OpenRaiser/Intern-Atlas huggingface dataset:
Policy Scan and Technology Strategy Design methodology
Venue: arXiv cs.CY 2018
Year: 2018

Increasingly accelerating technology advancement affects and disrupts almost all aspects of human society and civilization at large as we know it. Actually, this has been true since the technology started at the dawn of human society, yet the mere speed and magnitude of modern technology development brings about the situation where societies and economies have to adapt to the changing technological landscape as much as technologies have to integrate into the social fabric. The way to to achieve such integration in a changing and unpredictable world is to support the close interaction between the world of societal expectations and the world of technology. Policy Scan and Technology Strategy design methodology presented in this paper was developed precisely for the purpose of addressing specific types of 'ill-defined' problems in terms of observing, analyzing and integrating technology developments and availabilities with policy requirements, social governance and societal expectations. The methodology consists of conceptual tools and methods for developing concrete actions and products for guiding technology adoption for social change. It is geared towards increasingly complex and uncertain situations where existing analysis and problem solving methods often fail due to many non-linearities inherent in the social and technology worlds and, especially, at their area of their inter intersection. The development of the methodology followed the grounded theory construction process which requires a close relation to a specific context of an application domain, determined by actual interaction between the worlds of societal problems and technology. The chosen application domain of this research is the intersection of smart mobility problematics and opportunities, the rising autonomous driving technology, data privacy, provenance and security challenges, policies and legislation.
A Wireless Multimedia Sensor Network Platform for Environmental Event Detection Dedicated to Precision Agriculture
Venue: arXiv cs.CY 2018
Year: 2018

Precision agriculture has been considered as a new technique to improve agricultural production and support sustainable development by preserving planet resource and minimizing pollution. By monitoring different parameters of interest in a cultivated field, wireless sensor network (WSN) enables real-time decision making with regard to issues such as management of water resources for irrigation, choosing the optimum point for harvesting, estimating fertilizer requirements and predicting crop yield more accurately. In spite the tremendous advanced of scalar WSN in recent year, scalar WSN cannot meet all the requirements of ubiquitous intelligent environmental event detections because scalar data such as temperature, soil humidity, air humidity and light intensity are not rich enough to detect all the environmental events such as plant diseases and present of insects. Thus to fulfill those requirements multimedia data is needed. In this paper we present a robust multi-support and modular Wireless Multimedia Sensor Network (WMSN) platform, which is a type of wireless sensor network equipped with a low cost CCD camera. This WMSN platform may be used for diverse environmental event detections such as the presence of plant diseases and insects in precision agriculture applications.
Studying real-world societal problems in a STEM context through robotics
Venue: arXiv cs.CY 2018
Year: 2018

In this paper, we will discuss the design and implementation of three educational robotics workshops that focus on the use of robotics for addressing real-world societal problems. Through the workshops, we aimed to investigate how the integration of real-world problems to robotics can provide a multidisciplinary approach for child-robot interaction that allows students to engage with different STE(A)M concepts. In addition, we examined if this integration increased or strengthened students' interest in STEM/robotics education and careers. The workshops were designed and implemented in the context of the European project Educational Robotics for STEM (ER4STEM) with students from different age groups using Lego Mindstorms and Arduino kits.
Driving by the Elderly and their Awareness of their Driving Difficulties (Hebrew)
Venue: arXiv cs.CY 2018
Year: 2018

In the past twenty years the number of elderly drivers has increased for two reasons. one is the higher proportion of elderly in the population, and the other is the rise in the share of the elderly who drive. This paper examines the features of their driving and the level of their awareness of problems relating to it, by analysis preference survey that included interviews with 205 drivers aged between 70 and 80. The interviewees exhibited a level of optimism and self confidence in their driving that is out of line with the real situation. There is also a discrepancy between how their driving is viewed by others and their own assessment, and between their self assessment and their assessment of the driving of other elderly drivers, which they rate lower than their own. they attributed great importance to safety feature in cars, although they did not think that they themselves needed them, and most elderly drivers did not think there was any reason that they should stop driving, despite suggestions from family members and others that they should do so. A declared preference survey was undertaken to assess the degree of difficulty elderly drivers attribute to driving conditions. It was found that they are concerned mainly about weather condition, driving at night, and long journeys. Worry about night driving was most marked among women, the oldest drivers, and those who drove less frequently. In light of the findings, imposing greater responsibility on the health system should be considered. Consideration should also be given to issuing partial licenses to the elderly for daytime driving only, or restricted to certain weather conditions, dependent on their medical condition. Such flexibility will enable the elderly to maintain their life style and independence for a longer period on the one hand, and on the other, will minimize the risks to themselves and other.
A Tunable Measure for Information Leakage
Venue: arXiv cs.IT 2018
Year: 2018

