Reviewerly/exHarmony huggingface dataset:
Imperceptible AR Markers for Near-Screen Mobile Interaction

Owing to the pervasive use of displays and smartphones, mobile interactions with display screens have gained attention within the advertising and gaming industries as well as in human-computer interaction research. Communication through QR code-like markers and localization via AR markers are common examples of such interactions. However, these visible markers interfere with the display content; this problem is critical for localization over a wide range of interactions, and fewer markers result in less reliability and accuracy. Although some studies have addressed this issue, few have focused on near-screen interaction without additional hardware. To address this problem, we propose an easy-to-install localization method that uses an array of AR markers, which are made imperceptible to the human eye through chromaticity vibration at 30 Hz. We mainly focus on applications, such as digital signage, where users point their smartphones at the display content. Through four evaluations, we confirm that the pointing error is within 1 mm, and that the proposed system works, when the distance between the screen and smartphone is 4-24 times the size of the AR marker. In addition, we establish that our system is robust against rotation. Finally, we present two potential application scenarios, advertising and navigation.
Mint Centrality: A Centrality Measure for the Bitcoin Transaction Graph

In this work, we consider the graph of confirmed transactions in bitcoin. Understanding this graph is essential to discern the different economic activities conducted by the pseudonymous actors. In addition to traditional graph analysis methods, new metrics need to be engineered specifically for the bitcoin transaction graph. Hence, we propose a new centrality measure named mint centrality. The measure uses the inherent tree structure of transactions in bitcoin and their relation to the corresponding set of coinbase transactions, and can be evaluated with linear complexity. We present preliminary results of the mint centrality on the first 200,000 blocks of the public bitcoin blockchain.
Toward Proactive Refactoring: An Exploratory Study on Decaying Modules

Source code quality is often measured using code smell, which is an indicator of design flaw or problem in the source code. Code smells can be detected using tools such as static analyzer that detects code smells based on source code metrics. Further, developers perform refactoring activities based on the result of such detection tools to improve source code quality. However, such approach can be considered as reactive refactoring, i.e., developers react to code smells after they occur. This means that developers first suffer the effects of low quality source code (e.g., low readability and understandability) before they start solving code smells. In this study, we focus on proactive refactoring, i.e., refactoring source code before it becomes smelly. This approach would allow developers to maintain source code quality without having to suffer the impact of code smells. To support the proactive refactoring process, we propose a technique to detect decaying modules, which are non-smelly modules that are about to become smelly. We present empirical studies on open source projects with the aim of studying the characteristics of decaying modules. Additionally, to facilitate developers in the refactoring planning process, we perform a study on using a machine learning technique to predict decaying modules and report a factor that contributes most to the performance of the model under consideration.
Four equal-intensity foci generated by a Cantor–Thue–Morse zone plate

A Cantor–Thue–Morse zone plate (CTMZP) is proposed to generate four foci with equal intensities along the optic axis. Moreover, the intensity non-uniformity of four foci is low for the CTMZP with the large number of duplicate cells. In addition, the CTMZPs with the different sizes and numbers of duplicate cells can generate four equal-intensity foci with high intensities and unchanged intensity non-uniformity. Furthermore, four foci for the CTMZP located at the required axial positions, and the energy efficiency in the focus increases with the focal length. We will illustrate the construction method of the CTMZP in the details. It will be proved in the simulations and experiments that the proposed zone plate can generate four equal-intensity foci. The proposed zone plate can be applied to trap particles with an equal trapping force at four planes, and generate the same clear images of four planes in a single output plane of an image forming system simultaneously.
Training Generative Networks Using Random Discriminators

In recent years, Generative Adversarial Networks (GANs) have drawn a lot of attentions for learning the underlying distribution of data in various applications. Despite their wide applicability, training GANs is notoriously difficult. This difficulty is due to the min-max nature of the resulting optimization problem and the lack of proper tools of solving general (nonconvex, non-concave) min-max optimization problems. In this paper, we try to alleviate this problem by proposing a new generative network that relies on the use of random discriminators instead of adversarial design. This design helps us to avoid the min-max formulation and leads to an optimization problem that is stable and could be solved efficiently. The performance of the proposed method is evaluated using handwritten digits (MNIST) and Fashion products (Fashion-MNIST) data sets. While the resulting images are not as sharp as adversarial training, the use of random discriminator leads to a much faster algorithm as compared to the adversarial counterpart. This observation, at the minimum, illustrates the potential of the random discriminator approach for warm-start in training GANs.
Collaboration Matrix Factorization on Rate and Review for Recommendation

