nils-herrmann/clean_scirepeval huggingface dataset:
Achieving Fast and Lightweight SDN Updates with Segment Routing

In SDN, forwarding rules are frequently updated to adapt to network dynamics. During the procedure, path consistency needs to be preserved; otherwise, in-flight packets might meet with forwarding errors such as loops and black holes. Despite a large number of suggestions have been proposed, they take either a long duration or have high rule-space overheads, thus fail to be practical for large-scale high dynamic networks. In this paper, we propose FLUS, a Segment Routing (SR) based mechanism, to achieve fast and lightweight path updates. Basically, when a route needs a change, FLUS instantly employs SR to construct its desired new path by concatenating some fragments of the already existing paths. After the actual paths are established, FLUS then shifts incoming packets to them and disables the transitional ones. Such a design helps packets enjoy their new paths immediately without introducing rule-space overheads. This paper presents FLUS's segment allocation, path construction, and the corresponding optimal algorithms in detail. Our evaluation based on real and synthesized networks shows: FLUS can handle up to 92-100% updates using SR in real-time and save 72-88% rule overhead compared to prior methods.
Forensic analysis of encrypted instant messaging applications on Android

Smartphone market is growing day by day and according to Statista, as of 2017, 68.4% of the U.S. population uses smartphones. Similarly, the amount of information stored on these mobile devices is tremendous and ranging from personal details, contacts, applications data, to exchange of texts and media. This information can become a significant evidence during a digital forensics investigation and thereafter in courts. As Android is one of the leading smartphone operating systems worldwide, it is important to have the knowledge of Android forensics. Moreover, chat messaging between the users becoming the most prominent communication medium particularly among the youth. The exponential increase in the interception of chat messages on mobile devices led to implementation of end to end encryption. This is mainly due to the concerns raised on privacy and security of user data on smartphones. In this paper we analyze widely used encrypted Instant Messaging (IM) applications namely WeChat, Telegram, Viber and Whatsapp. We also show how these applications store data in the Android file system. In addition we also discuss forensic implications of the IM applications that are utilizing encryption. Analysis of artifacts collected from these applications is performed using the Android Debugging Bridge (ADB) tool and some other open source tools. Moreover, we also present the challenges faced during the collection of the forensically important artifacts.
Low-Cost Mitigation Against Cold Boot Attacks for an Authentication Token

Hardware tokens for user authentication need a secure and usable mechanism to lock them when not in use. The Pico academic project proposes an authentication token unlocked by the proximity of simpler wearable devices that provide shares of the token’s master key. This method, however, is vulnerable to a cold boot attack: an adversary who captures a running Pico could extract the master key from its RAM and steal all of the user’s credentials. We present a cryptographic countermeasure—bivariate secret sharing—that protects all the credentials except the one in use at that time, even if the token is captured while it is on. Remarkably, our key storage costs for the wearables that supply the cryptographic shares are very modest (256 bits) and remain constant even if the token holds thousands of credentials. Although bivariate secret sharing has been used before in slightly different ways, our scheme is leaner and more efficient and achieves a new property—cold boot protection. We validated the efficacy of our design by implementing it on a commercial Bluetooth Low Energy development board and measuring its latency and energy consumption. For reasonable choices of latency and security parameters, a standard CR2032 button-cell battery can power our prototype for 5–7 months, and we demonstrate a simple enhancement that could make the same battery last for over 9 months.
3D nonrigid registration via optimal mass transport on the GPU

In this paper, we present a new computationally efficient numerical scheme for the minimizing flow approach for optimal mass transport (OMT) with applications to non-rigid 3D image registration. The approach utilizes all of the gray-scale data in both images, and the optimal mapping from image A to image B is the inverse of the optimal mapping from B to A. Further, no landmarks need to be specified, and the minimizer of the distance functional involved is unique. Our implementation also employs multigrid, and parallel methodologies on a consumer graphics processing unit (GPU) for fast computation. Although computing the optimal map has been shown to be computationally expensive in the past, we show that our approach is orders of magnitude faster then previous work and is capable of finding transport maps with optimality measures (mean curl) previously unattainable by other works (which directly influences the accuracy of registration). We give results where the algorithm was used to compute non-rigid registrations of 3D synthetic data as well as intra-patient pre-operative and post-operative 3D brain MRI datasets.
Privacy-Preserving Multi-keyword Ranked Search over Encrypted Cloud Data

