OpenRaiser/Intern-Atlas huggingface dataset:
Quantum Temporal Logic
Venue: arXiv cs.LO 2019
Year: 2019

In this paper, we introduce a model of quantum concurrent program, which can be used to model the behaviour of reactive quantum systems and to design quantum compilers. We investigate quantum temporal logic, QTL, for the specification of quantum concurrent systems by suggesting the time-dependence of events. QTL employs the projections on subspaces as atomic propositions, which was established in the Birkhoff and von Neumann's classic treatise on quantum logic. For deterministic functional quantum program, We prove a quantum Böhm-Jacopini theorem which states that any such program is equivalent to a Q-While program. The decidability of basic QTL formulae for general quantum concurrent program is studied.
The Constrained Round Robin Algorithm for Fair and Efficient Allocation
Venue: arXiv cs.GT 2019
Year: 2019

We consider a multi-agent resource allocation setting that models the assignment of papers to reviewers. A recurring issue in allocation problems is the compatibility of welfare/efficiency and fairness. Given an oracle to find a welfare-achieving allocation, we embed such an oracle into a flexible algorithm called the Constrained Round Robin (CRR) algorithm, that achieves the required welfare level. Our algorithm also allows the system designer to lower the welfare requirements in order to achieve a higher degree of fairness. If the welfare requirement is lowered enough, a strengthening of envy-freeness up to one item is guaranteed. Hence, our algorithm can be viewed as a computationally efficient way to interpolate between welfare and approximate envy-freeness in allocation problems.
Estimating Color-Concept Associations from Image Statistics
Venue: arXiv cs.HC 2019
Year: 2019

To interpret the meanings of colors in visualizations of categorical information, people must determine how distinct colors correspond to different concepts. This process is easier when assignments between colors and concepts in visualizations match people's expectations, making color palettes semantically interpretable. Efforts have been underway to optimize color palette design for semantic interpretablity, but this requires having good estimates of human color-concept associations. Obtaining these data from humans is costly, which motivates the need for automated methods. We developed and evaluated a new method for automatically estimating color-concept associations in a way that strongly correlates with human ratings. Building on prior studies using Google Images, our approach operates directly on Google Image search results without the need for humans in the loop. Specifically, we evaluated several methods for extracting raw pixel content of the images in order to best estimate color-concept associations obtained from human ratings. The most effective method extracted colors using a combination of cylindrical sectors and color categories in color space. We demonstrate that our approach can accurately estimate average human color-concept associations for different fruits using only a small set of images. The approach also generalizes moderately well to more complicated recycling-related concepts of objects that can appear in any color.
Visual Place Recognition for Aerial Robotics: Exploring Accuracy-Computation Trade-off for Local Image Descriptors
Venue: arXiv cs.CV 2019
Year: 2019

Visual Place Recognition (VPR) is a fundamental yet challenging task for small Unmanned Aerial Vehicle (UAV). The core reasons are the extreme viewpoint changes, and limited computational power onboard a UAV which restricts the applicability of robust but computation intensive state-of-the-art VPR methods. In this context, a viable approach is to use local image descriptors for performing VPR as these can be computed relatively efficiently without the need of any special hardware, such as a GPU. However, the choice of a local feature descriptor is not trivial and calls for a detailed investigation as there is a trade-off between VPR accuracy and the required computational effort. To fill this research gap, this paper examines the performance of several state-of-the-art local feature descriptors, both from accuracy and computational perspectives, specifically for VPR application utilizing standard aerial datasets. The presented results confirm that a trade-off between accuracy and computational effort is inevitable while executing VPR on resource-constrained hardware.
Content and Colour Distillation for Learning Image Translations with the Spatial Profile Loss
Venue: arXiv cs.CV 2019
Year: 2019

Generative adversarial networks has emerged as a defacto standard for image translation problems. To successfully drive such models, one has to rely on additional networks e.g., discriminators and/or perceptual networks. Training these networks with pixel based losses alone are generally not sufficient to learn the target distribution. In this paper, we propose a novel method of computing the loss directly between the source and target images that enable proper distillation of shape/content and colour/style. We show that this is useful in typical image-to-image translations allowing us to successfully drive the generator without relying on additional networks. We demonstrate this on many difficult image translation problems such as image-to-image domain mapping, single image super-resolution and photo realistic makeup transfer. Our extensive evaluation shows the effectiveness of the proposed formulation and its ability to synthesize realistic images. [Code release: https://github.com/ssarfraz/SPL]
Featuring the topology with the unsupervised machine learning
Venue: arXiv cs.LG 2019
Year: 2019

