Miracle-dz/UniFAR-FTUs huggingface dataset:
Query Performance Analysis of NoSQL and Big Data

In the present era, Data Science is so often used for prognostic analysis instead of analyzing non-volatile data. Data has become important for research, industrial commerce and technology. Many of the repositories hold unstructured, voluminous data of different varieties generated at a high velocity. These include structured data (SD), semi-structured data (SSD) and unstructured data (USD) and collectively often referred as Big Data. The varieties of Big Data include text, audio, video, graphics and multimedia data. One of the important relevant issue is storage and retrieval of Big Data at a reasonably fast pace. NoSQL is a schema less approach that claims to handle this huge volume and variety of data efficiently. NoSQL can provide both vertical and horizontal scaling whereas conventional relational data models supports only vertical scaling. MongoDB and Cassandra are examples of NoSQL data models. The strength of our study is on hands-on implementation with multiple Big Data products used in the industry for last several years. In this empirical study, we present performance analysis of a few standard queries on SQL Server 2012, Cassandra and MongoDB data models. We make a comparative analysis of the access time on these platforms models by calculating the correlation coefficient between the number of row and access time with the increasing number of rows exceeding millions of data rows. The detailed study and interpretation of the experimental findings done in this paper would eventually contribute towards designing a general-purpose computation engine for Big data processing.
Primitive Shape Extraction for Objects in Point Cloud

Shape plays an important role in object recognition and shape modeling. In this paper, a comprehensive method is proposed to analyze and implement crucial primitive shape extraction including line, plane, cylinder and sphere. Linear shape is extracted by the proposed alpha-shape based method, and an algorithm that robustly computes the boundary of holes from incomplete objects is presented. The plane primitive shape is extracted by analyzing neighboring point distribution and the parameters that define the primitive shape. Reasonable primitive shape features is extracted accurately for further shape modeling. Experimental results demonstrate that our method could deal with different objects efficiently in different shapes.
Reliability of CMOS Integrated Memristive HfO2 Arrays with Respect to Neuromorphic Computing

CMOS integrated 4kbit 1T-1R memristive devices were examined in terms of device-to-device and pulse number dependent variability for the use in neuromorphic systems. Based on the variability of polycrystalline HfO2 based Resistive Random Access Memory (RRAM)devices, reliability issues for the implementation of stochastic learning rules are investigated. The switching variability of the memristive devices is demonstrated as a function of the voltage amplitude and pulse number. We demonstrate that the switching probability of the filamentary RRAM devices can emulate analog synaptic functionality. Finally, the endurance and retention characteristics of the memristive devices are analyzed to evaluate the performance and reliability of the cells used for neuromorphic computing.
A Differential balun-LNA for Multistandard Receivers in 0.13 μm BiCMOS

This paper presents an inductorless broadband differential low-noise amplifier (LNA) employing noise and distortion cancelling techniques in 0.13 $\mu$m SiGe BiCMOS process. The core design exploits a common-base (CB) input stage and a common-emitter (CE) stage with degeneration resistors to maximize balanced operation, while simultaneously canceling the noise and distortion of the CB-stage. With 1.6 V supply voltage, post simulation results show that the LNA covers 0.1 GHz to 3.5 GHz bandwidth and achieves a noise figure (NF) of 2 dB, voltage gain of 23 dB and third-order input intercept point (IIP3) of 0 dBm. The operating current of the LNA is 6.8 mA.
Identification and Ranking of Vulnerable Interdependences in Cyber-Physical Power System

This paper puts forward the identification and ranking method of the vulnerable interdependences in Cyber-Physical Power System (CPPS) and CPPS risk mitigation strategy. The direct identification and ranking of interdependences is transformed to ranking the weighted physical nodes power at physical side of the interdependences, and the weight are the values of importance evaluation matrix for the cyber nodes at the other side of the interdependences. The risk mitigation strategy is applied through the cyber layer topological transformation, which smoothes the difference of nodes’ degrees in the cyber layer and combines the traits of scale-free and small world networks. The effectiveness of the identification and ranking method, also the risk mitigation strategy are verified through simulating sequenced interdependences attack. It shows that the interdependences ranking method is valid to identify vulnerable interdependences that are important to the robustness of the CPPS, and the risk mitigation strategy can provide effective protection under interdependences attack.
Requirements and Challenges in Integration of Aggregated Personal Health Data for Inclusion into Formal Electronic Health Records (EHR)

