Miracle-dz/UniFAR-FTUs huggingface dataset:
Semantic-based Compound Keyword Search over Encrypted Cloud Data

Keyword search over encrypted data is essential for accessing outsourced sensitive data in cloud computing. In some circumstances, the keywords that the user searches on are only semantically related to the data rather than via an exact or fuzzy match. Hence, semantic-based keyword search over encrypted cloud data becomes of paramount importance. However, existing schemes usually depend upon a global dictionary, which not only affects the accuracy of search results but also causes inefficiency in data updating. Additionally, although compound keyword search is common in practice, the existing approaches only process them as single words, which split the original semantics and achieve low accuracy. To address these limitations, we initially propose a compound concept semantic similarity (CCSS) calculation method to measure the semantic similarity between compound concepts. Next, by integrating CCSS with Locality-Sensitive Hashing function and the secure <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="wang-ieq1-2847318.gif"/></alternatives></inline-formula>-Nearest Neighbor scheme, a semantic-based compound keyword search (SCKS) scheme is proposed. SCKS achieves not only semantic-based search but also multi-keyword search and ranked keyword search. Additionally, SCKS also eliminates the predefined global library and can efficiently support data update. The experimental results on real-world dataset indicate that SCKS introduces low overhead on computation and the search accuracy outperforms the existing schemes.
Camera-based measurement of cyclist motion

Heavy goods vehicles are overrepresented in cyclist fatality statistics in the United Kingdom relative to their proportion of total traffic volume. In particular, the statistics highlight a problem for vehicles turning left across the path of a cyclist on their inside. In this article, we present a camera-based system to detect and track cyclists in the blind spot. The system uses boosted classifiers and geometric constraints to detect cyclist wheels, and Canny edge detection to locate the ground contact point. The locations of these points are mapped into physical coordinates using a calibration system based on the ground plane. A Kalman Filter is used to track and predict the future motion of the cyclist. Full-scale tests were conducted using a construction vehicle fitted with two cameras, and the results compared with measurements from an ultrasonic-sensor system. Errors were comparable to the ultrasonic system, with average error standard deviation of 4.3 cm when the cyclist was 1.5 m from the heavy goods vehicles, and 7.1 cm at a distance of 1 m. When results were compared to manually extracted cyclist position data, errors were less than 4 cm at separations of 1.5 and 1 m. Compared to the ultrasonic system, the camera system requires simple hardware and can easily differentiate cyclists from stationary or moving background objects such as parked cars or roadside furniture. However, the cameras suffer from reduced robustness and accuracy at close range and cannot operate in low-light conditions.
Evaluation of Typing Key Layouts for Stylus Input

We studied initial user performance with and preference for alternative typing-key layouts for typing with a stylus. Previous analyses have indicated that, after asymptotic practice with this kind of typing, users should type fastest with certain nonstandard layouts. Although asymptotic performance is important in selecting a layout, it also is important to evaluate initial performance with and preference for layouts. Twelve participants used paper models of six different layouts to type four sentences. The layouts were the QWERTY, Dvorak, standard alphabetic, square alphabetic, and two digraph-based layouts. Performance and preference favored the QWERTY over the other layouts. The square alphabetic layout took a distant second place. QWERTY should be a product's default layout even if users can type with only a single finger or a stylus. If the QWERTY layout is not feasible, designers should consider using the square alphabetic layout for limited typing or a digraph-based layout for extensive typing.
Standardized representation of the LIDC annotations using DICOM

The Lung Imaging Data Consortium and Image Database Resource Initiative (LIDC) conducted a multi-site reader study that produced a comprehensive database of Computed Tomography (CT) scans for over 1000 subjects annotated by multiple expert readers. The result is hosted in the LIDC-IDRI collection of The Cancer Imaging Archive (TCIA). Annotations that accompany the images of the collection are stored using project-specific XML representation. This complicates their reuse, since no general-purpose tools are available to visualize or query those objects, and makes harmonization with other similar type of data non-trivial. To make the LIDC dataset more FAIR (Findable, Accessible, Interoperable, Reusable) to the research community, we prepared their standardized representation using the Digital Imaging and Communications in Medicine (DICOM) standard. This manuscript is intended to serve as a companion to the dataset to facilitate its reuse.
Gradient domain photomontage via interactive object selection

