Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Aged Garlic Ethyl Acetate Extract on Oxidative Stress and Cholinergic Function of Scopolamine-Induced Cognitive Impairment in Mice

This study was performed to investigate the effect of aged black garlic ethyl acetate extract on scopolamine-induced cognitive impairment in mice. Aged garlic ethyl acetate extract (BG) was administrated at a dose of 25 or 50 mg/ kg in scopolamine-induced mice. Cognitive ability was evaluated using a Morris water maze test and a passive avoidance test. BGs (50 mg/kg) shortened the latency time that was increased by scopolamine and increased the platform crossing numbers that was significantly shortened by scopolamine after 5 days training in the Morris water maze test (P<0.05). BG (50 mg/kg) also significantly prolonged the latency time in the passive avoidance test (P<0.05). Result from biochemical analysis showed that BG increased levels of glutathione, glutathione peroxidase activity, and glutathione reductase activity, whereas BG significantly inhibited lipid peroxidation (P<0.05). BG also attenuated cholinergic degradation through inhibiting acetylcholinesterase activity and increasing choline acetyltransferase activity (P<0.05). In conclusion, BG protected against scopolamine-induced cognitive impairment through decreasing oxidative damage and regulating cholinergic function in the brains of mice. BG may therefore be a beneficial food for protecting against neurodegeneration such as Alzheimer’s disease.
Distributed Minimum Degree Spanning Trees

The minimum degree spanning tree (MDST) problem requires the construction of a spanning tree T for graph G, such that the maximum degree of T is the smallest among all spanning trees of G. Let d be this MDST degree for a given graph. In this paper, we present a randomized distributed approximation algorithm for the MDST problem that constructs a spanning tree with maximum degree in O(d+log n ). With high probability in n, the algorithm runs in O((D + √n) log4 n) rounds, in the broadcast-CONGEST model, where D is the graph diameter and n is the graph size. We then show how to derandomize this algorithm, obtaining the same asymptotic guarantees on degree and time complexity, but now requiring the standard CONGEST model. Although efficient approximation algorithms for the MDST problem have been known in the sequential setting since the 1990's (finding an exact solution is NP-hard), our algorithms are the first efficient distributed solutions. We conclude by proving a lower bound that establishes that any randomized MDST algorithm that guarantees a maximum degree in ∼Ω (d) requires &Ø#8764;Ω (n1/3) rounds, and any deterministic solution requires ∼Ω (n1/2) rounds. These bounds proves our deterministic algorithm to be asymptotically optimal, and eliminates the possibility of significantly more efficient randomized solutions.
A PBIL for delay constrained virtual network function placement with load balancing

This paper investigates the virtual network function placement (VNF-P) problem in network function virtualization (NFV), with load balancing and delay issues considered. In this problem, utilization of server and link resources is jointly minimized for load balancing purpose while end-to-end delay is constrained for better user experience. An integer-encoding population based incremental learning algorithm (IEPBIL) is presented to tackle the problem above, where an elitism preservation scheme and a mutation operation are devised. Simulation results show that IEPBIL outperforms a number of evolutionary and heuristic algorithms in terms of the success rate for placing virtual network functions.
Depth estimation algorithm based on data-driven approach and depth cues for stereo conversion in three-dimensional displays

Abstract. With the advances in three-dimensional (3-D) display technology, stereo conversion has attracted much attention as it can alleviate the problem of stereoscopic content shortage. In two-dimensional (2-D) to 3-D conversion, the most difficult and challenging problem is depth estimation from a single image. In order to recover a perceptually plausible depth map from a single image, a depth estimation algorithm based on a data-driven method and depth cues is presented. Based on the human visual system mechanism, which is sensitive to the foreground object, this study classifies the image into one of two classes, i.e., nonobject image and object image, and then leverages different strategies on the basis of image type. The proposed strategies efficiently extract the depth information from different images. Moreover, depth image-based rendering technology is utilized to generate stereoscopic views by combining 2-D images with their depth maps. The proposed method is also suitable for 2-D to 3-D video conversion. Qualitative and quantitative evaluation results demonstrate that the proposed depth estimation algorithm is very effective for generating stereoscopic content and producing visually pleasing and realistic 3-D views.
Energy Efficient Network-on-Chip Architectures for Many-Core Near-Threshold Computing System

