Reviewerly/exHarmony huggingface dataset:
Evaluation and benchmarking of level set-based three forces via geometric active contours for segmentation of white blood cell nuclei shape

The segmentation of white blood cells and their nuclei is still difficult and challenging for many reasons, including the differences in their colour, shape, background and staining techniques, the overlapping of cells, and changing cell topologies. This paper shows how these challenges can be addressed by using level set forces via edge-based geometric active contours. In this work, three level set forces-based (curvature, normal direction, and vector field) are comprehensively studied in the context of the problem of segmenting white blood cell nuclei based on geometric flows. Cell images are first pre-processed, using contrast stretching and morphological opening and closing in order to standardise the image colour intensity, to create an initial estimate of the cell foreground and to remove the narrow links between lobes and cell bulges. Next, segmentation is conducted to prune out the white blood cell nucleus region from the cell wall and cytoplasm by combining the theory of curve evolution using curvature, normal direction, and vector field-based level set forces and edge-based geometric active contours. The overall performance of the proposed segmentation method is compared and benchmarked against existing techniques for nucleus shape detection, using the same databases. The three level set forces studied here (curvature, normal direction, and vector field) via edge-based geometric active contours achieve F-index values of 92.09%, 91.13%, and 90.76%, respectively, and the proposed segmentation method results in better performance than all other techniques for all indices, including Jaccard distance, boundary displacement error, and Rand index.
Situated Bayesian Reasoning Framework for Robots Operating in Diverse Everyday Environments

General-purpose robots operating in unstructured environments have the potential to benefit by leveraging abstract, commonsense knowledge for task execution. In this paper, we present an approach for automatically generating a compact semantic knowledge base, relevant to a robot’s particular operating environment, given only a small number of object labels obtained from object recognition or a robot’s task description. In order to cope with noise and non-deterministic data across our data sources, we formulate our representation as a statistical relational model represented as a Baysian Logic Network. We validate our approach in both abstract and real-world domains, demonstrating the robot’s ability to perform inference about object categories, locations and properties given a small amount of local information. Additionally, we present an approach for interactively validating the mined information with the help of a co-located user.
Optical encryption scheme for multiple-image based on spatially angular multiplexing and computer generated hologram

We propose an optical encryption scheme for multiple images based on angle multiplexing and computer generated hologram(CGH) in this paper. In the encryption process, the original images are firstly modulated by two random phase keys in Fresnel transform with different diffraction distances. Secondly, the modulated images are coherent superposition with solid angle multiplexed reference beams to generate interference fringes and form a compound image. Finally, the compound image is encoded to a CGH by Roman coding method. In the decrypted process, the corresponding experimental system using SLM is built, in which multiple images can be encrypted synchronously with high efficiency. Except random phase key, the proposed encryption system has multiple kind of keys including diffraction distance and wavelength to improve the security. Due to the characteristics of high storage efficiency and simple calculation, this optical multiple-image encryption scheme has important application prospect in improving the efficiency of information transmission and multi-user authentication.
Developing GDPR Compliant User Data Policies for Internet of Things

With recent adoption of Internet of Things (IoT) technologies and their use in industry, user data privacy concerns remain a major preoccupation of regulation bodies. The European General Data Protection Regulation (GDPR) enables users to control their data and be informed about any devices involved in collecting and processing this data. The overall objective is to enable individuals to have full rights and control over their data assets and to be able to transfer their data without any unmitigated risk. Blockchains provide the benefits of a distributed ledger that can securely manage digital transactions -- where the centralisation of data is eliminated. Blockchains have recently entered as an enabling technology into the IoT market, and used in a variety of different application areas. Blockchains enable the implementation of a more trusted system capable of processing operations between IoT services and sources of data. In smart buildings, for example, Blockchains support the formation of smart contracts as a means to give transactional capabilities to IoT devices, allowing users to keep data ownership and privacy using an immutable dataset. We describe how Blockchain technology can be used to develop an audit trail of data generated in IoT devices, enabling GDPR rules to be verified on such a trail. We describe how to translate a set of such rules into smart contracts to protect personal data in a transparent and automatic way.
Personalized and Automatic Model Repairing using Reinforcement Learning

When performing modeling activities, the chances of breaking a model increase together with the size of development teams and number of changes in software specifications. Model repair research mostly proposes two different solutions to this issue: fully automatic, non-interactive model repairing tools or support systems where the repairing choice is left to the developer's criteria. In this paper, we propose the use of reinforcement learning algorithms to achieve the repair of broken models allowing both automation and personalization. We validate our proposal by repairing a large set of broken models randomly generated with a mutation tool.
Teleporting quantum Fisher information for even and odd coherent states

