nils-herrmann/clean_scirepeval huggingface dataset:
Private Equality Test Using Ring-LWE Somewhat Homomorphic Encryption

We propose two secure protocols namely private equality test (PET) for single comparison and private batch equality test (PriBET) for batch comparisons of l-bit integers. We ensure the security of these secure protocols using somewhat homomorphic encryption (SwHE) based on ring learning with errors (ring-LWE) problem in the semi-honest model. In the PET protocol, we take two private integers input and produce the output denoting their equality or non-equality. Here the PriBET protocol is an extension of the PET protocol. So in the PriBET protocol, we take single private integer and another set of private integers as inputs and produce the output denoting whether single integer equals at least one integer in the set of integers or not. To serve this purpose, we also propose a new packing method for doing the batch equality test using few homomorphic multiplications of depth one. Here we have done our experiments at the 140-bit security level. For the lattice dimension 2048, our experiments show that the PET protocol is capable of doing any equality test of 8-bit to 2048-bit that require at most 107 milliseconds. Moreover, the PriBET protocol is capable of doing about 600 (resp., 300) equality comparisons per second for 32-bit (resp., 64-bit) integers. In addition, our experiments also show that the PriBET protocol can do more computations within the same time if the data size is smaller like 8-bit or 16-bit.
Human Ebola outbreak resulting from direct exposure to fruit bats in Luebo, Democratic Republic of Congo, 2007.

Twelve years after the Kikwit Ebola outbreak in 1995, Ebola virus reemerged in the Occidental Kasaï province of the Democratic Republic of Congo (DRC) between May and November 2007, affecting more than 260 humans and causing 186 deaths. During this latter outbreak we conducted several epidemiological investigations to identify the underlying ecological conditions and animal sources. Qualitative social and environmental data were collected through interviews with villagers and by direct observation. The local populations reported no unusual morbidity or mortality among wild or domestic animals, but they described a massive annual fruit bat migration toward the southeast, up the Lulua River. Migrating bats settled in the outbreak area for several weeks, between April and May, nestling in the numerous fruit trees in Ndongo and Koumelele islands as well as in palm trees of a largely abandoned plantation. They were massively hunted by villagers, for whom they represented a major source of protein. By tracing back the initial human-human transmission events, we were able to show that, in May, the putative first human victim bought freshly killed bats from hunters to eat. We were able to reconstruct the likely initial human-human transmission events that preceded the outbreak. This study provides the most likely sequence of events linking a human Ebola outbreak to exposure to fruit bats, a putative virus reservoir. These findings support the suspected role of bats in the natural cycle of Ebola virus and indicate that the massive seasonal fruit bat migrations should be taken into account in operational Ebola risk maps and seasonal alerts in the DRC.
Evaluating Effects of User Experience and System Transparency on Trust in Automation

Existing research assessing human operators' trust in automation and robots has primarily examined trust as a steady-state variable, with little emphasis on the evolution of trust over time. With the goal of addressing this research gap, we present a study exploring the dynamic nature of trust. We defined trust of entirety as a measure that accounts for trust across a human's entire interactive experience with automation, and first identified alternatives to quantify it using real-time measurements of trust. Second, we provided a novel model that attempts to explain how trust of entirety evolves as a user interacts repeatedly with automation. Lastly, we investigated the effects of automation transparency on momentary changes of trust. Our results indicated that trust of entirety is better quantified by the average measure of "area under the trust curve" than the traditional post-experiment trust measure. In addition, we found that trust of entirety evolves and eventually stabilizes as an operator repeatedly interacts with a technology. Finally, we observed that a higher level of automation transparency may mitigate the "cry wolf" effect -- wherein human operators begin to reject an automated system due to repeated false alarms.
A robust segmentation method for counting bovine milk somatic cells in microscope slide images

Mastitis is an infectious disease associated with the increased number of somatic cells in cow’s milk, and it is one of the most relevant cause of economic losses in dairy farming industries. In this paper, we propose a method capable of determine, with 99.7% accuracy, the number of these cells in microscope slide images. This level of accuracy is achieved by changing the image original RGB format to Lab color space and applying k-means clustering algorithm to remove debris and other background features. A new gray level thresholding is proposed, and the remaining bound cells are separated in the final segmentation step applying Watershed transform. Many microscope slide images with debris, contrast, and hue variation were used to validate the experimental results. Comparison between the proposed method and manual counting indicates that this new approach is a robust and promising solution to be incorporated in a future automated somatic cell counting system using optical microscopy.
AuTO: scaling deep reinforcement learning for datacenter-scale automatic traffic optimization