One of the most critical steps in photomontage is marking an object that is to be copied from a source image to a target image efficiently. Some unexpected and unwanted effects such as bleeding usually occur on the resulting image if the object is roughly marked, copied, and then pasted to the target region, even when using the Poisson equation approach. Moreover, original color information for the pasted object cannot be preserved, and color values for the boundary pixels of the pasted object may change and/or blurriness may occur when using some methods described in the literature. The proposed methods presented in this paper are designed to efficiently overcome these problems. The problem of rough selection is solved by employing the modified intelligent scissors method. In order to prevent diffusion around the boundary of the pasted object, a band region is created, and the texture information from the source image is used. In order to better preserve the color information of the pasted object, a fidelity term is added to the Poisson equation. Our approach for producing a photomontage is more efficient than other methods in the literature in terms of interactive object selection and automated band region creation. In our approach, readjustment is not necessary for required parameters in the operations. Qualitative and quantitative results prove the advantages of the proposed methods.
TUMOR DETECTION AND TISSUE SEGMENTATION IN MAGNETIC RESONANCE BRAIN IMAGES USING FUZZY AND OPTIMIZATION TECHNIQUES

Image processing plays a very important role in analyzing the images of different standards. Image enhancement, image restoration and image compression are the types of image processing techniques of which image segmentation process under Image enhancement technique is utilized extensively to analyze the medical images. An image is composed of several groups of pixels. Segmentation is an image processing technique, in which different regions of an image are segregated based upon the pixel intensities available within the image. Boundary and edge based segmentations are the two types of segmentation procedures, of which, boundary based segmentation is used widely. In the presence of several medical imaging techniques, Magnetic Resonance Imaging (MRI) technique occupies a predominant position in the area of diagnosing the organs of human body. The functionality of an MRI scanner entirely relies upon the 80% of hydrogen atom present in our body. The hydrogen atom of our human body is stimulated using resonant radio frequency signals. The excited hydrogen atom is capable of emitting the
A Comparison of Two Novel Direct Adaptive Control Methods under Actuator Failure Accommodation

In flight control systems it is crucial to accommodate actuator failures for safe operation. Modern flight control systems are equipped with multiple power sources and actuation systems that offer redundancy in the event of actuator failures. Traditional approaches to flight control system design involve scheduling of fixed gain controllers and control allocation to take full advantage of redundant actuators. Alternative adaptive control approaches have been extensively investigated recently to provide enhanced fault tolerance by augmenting an existing flight control design with adaptive elements. This paper provides a brief background of the state of the art in adaptive fault tolerant flight control and presents a comparison study of several novel direct neural network based adaptive control methods.
Joint Power and Sensing Optimization for Hybrid Cognitive Radios with limited CSIT

The problem of optimal Power Policy (PP) design for hybrid interweave/underlay Cognitive Radio (CR) systems is investigated, in the presence of imperfect Spectrum Sensing (SS). A limited Channel State Information at the Transmitter (CSIT) scenario is considered, that has never been studied before, where the Secondary Transmitter (STx) uses combined instantaneous and statistical CSIT. Given such CSIT, the optimal PP with the aim of maximizing the average transmission rate of the CR system, subject to a constraint on the average interference caused by STx transmission to the primary system, is found. The derived PP is then exploited for the development of an iterative framework for the problem of jointly optimizing the sensing time and the applied PP. By means of simulations, it is shown that the proposed joint SS and PP optimization framework offers a clear gain in terms of the achievable rate of the CR system, with respect to conventional underlay and interweave CR systems, especially for intermediate values of the average interference constraint and also for different primary user activity regimes.
GRAIL: an integrated artificial intelligence system for gene recognition and interpretation

The development of an integrated artificial intelligence system, GRAIL (gene recognition and analysis Internet link) is described. This system uses a combination of a multi-sensor/neural network, expert system, and parallel search tools to recognize and interpret genes in DNA sequences. A simple electronic mail (E-mail) interface makes the system accessible through Internet. The strength of the system in recognizing and interpreting genes in DNA sequences and the simple E-mail interface have already attracted more than 150 users. The success of the system is largely due to the multi-sensor/neural network approach and the integration of several AI tools. The modular development and flexible framework have made it easier to incorporate new knowledge and tools into the existing system. <<ETX>>
Human Activity Recognition from Sensor data using Random Forest Algorithm

The advancement of technologies have facilitated the monitoring of human activities through the embedded sensors in a smartphone. Since the smartphone is very common with the public, it has paved way for the researchers to work on activity recognition systems. In this work, we propose a system to recognize the basic activities of an individual. For this publicly available dataset was used. The data collected from the sensors are used to predict the activities. The proposed system obtained the highest accuracy of 99.6%. This system is implemented on R programming using Random Forest algorithm. Keywords: Human Activity Recognition, Sensor, Random Forest
Strategy Generation for Multiunit Real-Time Games via Voting