We present a scheme for implementing a quantum teleportation process using the Jaynes–Cummings model. For this, we study the interaction between an excited state of a two-level atom and a single electromagnetic field in a superposition of coherent states. The resulting entangled state may be considered as a good quantum channel for quantum teleportation protocol. By controlling the interaction field parameters inside the cavity, the average fidelity of the teleported state may be maximized. However, the weight and phase parameters in the teleported states are estimated by using quantum Fisher information. It allows one to show that the sensitivity of the teleported states fluctuates between maximum and minimum bounds for large numbers of photons. Evaluating different quantities of quantum entanglement, average fidelity, and the amount of quantum Fisher information shows that the odd coherent states are usually larger than those obtained using even coherent states.
MultiDepth: Single-Image Depth Estimation via Multi-Task Regression and Classification

We introduce MultiDepth, a novel training strategy and convolutional neural network (CNN) architecture that allows approaching single-image depth estimation (SIDE) as a multi-task problem. SIDE is an important part of road scene understanding. It, thus, plays a vital role in advanced driver assistance systems and autonomous vehicles. Best results for the SIDE task so far have been achieved using deep CNNs. However, optimization of regression problems, such as estimating depth, is still a challenging task. For the related tasks of image classification and semantic segmentation, numerous CNN-based methods with robust training behavior have been proposed. Hence, in order to overcome the notorious instability and slow convergence of depth value regression during training, MultiDepth makes use of depth interval classification as an auxiliary task. The auxiliary task can be disabled at test-time to predict continuous depth values using the main regression branch more efficiently. We applied MultiDepth to road scenes and present results on the KITTI depth prediction dataset. In experiments, we were able to show that end-to-end multitask learning with both, regression and classification, is able to considerably improve training and yield more accurate results.
The Function-Inversion Problem: Barriers and Opportunities

The task of function inversion is central to cryptanalysis: breaking block ciphers, forging signatures, and cracking password hashes are all special cases of the function-inversion problem. In 1980, Hellman showed that it is possible to invert a random function $$f{:}\,[N] \rightarrow [N]$$ in time $$T = \widetilde{O}(N^{2/3})$$ given only $$S = \widetilde{O}(N^{2/3})$$ bits of precomputed advice about f. Hellman’s algorithm is the basis for the popular “Rainbow Tables” technique (Oechslin 2003), which achieves the same asymptotic cost and is widely used in practical cryptanalysis. Is Hellman’s method the best possible algorithm for inverting functions with preprocessed advice? The best known lower bound, due to Yao (1990), shows that $$ST = \widetilde{\Omega }(N)$$, which still admits the possibility of an $$S = T = \widetilde{O}(N^{1/2})$$ attack. There remains a long-standing and vexing gap between Hellman’s $$N^{2/3}$$ upper bound and Yao’s $$N^{1/2}$$ lower bound. Understanding the feasibility of an $$S = T = N^{1/2}$$ algorithm is cryptanalytically relevant since such an algorithm could perform a key-recovery attack on AES-128 in time $$2^{64}$$ using a precomputed table of size $$2^{64}$$. For the past 29 years, there has been no progress either in improving Hellman’s algorithm or in strengthening Yao’s lower bound. In this work, we connect function inversion to problems in other areas of theory to (1) explain why progress may be difficult and (2) explore possible ways forward. Our results are as follows:
ILWAANet: An Interactive Lexicon-Aware Word-Aspect Attention Network for aspect-level sentiment classification on social networking

An Interactive Lexicon-Aware Word-Aspect Attention Network (ILWAAN) is proposed for aspect-level sentiment classification which deals with identifying the sentiment polarity of a specific aspect in its context and have potential application on social networking. In this model, effective multiple attention mechanisms (intra-attention and interactive-attention mechanisms) integrated with sentiment lexicon information are developed to form an aspect-specific representation at two levels: Phrase-level and Aggregation-level information. Specifically, an aspect and its context are fused with the sentiment lexicon information and learn their relationship representations by lexicon-aware attention operations. This allows the model to tries to incorporate the aspect information into the deep neural networks and learn to attend the correct sentiment context words conditioned on the informative aspect words. To evaluate the performance, we evaluate our model in three benchmark data: Twitter, Laptop, and Restaurant. The experimental results indicate that our models improve the performance for aspect-level sentiment classification.
A Deep Ensemble Learning Method for Effort-Aware Just-In-Time Defect Prediction