Traffic optimizations (TO, e.g. flow scheduling, load balancing) in datacenters are difficult online decision-making problems. Previously, they are done with heuristics relying on operators' understanding of the workload and environment. Designing and implementing proper TO algorithms thus take at least weeks. Encouraged by recent successes in applying deep reinforcement learning (DRL) techniques to solve complex online control problems, we study if DRL can be used for automatic TO without human-intervention. However, our experiments show that the latency of current DRL systems cannot handle flow-level TO at the scale of current datacenters, because short flows (which constitute the majority of traffic) are usually gone before decisions can be made.
 Leveraging the long-tail distribution of datacenter traffic, we develop a two-level DRL system, AuTO, mimicking the Peripheral & Central Nervous Systems in animals, to solve the scalability problem. Peripheral Systems (PS) reside on end-hosts, collect flow information, and make TO decisions locally with minimal delay for short flows. PS's decisions are informed by a Central System (CS), where global traffic information is aggregated and processed. CS further makes individual TO decisions for long flows. With CS&PS, AuTO is an end-to-end automatic TO system that can collect network information, learn from past decisions, and perform actions to achieve operator-defined goals. We implement AuTO with popular machine learning frameworks and commodity servers, and deploy it on a 32-server testbed. Compared to existing approaches, AuTO reduces the TO turn-around time from weeks to ~100 milliseconds while achieving superior performance. For example, it demonstrates up to 48.14% reduction in average flow completion time (FCT) over existing solutions.
Biotechnological Production of Fumaric Acid : The Effect of Morphology of Rhizopus arrhizus NRRL 2582

Fumaric acid is a platform chemical with many applications in bio-based chemical and polymer production. Fungal cell morphology is an important factor that affects fumaric acid production via fermentation. In the present study, pellet and dispersed mycelia morphology of Rhizopus arrhizus NRRL 2582 was analysed using image analysis software and the impact on fumaric acid production was evaluated. Batch experiments were carried out in shake flasks using glucose as carbon source. The highest fumaric acid yield of 0.84 g/g total sugars was achieved in the case of dispersed mycelia with a final fumaric acid concentration of 19.7 g/L. The fumaric acid production was also evaluated using a nutrient rich feedstock obtained from soybean cake, as substitute of the commercial nitrogen sources. Solid state fermentation was performed in order to produce proteolytic enzymes, which were utilised for soybean cake hydrolysis. Batch fermentations were conducted using 50 g/L glucose and soybean cake hydrolysate achieving up to 33 g/L fumaric acid concentration. To the best of our knowledge the influence of R. arrhizus morphology on fumaric acid production has not been reported previously. The results indicated that dispersed clumps were more effective in fumaric acid production than pellets and renewable resources could be alternatively valorised for the biotechnological production of platform chemicals.
Managing Complications of Fillers: Rare and Not-So-Rare

Fillers belong to the most frequently used beautifying products. They are generally well tolerated, but any one of them may occasionally produce adverse side effects. Adverse effects usually last as long as the filler is in the skin, which means that short-lived fillers have short-term side effects and permanent fillers may induce life-long adverse effects. The main goal is to prevent them, however, this is not always possible. Utmost care has to be given to the prevention of infections and the injection technique has to be perfect. Treatment of adverse effects is often with hyaluronidase or steroid injections and in some cases together with 5-fluorouracil plus allopurinol orally. Histological examination of biopsy specimens often helps to identify the responsible filler allowing a specific treatment to be adapted.
The behavioral economics of health and health care.

People often make decisions in health care that are not in their best interest, ranging from failing to enroll in health insurance to which they are entitled, to engaging in extremely harmful behaviors. Traditional economic theory provides a limited tool kit for improving behavior because it assumes that people make decisions in a rational way, have the mental capacity to deal with huge amounts of information and choice, and have tastes endemic to them and not open to manipulation. Melding economics with psychology, behavioral economics acknowledges that people often do not act rationally in the economic sense. It therefore offers a potentially richer set of tools than provided by traditional economic theory to understand and influence behaviors. Only recently, however, has it been applied to health care. This article provides an overview of behavioral economics, reviews some of its contributions, and shows how it can be used in health care to improve people's decisions and health.
Parametric estimation of distances with a 122 GHz FMCW radar system

Industrial applications for frequency modulated continuous wave (FMCW) radar systems often require the separation of close targets, which is limited by the available distance resolution of the radar. In general, for the evaluation of FMCW radar signals a fast Fourier transformation (FFT) based method is used. However, the FFT inherently limits the available distance resolution in dependence of the bandwidth. To overcome this limitation, super resolution algorithms or parametric methods can be used. A 122 GHz FMCW radar with a bandwidth of 1 GHz is used to take measurements of two targets, below the FFT limited distance resolution (150 mm) of the FMCW radar. It is shown by using an expectation maximization based algorithm that it is still possible to distinguish between both targets over the whole measurement distance. The maximum distance error remains in the millimeter range as long as the targets are separated by at least 50 mm, which equals an improved distance resolution of a factor 3 compared to the FFT. Furthermore, the influence of model order errors on the expectation maximization algorithm are analyzed.
Improvement of odometry for omnidirectional vehicle using optical flow information