Near threshold computing has unraveled a promising design space for energy efficient computing. However, it is still plagued by sub-optimal system performance. Application characteristics and hardware non-idealities of conventional architectures (those optimized for nominal voltage) prevent us from fully leveraging the potential of NTC systems. Increasing the computational core count still forms the bedrock of a multitude of contemporary works that address the problem of performance degradation in NTC systems. However, these works do not categorically address the shortcomings of the conventional on-chip interconnect fabric in a many core environment. In this work, we quantitatively demonstrate the performance bottleneck created by a conventional NTC architecture in many-core NTC systems. To reclaim the performance lost due to a sub-optimal NoC in many-core NTC systems, we propose BoostNoC—a power efficient, multi-layered network-on-chip architecture. BoostNoC improves the system performance by nearly 2× over a conventional NTC system, while largely sustaining its energy benefits. Further, capitalizing on the application characteristics, we propose two BoostNoC derivative designs: (i) PG BoostNoC; and (ii) Drowsy BoostNoC; to improve the energy efficiency by 1.4× and 1.37×, respectively over conventional NTC system.
Transport complexity of data dissemination in large-scale online social networks

In this paper, we aim to measure the transport difficulty of data dissemination in online social networks (OSNs). We focus on a usual type of interest-driven social sessions in OSNs, called Social-InterestCast, under which users autonomously determine whether to view the content from their followees depending on their interest. We define the fundamental limit of transport load as a new metric, called transport complexity, i.e., the minimally required transport load for an OSN in a given carrier network. We derive the transport complexity for Social-InterestCast sessions in a large-scale OSN in the carrier network with optimal communication architecture. The results can act as the common lower bounds on transport load for Social-InterestCast in any carrier networks. To the best of our knowledge, this is the first work to measure the transport difficulty for data dissemination in OSNs by modeling session patterns with the interest-driven characteristics.
Modeling and research of the orbital angular momentum conservation of vortex laser beams propagating in a random optical medium

Vortex light beams are used to compact data channels because they have orbital angular moments with an infinite number of possible quantum states. This allows the transmission of optical information in a single physical medium by encoding the data by different optical vortices. The aim of the paper is to modeling and analysis the state of orbital angular momentum of laser beams during propagation through free space and parabolic fiber in the presence of random fluctuations of the optical medium. The modeling results for Laguerre-Gauss beams showed that after the beams distortion by random noise, they self-regenerate with further propagation in an undisturbed medium, and it is possible to determine the initial state of the orbital angular momentum of the beam by means of binarization of the field expansion coefficients in angular harmonics.
Tree species classification for clarification of forest inventory data using Sentinel-2 images

Many ecological applications (e.g., human activity management in protected areas, conservation status assignment) require actual forest inventory data. However, frequent field research of forest areas is very expensive. Therefore, in practice, forest inventory data are slowly updated, approximately one time per decade. From the other hand, modern remote sensing systems combine high-quality imagery with short revisiting time and can be used for the forest inventory data clarification. Our paper presents an investigation of tree species classification based on seasonal Sentinel-2 data (2018) and the latest forest inventory information (2013–2014). The main advantages of Sentinel-2 satellites are a short revisiting time and a large field of view that is important in large area analysis. Our classification model was based on support vector machines method combined with specific spatial processing methods. We used the known inventory data for training and validation the classifier. Misclassified regions were further analyzed in ground surveys to produce the inventory data clarification. The paper addresses the optimal image dates selection, image preprocessing and classification procedure evaluation issues. The study was carried out for the territory of the Krasnosamarskoe forestry in Samara region, Russia. The experiments have shown that the proper Sentinel-2 data selection and classification procedure configuration allow reaching the classification accuracy of about 0.82 for the control sample. The ground survey confirmed that classification errors are mainly caused by the dominant tree species changes. Thus, we concluded that Sentinel-2 data can be effectively used for the forest inventory data clarification.
A miniaturized optical fiber tip high-temperature sensor based on concave-shaped Fabry-Perot cavity

A miniaturized optical fiber tip Fabry-Perot interferometer for high-temperature measurement is presented in this paper. The fabrication process of the diaphragm-free Fabry-Perot cavity is quite simple, involving only two steps: fusion splicing and cleaving. By adjusting the arc power during fusion splicing, a concave-shaped structure is obtained, through which the light is coupled/split into the wall of the spliced hollow core fiber. By cleaving the end-face of the hollow core fiber, a concave-shaped diaphragm-free Fabry-Perot interferometer is formed. The temperature response of the sensor was demonstrated, showing a high-temperature tolerance up to 1000 °C and a sensitivity of 0.01226 nm/°C.
Review on Speech Emotion Recognition

This paper surveys the state of the art of speech emotion recognition(SER), and presents an outlook on the trend of future SER technology. First, the survey summarizes and analyzes SER in detail from five perspectives, including emotion representation models, representative emotional speech corpora, emotion-related acoustic features extraction, SER methods and applications. Then, based on the survey, the challenges faced by current SER research are concluded. This paper aims to take a deep insight into the mainstream methods and recent progress in this field, and presents detailed comparison and analysis between these methods.
Health Benefits of Carotenoids: A Role of Carotenoids in the Prevention of Non-Alcoholic Fatty Liver Disease

