OpenRaiser/Intern-Atlas huggingface dataset:
Fair and Unbiased Algorithmic Decision Making: Current State and Future Challenges
Venue: arXiv cs.LG 2019
Year: 2019

Machine learning algorithms are now frequently used in sensitive contexts that substantially affect the course of human lives, such as credit lending or criminal justice. This is driven by the idea that `objective' machines base their decisions solely on facts and remain unaffected by human cognitive biases, discriminatory tendencies or emotions. Yet, there is overwhelming evidence showing that algorithms can inherit or even perpetuate human biases in their decision making when they are based on data that contains biased human decisions. This has led to a call for fairness-aware machine learning. However, fairness is a complex concept which is also reflected in the attempts to formalize fairness for algorithmic decision making. Statistical formalizations of fairness lead to a long list of criteria that are each flawed (or harmful even) in different contexts. Moreover, inherent tradeoffs in these criteria make it impossible to unify them in one general framework. Thus, fairness constraints in algorithms have to be specific to the domains to which the algorithms are applied. In the future, research in algorithmic decision making systems should be aware of data and developer biases and add a focus on transparency to facilitate regular fairness audits.
Efficient Algorithms to Test Digital Convexity
Venue: arXiv cs.CG 2019
Year: 2019

A set $S \subset \mathbb{Z}^d$ is digital convex if $conv(S) \cap \mathbb{Z}^d = S$, where $conv(S)$ denotes the convex hull of $S$. In this paper, we consider the algorithmic problem of testing whether a given set $S$ of $n$ lattice points is digital convex. Although convex hull computation requires $Ω(n \log n)$ time even for dimension $d = 2$, we provide an algorithm for testing the digital convexity of $S\subset \mathbb{Z}^2$ in $O(n + h \log r)$ time, where $h$ is the number of edges of the convex hull and $r$ is the diameter of $S$. This main result is obtained by proving that if $S$ is digital convex, then the well-known quickhull algorithm computes the convex hull of $S$ in linear time. In fixed dimension $d$, we present the first polynomial algorithm to test digital convexity, as well as a simpler and more practical algorithm whose running time may not be polynomial in $n$ for certain inputs.
A Tweet Dataset Annotated for Named Entity Recognition and Stance Detection
Venue: arXiv cs.CL 2019
Year: 2019

Annotated datasets in different domains are critical for many supervised learning-based solutions to related problems and for the evaluation of the proposed solutions. Topics in natural language processing (NLP) similarly require annotated datasets to be used for such purposes. In this paper, we target at two NLP problems, named entity recognition and stance detection, and present the details of a tweet dataset in Turkish annotated for named entity and stance information. Within the course of the current study, both the named entity and stance annotations of the included tweets are made publicly available, although previously the dataset has been publicly shared with stance annotations only. We believe that this dataset will be useful for uncovering the possible relationships between named entity recognition and stance detection in tweets.
Linear Network Coding: Effects of Varying the Message Dimension on the Set of Characteristics
Venue: arXiv cs.IT 2019
Year: 2019

It is known a vector linear solution may exist if and only if the characteristic of the finite field belongs to a certain set of primes. But, can increasing the message dimension make a network vector linearly solvable over a larger set of characteristics? To the best of our knowledge, there exists no network in the literature which has a vector linear solution for some message dimension if and only if the characteristic of the finite field belongs to a set $P$, and for some other message dimension it has a vector linear solution over some finite field whose characteristic does not belong to $P$. We have found that by \textit{increasing} the message dimension just by $1$, the set of characteristics over which a vector linear solution exists may get arbitrarily larger. However, somewhat surprisingly, we have also found that by \textit{decreasing} the message dimension just by $1$, the set of characteristics over which a vector linear solution exists may get arbitrarily larger. As a consequence of these finding, we prove two more results: (i) rings may be superior to finite fields in terms of achieving a scalar linear solution over a lesser sized alphabet, (ii) existences of $m_1$ and $m_2$ dimensional vector linear solutions guarantees the existence of an $(m_1 + m_2)$-dimensional vector linear solution only if the $m_1$ and $m_2$ dimensional vector linear solutions exist over the same finite field.
Blockchain enabled fog structure to provide data security in IoT applications
Venue: arXiv cs.DC 2019
Year: 2019

IoT provides services by connecting smart devices to the Internet, and exploiting data generated by said devices to enable value-added services to individuals and businesses. In such cases, if data is exposed, tampered or lost, the service would not behave correctly. In this article, we discuss data security in IoT applications across five dimensions: confidentiality, integrity, authenticity, non-repudiation and availability. We discuss how distributed ledger technology could be used to overcome these issues and propose to use a fog computing architecture as decentralized computational support to deploy the ledger.
Toward Explainable Fashion Recommendation
Venue: arXiv cs.CV 2019
Year: 2019

