Miracle-dz/UniFAR-FTUs huggingface dataset:
AIS Data Oriented Ships' Trajectory Mining and Forecasting Based on Trajectory Delimiter

Studying and analyzing the navigation trajectory pattern of a specific target ship or target cluster can provide important help for maritime traffic management, channel clustering analysis, and monitoring of key sea area. Therefore, this research has gradually become a necessary practical need. In order to analyze the ship's trajectory and master the rule of ship's navigation and action, this paper firstly introduces the trajectory delimiter based on the three dimensions of time, longitude and latitude to divide the ship's trajectory into different phases of navigation, and generate a trajectory transactional databases that can be used for trajectory pattern mining. Then, the frequent sequential pattern mining technique is used to extract the frequently used trajectory of marine vessels and the association rules that can be used for trajectory prediction are obtained. Using the real AIS data testing algorithms, the experimental results show that this method can effectively excavate the typical trajectory sequence of the ship and avoid the impact of sudden situations such as interference points and temporary change of channel. The association rules generated from the frequent trajectory sequence patterns can also be used for the trajectory forecasting.
Detection Of Shilling Attack In Collaborative Filtering Recommender System By Pca And Data Complexity

Collaborative filtering (CF) recommender system has been widely used for its well performing in personalized recommendation, but CF recommender system is vulnerable to shilling attacks in which shilling attack profiles are injected into the system by attackers to affect recommendations. Design robust recommender system and propose attack detection methods are the main research direction to handle shilling attacks, among which unsupervised PCA is particularly effective in experiment, but if we have no information about the number of shilling attack profiles, the unsupervised PCA will be suffered. In this paper, a new unsupervised detection method which combine PCA and data complexity has been proposed to detect shilling attacks. In the proposed method, PCA is used to select suspected attack profiles, and data complexity is used to pick out the authentic profiles from suspected attack profiles. Compared with the traditional PCA, the proposed method could perform well and there is no need to determine the number of shilling attack profiles in advance.
HotDASH: Hotspot Aware Adaptive Video Streaming Using Deep Reinforcement Learning

A large fraction of video content providers have adopted adaptive bitrate streaming over HTTP. The client player typically runs an adaptive bitrate (ABR) algorithm to decide upon the most optimal quality for the next few seconds of video playback. State-of-the-art ABR algorithms attempt to achieve an optimal trade-off among the competing objectives of high bitrate, less rebuffering, and high smoothness, in the face of unpredictable bandwidth variability. However, optimal bandwidth utilization does not necessarily ensure high quality of experience (QoE). Different users have different content preferences even within the same video, due to differences in team loyalties (in sport), character preferences (in movies and soaps), and so on. In this work, we present HotDASH, a system which enables opportune prefetching of user-preferred temporal video segments (called hotspots). HotDASH implements a prefetch module in the open source DASH player dash.js, which is powered by an optimal prefetch and bitrate decision engine. The decision engine is designed as a cascaded reinforcement learning (RL) model, implemented using a state-of-the-art actor-critic RL algorithm over a neural network. We train the neural network using trace-driven simulations over a large variety of bandwidth conditions. HotDASH outperforms all baseline algorithms, with a 16.2% QoE improvement over the best-performing baseline, and achieves 14.31% better average bitrate due to its ability to prefetch opportunistically.
Chemical Profiling of Xueshuan Xinmaining Tablet by HPLC and UPLC-ESI-Q-TOF/MS

Xueshuan Xinmaining Tablet (XXT) is a widely used traditional Chinese medicine for the treatment of stroke, chest pain, coronary heart disease, and angina pectoris caused by blood stasis. Having a multiple-component preparation, it is still far from meeting the requirements of modernization and standardization because its detailed chemical basis and action mechanism have not been clarified. In this work, the different batches of XXT samples were analyzed by HPLC and the typical sample was analyzed by UPLC-ESI-Q-TOF/MS to understand its chemical profiling. As a result, 77 chromatographic peaks were detected, among which 63 constituents were identified or tentatively characterized based on the comparison of retention time and UV spectra with authentic compounds as well as by summarized MS fragmentation rules and matching of empirical molecular formula with those of published components. This is the first systematic report on the chemical profiling of the commercial XXT products, which provides the sufficiently chemical evidence for the global quality evaluation of XXT products.
Implementation of 8 Bit Microprocessor Using Current Mode Logic (CML) Approach