The rapid growth of Wireless Sensor Networks and embedded devices as well as their implementation in the context of Smart Homes opens many possibilities for development of new quality solutions in, among others, eHealth field. In this sense, the idea was to implement the inclusion of personalized health data, collected via wireless sensors, such as e.g. personal trackers, into the formal Electronic Health Record (EHR) which forms a part of the Croatia's Central Health Information System. This consists of defining simple and extensible schemas for the most common and important types of data in healthcare and modeling a comprehensible, transparent and automatized ETL process (extract, transform, load) that would guarantee data integrity. Considering the sensitivity of data in question, privacy is considered a crucial challenge in defining the model to ensure patient's privacy is uncompromised.
CELL ELONGATION AND DIVISION IN SPIRILLUM ANULUS

The hypothesis that the main growth of the cell envelope of bacteria is from a growing point at one pole in unicellular bacteria and from the points of junction of cell wall and cross-wall in septate bacteria has been advanced by Bisset (1951, 1953, 1955, 1956). A corollary of this hypothesis is the consideration that the growth of the daughter cell from the mother cell is by a process resembling budding in that the mother cell retains most of its cell wall and mature flagella, whereas the bud has a newly grown cell wall and flagella in an early stage of development. The experimental evidence for this hypothesis is based on the division and arrangement of flagella in growing and dividing cells as well as the fact that at the putative growing point the cell walls appear thinner and less well developed than over the remainder of the cell. Stocker (1956), on the other hand, has advanced the hypothesis that cell growth and elongation occur diffusely by the intercalation of the cell wall along its length. Stocker's hypothesis is based on the investigation of the unilinear transmission of motility in nonmotile strains of Salmonella by transduction. The two hypotheses appear to be diametrically opposed although the possibility exists that both types of cell growth may occur among the various types of bacteria. It is highly improbable, however, that both types should occur in species of the same genus; species of the genus Salmonella were among the experimental organisms used by Bisset and co-workers. Since their experimental data are based on evidence found in fixed cells (flagellar stain preparations or in electron micrographs) and those of Stocker are derived from genetical considerations, it was believed that
Hierarchical Hybrid DVE-P2P Networking Based on Interests Clustering

In the massive multi-player online game scenes single P2P topology cannot sufficiently accomplish a variety of mission. To strengthen player experience, rapidly respond client request and improve P2P network scalability and flexibility. Firstly, we propose network architecture, which is hierarchical hybrid DVE-P2P based on interested cluster to achieve this purpose. Secondly, based on this network architecture and topology we propose three logical layers along with two physical layers to guarantee its security, flexibility, reliability, scalability and high throughput. Thirdly, based on Area of Interest we propose interested cluster to quickly share similarity resource with other peer in own cluster, selecting super node with excellent performance as central, virtual server, which is connected to Chord ring, to manage load balancing of interested cluster, at same time we present algorithms and strategies to load balancing for different peer, interested cluster and server. Finally, we perform experiments to show that our approaches significantly improve the DVE system's response speed, throughput, dynamic load balancing, load index and stability.
Introducing a Method for Combining Supervised and Semi-Supervised Methods in Fraud Detection

As electronic transactions are growing, fraud cases are also growing drastically. Detection of frauds is a complicated task and limiting fraud detection systems to certain kinds of detection methods like supervised or unsupervised methods does not seem efficient. In this paper, a combination framework for fraud detection systems, consisting of both supervised and semi-supervised methods in three main components namely rule-based component, trend-analysis-based component and, a scenario-based component is proposed. The rule-based component is the supervised part of the framework and decision tree which is a cost-insensitive classification algorithm is used for this component. In the trend-analysis-based component, which is the semi-supervised part of our proposed framework, the normal behavior of users are modeled and the extent of dissimilarities of newly-arrived transactions are calculated. Finally, in the scenario-based component which is another semi-supervised part of the proposed framework, the extent of similarities of the sequence of transactions to known fraud scenarios are calculated. The final result is gained through combining the results of all these three components using in a parallel mode. By combining the outputs of all these components together using the SUM function, the detection rate has increased remarkably (about 7%).
A Virtual Reality Video Stitching System Based on Mirror Pyramids

This paper introduces a virtual reality video acquisition method based on mirror pyramids. The system is designed to capture a high-definition stereo video, while the mirror pyramids allow users to shoot objects that are located close to the camera. First, we theoretically study the stereoscopic imaging of the specular reflection device and verify its feasibility. Second, we propose a complete stitching process that includes calibration, mask extraction, and fusion for the special imaging device. The mirror reflection of the 3D imaging method allows users to shoot close objects and achieve an excellent 3D effect.
Central nervous system tuberculosis in children: a review of 30 cases

