Reviewerly/exHarmony huggingface dataset:
Android-Based Lecturer Workload Simulation Application

Abstract In carrying out their duties, lecturers must conduct Tri dharma education activities which include teaching, research and service according to their respective fields of science. Every tri dharma activity carried out must be reported once every semester in the form of a lecturer workload report.The number of activities contained in the rubric makes many lecturers experience confusion to calculate how the credit performance in the Tridharma activities carried out. Therefore, it is necessary to have a simulation application that helps the calculation of credit performance of Tridharma activities in accordance with the BKD rubric. The lecturer workload simulation application can help lecturers to calculate credits for the Tridharma activities that have been carried out. The lecturer workload simulation application can also provide information on what physical evidence needs to be prepared by the lecturer.
Estimation of Human Activities by Fuzzy Classification Systems for Understanding Subject Persons' Situation

Human activities like stay, walk, squat, jogging, stair up and down have been estimated by our proposed fuzzy classification system. Data obtained by a wearable biometric sensor device have been used for estimating human activities. The sensor device can obtain electrocardiograms (ECG), 3-axis acceleration, body surface temperature, humidity, ambient temperature, and atmospheric pressure. Calculated body-angle, body vibration, and amount of change of pressure from sensor data have been used for making fuzzy rules. FIR filters have been used for pre-processing of data. Components of the attitude were extracted from the acceleration data. Five-fold and three-fold cross-validation methods have been used when the same person dataset and different person datasets were used for validation, respectively. MHEALTH datasets has also been validated on the fuzzy classification system. Three movements(stay, walk and jogging) have been classified with this system. From the experimental results, in every cases, every movement has been classified with more than 93% accuracy by our proposed method.
Study on the Future of Enterprise Communication by Cloud Session Border Controllers (SBC)

The purpose of the paper is to clearly elaborate on how Cloud Session Border Controllers (C-SBC) can be used in Cloud Unified Communication using Virtualization in Cloud Communication. With definite new generation of Communication, the event of High volume of Real-time communication and Intelligent communication traffic exploding everyday communication within Enterprises becomes complex and unmanageable, and the solution helps Enterprises in next-generation internetworking, Media Processing, and Transcoding, which is usually used for different next-generation services such as Web Real-time Communication (WebRtc) and Intelligent Communication involving minimum amount of physical cost of Hardware and Maintenance requirements with best of Cost efficiency and Profitability to a redefined low-cost architectural solution for Generation next Unified Communication.
Does pre-lecture Whatsapp intervention; enhance the knowledge of medical students in Clinical Microbiology?

INTRODUCTION: The most common method used to teach Clinical Microbiology is lecture. Lectures may not be interactive and interesting. Hence, the facilitator has to adopt newer teaching methods. WhatsApp is an interactive and interesting method to enhance medical education. Therefore, we decided to evaluate the effect of WhatsApp teaching, on the knowledge gain, in medical students.OBJECTIVE: To assess the knowledge gain, in medical students, using WhatsApp for teaching.METHODOLOGY: With institutional ethical committee approval taken, 146 medical students, who had smartphones and were using WhatsApp, were included in this study. By simple random sampling, an interventional study was conducted, by dividing them into two groups. The students in the intervention group (n = 73) were primed through WhatsApp, before the lecture session. The students in the control group (n = 73) attended the lecture, without prior exposure to the WhatsApp session. Ten pre- and post-lecture multiple-choice questions were given to both the groups. For the next lecture, crossover of the groups was done.RESULTS: 71.23% and 76.71% of the students in Group A and Group B, respectively, participated in the WhatsApp group discussion. In Session 1, the median score of pre-test of Batch A (intervention group) was 5 and Batch B (control group) was 4. The median score of post-test of both Batch A and Batch B was 7. In Session 2, the median score of pretest of Batch A (control group) was 4 and Batch B (intervention group) was 5. The median score of post-test of both Batch A and Batch B was 9.DISCUSSION: The percentage increase in knowledge, after the lecture, was statistically significant (P < 0.001). It is similar in both the Groups (A and B), in both the Sessions (1 and 2). This means that the control group (unexposed to the WhatsApp intervention) has also done well in the post-test. This is attributed to the lecture and not to the WhatsApp intervention.CONCLUSION: Although WhatsApp has several technical advantages, it has not contributed to the knowledge gain in the medical students.
Using Combinatorial Solutions and Atomic Weights to Play Competitive AI Clobber