Images of line drawings are generally composed of primitive elements. One of the most fundamental elements to characterize images is the topology; line segments belong to a category different from closed circles, and closed circles with different winding degrees are nonequivalent. We investigate images with nontrivial winding using the unsupervised machine learning. We build an autoencoder model with a combination of convolutional and fully connected neural networks. We confirm that compressed data filtered from the trained model retain more than 90% of correct information on the topology, evidencing that image clustering from the unsupervised learning features the topology.
Towards Multidimensional Verification: Where Functional Meets Non-Functional
Venue: arXiv cs.AR 2019
Year: 2019

Trends in advanced electronic systems' design have a notable impact on design verification technologies. The recent paradigms of Internet-of-Things (IoT) and Cyber-Physical Systems (CPS) assume devices immersed in physical environments, significantly constrained in resources and expected to provide levels of security, privacy, reliability, performance and low power features. In recent years, numerous extra-functional aspects of electronic systems were brought to the front and imply verification of hardware design models in multidimensional space along with the functional concerns of the target system. However, different from the software domain such a holistic approach remains underdeveloped. The contributions of this paper are a taxonomy for multidimensional hardware verification aspects, a state-of-the-art survey of related research works and trends towards the multidimensional verification concept. The concept is motivated by an example for the functional and power verification dimensions.
Modeling and Analysis of Integrated Proactive Defense Mechanisms for Internet-of-Things
Venue: arXiv cs.CR 2019
Year: 2019

As a solution to protect and defend a system against inside attacks, many intrusion detection systems (IDSs) have been developed to identify and react to them for protecting a system. However, the core idea of an IDS is a reactive mechanism in nature even though it detects intrusions which have already been in the system. Hence, the reactive mechanisms would be way behind and not effective for the actions taken by agile and smart attackers. Due to the inherent limitation of an IDS with the reactive nature, intrusion prevention systems (IPSs) have been developed to thwart potential attackers and/or mitigate the impact of the intrusions before they penetrate into the system. In this chapter, we introduce an integrated defense mechanism to achieve intrusion prevention in a software-defined Internet-of-Things (IoT) network by leveraging the technologies of cyberdeception (i.e., a decoy system) and moving target defense, namely MTD (i.e., network topology shuffling). In addition, we validate their effectiveness and efficiency based on the devised graphical security model (GSM)-based evaluation framework. To develop an adaptive, proactive intrusion prevention mechanism, we employed fitness functions based on the genetic algorithm in order to identify an optimal network topology where a network topology can be shuffled based on the detected level of the system vulnerability. Our simulation results show that GA-based shuffling schemes outperform random shuffling schemes in terms of the number of attack paths toward decoy targets. In addition, we observe that there exists a tradeoff between the system lifetime (i.e., mean time to security failure) and the defense cost introduced by the proposed MTD technique for fixed and adaptive shuffling schemes. That is, a fixed GA-based shuffling can achieve higher MTTSF with more cost while an adaptive GA-based shuffling obtains less MTTSF with less cost.
Popt4jlib: A Parallel/Distributed Optimization Library for Java
Venue: arXiv cs.DC 2019
Year: 2019

This paper describes the architectural design as well as key implementation details of the Open Source popt4jlib library (https://githhub.org/ioannischristou/popt4jlib) that contains a fairly large number of meta-heuristic and other exact optimization algorithms parallel/distributed Java implementations. Although we report on speedup and efficiency issues on some of the algorithms in the library, our main concern is to detail the design decisions for the key parallel/distributed infrastructure built into the library, so as to make it easier for developers to develop their own parallel implementations of the algorithms of their choice, rather than simply using it as an off-the-self application library.
Efficient Machine Learning for Large-Scale Urban Land-Use Forecasting in Sub-Saharan Africa
Venue: arXiv cs.CV 2019
Year: 2019

Urbanization is a common phenomenon in developing countries and it poses serious challenges when not managed effectively. Lack of proper planning and management may cause the encroachment of urban fabrics into reserved or special regions which in turn can lead to an unsustainable increase in population. Ineffective management and planning generally leads to depreciated standard of living, where physical hazards like traffic accidents and disease vector breeding become prevalent. In order to support urban planners and policy makers in effective planning and accurate decision making, we investigate urban land-use in sub-Saharan Africa. Land-use dynamics serves as a crucial parameter in current strategies and policies for natural resource management and monitoring. Focusing on Nairobi, we use an efficient deep learning approach with patch-based prediction to classify regions based on land-use from 2004 to 2018 on a quarterly basis. We estimate changes in land-use within this period, and using the Autoregressive Integrated Moving Average (ARIMA) model, our results forecast land-use for a given future date. Furthermore, we provide labelled land-use maps which will be helpful to urban planners.
Learning to Adapt Invariance in Memory for Person Re-identification
Venue: arXiv cs.CV 2019
Year: 2019

