OpenRaiser/Intern-Atlas huggingface dataset:
On Extending the Wireless Communications Range of Weather Stations using LoRaWAN
Venue: arXiv cs.NI 2019
Year: 2019

Consumer grade weather stations typically involve transmitting sensor information to a digital console that provides readouts of the data being collected. The wireless range of such system is confined to about 100 m, making their use limited to urban environments. We present a device that decodes the data being sent by the weather station and forwards them using the emerging LoRaWAN technology. We designed the device keeping in mind the peculiar conditions of Developing Countries, in particular the low cost and low power requirements. This allows interesting applications in the realm of disaster prevention and mitigation using a network of numerous weather stations.
A Fuzzy-Fuzzy Vault Scheme
Venue: arXiv cs.CR 2019
Year: 2019

In this paper an enhanced fuzzy vault scheme is proposed which we refer to as fuzzy-fuzzy vault scheme. The proposed scheme builds on the classical fuzzy vault by adding the concept of uncertainty and imprecision to the classical scheme. To lock a secret key K in the classical fuzzy vault the locking and unlocking elements are crisp or real elements and consequently the locking and unlocking operations are strict imperative. In the fuzzy-fuzzy vault scheme, Alice locks the secret key K using a set of fuzzy elements that belong to multi-fuzzy set A~ obtained from a universe public set of fuzzy elements in a multi-fuzzy set F~_q and projecting them on polynomial p. The elements in multi-fuzzy sets F~_q and A~ are fuzzy using m membership functions MF_i, i=1,2,...,m. Alice selects a set k of fuzzy elements fuzzy with a specific membership function MF_K from A~ to lock the vault. To hide the genuine locking points Alice generates a set of fuzzy chaff points that some of them do not lie on polynomial p while the other fuzzy chaff points may lie on polynomial p but fuzzy with different membership functions other than the membership function MF_K used to lock the vault. To unlock the fuzzy-fuzzy vault and retrieve the secret key K , Bob should have a set of unlocking fuzzy elements belonging to multi-fuzzy set B~ which substantially overlap with A~ is required. Then Bob selects t'_(TF_ki) fuzzy elements from B~ which are close to the t_(TF_k) fuzzy elements from A~ used by Alice to lock the vault. We show that adding uncertainty and imprecision by introducing fuzzy theory will enhance the security threshold of the fuzzy vault.
Classical Policy Gradient: Preserving Bellman's Principle of Optimality
Venue: arXiv cs.LG 2019
Year: 2019

We propose a new objective function for finite-horizon episodic Markov decision processes that better captures Bellman's principle of optimality, and provide an expression for the gradient of the objective.
Collaborating with Users in Proximity for Decentralized Mobile Recommender Systems
Venue: arXiv cs.IR 2019
Year: 2019

Typically, recommender systems from any domain, be it movies, music, restaurants, etc., are organized in a centralized fashion. The service provider holds all the data, biases in the recommender algorithms are not transparent to the user, and the service providers often create lock-in effects making it inconvenient for the user to switch providers. In this paper, we argue that the user's smartphone already holds a lot of the data that feeds into typical recommender systems for movies, music, or POIs. With the ubiquity of the smartphone and other users in proximity in public places or public transportation, data can be exchanged directly between users in a device-to-device manner. This way, each smartphone can build its own database and calculate its own recommendations. One of the benefits of such a system is that it is not restricted to recommendations for just one user - ad-hoc group recommendations are also possible. While the infrastructure for such a platform already exists - the smartphones already in the palms of the users - there are challenges both with respect to the mobile recommender system platform as well as to its recommender algorithms. In this paper, we present a mobile architecture for the described system - consisting of data collection, data exchange, and recommender system - and highlight its challenges and opportunities.
Word-based Domain Adaptation for Neural Machine Translation
Venue: arXiv cs.CL 2019
Year: 2019

In this paper, we empirically investigate applying word-level weights to adapt neural machine translation to e-commerce domains, where small e-commerce datasets and large out-of-domain datasets are available. In order to mine in-domain like words in the out-of-domain datasets, we compute word weights by using a domain-specific and a non-domain-specific language model followed by smoothing and binary quantization. The baseline model is trained on mixed in-domain and out-of-domain datasets. Experimental results on English to Chinese e-commerce domain translation show that compared to continuing training without word weights, it improves MT quality by up to 2.11% BLEU absolute and 1.59% TER. We have also trained models using fine-tuning on the in-domain data. Pre-training a model with word weights improves fine-tuning up to 1.24% BLEU absolute and 1.64% TER, respectively.
Word Embeddings for the Armenian Language: Intrinsic and Extrinsic Evaluation
Venue: arXiv cs.CL 2019
Year: 2019