A tunable measure for information leakage called \textit{maximal $α$-leakage} is introduced. This measure quantifies the maximal gain of an adversary in refining a tilted version of its prior belief of any (potentially random) function of a dataset conditioning on a disclosed dataset. The choice of $α$ determines the specific adversarial action ranging from refining a belief for $α=1$ to guessing the best posterior for $α= \infty$, and for these extremal values this measure simplifies to mutual information (MI) and maximal leakage (MaxL), respectively. For all other $α$ this measure is shown to be the Arimoto channel capacity. Several properties of this measure are proven including: (i) quasi-convexity in the mapping between the original and disclosed datasets; (ii) data processing inequalities; and (iii) a composition property.
Resource Allocation for Low-Latency Vehicular Communications with Packet Retransmission
Venue: arXiv cs.IT 2018
Year: 2018

Vehicular communications have stringent latency requirements on safety-critical information transmission. However, lack of instantaneous channel state information due to high mobility poses a great challenge to meet these requirements and the situation gets more complicated when packet retransmission is considered. Based on only the obtainable large-scale fading channel information, this paper performs spectrum and power allocation to maximize the ergodic capacity of vehicular-to-infrastructure (V2I) links while guaranteeing the latency requirements of vehicular-to-vehicular (V2V) links. First, for each possible spectrum reusing pair of a V2I link and a V2V link, we obtain the closed-form expression of the packets' average sojourn time (the queueing time plus the service time) for the V2V link. Then, an optimal power allocation is derived for each possible spectrum reusing pair. Afterwards, we optimize the spectrum reusing pattern by addressing a polynomial time solvable bipartite matching problem. Numerical results show that the proposed queueing analysis is accurate in terms of the average packet sojourn time. Moreover, the developed resource allocation always guarantees the V2V links' requirements on latency.
Distributed Algorithms for Minimum Degree Spanning Trees
Venue: arXiv cs.DS 2018
Year: 2018

The minimum degree spanning tree (MDST) problem requires the construction of a spanning tree $T$ for graph $G=(V,E)$ with $n$ vertices, such that the maximum degree $d$ of $T$ is the smallest among all spanning trees of $G$. In this paper, we present two new distributed approximation algorithms for the MDST problem. Our first result is a randomized distributed algorithm that constructs a spanning tree of maximum degree $\hat d = O(d\log{n})$. It requires $O((D + \sqrt{n}) \log^2 n)$ rounds (w.h.p.), where $D$ is the graph diameter, which matches (within log factors) the optimal round complexity for the related minimum spanning tree problem. Our second result refines this approximation factor by constructing a tree with maximum degree $\hat d = O(d + \log{n})$, though at the cost of additional polylogarithmic factors in the round complexity. Although efficient approximation algorithms for the MDST problem have been known in the sequential setting since the 1990's, our results are first efficient distributed solutions for this problem.
Cultural Investment and Urban Socio-Economic Development: A Geo-Social Network Approach
Venue: arXiv cs.SI 2018
Year: 2018

Being able to assess the impact of government-led investment onto socio-economic indicators in cities has long been an important target of urban planning. However, due to the lack of large-scale data with a fine spatio-temporal resolution, there have been limitations in terms of how planners can track the impact and measure the effectiveness of cultural investment in small urban areas. Taking advantage of nearly 4 million transition records for three years in London from a popular location-based social network service, Foursquare, we study how the socio-economic impact of government cultural expenditure can be detected and predicted. Our analysis shows that network indicators such as average clustering coefficient or centrality can be exploited to estimate the likelihood of local growth in response to cultural investment. We subsequently integrate these features in supervised learning models to infer socio-economic deprivation changes for London's neighbourhoods. This research presents how geo-social and mobile services can be used as a proxy to track and predict socio-economic deprivation changes as government financial effort is put in developing urban areas and thus gives evidence and suggestions for further policy-making and investment optimisation.
Fully Convolutional Networks with Sequential Information for Robust Crop and Weed Detection in Precision Farming
Venue: arXiv cs.CV 2018
Year: 2018

Reducing the use of agrochemicals is an important component towards sustainable agriculture. Robots that can perform targeted weed control offer the potential to contribute to this goal, for example, through specialized weeding actions such as selective spraying or mechanical weed removal. A prerequisite of such systems is a reliable and robust plant classification system that is able to distinguish crop and weed in the field. A major challenge in this context is the fact that different fields show a large variability. Thus, classification systems have to robustly cope with substantial environmental changes with respect to weed pressure and weed types, growth stages of the crop, visual appearance, and soil conditions. In this paper, we propose a novel crop-weed classification system that relies on a fully convolutional network with an encoder-decoder structure and incorporates spatial information by considering image sequences. Exploiting the crop arrangement information that is observable from the image sequences enables our system to robustly estimate a pixel-wise labeling of the images into crop and weed, i.e., a semantic segmentation. We provide a thorough experimental evaluation, which shows that our system generalizes well to previously unseen fields under varying environmental conditions --- a key capability to actually use such systems in precision framing. We provide comparisons to other state-of-the-art approaches and show that our system substantially improves the accuracy of crop-weed classification without requiring a retraining of the model.
Acyclic orientations with degree constraints
Venue: arXiv cs.CC 2018
Year: 2018

