OpenRaiser/Intern-Atlas huggingface dataset:
Privacy Salience: Taxonomies and Research Opportunities
Venue: arXiv cs.CY 2018
Year: 2018

Privacy is a well-understood concept in the physical world, with us all desiring some escape from the public gaze. However, while individuals might recognise locking doors as protecting privacy, they have difficulty practising equivalent actions online. Privacy salience considers the tangibility of this important principle; one which is often obscured in digital environments. Through extensively surveying a range of studies, we construct the first taxonomies of privacy salience. After coding articles and identifying commonalities, we categorise works by their methodologies, platforms and underlying themes. While web browsing appears to be frequently analysed, the Internet-of-Things has received little attention. Through our use of category tuples and frequency matrices, we then explore those research opportunities which might have been overlooked. These include studies of targeted advertising and its affect on salience in social networks. It is through refining our understanding of this important topic that we can better highlight the subject of privacy.
BitSurfing: Wireless Communications with Outsourced Symbol Generation
Venue: arXiv cs.NI 2018
Year: 2018

Nano-IoT enables a wide range of ground-breaking technologies, but face implementation challenges due to the extremity of the scale. Space restrictions pose severe power supply considerations, to the point where just a few packet transmissions are sufficient to deplete state-of-the-art supplies. In turn, this translates to difficulties in developing efficient protocols even for basic operations, such as addressing and routing. The present work proposes a new network adapter architecture that can address these challenges. The BitSurfing adapter does not generate packets and, hence, abolishes the need for the corresponding transmission circuitry and power consumption. Instead, it relies on an external symbol generator. The BitSurfing adapter reads incoming symbols, waiting for intended messages to appear in the symbol stream. A short (1-bit), low-energy pulse is then emitted to notify neighboring nodes. BitSurfing adapters are shown to exhibit perpetual (and even battery-less) operation, ability to operate without medium access control, while being completely transparent to applications. Moreover, their operation is event-driven, allowing for clock-less implementations. The new adapters are evaluated in a simulated multi-hop nano-IoT network and are shown to offer nearly-perfect packet delivery rates and practically no collisions, under any congestion level.
Example of a finite game with no Berge equilibria at all
Venue: arXiv cs.GT 2018
Year: 2018

The problem of the existence of Berge equilibria in the sense of Zhukovskii in normal form finite games in pure and in mixed strategies is studied. The example of a three-player game that has Berge equilibrium neither in pure nor in mixed strategies is given.
K-method of calculating the mutual influence of nodes in a directed weight complex networks
Venue: arXiv cs.SI 2018
Year: 2018

A new characteristic of paired nodes in a directed weight complex network is considered. A method (named as K-method) of the characteristics calculation for complex networks is proposed. The method is based on transforming the initial network with the subsequent application of the Kirchhoff rules. The scope of the method for sparse complex networks is proposed. The nodes of these complex networks are concepts of the real world, and the connections have a cause-effect character of the so-called "cognitive maps". Two new characteristics of concept nodes having a semantic interpretation are proposed, namely "pressure" and "influence" taking into account the influence of all nodes on each other.
A Distributed Collaborative Filtering Algorithm Using Multiple Data Sources
Venue: arXiv cs.IR 2018
Year: 2018

Collaborative Filtering (CF) is one of the most commonly used recommendation methods. CF consists in predicting whether, or how much, a user will like (or dislike) an item by leveraging the knowledge of the user's preferences as well as that of other users. In practice, users interact and express their opinion on only a small subset of items, which makes the corresponding user-item rating matrix very sparse. Such data sparsity yields two main problems for recommender systems: (1) the lack of data to effectively model users' preferences, and (2) the lack of data to effectively model item characteristics. However, there are often many other data sources that are available to a recommender system provider, which can describe user interests and item characteristics (e.g., users' social network, tags associated to items, etc.). These valuable data sources may supply useful information to enhance a recommendation system in modeling users' preferences and item characteristics more accurately and thus, hopefully, to make recommenders more precise. For various reasons, these data sources may be managed by clusters of different data centers, thus requiring the development of distributed solutions. In this paper, we propose a new distributed collaborative filtering algorithm, which exploits and combines multiple and diverse data sources to improve recommendation quality. Our experimental evaluation using real datasets shows the effectiveness of our algorithm compared to state-of-the-art recommendation algorithms.
Machine Learning Approaches to Hybrid Music Recommender Systems
Venue: arXiv cs.IR 2018
Year: 2018

