Miracle-dz/UniFAR-FTUs huggingface dataset:
Sentiment Classification in English Using a Self-Organizing Map Algorithm with Only the One-Dimensional Vectors and a Ruzicka Coefficient in a Parallel System

: Sentiment classification has been used in many different fields because it has many significant contributions in everyday life, such as in political activities, commodity production, and commercial activities. We have proposed a new model for big data sentiment classification by using a combination of an unsupervised learning algorithm of a machine learning with a Ruzicka Coefficient (RC) in this work. A Self-Organizing Map Algorithm (SOM) of the machine learning is used in clustering the documents of the testing data set (TES) comprising 7,500,000 documents, which are the 3,750,000 positive and the 3,750,000 negative in English, into either the positive group or the negative group of our training data set (TRA) which is 3,000,000 sentences including the 1,500,000 positive sentences and the 1,500,000 negative sentences in English. In this study, we do not use a vector space modeling (VSM). We do not use any multi-dimensional vectors according to both the VSM and many sentiment lexicons. We use many sentiment lexicons of our basis English sentiment dictionary (bESD). We use many one-dimensional vectors based on the sentiment lexicons. We use a similarity coefficient in this study. We do not use any one-dimensional vectors based on the VSM. We have achieved 88.64% accuracy of the TES. The execution time of the proposed model in a distributed network environment - DNE is less than that in a sequential system - SS. Many commercial applications and surveys of the sentiment classification can widely use the results of the proposed model.
Development and Validation of Teacher Emotional Support Scale: a structural equation modeling approach

Reviewing the literature indicated that no validated model was found that examine the extent to which teachers support their students emotionally in EFL classrooms. Therefore the present study elaborated on this issue through developing and validating a teacher emotional support scale in an Iranian English foreign language context. Main components of the scale have been specified based on Hamre and Pianta's (2007) theoretical framework. A large number of items were created primarily based on operational definitions of each component. After reviewing the items by a group of experts, the questionnaire was piloted and tested on a sample of 324 EFL teachers. Finally, the researchers evaluated the validity of the questionnaire through exploratory and confirmatory factor analysis.The results of the reliability of the questionnaire estimated through Cronbach’s alpha were 0.833. 17 items have been removed from analysis resulting in identifying 4 factors in exploratory factor analysis. The model was evaluated using AMOS 22 also indicated that the model was fit the data.the current study contributes to the field of English language education through designing and validating new instrument to assess teacher emotional support in EFL classrooms. Researchers and other practitioners who are involved in teaching English language can assess the extent to which teachers support students emotionally in similar pedagogical EFL context using this instrument. They also need to encourage teachers to improve their emotional skills by participating in teacher training courses.
A novel remote sensing images fusion algorithm combining extended NSST and modified PCNN

In order to more accurately realize fusion of remote sensing images, we propose a novel remote sensing images fusion algorithm combining extended non-subsampled shearlet transform (NSST) and modified pulse-coupled neural network (PCNN). Firstly, it makes histogram matching and intensity smoothing and filtering treatment on intensity component and full-color image of multi-spectral image. Secondly, such intensity component and full-color image are decomposed by extended NSST to get corresponding high-frequency and low-frequency coefficients. For low-frequency coefficients, fusion is made by sparse representation; for high-frequency coefficients, a modified pulse-coupled neural network (PCNN) strategy is put forward to process. Finally, the processed result is drawn by inverse transformation of the extended NSST and intensity-hue-saturation inverse transformation. The experimental results show that the proposed algorithm reserves as much spectral information as possible and improve spatial resolution; its visual effects and objective indexes are better than other classical fusion algorithms.
Weaving critical theory, fashion, electronics, and makerspaces in learning: fashioning circuits – a case study

ABSTRACT Fashioning Circuits is a humanities course designed to explore how fashion, electronics, social issues, and makerspaces interact with design and critical theory. Digital humanities scholars and practitioners have seen an emergence of public art and representation beyond the academy, specifically applying interdisciplinary university curricula to youth learners in the community. As this university course expands into the public domain, the social discourse around gender, technology, and fashion are threaded with the “making” opportunities to tinker and design in order to solve real-world problems in the community. This article is an ethnographic case study, which details the Fashioning Circuits learning experiences, specifically to understand how open education projects and shared curriculum knowledge can exist within the postsecondary and community learning environments. Through the intersection of media ecologies and contemporary fashion design offers this emerging pedagogical practice asks learners to build artifacts that engage with real-world problems. To encourage critical making, that is, an emphasis on the process rather than the product, it is important to center learning experiences around issues and ideas to find solutions and not focus on end technological outcomes alone.
Dynamic object counting application based on object detection and tracking

