Reviewerly/exHarmony huggingface dataset:
Separating Symmetric and Asymmetric Password-Authenticated Key Exchange

Password-Authenticated Key Exchange (PAKE) is a method to establish cryptographic keys between two users sharing a low-entropy password. In its asymmetric version, one of the users acts as a server and only stores some function of the password, e.g., a hash. Upon server compromise, the adversary learns $$H(\mathsf {pw})$$ . Depending on the strength of the password, the attacker now has to invest more or less work to reconstruct $$\mathsf {pw} $$ from $$H(\mathsf {pw})$$ . Intuitively, asymmetric PAKE seems more challenging than symmetric PAKE since the latter is not supposed to protect the password upon compromise. In this paper, we provide three contributions:
Exploring the Robustness of NMT Systems to Nonsensical Inputs

Neural machine translation (NMT) systems have been shown to give undesirable translation when a small change is made in the source sentence. In this paper, we study the behaviour of NMT systems when multiple changes are made to the source sentence. In particular, we ask the following question "Is it possible for an NMT system to predict same translation even when multiple words in the source sentence have been replaced?". To this end, we propose a soft-attention based technique to make the aforementioned word replacements. The experiments are conducted on two language pairs: English-German (en-de) and English-French (en-fr) and two state-of-the-art NMT systems: BLSTM-based encoder-decoder with attention and Transformer. The proposed soft-attention based technique achieves high success rate and outperforms existing methods like HotFlip by a significant margin for all the conducted experiments. The results demonstrate that state-of-the-art NMT systems are unable to capture the semantics of the source language. The proposed soft-attention based technique is an invariance-based adversarial attack on NMT systems. To better evaluate such attacks, we propose an alternate metric and argue its benefits in comparison with success rate.
Deep reinforcement learning for market making in corporate bonds: beating the curse of dimensionality

In corporate bond markets, which are mainly OTC markets, market makers play a central role by providing bid and ask prices for a large number of bonds to asset managers from all around the globe. Determining the optimal bid and ask quotes that a market maker should set for a given universe of bonds is a complex task. Useful models exist, most of them inspired by that of Avellaneda and Stoikov. These models describe the complex optimization problem faced by market makers: proposing bid and ask prices in an optimal way for making money out of the difference between bid and ask prices while mitigating the market risk associated with holding inventory. While most of the models only tackle one-asset market making, they can often be generalized to a multi-asset framework. However, the problem of solving numerically the equations characterizing the optimal bid and ask quotes is seldom tackled in the literature, especially in high dimension. In this paper, our goal is to propose a numerical method for approximating the optimal bid and ask quotes over a large universe of bonds in a model a la Avellaneda-Stoikov. Because we aim at considering a large universe of bonds, classical finite difference methods as those discussed in the literature cannot be used and we present therefore a discrete-time method inspired by reinforcement learning techniques. More precisely, the approach we propose is a model-based actor-critic-like algorithm involving deep neural networks.
An Integrated Method to Remove Color Cast and Contrast Enhancement for Underwater Image

Light scattering and absorption of light in water cause underwater images to be poorly contrasted, haze and dominated by a single color cast. A solution to this is to find methods to improve the quality of the image that eventually leads to better visualization. We propose an integrated approach using Adaptive Gray World (AGW) and Differential Gray-Levels Histogram Equalization for Color Images (DHECI) to remove the color cast as well as improve the contrast and colorfulness of the underwater image. The AGW is an adaptive version of the GW method where apart from computing the global mean, the local mean of each channel of an image is taken into consideration and both are weighted before combining them. It is applied to remove the color cast, thereafter the DHECI is used to improve the contrast and colorfulness of the underwater image. The results of the proposed method are compared with seven state-of-the-art methods using qualitative and quantitative measures. The experimental results showed that in most cases the proposed method produced better quantitative scores than the compared methods.
Diversity-aware Event Prediction based on a Conditional Variational Autoencoder with Reconstruction