Music recommender systems have become a key technology supporting the access to increasingly larger music catalogs in on-line music streaming services, on-line music shops, and private collections. The interaction of users with large music catalogs is a complex phenomenon researched from different disciplines. We survey our works investigating the machine learning and data mining aspects of hybrid music recommender systems (i.e., systems that integrate different recommendation techniques). We proposed hybrid music recommender systems based solely on data and robust to the so-called "cold-start problem" for new music items, favoring the discovery of relevant but non-popular music. We thoroughly studied the specific task of music playlist continuation, by analyzing fundamental playlist characteristics, song feature representations, and the relationship between playlists and the songs therein.
Using smartphones for low-cost robotics
Venue: arXiv cs.CY 2018
Year: 2018

Smartphones and robots can have an adversarial or a symbiotic relationship because they strive to serve overlapping customer needs. While smartphones are prevalent, humanoid robots are not. Even though considerable public and private resources are being invested in developing and commercializing humanoid robots, progress seems stalled and no humanoid robot can be said to be successful with consumers. A part from the obvious engineering differences between humanoids and smartphones, other economic factors influence this situation. On one hand, the product cycle of robots is slower than smartphones. This makes robot computing hardware, (as it with automobile's infotainment systems), perennially outdated when side-by-side to a smartphone. On the other hand, the incentives to develop Apps are high for smartphones and they are comparatively low for robot platforms. Here, we point to how smartphones could be used to lower hardware cost and foster robot app development.
Fundamental Limits of Dynamic Interference Management with Flexible Message Assignments
Venue: arXiv cs.IT 2018
Year: 2018

The problem of interference management is considered in the context of a linear interference network that is subject to long term channel fluctuations due to shadow fading. The fading model used is one where each link in the network is subject independently to erasure with probability p. It is assumed that each receiver in the network is interested in one unique message, which is made available at M transmitters. Optimal schemes from a Degrees of Freedom (DoF) viewpoint are analyzed for the setting of no erasures, and new schemes are proposed with better average DoF performance at higher probabilities of erasure. Additionally, for M=1, the average per user DoF is characterized for every value of p, and optimal message assignments are identified. For M>1, it is first established that there is no strategy for assigning messages to transmitters in networks that is optimal for all values of p. The optimal cooperative zero-forcing scheme for M=2 is then identified, and shown to be information-theoretically optimal when the size of the largest subnetwork that contains no erased links is at most five.
Using Textual Summaries to Describe a Set of Products
Venue: arXiv cs.CL 2018
Year: 2018

When customers are faced with the task of making a purchase in an unfamiliar product domain, it might be useful to provide them with an overview of the product set to help them understand what they can expect. In this paper we present and evaluate a method to summarise sets of products in natural language, focusing on the price range, common product features across the set, and product features that impact on price. In our study, participants reported that they found our summaries useful, but we found no evidence that the summaries influenced the selections made by participants.
Methods, Forms and Safety of Learning in Corporate Social Networks
Venue: arXiv cs.CY 2018
Year: 2018