Many circuits and Micro-Architectural innovations have been proposed to reduce voltage swing, delay and area in many individual processor units. For this purpose, in this work, a case study is presented with a possible new design methodology of microprocessor based on CML approach. It focuses on the applicability of Current mode logic for building digital circuits, which run at very low voltages and provide desirable performance with excellent energy savings compared to conventional CMOS. The 8-Bit Microprocessor was designed in 45nm technology using CML Approach at a power supply of 1V using cadence virtuoso tools. The design of 8 Bit microprocessor consists of two functional blocks i.e. data path unit and control unit. The internal blocks of Data path unit are 8 BIT ALU, 4*8 Register file, 8 Bit Tristate buffer and 8 Bit shifter. The control unit is designed using Finite State Machines
A Self-powered Wireless Inrush Current Measurement System

A self-powered inrush current measurement system based on the electromagnetic induction principle is proposed. Signal coil and power harvesting coil adopt Rogowski coils, which are nested in the current channel. Power supply unit draw out energy from inrush current. After signal conditioning, polarity, peak value and occurrence time of transient surge current can be acquired. The current information can be power-down stored, and wireless data transmission is achieved through ZigBee protocol. Experiments prove that the system can be self-powered by inrush current and realize linear measurement within the range of 10kA to 200kA.
Spam Detection Framework for Online Reviews Using Hadoop’ s Computational Capability

Nowadays, online reviews have become one of the vital elements for customers to do online shopping. Organizations and individuals use this information to buy the right products and make business decisions. This has influenced the spammers or unethical business people to create false reviews and promote their products to out-beat competitions. Sophisticated systems are developed by spammers to create bulk of spam reviews in any websites within hours. To tackle this problem, studies have been conducted to formulate effective ways to detect the spam reviews. Various spam detection methods have been introduced in which most of them extracts meaningful features from the text or used machine learning techniques. These approaches gave little importance on extracted feature type and processing rate. NetSpam[1] defines a framework which can classify the review dataset based on spam features and maps them to a spam detection procedure which performs better than previous works in predictive accuracy. In this work, a method is proposed that can improve the processing rate by applying a distributed approach on review dataset using MapReduce feature. Parallel programming concept using MapReduce is used for processing big data in Hadoop. The solution involves parallelising the algorithm defined in NetSpam and it defines a spam detection procedure with better predictive accuracy and processing rate.
Early Diagnosis of Alzheimer's Disease from MRI Images Using PNN

Here we propose a new method for the diagnosis of Alzheimer's disease from MRI images. The most prevalent, reliable and result-oriented methods for diagnosis of Alzheimer's disease are: Firstly, measuring the rate of atrophy of the hippocampus and total brain volume; Secondly, extracting information from the gray matter, white matter and cerebro spinal fluid (CSF) of the brain. In this work, different statistical features such as contrast, homogeneity, correlation, energy and also shape features are extracted from the MRI images. For every image a number of features are extracted and these are fed to the classifier. The images are classified into any of the three classes: Alzheimer's Disease (AD), Mild Cognitive Impairment (MCI) or Normal Control (NC). The proposed approach compares the performances of Probabilistic Neural Network(PNN), Support Vector Machines(SVM) and K-Nearest Neighbour(KNN) in terms of their accuracy, specificity and sensitivity. The MRI images are obtained from the ADNI database. ADNI is a clinically validated database of MRI, PET and FMRI images of subjects.
Comparative Study on Decentralized Cloud Collaboration(DCC)

