items in an ordered way. This form of coding has been most clearly demonstrated in the hippocampus, where different spatial information is represented in different gamma subcycles of a theta cycle. Other experiments have tested the functional importance of oscillations and their coupling. These involve correlation of oscillatory properties with memory states, correlation with memory performance, and effects of disrupting oscillations on memory. Recent work suggests that this coding scheme coordinates communication between brain regions and is involved in sensory as well as memory processes.
Electrical Breakdown of Gases: The Prebreakdown Stage

In this paper, a review of the theories and experiments devoted to the understanding of the development of the electrical breakdown of a gas insulated gap, i. e., the switching delay, is presented. The presentation is chronological. The classical Townsend and streamer models for breakdown are discussed; followed by a brief account of the continuous acceleration and avalanche-chain models. These last two models have been proposed primarily to describe breakdown at large electric fields. Then, the two-group model for breakdown at voltages above approximately 20-percent self-breakdown is presented. Finally, a brief analysis is given of the present state of the field and the direction it is takdng.