Reviewerly/exHarmony huggingface dataset:
Quantitative Analysis of Mobile Application User Interface Design

With the development and proliferation of mobile devices, the industry of mobile applications has grown into a massive market. Well-designed user interfaces are essential for a high-quality and popular mobile application. To understand the basic principles of UI designing, this work employs a data-driven quantitative approach. In this work, we build a dataset of mobile UI and propose a systematic approach for quantitative analysis of UI design using five measurable metrics. Our dataset and analytical methods make it possible to correlate the quantitative metrics with the qualitative features so that the quality of UI designs can be reliably studied and optimized. The evaluation shows that these proposed metrics can be correlated with the qualitative features, and some UI designing suggestions or insights are generated.
The Protection of Database Copyright in the Era of Big Data

Abstract The development of computer technology, mobile intercommunication, cloud computing and Internet of things embraces a limitless prospect for the application of big data. The paper reviews the definition of big data and summarizes the design features by proposing “the Five-Star Feature Model”. The paper then takes the protection of English corpus constructed in China as one special kind of big data to demonstrate the necessity of establishing a comprehensive protection legal system by revealing the setbacks of the Copyright Law in China. With reference to academic literature in China and other regulations in Western countries concerning copyright protection of database, the author finally recommends the complementary use of Law of the PRC against Unfair Competition for the commercial use of these corpora and further proposes to set a new special database right to guarantee the balance of copyright protection and public use of these big data.
A Flight Formation Control of a Micro Aerial Vehicle Swarm using a Motion Capture

Unmanned Aerial Vehicles (UAVs) have been used widely since they can be applied in various missions. Using a single UAV may not be able to carry out the mission thoroughly and efficiently for current operational demandings. Therefore, we are interested in designing a flight formation controller of a small fleet of four micro UAVs, called crazyflie, using a kinect motion capture together with an indoor local positioning system. The flight test results show that the crazyflie swarm can fly in a formation within the designated area using the centralized control approach. Moreover, the flight formations of the swarm such as a triangle pattern, a square pattern and a line pattern can be controlled using the movements of the body.
Evaluation of Machine Learning Algorithms for Worker’s Motion Recognition Using Motion Sensors

Construction tasks involve various activities composed of one or more body motions. It is essential to understand the dynamically changing behavior and state of construction workers to manage construction workers effectively with regards to their safety and productivity. While several research efforts have shown promising results in activity recognition, further research is still necessary to identify the best locations of motion sensors on a worker’s body by analyzing the recognition results for improving the performance and reducing the implementation cost. This study proposes a simulation-based evaluation of multiple motion sensors attached to workers performing typical construction tasks. A set of 17 inertial measurement unit (IMU) sensors is utilized to collect motion sensor data from an entire body. Multiple machine learning algorithms are utilized to classify the motions of the workers by simulating several scenarios with different combinations and features of the sensors. Through the simulations, each IMU sensor placed in different locations of a body is tested to evaluate its recognition accuracy toward the worker’s different activity types. Then, the effectiveness of sensor locations is measured regarding activity recognition performance to determine relative advantage of each location. Based on the results, the required number of sensors can be reduced maintaining the recognition performance. The findings of this study can contribute to the practical implementation of activity recognition using simple motion sensors to enhance the safety and productivity of individual workers.
Cellular Automata based Decentralized Cooperative Collision Avoidance Control for Multiple Mobile Robots

One of the main challenges in the decentralized implementation of mobile multi-robotic systems is the avoidance of collisions among agents and other obstacles in cluttered environments as well as the convergence of each robot to its desired destination. This paper presents a novel decentralized, cooperative navigation algorithm for a team of mobile robots based on the concept of cellular automata that is proven to guarantee collision avoidance at all times even under the presence of static non-cooperative obstacles. The algorithm does not need to differentiate among agents and obstacles and use a time-varying localization-based priority rule to guarantee safe motion while reducing the occurrence of deadlocks. A Monte Carlo simulation is performed showcasing the performance of the algorithm under different densities of obstacles and agents.
Architecture and Design Patterns for Distributed, Scalable Augmented Reality and Wearable Technology Systems