Real-time strategy (RTS) games are a challenging application for artificial intelligence (AI) methods. This is because they involve simultaneous play and adversarial reasoning that is conducted in real time in large state spaces. Many AI methods for playing RTS games rely on hard-coded strategies designed by human experts. The drawback of using such strategies is that they are often unable to adapt to new scenarios during gameplay. The contribution of this paper is a new approach, called strategy creation via voting (SCV), that uses a voting method to generate a large set of novel strategies from existing expert-based ones. Then, SCV uses an opponent modeling scheme during the game to choose which strategy from the generated pool of possibilities to use. By repeatedly choosing which strategy to use, SCV is able to adapt to different scenarios that might arise during the game. We implemented SCV as a bot for $\mu$RTS, a recognized RTS testbed. The results of a detailed empirical study show that SCV outperforms all approaches tested in matches played on large maps and is competitive in matches played on smaller maps.
Multi Criteria Decision Making (MCDM) Methods and its applications

: Multi Criteria Decision Making (MCDM) is the quantitative and qualitative method, problem has many solution but to find the solution and to get the appropriate decision regarding the solution is the application of MCDM. This paper gives the information about all the MCDM methods with the applications of maximum MCDM methods to various fields as per the literature. The aim is revel MCDM methods and their applications to understand the nature of MCDM for various problems. The utilization of MCDM to other field gives an idea for the new research area. The review helps to find the problems studied by different researcher and the solution they interpreted by using MCDM methods like ELECTRA, PROMETHEE, TOPSIS, AHP,GP etc. AHP, TOPSIS, MAUT were the most used methods but hybrid or integrated methods gives the solution for problem like location, finance, bankrupt, construction bridges, waste water and many more. This combination creates the new era in MCDM history.
A Logic Test Chip for Optimal Test and Diagnosis

The benefits of the continued progress in integrated circuit manufacturing have been numerous, most notably in the explosion of computing power in devices ranging from cell phones to cars. Key to this success has been strategies to identify, manage, and mitigate yield loss. One such strategy is the use of test structures to identify sources of yield loss early in the development of a new manufacturing process. However, the aggressive scaling of feature dimensions, the integration of new materials, and the increase in structural complexity in modern technologies has challenged the capabilities of conventional test structures. To help address these challenges, a new logic test chip, called the Carnegie Mellon Logic Characterization Vehicle (CM-LCV), has been developed. The CM-LCV utilizes a twodimensional array of functional unit blocks (FUBs) that each implement an innovative functionality. Properties including fault coverage, logical and physical design features, and fault distinguishability are shown to be composable within the FUB array; that is, they exist regardless of the size and composition of the FUB array. A synthesis flow that leverages this composability to adapt the FUB array to a wide range of test chip design requirements is presented. The connection between the innovative FUB functionality and orthogonal Latin squares is identified and used to analyze the universe of possible FUB functions. Two additional variants to the FUB array are also developed: heterogenous FUB arrays utilize multiple FUB functions to improve the synthesis flow performance, while pipelined FUB arrays incorporate sequential circuit elements (e.g., flip-flops and latches) that are absent from the original combinational FUB array.
IMPLEMENTASI MODEL PEMBELAJARAN BERBASIS MASALAH UNTUK MENINGKATKAN KOMUNIKASI MATEMATIS SISWA

Mathematical communication is very important for mathematic learning. But, mathematic learning in school can’t increase mathematical communication. This research aims to describe the implementation of problem based learning to enhance students mathematical communication know increase of the mathematical communication through problem based learning in class VII MTs. Maarif Balong Ponorogo.This research is a classroom action research (CAR) conducted in two cycles. This research subject is the students of class VII B MTs. Ma’arif Balong Ponorogo. The data collection technique used is a test block, at the end of each cycle to measure mathematical communication and observation to evaluate the adherence of teachers and students activities. The results showed that mathematical communication increased from the first cycle to the second cycle after the implementation of problem-based learning model. This score average has been increased in accordance with the indicator of the success that was determined by the researchers. Based on the analysis of learning process through observation sheets of problem-based learning model showed that the average percentage activities of teachersand students activities increased from the first cycle to the second cycle. Keywords: Problem Based Learning, Mathematical Communication, Increase
Mining Software Defects using Random tree

A relevant sub area of reverse engineering is identification of modules requiring re engineering, which focuses on predicting faulty modules. To this end, predicting faulty modules may use any available source of information such as source code and documentation. Similarly, the identified abstractions may take different forms such as module breakdown, structurecharts, entity-relationship diagrams, and software metric specifications. In this paper it is proposed to investigate the classification accuracy of random tree algorithms for software defect prediction.
Control method of the wearable manipulator based on EMG signals

