Reviewerly/exHarmony huggingface dataset:
Use case evolution analysis based on graph transformation with negative application conditions

Use Case (UC) quality impacts the overall quality and defect rate of a system, as they specify the expected behavior of an implementation. In a previous work, we have defined an approach for a step-by-step translation from UCs written in natural language to a formal description in terms of Graph Transformation (GT), where each step of the UC was translated to a transformation rule. This UC formalization enables the detection of several specification problems even before an actual implementation is produced, thus reducing development costs. In this paper, we extend our approach to handle UC evolution by defining evolution rules, which are described as higher-order rules, simultaneously changing the behavior of a set of transformation rules. We also support the use of negative application conditions (NAC) associated both to the transformation and evolution rules. Analysis of the interplay between the evolution rules and the rules describing UC steps shows the effects of an evolution and serves to identify potential impacts, even before the changes are actually carried out. Besides defining the theoretical foundations of UC evolution with NACs, we have implemented the evolution analysis technique in the Verigraph tool and used it to verify impacts in 3 different case studies. The results demonstrate the applicability and usefulness of our approach to help developers in the evolution process based on UCs.
Floating Houses Technology as Alternative Living on The Water

Abstract Increasing population has resulted in increased housing needs, but currently the available land for housing is decreasing. In addition, climate change has resulted in rising sea levels. With these three problems, it is necessary to develop a place to live with limited land and utilize the potential of Indonesian waters. The efforts that have been made by the current government by building settlements vertically, but have not been able to meet the needs of the residence. One type of house that can be developed by utilizing the potential of the waters is floating houses. A floating house is a residential building that is built on water by applying floating structures, materials, and shapes to suit the construction site. The floating house will function properly with proper planning, especially in the selection of technology at the bottom, floats, and the stability of the building above the water. Besides technology, other things that need to be taken into consideration are the selection of materials and the selection of the type of structure that will be applied. This technological innovation is expected to be able to meet the needs of residences that can utilize the territorial waters.
Fast confocal microscopy imaging based on deep learning

Confocal microscopy is the de-facto standard technique in bio-imaging for acquiring 3D images in the presence of tissue scattering. However, the point-scanning mechanism inherent in confocal microscopy implies that the capture speed is much too slow for imaging dynamic objects at sufficient spatial resolution and signal to noise ratio(SNR). In this paper, we propose an algorithm for super-resolution confocal microscopy that allows us to capture high-resolution, high SNR confocal images at an order of magnitude faster acquisition speed. The proposed Back-Projection Generative Adversarial Network (BPGAN) consists of a feature extraction step followed by a back-projection feedback module (BPFM) and an associated reconstruction network, these together allow for super-resolution of low-resolution confocal scans. We validate our method using real confocal captures of multiple biological specimens and the results demonstrate that our proposed BPGAN is able to achieve similar quality to high-resolution confocal scans while the imaging speed can be up to 64 times faster.
Motion Based Video Skimming

Automatic video summarization is a sustainable method that provides efficient browsing and searching mechanism for long videos. Video skimming is one of the popular ways to represent a summary of a full-length video. This work describes an unsupervised technique that automatically extracts the important clips from an input video and generates a summarized version of that video. The proposed scheme of video skimming is composed of three parts: extraction of motion based features, selection of important clips, detection, and removal of the shot boundary, if any, within a clip. Each frame is represented by a 32-dimensional feature vector that is generated using the slope and magnitude of the motion vectors. A set of representative frames of the entire video is obtained using the k-means clustering followed by the Maximal Spanning Tree (MxST). These representative frames are the center point of the clips to be generated. A window is considered around these representative frames and the clip is formed. A shot boundary may exist within the clip. To detect such a shot boundary, a method is proposed considering the variations present in the pixel intensities of the frames of a clip. The variation among the frames is captured using the standard deviation of the distribution of the pixel intensities. The clips are reformed in case the boundary is detected. Finally, the skim is generated by concatenating extracted video clips in a sequential manner. The obtained video summaries are concise and the proper representation of the input videos. The experiment is performed on two benchmark datasets namely SumMe and TVSum. Experimental results show that the proposed method outperforms the state-of-the-art methods.
Collaborative Filtering With Ranking-Based Priors on Unknown Ratings