In this work, we intrinsically and extrinsically evaluate and compare existing word embedding models for the Armenian language. Alongside, new embeddings are presented, trained using GloVe, fastText, CBOW, SkipGram algorithms. We adapt and use the word analogy task in intrinsic evaluation of embeddings. For extrinsic evaluation, two tasks are employed: morphological tagging and text classification. Tagging is performed on a deep neural network, using ArmTDP v2.3 dataset. For text classification, we propose a corpus of news articles categorized into 7 classes. The datasets are made public to serve as benchmarks for future models.
HPILN: A feature learning framework for cross-modality person re-identification
Venue: arXiv cs.CV 2019
Year: 2019

Most video surveillance systems use both RGB and infrared cameras, making it a vital technique to re-identify a person cross the RGB and infrared modalities. This task can be challenging due to both the cross-modality variations caused by heterogeneous images in RGB and infrared, and the intra-modality variations caused by the heterogeneous human poses, camera views, light brightness, etc. To meet these challenges a novel feature learning framework, HPILN, is proposed. In the framework existing single-modality re-identification models are modified to fit for the cross-modality scenario, following which specifically designed hard pentaplet loss and identity loss are used to improve the performance of the modified cross-modality re-identification models. Based on the benchmark of the SYSU-MM01 dataset, extensive experiments have been conducted, which show that the proposed method outperforms all existing methods in terms of Cumulative Match Characteristic curve (CMC) and Mean Average Precision (MAP).
Predicting risk of dyslexia with an online gamified test
Venue: arXiv cs.HC 2019
Year: 2019

Dyslexia is a specific learning disorder related to school failure. Detection is both crucial and challenging, especially in languages with transparent orthographies, such as Spanish. To make detecting dyslexia easier, we designed an online gamified test and a predictive machine learning model. In a study with more than 3,600 participants, our model correctly detected over 80% of the participants with dyslexia. To check the robustness of the method we tested our method using a new data set with over 1,300 participants with age customized tests in a different environment -- a tablet instead of a desktop computer -- reaching a recall of over 72% for the class with dyslexia for children 9 years old or older. Our work shows that dyslexia can be screened using a machine learning approach. An online screening tool based on our methods has already been used by more than 200,000 people.
Cryptanalysis of the SLAP Authentication Protocol
Venue: arXiv cs.CR 2019
Year: 2019

RFID (Radio Frequency Identification) is a powerful technology that, due to its numerous advantages, is supposed to replace the various identification systems such as barcodes or magnetic stripes in a short time. There are three devices involved in an RFID protocol: a Reader, a Tag, and a back-end Database. One of the key factors for the RFID technology is that, in order to be used on large scale, the price of the Tags has to be cheap: it cannot be expensive because who is supposed to use it would need a great amount of them, furthermore, Tags must have very small dimensions. The low-cost nature of such devices implies the impossibility to use standard cryptographic protocols on them, furthermore, Ultra-Lightweight Tags even lack the necessary computational power to generate random numbers. Many experts are trying to build secure protocols that involve just simple bitwise logical operations for these Tags, but, unfortunately, each of these protocols turned out to be vulnerable to some serious attack after short time from publication. The need for a secure RFID authentication protocol today seems more urgent than ever, because this technology is already used for security purposes, such as electronic passports. The challenge to build a secure protocol for Ultra-Lightweight RFID systems is so hard that in several years no one has been able to build one. In this thesis we analyze in great detail a recently proposed protocol for Ultra-Lightweight RFID systems called SLAP, with the aim of finding new vulnerabilities. SLAP has already been violated (along with a set of similar protocols) by Safkhani and Bagheri, that have recently published a de-synchronization attack. At the end of our analysis, we will propose an impersonification attack to the protocol, along with a fix for our attack and some considerations on the attacks proposed on this kind of protocols.
Deep Robust Single Image Depth Estimation Neural Network Using Scene Understanding
Venue: arXiv cs.CV 2019
Year: 2019

Single image depth estimation (SIDE) plays a crucial role in 3D computer vision. In this paper, we propose a two-stage robust SIDE framework that can perform blind SIDE for both indoor and outdoor scenes. At the first stage, the scene understanding module will categorize the RGB image into different depth-ranges. We introduce two different scene understanding modules based on scene classification and coarse depth estimation respectively. At the second stage, SIDE networks trained by the images of specific depth-range are applied to obtain an accurate depth map. In order to improve the accuracy, we further design a multi-task encoding-decoding SIDE network DS-SIDENet based on depthwise separable convolutions. DS-SIDENet is optimized to minimize both depth classification and depth regression losses. This improves the accuracy compared to a single-task SIDE network. Experimental results demonstrate that training DS-SIDENet on an individual dataset such as NYU achieves competitive performance to the state-of-art methods with much better efficiency. Ours proposed robust SIDE framework also shows good performance for the ScanNet indoor images and KITTI outdoor images simultaneously. It achieves the top performance compared to the Robust Vision Challenge (ROB) 2018 submissions.
Frequency-Dependent Perceptual Quantisation for Visually Lossless Compression Applications
Venue: arXiv cs.MM 2019
Year: 2019

