Reviewerly/exHarmony huggingface dataset:
A New Semantic Segmentation Model for Supplementing More Spatial Information

Semantic segmentation, aiming to assign semantic labels to each pixel, is broadly applied into many fields, such as video surveillance, medical image analysis, and autonomous driving. However, there are two challenges in semantic segmentation task: 1) the deficiency of rich contextual information; and 2) the lack of sufficient spatial information, all of which affect segmentation performance seriously. To solve these two challenges, the global feature capturing module (GFCM) and Conv Block are proposed in this paper to build a new model to improve segmentation performance. Specifically, GFCM, made of the global encoding module (GEM) and spatial attention module (SAM), is designed to extract adequate global contextual information and build global spatial dependencies. Composed of three convolution layers, Conv Block is proposed to preserve rich spatial information. Based on GFCM and Conv Block, a new model is designed, where a data-dependent upsampling operator (DUpsampling) is exploited to recover the pixel-wise prediction effectively. The extensive experiments have been made to prove the effectiveness of the design, and the new model achieves 73.69% mIoU on Cityscapes test set and 80.05% mIoU on PASCAL VOC 2012 test set without any post-processing.
Advertisement billboard detection and geotagging system with inductive transfer learning in deep convolutional neural network

In this paper, we propose an approach to detect and geotag advertisement billboard in real-time condition. Our approach is using AlexNet’s Deep Convolutional Neural Network (DCNN) as a pre-trained neural network with 1000 categories for image classification. To improve the performance of the pre-trained neural network, we retrain the network by adding more advertisement billboard images using inductive transfer learning approach. Then, we fine-tuned the output layer into advertisement billboard related categories. Furthermore, the detected advertisement billboard images will be geotagged by inserting Exif metadata into the image file. Experimental results show that the approach achieves 92.7% training accuracy for advertisement billboard detection, while for overall testing results it will give 71,86% testing accuracy.
Multi-Sensor Multi-Target Tracking Using Probability Hypothesis Density Filter

Compared with the single sensor tracking system, the multi-sensor tracking system has several advantages in target tracking, such as a larger field of view and higher tracking accuracy.Different from the multi-sensor filters based on the random finite set (RFS) theory, the product multi-sensor probability hypothesis density (PM-PHD) filter with a modified cardinality coefficient performs well in estimating the number of targets.Since the PM-PHD filter employs the iterative fusion structure, its state estimation is sensitive to the sensor parameters.Furthermore, to improve the cardinality estimation, the PM-PHD filter may estimate some false targets when miss-detection occurs.Addressing the above problems, this paper changes the fusion structure of the PM-PHD filter and presents a novel version of the PM-PHD filter.The main idea of the proposed algorithm is the combinations of measurement subsets and other factors.Both the cardinality estimation and the state estimation are obtained by fusing the target numbers and normalized PHDs of these combinations.Compared with other multi-sensor PHD filters, the proposed algorithm can handle the problems of miss-detection and false alarm effectively.Moreover, the simulation results and the theoretical analysis indicate that the new PM-PHD filter can deal with a harsh tracking environment.
Flying Squirrel Optimizer (FSO): A novel SI-based optimization algorithm for engineering problems

This paper provides a novel meta-heuristic optimization algorithm. The behaviors of flying squirrels in the nature are the main inspiration of this research. These behaviors include flying from tree to tree and walking on the ground or on a tree branch to find food. They also contact each other with chirp or squeak. This algorithm is named flying squirrel optimizer (FSO). Two main theories of motion were used for the simulation of flying and walking of the flying squirrels and they are Levy flight and normal random walk. FSO is also benchmarked on twelve mathematical functions and the answers are compared with MFO, PSO, GSA, BA, FPA, SMS, and FA. The results show that FSO can provide good results when compared with these well-known meta-heuristics approaches. Five classical engineering problems and a real issue in the field of dam engineering were employed to challenge the FSO abilities in solving engineering design problems. The results also show that the proposed FSO algorithm can be used on a wide range of problems with unknown search spaces.
Transtech: development of a novel translator for Roman Urdu to English

Advances in machine and language translation immerge new fields and research opportunities for researchers, whereas Natural Language Processing and Computational Linguistics deal with communication between natural languages and their interaction. The objective of this research is to develop and test a novel tactic to solve the issue of translation from Roman Urdu to the English language. The approach used to construct this practical model is divided into three stages; each stage works out to achieve its desired task. Self-maintained corpus alongwith its corresponding tag-set is used for tokenization. The syntactical structure is covered by writing Urdu POS tagger based on grammatical rules. We prepared the grammatical structures of different sentences for Roman Urdu to English translation. Since Roman script can be expressed in numerous ways, our grammatical structures fulfill the maximum possible needs of writing and produce the best possible English translation. We entered a sentence in Roman Urdu which gave the best possible translation in the English language. In comparison with Google Translator, Transtech worked better and gives more accurate results.
Toward a Policy-based Blockchain Agnostic Framework

