Reviewerly/exHarmony huggingface dataset:
A New Location-Based Topic Model for Event Attendees Recommendation

Event-based social networks (EBSNs) have gained increasing popularity and rapid growth, EBSNs provide services for users to create events and make plan to attend. Developing and creating recommendation models are important and hot issues in EBSNs in recent years, such as event recommendation to users. Although several recommendation models have been proposed, event attendees recommendation models are not fully studied. In this paper, we study the event attendees recommendation problem through empirical experiments. Because of the nature of new events and severe data sparsity in EBSNs, traditional recommender systems work less efficiently for event attendees recommendation problem. To solve this problem, we propose a new location-based topic model that is based on scores of users computed from three major factors extracted from previously attended events, namely content, location and time. The proposed model includes two phases. The first phase uses the topic modeling Latent Dirichlet Allocation (LDA) and Jensen Shannon divergence to compute the similarity of events based on their contents. The spatial and temporal factors are also calculated. The scores of previous events with an upcoming event are computed from a combination of these three factors. Previous events with high scores are selected, then users who are extracted from the selected events are scored by temporal factors of these events in the second phase. Finally, we recommend users with top scores to the upcoming event. A series of experiments were conducted on real data collected from Meetup Event and the results have demonstrated the improvement of our model over baseline methods.
Cybersecurity in the Era of Data Science: Examining New Adversarial Models

The ever-increasing volume, variety, and velocity of threats dictates a big data problem in cybersecurity and necessitates deployment of AI and machine-learning (ML) algorithms. The limitations and vulnerabilities of AI/ML systems, combined with complexity of data, introduce a new adversarial model, which is defined and discussed in this article.
Quality-Sensitive Training! Social Advertisement Generation by Leveraging User Click Behavior

Social advertisement has emerged as a viable means to improve purchase sharing in the context of e-commerce. However, humanly generating lots of advertising scripts can be prohibitive to both e-platforms and online sellers, and moreover, developing the desired auto-generator will need substantial gold-standard training samples. In this paper, we put forward a novel seq2seq model to generate social advertisements automatically, in which a quality-sensitive loss function is proposed based on user click behavior to differentiate training samples of varied qualities. Our motivation is to leverage the clickthrough data as a kind of quality indicator to measure the textual fitness of each training sample quantitatively, and only those ground truths that satisfy social media users will be considered the eligible and able to optimize the social advertisement generation. Specifically, under the qualified case, the ground truth should be utilized to supervise the whole training phase as much as possible, whereas in the opposite situation, the generated result ought to preserve the semantics of original input to the greatest extent. Simulation experiments on a large-scale dataset demonstrate that our approach achieves a significant superiority over two existing methods of distant supervision and three state-of-the-art NLG solutions.
Maximal sensitivity of Boolean nested canalizing functions

Boolean nested canalizing functions (NCF) have important applications in molecular regulatory networks, engineering and computer science. In the literature, there are two sensitivities to measure the complexity of a Boolean function. One is average sensitivity, the other one is maximal sensitivity. We follow the tradition and omit the word "maximal". In other words, in this paper, sensitivity is always maximal sensitivity. Using the past work of the authors and their coauthors on a characterization of NCF, we obtain the formula of the sensitivity of any NCF. We find that the sensitivity of any NCF is between ⌈n+22⌉ and n. Both lower and upper bounds are tight. We prove that the block sensitivity, hence the l-block sensitivity, is the same as the sensitivity for NCF. It is well known that monotone Boolean functions (MBF) also have this property. We characterize all functions which are both monotone and nested canalizing (MNCF). The closed formula of the cardinality of the set of MNCFs is also provided.
Developing Wood Identification System by Local Binary Pattern and Hough Transform Method

The aim of this paper is to develop the wood identification system using two methods, called, Local Binary Pattern (LBP) and Hough Transform (HT). Here, 12 (twelve) varies of wood species form Indonesia will be used as the data sets. The wood species are taken from wood anatomy laboratory, Puslitbang Hasil Hutan (P3HH). Here, the classification method of this research uses Euclidean Distance (ED) to determine the distance of two images of wood. From the classification results, using LBP method is shown better than HT. The weakness of HT method in this paper is HT method can only detecting the circle shape rather than arbitrary shape. By using LBP method, 6 of 12 species are 100% accurate detected. Beside, using HT method, only one species (Cratoxylon formosum) has accuracy 90%.
The integration of biometrics and AI

We've all been entertained by surreal examples of artificial intelligence (AI) in sci-fi movies: from smart homes that trigger a lockdown at the slightest hint of danger, to facial security scans that only grant access to invited guests. But these technologies are no longer confined to the world of fiction – they are becoming a reality for consumers.
Effects of Differential Geometry Parameters on Grid Generation and Segmentation of MRI Brain Image