Since the introduction of just-in-time effort aware defect prediction, many researchers are focusing on evaluating the different learning methods, which can predict the defect inducing changes in a software product. In order to predict these changes, it is important for a learning model to consider the nature of the dataset, its unbalancing properties and the correlation between different attributes. In this paper, we evaluated the importance of these properties for a specific dataset and proposed a novel methodology for learning the effort aware just-in-time prediction of defect inducing changes. Moreover, we devised an ensemble classifier, which fuses the output of three individual classifiers (Random forest, XGBoost, Multi-layer perceptron) to build an efficient state-of-the-art prediction model. The experimental analysis of the proposed methodology showed significant performance with 77% accuracy on the sample dataset and 81% accuracy on different datasets. Furthermore, we proposed a highly competent reinforcement learning technique to avoid false alarms in real time predictions.
Self-trapped quantum walks

We study the existence and characterization of self-trapping phenomena in discrete-time quantum walks. By considering a Kerr-like nonlinearity, we associate an acquisition of the intensity-dependent phase with the walker while it propagates on the lattice. Adjusting the nonlinear parameter $\ensuremath{\chi}$ and the quantum gates $\ensuremath{\theta}$, we will show the existence of different quantum walking regimes, including those with traveling solitonlike structures or localized by self-trapping. The latter scenario is absent for quantum gates close enough to the Pauli-$X$ gate. It appears for intermediate configurations and becomes predominant as quantum gates get closer to the Pauli-$Z$ gate. By using $\ensuremath{\chi}$ versus $\ensuremath{\theta}$ diagrams, we will show that the threshold between quantum walks with delocalized or localized regimes exhibits an unusual aspect in which an increment of the nonlinear strength can induce the system to transition from a localized to a delocalized regime.
Dataset for spatial distribution and pollution indices of heavy metals in the surface sediments of Emerald Lake, Tamil Nadu, India

The dataset for this manuscript with the spatial distribution of heavy metals such as Mn, Zn, Ni, Co, Pb, Cr, Cu and Fe, pollution indices of the surface sediments in the Emerald Lake, India, the significant source of pollution, are determined. The methods for calculation of pollution indices such as Enrichment Factor (EF), Contamination Degree (CD), modified Contamination degree (mCd) and Contamination factor (CF) are considered to know the level of heavy metal pollution in the study area. In this data, the results of pollution indices suggest that almost all the sites are polluted by heavy metals. The data could be very useful to metal pollutants and their sources in the surface sediments.
Accelerating Sparse Deep Neural Networks on FPGAs

Deep neural networks (DNNs) have been widely adopted in many domains, including computer vision, natural language processing, and medical care. Recent research reveals that sparsity in DNN parameters can be exploited to reduce inference computational complexity and improve network quality. However, sparsity also introduces irregularity and extra complexity in data processing, which make the accelerator design challenging. This work presents the design and implementation of a highly flexible sparse DNN inference accelerator on FPGA. Our proposed inference engine can be easily configured to be used in both mobile computing and high-performance computing scenarios. Evaluation shows our proposed inference engine effectively accelerates sparse DNNs and outperforms CPU solution by up to 4.7 × in terms of energy efficiency.
Performance Analysis for Heterogeneous Cloud Servers Using Queueing Theory

In this article, we consider the problem of selecting appropriate heterogeneous servers in cloud centers for stochastically arriving requests in order to obtain an optimal tradeoff between the expected response time and power consumption. Heterogeneous servers with uncertain setup times are far more common than homogenous ones. The heterogeneity of servers and stochastic requests pose great challenges in relation to the tradeoff between the two conflicting objectives. Using the Markov decision process, the expected response time of requests is analyzed in terms of a given number of available candidate servers. For a given system availability, a binary search method is presented to determine the number of servers selected from the candidates. An iterative improvement method is proposed to determine the best servers to select for the considered objectives. After evaluating the performance of the system parameters on the performance of algorithms using the analysis of variance, the proposed algorithm and three of its variants are compared over a large number of random and real instances. The results indicate that proposed algorithm is much more effective than the other four algorithms within acceptable CPU times.
Casino royale

The popularity of online gambling could bring negative social impact, and many countries ban or restrict online gambling. Taking China for example, online gambling violates Chinese laws and hence is illegal. However, illegal online gambling websites are still thriving despite strict restrictions, since they are able to make tremendous illicit profits by trapping and cheating online players. In this paper, we conduct the first deep analysis on illegal online gambling targeting Chinese to unveil its profit chain. After successfully identifying more than 967,954 suspicious illegal gambling websites, we inspect these illegal gambling websites from five aspects, including webpage structure similarity, SEO (Search Engine Optimization) methods, the abuse of Internet infrastructure, third-party online payment, and gambling group. Then we conduct a measurement study on the profit chain of illegal online gambling, investigating the upstream and downstream of these illegal gambling websites. We mainly focus on promotion strategies, third-party online payment, the abuse of third-party live chat services, and network infrastructures. Our findings shed the light on the ecosystem of online gambling and help the security community thwart illegal online gambling.
Linear Fusion Estimation for Range-Only Target Tracking With Nonlinear Transformation