The medical records of 30 children with central nervous system tuberculosis (CNS tuberculosis) who were treated between March, 1976, and February, 1989, were reviewed. All had cranial computerized tomography scans at presentation. The mean cerebrospinal fluid leukocyte count was 200/mm3, protein 239 mg/dl, glucose 25 mg/dl and CSF/serum glucose ratio 21%. Mantoux skin tests with 5 tuberculin units were ≥10 mm induration in 50%, and chest radiographs were positive in 40% of patients. Hydrocephalus was demonstrated by cranial computerized tomography in all 30 patients (100%). Cranial computerized tomography scan demonstrating hydrocephalus is a sensitive radiographic finding in children with central nervous system tuberculosis and should be part of the early evaluation of children with suspected central nervous system tuberculosis.
Help From and Help to Neighbors Among Older Adults in Europe

Neighborhoods can be a valuable source of social support particularly for older adults. Such support can be mutual; however, the influential factors in giving and receiving are unclear. This study investigated neighborhood help among the older European population using representative data for 17 countries from the Survey of Health, Ageing and Retirement in Europe (SHARE). The analyses were based on 104,059 observations of 39,113 respondents aged 50 years and older. In general, ~6% of all respondents provided recently neighborhood help, and 4% received help. Moreover, the results indicate a high degree of reciprocity in giving and receiving neighborhood help. However, the situation varied widely among age groups and countries. Our multilevel results suggest that the provision and receipt of help are driven by personal characteristics (age, sex, education, income, and retirement), health resources (subjective health, activities of daily living, and instrumental activities of daily living), living situation (homeownership, location, and length of time in a residence), social factors (marital status, partner, parents, and children), and contextual factors (gross domestic product, social expenditures, poverty rate, Gini index, population density, country-centered satisfaction with life, living, and relationships).
End-to-End Cascade CNN for Simultaneously Face Detection and Alignment

Real-world face detection and alignment demand an advanced discriminative model to address challenges by pose, lighting and expression. Recent studies have utilized the relation between face detection and alignment to make models computationally efficiency, but they ignore the connection between each cascade CNNs. In this paper, we combine detection, calibration and alignment in each cascade structure and propose an End-to-End cascade Online Hard Example Mining (OHEM) for training, which expert in accelerating convergence. Experiments on FDDB and AFLW demonstrate considerable improvement on accuracy and speed.
A Multi-agent Formation Control Algorithm in Interference Environment

Under the condition of satisfying certain communication topology, a multi-agent formation control algorithm based on the speed and direction of single-agent is proposed. For the situation that multi-agent communication system is vulnerable to external interference. Firstly, an agent guidance law is designed so that the speed direction of the robot can gradually converge to the desired direction during the motion. Secondly, in the last stage of motion, the agent realizes the cooperative control through the effective communication, so that the multi-agent can reach the target point at the same time with meeting the speed and direction constraints. The simulation results verify the effectiveness of the proposed algorithm.
Response of Switching Hole Traps in the Small-Area P-MOSFET Under Channel Hot-Hole Effect

This work elucidates two distinct behaviors of switching hole traps in small-area, SiON gated p-MOSFETs subjected to channel hot-hole (CHH) effect. First, in all devices tested, the occupancy rate (OR) of a trap could either be increased or decreased as the drain voltage $\vert V_{d}\vert$ is increased towards the gate voltage $\vert V_{g}\vert$. A further increase of $\vert V_{d}\vert$ would eventually lead to a decrease of OR. Second, the reduced emission probability is typically reversed upon removing the drain bias but in $\sim 11^{\mathrm{o}}{\%}$ of the traps, this is not the case, i.e. the hole-trapping became more permanent. Thus, besides those characteristics of single oxide traps already revealed through gate biasing studies, these new behaviors arising from drain biasing should also be accounted for in circuit reliability compact modeling.
An Empirical Study of Factors Influencing Undergraduates' Programming Learning Engagement in Blended Learning Environment

In some colleges and universities, programming has become one of the compulsory subjects for undergraduates. However, the programming language has strict logic and grammar, which has an impact on learners' engagement, thereby affecting the learning effect. In this study, 100 freshmen who enrolled the programming design courses at a university in Beijing were selected as the research object, and the influencing factors of programming learning engagement were studied from the perspective of self-regulation. The study found that there were significant differences in motivational regulation between different genders, different professional categories, and whether they have studied programming; there was a significant positive correlation between motivational regulation, task value and programming learning engagement. Regression analysis showed that motivational regulation and task value had a good positive predictive effect on learning engagement.
Content-aware Video Quality Modeling Based on Neural Network