Non-alcoholic fatty liver disease (NAFLD) is one of the most common chronic liver diseases with a prevalence of ~25% worldwide. NAFLD includes simple hepatic steatosis, non-alcoholic steatohepatitis, fibrosis, and cirrhosis, which can further progress to hepatocellular carcinoma. Therefore, effective strategies for the prevention of NAFLD are needed. The pathogenesis of NAFLD is complicated due to diverse injury insults, such as fat accumulation, oxidative stress, inflammation, lipotoxicity, and apoptosis, which may act synergistically. Studies have shown that carotenoids, a natural group of isoprenoid pigments, prevent the development of NAFLD by exerting antioxidant, lipid-lowering, anti-inflammatory, anti-fibrotic, and insulin-sensitizing properties. This review summarizes the protective action of carotenoids, with primary focuses on astaxanthin, lycopene, β-carotene, β-cryptoxanthin, lutein, fucoxanthin, and crocetin, against the development and progression of NAFLD.
DCDroid: automated detection of SSL/TLS certificate verification vulnerabilities in Android apps

Current Android applications (apps) often use Security Socket Layer(SSL)/Transport Layer Security(TLS) protocols to transmit users' information, as the implementation of SSL/TLS secures the transmission of sensitive information. However, for various reasons, Android developers fail to properly implement SSL/TLS during the development of an app, resulting in security risks. The improper implementations include trusting all certificates, trusting all domain names, or ignoring certificate verification errors. These improper implementations may result in Man-In-The-Middle(MITM) attacks or phishing attacks. In this work, we are motivated to detect vulnerabilities in implementation of SSL/TLS in Android apps by designing and implementing a tool called DCDroid (Detecting SSL/TLS Certificate verification vulnerabilities in Android apps) with the combination of static analysis and dynamic analysis. We focus on four types of vulnerable schema and locate the potential vulnerable code snippets in apps with static analysis. In dynamic analysis, we prioritize the triggering of User Interface(UI) components based on the results with static analysis to confirm the misuse of SSL/TLS. The dynamic analysis benefits from the static analysis and removes false positives. With DCDroid we analyze 960 apps from Google Play and 1253 apps from 360app. The experimental results show that 457 (20.65%) apps contain potential security risks in the implementation of SSL/TLS. Guided by the static analysis, we further confirm that 248 (11.21%) out of 2213 apps are truly vulnerable to MITM and phishing attacks. By analyzing the categories, ranks and version evolution of these detected vulnerable apps, we find that apps of News&Books are more likely to introduce SSL/TLS risks. We also find that the fix cycle of the risk is very long. We provide suggestions on SSL/TLS certificate verification to Android developers in order to deal with the SSL/TLS certificate verification vulnerabilities.
Towards big data processing in clouds: An online cost-minimization approach

Due to its elastic and on-demand nature of resource provisioning, cloud computing provides a cost effective and powerful technology for the processing of big data. Under this paradigm, Data Service Provider (DSP) may rent geographically distributed datacenters to process their large amount of data. As the data are dynamically generated and the resource pricing varies over time, moving the data from differently geographic locations to different datacenters while provisioning adequate computation resource to process them is an essential task to achieve cost effectiveness for DSP. In this paper, a joint online approach is proposed to address this task. We formulate the problem into a joint stochastic optimization problem, which is then decoupled into two independent subproblems via the Lyapunov framework. Our method is able to minimize the long-term time average cost including computing cost, storage cost, bandwidth cost and latency cost. Theoretical analysis shows that our online algorithm can produce a solution within an upper bound to the optimal solution achieved through offline computing and guarantee that the data processing can be completed with preset delays.
The Recoverable Consensus Hierarchy

Herlihy's consensus hierarchy ranks the power of various synchronization primitives for solving consensus in a model where asynchronous processes communicate through shared memory, and may fail by halting. This paper revisits the consensus hierarchy in a model with crash-recovery failures, where the specification of consensus, called recoverable consensus (RC) in this paper, is weakened by allowing non-terminating executions when a process fails infinitely often. Two variations of this model are considered: with independent process failures, and with simultaneous (i.e., system-wide) process failures. We prove several fundamental results: (i) any synchronization primitive at level 2 in the conventional consensus hierarchy remains at level 2 in the RC hierarchy if failures are simultaneous; (ii) any commutative or overwriting primitive (including Test-And-Set, Fetch-And-Add, and Fetch-And-Store) at level 2 in the conventional consensus hierarchy drops to level 1 in the RC hierarchy if failures are independent, unless the number of such failures is bounded; and (iii) there exists a primitive at level 2 in the conventional consensus hierarchy that remains at level 2 in the RC hierarchy. Result (iii) implies that result (ii) cannot be generalized to all primitives at level 2 in the conventional consensus hierarchy. To our knowledge, this collection of results exhibits the first separation between the simultaneous and independent crash-recovery failure models with respect to the computability of consensus.
Urban crowd flow forecasting based on cellular network

