Miracle-dz/UniFAR-FTUs huggingface dataset:
Confidence Index Analysis of Twitter Users Timeline

With the development of information technologies and the increase of means and facilities for accessing the internet, the number of users of popular social networks has started to increase rapidly. Twitter is the most requested microblog site with 336 million active users. On Twitter, users can instantly express ideas, emotions and reactions with tweets. Scalable data from microblog sites, where fast and effective responses can be obtained, can be used in political, social and economic areas. It is possible to analyze the characteristics, trends and behaviors of the users by revealing their interaction. In recent years, especially the emotional analysis of users has become very popular. By analyzing the characters, effects and trends of users in Twitter, business intelligence applications can be developed and individual and social strategies can be created.In this study, the Twitter profiles of Turkey presidential election candidate were examined. Profiles was measured by physical and emotional metrics such as density, reciprocity, centralization, modularity, polarity, subjectivity. Users profiles confidence level and social authority indexes was determined. Relations with each other have been revealed. By examining these metrics, the effects of social media usage habits on users’ followers are examined. Social media performances were measured from each candidate’s tweets and follower analysis. Confidence indices related to 6-week time series were extracted using hierarchical classification algorithm.
Theoretical Study on Working Capital Management Practices for Trainees

Financial management directly associated with assets and liabilities enabling long or short term firm ailment. Enables vital activity of company with proper planning, shaping, regulating and monitoring both the material and financial resource required for the flawless organizational goal achievements. This study provides the details of working capital management practice essential in the evolving company. Management of every asset, namely inventory management cash management, receivable management crucially needed finance management on an organization. This gives the wholly general understanding for the novices on working capital and management of assets and liabilities of the firm. This study analyzes the liquidity and working capital management in developing management company. The financial position analysis comprises the process of identification of monetary strength and dimness of the company based on working capital management.
A Convenient Indoor Positioning Method

It is often difficult for people to reach their destinations When they enter unfamiliar buildings. This brings huge inconvenience to both daily life and emergency cases. However, current indoor positioning methods are not practical because they require deploying a large number of devices within the buildings. In order to solve the existed problem, this paper proposes a positioning method that fuses the data from inertial navigation system, floor plans of buildings and the distributed data of the evacuation signs. This positioning method achieves the required precision with no need for deploying extra devices in the buildings. This paper adopts particle filter to implement the positioning method. Matlab is used to simulate the process of observing, calculating and positioning. The original walking path, observed path and predicted path were provided through the simulation. Positioning error is also simulated to evaluate the accuracy. According to the results, the proposed positioning method can truly reduce the positioning error compared with the previous methods. This paper also simulates the effects on positioning error from the number of particles, the radius of particle distribution, the relative weight of evacuation signs and the recognition rate. Results and analysis are provided in this article
Design of CNC Contour Error Compensation Based on Geometric Trajectory

In the face of the existing large-scale processing requirements of the 3C industry, the traditional mode of reducing the contour error requires repeated trials, which relies on the experience of the staff and results in low processing efficiency. Therefore, this paper presents a self-learning contour error compensation study based on geometric trajectories, which enables the machine to learn from past processing experience to predict and compensate for errors in advance to achieve the high-quality parts processing. Experiments show that the algorithm can effectively reduce the machining contour error and improve the machining accuracy.
Providing VNF Services with Pipe&Hose Model Based Nonblocking SDN Networks

Combining Virtual Network Functions (VNF) and Software Defined Networking (SDN) enables fine-grained traffic steering to provide required network services to flows. However, online routing calculation and flow table enforcement incurs an non-negligible overhead. In this work, we propose to design a nonblocking network with fixed routing and VNF provisioning schemes to improve the network performance (e.g. the flow completion time). We first formulate this problem as a linear programming (LP) problem with infinite number of constraints, and leverage primal-dual technology to reformulate the problem as a polynomial size LP. The LP formulation is also extended to reconfigure the network when some components become unavailable, in order to keep the network nonblocking. Since solving the LP formulation is time consuming or even impossible in large scale networks, an efficient algorithm based on optimization decomposition and column generation is proposed to find a near optimal solution quickly. Simulation results show that nonblocking networks can speed up 60% of the flows by 2x, with a small increase in the required network capacity, compared with approaches that do not use the nonblocking networks.
Variation-Aware Hardware Trojan Detection through Power Side-channel