Our research goal is to realize a robust navigation in indoor and outdoor environment for autonomous vehicle. An omnidirectional vehicle driven by four Mecanum wheels was chosen for our research platform. Mecanum wheel has 16 tilted rollers (45 degrees against the direction of wheel rotation) around the wheel, so the vehicle moves omnidirectionally by controlling these wheels independently. However, it has a disadvantage of odometry because of wheels’ slippage. Particularly, when the robot moves laterally, same wheels’ rotations generate different traveling distance according to a friction of a ground surface. To cope with the problem, we estimate robot’s position by detecting optical flow of ground image using vision sensor (visual dead-reckoning). The estimation method is inaccurate comparing with odometry, but it is independent from friction of ground surface. Therefore, the estimated vehicle position can be improved by fusing odometry and visual dead-reckoning based on maximum likelihood technique. This paper describes an odometry method and a visual dead-reckoning method for omnidirectional vehicle, and fusion technique to improve the estimated position of the vehicle. Finally, experimental results support above technique.
Training multi-layered neural network neocognitron

This paper proposes new learning rules suited for training multi-layered neural networks and applies them to the neocognitron. The neocognitron is a hierarchical multi-layered neural network capable of robust visual pattern recognition. It acquires the ability to recognize visual patterns through learning. For training intermediate layers of the hierarchical network of the neocognitron, we use a new learning rule named add-if-silent. By the use of the add-if-silent rule, the learning process becomes much simpler and more stable, and the computational cost for learning is largely reduced. Nevertheless, a high recognition rate can be kept without increasing the scale of the network. For the highest stage of the network, we use the method of interpolating-vector. We have previously reported that the recognition rate is greatly increased if this method is used during recognition. This paper proposes a new method of using it for both learning and recognition. Computer simulation demonstrates that the new neocognitron, which uses the add-if-silent and the interpolating-vector, produces a higher recognition rate for handwritten digits recognition with a smaller scale of the network than the neocognitron of previous versions.
Long-lasting hyperalgesia induced by fentanyl in rats: preventive effect of ketamine.

It has been reported that mu-opioid receptor activation leads to a sustained increase in glutamate synaptic effectiveness at the N-methyl-D-aspartate (NMDA) receptor level, a system associated with central hypersensitivity to pain. One hypothesis is that postoperative pain may result partly from the activation of NMDA pain facilitatory processes induced by opiate treatment per se. The authors tested here the effectiveness of the opiate analgesic fentanyl for eliciting a delayed enhancement in pain sensitivity.



The consequences of four bolus injections (every 15 min) of fentanyl (20-100 microg/kg per injection, subcutaneously) on immediate (for several hours) and long-term (for several days) sensitivity to nociceptive stimuli in the rat (paw-pressure vocalization test) were evaluated. The effects of the combination of the NMDA-receptor antagonist ketamine (10 mg/kg, subcutaneously) with fentanyl also were assessed.



Fentanyl administration exhibited a biphasic time-dependent effect: first, an early response (for 2-5 h) associated with a marked increase in nociceptive threshold (analgesia), and second, a later response associated with sustained lowering of the nociceptive threshold (5 days for the longest effect) below the basal value (30% of decrease for the maximal effect) indicative of hyperalgesia. The higher the fentanyl dose used, the more pronounced was the fentanyl-induced hyperalgesia. Ketamine pretreatment, which had no analgesic effect on its own, enhanced the earlier response (analgesia) and prevented the development of long-lasting hyperalgesia.



Fentanyl activates NMDA pain facilitatory processes, which oppose analgesia and lead to long-lasting enhancement in pain sensitivity.
Software Protection and Application Security : Understanding the Battleground ?