The application of deep learning in traditional industries has not gained much attention. However, deep learning has a great potential to be transplanted to other fields. And we managed to apply two techniques in deep learning, object detection and tracking, in dynamic object counting. We test it on one of the basic problem in steel industry, rebar counting. To cope with this, we used an infrared camera to collect video of rebar on the spot so that rebar can be distinguished from background apparently. Then we use the video to complete counting work. We divided the counting process into two parts: detection and tracking. We improved SSD model to satisfy the detection demand of accuracy and speed, and use KCF to track. Given the fact that the rebar objects in video are scale-invariable, we reduced the feature map numbers as well as the anchors and gained a considerable speed-up, without worsening the accuracy. To getting rid of the error from the vibration of conveyor belt, we improved the tracking algorithm and make a satisfactory result. The application of our object counting system is not limited in rebar counting, and it can be transplanted to some other field.
Performance and Classification Evaluation of J48 Algorithm and Kendall’s Based J48 Algorithm (KNJ48)

- We have been using the most popular algorithm J48 for classification of data. The J48 algorithm is used to classify different applications and perform accurate results of the classification. J48 algorithm is one of the best machine learning algorithms to examine the data categorically and continuously. When it is used for instance purpose, it occupies more memory space and depletes the performance and accuracy in classifying medical data. Our proposed method is to measure the improved performance and produce higher rate of accuracy. For this research, the dengue dataset was collected from various government hospitals in Krishnagiri District. To measure the entropy of information and to identify the dataset and to increase the accuracy of J48 algorithm, the entropy of J48 is modified with Kendall’s Rank Correlation Coefficient algorithm (KNJ48) to improve the accuracy of classification and performance time. Thus, it is modified as Kendall’s New Rank Correlation Coefficient J48 algorithm (KNJ48) for better performance.
Prediction of dynamic responses of floating structures using NARX with mirroring technique

Displacements, velocities and accelerations of Six Degree of freedom of a single floating structure was predicted using Time Series NARX feedback neural Networks. The nonlinear autoregressive network with exogenous inputs (NARX) is a recurrent dynamic network, with feedback connections enclosing several layers of the network is based on the linear ARX model, which is commonly used in time-series modelling is used in this study. Time series data of displacements of a single floating structure was used for training and testing the ANN model. In the training stage, this time series data of environment parameters was used as input and dynamic responses was used as target. Benchmarking result and error prediction was compared between two techniques of Neural Network training. The prediction result of the model responses can be concluded that NARX with mirroring technique increase the accuracy and can be used to predict time series of dynamic responses of floating structures.
A deadlock‐free lock‐based synchronization for GPUs

Graphics Processing Units (GPUs) have evolved from pure graphics applications toward general purpose applications, often referred to as GPGPU computing. However, its scope is still limited to data‐parallel applications that require little synchronization. As synchronization on GPUs is quite costly, synchronization requirements in GPUs are usually realized using existing synchronization primitives like atomic operations and barriers. These approaches either incur significant overhead or place certain restrictions in their usage, affecting the scalability/scope of such applications. The lack of adequate support for fine‐grained synchronization has restricted the realization of irregular algorithms on GPUs, wherein control flow and memory access patterns are data‐dependent and unpredictable. Recently, there has been an interest in building relationship between lock‐step semantics and interleaving semantics and to develop lock‐based synchronization mechanism for GPUs to overcome these issues. GPUs follow SIMD, and hence, when adapted for general purpose computing, new distinct deadlock scenarios arise. In this paper, we discuss various deadlock scenarios that can happen in GPUs, and present a modeling of deadlocks in GPUs. We shall first illustrate such deadlock scenarios in GPU applications, and then describe a novel lock‐based deadlock‐free, fine‐grained synchronization mechanism for GPU architectures that overcomes deadlocks without a significant overhead. We further establish the correctness of our methods and discuss the performance overheads.
A new method of location detection with knowledge transfer learning

This paper propose a new method of location detection with knowledge transfer learning(KTL-LD). Processing Framework was developed and compared with existed experimental results. In this Framework, the label extraction model is used to extract the Weibo user tags, using the existing word embedding model as the source domain, and applying the knowledge transfer learning model to transfer the knowledge to the target domain, which obtain the target domain vector, then the classification model is used to train and predict the user’s location. This method does not require manual tagging data, the training time of this model is effectively reduced, and the ability of generalization is improved as well.
Synthesis and antimicrobial properties of flowerlike copper nanomaterials

