Reviewerly/exHarmony huggingface dataset:
Cryptanalysis of a public authentication protocol for outsourced databases with multi-user modification

Nowadays more and more enterprises prefer to outsource their databases to remote cloud servers, they also want to access/query/update the remote outsourced databases just as accessing/quering/updating on the local database systems. Thus how to ensure the outsourced database being correct/confidential/integral is very important, especially how to ensure the correctness and completeness of the query results are big challenge problems. Aiming at solving these problems, recently Song et al. proposed a public authentication protocol for outsourced databases with multi-user modification, unfortunately in this short paper we show their proposal is not secure.
Geotagging tweets to landmarks using convolutional neural networks with text and posting time

Geotagged tweets serve many important applications, e.g., crisis management, but only a small proportion of tweets are explicitly geotagged. We propose a Convolutional Neural Network (CNN) architecture for geotagging tweets to landmarks, based on the text in tweets and other meta information, such as posting time and source. Using a dataset of Melbourne tweets, experimental results show that our algorithm out-performed various state-of-the-art baselines.
Automatically Identifying Bug Entities and Relations for Bug Analysis

During the bug fixing process, developers usually analyze the historical relevant bug reports in bug repository to support various bug analysis activities, i.e., bug understanding, bug localization, bug fixing, etc. There are rich semantics and relations in the bug reports, which are useful for bug analysis. Entity recognition and relation extraction are useful to represent semantics and relations in the text. However, the text in the bug reports are often in a free-style form, and include lots of noisy information. Therefore, how to extract and express rich semantics and relations in the bug reports is difficult, but important for bug analysis. To address this challenge, we propose an approach, which incorporates the neural networks RNN with dependency parser to automatically extract bug entities and their relations from bug reports. A preliminary empirical evaluation demonstrates that our approach is effective to extract bug entities and their relations from bug reports in the bug repository.
On pre-solution matrices of fuzzy relation equations over complete Brouwerian lattices

Each solution of a system of fuzzy relation equations over a complete Brouwerian lattice can be decomposed into a matrix which we call pre-solution matrix. Every pre-solution matrix can induce at least one solution. In this paper, we discuss connections between solutions and pre-solution matrices of a system of fuzzy relation equations over a complete Brouwerian lattice. Then, we explore properties of pre-solution matrices and characterize lower pre-solution matrices. Moreover, we discuss constructions of pre-solution matrices.
Reverse Parking a Car-Like Mobile Robot with Deep Reinforcement Learning and Preview Control

This paper presents a control technique for reverse parking car-like vehicles based on deep reinforcement learning and preview control. The deep deterministic policy gradient (DDPG) algorithm is used for training a neurocontroller using a reward function defined in terms of the desired final state of the system. A preview control approach is employed to leverage knowledge of a known a priori reference input to generate a predictive control signal coupled into the neurocontroller output. Simulation results are presented to validate the proposed method. Moreover, these results show that incorporating a preview control signal improves the parking time.
Method for the prediction of time series using small sets of experimental samples

The paper is concerned with the method of prediction of time series based on the concepts of system identification. The distinctive property of the method is the use of small sets of experimental samples of data. The latter create some basis for building so-called learning subsets, which are used to construct particular prediction models. Values of variables predicted by different particular models allow calculating the desired variables by using a batch voting technique. The method can be used for short-term prediction of data values at future time instants based on the analysis of a brief history of the process under consideration. It can be useful in cases of processing very large arrays of data samples, when the researcher has to confirm his (her) attention to only a small part of the samples received at the last instants of time, in view of the limited memory of the computer or in cases when very slow processes are analyzed. A special place in the paper is given to the example in which the computational aspects of the proposed method are considered in detail.
Efficient matrix approach for classical inference in state space models

Reformulating a Gaussian state space model in matrix form, we obtain expressions for the likelihood function and the smoothing vector that are generally more efficient than the standard recursive algorithm. We also retrieve filtering weights and deal with data irregularities.
SPaSe - Multi-Label Page Segmentation for Presentation Slides