Forecasting the crowd flows in a city is crucial for public safety, traffic management and urban planning. Researchers proposed several methods to forecast the crowd flows. However, they omitted the acquisition of comprehensive flow data. Taxis' GPS trajectory data and bike sharing system data are often used in these works as the flow data. But they are not able to reflect the comprehensive crowd flows in a city, since they only contain the transitions of a specific transportation mode. In this paper, we propose to extract comprehensive crowd flows from mobile flow records (MFRs), a finegrained cellular data. We also use a Convolution Neural Network (CNN) based method to forecast crowd flows and compare it with traditional time series regression models. The experiments on a large-scale cellular dataset show that CNN based method can reduce the error by 28% to 77%.
Towards a Theory of Randomized Shared Memory Algorithms

Randomization has become an invaluable tool to overcome some of the problems associated with asynchrony and faultiness. Allowing processors to use random bits helps to break symmetry, and to reduce the likelihood of undesirable schedules. As a consequence, randomized techniques can lead to simpler and more efficient algorithms, and sometimes to solutions of otherwise unsolvable computational problems. However, the design and the analysis of randomized shared memory algorithms remains challenging. This talk will give an overview of recent progress towards developing a theory of randomized shared memory algorithms. For many years, linearizability [6] has been the gold standard of distributed correctness conditions, and the corner stone of modular programming. In deterministic algorithms, implemented linearizable methods can be assumed to be atomic. But when processes can make random choices, the situation is not the same: Probability distributions of outcomes of algorithms using linearizable methods may be very different from those using equivalent atomic operations [4]. In general, modular algorithm design is much more difficult for randomized algorithms than for deterministic ones. The first part of the talk will present a correctness condition [2, 5] that is suitable for randomized algorithms in certain settings, and will explain why in other settings no such correctness condition exists [3] and what we can do about that. To this date, almost all randomized shared memory algorithms are Las Vegas, meaning they permit no error. Monte Carlo algorithms, which allow errors to occur with small probability, have been studied thoroughly for sequential systems. But in the shared memory world such algorithms have been neglected. The second part of this talk will discuss recent attempts to devise Monte Carlo algorithms for fundamental shared memory problems (e.g., [1]). It will also present some general techniques, that have proved useful in the design of concurrent randomized algorithms.
Towards an Automatic Quality Evaluation of Natural Language Software Specifications

Requirements Specification is a crucial phase of the software life-cycle. Providing the software engineer with tools and techniques for evaluating quality of Software Requirements may solve some of the problems due to possible lacks of quality of them. In this paper, first a feasible Quality Model is defined for Software Requirements written in Natural Language (NL). The term feasible means that the Quality Properties of this Quality Model can be evaluated by means of Indicators automatically and numerically measurable. Then, the results of the validation of this Quality Model on industrial cases are presented. The encouraging outcomes of the validation have been taken into account for starting the realization of an automatic tool for verifying the adherence to syntax/lexical constraints of the requirement sentences to guarantee the conformance to the Quality Model. This tool should be a valid support for the reviewer of NL Software Requirements. EMAIL:: lami@iei.pi. cnr.it
A Weighted Mini-Bucket Bound for Solving Influence Diagram

Influence diagrams provide a modeling and inference framework for sequential decision problems, representing the probabilistic knowledge by a Bayesian network and the preferences of an agent by utility functions over the random variables and decision variables. The time and space complexity of computing the maximum expected utility (MEU) and its maximizing policy are exponential in the induced width of the underlying graphical model, which is often prohibitively large. In this paper, we develop a weighted mini-bucket approach for bounding the MEU. These bounds can be used as a standalone approximation that can be improved as a function of a controlling i-bound parameter, or as a heuristic function to guide subsequent search. We evaluate the scheme empirically against the current state of the art, illustrating its potential.
An energy-efficient resource allocation strategy for vehicular networks

Vehicular networks have drawn many researchers' attention for the reason that vehicular networks can change the operation of vehicles into a safer and greener state. Resource allocation is one of important problems in vehicular networks. In this paper, to achieve a good trade-off between energy efficiency and transmission latency, a novel resource allocation method is proposed based on actor-critic reinforcement learning approach. As known, actor-critic reinforcement learning is an acknowledged solution for the problem with continuous valued state and action variables. Further, considering that edge computing technology for vehicular networks, the architecture of fog radio access network (F-RAN) is exploited to formulate the resource allocation problem in this paper. By adopting fog computing, the transmission delay is decreased because the F-RAN nodes with computing ability, are very close to vehicles so that the related information message does not need to be transmitted through the whole vehicular network. Further, to increase access capacity, the proposed method is deployed into non-orthogonal multiple access (NOMA) system.