According to the sparseness of rating information, the quality of recommender systems has been greatly restricted. In order to solve this problem, much auxiliary information has been used, such as social networks, review information, and item description. Convolutional neural networks (CNNs) have been widely employed by recommender systems, it greatly improved the rating prediction's accuracy especially when combined with traditional recommendation methods. However, a large amount of research focuses on the consistency between the rating-based latent factor and review-based latent factor. But in fact, these two parts are completely different. In this article, the authors propose a model named collaboration matrix factorization (CMF) that combines a projection method with a convolutional matrix factorization (ConvMF) to extract the collaboration between rating-based latent factors and review-based latent factors that comes from the results of the CNN process. Extensive experiments on three real-world datasets show that the projection method achieves significant improvements over the existing baseline.
Abnormality Detection Approach using Deep Learning Models in Smart Home Environments

The rising number of elderly populations has become a common concern in many countries. As one of the solutions, smart homes have been developed to help them live independently in their own homes. However, the accurate interpretation in monitoring human situations is still limited. This paper presents an abnormality detection approach that can monitor smart home residents' behavior and identify any abnormalities regarding their daily routines. In particular, this study investigates the use of two deep learning models that are commonly used in the pattern recognition communities, which are known as Multi-Layer Perceptron (MLP) and Recurrent Neural Network (RNN). The learned models are used to classify between normal and abnormal situations and their performance are then compared using a publicly available smart home dataset. Experimental results show that MLP has significant performance and outperforms RNN in terms of accuracy.
Fixpoint reuse for incremental JavaScript analysis

Frequently updated programs cause the cost of static analysis to be multiplied by the number of program versions. When the baseline cost is high (for example, analyzing JavaScript), this multiplicative factor can be prohibitive. As an example, JavaScript-based browser addons are continually updated and there are known instances where malicious code has been injected into such updates; thus the addons must be repeatedly vetted each time an update happens.
The Formal Reference Model for Software Requirements

The formal reference model for software requirements must be useful for specification of mappings from both functional and non-functional requirements. The topological functioning model (TFM) can serve as such reference model for specifying mappings from software requirements to functional characteristics and structure of the modeled system. Different types of mapping of functional requirements and their aspects such as completeness and overlapping have mathematical background and can be described using mathematical constructs (the inclusion predicate, disjoint predicate, covering predicate, projection, and separation family of functions), as well as meta-classes in the metamodel. This paper continues the previous work and illustrates the way how specification of the TFM functional characteristics and causal relationships can be extended and can represent mappings from the requirements as tuples of TFM functional features extended with requirements sets and mapping characteristics, namely, completeness and overlapping for functional requirements, and, additionally, scope and dynamic characteristics for non-functional ones. This allows formal tracing from the whole set of requirements to software implementing constructs via TFM elements and vice versa, that could be useful for further architectural decisions and development of test cases.
A QR Code Secret Hiding Scheme against Contrast Analysis Attack for the Internet of Things

Due to the advantages of larger content and error correction capability, quick response (QR) code is commonly used as a tagging technology for the Internet of Things (IoT) recently. However, the cover message of QR code can be easily decoded by a QR code reader, which causes the security and privacy of the cover message to raise the important issues. In this paper we present a new secret hiding scheme based on QR code. The proposed scheme has low computational complexity and is suitable for low-power devices in IoT systems because of utilizing the error correction property of QR code to hide secret information. The proposed scheme hides the secret information without changing the cover message of QR code and the user can get the cover message by using a general scanner, which contributes to reducing attacker’s curiosity. The hidden secret information can be read by a special scanner with the help of the user key. One thing which is better than other known schemes is that the proposed scheme can resist contrast analysis attack. In addition, experimental results show the proposed scheme has feasibility, low computational complexity, and high hiding payload.
WeChat knowledge service system of university library based on SoLoMo: A holistic design framework