Many studies have been conducted so far to build systems for recommending fashion items and outfits. Although they achieve good performances in their respective tasks, most of them cannot explain their judgments to the users, which compromises their usefulness. Toward explainable fashion recommendation, this study proposes a system that is able not only to provide a goodness score for an outfit but also to explain the score by providing reason behind it. For this purpose, we propose a method for quantifying how influential each feature of each item is to the score. Using this influence value, we can identify which item and what feature make the outfit good or bad. We represent the image of each item with a combination of human-interpretable features, and thereby the identification of the most influential item-feature pair gives useful explanation of the output score. To evaluate the performance of this approach, we design an experiment that can be performed without human annotation; we replace a single item-feature pair in an outfit so that the score will decrease, and then we test if the proposed method can detect the replaced item correctly using the above influence values. The experimental results show that the proposed method can accurately detect bad items in outfits lowering their scores.
Multiple Importance Sampling for Efficient Symbol Error Rate Estimation
Venue: arXiv cs.IT 2019
Year: 2019

Digital constellations formed by hexagonal or other non-square two-dimensional lattices are often used in advanced digital communication systems. The integrals required to evaluate the symbol error rate (SER) of these constellations in the presence of Gaussian noise are in general difficult to compute in closed-form, and therefore Monte Carlo simulation is typically used to estimate the SER. However, naive Monte Carlo simulation can be very inefficient and requires very long simulation runs, especially at high signal-to-noise ratios. In this letter, we adapt a recently proposed multiple importance sampling (MIS) technique, called ALOE (for `At Least One rare Event'), to this problem. Conditioned to a transmitted symbol, an error (or rare event) occurs when the observation falls in a union of half-spaces or, equivalently, outside a given polytope. The proposal distribution for ALOE samples the system conditionally on an error taking place, which makes it more efficient than other importance sampling techniques ALOE provides unbiased SER estimates with simulation times orders of magnitude shorter than conventional Monte Carlo.
Low Precision Constant Parameter CNN on FPGA
Venue: arXiv cs.DC 2019
Year: 2019

We report FPGA implementation results of low precision CNN convolution layers optimized for sparse and constant parameters. We describe techniques that amortizes the cost of common factor multiplication and automatically leverage dense hand tuned LUT structures. We apply this method to corner case residual blocks of Resnet on a sparse Resnet50 model to assess achievable utilization and frequency and demonstrate an effective performance of 131 and 23 TOP/chip for the corner case blocks. The projected performance on a multichip persistent implementation of all Resnet50 convolution layers is 10k im/s/chip at batch size 2. This is 1.37x higher than V100 GPU upper bound at the same batch size after normalizing for sparsity.
Achlys : Towards a framework for distributed storage and generic computing applications for wireless IoT edge networks with Lasp on GRiSP
Venue: arXiv cs.DC 2019
Year: 2019

Internet of Things (IoT) has gained substantial attention over the past years. And the main discussion has been how to process the amount of data that it generates which has lead to the edge computing paradigm. Wether it is called fog1, edge or mist, the principle remains that cloud services must become available closer to clients. This documents presents ongoing work on future edge systems that are built to provide steadfast IoT services to users by bringing storage and processing power closer to peripheral parts of networks. Designing such infrastructures is becoming much more challenging as the number of IoT devices keeps growing. Production grade deployments have to meet very high performance requirements, and end-to-end solutions involve significant investments. In this paper, we aim at providing a solution to extend the range of the edge model to the very farthest nodes in the network. Specifically, we focus on providing reliable storage and computation capabilities immediately on wireless IoT sensor nodes. This extended edge model will allow end users to manage their IoT ecosystem without forcibly relying on gateways or Internet provider solutions. In this document, we introduce Achlys, a prototype implementation of an edge node that is a concrete port of the Lasp programming library on the GRiSP Erlang embedded system. This way, we aim at addressing the need for a general purpose edge that is both resilient and consistent in terms of storage and network. Finally, we study example use cases that could take advantage of integrating the Achlys framework and discuss future work for the latter.
Solving inverse problems via auto-encoders
Venue: arXiv cs.IT 2019
Year: 2019