A hardware Trojan (HT) denotes the malicious addition or modification of circuit elements. The purpose of this work is to improve the HT detection sensitivity in ICs using power side-channel analysis. This paper presents three detection techniques in power based side-channel analysis by increasing Trojan-to-circuit power consumption and reducing the variation effect in the detection threshold. Incorporating the three proposed methods has demonstrated that a realistic fine-grain circuit partitioning and an improved pattern set to increase HT activation chances can magnify Trojan detectability.
Detecting Latent Structure Uncertainty with Structural Entropy

This paper proposes a new method for detecting the uncertainty of a latent structure. We consider the case where the latent structure of dataset changes gradually over time, with the goal of selecting the optimal model at any given time. In selecting the optimal model, we use the minimum description length (MDL) principle, specifically the normalized maximum likelihood (NML), which is the optimal code length in the sense of Shtarkov’s minimax regret. To detect the uncertainty of a latent structure, the main idea proposed here is that the uncertainty of the model selection will increase at the initial stage when the model change occurs. Here, we propose a new indicator called "structural entropy (SE)", which defines model selection uncertainty based on the MDL principle. We use several models for model selection, including the clustering structures of the Gaussian mixture model and Poisson mixture model, and a time-dependent structure model such as the autoregression model. We show the behavior of the proposed indicator (SE) using an artificial dataset and a real marketing dataset.
A Multi-Energy Microgrid Modelling and Optimization Method Based on Exergy Theory

Multi-energy system is a promising solution to improve energy efficiency. In this paper, exergy analysis is applied to the natural gas based multi-energy microgrid. The exergy theory is developed on the basis of the second law of thermodynamic. Exergy represents both quantity and availability of energy and exergy is defined by the maximum potential useful work in a certain amount of energy. Multienergy flows of electricity, gas, cooling and heating power are represented by exergy. By applying improved non-dominated sorting genetic algorithm, the pareto front of minimum unit cost of output exergy is reached and the output curves of system are obtained. Simulation results show the usability of exergy theory to multi-energy microgrid and the better energy-saving performance of the microgrid system in comparison to some on-running projects.
Modeling of Power GaN HEMT for Switching Circuits Applications Using Levenberg-Marquardt Algorithm

This paper presents a generic process to characterize and model the power Gallium Nitride (GaN) High Electron Mobility Transistor (HEMT). Both static and thermal characteristics of a 650V, 30A GaN HEMT have been modelled using Levenberg-Marquardt Algorithm. The resulted Spice model is then used to converge simulation from experiment characteristics of a designed switching circuit. The accuracy and good convergence of the improved model provide a good way to research the power converters with GaN HEMTs by a simulation approach.
Practical Cross Program Memoization with KeyChain

Cross program memoization (CPM) reduces resource utilization and improves response times by enabling data processing systems to re-use previously computed results between programs. An under-explored requirement to implementing CPM in general purpose data processing systems like Apache Spark is computing identifiers for results of user-defined functions that are valid between programs while avoiding degrading system performance when sharing is not possible. In this paper we describe and evaluate a technique, called KeyChain, that computes keys for intermediate and final results of programs with user-defined functions. We use KeyChain to implement CPM in Apache Spark, and show that KeyChain’s simple design means it can be easily added to relevant systems, incurs low runtime overheads, and enables heuristic detection of equivalent programs so that CPM can be added to more systems and useful results can be more widely re-used.
Fast Multi-Contact Whole-Body Motion Planning with Limb Dynamics

We present a new method for multi-contact motion planning which efficiently encodes internal dynamics of the robot without needing to use full models. Our approach is based on a five-mass model which is formulated by Cartesian points instead of joint angles. We solve direct optimization problems which include distance constraints between these points, Newtonian equations and integration constraints. We consider a given rhythm of contact switches but leave the phase-timings and contact positions free inside the optimization to provide more flexibility. Due to simpler equations and sparser problem structures, we can achieve very short optimization times in the order of few hundred milliseconds, which make the method suitable for application of online model predictive control. Aside from contact position and time adjustment properties, we can include precise foothold regions and synthesize dynamic motions by taking internal dynamics and momentums into account.
Learning Embedding Space for Clustering From Deep Representations