Cloud collaboration is a billion-dollar industry, for sharing, storing, and co-authoring files. In the current age of information technology, cloud collaboration expects to see a significant amount of growth, as more organizations look to leverage the benefits of the industry specifically in the areas of flexibility, cost-efficiency, and security[1]. However, existing systems basically operates in a centralized cluster to achieve high performance, though they have a demand solving indisputable benefits, there are several inherent weaknesses such as high server costs for service providers, illegal data mining in trust-based architecture, security loopholes, and unethical government surveillance. Therefore, a large-scale resource sharing decentralized system can mitigate these traditional server expenses, data failure, and outage, as well as the enhanced security, and privacy of data. This dissertation presents a background to the problem, its impact on adaption, existing research background, and proposing design for storing, sharing, and coauthor files. The Design presents a decentralized resource (storage and network) sharing system, with real-time collaborative editing, peer (node) management, and redundancy schemes to manage fault tolerance of the distributed storage.
Alarm System For Bed Exit And Prolonged Bed Rest

In this study, we designed a system that senses bed pressure levels and provides alarms for bed exit and prolonged bed rest; the system uses pressure sensors, a single-chip microcontroller, and a computer. The designed system enables reducing bed exit falls due to muscle weakness among hospitalized patients, preventing pressure injury among patients restricted to prolonged bed rest, preventing wandering behavior among patients with dementia, and reducing the workload of nurses. Furthermore, the system can serve as an adequate substitute in situations in which 24-hours monitoring is impractical; additionally, it is a low-cost alternative to existing alarm systems. Therefore, this alarm system can be adopted as common medical-care equipment for hospitals and nursing homes.
Optimal Multipole Orders in TE Imaging of PEC Scatterers

In this paper we introduce the concept of optimal multiple orders utilized in imaging of PEC scatterers using TE polarized fields. Different objects with an increasing multiple scattering effect were used for testing, and satisfying results were obtained. The proposed concept has the potential of significantly reducing the computational time needed for reconstructing of the final images, since only one multipole order is to be used.
A Flexible Clustering Algorithm Based On Statistics Bariables Granule

Cluster analysis technique, as a promising data analysis method, still faces many challenges when processing today's com- plex data set. There are mainly three effectivity problems which are considered in this paper. First, the prior knowledge, which is usually unavailable, is needed. Second, parameter tuning is diffi- cult. Third, it usually provides little knowledge about the struc- ture of clusters. Therefore, we present a clustering algorithm based on statistics granular merging of data blocks. A database is broken down into many data granule and then be gradually merged into small statistics homonomous subsets. This approach has not imposed needs for prior knowledge. Its tuning parameters is relatively easy to use. It effectively discovers the clusters and the noises in a computationally efficient way. The behavior of the proposed algorithm is illustrated on several 2D shape data sets, and the state-of-the-art performance comparing with some pop- ular algorithms is demonstrated on several real world data sets.
Men’s Overpersistence and the Gender Gap in Science and Mathematics

Large and long-standing gaps exist in the gender composition of science, technology, engineering, and mathematics (STEM) fields. Abundant research has sought to explain these gaps, typically focusing on women, though these gaps result from the decisions of men as well as women. Here we study gender differences in STEM persistence with a focus on men’s choices, finding that men persist in these domains even where opting out could lead to greater material payoffs. Study 1 employed a novel experimental paradigm for measuring “overpersistence,” finding that undergraduate men chose mathematics questions over verbal questions at higher rates than undergraduate women on a test in which mathematics questions were substantially more difficult than verbal questions and participants were paid for correct answers. Study 2 analyzed data from a nationally representative longitudinal survey, finding that men are more likely than women to retake college STEM courses after failing them and that men’s STEM retaking after failure is as likely to lead to lower later life earnings as to higher earnings. Finally, in Study 3, we used a survey-embedded experiment to examine the intervening factors driving men’s overpersistence in a diverse sample of adults. Integrating prior theoretical work, we find evidence for a model in which cultural stereotypes of male superiority in mathematics lead men both to be more confident in and identify more with the mathematics domain, factors that in turn lead men to pursue math to a greater extent than women.
3D Face Generation from Sketch Using ASM and 3DMM