Advanced collaborative filtering methods based on explicit feedback assume that unknown ratings are missing not at random. The state-of-the-art algorithm hypothesizes that unknown items are weakly rated and sets an explicit prior to unknown ratings. However, the prior assuming unknown ratings be close to zero may be questionable and it is challenging to set appropriate prior ratings for unknown items. In this article, to avert the use of prior ratings, we propose a ranking-based prior by hypothesizing that each user's unknown ratings are close to each other. This prior essentially acts as a regularizer to penalize the discrepancy of predicted ratings between any two unknown items. With the ranking-based prior, we design a generic collaborative filtering framework for explicit feedback and develop an efficient optimization algorithm for parameter learning. We finally evaluate the proposed algorithms on four real-world rating datasets. The results show that the proposed algorithms consistently outperform the state-of-the-art baselines and that the ranking-based prior leads to superior recommendation accuracy.
Autonomous Navigation of Holonomic Mobile Robot Using Point-to-Line Algorithm

This paper consists of a novel approach for autonomous navigation of a four-wheel Omni-directional robot using inexpensive sensors for localization and low computation method for planning a trajectory. The autonomous navigation system is such that the robot can navigate with any orientation. An analytical approach is introduced for tracing a sinusoidal path which can be extended to trace any path whose equation is known.
Adaptive Weight Update Algorithm for Target Tracking of UUV Based on Improved Gaussian Mixture Cubature Kalman Filter

The Gaussian mixture filter can solve the non-Gaussian problem of target tracking in complex environment by the multimode approximation method, but the weights of the Gaussian component of the conventional Gaussian mixture filter are only updated with the arrival of the measurement value in the measurement update stage. When the nonlinear degree of the system is high or the measurement value is missing, the weight of the Gauss component remains unchanged, and the probability density function of the system state cannot be accurately approximated. To solve this problem, this paper proposes an algorithm to update adaptive weights for the Gaussian components of a Gaussian mixture cubature Kalman filter (CKF) in the time update stage. The proposed method approximates the non-Gaussian noise by splitting the system state, process noise, and observation noise into several Gaussian components and updates the weight of the Gaussian components in the time update stage. The method contributes to obtaining a better approximation of the posterior probability density function, which is constrained by the substantial uncertainty associated with the measurements or ambiguity in the model. The estimation accuracy of the proposed algorithm was analyzed using a Taylor expansion. A series of extensive trials was performed to assess the estimation precision corresponding to various algorithms. The results based on the data pertaining to the lake trial of an unmanned underwater vehicle (UUV) demonstrated the superiority of the proposed algorithm in terms of its better accuracy and stability compared to those of conventional tracking algorithms, along with the associated reasonable computational time that could satisfy real-time tracking requirements.
The Use of Students’ Digital Portraits in Creating Smart Higher Education: A Case Study of the AI Benefits in Analyzing Educational and Social Media Data

The problem of a smooth and synchronous integration of smart education into the traditional educational system of higher education institutions (HEIs) and transforming it into a person-centered, blended, systematic adaptive educational environment is becoming a new paradigm of higher education institutions (HEIs). But there are a few studies that discuss the role of artificial intelligence (AI) in providing comprehensive and predictive analysis not limited to educational outcomes but focused on students’ individual portraits and aimed at improving the quality of education and provision of an integrated educational environment. This paper investigates role, application, and challenges of applying AI to targets and features of smart education concept: proactive, predictive analysis based on instruments aimed at building up a system that not only records students’ performance but also predicts and influences it. This study offers the concept of the comprehensive system of the university targeting to provide predictive analysis of students’ performance based on quantitative and qualitative information from multiple sources concerning students’ education, research performance, and social network information joined into unified, comprehensive dataset of each student (digital portraits) that allows to design individual tracks and use advising system to correct this track according to students’ needs and personal goals. As a result, we can predict students’ dropout with almost 90% accuracy. The main research problem of the study is to observe the implementation of AI instruments in higher education according to the needs and goals of universities and personal goals of each student and investigate the impact of this in blending different types of educational activity and creation of the integrated educational environment. Our findings are based on the analysis of the information about more than 20,000 students in 2013–2019.
Ensembling Handcarafted Features to Representation Learning for Content Based Image Classification