The paper discusses methods, forms and safety issues of social network usage for school students. Taking into consideration the growing interest of students to electronic communication in social networks (ESN), it was described their place in the information educational environment. There were made the classification of objects and use of ESN to help teachers and school authority to teach students in the corporate social network. The basic components of corporate social networks were revealed: forms of learning activity (individual, group, and collective), forms of learning (quiz, debates, discussions, photo-story, essay contest, a virtual tour, mini design web quest, and conference video-lesson), and database. They were defined particular aspects of the use of certain forms for students training in ESN according to the type of social objects (messages, individual messages, video files, photos, audio files, documents, comments, and blitz-survey). Student safety when using ESN is discussed as well.
Gnirut: The Trouble With Being Born Human In An Autonomous World
Venue: arXiv cs.CY 2018
Year: 2018

What if we delegated so much to autonomous AI and intelligent machines that They passed a law that forbids humans to carry out a number of professions? We conceive the plot of a new episode of Black Mirror to reflect on what might await us and how we can deal with such a future.
An agent-based model of an endangered population of the Arctic fox from Mednyi Island
Venue: arXiv cs.MA 2018
Year: 2018

Artificial Intelligence techniques such as agent-based modeling and probabilistic reasoning have shown promise in modeling complex biological systems and testing ecological hypotheses through simulation. We develop an agent-based model of Arctic foxes from Medniy Island while utilizing Probabilistic Graphical Models to capture the conditional dependencies between the random variables. Such models provide valuable insights in analyzing factors behind catastrophic degradation of this population and in revealing evolutionary mechanisms of its persistence in high-density environment. Using empirical data from studies in Medniy Island, we create a realistic model of Arctic foxes as agents, and study their survival and population dynamics under a variety of conditions.
Rectification from Radially-Distorted Scales
Venue: arXiv cs.CV 2018
Year: 2018

This paper introduces the first minimal solvers that jointly estimate lens distortion and affine rectification from repetitions of rigidly transformed coplanar local features. The proposed solvers incorporate lens distortion into the camera model and extend accurate rectification to wide-angle images that contain nearly any type of coplanar repeated content. We demonstrate a principled approach to generating stable minimal solvers by the Grobner basis method, which is accomplished by sampling feasible monomial bases to maximize numerical stability. Synthetic and real-image experiments confirm that the solvers give accurate rectifications from noisy measurements when used in a RANSAC-based estimator. The proposed solvers demonstrate superior robustness to noise compared to the state-of-the-art. The solvers work on scenes without straight lines and, in general, relax the strong assumptions on scene content made by the state-of-the-art. Accurate rectifications on imagery that was taken with narrow focal length to near fish-eye lenses demonstrate the wide applicability of the proposed method. The method is fully automated, and the code is publicly available at https://github.com/prittjam/repeats.
Visualizing Design Erosion: How Big Balls of Mud are Made
Venue: arXiv cs.SE 2018
Year: 2018

Software systems are not static, they have to undergo frequent changes to stay fit for purpose, and in the process of doing so, their complexity increases. It has been observed that this process often leads to the erosion of the systems design and architecture and with it, the decline of many desirable quality attributes, such as maintainability. This process can be captured in terms of antipatterns-atomic violations of widely accepted design principles. We present a visualisation that exposes the design of evolving Java programs, highlighting instances of selected antipatterns including their emergence and cancerous growth. This visualisation assists software engineers and architects in assessing, tracing and therefore combating design erosion. We evaluated the effectiveness of the visualisation in four case studies with ten participants.
Anaconda: A Non-Adaptive Conditional Sampling Algorithm for Distribution Testing
Venue: arXiv cs.DS 2018
Year: 2018