The booming of High Definition (HD) and UltraHD (UHD) videos in the last decade has greatly promoted the use of video display on diversified terminals such as TV, tablets and smartphones. To present more video contents with high quality to users, it is imperative to assess the visual quality of videos. The approach denoted as Video Quality Assessment (VQA) has become an appealing problem to researchers. In this work, we take the video contents into consideration besides of media format and pixel quality. This paper utilizes four measures, picture resolution, bitrates, Spatial Information (SI) and Temporal Information (TI), to represent the visual quality in four separate dimensions. Then, a subjective database is constructed to train a neural network that is capable of scaling content-aware video qualities. Experiments have shown that our method is superior to human observers in terms of correlations to Mean Opinion Score (MOS) values.
High-speed Stereo Matching Algorithm for Ultra-high Resolution Binocular Image

This paper proposes a high-speed stereo matching algorithm (HSSM) for ultra-high resolution binocular images. By distinguishing the distances of objects in the acquired images, the size of the left and right images is determined. For example, for nearby objects, the binocular camera collects the left and right images that have large parallaxes and contain so many pixels, which consumes large resources in the process of matching. Therefore, a smaller size image is used to match close-range objects in the proposed algorithm. On the contrary, large-size images are used to match distant objects to ensure the matching accuracy of distant objects, and finally the result of hierarchical matching. Combine and get a disparity map of the left and right images captured by the binocular camera. This kind of matching method similar to image pyramid has been verified by our large number of comparative experiments. Compared with the previous binocular stereo matching algorithm, the binocular stereo matching ultra-high resolution image can be greatly guaranteed under the premise of ensuring accuracy. It reduces the time consumption in the matching process and is highly efficient.
Voltammetric Technique for Eugenol Analysis Using Polyoctyltriethoxysilane Molecular Imprinted Polymer Electrode

Eugenol plays an important role in medicine and perfume industry. It has strong antioxidant property. In this paper a polyoctyltriethoxysilane molecular imprinted polymer (MIP) electrode has been reported to separate different concentration of eugenol samples. The data analysis techniques; Principal Component Analysis (PCA), box plot and radar plot were used. Separability index (SI) was also reported to distinguish different concentration of eugenol samples. The prepared MIP electrode also shows a good linearity over the concentration range 1 to 200-M. The cyclic voltammetry results were found quite promising, thus justifying the suitability of the polyoctyltriethoxysilane MIP electrode for the in-situ detection of eugenol.
Large-Scale Evaluation of the Efficiency of Runtime-Verification Tools in the Wild

Runtime verification (RV) is a field of study which suffers from a lack of dedicated benchmarks. Many published evaluations of RV tools rely on workloads which are not representative of real-world programs. In this paper, we present a methodology to automatically discover relevant open-source projects for evaluating RV tools. This is done by analyzing unit tests on a large number of projects hosted on GitHub. Our evaluation shows that analyzing a large number of open-source projects—instead of a handful of manually selected workloads—provides better insight into the behavior of three state-of-the-art RV tools (JavaMOP, MarQ, and Muffin) based on two metrics (memory utilization and runtime overhead). By monitoring test executions of a large number of projects, we show that none of the evaluated RV tools wins for both metrics.
A JavaScript Transpiler for Escaping from Complicated Usage of Cloud Services and APIs

We propose Escapin, a JavaScript transpiler for developing application programs that consume APIs and are deployed on cloud services in order to obtain new business concepts by trial-and-error iterations. Escapin tackles two difficulties in consuming API and using cloud services; 1) understanding the correct usage of cloud services and APIs requires a lot of time, and 2) programs using cloud service SDKs and HTTP clients are complicated. Escapin introduces two new features to the ECMAScript 2018 specification for simplifying application programs: importing APIs as objects and exporting objects and functions as cloud service resources. Escapin transpiles a JavaScript program independent from any platform into a JavaScript program containing concrete usages of APIs and cloud services. Escapin encourages engineers to write a callback-agnostic program, which represents asynchronous processing as a completely synchronized procedure without any asynchronization feature such as callback, Promise, async/await, etc. Escapin also accepts programs having nesting callbacks. Escapin destructures nesting callbacks in the programs to obtain a callback-agnostic program. Our experimental results show that in the task of developing an application that consumes Uber API and is deployed on Amazon Web Services, Escapin statistically significantly reduces development time and the metrics regarding the simplicity of source code with huge effects: lines of code, cyclomatic complexity, and cognitive complexity.
Observation of the Kondo effect in a spin-3/2 hole quantum dot

We report for the first time the observation of the Kondo effect in a spin-3/2 hole quantum dot formed near pinch-off in a GaAs quantum wire. We clearly observe two distinctive hallmarks of quantum dot Kondo physics. First, the zero-bias peak in the differential conductance splits an in-plane magnetic field and the splitting is independent of gate voltage. Second, the splitting rate is twice as large as that for the lowest one-dimensional subband. We show that the Zeeman splitting of the zero-bias peak is highly anisotropic and attribute this to the strong spinorbit interaction for holes in GaAs.