Content Based Image Classification (CBIC) has garnered massive interest within research community for it’s applications and significances in multiple areas. This paper has explored a robust feature vector definition scheme based on fusion of handcrafted features to neural network based feature extraction, popularly known as representation learning. The extracted handcrafted features are primarily evaluated with classification results to identify the best color space for significant descriptor definition. Further, feature vector extraction using pre-trained Convolution Neural Network is exercised for evaluating classification performance with representation learning features. Finally, an early fusion of handcrafted feature and representation learning feature is accomplished to examine the classification performance of the model. The test bed is prepared using Wang dataset having 10 different categories of 1000 images. Six different color spaces are explored to finalize the best fit for feature extraction with the proposed handcrafted technique. Performance analyses of the proposed model have turned out to be superior compared to state-of-the-art techniques and have established the importance of fusion based approach in enhancing the accuracy of content based image classification.
Preparing for Projects

As Information Technology academics we seek to prepare our students for their future endeavours, and these are generally focused on an IT professional career. To commence such a career, our graduates need a range of skills, both technical and professional and the ability to demonstrate these skills. In this paper a study of a graduating class of IT students is examined to explore their own identification and evaluation of their skills. Their self-rated skills are compared to a peer evaluation, assessor evaluation, and an evaluation from the industry partner to determine the accuracy of their self-rated skills evaluation.
Tracking cryptographic keys and encrypted data using position verification

Position verification is an emerging field of quantum cryptography. Its goal is to verify whether a distant communicating party is telling the truth about where they are. However, the problem is usually formulated in a way that the position is the only credential of that party, which cannot guarantee uniqueness. In this study, the authors show how a practically secure position verification algorithm – assuming it exists – might be used to track (i.e. repeatedly verify the position) of some unique key or cipher text. To achieve this, they rely on pre-prepared position verification data called trackers. They also propose three algorithms that implement their general tracking scheme and examine some questions related to their security. These implementations include shuffling trackers into valuable data and hiding their memory address through a random permutation; using CNOT operations to entangle valuable data and trackers; and using random qubit strings from which either trackers or secret keys can be produced at will. These methods may be used to track a diplomatic package or reveal the location of a malicious party during a denial of service attack.
Detection of Moving Object Based on FPGA

Detecting a moving object is very crucial in surveillance application. Many algorithms have been developed for object detection, recognition and analysis. The algorithms involve the trajectory of detected object by locating position in each captured image. In the proposed work a system is implemented to detect foreground object in any environmental condition and the model is implemented on FPGA board. Real time foreground object detection is a very complex task. Proposed system detects object by background subtraction algorithm based on dynamic thresholding. Background subtraction algorithm detects moving object by subtracting current frame from reference background frame. The system is simulated using MATLAB and implemented on FPGA board.
Development of embedded system in monitoring temperature and humidity as supporting smart farm

Abstract Enabling the environment is an important factor in developing livestock business. The temperature and humidity of the cage are very important aspects to consider. Inappropriate livestock cage temperature can affect livestock productivity and survival. The purpose of this research is to design a web-based temperature and humidity monitoring system, in the form of automatic regulation and measurement. In this research, embedded systems techniques are applied to build temperature monitoring systems by combining sensors and microcontrollers. Monitoring the temperature and humidity conditions of the enclosure is done through a sensor network and the classification of conditions is carried out by an intelligent system that produces web-based decision support. The sensor used to measure temperature and humidity in this monitoring system is the DHT22 sensor, while the controller is used NodeMCU. The results of this study are measurement data can be displayed on web pages that can be accessed in realtime. NodeMCU devices can be used as controllers for temperature and humidity data and successfully send data to a database that can be displayed on the website interface. Monitoring humidity and temperature is a great and easy way to record and track any changes which can help decision-makers to prepare or follow suit procedures to prevent conditions that can cause livestock.
Prediction Mechanism – A Novel Approach for OverLoad Management in a Distributed Computing System

The fast growing nature of internet in the modern era promises a vast future for large scale distributed systems. There by locally scattered distributed resources are collectively pooled and used in a cooperative manner to achieve high computational efficiency. The server on getting a request from the client, in-order to process a job, selects the best processing node using multi-QoS-oriented resource selection algorithm. The directory node accomplishes this task by considering the quality attributes of various resources and reputation information of the processing node. Due to this, the high quality processing nodes get easily overloaded and eventually a deadlock can occur. To avoid over-loading, a load balancing algorithm using job migration is used. In this paper, we implement the ideology of job migration using prediction mechanism, which will help in the effective avoidance of overloading in the processing nodes and thereby aids in increasing the performance.
On varieties defined by large sets of quadrics and their application to error-correcting codes