This paper presents a novel reference software architecture and supporting pattern language for an augmented reality authoring and training system. Industry-based augmented reality training is considered an essential element of the next techno-industrial revolution. These next generation learning environments allow a trainee to offload complexity and giving them live (or on-demand) feedback on their progress and performance through workplace augmentation is already being taken up by industry forerunners. This reference architecture - for wearable experience for knowledge intensive training - incorporates head-mounted augmented vision, an array of wearable sensors that monitor movement and physiological signals, a data-layer managing sensor data and a cloud-based repository for storing information about the activity and workplace. Moreover, this architecture has been tested in a range of knowledge intensive workplaces, in the aeronautic, medical and space industries. Two iterations of the architecture were developed and validated, together with over 500 participants, producing datasets on activity performance, physiological state and assessment of the platform. The components and links of the architecture are presented here as generalizable design patterns to support wider development. We then propose a pattern language for augmented reality training applications.
Mutual Relationship between the Neural Network Model and Linear Complexity for Pseudorandom Binary Number Sequence

Machine learning (ML) technology has been getting popular in many applications where ML purposes to analyze or classify data, or predicting the phenomenon follows from the previous conditions, for example. However, the spread of ML technologies allows an attacker to use them into the attack for the sake of sniffing secret information. Since the randomness has been used for an inseparable part of the cryptographic applications to ensure the security, the resistance of a random sequence against analysis based on the ML technologies have to be required. The authors anticipate having the mutual relationship between the classical properties of the randomness, linear complexity (LC) in particular, and the structure of a neural network (NN), which is a class of ML. In this research, the authors find that the strength of each connection between nodes in the NN is relevant to the linear recurrence relation of the target sequence by observing parameters after complete learning. In other words, the difficulty of predicting the next bits from a given sequence would be discussed based on the LC of a sequence in most cases. The experimental results are introduced to clarify the black box in this research.
Combining SentiStrength and Multilayer Perceptron in Twitter Sentiment Classification

The advancement of internet technology has caused the use of social media to become the people lifestyle. The company and the government use social media as instant feedback to get user sentiments regarding their comments or reviews. The sentiment is an opinion or view that based on excessive feelings towards something. The method for knowing positive or negative sentiments from someone’s comments can be done manually by humans to analyze comments one by one or automatically by machine learning to do classifications. Machine learning requires training data and test data that have positive and negative labels. Generally, data labeling is done manually by humans. In this study, we used machine learning to classify sentiments with data collected from Twitter. Machine learning method used is Multilayer Perceptron and Naive Bayes as a comparison. Labeling dataset using manual method. For addition training data, labeled data was generated using an English lexicon dictionary called SentiStrength. Feature extraction uses vectorization and TF-IDF. This study aims to measure effect of adding training data generated using SentiStrength from unlabeled data during learning process to accuracy of machine learning model. Classification model testing uses data of 627 tweets. The result is addition of training data to increase average accuracy by 5% of initial accuracy. Multilayer Perceptron is more accurate than Naive Bayes with the highest accuracy ratio of 77.71% and 76.07%.
Semantic Modeling Approach for Software Vulnerabilities Data Sources

Data sources describing software security vulnerabilities are commonly used by software engineers not only increase the security of software systems but also enhance software productivity and reduce maintenance costs. However, with the constantly growing amount of available security vulnerability information and this information being spread across heterogeneous resources, software developers are struggling in taking full advantage of these resources. The Semantic Web and its supporting technology stack have been widely promoted to support the modeling, reuse and interoperability among heterogeneous data sources. In our research we present a Semantic Web enabled knowledge model which provides a formal and semi-automated approach for unifying vulnerability information resources. As part of this knowledge modeling approach, we also take advantage of Formal Concept Analysis (FCA) to identify vulnerability related knowledge concepts and model them at various abstraction levels. We illustrate the applicability and flexibility of our approach through several usage examples that take advantage of our unified knowledge model and Semantic Web inference services to provide new types of vulnerability analysis.
Bilateral Loop Filter in Combination with SAO

This paper describes a bilateral filter that is being proposed as a coding tool for the Versatile Video Codec (VVC). The filter acts as a loop filter in parallel with the sample-adaptive offset (SAO) filter. Both the proposed filter and SAO act on the same input samples, each filter produces an offset, and these offsets are then added to the input sample to produce an output sample that, after clipping, goes to the next stage. The method has been implemented and tested according to the common test conditions in VVC test model version 5.0. For the all-intra configuration, we report a BD rate figure of -0.4% with an encoder run time increase of 6% and a decoder run time increase of 4%. For the random access configuration, the BD rate figure is -0.5% with an encoder run time increase of 2% and a decoder run time increase of 2%.
Counter based suffix tree for DNA pattern repeats