We introduce the first benchmark dataset for slide-page segmentation. Presentation slides are one of the most prominent document types used to exchange ideas across the web, educational institutes and businesses. This document format is marked with a complex layout which contains a rich variety of graphical (e.g. diagram, logo), textual (e.g. heading, affiliation) and structural components (e.g. enumeration, legend). This vast and popular knowledge source is still unattainable by modern machine learning technique due to lack of annotated data. To tackle this issue, we introduce SPaSe (Slide Page Segmentation), a novel dataset containing in total 2000 slides with dense, pixel-wise annotations of 25 classes. We show that slide segmentation reveals some interesting properties that characterize this task. Unlike the common image segmentation problem, disjoint classes tend to have a high overlap of regions, thus posing this segmentation task as a multi-label problem. Furthermore, many of the frequently encountered classes in slides are location sensitive (e.g. title, footnote). Hence, we believe our dataset represents a challenging and interesting benchmark for novel segmentation models. Finally, we evaluate state-of-the-art deep segmentation models on our dataset and show that it is suitable for developing deep learning models without any need of pre-training. Our dataset will be released to the public to foster further research on this interesting task.
Automatic Kalman-filter-based wavelet shrinkage denoising of 1D stellar spectra

We propose a non-parametric method to denoise 1D stellar spectra based on wavelet shrinkage followed by adaptive Kalman thresholding. Wavelet shrinkage denoising involves applying the Discrete Wavelet Transform (DWT) to the input signal, `shrinking' certain frequency components in the transform domain, and then applying inverse DWT to the reduced components. The performance of this procedure is influenced by the choice of base wavelet, the number of decomposition levels, and the thresholding function. Typically, these parameters are chosen by `trial and error', which can be strongly dependent on the properties of the data being denoised. We here introduce an adaptive Kalman-filter-based thresholding method that eliminates the need for choosing the number of decomposition levels. We use the `Haar' wavelet basis, which we found to be the best-suited for 1D stellar spectra. We introduce various levels of Poisson noise into synthetic PHOENIX spectra, and test the performance of several common denoising methods against our own. It proves superior in terms of noise suppression and peak shape preservation. We expect it may also be of use in automatically and accurately filtering low signal-to-noise galaxy and quasar spectra obtained from surveys such as SDSS, Gaia, LSST, PESSTO, VANDELS, LEGA-C, and DESI.
Health Evaluation of MVB Based on SVDD and Sample Reduction

Multifunction vehicle bus (MVB) is the most widely used train communication network whose performance degradation and anomaly will heavily affect the train's safe and stable operation. However, current scheduled maintenance and post-failure maintenance of MVB cannot detect the early anomaly and evaluate the health condition of the network in time. This paper provides a method to detect the anomaly and evaluate the health condition of MVB based on a one-class classification (OCC) algorithm called density-based sample reduction for support vector data description (DBSRSVDD). First, network features are extracted from physical layer waveform parameters. In order to reduce the computational complexity of SVDD, a sample reduction operation is conducted to screen out the edge samples as support vector candidates. Then, the SVDD models representing the normal patterns of a single MVB node are trained based on the support vector candidates. Performance degradation of the node is quantified by the distance between the tested sample and the trained hyper sphere. The whole network's health condition is the linear weighted sum of the nodes' scores based on their bandwidth occupancy. The experimental results show that the proposed method can detect the anomaly and degradation of MVB successfully, improve accuracy, and reduce training time compared with the existing methods.
LUCFER: A Large-Scale Context-Sensitive Image Dataset for Deep Learning of Visual Emotions

Still image emotion recognition has been receiving increasing attention in recent years due to the tremendous amount of social media content available on the Web. Opinion mining, visual emotion analysis, search and retrieval are among the application areas, to name a few. While there exist works on the subject, offering methods to detect image sentiment; i.e. recognizing the polarity of the image, less efforts focus on emotion analysis; i.e. dealing with recognizing the exact emotion aroused when exposed to certain visual stimuli. Main gaps tackled in this work include (1) lack of large-scale image datasets for deep learning of visual emotions and (2) lack of context-sensitive single-modality approaches in emotion analysis in the still image domain. In this paper, we introduce LUCFER (Pronounced LU-CI-FER), a dataset containing over 3.6M images, with 3-dimensional labels; i.e. emotion, context and valence. LUCFER, the largest dataset of the kind currently available, is collected using a novel data collection pipeline, proposed and implemented in this work. Moreover, we train a context-sensitive deep classifier using a novel multinomial classification technique proposed here via adding a dimensionality reduction layer to the CNN. Relying on our categorical approach to emotion recognition, we claim and show empirically that injecting context to our unified training process helps (1) achieve a more balanced precision and recall, and (2) boost performance, yielding an overall classification accuracy of 73.12% compared to 58.3% achieved in the closest work in the literature.
Parametric Modeling of Microwave Components Based on Semi-Supervised Learning