This work considers the problem of unsupervised domain adaptation in person re-identification (re-ID), which aims to transfer knowledge from the source domain to the target domain. Existing methods are primary to reduce the inter-domain shift between the domains, which however usually overlook the relations among target samples. This paper investigates into the intra-domain variations of the target domain and proposes a novel adaptation framework w.r.t. three types of underlying invariance, i.e., Exemplar-Invariance, Camera-Invariance, and Neighborhood-Invariance. Specifically, an exemplar memory is introduced to store features of samples, which can effectively and efficiently enforce the invariance constraints over the global dataset. We further present the Graph-based Positive Prediction (GPP) method to explore reliable neighbors for the target domain, which is built upon the memory and is trained on the source samples. Experiments demonstrate that 1) the three invariance properties are indispensable for effective domain adaptation, 2) the memory plays a key role in implementing invariance learning and improves the performance with limited extra computation cost, 3) GPP could facilitate the invariance learning and thus significantly improves the results, and 4) our approach produces new state-of-the-art adaptation accuracy on three re-ID large-scale benchmarks.
POINTS -- Playful objects for inclusive, personalized movement games
Venue: arXiv cs.HC 2019
Year: 2019

Promoting exercise and promoting fun in sport and activity is a common goal of schools. However, children and adolescents do not exercise enough, which can favor a number of chronic illnesses. Exercise and sports often require coordination of visual perception and reaction, which is an additional barrier for visually impaired (blind and partially sighted) people. Due to their highly motivating appeal, games promoting physical activity (exertion games) have become increasingly popular. Although accessible exertion games have been developed, they do not consider the different abilities of players. Especially in team sports player roles that consider individual abilities can foster inclusion. To personalize roles and assign certain abilities to players, wearable technology can play an important role. In this position paper we present ideas how digital objects can be used to design exertion games for visually impaired students and we reflect how wearable technology can be used for personalized player roles.
Two-Stream Video Classification with Cross-Modality Attention
Venue: arXiv cs.CV 2019
Year: 2019

Fusing multi-modality information is known to be able to effectively bring significant improvement in video classification. However, the most popular method up to now is still simply fusing each stream's prediction scores at the last stage. A valid question is whether there exists a more effective method to fuse information cross modality. With the development of attention mechanism in natural language processing, there emerge many successful applications of attention in the field of computer vision. In this paper, we propose a cross-modality attention operation, which can obtain information from other modality in a more effective way than two-stream. Correspondingly we implement a compatible block named CMA block, which is a wrapper of our proposed attention operation. CMA can be plugged into many existing architectures. In the experiments, we comprehensively compare our method with two-stream and non-local models widely used in video classification. All experiments clearly demonstrate strong performance superiority by our proposed method. We also analyze the advantages of the CMA block by visualizing the attention map, which intuitively shows how the block helps the final prediction.
Evaluating an Immersive Space-Time Cube Geovisualization for Intuitive Trajectory Data Exploration
Venue: arXiv cs.HC 2019
Year: 2019

A Space-Time Cube enables analysts to clearly observe spatio-temporal features in movement trajectory datasets in geovisualization. However, its general usability is impacted by a lack of depth cues, a reported steep learning curve, and the requirement for efficient 3D navigation. In this work, we investigate a Space-Time Cube in the Immersive Analytics domain. Based on a review of previous work and selecting an appropriate exploration metaphor, we built a prototype environment where the cube is coupled to a virtual representation of the analyst's real desk, and zooming and panning in space and time are intuitively controlled using mid-air gestures. We compared our immersive environment to a desktop-based implementation in a user study with 20 participants across 7 tasks of varying difficulty, which targeted different user interface features. To investigate how performance is affected in the presence of clutter, we explored two scenarios with different numbers of trajectories. While the quantitative performance was similar for the majority of tasks, large differences appear when we analyze the patterns of interaction and consider subjective metrics. The immersive version of the Space-Time Cube received higher usability scores, much higher user preference, and was rated to have a lower mental workload, without causing participants discomfort in 25-minute-long VR sessions.
Set-Based Models for Cryptocurrency Software
Venue: arXiv cs.SE 2019
Year: 2019