Typical event sequences are an important class of commonsense knowledge. Formalizing the task as the generation of a next event conditioned on a current event, previous work in event prediction employs sequence-to-sequence (seq2seq) models. However, what can happen after a given event is usually diverse, a fact that can hardly be captured by deterministic models. In this paper, we propose to incorporate a conditional variational autoencoder (CVAE) into seq2seq for its ability to represent diverse next events as a probabilistic distribution. We further extend the CVAE-based seq2seq with a reconstruction mechanism to prevent the model from concentrating on highly typical events. To facilitate fair and systematic evaluation of the diversity-aware models, we also extend existing evaluation datasets by tying each current event to multiple next events. Experiments show that the CVAE-based models drastically outperform deterministic models in terms of precision and that the reconstruction mechanism improves the recall of CVAE-based models without sacrificing precision.
Image quality assessment using BSIF, CLBP, LCP, and LPQ operators

The pervasiveness of digital devices like mobile phones, tablets, and personal computers established the presence of image contents as an essential media in several communication applications. In this scenario, the quality of the displayed images is directly correlated with the sense of communication excellence experienced by the users of these applications. Therefore, the development of techniques for assessing the quality of images, as perceived by human observers, is crucial for current multimedia applications. These techniques can either utilize the full prior information from a reference image (full-reference metrics), partial features of the reference (reduced-reference metrics) or exclusively the test image (no-reference metrics). In this paper, an effective no-reference image quality assessment approach is proposed based on the binarized statistical image features (BSIF), the completed local binary patterns (CLBP), the local configuration patterns (LCP), and the local phase quantization (LPQ) descriptors. The statistics of these descriptors is thoroughly evaluated using three popular databases: LIVE, TID2013, and CSIQ. Experimental results evince the correlation of quality scores provided by the observer with the proposed metrics, that indicate a fine performance when compared with several state-of-the-art image quality assessment methods.
Incorporating Blockchain into RDF Store at the Lightweight Edge Devices

RDF stores provide a simple abstraction for publishing and querying data, that is becoming a norm in data sharing practice. They also empower the decentralised architecture of data publishing for the Web or IoT-driven systems. Such architecture shares a lot in common with blockchain infrastructure and technologies. Therefore, there are emerging interests in marrying RDF stores and blockchain to realise desirable but speculative benefits of blockchain-powered data sharing. This paper presents the first RDF store with blockchain that enables lightweight edge devices to control of the data sharing processes (personal, IoT data). Our novel approach on the deep integration of the storage design for RDF store enables the ability to enforce controlling measures on access methods and auditing policies over data elements via smart contracts before they fetched from the sources to the consumers. Our experiments show that the prototype system delivers an effective performance for a processing load of 1 billion triples on a small network of lightweight nodes which costs less than a commodity PC.
A logical-based corpus for cross-lingual evaluation

At present, different deep learning models are presenting high accuracy on popular inference datasets such as SNLI, MNLI, and SciTail.However, there are different indicators that those datasets can be exploited by using some simple linguistic patterns.This fact poses difficulties to our understanding of the actual capacity of machine learning models to solve the complex task of textual inference.We propose a new set of syntactic tasks focused on contradiction detection that require specific capacities over linguistic logical forms such as: Boolean coordination, quantifiers, definite description, and counting operators.We evaluate two kinds of deep learning models that implicitly exploit language structure: recurrent models and the Transformer network BERT.We show that although BERT is clearly more efficient to generalize over most logical forms, there is space for improvement when dealing with counting operators.Since the syntactic tasks can be implemented in different languages, we show a successful case of crosslingual transfer learning between English and Portuguese.
Secure Federated Submodel Learning

Federated learning was proposed with an intriguing vision of achieving collaborative machine learning among numerous clients without uploading their private data to a cloud server. However, the conventional framework requires each client to leverage the full model for learning, which can be prohibitively inefficient for resource-constrained clients and large-scale deep learning tasks. We thus propose a new framework, called federated submodel learning, where clients download only the needed parts of the full model, namely submodels, and then upload the submodel updates. Nevertheless, the "position" of a client's truly required submodel corresponds to her private data, and its disclosure to the cloud server during interactions inevitably breaks the tenet of federated learning. To integrate efficiency and privacy, we have designed a secure federated submodel learning scheme coupled with a private set union protocol as a cornerstone. Our secure scheme features the properties of randomized response, secure aggregation, and Bloom filter, and endows each client with a customized plausible deniability, in terms of local differential privacy, against the position of her desired submodel, thus protecting her private data. We further instantiated our scheme with the e-commerce recommendation scenario in Alibaba, implemented a prototype system, and extensively evaluated its performance over 30-day Taobao user data. The analysis and evaluation results demonstrate the feasibility and scalability of our scheme from model accuracy and convergency, practical communication, computation, and storage overheads, as well as manifest its remarkable advantages over the conventional federated learning framework.
Energy-Aware Analog Aggregation for Federated Learning with Redundant Data

