Reviewerly/exHarmony huggingface dataset:
Substation pointer meters detection and reading based on CNN

Image processing methods based on feature matching are generally used for detecting and recognizing pointer meters in substation. Under the influence of environmental factors, such methods run into problems with low detection accuracy and reading success rate, when deployed in substation inspection robots. To improve the situation, a new method based on CNN (Convolutional Neural Network) for detecting and reading meters is proposed in this paper, through analyzing existing meter recognition process in robot’s vision subsystem. The new method detects and segments pointer meters using YOLOv3 (You Only Look Once) and U-Net separately, classifies scale values using AlexNet, and finally estimates readings though post-processing based on CNN models. The field experiment shows that, the proposed method has improved the reading success rate by 45% comparing to that of the conventional methods, while keeping the deviation within the permissible limits.
Stitcher: Correlating digital forensic evidence on internet-of-things devices

The increasing adoption of Internet-of-Things (IoT) devices present new challenges to digital forensic investigators and law enforcement agencies when investigation into cybercrime on these new platforms are required. However, there has been no formal study to document actual challenges faced by investigators and whether existing tools help them in their work. Prior issues such as the correlation and consistency problem in digital forensic evidence have also become a pressing concern in light of numerous evidence sources from IoT devices. Motivated by these observations, we conduct a user study with 39 digital forensic investigators from both public and private sectors to document the challenges they faced in traditional and IoT digital forensics. We also created a tool, Stitcher, that addresses the technical challenges faced by investigators when handling IoT digital forensics investigation. We simulated an IoT crime that mimics sophisticated cybercriminals and invited our user study participants to utilize Stitcher to investigate the crime. The efficacy of Stitcher is confirmed by our study results where 96.2% of users indicated that Stitcher assisted them in handling the crime, and 61.5% of users who used Stitcher with its full features solved the crime completely.
International Workshop on Deep Video Understanding

This is the introduction paper to the International Workshop on Deep Video Understanding, organized at the 22nd ACM Interational Conference on Multimodal Interaction. In recent years, a growing trend towards working on understanding videos (in particular movies) to a deeper level started to motivate researchers working in multimedia and computer vision to present new approaches and datasets to tackle this problem. This is a challenging research area which aims to develop a deep understanding of the relations which exist between different individuals and entities in movies using all available modalities such as video, audio, text and metadata. The aim of this workshop is to foster innovative research in this new direction and to provide benchmarking evaluations to advance technologies in the deep video understanding community.
Constructing the Seismograms of Future Earthquakes in Yunnan, China, Using Compressed Sensing

Abstract The ability to calculate the seismogram of an earthquake at a local or regional scale is critical but challenging for many seismological studies because detailed knowledge about the 3D heterogeneities in the Earth’s subsurface, although essential, is often insufficient. Here, we present an application of compressed sensing technology that can help predict the seismograms of earthquakes at any position using data from past events randomly distributed in the same area in Jinggu County, Yunnan, China. This first data-driven approach for calculating seismograms generates a large dataset in 3D with a volume encompassing an active fault zone. The input number of earthquakes comprises only 1.27% of the total output events. We use the output data to create a database intended to find the best-matching waveform of a new event by applying an earthquake search engine, which instantly reveals the hypocenter and focal-mechanism solution.
Design of an Artificial Game Entertainer by Reinforcement Learning

Games are often used for performance evaluation of artificial intelligence (AI) methods. Most AI studies using the games aim to design a computer player which plays a game better than humans. On the other hand, video game companies develop one-to-one games such as Go and Reversi and design computer opponents which entertain human players to have them play the games a lot. However, it takes much time to design such computer opponents. In this paper, we propose a reinforcement learning method for automatically designing an AI player entertaining the human players, especially those who are not good at playing games, in the one-to-one games. There are several ways to entertain them. One of the ways is to use a computer opponent which is neither too strong nor too weak, and the proposed method designs such an artificial game entertainer. The performance of the proposed method is evaluated through numerical experiments.
An efficient low-cost calibration board for Long-wave infrared (LWIR) camera