Clustering is one of the most fundamental unsupervised tasks in machine learning and is elementary in the exploration of high volume data. Recent works propose using deep neural networks for clustering, owing to their ability to learn powerful representations of the data. In this work, we present a novel clustering approach using deep neural networks that simultaneously learns feature representations and embeddings suitable for clustering by encouraging separation of natural clusters in the embedding space. More specifically, an autoencoder is employed to learn representations of the data. Then a mapping from autoencoder representation space to an embedding space is learned using a deep neural network we call Representation Network. This neural network promotes separation between natural clusters by minimizing cross-entropy between two probability distributions that denote pairwise similarity in autoencoder latent space and representation network’s embedding space. The resultant optimization problem can be solved effectively by jointly training the autoencoder and the representation network using minibatch stochastic gradient descent and backpropagation. Ultimately we obtain a K-Means friendly embedding space. Experimental results show that despite being a simple model, the proposed approach outperforms a broad range of recent approaches on Reuters dataset, other autoencoder based models on MNIST dataset and produces consistently good results that are very competitive with other complex and hybrid models.
A New Type of Soft Actuator: Design and Fabrication

Soft robot is a new form of robot and the actuator is the key to its design. This paper designed a new type of soft actuator, which has the ability of stiffness tuning in addition to elongation and three-dimensional bending. This paper introduced the structure of the actuator and its principle of elongation, bending and stiffness tuning. Besides, manufacturing and steps for the soft actuator was proposed and the prototype was fabricated by the proposed methods and steps. Finally, based on the fabricated actuator, we demonstrated its elongation, single chamber bending and multi-chamber driving bending.
The DQN Model Based on the Dual Network for Direct Marketing

Direct marketing is the issue of sequential decision making with the goal of maximizing total benefits accrued over a period of time instead of immediate benefits. Thus, it is natural to be formulated as a Reinforcement Learning problem. With using of neural network, the DQN model has better function approximation ability and hidden state learning ability, but it is challenge to find and train a single network for both purposes simultaneously. In this paper, a DQN model based on the dual network for direct marketing is proposed. Furthermore, we improved this model from two aspects of network structure and training methods. Besides, we conducted experiments using an on-purpose advertising simulation based on the KDD Cup 1998 data set. Comparing to the state-of-arts, our experimental results demonstrated more effective in different angles.
Three-dimensional computed tomography and composition analysis of porcelain insulators for 154 kV power transmission lines

Porcelain insulators were removed from 154 kV power transmission lines from three locations that were installed between 1969 and 1977. Visual inspection, three-dimensional computed tomography (3D-CT), and X-ray fluorescence (XRF) analyses were carried out. The 3D-CT analysis shows the presence of a number of pores. It was found that the insulating resistance of the porcelain insulators decreases with increasing number of pores and total pore area. Since most of the pores are located in the cement area, it is expected that the decrease in insulating resistance is related to the deterioration of the cement. The XRF analysis reveals that a high percentage of Fe exists in the cap. The Fe2O3 content, which indicates corrosion, is the highest at 95.7% in Region C (48 years), followed by 88.4% in Region A (40 years), and 81.65% in Region B (38 years). High levels of Al2O3 and SiO2 were detected in Regions B and C where the number of yellow dust days is high. In the case of the cement, the water-absorbing CaO component is 62.87% on average. In Region C where the insulators are the oldest, higher CaO is detected. This shows water absorbion greatly affected the insulation deterioration of the porcelain insulator. It was found that the Al content of the porcelain insulator from Region C, for which the main material is cristobalite, is lower than that from other areas using alumina. The components found by XRF analysis are dependent on the age, the main materials, and the environmental conditions where the insulators were installed.
Reinforcement Learning Controller Trained with Dynamic Performance Index and Its Application to 3-DOF Helicopter

In this paper, the dynamic performance index in the classical control theory is introduced into the reward of reinforcement learning. The incompatibility of large initial error with small operation error when using binary reward are solved. In the simulation experiment based on 3-DOF helicopter platform, the feasibility is proved, and the comparison result between this method and binary rewarding method is given. Experiments show that this method enables the 3-DOF helicopter to track time-varying signals. It can also cope with the large initial error and has better dynamic performance.
GPUPerfML: A Performance Analytical Model Based on Decision Tree for GPU Architectures

GPU has been applied to many fields for its excellent parallel computing ability, but the complexity of GPU architecture makes it hard to find the performance bottlenecks of GPU applications. Although profiling tools exist, they only provide a large number of data that programmers can not well understand. Some mathematical modelling methods are presented for analysis, but they are often too complicated and time-consuming for users. A performance evaluation method named GPUPerfML is proposed, which combines decision tree and theoretical analytical model to locate performance bottlenecks of GPU applications and guide application optimization. Based on its feature selection, decision tree is used to extract the sorted influential features from a large number of features. The proposed theoretical analysis model defines the categories of performance issues and establishes mapping relationships between features and performance issues, so as to quickly and directly identify performance bottlenecks. Besides, theoretical analysis can be used to guide decision tree building and features analysis, which improves and guarantees the accuracy of the proposed method. The experiments on three common applications (Matrix transpose, Parallel Reduction, and BFS) show that it can identify performance bottlenecks accurately and quickly, to help programmers write high-efficient programs conveniently.
Boundary Artifacts Removal of Synthesis View by Pre-filtering the Texture Image in 3D Video