In recent years, the string datasets have increased exponentially, so is the need to process them. Most of these datasets have been deeply rooted in the field of bioinformatics since the entire characteristics of any living organism is encoded in their genes. Genes consist of nucleic bases which will, therefore, makeup the entire genome. A genome is made of a concatenation of different types of nucleic bases. To efficiently extract the information encrypted in these sequences there is a need to use algorithms to decrypt it. Most available methods use the data structure commonly referred to as the suffix tree. They have tremendously evolved over the years, and the on-line construction of the suffix tree is deemed as the best data structure, however, it is not optimal when it comes to finding repeated sequences because of many traversals algorithm will have to do when identifying repeats. To improve the speed and of finding repeats we developed a counter based suffix tree algorithm. Our work presents a novel algorithm of constructing a counter based suffix tree without losing its properties. The counter based suffix tree time complexity is θ(n) where n represents the length of a string. Which is the same as the fastest suffix tree implementation. We have shown that the counter based suffix tree will reduce the search time when identifying repeats. We have proved that a counter based suffix tree can be developed during construction.
More About Covariance Descriptors for Image Set Coding: Log-Euclidean Framework Based Kernel Matrix Representation

We consider a family of structural descriptors for visual data, namely covariance descriptors (CovDs) that lie on a non-linear symmetric positive definite (SPD) manifold, a special type of Riemannian manifolds. We propose an improved version of CovDs for image set coding by extending the traditional CovDs from Euclidean space to the SPD manifold. Specifically, the manifold of SPD matrices is a complete inner product space with the operations of logarithmic multiplication and scalar logarithmic multiplication defined in the Log-Euclidean framework. In this framework, we characterise covariance structure in terms of the arc-cosine kernel which satisfies Mercer's condition and propose the operation of mean centralization on SPD matrices. Furthermore, we combine arc-cosine kernels of different orders using mixing parameters learnt by kernel alignment in a supervised manner. Our proposed framework provides a lower-dimensional and more discriminative data representation for the task of image set classification. The experimental results demonstrate its superior performance, measured in terms of recognition accuracy, as compared with the state-of-the-art methods.
Coverless Image Steganography Framework with Increased Payload Capacity

This paper proposes a coverless image steganog- raphy framework with increased payload hiding capacity for confidential information transmission. This is superior to existing coverless schemes which require transmitting a stream of stego images in sequence due to the issue of small hiding capacity per stego image. In addition, by directly linking the secret with existing or generated images, coverless schemes are proven to be more secure as compared to traditional image steganography which embeds confidential message by modifying pixel values of a given image (known as cover). Specifically, we build a dictionary- based encoding scheme using images of the MNIST handwritten digit dataset to generate the stego image. In this dataset, thousands of images carry the same digit but have variance in their shapes and pixel depth. Thus it serves as a good codebook to represent information. The proposed coverless steganography scheme holds necessary resistance to image processing operations like JPEG compression, image binarization, image scaling and image noise, i.e., the hidden message can be successfully decoded from the processed stego images.
A Zero-Knowledge Proof System with Algebraic Geometry Techniques

Current requirements for ensuring data exchange over the internet to fight against security breaches have to consider new cryptographic attacks. The most recent advances in cryptanalysis are boosted by quantum computers, which are able to break common cryptographic primitives. This makes evident the need for developing further communication protocols to secure sensitive data. Zero-knowledge proof systems have been around for a while and have been considered for providing authentication and identification services, but it has only been in recent times that its popularity has risen due to novel applications in blockchain technology, Internet of Things, and cloud storage, among others. A new zero-knowledge proof system is presented, which bases its security in two main problems, known to be resistant, up to now, against quantum attacks: the graph isomorphism problem and the isomorphism of polynomials problem.
DWT Based Compression Algorithm on Acne Face Images

Every day, a huge figure of medical face images are created by dermatologist and medical imaging center for research, surgical, and acne diagnostics. All of such images are kept for future remark and citation of the patients consequently, such medical images are required to endure the compression process prior to its storage. Acne face images have several parts which are having more information than the supplementary part of face called the region of interest (ROI). The lossless compression approach is essential in archival and communiqué of remedial images and it is found that discrete wavelet transform is used in this rationale. In this paper, a new algorithm is anticipated to choose automatically a part or patch of an image from the whole image which contains more information and is to be sent uncompressed.
Design and Implementation of Ludo Game Using Automata Theory