In this study, we develop a WeChat knowledge service system (WKSS) in university library based on SoLoMo. The aim is to build a comprehensive, open, mobile and smart knowledge service environment. It can realise the interaction between the three users, library and knowledge, and promote the dissemination and sharing of knowledge. By referencing the Internet frontier concept SoLoMo, this study designs a new mobile smart service system, including the system architecture design, the content design and the data association design. Then, this study develops the system, including the running environment configuration, the development of workflow, the core module and the system implementation. This system enables the provision of accurate, specific and more personalised service to each user. It also includes a portable mobile terminal to increase the accuracy of context awareness and enhance user convenience. This study makes up for the shortcomings of the library and increases the functions of personalisation, mobility and intelligence. It extends the way of mobile service in libraries and provides readers with better library mobile services, which was liked by readers.
Simultaneous meta-data and meta-classifier selection in multiple classifier system

In ensemble systems, the predictions of base classifiers are aggregated by a combining algorithm (meta-classifier) to achieve better classification accuracy than using a single classifier. Experiments show that the performance of ensembles significantly depends on the choice of meta-classifier. Normally, the classifier selection method applied to an ensemble usually removes all the predictions of a classifier if this classifier is not selected in the final ensemble. Here we present an idea to only remove a subset of each classifier's prediction thereby introducing a simultaneous meta-data and meta-classifier selection method for ensemble systems. Our approach uses Cross Validation on the training set to generate meta-data as the predictions of base classifiers. We then use Ant Colony Optimization to search for the optimal subset of meta-data and meta-classifier for the data. By considering each column of meta-data, we construct the configuration including a subset of these columns and a meta-classifier. Specifically, the columns are selected according to their corresponding pheromones, and the meta-classifier is chosen at random. The classification accuracy of each configuration is computed based on Cross Validation on meta-data. Experiments on UCI datasets show the advantage of proposed method compared to several classifier and feature selection methods for ensemble systems.
On the Usefulness of Different Expert Question Types for Fault Localization in Ontologies

When ontologies reach a certain size and complexity, faults such as inconsistencies or wrong entailments are hardly avoidable. Locating the faulty axioms that cause these faults is a hard and time-consuming task. Addressing this issue, several techniques for semi-automatic fault localization in ontologies have been proposed. Often, these approaches involve a human expert who provides answers to system-generated questions about the intended (correct) ontology in order to reduce the possible fault locations. To suggest as few and as informative questions as possible, existing methods draw on various algorithmic optimizations as well as heuristics. However, these computations are often based on certain assumptions about the interacting user and the metric to be optimized. In this work, we critically discuss these optimization criteria and suppositions about the user. As a result, we suggest an alternative, arguably more realistic metric to measure the expert’s effort and show that existing approaches do not achieve optimal efficiency in terms of this metric. Moreover, we detect that significant differences regarding user interaction costs arise if the assumptions made by existing works do not hold. As a remedy, we suggest a new notion of expert question that does not rely on any assumptions about the user’s way of answering. Experiments on faulty real-world ontologies testify that the new querying method minimizes the necessary expert consultations in the majority of cases and reduces the computation time for the best next question by at least 80 % in all scenarios.
Optimal Collision-Free Navigation for Multi-Rotor UAV Swarms in Urban Areas

The use of micro unmanned aerial vehicles (UAVs) have gained a lot of interests especially for smart city applications due to their three- dimensional (3D) mobility and flexibility. Path planning is one of the most important UAV problems that needs to be addressed. In this paper, we focus on determining the optimal routes for a fleet of UAVs in urban cities that minimize the total arrival time of all UAV swarms while respecting their energy consumption constraints and avoiding the risk of collision. Through a mixed integer linear program, we develop a safe navigation framework for realistic 3D maps where charging stations are made available to recharge UAV batteries on their ways to destination. Our results investigate different scenarios for selected system parameters and illustrate different collision avoidance techniques followed by the UAVs.
MLP-IA: Multi-label User Profile Based on Implicit Association Labels