Accurate segmentation of brain tissue in magnetic resonance images (MRI) is a difficult task due to different types of brain abnormalities. In this paper, we review the deformation method focus on the construction of diffeomorphisms to generate the 2D MRI image grids and extend it to the 3D MRI images, address clearly a new formation of the deformation problem for moving domains and construction of diffeomorphisms through a completely different approach. The idea is to control directly the first order differential operators including the Jacobian determinant (JD), divergence (DIV), and curl vector (CV) generated by the 2D and 3D MRI image grids and use them as CNN channels with other modalities (T1-weighted, T1-IR, and T2-FLAIR) or single T1-weighted modality to improve the performance of brain segmentation. More importantly, we discuss the influence of three optimization parameters to the generation of the 2D and 3D MRI image grids by both theoretical analysis and the numerical experiments. We test this method on the IBSR dataset and MRBrainS18 dataset based on VoxResNet and verify the influence of three parameters on the accuracy of MRI brain segmentation. Finally, we also compare the segmentation performance of our method in three networks, including 2D U-Net, 3D U-Net, and VoxResNet. We believe the proposed method can advance the performance in brain segmentation and clinical diagnosis.
Vulnerability Assessment of Sensor Systems

There are more and more applications of sensors in today's world. Moreover, sensor systems are getting more complex and they are used for many high-risk security-critical purposes. Security assurance is a key issue for sensors and for other information technology (IT) products. Still, sensor security facilities and methodologies are relatively poor compared to other IT products. That is why a methodical approach to the sensor IT security is needed, i.e., risk management, implementation of countermeasures, vulnerability removal, and security evaluation and certification. The author proposes to apply the main security assurance methodology specified in ISO/IEC 15408 Common Criteria to solve specific security problems of sensors. A new Common Criteria compliant method is developed which specifies the vulnerability assessment process and related data in a structured way. The input/output data of the introduced elementary evaluation processes are modeled as ontology classes to work out knowledge bases. The validation shows that sensor-specific knowledge can be acquired during the vulnerability assessment process and then placed in knowledge bases and used. The method can be applied in different IT products, especially those with few certifications, such as sensors. The presented methodology will be implemented in a software tool in the future.
Towards a Practical Cluster Analysis over Encrypted Data

Cluster analysis is one of the most significant unsupervised machine learning methods, and it is being utilized in various fields associated with privacy issues including bioinformatics, finance and image processing. In this paper, we propose a practical solution for privacy-preserving cluster analysis based on homomorphic encryption (HE). Our work is the first HE solution for the mean-shift clustering algorithm. To reduce the super-linear complexity of the original mean-shift algorithm, we adopt a novel random sampling method called dust sampling approach, which perfectly suits with HE and achieves the linear complexity. We also substitute non-polynomial kernels by a new polynomial kernel so that it can be efficiently computed in HE. The HE implementation of our modified mean-shift clustering algorithm based on the approximate HE scheme HEAAN shows prominent performance in terms of speed and accuracy. It takes approx. 30 min with $$99\%$$ accuracy over several public datasets with hundreds of data, and even for the dataset with 262, 144 data, it takes 82 min only when SIMD operations in HEAAN is applied. Our results outperform the previously best known result (SAC 2018) by over 400 times.
Designing Graph Databases With GRAPHED

In recent years, graph database systems have become very popular and been deployed mainly in situations where the relationship between data is significant, such as in social networks. Although they do not require a particular schema design, a data model contributes to their consistency. Designing diagrams is an approach to satisfying this demand for a conceptual data model. While researchers and companies have been developing concepts and notations for graph database modeling, their notations focus on their specific implementations. In this article, the authors propose a diagram to address this lack of a generic and comprehensive notation for graph databases modeling, named GRAPHED (Graph Description Diagram for Graph Databases). The authors verified the effectiveness and compatibility of GRAPHED in two case studies: fraud identification, and a biological network model.
A Detection and Verification Model Based on SSD and Encoder-Decoder Network for Scene Text Detection