Computation and automata theory is very frequently used in the lexical analysis of compilers and programming languages, image compression, bioinformatics, morphological analysis etc., This research work proposed the design of Ludo game using automata and computation theory. Deterministic finite state automata (DFA) and Non-Deterministic finite state automata (NDFA) are used at different levels to design LUDO for multi-player gaming.
Dense Cross-Modal Correspondence Estimation With the Deep Self-Correlation Descriptor

We present the deep self-correlation (DSC) descriptor for establishing dense correspondences between images taken under different imaging modalities, such as different spectral ranges or lighting conditions. We encode local self-similar structure in a pyramidal manner that yields both more precise localization ability and greater robustness to non-rigid image deformations. Specifically, DSC first computes multiple self-correlation surfaces with randomly sampled patches over a local support window, and then builds pyramidal self-correlation surfaces through average pooling on the surfaces. The feature responses on the self-correlation surfaces are then encoded through spatial pyramid pooling in a log-polar configuration. To better handle geometric variations such as scale and rotation, we additionally propose the geometry-invariant DSC (GI-DSC) that leverages multi-scale self-correlation computation and canonical orientation estimation. In contrast to descriptors based on deep convolutional neural networks (CNNs), DSC and GI-DSC are training-free (i.e., handcrafted descriptors), are robust to cross-modality, and generalize well to various modality variations. Extensive experiments demonstrate the state-of-the-art performance of DSC and GI-DSC on challenging cases of cross-modal image pairs having photometric and/or geometric variations.
Embedded Real-Time System for Traffic Sign Recognition on ARM Processor

This article proposes the design of a novel hardware embedded system used for automatic real-time road sign recognition. The algorithm used was implemented in two main steps. The first step, which detects the road signs, is performed by the maximally stable extremal region method on HSV color space. The second step enables the recognition of the detected signs by using the oriented fast and rotated brief features method. The novelty of the embedded hardware system, on an ARM processor, leads to a real-time implementation of the ADAS applications. The proposed system was tested on the Belgium Traffic Sign Detection and Recognition Benchmark and on the German Traffic Signs Datasets. The proposed approach attained a high detection and recognition rate with real-world situations. The achieved results are acceptable when compared to state-of-the-art systems.
Whitebox Induction of Default Rules Using High-Utility Itemset Mining

We present a fast and scalable algorithm to induce non-monotonic logic programs from statistical learning models. We reduce the problem of search for best clauses to instances of the High-Utility Itemset Mining (HUIM) problem. In the HUIM problem, feature values and their importance are treated as transactions and utilities respectively. We make use of TreeExplainer, a fast and scalable implementation of the Explainable AI tool SHAP, to extract locally important features and their weights from ensemble tree models. Our experiments with UCI standard benchmarks suggest a significant improvement in terms of classification evaluation metrics and training time compared to ALEPH, a state-of-the-art Inductive Logic Programming (ILP) system.
When to remember where you came from

For trajectory data that tend to have beyond first-order (i.e., non-Markovian) dependencies, higher-order networks have been shown to accurately capture details lost with the standard aggregate network representation. At the same time, representation learning has shown success on a wide range of network tasks, removing the need to hand-craft features for these tasks. In this work, we propose a node representation learning framework called EVO or Embedding Variable Orders, which captures non-Markovian dependencies by combining work on higher-order networks with work on node embeddings. We show that EVO outperforms baselines in tasks where high-order dependencies are likely to matter, demonstrating the benefits of considering high-order dependencies in node embeddings. We also provide insights into when it does or does not help to capture these dependencies. To the best of our knowledge, this is the first work on representation learning for higher-order networks.
Human Face Sentiment Classification Using Synthetic Sentiment Images with Deep Convolutional Neural Networks

Image is one of the most important ways for users to express their emotions on social networks. In this paper, we use the deep convolutional neural networks to solve the problem of image sentiment analysis from visual content. Because training a neural network requires a large number of data sets to provide good training performance, we cannot obtain such a real human emotion training set, because emotions are subjective, and multiple people need to provide annotations for the images, which requires a lot of manpower. This study proposes to incorporate synthetic face images into the training set to substantially increase the size of the training set. We use only synthetic face images, real facial images, and mixtures of synthetic and real facial images in the training set. Our experiments show that by using only 4026 real images, where each image is supplemented by the synthetic image to the same data set size (Anger: 1063 + 937 true, Disgust: 1857 + 143 true, Fear: 1802 + 198 true, Happy: 2000 true, Sad: 1252 + 748 true) total of 10,000 images, can reach 87.79%, 74.19%, 86.99%, 79.80% average testing accuracy in each testing set in human face sentiment classification.