We investigate distribution testing with access to non-adaptive conditional samples. In the conditional sampling model, the algorithm is given the following access to a distribution: it submits a query set $S$ to an oracle, which returns a sample from the distribution conditioned on being from $S$. In the non-adaptive setting, all query sets must be specified in advance of viewing the outcomes. Our main result is the first polylogarithmic-query algorithm for equivalence testing, deciding whether two unknown distributions are equal to or far from each other. This is an exponential improvement over the previous best upper bound, and demonstrates that the complexity of the problem in this model is intermediate to the the complexity of the problem in the standard sampling model and the adaptive conditional sampling model. We also significantly improve the sample complexity for the easier problems of uniformity and identity testing. For the former, our algorithm requires only $\tilde O(\log n)$ queries, matching the information-theoretic lower bound up to a $O(\log \log n)$-factor. Our algorithm works by reducing the problem from $\ell_1$-testing to $\ell_\infty$-testing, which enjoys a much cheaper sample complexity. Necessitated by the limited power of the non-adaptive model, our algorithm is very simple to state. However, there are significant challenges in the analysis, due to the complex structure of how two arbitrary distributions may differ.
Phase Retrieval for Signals in Union of Subspaces
Venue: arXiv cs.IT 2018
Year: 2018

We consider the phase retrieval problem for signals that belong to a union of subspaces. We assume that amplitude measurements of the signal of length $n$ are observed after passing it through a random $m \times n$ measurement matrix. We also assume that the signal belongs to the span of a single $d$-dimensional subspace out of $R$ subspaces, where $d\ll n$. We assume the knowledge of all possible subspaces, but the true subspace of the signal is unknown. We present an algorithm that jointly estimates the phase of the measurements and the subspace support of the signal. We discuss theoretical guarantees on the recovery of signals and present simulation results to demonstrate the empirical performance of our proposed algorithm. Our main result suggests that if properly initialized, then $O(d+\log R)$ random measurements are sufficient for phase retrieval if the unknown signal belongs to the union of $R$ low-dimensional subspaces.
Robust Deep Multi-modal Learning Based on Gated Information Fusion Network
Venue: arXiv cs.CV 2018
Year: 2018

The goal of multi-modal learning is to use complimentary information on the relevant task provided by the multiple modalities to achieve reliable and robust performance. Recently, deep learning has led significant improvement in multi-modal learning by allowing for the information fusion in the intermediate feature levels. This paper addresses a problem of designing robust deep multi-modal learning architecture in the presence of imperfect modalities. We introduce deep fusion architecture for object detection which processes each modality using the separate convolutional neural network (CNN) and constructs the joint feature map by combining the intermediate features from the CNNs. In order to facilitate the robustness to the degraded modalities, we employ the gated information fusion (GIF) network which weights the contribution from each modality according to the input feature maps to be fused. The weights are determined through the convolutional layers followed by a sigmoid function and trained along with the information fusion network in an end-to-end fashion. Our experiments show that the proposed GIF network offers the additional architectural flexibility to achieve robust performance in handling some degraded modalities, and show a significant performance improvement based on Single Shot Detector (SSD) for KITTI dataset using the proposed fusion network and data augmentation schemes.
Preference-Based Monte Carlo Tree Search
Venue: arXiv cs.AI 2018
Year: 2018

Monte Carlo tree search (MCTS) is a popular choice for solving sequential anytime problems. However, it depends on a numeric feedback signal, which can be difficult to define. Real-time MCTS is a variant which may only rarely encounter states with an explicit, extrinsic reward. To deal with such cases, the experimenter has to supply an additional numeric feedback signal in the form of a heuristic, which intrinsically guides the agent. Recent work has shown evidence that in different areas the underlying structure is ordinal and not numerical. Hence erroneous and biased heuristics are inevitable, especially in such domains. In this paper, we propose a MCTS variant which only depends on qualitative feedback, and therefore opens up new applications for MCTS. We also find indications that translating absolute into ordinal feedback may be beneficial. Using a puzzle domain, we show that our preference-based MCTS variant, wich only receives qualitative feedback, is able to reach a performance level comparable to a regular MCTS baseline, which obtains quantitative feedback.
Knowledge-aware Autoencoders for Explainable Recommender Sytems
Venue: arXiv cs.IR 2018
Year: 2018