In 3D video systems, texture image of reference view may include blurred edges while the edges of depth image are often sharp. It induces artifacts in the generated synthesis view due to the inconsistency between the edges of the texture and depth images. We propose a new method to remove such boundary artifacts, which is achieved by pre-filtering the regions of the texture image near boundaries with the guidance of its associated depth image. Experiments show that the proposed method well removes the artifacts of the synthesis view.
Profile Face Recognition using Local Binary Patterns with Artificial Neural Network

Face recognition is a process of identifying a person based on facial features. It is one of the most popular and widely used biometrics technique today. However, there are challenges regarding robust, rotation-invariant, real-time face recognition systems. Few challenges can be listed as changes in illumination, rotations of the face, different head poses and occlusion etc. This study mainly consists of four parts, namely face detection from the image, feature extraction, training the neural network and recognition. Face detection algorithm is used to detect the face from the given image. The most useful and unique features of the face image are extracted in the feature extraction phase. The face area is first divided into K2 sub regions from which Local Binary Pattern (LBP) histograms are extracted and concatenated into a single feature vector which is used to train the Neural Network. In the recognition phase, face recognition is done by trained Neural Network. The system is trained and tested on Ondokuz Mayis University database.
Adaptive Search Area in the Object Tracking

Object tracking is the automatic determining of position and size information of the target object in a video along the frames. Object tracking methods generally use a search area in a smaller size, rather than using an entire frame view to extract position and size information of any frame target object. This search area is usually chosen so that the object position in the previous frame is centered and will be “k” times the object size. In the general, after the object position in the frame is determined, the relevant object in the search area is taken as positive and the remaining part is taken as negative and classifier updates are made. In this way, the object can be strong against the appearance changes throughout the frame. The size of the search area in the frame directly affects object tracking performance and speed performance. The oversized selection of the search area can reduce speed performance on object tracking while allowing a more powerful classifier to be trained. Likewise, if the search area is selected too small, then the object will run at very high speeds. In this study, an adaptive search area (UAA) algorithm is proposed, which can adaptive select the search area used for object tracking. The developed UAA algorithm is applied to HCFT (Hierarchical Convolutional Features for Visual Tracking) method for the determine the effects on tracking performance. Then, practical tests were conducted to compare with the original HCFT method. As a result of the tests, UAA method has been found to give very strong results in terms of speed and performance. With the UAA algorithm developed, object detection can be performed at higher speeds, keeping the search area small in video scenarios. On the other hand, in difficult scenarios, the object tracking accuracy is improved by keeping the search area larger. Thanks to the improved UAA algorithm, it has made object tracking at higher speeds by reducing the search area in video scenarios in which the object detection is simple. On the other hand, in difficult scenarios, the object tracking accuracy is improved by keeping the search area larger.
A Compact Integration of a 77 GHz FMCW Radar System Using CMOS Transmitter and Receiver Adopting On-Chip Monopole Feeder

This paper presents 77-GHz CMOS radar transmitter and receiver chips equipped with monopole feeders for use in frequency-modulated continuous-wave radar systems. The on-chip monopole feeder can feed not only the aperture of waveguides but also the slots on planar circuits by simply attaching the chips to a printed circuit board using a low-cost non-conductive epoxy. With the aid of a high ratio multiplier and the proposed on-chip feeder, a radar system can easily be integrated without needing to use precise and expensive millimeter-wave packaging technologies. In order to experimentally confirm this, the chips integrated with waveguides are first measured and a full radar system integrated on the planar circuit is evaluated using the microstrip patch antennas. Finally, with the benefit of compact integration technology, the design of a five-channel 3-D environmental sensing radar for small-unmanned aerial vehicles is presented.
Impact Assessment of Electric Vehicle Charging on Distribution Transformers Including State-of-Charge

In this study, the impact of electric vehicle (EV) battery charging on distribution transformer (DT) is assessed in addition to the conventional loads connected to the grid. Current harmonic spectrums of different charging levels are used to analyze the effects of commercially available EV chargers on the losses, temperature rise and lifetime of a sample 1500 kVA transformer. The harmonic spectrum data take into account the state-of-charge (SOC) for more accuracy. The results for Level I/II chargers and Level III chargers are both discussed.