The default quantisation algorithms in the state-of-the-art High Efficiency Video Coding (HEVC) standard, namely Uniform Reconstruction Quantisation (URQ) and Rate-Distortion Optimised Quantisation (RDOQ), do not take into account the perceptual relevance of individual transform coefficients. In this paper, a Frequency-Dependent Perceptual Quantisation (FDPQ) technique for HEVC is proposed. FDPQ exploits the well-established Modulation Transfer Function (MTF) characteristics of the linear transformation basis functions by taking into account the Euclidean distance of an AC transform coefficient from the DC coefficient. As such, in luma and chroma Cb and Cr Transform Blocks (TBs), FDPQ quantises more coarsely the least perceptually relevant transform coefficients (i.e., the high frequency AC coefficients). Conversely, FDPQ preserves the integrity of the DC coefficient and the very low frequency AC coefficients. Compared with RDOQ, which is the most widely used transform coefficient-level quantisation technique in video coding, FDPQ successfully achieves bitrate reductions of up to 41%. Furthermore, the subjective evaluations confirm that the FDPQ-coded video data is perceptually indistinguishable (i.e., visually lossless) from the raw video data for a given Quantisation Parameter (QP).
Class-specific Differential Detection in Diffractive Optical Neural Networks Improves Inference Accuracy
Venue: arXiv cs.NE 2019
Year: 2019

Diffractive deep neural networks have been introduced earlier as an optical machine learning framework that uses task-specific diffractive surfaces designed by deep learning to all-optically perform inference, achieving promising performance for object classification and imaging. Here we demonstrate systematic improvements in diffractive optical neural networks based on a differential measurement technique that mitigates the non-negativity constraint of light intensity. In this scheme, each class is assigned to a separate pair of photodetectors, behind a diffractive network, and the class inference is made by maximizing the normalized signal difference between the detector pairs. Moreover, by utilizing the inherent parallelization capability of optical systems, we reduced the signal coupling between the positive and negative detectors of each class by dividing their optical path into two jointly-trained diffractive neural networks that work in parallel. We further made use of this parallelization approach, and divided individual classes among multiple jointly-trained differential diffractive neural networks. Using this class-specific differential detection in jointly-optimized diffractive networks, our simulations achieved testing accuracies of 98.52%, 91.48% and 50.82% for MNIST, Fashion-MNIST and grayscale CIFAR-10 datasets, respectively. Similar to ensemble methods practiced in machine learning, we also independently-optimized multiple differential diffractive networks that optically project their light onto a common detector plane, and achieved testing accuracies of 98.59%, 91.06% and 51.44% for MNIST, Fashion-MNIST and grayscale CIFAR-10, respectively. Through these systematic advances in designing diffractive neural networks, the reported classification accuracies set the state-of-the-art for an all-optical neural network design.
Learning Individual Causal Effects from Networked Observational Data
Venue: arXiv cs.SI 2019
Year: 2019

Convenient access to observational data enables us to learn causal effects without randomized experiments. This research direction draws increasing attention in research areas such as economics, healthcare, and education. For example, we can study how a medicine (the treatment) causally affects the health condition (the outcome) of a patient using existing electronic health records. To validate causal effects learned from observational data, we have to control confounding bias -- the influence of variables which causally influence both the treatment and the outcome. Existing work along this line overwhelmingly relies on the unconfoundedness assumption that there do not exist unobserved confounders. However, this assumption is untestable and can even be untenable. In fact, an important fact ignored by the majority of previous work is that observational data can come with network information that can be utilized to infer hidden confounders. For example, in an observational study of the individual-level treatment effect of a medicine, instead of randomized experiments, the medicine is often assigned to each individual based on a series of factors. Some of the factors (e.g., socioeconomic status) can be challenging to measure and therefore become hidden confounders. Fortunately, the socioeconomic status of an individual can be reflected by whom she is connected in social networks. With this fact in mind, we aim to exploit the network information to recognize patterns of hidden confounders which would further allow us to learn valid individual causal effects from observational data. In this work, we propose a novel causal inference framework, the network deconfounder, which learns representations to unravel patterns of hidden confounders from the network information. Empirically, we perform extensive experiments to validate the effectiveness of the network deconfounder on various datasets.
Finding a Generator Matrix of a Multidimensional Cyclic Code
Venue: arXiv cs.IT 2019
Year: 2019

We generalize Sepasdar's method for finding a generator matrix of two-dimensional cyclic codes to find an independent subset of a general multicyclic code, which may form a basis of the code as a vector subspace. A generator matrix can be then constructed from this basis.
Mastery Learning-Like Teaching with Achievements
Venue: arXiv cs.CY 2019
Year: 2019