Recommender Systems have been widely used to help users in finding what they are looking for thus tackling the information overload problem. After several years of research and industrial findings looking after better algorithms to improve accuracy and diversity metrics, explanation services for recommendation are gaining momentum as a tool to provide a human-understandable feedback to results computed, in most of the cases, by black-box machine learning techniques. As a matter of fact, explanations may guarantee users satisfaction, trust, and loyalty in a system. In this paper, we evaluate how different information encoded in a Knowledge Graph are perceived by users when they are adopted to show them an explanation. More precisely, we compare how the use of categorical information, factual one or a mixture of them both in building explanations, affect explanatory criteria for a recommender system. Experimental results are validated through an A/B testing platform which uses a recommendation engine based on a Semantics-Aware Autoencoder to build users profiles which are in turn exploited to compute recommendation lists and to provide an explanation.
Unifying Inference for Bayesian and Petri Nets
Venue: arXiv cs.LO 2018
Year: 2018

Recent work by the authors equips Petri occurrence nets (PN) with probability distributions which fully replace nondeterminism. To avoid the so-called confusion problem, the construction imposes additional causal dependencies which restrict choices within certain subnets called structural branching cells (s-cells). Bayesian nets (BN) are usually structured as partial orders where nodes define conditional probability distributions. In the paper, we unify the two structures in terms of Symmetric Monoidal Categories (SMC), so that we can apply to PN ordinary analysis techniques developed for BN. Interestingly, it turns out that PN which cannot be SMC-decomposed are exactly s-cells. This result confirms the importance for Petri nets of both SMC and s-cells.
Using statistical encoding to achieve tree succinctness never seen before
Venue: arXiv cs.DS 2018
Year: 2018

We propose a new succinct representation of labeled trees which represents a tree T using |T|H_k(T) number of bits (plus some smaller order terms), where |T|H_k(T) denotes the k-th order (tree label) entropy, as defined by Ferragina at al. 2005. Our representation employs a new, simple method of partitioning the tree, which preserves both tree shape and node degrees. Previously, the only representation that used |T|H_k(T) bits was based on XBWT, a transformation that linearizes tree labels into a single string, combined with compression boosting. The proposed representation is much simpler than the one based on XBWT, which used additional linear space (bounded by 0.01n) hidden in the "smaller order terms" notion, as an artifact of using zeroth order entropy coder; our representation uses sublinear additional space (for reasonable values of k and size of the label alphabet σ). The proposed representation can be naturally extended to a succinct data structure for trees, which uses |T|H_k(T) plus additional O(|T|k log_σ/ log_σ |T| + |T| log log_σ |T|/ log_σ |T|) bits and supports all the usual navigational queries in constant time. At the cost of increasing the query time to O(log log |T|/ log |T|) we can further reduce the space redundancy to O(|T| log log |T|/ log_σ |T|) bits, assuming k <= log_σ |T|. This is a major improvement over representation based on XBWT: even though XBWT-based representation uses |T|H_k(T) bits, the space needed for structure supporting navigational queries is much larger: (...)
Are Efficient Deep Representations Learnable?
Venue: arXiv cs.LG 2018
Year: 2018

Many theories of deep learning have shown that a deep network can require dramatically fewer resources to represent a given function compared to a shallow network. But a question remains: can these efficient representations be learned using current deep learning techniques? In this work, we test whether standard deep learning methods can in fact find the efficient representations posited by several theories of deep representation. Specifically, we train deep neural networks to learn two simple functions with known efficient solutions: the parity function and the fast Fourier transform. We find that using gradient-based optimization, a deep network does not learn the parity function, unless initialized very close to a hand-coded exact solution. We also find that a deep linear neural network does not learn the fast Fourier transform, even in the best-case scenario of infinite training data, unless the weights are initialized very close to the exact hand-coded solution. Our results suggest that not every element of the class of compositional functions can be learned efficiently by a deep network, and further restrictions are necessary to understand what functions are both efficiently representable and learnable.