Blockchain technology is developing at a rapid pace. For consumers that are only interested in simplifying processes and reducing costs by using a blockchain solution a new problem has occurred. It is not only necessary to select the appropriate solution, but also to configure and operate considering blockchain interoperability with different chains. For the industry, there is a need for solutions based on open standards. This paper proposes a Policy-based Blockchain Agnostic Framework that not only connects different blockchains but also facilitates their configuration and operation. Moreover, a case study is presented considering the cold-chain supply-chain, where each actor defines its policies based on blockchain requirements.
Considerations on explainable AI and users’ mental models

As the aim of explaining is understanding, XAI is successful when the user has a good understanding of the AI system. This paper shows, using theories from the social sciences and HCI, that appropriately capturing and accounting for the user’s mental model while explaining is key to successful XAI.
What's to Know? Uncertainty as a Guide to Asking Goal-Oriented Questions

One of the core challenges in Visual Dialogue problems is asking the question that will provide the most useful information towards achieving the required objective. Encouraging an agent to ask the right questions is difficult because we don't know a-priori what information the agent will need to achieve its task, and we don't have an explicit model of what it knows already. We propose a solution to this problem based on a Bayesian model of the uncertainty in the implicit model maintained by the visual dialogue agent, and in the function used to select an appropriate output. By selecting the question that minimises the predicted regret with respect to this implicit model the agent actively reduces ambiguity. The Bayesian model of uncertainty also enables a principled method for identifying when enough information has been acquired, and an action should be selected. We evaluate our approach on two goal-oriented dialogue datasets, one for visual-based collaboration task and the other for a negotiation-based task. Our uncertainty-aware information-seeking model outperforms its counterparts in these two challenging problems.
Transitioning to smart green growth: lessons from history

This chapter identifies and brings together two major trends in economic growth: the knowledge economy and green growth. It uses lessons from periods of rapid economic growth to understand how financial markets may deliver a new golden age and suggests what policy makers can learn from these historical patterns. It asserts that the combination of ICT with a globalised context requiring sustainable growth looks towards a future in which ‘smart green growth’ is probably the only direction that can bring the next golden age.
Breaking the Spin Cycle: Teaching Complexity in the Age of Fake News

This article describes a discussion-based approach for teaching college students to identify the characteristics of ethical journalism and scholarly writing, by comparing fake news with credible information in a strategically planned slideshow. Much has been written on the need to instruct our students about disinformation. This librarian shares a lesson plan that engages students' critical thinking skills by using a blend of humor, analysis, and a compelling visual presentation. The teaching method is contextualized by research on the distrust of the press and scientific evidence since the rise of hyper-partisan cable news, Russian troll farms, and alternative facts.
Dynamic Clustering Algorithms via Small-Variance Analysis of Markov Chain Mixture Models

Bayesian nonparametrics are a class of probabilistic models in which the model size is inferred from data. A recently developed methodology in this field is small-variance asymptotic analysis, a mathematical technique for deriving learning algorithms that capture much of the flexibility of Bayesian nonparametric inference algorithms, but are simpler to implement and less computationally expensive. Past work on small-variance analysis of Bayesian nonparametric inference algorithms has exclusively considered batch models trained on a single, static dataset, which are incapable of capturing time evolution in the latent structure of the data. This work presents a small-variance analysis of the maximum a posteriori filtering problem for a temporally varying mixture model with a Markov dependence structure, which captures temporally evolving clusters within a dataset. Two clustering algorithms result from the analysis: D-Means, an iterative clustering algorithm for linearly separable, spherical clusters; and SD-Means, a spectral clustering algorithm derived from a kernelized, relaxed version of the clustering problem. Empirical results from experiments demonstrate the advantages of using D-Means and SD-Means over contemporary clustering algorithms, in terms of both computational cost and clustering accuracy.
Fast View Synthesis with Deep Stereo Vision

Novel view synthesis is an important problem in computer vision and graphics. Over the years a large number of solutions have been put forward to solve the problem. However, the large-baseline novel view synthesis problem is far from being solved. Recent works have attempted to use Convolutional Neural Networks (CNNs) to solve view synthesis tasks. Due to the difficulty of learning scene geometry and interpreting camera motion, CNNs are often unable to generate realistic novel views. In this paper, we present a novel view synthesis approach based on stereo-vision and CNNs that decomposes the problem into two sub-tasks: view dependent geometry estimation and texture inpainting. Both tasks are structured prediction problems that could be effectively learned with CNNs. Experiments on the KITTI Odometry dataset show that our approach is more accurate and significantly faster than the current state-of-the-art. The code and supplementary material will be publicly available. Results could be found here this https URL
Recursive Programs for Document Spanners

