Reviewerly/exHarmony huggingface dataset:
Proactive identification of query failure

ABSTRACT When a user fails to find any useful information to support the task at hand after issuing a query, the user experiences a query failure . Since users possess limited cognitive resources, query failures often lead to user frustration as no clear benefit is obtained from the associated search interactions. Therefore, to improve users' search experiences, we conducted a controlled‐lab study with 40 participants, seeking to explore the extent to which query failures can be proactively identified before users start examining the retrieved results . Specifically, based on the data collected from 693 query segments generated in 80 search sessions, we used past search behaviors and current query attributes as features to build classifiers and examined the performance in capturing query failures. We report that (1) analytics algorithms utilizing past search behavioral data have significantly better performances than the baseline model in tasks of different types, and (2) The knowledge of users' search intentions can help improve the performance of the prediction model. Results pave way for developing proactive system supports for task‐based search interactions.
Hash function design for cloud storage data auditing

Cloud based storage is being widely used as a viable solution to the problem of data storage in contexts where financial and practical considerations prohibit the use of locally based hardware and software resources. User reservations and legal constraints however have given rise to questions about the verifiability of the integrity of the stored data, especially in the case of public cloud infrastructure. A new problem has hence arisen, that of auditing stored files in order to obtain Proof of Retrievability. Secure cloud storage systems are limited by the overheads they require in order to provide the required security levels. Combined use of cloud and local computational resources is necessary in order to enable the desirable user experiences. With increasing local processing capacities, the most significant relevance is encountered in the Big Data Processing paradigm. The volumes of data that need to be processed are overwhelming to such an extent that approaches which use unlimited amounts of power, for processing and storage are not feasible. This paper focuses on a recent study of hash function requirements for big data applications and an associated key – based hash function design technique that makes the real – time collection, summarization, analysis and decision making based on streaming data. A file auditing technique is proposed that uses fundamental big data mass processing operations in order to develop an efficient and reliable proof of recoverability algorithm.
Comparative Study of Arabic Stemming Algorithms for Topic Identification

Stemming process is one of the important pre-processing steps in different natural language process tasks such as text mining and information retrieval. Yet, stemming process can be considered as a difficult step to realize according to the used language. In fact, due to the complex morphology of Arabic language, stemming results can be influenced. Thus, several algorithms have been proposed in order to overcome stemming problems. In this paper, we investigate different stemming algorithms by presenting a comparative study in the field of Arabic topic identification.
Voice Input Interface Failures and Frustration

We identify different types of failures in a voice user interface application, from both developer and user perspectives. Our preliminary experiment suggests that user-perceived Pattern Match Failure may have a strong negative effect on user frustration; based on this result, we conduct power analysis to obtain more conclusive results in a future experiment.
DVI (HDMI) and DisplayPort digital video interfaces in electromagnetic eavesdropping process

Increasingly common usage of new technologies in information processing generates escalating threat of confidentiality loss. Utilizing current VGA analog standard based means raises many doubts. It is a widely held view that this standard is highly susceptible to electromagnetic infiltration. Digital technologies are supposed to ensure a higher security level. This also applies to data processed in graphic form, especially those being transmitted to graphic display devices, such as computer monitors using DVI and DisplayPort standards. This article presents a comparative analysis of graphic technologies (DVI and DisplayPort) in terms of the electromagnetic safety of processed data. Analyses shown are based both on simulations results and practical study. The products of recreating the data using registered sensitive emission signals for studied graphical standards are presented.
Mixed-dense connection networks for image and video super-resolution

Efficiency of gradient propagation in intermediate layers of convolutional neural networks is of key importance for super-resolution task. To this end, we propose a deep architecture for single image super-resolution (SISR), which is built using efficient convolutional units we refer to as mixed-dense connection blocks (MDCB). The design of MDCB combines the strengths of both residual and dense connection strategies, while overcoming their limitations. To enable super-resolution for multiple factors, we propose a scale-recurrent framework which reutilizes the filters learnt for lower scale factors recursively for higher factors. This leads to improved performance and promotes parametric efficiency for higher factors. We train two versions of our network to enhance complementary image qualities using different loss configurations. We further employ our network for video super-resolution task, where our network learns to aggregate information from multiple frames and maintain spatio-temporal consistency. The proposed networks lead to qualitative and quantitative improvements over state-of-the-art techniques on image and video super-resolution benchmarks.
A Distributed Flow Correlation Attack to Anonymizing Overlay Networks Based on Wavelet Multi-resolution Analysis