This article is concerned with the multisensor fusion estimation for target tracking with range-only wireless sensor networks. By employing a nonlinear transformation and a measurement fusion, the nonlinear distance measurements are transformed into a linear measurement with respect to the position of the target, which avoids the instability problem of nonlinear filtering. However, after the transformation, the new measurement noises are no longer Gaussian and cross uncorrelated. Taking the unmodeled disturbances into account, as well as the new noise properties, an adaptive factor is introduced by hypothesis test based on the posterior residual to improve the estimation performance, where only the root of a quadratic equation is required to be solved. Finally, both simulations and experiments of a target tracking example are presented to show the effectiveness of the proposed methods.
A multi-institutional study of inquiry-based lab activities using the Augmented Reality Sandbox: impacts on undergraduate student learning

We developed and tested different pedagogical treatments using an Augmented Reality (AR) Sandbox to teach introductory geoscience students about reading topographic maps at five institutions in both pilot and full implementation studies. The AR Sandbox treatments were characterized as 1) unstructured play, 2) a semi-structured lesson, and 3) a structured lesson. The success of each was contrasted with the control condition of a traditional topographic map lab without the AR Sandbox. Students completed a subset of questions from the Topographic Maps Assessment (TMA) and a series of mental rotation questions post-implementation. No significant differences were found on TMA post-test scores between groups who used the unstructured Sandbox play treatment compared to the control condition. Semi-structured and structured lesson formats similarly failed to produce a statistically significant difference on the TMA post-test. This indicates that no single treatment worked universally better than another. However, regression analysis showed two factors significantly predicted performance on the TMA, including spatial performance and self-assessed knowledge (or confidence) of topographic maps. Of the groups that used the Sandbox, students with low and high scores on the mental rotation test performed best on the TMA following the structured treatment.
Insights into the effects of control parameters and mutation strategy on self-adaptive ensemble-based differential evolution

This work explores the challenges in identifying appropriate and significant parameter configurations in differential evolution (DE) under the influence of population diversity and dimension size. For most DE algorithms, the configuration of control parameters is a vital prerequisite for balancing exploration and exploitation within the confinement of a search space. This study investigates the implementation of various adaptive parameter setting configurations on benchmark functions via the proposal of an algorithmic scheme called self-adaptive ensemble-based DE (SAEDE). This algorithm uses self-adaptive and ensemble mechanisms to set the relevant parameters for each generation. SAEDE is compared with two other ensemble-based DEs, and their performance is evaluated using 34 benchmark functions consisting of 20 low dimensions and 14 high dimensions. Furthermore, the convergence of these DEs is tested by using Q-measure. Experimental results indicate that SAEDE achieves the highest frequency of maximum success rate in 28 out of the 34 benchmark functions. SAEDE also achieves the lowest Q-measure of 4237318. These findings show the competitiveness and efficiency of SAEDE in locating optimal solutions while avoiding exhaustive searches of suitable parameters by users in terms of achieving optimization while minimizing the dependency on user setting.
Lifelong Learning in Sensor-Based Human Activity Recognition

Human activity recognition (HAR) systems will be increasingly deployed in real-world environments and for long periods of time. This significantly challenges current approaches to HAR, which have to account for changes in activity routines, the evolution of situations, and sensing technologies. Driven by these challenges, in this paper, we argue the need to move beyond learning to lifelong machine learning—with the ability to incrementally and continuously adapt to changes in the environment being learned. We introduce a conceptual framework for lifelong machine learning to structure various relevant proposals in the area and identify some key research challenges that remain.
A Control Oriented Cyber-Secure Strategy Based on Multiple Sensor Fusion

This paper introduces a cyber-secure strategy for radar tracking systems. Two common cyber attacks including denial-of-service (DoS) and false data injection (deception) attacks are investigated. The proposed secure control strategy consists of two subsystems: 1) an attack detection and isolation (ADI) subsystem, and 2) a resilient observer (RO) subsystem. The ADI subsystem is used to observe the state of the system using a bank of Kalman Filters and multi-sensor measurements. Then, residuals generated by local Kalman filters are used to isolate the cyber attacks. Afterward, ordered weighted averaging (OWA) operator is utilized to drive a resilient observer to estimate the real correct value of variables such as position under cyber attacks. Weighting factors of the OWA operator are derived using the covariance matrix, and proof of convergence is provided. Simulation studies on a radar tracking system show that the proposed secure control strategy using multi-sensor fusion enhances the performance of the system and results in a more resilient control system against cyber attacks.