The advent of cloud computing, data owners are motivated to outsource their complex data management systems from local sites to commercial public cloud for great flexibility and economic savings. But for protecting data privacy, sensitive data has to be encrypted before outsourcing, which obsoletes traditional data utilization based on plaintext keyword search. Thus, enabling an encrypted cloud data search service is of paramount importance. Considering the large number of data users and documents in cloud, it is crucial for the search service to allow multi-keyword query and provide result similarity ranking to meet the effective data retrieval need. Related works on searchable encryption focus on single keyword search or Boolean keyword search, and rarely differentiate the search results. In this paper, for the first time, we define and solve the challenging problem of privacy-preserving multi-keyword ranked search over encrypted cloud data (MRSE), and establish a set of strict privacy requirements for such a secure cloud data utilization system to become a reality. Among various multi-keyword semantics, we choose the efficient principle of " coordinate matching " , i.e., as many matches as possible, to capture the similarity between search query and data documents, and further use " inner product similarity " to quantitatively formalize such principle for similarity measurement. We first propose a basic MRSE scheme using secure inner product computation, and then significantly improve it to meet different privacy requirements in two levels of threat models. Thorough analysis investigating privacy and efficiency guarantees of proposed schemes is given, and experiments on the real-world dataset further show proposed schemes indeed introduce low overhead on computation and communication.  Due to the rapid expansion of data, the data owners tend to store their data into the cloud to release the burden of data storage and maintenance . However, as the cloud customers and the cloud server are not in the same trusted domain, our outsourced data may be under the exposure to the risk. Thus, before sent to the cloud, the sensitive data needs to be encrypted to protect for data privacy and combat unsolicited accesses. Unfortunately, the traditional plaintext search methods cannot be directly applied to the encrypted cloud data any more. The traditional information retrieval (IR) has already provided multi-keyword ranked search for the data user. In the same way, the cloud server needs provide the data user with the similar function, while protecting data and search privacy. It …
Effect of thread embedding acupuncture for facial wrinkles and laxity: a single-arm, prospective, open-label study

There is a growing trend for patients to seek the least invasive treatments with less risk of complications and downtime for facial rejuvenation. Thread embedding acupuncture has become popular as a minimally invasive treatment. However, there is little clinical evidence in the literature regarding its effects.



This single-arm, prospective, open-label study recruited participants who were women aged 40-59 years, with Glogau photoaging scale III-IV. Fourteen participants received thread embedding acupuncture one time and were measured before and after 1 week from the procedure. The primary outcome was a jowl to subnasale vertical distance. The secondary outcomes were facial wrinkle distances, global esthetic improvement scale, Alexiades-Armenakas laxity scale, and patient-oriented self-assessment scale.



Fourteen participants underwent thread embedding acupuncture alone, and 12 participants revisited for follow-up outcome measures. For the primary outcome measure, both jowls were elevated in vertical height by 1.87 mm (left) and 1.43 mm (right). Distances of both melolabial and nasolabial folds showed significant improvement. In the Alexiades-Armenakas laxity scale, each evaluator evaluated for four and nine participants by 0.5 grades improved. In the global aesthetic improvement scale, improvement was graded as 1 and 2 in nine and five cases, respectively. The most common adverse events were mild bruising, swelling, and pain. However, adverse events occurred, although mostly minor and of short duration.



In this study, thread embedding acupuncture showed clinical potential for facial wrinkles and laxity. However, further large-scale trials with a controlled design and objective measurements are needed.
Greater intrasex phenotype variability in males than in females is a fundamental aspect of the gender differences in humans.

Human studies of intrasex variability have shown that males are intellectually more variable. Here we have performed retrospective statistical analysis of human intrasex variability in several different properties and performances that are unrelated or indirectly related to intelligence: (a) birth weights of nearly 48,000 babies (Medical Birth Registry of Norway); (b) adult weight, height, body mass index and blood parameters of more than 2,700 adults aged 18-90 (NORIP); (c) physical performance in the 60 meter dash event of 575 junior high school students; and (d) psychological performance reflected by the results of more than 222,000 undergraduate university examination grades (LIST). For all characteristics, the data were analyzed using cumulative distribution functions and the resultant intrasex variability for males was compared with that for females. The principal finding is that human intrasex variability is significantly higher in males, and consequently constitutes a fundamental sex difference.
COMPARISON OF SCALAR AND VECTOR CONTROL STRATEGIES OF INDUCTION MOTORS

Currently the use of three-phase induction machines is widespread in industrial applications due to several methods available to control the speed and torque of the motor. Many applications require that the same torque be available at all revolutions up to the nominal value. In this paper two control methods are compared: scalar control and vector control. Scalar control is a relatively simple method. The purpose of the technique is to control the magnitude of the chosen control quantities. At the induction motor the technique is used as Volts/Hertz constant control. Vector control is a more complex control technique, the evolution of which was inevitable, too, since scalar control cannot be applied for controlling systems with dynamic behaviour. The vector control technique works with vector quantities, controlling the desired values by using space phasors which contain all the three phase quantities in one phasor. It is also known as field-oriented control because in the course of implementation the identification of the field flux of the motor is required. This paper reports on the changing possibilities of the revolution – torque characteristic curve, and demonstrates the results of the two control methods with simulations. The simulations and the applied equivalent circuit parameters are based on real measurements done with no load, with direct current and with locked-rotor.
The Bridged T-Coil [A Circuit for All Seasons]