Clobber is a two-player abstract-strategy game with no element of random chance. Introduced by game theorists Michael H. Albert, J. P. Grossman and Richard Nowakowski in 2001, it has subsequently been extensively studied for its combinatorial properties. Since 2005, it has been a major event in the Computer Olympiad.Although the concept of endgame tables has been introduced for Clobber and a use case suggested, usage of such has been scant in the Computer Olympiad or in the further development of other areas of Combinatorial Game Theory. We remedy this gap in this paper and demonstrate how to implement and use combinatorial solutions and atomic weights in an endgame table to perfect the play of Clobber by AI.We find that with the proper leveraging of endgame tables, it is possible to play perfectly on many Clobber positions of up to 70 pieces (on a 10x10 opening position) within time limits of most tournaments, and the solutions of Clobber on some significantly sized boards are likely reachable in the near future.
Comprehensive analysis of face recognition techniques: A survey

Face recognition is a dynamic problem in computer vision. Even though there is a great progress in this area, it still faces various problems like illumination, pose, aging, facial expression and partial occlusion etc. In this paper, we describe various constraints affecting the face recognition. After, various face recognition techniques are discussed in quantitative manner along with their applications.
Human-Machine Coadaptation Based on Reinforcement Learning with Policy Gradients

The problem of adaptive human-machine interaction is investigated. It is sought that not only the human learns how to perform a task with a novel machine, but the machine itself co-adapts to the human style in the interaction. This requires solving the problem of two agents co-adapting or co-learning at the same time. Due to the lack of human learning and performance models, it is hypothesized that reinforcement learning with policy gradient algorithms are good candidates for addressing this problem with robustness and fast convergence.
Clustering versus Incremental Learning Multi-Codebook Fuzzy Neural Network for Multi-Modal Data Classification

One of the challenges in machine learning is a classification in multi-modal data. The problem needs a customized method as the data has a feature that spreads in several areas. This study proposed a multi-codebook fuzzy neural network classifiers using clustering and incremental learning approaches to deal with multi-modal data classification. The clustering methods used are K-Means and GMM clustering. Experiment result, on a synthetic dataset, the proposed method achieved the highest performance with 84.76% accuracy. Whereas on the benchmark dataset, the proposed method has the highest performance with 79.94% accuracy. The proposed method has 24.9% and 4.7% improvements in synthetic and benchmark datasets respectively compared to the original version. The proposed classifier has better accuracy compared to a popular neural network with 10% and 4.7% margin in synthetic and benchmark dataset respectively.
The Teaching Quality Assessment System for Application-Oriented Undergraduate Colleges and Universities Based on the WEB Platform

With the development of the higher education in China, many new problems and challenges arise in the teaching and management of colleges and universities. With the increase of the enrollment in colleges and universities, the quality of graduates decreases and their ability to work is questioned. The teaching quality of colleges and universities has become the focus of our social attention, so it is particularly important to effectively evaluate the quality of our teaching. The teaching quality assessment is one of the important means to check the teaching quality of colleges and universities, which is a routine assessment work carried out every semester in colleges and universities. The evaluation of the teaching quality is of great significance to schools, teachers and students. The teaching quality evaluation system based on the web platform developed in this paper is a convenient and efficient teaching quality evaluation system adapted to the current new situation.
An Extended Polyalphabetic Cipher Using Directional Shift Patterns

Security is a crucial requirement of any communication system, and it becomes a predominant as most of the businesses are done online, and confidentiality of data is a prime security requirement and is being addressed through innumerous mechanisms. Secrecy of data in significant communication is being ensured from ancient times. There are various techniques to provide confidentiality services to a system. In this paper, we discussed various classical techniques of encryption and proposed an enhanced version of polyalphabetic cipher, which is more secure than the later one. Even though the proposed work may not be accepted in comparison with current encryption mechanisms, it will secure as an attempt to revisit the classical techniques and design new techniques inline to them.
Ultra HD Video and Audio Broadcast System for Shared Living Room

HD video and high reproduction audio are two important playback indicators. The 8K ultra-high-definition video player developed by our team has reached a practical level. The multi-channel sound system (DMS) is a new multi-channel sound-recovery system based on the sound field optimization principle and Huygens principle developed by CUC. Through experiments and theoretical analysis, this paper studies the operation mechanism of ultra-high definition shared living room audio and video playback system, summarizes the common techniques of audio and video synchronization at home and abroad, and provides a feasible synchronous implementation method, which makes the audio and video synchronization error range. To meet the requirement of relevant national standards, the ultra-high-definition video and audio broadcast system shared living room is realized.
Research on Weak Chirp Signal Detection Based on Cubic Spline Interpolation