In this note we study the complexity of some generalizations of the notion of $st$-numbering. Suppose that given some functions $f$ and $g$, we want to order the vertices of a graph such that every vertex $v$ is preceded by at least $f(v)$ of its neighbors and succeeded by at least $g(v)$ of its neighbors. We prove that this problem is solvable in polynomial time if $fg\equiv 0$, but it becomes NP-complete for $f\equiv g \equiv 2$. This answers a question of the first author posed in 2009.
Type variables in patterns
Venue: arXiv cs.PL 2018
Year: 2018

For many years, GHC has implemented an extension to Haskell that allows type variables to be bound in type signatures and patterns, and to scope over terms. This extension was never properly specified. We rectify that oversight here. With the formal specification in hand, the otherwise-labyrinthine path toward a design for binding type variables in patterns becomes blindingly clear. We thus extend ScopedTypeVariables to bind type variables explicitly, obviating the Proxy workaround to the dustbin of history.
DIR-ST$^2$: Delineation of Imprecise Regions Using Spatio--Temporal--Textual Information
Venue: arXiv cs.IR 2018
Year: 2018

An imprecise region is referred to as a geographical area without a clearly-defined boundary in the literature. Previous clustering-based approaches exploit spatial information to find such regions. However, the prior studies suffer from the following two problems: the subjectivity in selecting clustering parameters and the inclusion of a large portion of the undesirable region (i.e., a large number of noise points). To overcome these problems, we present DIR-ST$^2$, a novel framework for delineating an imprecise region by iteratively performing density-based clustering, namely DBSCAN, along with not only spatio--textual information but also temporal information on social media. Specifically, we aim at finding a proper radius of a circle used in the iterative DBSCAN process by gradually reducing the radius for each iteration in which the temporal information acquired from all resulting clusters are leveraged. Then, we propose an efficient and automated algorithm delineating the imprecise region via hierarchical clustering. Experiment results show that by virtue of the significant noise reduction in the region, our DIR-ST$^2$ method outperforms the state-of-the-art approach employing one-class support vector machine in terms of the $\mathcal{F}_1$ score from comparison with precisely-defined regions regarded as a ground truth, and returns apparently better delineation of imprecise regions. The computational complexity of DIR-ST$^2$ is also analytically and numerically shown.
Hierarchical Information Quadtree: Efficient Spatial Temporal Image Search for Multimedia Stream
Venue: arXiv cs.MM 2018
Year: 2018

Massive amount of multimedia data that contain times- tamps and geographical information are being generated at an unprecedented scale in many emerging applications such as photo sharing web site and social networks applications. Due to their importance, a large body of work has focused on efficiently computing various spatial image queries. In this paper,we study the spatial temporal image query which considers three important constraints during the search including time recency, spatial proximity and visual relevance. A novel index structure, namely Hierarchical Information Quadtree(\hiq), to efficiently insert/delete spatial temporal images with high arrive rates. Base on \hiq an efficient algorithm is developed to support spatial temporal image query. We show via extensive experimentation with real spatial databases clearly demonstrate the efficiency of our methods.
Learning to Grasp from a Single Demonstration
Venue: arXiv cs.CV 2018
Year: 2018

Learning-based approaches for robotic grasping using visual sensors typically require collecting a large size dataset, either manually labeled or by many trial and errors of a robotic manipulator in the real or simulated world. We propose a simpler learning-from-demonstration approach that is able to detect the object to grasp from merely a single demonstration using a convolutional neural network we call GraspNet. In order to increase robustness and decrease the training time even further, we leverage data from previous demonstrations to quickly fine-tune a GrapNet for each new demonstration. We present some preliminary results on a grasping experiment with the Franka Panda cobot for which we can train a GraspNet with only hundreds of train iterations.
Computational Complexity of Motion Planning of a Robot through Simple Gadgets
Venue: arXiv cs.CC 2018
Year: 2018