The process of geometric camera calibration for long-wave infrared (LWIR) camera is essential for determining the camera’s intrinsic and extrinsic parameters, which maps 3D world coordinate points onto 2D image coordinate points. In this paper, new calibration board materials are proposed for making a plane checkerboard to calibrate the LWIR camera. These materials are low-cost items and, to the best of our knowledge, has not been used so far for calibration of infrared camera. The resulting calibration board exhibited high thermal infrared image contrast and showed consistent checkerboard corners’ detection to accomplish the geometric calibration of LWIR wavebands camera. The suggested method is tested using three different LWIR cameras in an indoor environment. The quality of new proposed board thermal imaging is compared with existing calibration board made by paint. The calibration approach is experimented using “MATLAB Single Camera Calibrator Tool” for each of the three LWIR cameras and showed that the overall calibration mean reprojection error is less than 0.35 pixel. The proposed calibration board is beneficial in terms of precision, low-cost built-up and easy reusability.
Data Classification with Qubit Superposition Approach in Quantum Perceptron

Quantum Perceptron is a combination method of artificial neural network and the concept of quantum computing. There are several proposed methods of quantum neural network where the methods have their own approach by using the concept of computing quantum. However, for several methods do not give detail exposure in deciding neural network architecture. Neural network architecture is most influential in the process of learning and testing in data classification. Therefore, presented some models of artificial neural network architecture using the concept of quantum computing is a superposition qubit. The qubit superposition is used to transform attribute values in the dataset so that the dataset can be used in the learning and testing process to obtain the value of the learning rate and the accuracy of the test. The dataset used is User Knowledge Modeling Dataset from the UCI Machine Learning Repository which contains students' knowledge about DC electric machine topic. The final result of this research shows that the value of learning rate and accuracy level of testing is very good in classifying the data.
Information Extraction from Arabic Law Documents

Information hidden in unstructured or semi-structured law documents can be very useful but may not be readily accessible. To get this information, an information extraction (IE) system is needed. Making extracted information available in structured form enables answering complex queries that may go well beyond simple keyword search and thus may be of interest to law professionals. In this paper we address the issue of Arabic information extraction from law documents. We describe a system we developed to extract important information, that may be of interest to potential users of these documents, with minimal human intervention. We employs a hybrid approach that utilizes machine learning and rule-based methods and Arabic NLP to facilitate the extraction of needed information. The approach was applied to a limited class of Arabic law documents and we are working on extending it to other document types and to other fields.
Implementation of Generate and Test Algorithm for Junior High School Zoning System in Malang

Admission of new students in 2019 through zoning still faces many obstacles, one of which is the readiness of the application to determine the distance of a student's house from the nearest recommended school. The purpose of this study is to provide better recommendations regarding the shortest and most accurate distance in the zoning system for admission of junior high school students in Malang by using the Generate and Test algorithm without having to do it manually or using other map applications. The results of this study indicate that the distance value obtained from the haversine formula corresponds to the original distance measured through a zoning application that applies the Generate and Test algorithm to measure the closest distance from the student's location. The recommended distance is given through a process of repetition and comparison of nodes. By using the generate and test algorithm for the search process the shortest distance from the user to the destination school has an average time of 6.2MB, the level of accuracy is the same as the Haversine calculation.
Authorship Attribution using data from Reddit forum

As the online social networks become more and more part of people’s daily lives, analyzes of the content posted in these media to avoid the circulation of fake news or doubtful authorship become necessary. This paper analyzes comments from a Reddit forum community in order to evaluate different representations of the text and using artificial intelligence classification techniques for the authorship attribution in the context of social networks in the forum style. The results showed that, for each scenario, a given combination of classifier and selected characteristics (different representations) is more recommended and presents good efficiency in the distinction among authors.
Efficient Compression Algorithm for Multimedia Data

In this work, we consider the problem of Cosine Similarity preserving dimensionality reduction (compression) for the sparse binary dataset. [18] suggested a compression algorithm for high dimensional, sparse, binary data for preserving Inner product and Hamming distance. In this work, we show that their proposed algorithm also works well for Cosine Similarity. We present a theoretical analysis of the dimension reduction bound and complement it with rigorous experimentation on real-world datasets. We compare our results with the state-of-the-art for the considered problem - SimHash [8], MinHash [21], Circulant Binary Embedding [25], and Densified one Permutation Hashing [20], and show that our result offers a significant saving in the compression time and the number of random bits required for the compression, and simultaneously provides comparable performance.
CLRGaze: Contrastive Learning of Representations for Eye Movement Signals