In response to the problem of weak chirp signal detection, a method based on Short-time Fourier transform (STFT) and cubic spline interpolation is proposed. The scheme mainly exploits the continuous time-frequency characteristic of chirp signals to extract them from background noise. To be specific, we short-time Fourier transform the input signals into coherent integration of frequency-shift sample sequences which are with complex envelopes, then achieve a time-frequency curve and numerically analyze it with cubic spline interpolation, in order to eliminate the noise to the utmost extent as well as to detect chirp signals effectively. The algorithm remains available in quite a low signal-to-noise (SNR) with low complexity. Simulation results show its fine detection performance in -18dB condition, which can meet the demand of electronic reconnaissance.
Inclination Manipulator

This study aims to use the redirected walking technique that makes users of virtual reality systems feel like they are walking on an inclined surface while actually walking on a flat one in a real space. However, when the appearance of the virtual inclination becomes too significant, the absence of rotational force components applied to the body causes a feeling of incongruity. In this paper, we propose a novel technique to effectively manipulate the perception of inclination of a virtual walking surface using haptic cues to complement the rotational force components. Based on this approach, we developed a system to generate the rotational force components by loading a weight at the user’s back, which shifts when the configuration of virtual inclination changes. The experiments conducted using the proposed system verified the feasibility of our approach.
Night Pedestrian Detection Based on Fuzzy Reasoning

Night pedestrian detection has attracted attention of researchers from academic institutions and industry companies for many years especially on surveillance systems and assistive night vision systems for intelligent vehicles. However, most detection approaches rely on complicated algorithms to provide excellent detection performance. This paper aims to develop a computationally efficient and effective detection method for night pedestrian detection with an infrared camera. Because of unclear human body features, the upper body of humans is chosen for the basis for pedestrian detection during the night. Two feature parameters including the angle between the head and the body and the width ratio of the head with respect to the body are applied to distinguish non-human and human as well as single person and multiple persons based on fuzzy reasoning technique. Experimental results on forty-four images from four different scenes for night pedestrian detection demonstrate promising performance using the proposed detection algorithm.
Generating images with desired properties using the DiscoGAN model enhanced with repeated property construction

The idea of image-to-image translation is to take advantage in certain areas such as adding the sharpness to images and improving the semantic segmentation. The most popular models for solving problems are generative adversarial network (GAN) [1] models such as DiscoGAN [2] and CycleGAN [3]. In training process, input images with no desired properties, and output images with the desired properties are fed into the generative model to train the model. After training, the model can synthesize the desired properties from the input images without those properties. However, in practical usage, an input image may be different from the training process because the input image may be the image with or without the desired properties. This research proposes the method of training the generative model by giving input images with and without desired properties in the same way as when the model is used. Our proposed model enhances DiscoGAN with repeated property construction to generate images with desired properties. The model can use unpaired data as the training data, which makes data preparation more efficiently and more comprehensive than paired data. The proposed model obtained approximately 8% better Fréchet Inception Distance (FID) [4] score compared to the DiscoGAN model.
Ellipsoid Modeling Method for Coding of Face Depth Picture

In this paper, we propose an ellipsoid modeling method for coding of a face depth picture.The ellipsoid modeling is firstly based on a point of a nose tip which is defined as the lowest value of the depth in the picture.The proposed ellipsoid representation is simplified through a difference of depth values between in the nose tip and in left or right boundary point of the face.Parameters of the ellipsoid are calculated through coordinates and depth values to minimize differences from the actual depth pixels.A picture is predicted by the modeled ellipsoid for coding of the face depth picture.In simulation results, an average MSEs between the face depth picture and the predicted picture is measured as 20.3.
Design of a Reading Fluency Assist Tool Based on Pause Metrics into Reading Aloud

The purpose of this research is to develop an assessment tool for reading aloud by pupils. We have proposed some metrics for evaluating reading fluency based on metrics of pauses in reading. In this paper, we developed a full-automated evaluation tool and a visualization method for reading using these indicators. We also propose a feedback interface based on heat-map method.
Relations of the E-Derivative, Derivative, Algebraic Immunity, and Algebraic Degree of Balanced H Boolean Functions

We examine the algebraic immunity and algebraic degree of balanced H Boolean functions and resilient H Boolean functions that exhibit propagation, balance, and correlation immunity by using a derivative of Boolean functions and e-derivative that we defined ourselves. We determine that e-derivatives decide the algebraic immunity of balanced H Boolean functions and resilient H Boolean functions. We also identify that derivatives partly decide the algebraic degree of functions. Meanwhile, we deduce that without changing the algebraic immunity order, the algebraic degree of balanced H Boolean functions can be improved through a change in the e-derivative, and as a result, algebraic complexity is also improved. Furthermore, a method to develop resilient H Boolean functions is developed with the use of generalized complement transformation and cascade operation.
Intuitive Industrial Robot Programming Interface with Augmented Reality Environment