Compressed sensing (CS) is about recovering a structured signal from its under-determined linear measurements. Starting from sparsity, recovery methods have steadily moved towards more complex structures. Emerging machine learning tools such as generative functions that are based on neural networks are able to learn general complex structures from training data. This makes them potentially powerful tools for designing CS algorithms. Consider a desired class of signals $\cal Q$, ${\cal Q}\subset{R}^n$, and a corresponding generative function $g:{\cal U}^k\rightarrow {R}^n$, ${\cal U}\subset {R}$, such that $\sup_{{\bf x}\in {\cal Q}}\min_{{\bf u}\in{\cal U}^k}{1\over \sqrt{n}}\|g({\bf u})-{\bf x}\|\leq δ$. A recovery method based on $g$ seeks $g({\bf u})$ with minimum measurement error. In this paper, the performance of such a recovery method is studied, under both noisy and noiseless measurements. In the noiseless case, roughly speaking, it is proven that, as $k$ and $n$ grow without bound and $δ$ converges to zero, if the number of measurements ($m$) is larger than the input dimension of the generative model ($k$), then asymptotically, almost lossless recovery is possible. Furthermore, the performance of an efficient iterative algorithm based on projected gradient descent is studied. In this case, an auto-encoder is used to define and enforce the source structure at the projection step. The auto-encoder is defined by encoder and decoder (generative) functions $f:{R}^n\to{\cal U}^k$ and $g:{\cal U}^k\to{R}^n$, respectively. We theoretically prove that, roughly, given $m>40k\log{1\over δ}$ measurements, such an algorithm converges to the vicinity of the desired result, even in the presence of additive white Gaussian noise. Numerical results exploring the effectiveness of the proposed method are presented.
Status from a Random Field: How Densely Should One Update?
Venue: arXiv cs.IT 2019
Year: 2019

In many applications, status information of a general spatial process, in contrast to a point information source, is of interest. In this paper, we consider a system where status information is drawn from a random field and transmitted to a fusion center through a wireless multiaccess channel. The optimal density of spatial sampling points to minimize the remote status estimation error is investigated. Assuming a one-dimensional Gauss Markov random field and an exponential correlation function, closed-form expressions of remote estimation error are obtained for First-Come First-Served (FCFS) and Last-Come First-Served (LCFS) service disciplines. The optimal spatial sampling density for the LCFS case is given explicitly. Simulation results are presented which agree with our analysis.
Memory Augmented Deep Generative models for Forecasting the Next Shot Location in Tennis
Venue: arXiv cs.LG 2019
Year: 2019

This paper presents a novel framework for predicting shot location and type in tennis. Inspired by recent neuroscience discoveries we incorporate neural memory modules to model the episodic and semantic memory components of a tennis player. We propose a Semi Supervised Generative Adversarial Network architecture that couples these memory models with the automatic feature learning power of deep neural networks and demonstrate methodologies for learning player level behavioural patterns with the proposed framework. We evaluate the effectiveness of the proposed model on tennis tracking data from the 2012 Australian Tennis open and exhibit applications of the proposed method in discovering how players adapt their style depending on the match context.
Deep Supervised Hashing leveraging Quadratic Spherical Mutual Information for Content-based Image Retrieval
Venue: arXiv cs.CV 2019
Year: 2019

Several deep supervised hashing techniques have been proposed to allow for efficiently querying large image databases. However, deep supervised image hashing techniques are developed, to a great extent, heuristically often leading to suboptimal results. Contrary to this, we propose an efficient deep supervised hashing algorithm that optimizes the learned codes using an information-theoretic measure, the Quadratic Mutual Information (QMI). The proposed method is adapted to the needs of large-scale hashing and information retrieval leading to a novel information-theoretic measure, the Quadratic Spherical Mutual Information (QSMI). Apart from demonstrating the effectiveness of the proposed method under different scenarios and outperforming existing state-of-the-art image hashing techniques, this paper provides a structured way to model the process of information retrieval and develop novel methods adapted to the needs of each application.
Predicting Variable Types in Dynamically Typed Programming Languages
Venue: arXiv cs.PL 2019
Year: 2019

Dynamic Programming Languages are quite popular because they increase the programmer's productivity. However, the absence of types in the source code makes the program written in these languages difficult to understand and virtual machines that execute these programs cannot produced optimized code. To overcome this challenge, we develop a technique to predict types of all identifiers including variables, and function return types. We propose the first implementation of $2^{nd}$ order Inside Outside Recursive Neural Networks with two variants (i) Child-Sum Tree-LSTMs and (ii) N-ary RNNs that can handle large number of tree branching. We predict the types of all the identifiers given the Abstract Syntax Tree by performing just two passes over the tree, bottom-up and top-down, keeping both the content and context representation for all the nodes of the tree. This allows these representations to interact by combining different paths from the parent, siblings and children which is crucial for predicting types. Our best model achieves 44.33\% across 21 classes and top-3 accuracy of 71.5\% on our gathered Python data set from popular Python benchmarks.
Relational Proofs for Quantum Programs
Venue: arXiv cs.LO 2019
Year: 2019

Relational verification of quantum programs has many potential applications in quantum and post-quantum security and other domains. We propose a relational program logic for quantum programs. The interpretation of our logic is based on a quantum analogue of probabilistic couplings. We use our logic to verify non-trivial relational properties of quantum programs, including uniformity for samples generated by the quantum Bernoulli factory, reliability of quantum teleportation against noise (bit and phase flip), security of quantum one-time pad and equivalence of quantum walks.
Deep Grid Net (DGN): A Deep Learning System for Real-Time Driving Context Understanding
Venue: arXiv cs.CV 2019
Year: 2019