Federated learning (FL) enables workers to learn a model collaboratively by using their local data, with the help of a parameter server (PS) for global model aggregation. The high communication cost for periodic model updates and the non-independent and identically distributed (i.i.d.) data become major bottlenecks for FL. In this work, we consider analog aggregation to scale down the communication cost with respect to the number of workers, and introduce data redundancy to the system to deal with non-i.i.d. data. We propose an online energy-aware dynamic worker scheduling policy, which maximizes the average number of workers scheduled for gradient update at each iteration under a long-term energy constraint, and analyze its performance based on Lyapunov optimization. Experiments using MNIST dataset show that, for non-i.i.d. data, doubling data storage can improve the accuracy by 9.8% under a stringent energy budget, while the proposed policy can achieve close-to-optimal accuracy without violating the energy constraint.
Identifying Students’ Attitudes Regarding Augmented Reality Applications in Science Classes

As one of the developing technologies and increased use in the field of ed-ucation, Augmented Reality (AR) technology adds a digital (audio, video, graphic and GPS location) layer to authentic world images. The aim of this study is to determine students’ attitudes regarding AR contents developed for science education. The study group consists of 77 7th grade students studying in a private college during 2018-2019 academic year spring se-mester. The study adopted quantitative data collection method and imple-mented Attitude Scale for Augmented Reality Applications. The collected data were analyzed through paired samples statistics, mean and standard deviation. The data analysis showed that students were satisfied with AR applications, found it interesting and easy to use and would like to use it in the future.
Zero-Shot Deep Learning for Media Mining: Person Spotting and Face Clustering in Video Big Data

The analysis of frame sequences in talk show videos, which is necessary for media mining and television production, requires significant manual efforts and is a very time-consuming process. Given the vast amount of unlabeled face frames from talk show videos, we address and propose a solution to the problem of recognizing and clustering faces. In this paper, we propose a TV media mining system that is based on a deep convolutional neural network approach, which has been trained with a triplet loss minimization method. The main function of the proposed system is the indexing and clustering of video data for achieving an effective media production analysis of individuals in talk show videos and rapidly identifying a specific individual in video data in real-time processing. Our system uses several face datasets from Labeled Faces in the Wild (LFW), which is a collection of unlabeled web face images, as well as YouTube Faces and talk show faces datasets. In the recognition (person spotting) task, our system achieves an F-measure of 0.996 for the collection of unlabeled web face images dataset and an F-measure of 0.972 for the talk show faces dataset. In the clustering task, our system achieves an F-measure of 0.764 and 0.935 for the YouTube Faces database and the LFW dataset, respectively, while achieving an F-measure of 0.832 for the talk show faces dataset, an improvement of 5.4%, 6.5%, and 8.2% over the previous methods.
Transfer learning extensions for the probabilistic classification vector machine

Transfer learning is focused on the reuse of supervised learning models in a new context. Prominent applications can be found in robotics, image processing or web mining. In these fields, the learning scenarios are naturally changing but often remain related to each other motivating the reuse of existing supervised models. Current transfer learning models are neither sparse nor interpretable. Sparsity is very desirable if the methods have to be used in technically limited environments and interpretability is getting more critical due to privacy regulations. In this work, we propose two transfer learning extensions integrated into the sparse and interpretable probabilistic classification vector machine. They are compared to standard benchmarks in the field and show their relevance either by sparsity or performance improvements.
Relevant SMS Spam Feature Selection Using Wrapper Approach and XGBoost Algorithm