Text detection in natural scene image is challenging due to text variation in size, orientation, color and complex background, contrast, and resolution. In this paper, we focus on the long text detection in complex background. In order to deal with multi-scale text variation and exploit the recognition result to enhance the detection performance, we propose a detection and verification model based on SSD and encoder-decoder network for scene text detection. First, we present a text localization neural network based on SSD, which incorporates a text detection layer into the standard SSD model and can detect horizontal texts, especially long and dense Chinese texts in natural scenes more effectively. Second, a text verification model based on the encoder-decoder network is designed to recognize and verify the initial detection results, in order to eliminate non-text areas that are falsely detected as text areas. A series of experiments have been conducted on our constructed horizontal text detection dataset, which is composed of the horizontal text images in ICDAR 2017 Competition on Reading Chinese Text in the Wild (RCTW 2017) and some scene images taken by cameras. Compared with previous approaches, experimental results show that our method has achieved the highest recall rate of 0.784 and competitive precision rate in text detection, indicating the effectiveness of our proposed method.
Impact of Visuospatial Abilities on Perceived Enjoyment of Students toward an AR-Simulation System in a Physics Course

Visualization of electromagnetism phenomena is difficult to achieve for learners with low spatial abilities. We are addressing this difficulty by using augmented reality interfaces to create 3D magnetic field representations as aid to student conceptualization about electromagnetic phenomena. Results of the study suggest that students with low spatial abilities perceive more enjoyment when using the AR application than those who had slightly better spatial abilities.
Fueling AI with public displays?

Interactive public displays have matured into highly capable two-way interfaces. They can be used for efficiently delivering information to people as well as for collecting insights from their users. While displays have been used for harvesting opinions and other content from users, surprisingly little work has looked into exploiting such screens for the consensual collection of tagged data that might be useful beyond one application. We present a field study where we collected biometrically tagged data using public kiosk-sized interactive screens. During 61 days of deployment time, we collected 199 selfie videos, cost-efficiently and with consent to leverage the videos in any non-profit research. 78 of the videos also had metadata attached to them. Overall, our studies indicate that people are willing to donate even highly sensitive data about themselves in public but that, at the same time, the participants had specific ethical and privacy concerns over the future of their data. Our study paves the way forward toward a future where volunteers can ethically help advance innovations in computer vision research across a variety of exciting application domains, such as health monitoring and care.
Knowledge Complacency and Decision Support Systems

Decision support systems (DSS), which are often based on complex statistical, machine learning, and AI models, have increasingly become a core part of data analytics and sensemaking processes. Automation complacency - a state characterized by over-trust in intelligent systems - has the potential to result in catastrophic performance failure. An under-investigated factor in automation complacency research is the effect that DSS might have on human learning of domain concepts. In this paper, we perform a comparative analysis of two studies of users interacting with decision aids to understand how knowledge retention is affected by the competence and presentation of a DSS. Our results indicate that while humans have the opportunity to learn and internalize domain concepts while being supported by a DSS, features that make the DSS appear more competent, persuasive, or customizable may lead a user to form incorrect beliefs about a domain.
Hierarchical solution of non-convex optimal control problems with application to autonomous driving

This article considers the problem of planning collision-free reference trajectories for autonomous vehicles. Because optimal solutions to the problem may be hard to obtain in real time, we devise a procedure which yields potentially suboptimal but feasible solutions under standard assumptions. A hierarchical decomposition of the original problem based on a novel interpretation of the maneuver concept allows to consider independently the questions of which maneuver to choose and how to execute it in detail. The computation of trajectories relies on mixed-integer programming and can be performed efficiently based on the proposed decomposition, while controllable sets are used to assess the feasibility of a maneuver prior to its execution. An exemplary overtaking scenario demonstrates the efficacy of our approach.
When is Ontology-Mediated Querying Efficient?

In ontology-mediated querying, description logic (DL) ontologies are used to enrich incomplete data with domain knowledge which results in more complete answers to queries. However, the evaluation of ontology-mediated queries (OMQs) over relational databases is computationally hard. This raises the question when OMQ evaluation is efficient, in the sense of being tractable in combined complexity or fixed-parameter tractable. We study this question for a range of ontology-mediated query languages based on several important and widely-used DLs, using unions of conjunctive queries as the actual queries. For the DL ELHI⊥, we provide a characterization of the classes of OMQs that are fixed-parameter tractable. For its fragment ELH⊥ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dr</sup> , which restricts the use of inverse roles, we provide a characterization of the classes of OMQs that are tractable in combined complexity. Both results are in terms of equivalence to OMQs of bounded tree width and rest on a reasonable assumption from parameterized complexity theory. They are similar in spirit to Grohe's seminal characterization of the tractable classes of conjunctive queries over relational databases. We further study the complexity of the meta problem of deciding whether a given OMQ is equivalent to an OMQ of bounded tree width, providing several completeness results that range from NP to 2ExpTIME, depending on the DL used. We also consider the DL-Lite family of DLs, including members that, unlike εLHI <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">⊥</sub> , admit functional roles.
LAME