Government agencies rely more and more heavily on the availability of flexible and intelligent solutions for the interception and analysis of Internet-based telecommunications. Unfortunately, the global lawful interception market has been recently put into a corner by the emerging sophisticated encryption, obfuscation and anonymization technologies provided by modern overlay communication infrastructures. To face this challenge, this work proposes a novel strategy for defeating the anonymity of traffic flows, collected within and at the exit of these anonymizing networks, relying on distributed flow-capture, characterization and correlation attacks driven by wavelet-based multi-resolution analysis. Such a strategy, starting from a properly formalized attack model, results in an effective and promising framework that can be easily deployed on real-life network equipment and can potentially scale by working according to different distribution/parallelization scenarios.
Multi-objective mobile robot path planning algorithm based on adaptive genetic algorithm

Nowadays, mobile robots are more and more widely used in some fields, such as domestic, industry, service. Global path planning is an important technology of robot autonomous navigation. It is related to running speed, running smoothness and safety of robots. In order to solve the problem of insufficient security distance provided by most algorithms, a multi-objective path planning algorithm based on adaptive genetic algorithm that considers path length, path smoothness and safety simultaneously is proposed in this paper. At first, construct navigation maps on the basis of working environments with artificial potential field. Then, several navigation paths are initialized based on the complexity of scenarios. At last, the paths are optimized by the adaptive genetic algorithm, and the optimal path considering length, smoothness and safety is obtained in the light of the Pareto frontier method. In order to achieve the optimal purpose, adaptive operator and supervisory operator were proposed to add or delete path nodes adaptively according to the map complexity. Finally, the performance of the proposed algorithm is verified based on two realistic maps with different complexity. Experimental results show that the proposed algorithm can obtain secure path than other methods, while simultaneously considering length and smoothness.
Causal Inference of Optimal Control Water Level and Inflow in Reservoir Optimal Operation Using Fuzzy Cognitive Map

Reservoir optimal operation (ROO) has always been a hot issue in the field of water resources management. Analysis of the relationship of optimal control water level and inflow is conducive to understanding and solving ROO under deterministic inflow conditions. The current research uses a fuzzy cognitive map (FCM) as a tool to effectively model complex systems and then extracts systematic relationship diagrams from the dataset. A new fuzzy cognitive map with offset (FCM-O) is proposed to overcome the causal inference error caused by non-linear mapping of the activation function in a traditional FCM. With the application of inferring the causal relationship between the optimal control water level and inflow of ROO for the Three Gorges Reservoir (TGR), the experimental results show that, compared with FCM in the min data error, FCM-O reduces 11.11% and 7.14% in the training and the testing, respectively. Also, the experimental results of FCM-O are more reasonable than those of FCM. Finally, the following conclusions about the causal inference of optimal control water level and inflow in ROO for TGR are drawn: (1) The optimal control water level in September, October and November needs to be raised as much as possible to raise the water head of power generation, which is mainly affected by the constraints of the maximum operating water level of the reservoir rather than inflow; (2) the optimal control water level in January, February and March is positively affected by the inflow of the adjacent months; (3) the optimal control water level in April is due to the approaching flood season. In order to prevent water discarding, the water level is low and the optimum operation space is small. All of those shows that FCM-O is more competent than FCM in the causal relationship between optimal control water level and inflow in ROO.
PatchRank: Ordering updates for SCADA systems

Securing SCADA is a challenging task for the research community as well as the industry. SCADA networks form the basis of industrial productivity. Industry 4.0 is likely to see more expansive use of SCADA & IIoT for enhanced productivity. These complex systems consist of numerous vulnerable subsystems. It is challenging for the timely application of patches to all the vulnerabilities, due to resource constraints and the high cost of the patch process. Usually, the more severe (attack probable) weaknesses are patched first to secure the system. Often organizations ignore the vulnerabilities in the “critical” node in favor of securing a vulnerability in an isolated subsystem. Therefore, the sequence in which patches are applied needs to be prioritized. State of the art indicates that patch prioritization is primarily an art rather than any significant methodology being followed.This paper proposes PatchRank - a patch prioritization method for the SCADA systems based on Viable System Model, Common Vulnerability Scoring System, and Game theory. PatchRank provides a ranking of vulnerable nodes/subsystems as well as a ranking of subsystem vulnerabilities, thereby allowing well-formed strategies for patch management. This paper also proposes a “Usable Secure State” to define a security assurance level. A comparative analysis of PatchRank with other benchmark algorithms, i.e., SecureRank, CVSS, and density based prioritization shows that PatchRank converges to a usable secure state faster.
A New Radar Signal Modulation Recognition Algorithm Based on Time-frequency Transform