Let U be a (k−12−1)-dimensional subspace of quadratic forms defined on Fk with the property that U does not contain any reducible quadratic form. Let V(U) be the points of PG(k−1,F) which are zeros of all quadratic forms in U. We will prove that if there is a group G which fixes U and no line of PG(k−1,F) and V(U) spans PG(k−1,F) then any hyperplane of PG(k−1,F) is incident with at most k points of V(U). If F is a finite field then the linear code generated by the matrix whose columns are the points of V(U) is a k-dimensional linear code of length |V(U)| and minimum distance at least |V(U)|−k. A linear code with these parameters is an MDS code or an almost MDS code. We will construct examples of such subspaces U and groups G, which include the normal rational curve, the elliptic curve, Glynn's arc from Glynn (1986) and other examples found by computer search. We conjecture that the projection of V(U) from any k−4 points is contained in the intersection of two quadrics, the common zeros of two linearly independent quadratic forms. This would be a strengthening of a classical theorem of Fano, which itself is an extension of a theorem of Castelnuovo, for which we include a proof using only linear algebra.
Explicit constructions of cyclic and negacyclic codes of length 3ps over 𝔽pm + u𝔽pm

Let [Formula: see text] be a prime such that [Formula: see text]. The algebraic structures of all cyclic and negacyclic codes of length [Formula: see text] over the finite commutative chain ring [Formula: see text] are obtained that the conditions depend on the factorization of polynomial [Formula: see text] over [Formula: see text]. Therefore, we classify the structures of cyclic and negacyclic codes of length [Formula: see text] over [Formula: see text] into 2 cases, i.e., [Formula: see text] and [Formula: see text]. From that we obtain the number of all cyclic and negacyclic codes of length [Formula: see text] over [Formula: see text]. After that, we give some situations for such cyclic and negacyclic codes are self-dual codes.
Polarized Near-Infrared Light Emission for Eye Gaze Estimation

The number of near-infrared light-emitting diodes (LEDs) is increasing to improve the accuracy and robustness of eye-tracking methods, and it is necessary to determining the identifiers (IDs) of the LEDs when applying multiple light sources. Therefore, we propose polarized near-infrared light emissions for an eye gaze estimation. We succeeded in determining the IDs of LEDs using polarization information. In addition, we remove glints from the cornea for correctly detecting the pupil center. We confirmed the effectiveness of using polarized near-infrared light emissions through evaluation experiments.
Adaptive Odometry and IMU Sensor Fusion for KUKA youBot Mobile Robot Using Analytical Time Update

Odometry and inertial measurement units are fundamental components of modern vehicle localization systems along with the low-sampling-rate absolute position sensors like GPS and LIDAR. This paper considers their tight fusion and adaptive filtering methods to determine their bias and deviation online. The performance of CMAUKF (Covariance Matching-based Adaptive Kalman Filter) and the WRWAUKF (Windowing and Random Weighting Adaptive Unscented Kalman Filter) is investigated via numerical simulations. Furthermore, a robot-specific exact analytical formula is utilized in the time update step instead of applying the generic unscented transformation in order to decrease the computational cost of the methods.
On the impact of architecture design decisions on the quality of blockchain-based applications

Abstract In software architectures, architectural design decisions (ADDs) strongly influence the quality of the resulting software system. Wrong decisions lead to low-quality systems and are difficult to repair later on in the development process. As of today, little is known about the impact of certain ADDs for the development of architectures for blockchain-based systems. Thus, it is difficult to predict the outcome of certain ADDs when developing architectures for such systems. In the following, we propose a simulation-based approach for blockchain architectures in which the impact of certain ADDs on certain quality attributes can be simulated. To this end, we first implemented a simulation environment for blockchain architectures. The simulation environment was then used to execute a series of experiments from which we derived a set of hypotheses about the impact of certain ADDs on quality attributes for blockchain architectures. Finally, we tested the hypotheses using statistical analyses and derived an empirical model for blockchain architectures based on the outcome of the analysis. The model can be used by architects to predict the effect of certain decisions in the design of blockchain architectures before implementing them.
Conformal Wearable Devices for Expressive On-Skin Interaction

In this demonstration we showcase our recent work on conformal wearable devices that can enable expressive interaction on skin. Our interactive exhibits demonstrate a variety of functionalities including ultrathin wearable devices for high-resolution touch input, expressive interaction on body landmarks, visual output, and physiological sensing. Our work furthermore demonstrates fabrication techniques to offer customization and personalization of epidermal devices, ultimately providing an intimate coupling with the human body.