Emin Gün Sirer once said: It's clear that writing a robust, secure smart contract requires extreme amounts of diligence. It's more similar to writing code for a nuclear power reactor, than to writing loose web code [...] Yet the current Solidity language and underlying EVM seems designed more for the latter. Formal methods (FM) are mathematics-based software development methods aimed at producing "code for a nuclear power reactor". That is, due application of FM can produce bug-free, zero-defect, correct-by-construction, guaranteed, certified software. However, the software industry seldom use FM. One of the main reasons for such a situation is that there exists the perception (which might well be a fact) that FM increase software costs. On the other hand, FM can be partially applied thus producing high-quality software, although not necessarily bug-free. In this paper we outline some FM related techniques whose application the cryptocurrency community should take into consideration because they could bridge the gap between "loose web code" and "code for a nuclear power reactor".
Color Crafting: Automating the Construction of Designer Quality Color Ramps
Venue: arXiv cs.HC 2019
Year: 2019

Visualizations often encode numeric data using sequential and diverging color ramps. Effective ramps use colors that are sufficiently discriminable, align well with the data, and are aesthetically pleasing. Designers rely on years of experience to create high-quality color ramps. However, it is challenging for novice visualization developers that lack this experience to craft effective ramps as most guidelines for constructing ramps are loosely defined qualitative heuristics that are often difficult to apply. Our goal is to enable visualization developers to readily create effective color encodings using a single seed color. We do this using an algorithmic approach that models designer practices by analyzing patterns in the structure of designer-crafted color ramps. We construct these models from a corpus of 222 expert-designed color ramps, and use the results to automatically generate ramps that mimic designer practices. We evaluate our approach through an empirical study comparing the outputs of our approach with designer-crafted color ramps. Our models produce ramps that support accurate and aesthetically pleasing visualizations at least as well as designer ramps and that outperform conventional mathematical approaches.
Robust Deep Sensing Through Transfer Learning in Cognitive Radio
Venue: arXiv cs.IT 2019
Year: 2019

We propose a robust spectrum sensing framework based on deep learning. The received signals at the secondary user's receiver are filtered, sampled and then directly fed into a convolutional neural network. Although this deep sensing is effective when operating in the same scenario as the collected training data, the sensing performance is degraded when it is applied in a different scenario with different wireless signals and propagation. We incorporate transfer learning into the framework to improve the robustness. Results validate the effectiveness as well as the robustness of the proposed deep spectrum sensing framework.
Exploring Challenges and Opportunities in Cybersecurity Risk and Threat Communications Related To The Medical Internet Of Things (MIoT)
Venue: arXiv cs.CY 2019
Year: 2019

As device interconnectivity and ubiquitous computing continues to proliferate healthcare, the Medical Internet of Things (MIoT), also well known as the, Internet of Medical Things (IoMT) or the Internet of Healthcare Things (IoHT), is certain to play a major role in the health, and well-being of billions of people across the globe. When it comes to issues of cybersecurity risks and threats connected to the IoT in all of its various flavors the emphasis has been on technical challenges and technical solution. However, especially in the area of healthcare there is another substantial and potentially grave challenge. It is the challenge of thoroughly and accurately communicating the nature and extent of cybersecurity risks and threats to patients who are reliant upon these interconnected healthcare technologies to improve and even preserve their lives. This case study was conducted to assess the scope and depth of cybersecurity risk and threat communications delivered to an extremely vulnerable patient population, semi-structured interviews were held with cardiac medical device specialists across the United States. This research contributes scientific data in the field of healthcare cybersecurity and assists scholars and practitioners in advancing education and research in the field of MIoT patient communications.
b-continuity and Partial Grundy Coloring of graphs with large girth
Venue: arXiv cs.DM 2019
Year: 2019

A b-coloring of a graph is a proper coloring such that each color class has at least one vertex which is adjacent to each other color class. The b-spectrum of $G$ is the set $S_{b}(G)$ of integers $k$ such that $G$ has a b-coloring with $k$ colors and $b(G)=\max S_{b}(G)$ is the b-chromatic number of $G$. A graph is b-continous if $S_{b}(G)=[χ(G),b(G)]\cap \mathbb{Z}$. An infinite number of graphs that are not b-continuous is known. It is also known that graphs with girth at least 10 are b-continuous. A partial Grundy coloring is a proper coloring $f:V(G)\rightarrow \{1,\ldots,k\}$ such that each color class $i$ contains some vertex $u$ that is adjacent to every color class $j$ such that $j<i$. The partial Grundy number of $G$ is the maximum value $\partialΓ(G)$ for which $G$ has a partial Grundy coloring. In this work, we prove that graphs with girth at least 8 are b-continuous, and that the b-spectrum of a graph $G$ with girth at least 7 contains the integers between $2χ(G)$ and $b(G)$. We also prove that $\partialΓ(G)$ equals a known upper bound when $G$ is a graph with girth at least 7. These results generalize previous ones by Linhares-Sales and Silva (2017), and by Shi et al.(2005).