Eye movements are intricate and dynamic biosignals that contain a wealth of cognitive information about the subject. However, these are ambiguous signals and therefore require meticulous feature engineering to be used by machine learning algorithms. We instead propose to learn feature vectors of eye movements in a self-supervised manner. We adopt a contrastive learning approach and propose a set of data transformations that encourage a deep neural network to discern salient and granular gaze patterns. This paper presents a novel experiment utilizing six eye-tracking data sets despite different data specifications and experimental conditions. We assess the learned features on biometric tasks with only a linear classifier, achieving 84.6 % accuracy on a mixed dataset, and up to 97.3% accuracy on a single dataset. Our work advances the state of machine learning for eye movements and provides insights into a general representation learning method not only for eye movements but also for similar biosignals.
Exact O(N<sup>2</sup>) Hyper-Parameter Optimization for Gaussian Process Regression

Hyper-parameter optimization remains as the core issue of Gaussian process (GP) for machine learning nowadays. The benchmark method using maximum likelihood (ML) estimation and gradient descent (GD) is impractical for processing big data due to its O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) complexity. Many sophisticated global or local approximation models have been proposed to address such complexity issue. In this paper, we propose two novel and exact GP hyper-parameter training schemes by replacing ML with cross-validation (CV) as the fitting criterion and replacing GD with a non-linearly constrained alternating direction method of multipliers (ADMM) as the optimization method. The proposed schemes are of O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) complexity for any covariance matrix without special structure. We conduct experiments based on synthetic and real datasets, wherein the proposed schemes show excellent performance in terms of convergence, hyper-parameter estimation, and computational time in comparison with the traditional ML based routines.
Towards an Approach to Identify Obsolete Features based on Importance and Technical Debt

Many of today's software systems are maintained over years or even decades. To ensure that software remains useful, new features have to be added or old features have to be adapted to respond to new or changed requirements. As time goes on, some of the features become obsolete, i.e., are not needed anymore. Typically, these features are not removed because of various reasons, e.g., because removing them might be considered too costly, the costs of keeping unused features is considered low, or because of the "sunk cost fallacy", i.e., that a feature is considered worth to keep because of the previously invested resources (time, money or effort) to build it. The consequences of keeping unused source code can impact maintainability, technical debt, performance, and extensibility of the system. This can lead to lower development productivity and to a reduced innovation ability, consequently reducing competitiveness on the market. This paper aims to present an approach to identify features based on their value and on costs for keeping or removing them.
A robust medical image watermarking framework based on SVD and DE in Integer DCT domain (Workshop Paper)

A secure medical image watermarking framework using SVD, DE, step space filling curve and IntDCT is proposed. The proposed algorithm starts with partition of the host medical image into 8×8 blocks and then IntDCT is applied in each block. By collecting integer DC coefficients, a feeble resolution medical image is obtained. SVD is applied on this feeble resolution image. For maintaining a strong relationship between imperceptibility and robustness, the popular differential evolution (DE) technique is chosen to examine the most suitable scaling factors. The singular values of the host image are modified with the singular values of the watermark to insert the watermark in the host image. Here, DE is playing a very important role to determine the most suitable scaling factors to the embedded process for maintaining the dilemma between robustness and imperceptibility without degrading the nobility of medical images. To increase the security of the watermark image, first it is scuffled with the help of step space filling curve before the embedding process. The experimental observations and results proved the efficiency of the proposed framework. It maintains the quality of the watermarked images and the watermark can be extracted from the seriously malformed images.
Nonlinear Fuzzy Modelling of Dynamic Objects with Fuzzy Hybrid Particle Swarm Optimization and Genetic Algorithm

Algorithms based on populations are a very popular family of methods for solving optimization problems. One of the more frequently used representatives of this group is the Particle Swarm Optimization algorithm. The social learning mechanism used in the Particle Swarm Optimization algorithm allows this method to converge quickly. However, it can lead to catching the swarm in the local optimum. The solution to this issue may be the use of genetic operators whose random nature allows them to leave this point. The degree of use of these operators can be controlled using a neuro-fuzzy system. Such a mechanism exists in the FSHPSO-E algorithm presented in our previous paper. To test it, we used the set of benchmark functions widely adapted in the literature. The results proved effectiveness, efficiency, and scalability of this solution. In this paper, we show the effectiveness of this method in solving practical problems of optimization of fuzzy-neural systems used to model non-linear dynamic objects.
Real-time Environmental Control System for Disability Access under Node-MCU Platform