Grid maps obtained from fused sensory information are nowadays among the most popular approaches for motion planning for autonomous driving cars. In this paper, we introduce Deep Grid Net (DGN), a deep learning (DL) system designed for understanding the context in which an autonomous car is driving. DGN incorporates a learned driving environment representation based on Occupancy Grids (OG) obtained from raw Lidar data and constructed on top of the Dempster-Shafer (DS) theory. The predicted driving context is further used for switching between different driving strategies implemented within EB robinos, Elektrobit's Autonomous Driving (AD) software platform. Based on genetic algorithms (GAs), we also propose a neuroevolutionary approach for learning the tuning hyperparameters of DGN. The performance of the proposed deep network has been evaluated against similar competing driving context estimation classifiers.
Sentence transition matrix: An efficient approach that preserves sentence semantics
Venue: arXiv cs.CL 2019
Year: 2019

Sentence embedding is a significant research topic in the field of natural language processing (NLP). Generating sentence embedding vectors reflecting the intrinsic meaning of a sentence is a key factor to achieve an enhanced performance in various NLP tasks such as sentence classification and document summarization. Therefore, various sentence embedding models based on supervised and unsupervised learning have been proposed after the advent of researches regarding the distributed representation of words. They were evaluated through semantic textual similarity (STS) tasks, which measure the degree of semantic preservation of a sentence and neural network-based supervised embedding models generally yielded state-of-the-art performance. However, these models have a limitation in that they have multiple parameters to update, thereby requiring a tremendous amount of labeled training data. In this study, we propose an efficient approach that learns a transition matrix that refines a sentence embedding vector to reflect the latent semantic meaning of a sentence. The proposed method has two practical advantages; (1) it can be applied to any sentence embedding method, and (2) it can achieve robust performance in STS tasks irrespective of the number of training examples.
Analysing user identity via time-sensitive semantic edit distance (t-SED): A case study of Russian trolls on Twitter
Venue: arXiv cs.CY 2019
Year: 2019

In the digital era, individuals are increasingly profiled and grouped based on the traces they leave behind in online social networks such as Twitter and Facebook. In this paper, we develop and evaluate a novel text analysis approach for studying user identity and social roles by redefining identity as a sequence of timestamped items (e.g. tweet texts). We operationalise this idea by developing a novel text distance metric, the time-sensitive semantic edit distance (t-SED), which accounts for the temporal context across multiple traces. To evaluate this method we undertake a case study of Russian online-troll activity within US political discourse. The novel metric allows us to classify the social roles of trolls based on their traces, in this case tweets, into one of the predefined categories left-leaning, right-leaning, and news feed. We show the effectiveness of the t-SED metric to measure the similarities between tweets while accounting for the temporal context, and we use novel data visualisation techniques and qualitative analysis to uncover new empirical insights into Russian troll activity that have not been identified in previous work. Additionally, we highlight a connection with the field of Actor-Network Theory and the related hypotheses of Gabriel Tarde, and we discuss how social sequence analysis using t-SED may provide new avenues for tackling a longstanding problem in social theory: how to analyse society without separating reality into micro versus macro levels.
IEDM, an Ontology for Irradiation Experiment Data Management
Venue: arXiv cs.AI 2019
Year: 2019

Irradiation experiments (IE) are an essential step in the development of High-Energy Physics (HEP) particle accelerators and detectors. They assess the radiation hardness of materials used in HEP experimental devices by simulating, in a short time, the common long-term degradation effects due to their bombardment by high-energy particles. IEs are also used in other scientific and industrial fields such as medicine (e.g., for cancer treatment, medical imaging, etc.), space/avionics (e.g., for radiation testing of payload equipment) as well as in industry (e.g., for food sterilization). Usually carried out with ionizing radiation, these complex processes require highly specialized infrastructures: the irradiation facilities. Currently, hundreds of such facilities exist worldwide. To help develop best practices and promote computer-assisted handling and management of IEs, we introduce IEDM, a new OWL-based Irradiation Experiment Data Management ontology. This paper provides an overview of the classes and properties of IEDM. Since one of the key design choices for IEDM was to maximize the reuse of existing foundational ontologies such as the Ontology of Scientific Experiments (EXPO), the Ontology of Units of Measure (OM) and the Friend-of-a-Friend Ontology (FOAF), we discuss the methodological issues of the integration of IEDM with these imported ontologies. We illustrate the use of IEDM via an actual IE recently performed at IRRAD, the CERN proton irradiation facility. Finally, we discuss other motivations for this work, including the use of IEDM for the generation of user interfaces for IE management, and their impact on our methodology.