To solve the problem that exist methods of radar signal modulation recognition do not perform well under low SNR, we propose a new modulation recognition algorithm based on time-frequency transform. The algorithm applies time-frequency transform to analysis radar signal in time-frequency domain. Meanwhile, AlexNet, Hu moments and t-SNE are the detailed methods in algorithm to improve the recognition rate. The AlexNet can extract the detailed feature of radar signal based on transfer learning, while the Hu moments can express the shape feature. Thus the fusion of two kinds of feature further improve the recognition rate. The simulation results show that the proposed algorithm can deal with the small sample of radar signal under low SNR. And the recognition rate is 95% at the SNR of 4dB. The proposed algorithm can be employed in electronic reconnaissance for effective reconnaissance under complex environment.
Nonlinear Filtering Method Using a Switching Error Model for Outlier-Contaminated Observations

This article considers a nonlinear filtering method for handling outliers. The presence of outliers that are gross observation errors can greatly reduce the accuracy of filtering methods that assume Gaussian distributed errors. There are some existing methods that assume a Gaussian observation error, and estimate the error covariance matrix at each time step to avoid overfitting to the outliers. However, the estimates of the covariance matrix under such methods can become unstable. This results in underfitting or overfitting to observations when filtering. This paper presents a new method for handling outliers in nonlinear filtering problems by extending the unscented Kalman filter. In this method, two Gaussian observation error models with distinct covariance matrices are used: one for observations with regular errors, and another with a larger variance specified by a scale parameter for outliers. In addition to the system state, this method estimates an indicator variable that switches between the two models and the scale parameter for outliers. With the inclusion of the indicator variable and scale parameter, the estimated error covariance matrix can handle both regular observations and outliers appropriately at each time step. Furthermore, by estimating the scale parameter, the proposed method can be applied to a dataset without additional tuning to account for outlier characteristics. Through numerical experiments, we find that our method can estimate the system state better than existing methods for both datasets with and without outliers.
Risk Probability Minimization Problems for Continuous-Time Markov Decision Processes on Finite Horizon

This article deals with a risk probability minimization problem for finite horizon continuous-time Markov decision processes with unbounded transition rates and history-dependent policies. Only using the assumption of nonexplosion of the controlled state process as well as the finiteness of actions available at each state, we not only establish the existence and uniqueness of a solution to the corresponding optimality equation, but also prove the existence of a risk probability optimal policy. Finally, we give two examples to illustrate our results: one example shows that the value iteration algorithm is provided for computing both the value function and an optimal risk probability policy, and the other shows the differences between the conditions in this article and those in the previous literature.
An Interactive Web Application for the Creation, Organization,and Visualization of Repeat Photographs

Repeat photography describes the process of photographing the same scene from the same camera position at different points in time. Goals are for example the detection and measurement of changes in the scene. In this paper we will present re.photos (www.re.photos), a working and freely accessible system for re- peat photography which offers online methods for the interac- tive and user friendly creation as well as the interactive browsing, searching, locating, rating, sharing, and discussing of repeat photo- graphs. Users can add historic images, called templates, for which other users or they themself can record a new image and create a re- peat photograph. Creating repeat photographs involves registering the two images, i. e., aligning the images such that the same objects are at the same pixel locations in both images. Our system offers a new interactive registration method, allowing control over the registration process, since automatic methods yield poor results. Previous works addressed only subproblems and did not result in usable and freely accessible software as our web platform.
Using behavioral biometric sensors of mobile phones for user authentication

In this paper, we use Hand Movement Orientation and Grasp (HMOG) sensor data to authenticate smart phone users. The way a user holds, grasps a mobile phone or touches to it are all key factors for authentication. At the moment of a user makes an event on his/her smart phone, three sensors automatically collect data about magnitude, angular speed and acceleration. Moreover, touching and holding events also produce data about pressure and coordinates. In this paper, we build four types of machine learning algorithms (decision forest, boosted decision tree, support vector machine, logistic regression) to predict user authentication. The data used in this experiment (HMOG) are collected from 100 students. We compare the results of the algorithms and for our scenario, we show that decision forest algorithm with normalized data gives best results.
Variance-Based Feature Importance in Neural Networks