We initiate a general theory for analyzing the complexity of motion planning of a single robot through a graph of "gadgets", each with their own state, set of locations, and allowed traversals between locations that can depend on and change the state. This type of setup is common to many robot motion planning hardness proofs. We characterize the complexity for a natural simple case: each gadget connects up to four locations in a perfect matching (but each direction can be traversable or not in the current state), has one or two states, every gadget traversal is immediately undoable, and that gadget locations are connected by an always-traversable forest, possibly restricted to avoid crossings in the plane. Specifically, we show that any single nontrivial four-location two-state gadget type is enough for motion planning to become PSPACE-complete, while any set of simpler gadgets (effectively two-location or one-state) has a polynomial-time motion planning algorithm. As a sample application, our results show that motion planning games with "spinners" are PSPACE-complete, establishing a new hard aspect of Zelda: Oracle of Seasons.
A fast algorithm for solving linearly recurrent sequences
Venue: arXiv cs.SC 2018
Year: 2018

We present an algorithm which computes the $D^{th}$ term of a sequence satisfying a linear recurrence relation of order $d$ over a field $K$ in $O( \mathsf{M}(\bar d)\log(D) + \mathsf{M}(d)\log(d))$ operations in $K$, where $\bar d \leq d$ is the degree of the squarefree part of the annihilating polynomial of the recurrence and $\mathsf{M}$ is the cost of polynomial multiplication in $K$. This is a refinement of the previously optimal result of $O( \mathsf{M}(d)\log(D) )$ operations, due to Fiduccia.
Consistent Position Bias Estimation without Online Interventions for Learning-to-Rank
Venue: arXiv cs.LG 2018
Year: 2018

Presentation bias is one of the key challenges when learning from implicit feedback in search engines, as it confounds the relevance signal with uninformative signals due to position in the ranking, saliency, and other presentation factors. While it was recently shown how counterfactual learning-to-rank (LTR) approaches \cite{Joachims/etal/17a} can provably overcome presentation bias if observation propensities are known, it remains to show how to accurately estimate these propensities. In this paper, we propose the first method for producing consistent propensity estimates without manual relevance judgments, disruptive interventions, or restrictive relevance modeling assumptions. We merely require that we have implicit feedback data from multiple different ranking functions. Furthermore, we argue that our estimation technique applies to an extended class of Contextual Position-Based Propensity Models, where propensities not only depend on position but also on observable features of the query and document. Initial simulation studies confirm that the approach is scalable, accurate, and robust.
Adaptations of ROUGE and BLEU to Better Evaluate Machine Reading Comprehension Task
Venue: arXiv cs.CL 2018
Year: 2018

Current evaluation metrics to question answering based machine reading comprehension (MRC) systems generally focus on the lexical overlap between the candidate and reference answers, such as ROUGE and BLEU. However, bias may appear when these metrics are used for specific question types, especially questions inquiring yes-no opinions and entity lists. In this paper, we make adaptations on the metrics to better correlate n-gram overlap with the human judgment for answers to these two question types. Statistical analysis proves the effectiveness of our approach. Our adaptations may provide positive guidance for the development of real-scene MRC systems.
Temporal Limits of Privacy in Human Behavior
Venue: arXiv cs.CY 2018
Year: 2018

Large-scale collection of human behavioral data by companies raises serious privacy concerns. We show that behavior captured in the form of application usage data collected from smartphones is highly unique even in very large datasets encompassing millions of individuals. This makes behavior-based re-identification of users across datasets possible. We study 12 months of data from 3.5 million users and show that four apps are enough to uniquely re-identify 91.2% of users using a simple strategy based on public information. Furthermore, we show that there is seasonal variability in uniqueness and that application usage fingerprints drift over time at an average constant rate.
Steganography Security: Principle and Practice
Venue: arXiv cs.MM 2018
Year: 2018

This paper focuses on several theoretical issues and principles in steganography security, and defines four security levels by analyzing the corresponding algorithm instances. In the theoretical analysis, we discuss the differences between steganography security and watermarking security. The two necessary conditions for the steganography security are obtained. Under the current technology situation, we then analyze the indistinguishability of the cover and stego-cover, and consider that the steganography security should rely on the key secrecy with algorithms open. By specifying the role of key in steganography, the necessary conditions for a secure steganography algorithm in theory are formally presented. When analyzing the security instances, we have classified the steganalysis attacks according to their variable access to the steganography system, and then defined the four security levels. The higher level security one has, the higher level attacks one can resist. We have also presented algorithm instances based on current technical conditions, and analyzed their data hiding process, security level, and practice requirements.
Conceptualizing Blockchains: Characteristics & Applications
Venue: arXiv cs.CY 2018
Year: 2018

Blockchain technology has recently gained widespread attention by media, businesses, public sector agencies, and various international organizations, and it is being regarded as potentially even more disruptive than the Internet. Despite significant interest, there is a dearth of academic literature that describes key components of blockchains and discusses potential applications. This paper aims to address this gap. This paper presents an overview of blockchain technology, identifies the blockchain's key functional characteristics, builds a formal definition, and offers a discussion and classification of current and emerging blockchain applications.