The bridged T-coil, often simply called the T-coil, is a circuit topology that extends the bandwidth by a greater factor than does inductive peaking. Many high-speed amplifiers, line drivers, and input/output (I/O) interfaces in today's wireline systems incorporate on-chip T-coils to deal with parasitic capacitances. In this article, we introduce and analyze the basic structure and study its applications.
Cross-view and multi-view gait recognitions based on view transformation model using multi-layer perceptron

Gait has been shown to be an efficient biometric feature for human identification at a distance from a camera. However, performance of gait recognition can be affected by various problems. One of the serious problems is view change which can be caused by change of walking direction and/or change of camera viewpoint. This leads to a consequent difficulty of across-view gait recognition where probe and gallery gaits are captured from different views. In this study, a novel method is proposed to solve the above difficulty based on View Transformation Model (VTM) under an uncalibrated single camera system. VTM is constructed based on regression processes by adopting Multi-Layer Perceptron (MLP) as a regression tool. VTM smoothly estimates gait feature from one view using motion information in a well selected Region of Interest (ROI) on gait feature from another view. Thus, pre-trained VTMs can normalize gait features from across views into a same view before gait similarity measurement is carried out. Comprehensively, the proposed method is logically extended for multi-view gait recognition where gallery gaits from multiple views are used to recognize probe gait from single view. This is addressed using multi-view to one-view transformation where VTM is now employed to estimate gait feature from single view using motion information in well selected ROIs on gait features from multiple views. The proposed method is tested on a large benchmark gait database which contains 124 subjects from 11 views. Extensive experimental results demonstrate that our method significantly outperforms other baseline methods in literature for both across-view and multi-view gait recognitions. In our experiments, particularly, average accuracies of 99%, 98% and 93% are achieved under multiple view gait recognition by using 5 cameras, 4 cameras and 3 cameras respectively.
Autonomous Ramp Merge Maneuver Based on Reinforcement Learning with Continuous Action Space

Ramp merging is a critical maneuver for road safety and traffic efficiency. Most of the current automated driving systems developed by multiple automobile manufacturers and suppliers are typically limited to restricted access freeways only. Extending the automated mode to ramp merging zones presents substantial challenges. One is that the automated vehicle needs to incorporate a future objective (e.g. a successful and smooth merge) and optimize a long-term reward that is impacted by subsequent actions when executing the current action. Furthermore, the merging process involves interaction between the merging vehicle and its surrounding vehicles whose behavior may be cooperative or adversarial, leading to distinct merging countermeasures that are crucial to successfully complete the merge. In place of the conventional rule-based approaches, we propose to apply reinforcement learning algorithm on the automated vehicle agent to find an optimal driving policy by maximizing the long-term reward in an interactive driving environment. Most importantly, in contrast to most reinforcement learning applications in which the action space is resolved as discrete, our approach treats the action space as well as the state space as continuous without incurring additional computational costs. Our unique contribution is the design of the Q-function approximation whose format is structured as a quadratic function, by which simple but effective neural networks are used to estimate its coefficients. The results obtained through the implementation of our training platform demonstrate that the vehicle agent is able to learn a safe, smooth and timely merging policy, indicating the effectiveness and practicality of our approach.
Healthcare Waste Generation Worldwide and Its Dependence on Socio-Economic and Environmental Factors

This paper examines the dependence of the healthcare waste (HCW) generation rate on several social-economic and environmental parameters. Correlations were calculated between the quantities of healthcare waste generated (expressed in kg/bed/day) versus economic indices (GDP, healthcare expenditure per capita), social indices (HDI, IHDI, MPI, life expectancy, mean years of schooling, HIV prevalence, deaths due to tuberculosis and malaria, and under five mortality rate), and an environmental sustainability index (total CO2 emissions) from 42 countries worldwide. The statistical analysis included the examination of the normality of the data and the formation of linear multiple regression models to further investigate the correlation between those indices and HCW generation rates. Pearson and Spearman correlation coefficients were also calculated for all pairwise comparisons. Results showed that the life expectancy, the HDI, the mean years of schooling and the CO2 emissions positively affect the HCW generation rates and can be used as statistical predictors of those rates. The resulting best reduced regression model included the life expectancy and the CO2 emissions and explained 85% of the variability of the response.
Temporal Summary Images: An Approach to Narrative Visualization via Interactive Annotation Generation and Placement