The flower-like copper nanomaterials modified by polyvinylpyrrolidone(PVP) and carboxymethyl chitosan(NOCC) were successfully synthesized by chemical reduction method in aqueous solution,respectively.The morphologies and structures of the copper/polymer nanocomposites were characterized by means of scanning electron microscopy(SEM),transmission electron microscopy(TEM) and X-ray powder diffraction(XRD). The results showed that the as-synthesized surface modified Cu nanoparticles exhibited a face centered cubic crystalline structure.A flowerlike self-assembled nanostructure was observed by SEM with PVP and NOCC as soft template.The minimal inhibitory concentration(MIC) and minimum bactericidal concentration(MBC) against Escherichia coli(E.coli),Staphylococcus aureus(S.aureus) and pseudomonas aeruginosa(P.aeruginosa) were evaluated by the method of inhibition zones and broth dilution.Biological experiments showed that the as-synthesized Cu nanomaterials exhibited excellented antibacterial properties and had a potential application prospect.
Design considerations and optimisation of clock circuit for ultra-low power sub-threshold applications

ABSTRACT Clock system plays a vital role in governing the reliability, power consumption and performance of synchronous system. In today’s era of portable electronics, power consumption has emerged as a forefront design metrics. Sub-threshold operation of devices is an excellent option to have an ultra-low power system. However, degraded performance and exacerbated variability are the major concerns of the sub-threshold circuits. Furthermore, as contradictory to the conventional super threshold circuits, the device resistance for the sub-threshold circuits is quite high compared to the interconnect resistance. This paper therefore, explores the design constraints for sub-threshold clock system. It further explores the suitability of buffered and un-buffered clock system in deep sub-threshold regime. The impact of supply voltage scaling and 3σ/µ variability for clock system is investigated in this work. An attempt to improve the slew of the clock system with un-buffered tree is made by redesigning the tapered H tree which exhibits better slew and robustness in sub-threshold regime.
Penerapan Fuzzy Topsis Untuk Seleksi Penerima Bantuan Kemiskinan

Some factors that cause mis target of poor relief are inacurate criteria of poor citizen as well as inexactly method that make error in manual calculation. Fuzzy Technique for Order Preference by Similarity to Ideal Solution Method (Fuzzy TOPSIS) is used to select the receiver of poor relief in order to make alternative ranking to compare. It's implementation is by using excel and matlab with ten alternative (village) which is will compare based on their criteria and subcriteria. The criteria that will be use is the look of their house, family (card) member, ownership of house land, and their work for living, monthly revenue, and level of education. The output of this method is total preference point of all criteria. The highest point will be priority to have poor relief. Its validation will be done by comparing the result of Fuzzy TOPSIS through Ms. Excel and Matlab and have the same results. The last decision will be at the stakeholder and this Fozzy TOPSIS results can be used to decision making acceleration. The data that used can be from related official and by library investigation.
A Modified Just Noticeable Depth Difference Model Built in Perceived Depth Space

This paper proposes a modified just noticeable depth difference (JNDD) (JNDiD) model in perceived depth space. The JNDiD model improves the accuracy of current JNDD models by also taking into account the blurriness caused by the change in accommodation when a 3-D object is perceived far from the screen. This change in accommodation is a result of the convergence-accommodation conflict, in which convergence plays a leading role. In the JNDiD model described in this paper, the JNDD threshold is the addition between a base threshold and an additional threshold. Adapted to different blurring effects in three depth regions, the additional threshold is defined as a three-piecewise linear function. The proposed model also attempts to separate the JNDD modeling from the display modeling and viewing conditions in perceived depth space, making it applicable for different types of displays given their specific model parameters. With the help of model parameter transfer functions, the JNDiD model in perceived depth space and the corresponding one in any specific stimulated depth space can be easily transformed to each other. Experimental results demonstrate the effectiveness and superiority of the JNDiD model in perceived depth space.
Broadband Characterization of 3D Printed Samples with Graded Permittivity

This work presents the broadband characterization of the dielectric permittivity of 3-D printed samples with infill ratios in the range 10%-100%. The procedure is based on the measured S-parameters of a microstrip line loaded with the material under test as a superstrate. Measurement results are within a 15% error margin related to theoretical models and narrow-band results recently reported, and provide useful information on how to obtain arbitrary dielectric constant out of a material with fixed properties.
Hockey Pose Estimation and Action Recognition using Convolutional Neural Networks to Ice Hockey