The major challenge that has not received much attention in automatic sketch to image face recognition is to achieve age invariance. 3D face modeling using image has been used to overcome many challenges of face recognition, as it is invariant to changes of viewpoint, illumination, background clutter and occlusions. Keeping all the challenges associated with sketches, 3D modeling and automatic sketch to image face recognition, we propose a method for reconstructing a 3D face model using only one frontal face sketch. The methodologies used for doing so are Active Shape Modeling (ASM) and three Dimensional Morphable Model (3DMM). ASM is used for getting the landmark points on the face based on the shape while 3DMM is used for reconstructing the 3D face model by using those points.
Combined Pharmacological Inhibition of Cyclophilins, FK506-Binding Proteins, Hsp90, and Hsp70 Protects Cells From Clostridium botulinum C2 Toxin

The Clostridium botulinum C2 toxin is an exotoxin causing severe enterotoxic symptoms. The C2 toxin consists of the binding/translocation component C2II, and the enzymatic active component C2I. After proteolytic activation, C2IIa forms heptamers that bind C2I. The C2IIa/C2I complex is taken up into mammalian target cells via receptor-mediated endocytosis. Acidification of endosomes leads to conformational changes in both components. C2IIa heptamers form a pore into the endosomal membrane, and C2I becomes unfolded and translocates through the narrow C2IIa pores into the cytosol of the cell. Here, C2I covalently transfers an ADP-ribose moiety from its co-substrate NAD+ onto G-actin, which leads to depolymerization of F-actin resulting in rounding up of adherent cells. Translocation of C2I into the cytosol depends on the activity of the chaperones Hsp90 and Hsp70 and peptidyl-prolyl cis/trans isomerases of the cyclophilin (Cyp) and FK506-binding protein (FKBP) families. Here, we demonstrated that C2I is detected in close proximity with Hsp90, Cyp40, and FKBP51 in cells, indicating their interaction. This interaction was dependent on the concentration of C2 toxin and detected in mammalian Vero and human HeLa cells. Moreover, the present study reveals that combination of radicicol, VER-155008, cyclosporine A, and FK506, which are specific pharmacological inhibitors of Hsp90, Hsp70, Cyps, and FKBPs, respectively, resulted in a stronger inhibition of intoxication of cells with C2 toxin compared to application of the single inhibitors. Thus, the combination of inhibitors showed enhanced protection of cells against the cytotoxic effects of C2 toxin. Cell viability was not significantly impaired by application of the inhibitor combination. Moreover, we confirmed that the combination of radicicol, VER-155008, CsA, and FK506 in particular inhibit the membrane translocation step of C2I into the cytosol whereas receptor binding and enzyme activity of the toxin were not affected. Our findings further characterize the mode of action of Hsp90, Hsp70, Cyps, and FKBPs during membrane translocation of bacterial toxins and furthermore supply starting points for developing of novel therapeutic strategies against diseases caused by bacterial toxins that depend on Hsp90, Hsp70, Cyps, and FKBPs.
Graph-Based Technique for Extracting Keyphrases in a Single-Document (GTEK)

In this paper, a novel Graph-based Technique for Extracting Keyphrases in a single document (GTEK) is introduced to be used in extractive summarization of text. GTEK is based on the graph-based representation of text, which depends on terms and phrase numeration in sentences rather than some structural document features. GTEK considers the impact of the sentence on the phrases in a document, motivated by the fact that a phrase may be important if it appears in the most important sentences in the document. The Graph-based Growing Self-Organizing Map (G-GSOM) is used to group the sentences into graph-based clusters. TextRank algorithm is applied on graphs of clusters under the assumption that the top-ranked nodes should represent the most important sentences, where the most frequent phrases in these sentences are selected as document keyphrases. Experimental results show that our innovative technique extracts the most keyphrases of two datasets.
A Self-Adaptive Network for HPC Clouds: Architecture, Framework, and Implementation