This paper describes the design of a second-year, 20 ECTS credit course on imperative and object-oriented programming. The key design rhetoric is encouraging students to assume responsibility for their own learning, and, to this end, elements from mastery learning and other teaching strategies are used in concert to construct an achievement-based system where students are put in charge of how and when they are examined. In addition to describing the elements of the course design, the paper reports on experiences from teaching in this format, and how the format has evolved over time.
A Federated Authorization Framework for Distributed Personal Data and Digital Identity
Venue: arXiv cs.CR 2019
Year: 2019

The digital identity problem is a complex one in large part because it involves personal data, the algorithms which compute reputations on the data and the management of the identifiers that are linked to personal data. The reality of today is that personal data of an individual is distributed throughout the Internet, in both private and public institutions, and increasingly also on the user's devices. In order to empower individuals to have a say in who has access to their personal data and to enable individuals to make use of their data for their own purposes, a coherent and scalable access authorization architecture is required. Such an architecture must allow different data holders, data providers and user-content generators to respond to an individual's wishes with regards to consent in a federated fashion. This federation must allow an individual to easily manage access policies and provide consent as required by current and forthcoming data privacy regulations. This paper describes the User Managed Access (UMA) architecture and protocols that provide the foundation for scalable access authorization.
Partial Server Pooling in Redundancy Systems
Venue: arXiv cs.NI 2019
Year: 2019

Partial sharing allows providers to possibly pool a fraction of their resources when full pooling is not beneficial to them. Recent work in systems without sharing has shown that redundancy can improve performance considerably. In this paper, we combine partial sharing and redundancy by developing partial sharing models for providers operating multi-server systems with redundancy. Two M/M/N queues with redundant service models are considered. Copies of an arriving job are placed in the queues of servers that can serve the job. Partial sharing models for cancel-on-complete and cancel-on-start redundancy models are developed. For cancel-on-complete, it is shown that the Pareto efficient region is the full pooling configuration. For a cancel-on-start policy, we conjecture that the Pareto frontier is always non-empty and is such that at least one of the two providers is sharing all of its resources. For this system, using bargaining theory the sharing configuration that the providers may use is determined. Mean response time and probability of waiting are the performance metrics considered.
Federated AI lets a team imagine together: Federated Learning of GANs
Venue: arXiv cs.AI 2019
Year: 2019

Envisioning a new imaginative idea together is a popular human need. Imagining together as a team can often lead to breakthrough ideas, but the collaboration effort can also be challenging, especially when the team members are separated by time and space. What if there is a AI that can assist the team to collaboratively envision new ideas?. Is it possible to develop a working model of such an AI? This paper aims to design such an intelligence. This paper proposes a approach to design a creative and collaborative intelligence by employing a form of distributed machine learning approach called Federated Learning along with fusion on Generative Adversarial Networks, GAN. This collaborative creative AI presents a new paradigm in AI, one that lets a team of two or more to come together to imagine and envision ideas that synergies well with interests of all members of the team. In short, this paper explores the design of a novel type of AI paradigm, called Federated AI Imagination, one that lets geographically distributed teams to collaboratively imagine.
Pixel DAG-Recurrent Neural Network for Spectral-Spatial Hyperspectral Image Classification
Venue: arXiv cs.CV 2019
Year: 2019

Exploiting rich spatial and spectral features contributes to improve the classification accuracy of hyperspectral images (HSIs). In this paper, based on the mechanism of the population receptive field (pRF) in human visual cortex, we further utilize the spatial correlation of pixels in images and propose pixel directed acyclic graph recurrent neural network (Pixel DAG-RNN) to extract and apply spectral-spatial features for HSIs classification. In our model, an undirected cyclic graph (UCG) is used to represent the relevance connectivity of pixels in an image patch, and four DAGs are used to approximate the spatial relationship of UCGs. In order to avoid overfitting, weight sharing and dropout are adopted. The higher classification performance of our model on HSIs classification has been verified by experiments on three benchmark data sets.
Learning to Predict Novel Noun-Noun Compounds
Venue: arXiv cs.CL 2019
Year: 2019

We introduce temporally and contextually-aware models for the novel task of predicting unseen but plausible concepts, as conveyed by noun-noun compounds in a time-stamped corpus. We train compositional models on observed compounds, more specifically the composed distributed representations of their constituents across a time-stamped corpus, while giving it corrupted instances (where head or modifier are replaced by a random constituent) as negative evidence. The model captures generalisations over this data and learns what combinations give rise to plausible compounds and which ones do not. After training, we query the model for the plausibility of automatically generated novel combinations and verify whether the classifications are accurate. For our best model, we find that in around 85% of the cases, the novel compounds generated are attested in previously unseen data. An additional estimated 5% are plausible despite not being attested in the recent corpus, based on judgments from independent human raters.