In the artificial neural network (ANN), the most time-consuming part for parametric modeling of microwave components is the collection of training datasets from full-wave electromagnetic simulations. However, the reported models for parametric modeling of EM behaviors are based on supervised learning, in which the labeled sampling data from full-wave EM simulations ought to be sufficient for ANN training. Thus, the number of full-wave simulations is the main factor that influences the effectiveness of collecting training and testing samples. Based on the dynamic adjustment kernel extreme learning machine, this paper proposes a semi-supervised learning model lying between supervised learning and unsupervised learning to largely reduce the number of required training samples. The proposed model contains two training processes, the initial training, and the self-training. In the initial training process, a small number of training samples from full-wave simulations are used to make the model rapidly converge. Then, in the self-training process, the model produces unlabeled training datasets to train itself till the testing accuracy is satisfied. Two numerical examples of a microstrip-to-microstrip vertical transition and a dual-band four-pole filter are employed to verify the effectiveness of the semi-supervised learning model.
The Groningen Gasquakes: Foreseeable Surprises, Complications of Hard Science, and the Search for Effective Risk Communication

Editorial| March 06, 2019 The Groningen Gasquakes: Foreseeable Surprises, Complications of Hard Science, and the Search for Effective Risk Communication Charles Vlek Charles Vlek aDepartment of Behavioural and Social Sciences, University of Groningen, Grote Kruisstraat 2/I, 9712 TS Groningen, The Netherlands, c.a.j.vlek@rug.nl Search for other works by this author on: GSW Google Scholar Seismological Research Letters (2019) 90 (3): 1071–1077. https://doi.org/10.1785/0220180368 Article history first online: 29 Apr 2019 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Search Site Citation Charles Vlek; The Groningen Gasquakes: Foreseeable Surprises, Complications of Hard Science, and the Search for Effective Risk Communication. Seismological Research Letters 2019;; 90 (3): 1071–1077. doi: https://doi.org/10.1785/0220180368 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietySeismological Research Letters Search Advanced Search Growing seismicity in Europe's largest gas field in the northeastern Netherlands has caused widespread building damage, social concern, and political upheaval. This has led to a massive research effort, elaborate damage‐handling procedures, delayed building reinforcement, and various lawsuits against the state. Through successive government decisions, annual gas extraction has been decreasing since 2014, with 55‐yr‐old field operations now to be concluded in 2030. In this commentary,1 the 28‐yr history and likely further development of gas extraction with earthquakes is statistically summarized. Then a response is given to the critical opinion about the Groningen case by Sintubin (2018a), which... You do not have access to this content, please speak to your institutional administrator if you feel you should have access.
Image of a University Professor: Students’ Views and Priorities

In the light of transformations, which happen in the contemporary society in general and in the higher education sphere in particular, the practical and theoretical significance of the problem of University professor image making is growing. The purpose of this paper is to find out what today's students think about their professors, which parts of their images students find the most important and prioritized. To this end, an experimental research of student's opinions about university professor's image was carried out. This paper provides the analysis of its results. The method of research consisted of oral and written interviews, as well as interviews in social networks. The analysis resulted in considering key features of the image of a contemporary university professor and in giving recommendations on the technology of positive image making. The paper shows that the dominant features of the image are professors' professionalism and expertise in a specific subject area, their pedagogical competence as an ability to implement productive educational process and an emotional intelligence. The paper contains many practical examples which show the legitimacy of students' priorities in part of image features of their professors .
High-Level Synthesis of Online K-Means Clustering Hardware for a Real-Time Image Processing Pipeline

The growing need for smart surveillance solutions requires that modern video capturing devices to be equipped with advance features, such as object detection, scene characterization, and event detection, etc. Image segmentation into various connected regions is a vital pre-processing step in these and other advanced computer vision algorithms. Thus, the inclusion of a hardware accelerator for this task in the conventional image processing pipeline inevitably reduces the workload for more advanced operations downstream. Moreover, design entry by using high-level synthesis tools is gaining popularity for the facilitation of system development under a rapid prototyping paradigm. To address these design requirements, we have developed a hardware accelerator for image segmentation, based on an online K-Means algorithm using a Simulink high-level synthesis tool. The developed hardware uses a standard pixel streaming protocol, and it can be readily inserted into any image processing pipeline as an Intellectual Property (IP) core on a Field Programmable Gate Array (FPGA). Furthermore, the proposed design reduces the hardware complexity of the conventional architectures by employing a weighted instead of a moving average to update the clusters. Experimental evidence has also been provided to demonstrate that the proposed weighted average-based approach yields better results than the conventional moving average on test video sequences. The synthesized hardware has been tested in real-time environment to process Full HD video at 26.5 fps, while the estimated dynamic power consumption is less than 90 mW on the Xilinx Zynq-7000 SOC.
Tangible Interactions for Privacy Management