Multi-monitor setups are common in various domains such as stock exchange, ship bridges, and emergency control centers. We developed a light display for such a multi-monitor setup, that is controlled by eye-tracker input to guide the attention of a user from the currently focused display to another display, that is in demand of attention. Together with HCI experts for light displays we designed, implemented, and evaluated three different strategies for attention guidance with our light display in a user study. We found that ambient guidance cues that begin at the current focus of the user are faster in guiding the user to a target in the periphery than ambient guidance cues in the peripheral vision of the user.
Special issue on problem-solving, creativity and spatial reasoning

Problem-solving, creativity and spatial reasoning are high level abilities of cognitive systems with high potential for synergies. However, they have been treated separately by different fields. This special issue presents research work on these topics, aiming to observe their interrelations in order to create theoretical approaches, methodologies and computational tools to advance work on creativity and spatial problem-solving in cognitive systems.
Recognizing Multi-Agent Plans When Action Models and Team Plans Are Both Incomplete

Multi-Agent Plan Recognition (MAPR) aims to recognize team structures (which are composed of team plans) from the observed team traces (action sequences) of a set of intelligent agents. In this article, we introduce the problem formulation of MAPR based on partially observed team traces, and present a weighted MAX-SAT–based framework to recognize multi-agent plans from partially observed team traces with the help of two types of auxiliary knowledge to help recognize multi-agent plans, i.e., a library of incomplete team plans and a set of incomplete action models. Our framework functions with two phases. We first build a set of hard constraints that encode the correctness property of the team plans, and a set of soft constraints that encode the optimal utility property of team plans based on the input team trace, incomplete team plans, and incomplete action models. After that, we solve all of the constraints using a weighted MAX-SAT solver and convert the solution to a set of team plans that best explain the structure of the observed team trace. We empirically exhibit both effectiveness and efficiency of our framework in benchmark domains from International Planning Competition (IPC).
Using Scientific Visualization Techniques to Visualize Parallel Network Simulations

Although parallel discrete event simulation has been used to simulate and study the performance of various network topologies, little effort has been spent on visualizing the time series data that result from these simulations. Visualization can be useful in multiple aspects of simulation, from debugging and validating models to gaining deeper insights from the data. In this paper, we present our preliminary work in developing 3-dimensional animations of data from optimistic parallel discrete event simulations. The visualizations are developed by using VTK and ParaView, and examples are shown on fat-tree and dragonfly network models using the ROSS simulator. We also discuss our plans for future work with the visualizations and their integration into an in situ analysis and visualization system being currently developed.
On the secrecy of a simple and effective implementation of BB84 quantum cryptography protocol

A simple and effective fiber implementation of the BB84 protocol is proposed, which does not use phase modulators at the receiving side and do not require adjustment of the polarization state at the output of the communication channel. The implementation of the protocol permits to prove of the secrecy of keys based on the fundamental entropic uncertainty relations. The secret key length is expressed only in terms of the values observed on the reception side, with various detector parameters, such as quantum efficiency and the probability of dark counts, are taken into account automatically for the secret key length, but implicitly in terms of the observed parameters.
Optimized FPGA Implementation of the CRC Using Parallel Pipelining Architecture

Cyclic Redundancy Check (CRC) plays a major role in data storage, communication systems and networking environment fields to detect errors. The speed of transmitting data with optimized hardware utilization are the main challenges nowadays. Thus, CRC calculation becomes a bottleneck for the system implementations. The aim of this paper is to design and implement the CRC5 and CRC8 systems that are used for USB token packet and ATM protocols, respectively. A parallel pipelining method is used to implement the proposed CRC architecture for both CRC encoder and decoder systems to achieve high throughput data with optimized hardware resources. A proposed architecture doesn't base on Look-Up Table (LUT) to store pre-calculated CRC values or F-matrix in its implementation as in the previous works. The system designed and implemented based on Spartan-3E FPGA chip, Very High-Speed Integrated Circuit Description Language (VHDL) used to write the related code. The whole proposed architecture is functionally simulated and verified using the Xilinx ISE 9.2i simulator.
Optimal Linear Exponential Quadratic Gaussian Estimation With Intermittent Observations

This letter is concerned with the problem of optimal linear exponential quadratic state estimation for discrete-time Gauss-Markov systems with intermittent observations. The optimal estimator and optimal cost are derived via an information state approach. The necessary and sufficient condition for the existence of the estimator is provided. For a special case when a scalar parameter in the exponential cost goes to zero, the derived estimator reduces to the corresponding Kalman filter. It is also interesting to note that the resulting estimator has the same form as that obtained from the H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> setting.