A document spanner models a program for Information Extraction (IE) as a function that takes as input a text document (string over a finite alphabet) and produces a relation of spans (intervals in the document) over a predefined schema. A well-studied language for expressing spanners is that of the regular spanners: relational algebra over regex formulas, which are regular expressions with capture variables. Equivalently, the regular spanners are the ones expressible in non-recursive Datalog over regex formulas (which extract relations that constitute the extensional database). This paper explores the expressive power of recursive Datalog over regex formulas. We show that such programs can express precisely the document spanners computable in polynomial time. We compare this expressiveness to known formalisms such as the closure of regex formulas under the relational algebra and string equality. Finally, we extend our study to a recently proposed framework that generalizes both the relational model and the document spanners.
Architectural Patterns for Secure IoT Orchestrations

The vast amount of connected devices on the Internet of Things (IoT) creates an enormous potential for new applications, by leveraging synergies arising through the convergence of consumer, business and industrial Internet, and creating open, global networks connecting people, data, and “things”. In this context, the SEMIoTICS project aims to develop a pattern-driven framework, built upon existing IoT platforms, to enable and guarantee secure and dependable actuation and semi-autonomic behaviour in IoT/Industrial IoT applications. To achieve this, patterns are used to encode proven dependencies between the security, privacy, dependability and interoperability (SPDI) properties of individual smart objects and corresponding properties of orchestrations (composition) involving them. This paper sketches this approach followed by SEMIoTICS, whereby the SPDI patterns are used to generate IoT orchestrations with proven SPDI properties at design time, while at runtime these properties are monitored in real-time, across system layers, triggering adaptations to return the deployed orchestration to the desired SPDI state, when needed.
Distantly Supervised Biomedical Knowledge Acquisition via Knowledge Graph Based Attention

The increased demand for structured scientific knowledge has attracted considerable attention in extracting scientific relation from the ever growing scientific publications. Distant supervision is widely applied approach to automatically generate large amounts of labelled data with low manual annotation cost. However, distant supervision inevitably accompanies the wrong labelling problem, which will negatively affect the performance of Relation Extraction (RE). To address this issue, (Han et al., 2018) proposes a novel framework for jointly training both RE model and Knowledge Graph Completion (KGC) model to extract structured knowledge from non-scientific dataset. In this work, we firstly investigate the feasibility of this framework on scientific dataset, specifically on biomedical dataset. Secondly, to achieve better performance on the biomedical dataset, we extend the framework with other competitive KGC models. Moreover, we proposed a new end-to-end KGC model to extend the framework. Experimental results not only show the feasibility of the framework on the biomedical dataset, but also indicate the effectiveness of our extensions, because our extended model achieves significant and consistent improvements on distant supervised RE as compared with baselines.
Selective ensemble learning method for belief-rule-base classification system based on PAES

Traditional Belief-Rule-Based (BRB) ensemble learning methods integrate all of the trained sub-BRB systems to obtain better results than a single belief-rule-based system. However, as the number of BRB systems participating in ensemble learning increases, a large amount of redundant sub-BRB systems are generated because of the diminishing difference between subsystems. This drastically decreases the prediction speed and increases the storage requirements for BRB systems. In order to solve these problems, this paper proposes BRBCS-PAES: a selective ensemble learning approach for BRB Classification Systems (BRBCS) based on Pareto-Archived Evolutionary Strategy (PAES) multi-objective optimization. This system employs the improved Bagging algorithm to train the base classifier. For the purpose of increasing the degree of difference in the integration of the base classifier, the training set is constructed by the repeated sampling of data. In the base classifier selection stage, the trained base classifier is binary coded, and the number of base classifiers participating in integration and generalization error of the base classifier is used as the objective function for multi-objective optimization. Finally, the elite retention strategy and the adaptive mesh algorithm are adopted to produce the PAES optimal solution set. Three experimental studies on classification problems are performed to verify the effectiveness of the proposed method. The comparison results demonstrate that the proposed method can effectively reduce the number of base classifiers participating in the integration and improve the accuracy of BRBCS.
An Algorithmic Framework For Differentially Private Data Analysis on Trusted Processors

Differential privacy has emerged as the main definition for private data analysis and machine learning. The global model of differential privacy, which assumes that users trust the data collector, provides strong privacy guarantees and introduces small errors in the output. In contrast, applications of differential privacy in commercial systems by Apple, Google, and Microsoft, use the local model. Here, users do not trust the data collector, and hence randomize their data before sending it to the data collector. Unfortunately, local model is too strong for several important applications and hence is limited in its applicability. In this work, we propose a framework based on trusted processors and a new definition of differential privacy called Oblivious Differential Privacy, which combines the best of both local and global models. The algorithms we design in this framework show interesting interplay of ideas from the streaming algorithms, oblivious algorithms, and differential privacy.
Scada systems in the cloud and fog environments: Migration scenarios and security issues