Multi-Label user profile is widely used and have made great contributions in the field of recommendation systems, personalized searches, etc. Current researches on multi-label user profile either ignore the associations among labels or only consider the explicit associations among them, which are not sufficient to take full advantage of the internal associations. In this paper, a new insight is presented to mine the internal correlation among implicit association labels. To take advantage of this insight, a multi-label propagation method with implicit associations (MLP-IA) is proposed to get user profile. A probability matrix is first designed to record the implicit associations and then combine the multi-label propagation method with this probability matrix to get more accurate user profile. Finally, this method proves to be convergent and faster than traditional label propagation algorithm. Experiments on six real-world datasets in Weibo show that, compared with state-of-the-art methods, our approach can accelerate the convergence and its performance is significantly better than the previous ones.
Evolving graphs with horizontal gene transfer

We introduce a form of neutral Horizontal Gene Transfer (HGT) to Evolving Graphs by Graph Programming (EGGP). We introduce the µ × λ evolutionary algorithm, where µ parents each produce λ children who compete with only their parents. HGT events then copy the entire active component of one surviving parent into the inactive component of another parent, exchanging genetic information without reproduction. Experimental results from 14 symbolic regression benchmark problems show that the introduction of the µ × λ EA and HGT events improve the performance of EGGP. Comparisons with Genetic Programming and Cartesian Genetic Programming strongly favour our proposed approach.
A hierarchical autoencoder learning model for path prediction and abnormality detection

In this paper, we introduce an unsupervised hierarchical framework for modeling trajectories in surveillance scenarios. Inspired by the object recognition field, a novel feature representation optimized for a neural network learning architecture is proposed. Low levels of the hierarchy capture local spatio-temporal motion attributes such as spatial orientation and speed, while higher levels contribute to obtaining richer semantic information. The bottom-up construction of the hierarchical framework exploits the inherent statistical correlations between neighboring elements using an increasing spatio-temporal grid. Cross-entropy based optimization in combination with autoencoders is used to learn weights for subsequent hierarchical layers. Finally, the Bayesian probabilistic framework built on top of the hierarchical model is proposed for applications such as long-term path prediction and abnormality detection. We demonstrate the efficiency of the proposed model on both indoor and outdoor datasets, achieving results comparable with state-of-the-art methods.
Big Bangs and Small Pops: On Critical Cyclomatic Complexity and Developer Integration Behavior

Continuous integration is believed by many to improve software quality, including cyclomatic complexity. In this paper an exploratory study investigates the relationship between continuous integration behavior among developers and the cyclomatic complexity - particularly at critical levels - of the source code they commit, as a correct understanding of the consequences of a software development practice, particularly one as popular as continuous integration, is of crucial importance to industry professionals when deciding on improvement efforts. It is found that developer behavior differs significantly, as does their knowledge and awareness of cyclomatic complexity as a concept. We find that even though large commits ("big bangs") contribute proportionately slightly more to cyclomatic complexity, the effect is much less pronounced for critical cyclomatic complexity. It is also found that complex code tends to overlap with high levels of change activity, and that some developers deliberately change their integration behavior in the face of such hotspots, as a strategy to avoid merge conflicts.
Towards a reference model for sensor-supported learning systems

Sensor-supported systems have been adapted for various domains, such as healthcare, sports, and education. In this paper, we propose a conceptual reference model of sensor-supported learning systems dimensions based on a review of recent articles in this field. The reference model is based on the identification of different observable properties of learning systems, how sensors are used to support learning, and educational objectives. The proposed reference model can be used to represent the capabilities of current sensor-supported learning systems and provide a basis for the development of future systems.
InterCriteria Analysis of Different Hybrid Ant Colony Optimization Algorithms for Workforce Planning

Every organization andFidanova, Stefka factory optimize their production process with a help of workforce planing. The aim is minimization of the assignment costs of the workers, who will do the jobs. The problem is very complexRoeva, Olympia and needs exponential number of calculations, therefore special algorithms are developed to be solved. The problem is to select employers and to assign them to the jobs to be performed. This problem has very strong constraints and it is difficult to find feasible solutions. The objective is to fulfil the requirements and to minimizeLuque, Gabriel the assignment cost. We propose a hybrid Ant Colony Optimization (ACO) algorithm to solve the workforce problem, which is a combination between ACO and an appropriate local search procedure. In thisPaprzycki, Marcin investigation InterCriteria Analysis (ICrA) is applied over numerical results obtained from ACO algorithms with the suggested different variants of local search procedures. Based on ICrA the ACO hybrid algorithms performance is examined and compared.