Clouds offer flexible and economically attractive compute and storage solutions for enterprises. However, the effectiveness of cloud computing for high-performance computing (HPC) systems still remains questionable. When clouds are deployed on lossless interconnection networks, like InfiniBand (IB), challenges related to load-balancing, low-overhead virtualization, and performance isolation hinder full potential utilization of the underlying interconnect. Moreover, cloud data centers incorporate a highly dynamic environment rendering static network reconfigurations, typically used in IB systems, infeasible. In this paper, we present a framework for a self-adaptive network architecture for HPC clouds based on lossless interconnection networks, demonstrated by means of our implemented IB prototype. Our solution, based on a feedback control and optimization loop, enables the lossless HPC network to dynamically adapt to the varying traffic patterns, current resource availability, workload distributions, and also in accordance with the service provider-defined policies. Furthermore, we present IBAdapt, a simplified ruled-based language for the service providers to specify adaptation strategies used by the framework. Our developed self-adaptive IB network prototype is demonstrated using state-of-the-art industry software. The results obtained on a test cluster demonstrate the feasibility and effectiveness of the framework when it comes to improving Quality-of-Service compliance in HPC clouds.
Image Quality Measures Based Face Spoofing Detection Algorithm for Online Social Media

Recent years, selfie becomes an important part of photography and considered as the powerful and trustful communication media. Nowaday's technology has been advanced much more so any one can manipulate image with available image editing software. So it became difficult to identify whether the image is spoofed or not. Many spoof detection algorithm are being developed to find fake face in biometrics system. But face spoofing recognition still persists as a challenge in online social media. In this paper, a fast and robust algorithm is proposed to detect fake faces from photographs which have shared through the online social networks. This algorithm extracts face segments in different color spaces and estimates the illuminate map of the region. In addition, this algorithm measures the image quality parameters of segment and compares with the back ground region. Reference based and Non reference based image quality parameters are used to generate feature vectors. Quadratic Discriminant Analyzer (QDA) classifier is used to recognize the fake face. The proposed algorithm is successfully tested with DSO-1, DSI-1 dataset and the result shows 96.5% accuracy in detecting fake faces.
A Unique Low Power Network-an-Chip Virtual Channel Router

IP cores on a chip have been drastically increasing with technology scaling. Integration of these cores on a single chip has been a challenge and Network on Chip (NoC) is the cognitive solution to this due to its faster data transmission, smaller area and lower power consumption. Routers constitute a significant part of NoC. It is imperative to design a router that is area and power efficient. The proposed router utilizes the concept of acyclic sorting operation, which replaces the cyclic round robin arbitration to obtain a low power network on chip router architecture, and this architecture is described in detail in this paper. This design achieves 18.03% decrease in area and 24.27% decrease in power with a marginal 16.41 % increase in delay when compared with the Shield router architecture.
Indoor Space Model Based on Knowledge Graph

In indoor space, a key issue that indoor location services need to solve is how to model the indoor space. With the rapid development of personal services in the indoor space, more and more personalized information has emerged. How to integrate this personalized information into the management of indoor space data has become the focus of attention. To solve this problem, considering the geometric, topological and personalized information of the indoor space, an indoor space model based on the knowledge map was established. First, combining people's perceptions of indoor space, the indoor space is divided from both functional and institutional perspectives. Secondly, the new concept, attributes and relations are expanded to build an indoor space ontology based on existing ontology to describe the indoor space characteristic. Thirdly, we build knowledge graph to describe the personalized information of the indoor entity. Finally, an example is proposed to illustrate the feasibility and effectiveness of the indoor space model.