We provide a state-of-the-art explication of application security and software protection. The relationship between application security and data security, network security, and software security is discussed. Three simplified threat models for software are sketched. To better understand what attacks must be defended against in order to improve software security, we survey software attack approaches and attack tools. A simplified software security view of a software application is given, and along with illustrative examples, used to motivate a partial list of software security requirements for applications.
Model Predictive Control of PV Sources in a Smart DC Distribution System: Maximum Power Point Tracking and Droop Control

In a dc distribution system, where multiple power sources supply a common bus, current sharing is an important issue. When renewable energy resources are considered, such as photovoltaic (PV), dc/dc converters are needed to decouple the source voltage, which can vary due to operating conditions and maximum power point tracking (MPPT), from the dc bus voltage. Since different sources may have different power delivery capacities that may vary with time, coordination of the interface to the bus is of paramount importance to ensure reliable system operation. Further, since these sources are most likely distributed throughout the system, distributed controls are needed to ensure a robust and fault tolerant control system. This paper presents a model predictive control-based MPPT and model predictive control-based droop current regulator to interface PV in smart dc distribution systems. Back-to-back dc/dc converters control both the input current from the PV module and the droop characteristic of the output current injected into the distribution bus. The predictive controller speeds up both of the control loops, since it predicts and corrects error before the switching signal is applied to the respective converter.
On learning hierarchical classifications

Many significant real-world classification tasks involve a large number of categories which are arranged in a hierarchical structure; for example, classifying documents into subject categories under the library of congress scheme, or classifying world-wide-web documents into topic hierarchies. We investigate the potential benefits of using a given hierarchy over base classes to learn accurate multi-category classifiers for these domains. First, we consider the possibility of exploiting a class hierarchy as prior knowledge that can help one learn a more accurate classifier. We explore the benefits of learning categorydiscriminants in a “hard” top-down fashion and compare this to a “soft” approach which shares training data among sibling categories. In doing so, we verify that hierarchies have the potential to improve prediction accuracy. But we argue that the reasons for this can be subtle. Sometimes, the improvement is only because using a hierarchy happens to constrain the expressiveness of a hypothesis class in an appropriate manner. However, various controlled experiments show that in other cases the performance advantage associated with using a hierarchy really does seem to be due to the “prior knowledge” it encodes.
Urethral duplication in a 12-year-old child.

Urethral duplication is a rare congenital malformation affecting mainly boys. The authors report a case in a Cameroonian child who was diagnosed and managed at the Gynaeco-Obstetric and Paediatric Hospital, Yaounde. The malformation was characterized by the presence of an incontinent epispadic urethra and a normal apical urethra. We describe the difficulties faced in the management of this disorder in a developing country.
Student Engagement in Blended Learning Environments with Lecture-Based and Problem-Based Instructional Approaches

This study investigates how blending of different instructional approaches with technology affects students’ engagement. A computer networks course was designed and implemented for the first eight weeks of the semester as a lecture-based blended learning environment and for the second eight weeks of the semester as a problem-based blended learning environment. A single group repeated measures research design was carried out to understand if there are significant differences in measures of student engagement between these two blended learning approaches. Repeated measure ANOVA analysis on the data collected from 89 students revealed that Active Learning and Total Time on Task indicators of student engagement were significantly higher in the problem-based part of the course. Interaction and Level of Academic Challenge components and course satisfaction did not show any significant differences between the two parts. Regression analysis showed that the difference in Active Learning is not due to student individual differences but rather the learning environment provided in the problem-based blended learning.
Design and implementation of secure enterprise network based on DMVPN

It aims at existing defects of traditional VPN in constructing enterprise network, analyses problems which must be considered in designing secure enterprise network, puts forward solution of DMVPN (Dynamic Multipoint VPN) technique to solve the problems that traditional VPN has not solved by now. At the same time, it expatiates on implementation mechanism of DMVPN, puts forward a concrete case that to adopt DMVPN technique constructs secure enterprise network of some business chain organization, and network performance indexes are tested. From the results of test, DMVPN network entirely satisfies the actual requirements that enterprise uses network. It offers a mode which is rapid, convenient and economical investment to enterprise for building secure network.
Loosely coupled GPS/INS integration with Kalman filtering for land vehicle applications

Nowadays, global positioning system (GPS) has been used widely in land vehicles to provide positioning services. However, information from this stand-alone device may be interrupted under some circumstances such as: urban environment, under the tunnels, etc. In addition, low-rate sample time typically 1Hz is another drawback of GPS. Therefore, GPS receiver can be augmented with the inertial navigation system (INS) to provide faster positioning information. By fusing GPS and INS data, the errors are bounded and accuracy increases considerably even when using low-cost INS and GPS. This paper presents a method of INS/GPS integration where a loosely coupled model is formulated and an extended Kalman filter is then applied to estimate information about position, velocity, and acceleration. Experiment results show that the processing time is reduced significantly but the estimated errors are acceptable (less than 1 meters) when applying the proposed integration method under the good signal of GPS. The performance evaluation is also implemented on several road trajectories in urban city that the 3 meters precision could be reached. In addition, accurate positioning and navigation results are still available from 9 to 14 seconds of GPS outages with the position errors spread from 3 to 10 meters (RMS).