An Environmental Control Unit (ECU) is an electronic device that helps a person to perform daily tasks and control the surrounding environment. Hand gesture can be employed to perform this purpose via special wearable gloves. In this paper, we propose a low-cost Human-Computer Interaction (HCI) method using Node MCU platform. Finger and palm movement is captured based on the output of a flex and accelerometer sensors. The output voltage of the flex sensor represents the degree of a finger's bending while the output of the accelerometer determines the 3-axis of palm movement in order to operate up to three devices. This glove is connected to an Internet-of-Thing (IoT) platform that supports wireless communication through a web server in order to make the ECU wire-free and hence increase the portability of the system. Comparisons with the related work confirm that the proposed system is simple, accurate and easy to implement.
Navigation Application of Q-learning Neural Network

Traditional navigation application has a problem with adaption for changing environment and time complexity. Reinforcement learning gives a better solution for time complexity. Taking Q-learning for example, thought Q-value(score) for every state, Q-learning can choose action instantly, but it should take lots of memory to save Q-table (table for saving Q-value). Neural network is always a good idea to save memory and adapt in every kind of environment. By integrating the above concept, we finally use Deep Q-learning neural network (DQN) to do path planning and give a method to score the action and environment for make the choice of action.
Chapter 4. Patterns of intra-individual variation in a Swiss WhatsApp corpus

Analysing written data from a large WhatsApp corpus, which was collected in Switzerland in 2014, this study examines how individuals may change their linguistic behaviour over time and how such changes are connected to the language use of the interlocutor. Therefore, this is one of the very first studies analysing intra-individual real-time change and long-term accommodation processes in written digital data. Focussing on WhatsApp chats that are primarily written in Swiss German, evidence for the possibility of rapid changes within the language use of individuals on several linguistic levels is presented: dialectal variation, phoneme-grapheme correspondence, punctuation, lexis, and use of emojis/emoticons. Furthermore, based on multiple examples from the corpus, several different types of long-term accommodation processes in two-person chats are distinguished.
Introduction to Quantum Computing

Abstract This chapter gives a brief introduction to quantum computing, which is the discipline of studying algorithms based on the principles of quantum theory. It outlines the two fundamental quantum algorithms, which are known as Grover’s and Shor’s algorithm, which are able to solve number-theoretical problems that are intractable for present conventional computers. Thus, this chapter also shows the impact of these quantum algorithms on present cryptography under the assumption of the existence of a large-scale quantum computer, concluding that quantum computing poses a serious threat to public-key cryptosystems, because their underlying mathematical problems can be solved efficiently by using Shor’s algorithm.
Effective Cryptographic Renewal Method with Rivest-Shamir-Adleman, Modified Artificial Bee Colony Optimization to Achieve Secure Cloud Storage and Computation in Cloud Computing

With the tremendous development of the online information, the term “Cloud Computing” offers multiple kinds of outsourcing and storage services to the user. Data security and privacy issues introduces multiple hurdles into it such as data leakage, calculating the key for data encryption, lack of proper security policies and renewal of application in cloud environment. One of the main issues is how to store encrypted data, decrypt it when required and renewal of application to face the security challenges. In this work we have proposed a secured and timestamp value based approach using RSA-MABC algorithm for encryption and decryption process. The work has been simulated in Cloudsim and results show our approach takes less time for encryption and decryption with added security features of renewing the secret key based on timestamp. Our results of encryption and decryption time along with the results of t test shows that our work is better than exiting approaches.
Smart Tuition Finder: An Educational App and SDGs

Quality education is a basic right of every individual. People around the world agree that education is the key element in making progress towards a civilized society. In a culture where other than traditional schooling, assisting through tuition is common, finding a skillful and trustworthy tutor is always a difficult task. This becomes more difficult for metropolitan cities like Karachi where people face difficulty in finding a good tutor due to the lack of trust, safety, security and transportation costs. In this paper, we will discuss an application called, Smart Tuition Finder, a location-based searching system that can help in finding a tutor close to one's location. A recommendation system and a safe payment method make this system secure and reliable. The application is based on the concept of a shared economy and works on an Escrow model. We analyze and discuss how such an application can help in achieving Sustainable Development Goals(SDG). We use the Feature Vs Goals framework to analyze how specific features can help in achieving SDGs. Such a framework can help to understand which features can be incorporated into any existing or new application to achieve certain goals.