Human integration with the exoskeleton, so that it correctly reflects the intentions of the user, requires the use of an appropriate control method containing an intent detection algorithm. The article presents the assumptions concerning the construction of the upper limb exoskeleton, the preliminary research procedure and the pre-developed methods of controlling the assistance manipulator based on the analysis of the electromyographic signal (EMG) characteristics and the use of neural networks.
Real-time Recognition of Shadow from Deep Edge Detection

In this work we address the problem of fast shadow detection from single images of natural scenes. Different from traditional methods that employ expensive optimization methods, we propose a fast semantic-aware Convolutional Neural Network learning framework which trains on different kinds of patches, while integrating semantic shadow information. We primarily cluster pixels based on their material similarities, then in addition to considering individual regions separately, we exploit a higher level interactions between the neighbouring regions. We process the shadow edge pixels between the segments, and relate the regions together.
Design of shunt hybrid active power filter (SHAPF) to reduce harmonics in AC side due to Non-linear loads

The quality of power is a major concern because of harmonics in the lines due to various sources. Passive filters have been used to eliminate the specific harmonics. In this paper shunt hybrid active power filter which consists of shunt active filter and shunt passive filter is designed to reduce harmonics in source side for non linear loads. The shunt passive filter includes a tuned RLC circuit that behaves like a band-pass filter for a particular harmonic content. The shunt active filter is a VSI that produces reverse harmonic current based on Clarke’s transformation. This paper presents the control strategy by implementing instantaneous reactive power theory to design Shunt Hybrid Active Filter (SHAPF) for a non linear load. The proposed control technique is simulated using MATLAB SIMULINK, Results prove there is considerable reduction in THD of source current from 30.35% to 3.25% with the proposed controller on shunt hybrid active power filter. The performance of SHAPF is better than Passive and Shunt Active Power Filters.
Survey of Object Detection using Deep Neural Networks

: Object detection using deep neural network especially convolution neural networks. Object detection was previously done using only conventional deep convolution neural network whereas using regional based convolution network [3] increases the accuracy and also decreases the time required to complete the program. The dataset used is PASCAL VOC 2012 which contains 20 labels. The dataset is very popular in image recognition, object detection and other image processing problems. Supervised learning is also possible in implementing the problem using Decision trees or more likely SVM. But neural network work best in image processing because they can handle images well.
Design and Analysis Methodology for Modular Multilevel Converters (MMC)

The design and analysis of the modular multilevel converter (MMC) are usually performed with simulation softwares in an interactive mode. Each parameter is individually set in order to analyze the effect on the converter output, which in turn, demands a lot of time of the user. In this context, this paper proposes a methodology based on Double Fourier Transform so as to provide support to the parameterization of modular multilevel converter (MMC). This methodology allows the designer to choose the appropriate converter, considering its main specifications, from analysis of the results to variations in the amplitude and frequency modulation rates as well as the number of modules used. The proposed methodology was developed in MATLAB software, computationally validated by electromagnetic transient analysis software PSCAD / EMTDC, and experimentally certified through implementation in FPGA Xilinx Spartan-3.
Visualizing Assumptions and Results in Network Meta-analysis: The Network Graphs Package

Network meta-analysis has been established in recent years as a particularly useful evidence synthesis tool. However, it is still challenging to develop understandable and concise ways to present data, assumptions, and results from network meta-analysis to inform decision making and evaluate the credibility of the results. In this article, we provide a suite of commands with graphical tools to facilitate the understanding of data, the evaluation of assumptions, and the interpretation of findings from network meta-analysis.
A Survey on Offline Signature Verification

In the era of growing technology, security is the major concern to avoid fakes and forgeries. There are various Biometric systems which help in personal identification, amongst those verification systems, one system is Signature Verification System. Signatures can be verified using online and offline systems. This paper explains the significance of offline systems and presents the survey of various approaches related to offline signature verification systems. Further, this paper provides the comparison of those approaches along with the features used, to help the researchers working in this field.
A utility-based joint power and rate adaptive algorithm in wireless ad hoc networks

In this paper, a utility-based joint power and rate adaptive algorithm (UPRA) is proposed to alleviate the influence of channel variation by using softened signal-to-interference-and-noise ratio (SINR) requirements. The transmission power and rate of each user will be adjusted to maximize its own net-utility according to the states of channel, which results in a balance between the effective transmission rate and power consumption. Although the UPRA works based on a non-cooperative game, cooperation among users can be achieved so that the throughput of the whole network will be improved. The convergence of the algorithm has also been studied in both feasible and infeasible cases.