This paper addresses scenarios and security issues when migrating SCADA systems to cloud and fog environments. Migration strategies to the cloud refer to different cloud infrastructures (public, private or hybrid) as well as selection of cloud service. Benefits of cloud-based SCADA systems mainly refer to improving economic efficiency. We further address migration risks, with regards to quality of service and cyber security. Challenges in security provisioning encompass security solutions, risk management and test environment. Finally, we address emerging evolution of SCADA toward fog computing, including the three-tier system?s architecture and security issues.
Optical control of entanglement and coherence for polar molecules in pendular states

Quantum entanglement and coherence are both essential physical resources in quantum theory. Cold polar molecules have long coherence time and strong dipole-dipole interaction and thus have been suggested as a platform for quantum information processing. In this paper, we employ the pendular states of the polar molecules trapped in static electric fields as the qubits, and put forward several theoretical schemes to generate the entanglement and coherence for two coupled dipoles by using optimal control theory. Through the designs of appropriate laser pulses, the transitions from the ground state to the Bell state and maximally coherent state can be realized with high fidelities 0.9906 and 0.9943 in the two-dipole system, respectively. Meanwhile, we show that the degrees of entanglement and coherence between the two pendular qubits are effectively enhanced with the help of optimized control fields. Furthermore, our schemes are generalized to the preparation of the Hardy state and even to the creation of arbitrary two-qubit states. Our findings can shed some light on the implementation of quantum information tasks with the molecular pendular states.
Mixtures of Nonlinear Poisson Autoregressions

We study nonlinear infinite order Markov switching integer‐valued ARCH models for count time series data. Markov switching models take into account complex dynamics and can deal with several stylistic facts of count data including proper modelling of nonlinearities, overdispersion and outliers. We study structural properties of those models. Under mild conditions, we prove consistency and asymptotic normality of the maximum likelihood estimator for the case of finite order autoregression. In addition, we give conditions which imply that the marginal likelihood ratio test, for testing the number of regimes, converges to a Gaussian process. This result enables us to prove that the BIC provides a consistent estimator for selecting the true number of regimes. A real data example illustrates the methodology and compares this approach with alternative methods.
Dirty Data: Content Moderation, Regulatory Outsourcing, and The Cleaners

Longtime FQ Editorial Board member and 2018 MacArthur Fellow Lisa Parks charts the shift in critical focus from the potential of social media platforms to unite people around progressive causes to the need for “content moderation,” the practice of cleaning up digital pollution. Parks centers her analysis on The Cleaners (2018), Moritz Riesewieck and Hans Block's provocative documentary that delves into the lives and worlds of commercial content moderators at an unnamed company in the Philippines. The film's account of these digital-labor conditions prompts Parks' critical reflection on a series of issues: the delegation of U.S. media regulation to globally outsourced workers, the problematic trope of “cleaning,” the business of historical sanitization, and the black-boxing of infrastructural information.
Flex Sensor Compensator via Hammerstein–Wiener Modeling Approach for Improved Dynamic Goniometry and Constrained Control of a Bionic Hand

In this paper, a new control-centric approach is introduced to model the characteristics of flex sensors on a goniometric glove, which is designed to capture the user hand gesture that can be used to wirelessly control a bionic hand. The main technique employs the inverse dynamic model strategy along with a black-box identification for the compensator design, which is aimed to provide an approximate linear mapping between the raw sensor output and the dynamic finger goniometry. To smoothly recover the goniometry on the bionic hand’s side during the wireless transmission, the compensator is restructured into a Hammerstein–Wiener model, which consists of a linear dynamic system and two static nonlinearities. A series of real-time experiments involving several hand gestures have been conducted to analyze the performance of the proposed method. The associated temporal and spatial gesture data from both the glove and the bionic hand are recorded, and the performance is evaluated in terms of the integral of absolute error between the glove’s and the bionic hand’s dynamic goniometry. The proposed method is also compared with the raw sensor data, which has been preliminarily calibrated with the finger goniometry, and the Wiener model, which is based on the initial inverse dynamic design strategy. Experimental results with several trials for each gesture show that a great improvement is obtained via the Hammerstein–Wiener compensator approach where the resulting average errors are significantly smaller than the other two methods. This concludes that the proposed strategy can remarkably improve the dynamic goniometry of the glove, and thus provides a smooth human–robot collaboration with the bionic hand.