The integration costs of industrial robots into small medium-sized enterprises (SMEs) are nowadays one of the main obstacles, to make automated solutions in this sector profitable. An essential role plays the human-robot interface (HRI), which is in robots standard implementation for averagely trained technicians overly complicated and time consuming. A new intuitive HRI, based on augmented reality, is presented to shorten programming phase during in commissioning of robot cells in the production line. The proposed system was implemented on a portable computer with a head-mounted camera and wearable display for the operator. In demonstrative scenery was with the presented hardware setup shown, how basic visualization and simulation functionalities and intuitive programming procedures of industrial robots in the AR environment could be implemented. In addition to the trajectory visualization in an AR environment, the robot’s gripper pose simulation was shown, as it would be visible in operators’ field of view. With the usage of a 6D camera tracked AR-Stylus, combined with the projection of the robots’ gripper 3D model in the AR environment, an elevated level of operator integration into AR environment can be achieved.
Easy Extrinsic Calibration of VR System and Multi-camera Based Marker-Less Motion Capture System

This paper proposes a novel easy extrinsic calibration algorithm for an off-the-shelf VR system and a multi-camera based marker-less motion capture system. To realize interactions between 3D user motions and virtual objects reconstructed from multi-view videos in a common 3D space, the extrinsic calibration of the VR system and the multiple cameras must be conducted beforehand. This calibration, which involves estimating the pose and position of each coordinate system, is a key technology for handling 3D information in a system with various type of input sources. In general, extrinsic calibration is carried out by identifying and utilizing some common 3D points. However, since most of off-the-shelf VR systems do not include any imaging device, it is difficult to apply conventional automatic calibration approaches as there are no common points shared with the cameras. Against this problem, this paper introduces an easy calibration algorithm by generating corresponding points from the trajectories of the user's motion with VR devices and 3D human pose reconstructed from multi-view videos. Our study provides the following two contributions; (1) our method does not need to introduce additional devices, such as a chessboard and (2) our method does not need manual processes as the extrinsic parameters are automatically estimated. We demonstrate the performance of the proposed method in a practical scenario.
Radar Emitter Recognition Based on LE-SVDD Classifier

For existing radar emitter recognition method, the performance declines sharply with decreasing of training sample numbers. To overcome this problem, a LE-SVDD classifier is proposed and applied in radar emitter recognition. In the LE-SVDD classifier, Laplacian Eigenmaps (LE) algorithm, which belongs to manifold learning field, is employed to improve SVDD classifier. And meanwhile, the strong generalization capability of SVDD classifier is inherited in it. So it is suitable for radar emitter recognition under a small-scale training sample set. The experimental results show that: when the number of training sample is small, higher radar emitter recognition accuracy can be achieved by the proposed method.
Adding New Classes in Semantic Segmentation

To implement semantic segmentation and assign one of the classes to each pixel, a large amount of pixel labelled images are required. However, annotations in existing image datasets are limited both in terms of quantity and diversity owing to the heavy annotation cost. Therefore, in this study, we examined a method to readily add new classes of training images and evaluate feasibility by testing semantic segmentation on car-mounted camera images.
Hardware evolution of AES algorithm on FPGA

Hardware evolution adopts the principle from evolutionary computing which finds the optimal solution to a problem using an evolutionary algorithm. Hardware evolution can create new hardware circuit from basic units like gates without human intervention. Evolution at a more abstract level is called function level evolution. The paper presents a functional level evolutionary system, which evolves AES algorithms from basic operations Substitute Byte, Shift Rows, Mix Column and Add Round Key. The Genetic algorithm is used for evolution with confusion, diffusion, and randomness as fitness function parameters. The average percentage of output that passes randomness test (test value ≥0.01) is above 98%.
Patterns and Colors: A Simple yet Innovative Way to Solve Pattern Tracking

Authentication is one of the essential defense mechanisms to prevent unauthorized access to our personal gadgets. Several authentication schemes have been proposed over the years, such as passwords, PIN and biometrics. Although there are many types of authentication methods equipped in our devices, they still have their limitations. This study compares the different types of commonly used knowledge-based authentication systems and identifies the shortcomings of these authentication methods by thoroughly exploring their vulnerabilities. In this study, we propose an innovative idea that modifies the existing pattern-lock authentication method by using a combination of patterns, colors and randomization to authenticate users to their mobile device. This proposed method can prevent unauthorized users from shoulder-surfing and analyzing the static repeated patterns drawn by users on their mobile devices.