Due to the ubiquity of IoT devices, privacy violations can now occur across our cyber-physical-social lives. An individual is often not aware of the possible privacy implications of their actions and commonly lacks the ability to dynamically control the undesired access to themselves or their information. Present approaches to privacy management lack an immediacy of feedback and action, tend to be complex and non-engaging, are intrusive and socially inappropriate, and are inconsistent with users' natural interactions with the physical and social environment. This results in ineffective end-user privacy management. To address these challenges, I focus on designing tangible systems, which promise to provide high levels of stimulation, rich feedback, direct, and engaging interaction experiences. This is achieved through intuitive awareness mechanisms and control interactions, conceptualizing interaction metaphors, implementing tangible interfaces for privacy management and demonstrating their utility within various real life scenarios.
ETGuard: Detecting D2D attacks using wireless Evil Twins

In this paper, we demonstrate a realistic variant of wireless Evil Twins (ETs) for launching device to device (D2D) attacks over the network, particularly for Android. We show an attack where an ET infects an Android device before the relay of network traffic through it, and disappears from the network immediately after inflicting the device. The attack leverages the captive portal facility of wireless networks to launch D2D attack. We configure an ET to launch a malicious component of an already installed app in the device on submission of the portal page. In this paper, we present an online, incremental, automated, fingerprinting based pre-association detection mechanism named as ETGuard which works as a client-server mechanism in real-time. The fingerprints are constructed from the beacon frames transmitted by the wireless APs periodically to inform client devices of their presence and capabilities in a network. Once detected, ETGuard continuously transmits deauthentication frames to prevent clients from connecting to an ET. ETGuard outperforms the existing state-of-the-art techniques from various perspectives. Our technique does not require any expensive hardware, does not modify any protocols, does not rely on any network specific parameters such as Round Trip Time (RTT), number of hops, etc., can be deployed in a real network, is incremental, and operates passively to detect ETs in real-time. To evaluate the efficiency, we deploy ETGuard in 802.11a/b/g wireless networks. The experiments are conducted using 12 different attack scenarios where each scenario differs in the source used for introducing an ET. ETGuard effectively detects ETs introduced either through a hardware, software, or mobile hotspot with high accuracy, only one false positive scenario, and no false negatives.
Slots-Memento

Mementos carry personal symbolic meaning and can be used to privately reflect on the past or share memories[28]. Older adults spend much time collecting mementos but spend less time telling stories behind the mementos. Once they pass away, stories behind mementos vanish with their owners. In this paper, we present Slots-Memento, a tangible device aiming to facilitate intergenerational story sharing and preservation for older adults. It builds on the metaphor of slot-machine, and integrates functions of memento photo display, story recording, and preservation. Our design process started with context inquiry, older adults and young adults were recruited, aiming to understand the status quo of their memento storytelling, and define design requirements. A preliminary evaluation was conducted, discussion and future work are in the final part.
Lights, Music, Stamps! Evaluating Mealtime Tangibles for Preschoolers

We present an evaluation of three prototype tangible user interfaces (TUIs) for mealtimes for preschoolers. Building on past work identifying value tensions between adults' and children's perspectives at meals, we evaluated TUIs to address different tensions in this context (for example, the tension between children's interest in experimenting with food versus adults' interest in cleanliness). Thirteen pre-school children and their parents tried out the prototypes, as did an additional seven preschool teachers. Adults and children alike were excited by the prototypes; parents were surprised by children's increased food intake, and children used the prototypes to engage in artistic expression with food traces. However, children's increased consumption was extrinsically motivated by the prototypes rather than intrinsically motivated by hunger. We conclude that TUIs have the potential to enhance shared meals between children and adults but also have the potential to distract or persuade children in inappropriate ways. We present design guidance differentiating these two outcomes, such as incorporating the TUI into pre-existing mealtime objects and routines.
Unified power and performance analysis of cloud computing infrastructure using stochastic reward nets

In this paper, power and performance of cloud infrastructures are analytically modeled and numerically evaluated using stochastic reward nets (SRNs). The main objective of the proposed model is to analyze the impact of the temperature and network traffic on power and performance of cloud computing systems, which has been ignored in most of previously presented approaches. In the proposed model, incoming tasks are divided into two categories, cold and hot, demonstrating different power consumption patterns, that can fail due to bandwidth constraints and congestion. By applying the proposed model, different resource allocation scenarios can be evaluated and compared, in terms of power and performance, when hot and cold tasks are assigned to virtual machines and the network congestion is taken into consideration. The results obtained from the proposed model, cross-validated with the CloudSim framework, show that the scenario which uses the minimum number of racks and the maximum number of servers is optimal in terms of both the power and performance measures.