Visualization is a powerful technique for analysis and communication of complex, multidimensional, and time-varying data. However, it can be difficult to manually synthesize a coherent narrative in a chart or graph due to the quantity of visualized attributes, a variety of salient features, and the awareness required to interpret points of interest (POls). We present Temporal Summary Images (TSIs) as an approach for both exploring this data and creating stories from it. As a visualization, a TSI is composed of three common components: (1) a temporal layout, (2) comic strip-style data snapshots, and (3) textual annotations. To augment user analysis and exploration, we have developed a number of interactive techniques that recommend relevant data features and design choices, including an automatic annotations workflow. As the analysis and visual design processes converge, the resultant image becomes appropriate for data storytelling. For validation, we use a prototype implementation for TSIs to conduct two case studies with large-scale, scientific simulation datasets.
Phosphorylation meets ubiquitination: the control of NF-[kappa]B activity.

NF-kappaB (nuclear factor-kappaB) is a collective name for inducible dimeric transcription factors composed of members of the Rel family of DNA-binding proteins that recognize a common sequence motif. NF-kappaB is found in essentially all cell types and is involved in activation of an exceptionally large number of genes in response to infections, inflammation, and other stressful situations requiring rapid reprogramming of gene expression. NF-kappaB is normally sequestered in the cytoplasm of nonstimulated cells and consequently must be translocated into the nucleus to function. The subcellular location of NF-kappaB is controlled by a family of inhibitory proteins, IkappaBs, which bind NF-kappaB and mask its nuclear localization signal, thereby preventing nuclear uptake. Exposure of cells to a variety of extracellular stimuli leads to the rapid phosphorylation, ubiquitination, and ultimately proteolytic degradation of IkappaB, which frees NF-kappaB to translocate to the nucleus where it regulates gene transcription. NF-kappaB activation represents a paradigm for controlling the function of a regulatory protein via ubiquitination-dependent proteolysis, as an integral part of a phosphorylationbased signaling cascade. Recently, considerable progress has been made in understanding the details of the signaling pathways that regulate NF-kappaB activity, particularly those responding to the proinflammatory cytokines tumor necrosis factor-alpha and interleukin-1. The multisubunit IkappaB kinase (IKK) responsible for inducible IkappaB phosphorylation is the point of convergence for most NF-kappaB-activating stimuli. IKK contains two catalytic subunits, IKKalpha and IKKbeta, both of which are able to correctly phosphorylate IkappaB. Gene knockout studies have shed light on the very different physiological functions of IKKalpha and IKKbeta. After phosphorylation, the IKK phosphoacceptor sites on IkappaB serve as an essential part of a specific recognition site for E3RS(IkappaB/beta-TrCP), an SCF-type E3 ubiquitin ligase, thereby explaining how IKK controls IkappaB ubiquitination and degradation. A variety of other signaling events, including phosphorylation of NF-kappaB, hyperphosphorylation of IKK, induction of IkappaB synthesis, and the processing of NF-kappaB precursors, provide additional mechanisms that modulate the level and duration of NF-kappaB activity.
Investigating Cybersecurity Issues In Active Traffic Management Systems

Active Traffic Management (ATM) systems have been introduced by transportation agencies to manage recurrent and non-recurrent congestion. ATM systems rely on the interconnectivity of components made possible by wired and/or wireless networks. Unfortunately, this connectivity that supports ATM systems also provides potential system access points that results in vulnerability to cyberattacks. This is becoming more pronounced as ATM systems begin to integrate internet of things (IoT) devices. Hence, there is a need to rigorously evaluate ATM systems for cyberattack vulnerabilities, and explore design concepts that provide stability and graceful degradation in the face of cyberattacks. In this research, a prototype ATM system along with a real-time cyberattack monitoring system were developed for a 1.5-mile section of I-66 in Northern Virginia. The monitoring system detects deviation from expected operation of an ATM system by comparing lane control states generated by the ATM system with lane control states deemed most likely by the monitoring system. This comparison provides the functionality to continuously monitor the system for abnormalities that would result from a cyberattack. In case of any deviation between two sets of states, the monitoring system displays the lane control states generated by the back-up data source. In a simulation experiment, the prototype ATM system and cyberattack monitoring system were subject to emulated cyberattacks. The evaluation results showed that the ATM system, when operating properly in the absence of attacks, improved average vehicle speed in the system to 60mph (a 13% increase compared to the baseline case without ATM). However, when subject to cyberattack, the mean speed reduced by 15% compared to the case with the ATM system and was similar to the baseline case. This illustrates that the effectiveness of the ATM system was negated by cyberattacks. The monitoring system however, allowed the ATM system to revert to an expected state with a mean speed of 59mph and reduced the negative impact of cyberattacks. These results illustrate the need to revisit ATM system design concepts as a means to protect against cyberattacks in addition to traditional system intrusion prevention approaches.