This paper proposes a new method to measure the relative importance of features in Artificial Neural Networks (ANN) models. Its underlying principle assumes that the more important a feature is, the more the weights, connected to the respective input neuron, will change during the training of the model. To capture this behavior, a running variance of every weight connected to the input layer is measured during training. For that, an adaptation of Welford’s online algorithm for computing the online variance is proposed. When the training is finished, for each input, the variances of the weights are combined with the final weights to obtain the measure of relative importance for each feature. This method was tested with shallow and deep neural network architectures on several well-known classification and regression problems. The results obtained confirm that this approach is making meaningful measurements. Moreover, results showed that the importance scores are highly correlated with the variable importance method from Random Forests (RF).
Improved A* Multi-AGV Path Planning Algorithm Based on Grid-Shaped Network

In this paper, a kind of two-dimensional coded mark is designed to guarantee that the multi-AGV could locate and execute motion commands in a grid-shaped path network. Then, according to the multi-AGV grid-shaped path network, an improved $\mathrm {A}^{*}$ path planning method is proposed. Finally, the path planning and collision avoidance of multi-AGV in the grid-shaped network is realized, and the effectiveness of the proposed method is demonstrated by numerical simulation experiments.
Recursive Noise Adaptive Extended Object Tracking by Variational Bayesian Approximation

This paper proposes an adaptive extended object tracking algorithm with unknown timevarying sensor error covariance in linear state-space models.The proposed algorithm employs Inverse Wishart distribution to describe the full covariance.To produce an analytical solution, the measurement likelihood function introduces a latent variable to obtain an augmented form.Then, the latent variable is involved into the estimated list of quantities.To hold a recursive estimation framework, the proposed algorithm selects variational Bayesian (VB) inference to approximate the joint posterior distribution of estimated quantities.The VB inference minimizes Kullback-Leibler divergence between the true and approximate posterior density to obtain a convergent solution.Simulation experiments with unknown covariance demonstrate the effectiveness of the proposed algorithm.
Understanding Assimilation-contrast Effects in Online Rating Systems

“Unbiasedness,” which is an important property to ensure that users’ ratings indeed reflect their true evaluations of products, is vital both in shaping consumer purchase decisions and providing reliable recommendations in online rating systems. Recent experimental studies showed that distortions from historical ratings would ruin the unbiasedness of subsequent ratings. How to “discover” historical distortions in each single rating (or at the micro-level), and perform the “debiasing operations” are our main objective. Using 42M real customer ratings, we first show that users either “assimilate” or “contrast” to historical ratings under different scenarios, which can be further explained by a well-known psychological argument: the “Assimilate-Contrast” theory. This motivates us to propose the Historical Influence Aware Latent Factor Model (HIALF), the “first” model for real rating systems to capture and mitigate historical distortions in each single rating. HIALF allows us to study the influence patterns of historical ratings from a modelling perspective, which perfectly matches the assimilation and contrast effects observed in experiments. Moreover, HIALF achieves significant improvements in predicting subsequent ratings and characterizing relationships in ratings. It also contributes to better recommendations, wiser consumer purchase decisions, and deeper understanding of historical distortions in both honest rating and misbehaving rating settings.
Real-time Person Orientation Estimation using Colored Pointclouds

Robustly estimating the orientations of people is a crucial precondition for a wide range of applications. Especially for autonomous systems operating in populated environments, the orientation of a person can give valuable information to increase their acceptance. Given people's orientations, mobile systems can apply navigation strategies which take people's proxemics into account or approach them in a human like manner to perform human robot interaction (HRI) tasks. In this paper, we present an approach for person orientation estimation based on performant features extracted from colored point clouds, formerly used for a two class person attribute classification. The classification approach has been extended to the continuous domain while treating the problem of orientation estimation in real time. We compare the performance of orientation estimation treated as a multi-class as well as a regression problem. The proposed approach achieves a mean angular error (MAE) of 15.4° at 14.3ms execution time and can be further tuned to 12.2° MAE with 79.8ms execution time. This can compete with accuracies from state-of-the-art and even deep learning based skeleton estimation approaches while retaining the real-time capability on a standard CPU.
Virtual Reality and Oceanography: Overview, Applications, and Perspective

With the ongoing, exponential increase in ocean data from autonomous platforms, satellites, models, and in particular, the growing field of quantitative imaging, there arises a need for scalable and cost-efficient visualization tools to interpret these large volumes of data. With the recent proliferation of consumer grade head-mounted displays, the emerging field of virtual reality (VR) has demonstrated its benefit in numerous disciplines, ranging from medicine to archaeology. However, these benefits have not received as much attention in the ocean sciences. Here, we summarize some of the ways that virtual reality has been applied to this field. We highlight a few examples in which we (the authors) demonstrate the utility of VR as a tool for ocean scientists. For oceanic datasets that are well-suited for three-dimensional visualization, virtual reality has the potential to enhance the practice of ocean science.