Human pose estimation and action recognition in ice hockey are one of the biggest challenges in computer vision-driven sports analytics, with a variety of difficulties such as bulky hockey wear, color similarity between ice rink and player jersey and the presence of additional sports equipment used by the players such as hockey sticks. As such, deep neural network architectures typically used for sports including baseball, soccer, and track and field perform poorly when applied to hockey. This research involves the design and implementation of deep neural networks for both pose estimation and action recognition can effectively evaluate the pose and the actions of a hockey player. First, a pre-trained convolutional neural network, known as the stacked hourglass network, is used to determine a hockey player′s body placement in video frames, also known as pose estimation. The proposed method provides a tool to analyze the pose of a hockey player via broadcast video which aids in the eventual assessment of a hockey player′s speed, shot accuracy, or other metrics. The algorithm demonstrated to be successful since it identifies on average 81.56% of the joints of a hockey player on a set of test images. Furthermore, inspired by the idea that modeling the pose of a hockey stick can improve hockey player pose estimation, a novel deep learning computer vision architecture known as the HyperStackNet has been designed and implemented for joint player and stick pose estimation. In addition to improving player pose estimation, the HyperStackNet architecture enables improved transfer learning from pre-trained stacked hourglass networks trained on a different domain. Experimental results demonstrate that when the HyperStackNet is trained to detect 18 different joint positions on a hockey player (including the hockey stick), the accuracy is 98.8% on the test dataset, thus demonstrating its efficacy for handling complex joint player and stick pose estimation from video. Extending from pose recognition, this research involves the development of an algorithm for accurate recognition of actions for hockey. To perform this action recognition, a convolutional neural network estimates actions through unifying latent pose and action recognition. The action recognition hourglass network, or ARHN, is designed to interpret player actions in ice hockey video using estimated pose. ARHN has three components. The first component is the latent pose estimator, the second transforms latent features to a common frame of reference, and the third performs action recognition. Since no benchmark dataset for pose estimation or action recognition is available for hockey players, we first had to generate such an annotated dataset. Experimental results show action recognition accuracy of 65% for four types of actions in hockey. When similar poses are merged to three and two classes, the accuracy rate increases to 71% and 78%, proving the potential of the methodology for automated action recognition in hockey.
Efficient Mitchell’s Approximate Log Multipliers for Convolutional Neural Networks

This paper proposes energy-efficient approximate multipliers based on the Mitchell’s log multiplication, optimized for performing inferences on convolutional neural networks (CNN). Various design techniques are applied to the log multiplier, including a fully-parallel LOD, efficient shift amount calculation, and exact zero computation. Additionally, the truncation of the operands is studied to create the customizable log multiplier that further reduces energy consumption. The paper also proposes using the one’s complements to handle negative numbers, as an approximation of the two’s complements that had been used in the prior works. The viability of the proposed designs is supported by the detailed formal analysis as well as the experimental results on CNNs. The experiments also provide insights into the effect of approximate multiplication in CNNs, identifying the importance of minimizing the range of error.The proposed customizable design at $w$w = 8 saves up to 88 percent energy compared to the exact fixed-point multiplier at 32 bits with just a performance degradation of 0.2 percent for the ImageNet ILSVRC2012 dataset.
Certifying multi-power RSA

In this study, the authors present two rigorous algorithms to certify the trapdoor permutation property of the RSA function RS A N , e ( x ) := x e mod N , where N = p r q is a multi-power RSA modulus with unknown factorisation and r is a known positive integer. Their work gives effective certification for a prime exponent e when e ≥ 2 N ( gcd ( r , e − 1 ) ) / ( r + 1 ) 2 + ϵ and for a composite integer e = e 1 s 1 e 2 s 2 ⋯ e u s u when e i ≥ 2 N ( gcd ( r , e i − 1 ) ) / ( r + 1 ) 2 + ϵ for i = 1 , … , u , where e i is a known prime, s i is a positive integer, and ϵ > 0 is some small enough constant. The algorithms apply Coppersmith's method for solving univariate modular polynomial equations and run in time O ( ϵ − 7 C log 2 N ) , where C ≤ u r 2 is a constant number.
Designing Hands-On Robotics Courses for Students with Visual Impairment or Blindness

School laboratories let students playfully experience the fundamentals of, for example, robotics, computer science, and technology-related topics. By working with LEGO Mindstorms, secondary school students get a chance to learn on a cognitive, emotional, and haptic level and gain experiences with the aid of even more advanced robotics. However, due to an impairment or lack of sight, it is hardly possible for some students to fully participate in a programming process or in building a robot. To overcome this unintentional discrimination, the interdisciplinary student laboratory “RoboScope” at RWTH Aachen University has teamed up with a group of experts to develop a barrier-free robotic course. Since then, the course has been tested and implemented based on concurrent evaluations and frequently held at RWTH and several other German schools. The presented work covers an overview of different kinds of visual impairment and lab settings and the development cycle of the courses at RWTH from design to test -ing, implementation, and further development regarding the evaluations. Evaluations show that students who are visually impaired or blind appreciate the opportunity to participate in the field of robotics. An insight into the evaluation concept that differs from “regular” courses in the “Roboscope,” as well as the results are used for further development.