In recent years with the widely usage of mobile devices, the problem of SMS Spam increased dramatically. Receiving those undesired messages continuously can cause frustration to users. And sometimes it can be harmful, by sending SMS messages containing fake web pages in order to steal users’ confidential information. Besides spasm number of hazardous actions, there is a limited number of spam filtering software. According to this paper, XGBoost algorithm used for handling SMS spam detection problem. Number of structural features was collected from previous studies. 15 structural features were extracted from Tiago’s dataset, which is the most frequently used dataset by researchers. For selecting the optimal relevant features, two different types of wrapper feature selection algorithms were used in order to reduce and select best relevant features. The accuracy and performance obtained by the selected features via sequential backward selection method was better comparing to sequential forward selection method. The extracted nine optimal features can be a good representation of a spam SMS message. Additionally, the classification accuracy obtained by the proposed method using nine optimal features with XGBoost algorithm is 98.64 using 10-fold cross validation.
VIRTUAL REALITY SIMULATION AS THERAPY FOR POSTTRAUMATIC STRESS DISORDER (PTSD)

Virtual reality (VR) technology has the potential to make a person experience anything, anytime, anywhere. It has the ability to influence the human brain that it assumes to be present somewhere that it is really not. In this paper, we exploit this application of the VR technology to simulate virtual environments that can help with PTSD therapy for people affected by trauma due to accident, war, sexual abuse and so on. Several sensors are used to gather the user movements on a motion platform and replicate it in the virtual environment with the help of a Raspberry Pi board and Unreal Developer’s kit. It has flexible interfaces that the clinician can modify the virtual environment according to the requirement for the patient.
NiuTrans Submission for CCMT19 Quality Estimation Task

This paper describes our system submitted for the CCMT 2019 Quality Estimation (QE) Task, including sentence-level and word-level. We propose a new method based on predictor-estimator architecture [7] in this task. For the predictor, we adopt Transformer-DLCL [17] (dynamic linear combination of previous layers) as our feature extracting models. In order to obtain the information of translations in both directions, we use right-to-left and left-to-right two models, concatenate two feature vectors as whole quality feature vectors. For the estimator, we use a multi-layer bi-directional GRU to predict HTER scores or OK/BAD labels for different tasks. We pre-train the predictor according to machine translation (MT) method with bilingual data from WMT2019 EN-ZH task, and then jointly train predictor and estimator with the QE task data. We also construct 50K pseudo data in different methods in respond to the data scarcity. The final system integrates multiple single models to generate results.
SKETRACK: Stroke-Based Recognition of Online Hand-Drawn Sketches of Arrow-Connected Diagrams and Digital Logic Circuit Diagrams

Digitalization of handwritten documents has created a greater need for accurate online recognition of hand-drawn sketches. However, the online recognition of hand-drawn diagrams is an enduring challenge in human-computer interaction due to the complexity in extracting and recognizing the visual objects reliably from a continuous stroke stream. This paper focuses on the design and development of a new, efficient stroke-based online hand-drawn sketch recognition scheme named SKETRACK for hand-drawn arrow diagrams and digital logic circuit diagrams. The fundamental parts of this model are text separation, symbol segmentation, feature extraction, classification, and structural analysis. The proposed scheme utilizes the concepts of normalization and segmentation to isolate the text from the sketches. Then, the features are extracted to model different structural variations of the strokes that are categorized into the arrows/lines and the symbols for effective processing. The strokes are clustered using the spectral clustering algorithm based on p-distance and Euclidean distance to compute the similarity between the features and minimize the feature dimensionality by grouping similar features. Then, the symbol recognition is performed using modified support vector machine (MSVM) classifier in which a hybrid kernel function with a lion optimized tuning parameter of SVM is utilized. Structural analysis is performed with lion-based task optimization for recognizing the symbol candidates to form the final diagram representations. This proposed recognition model is suitable for simpler structures such as flowcharts, finite automata, and the logic circuit diagrams. Through the experiments, the performance of the proposed SKETRACK scheme is evaluated on three domains of databases and the results are compared with the state-of-the-art methods to validate its superior efficiency.
Blockchain Applications in Cybersecurity

Blockchain has been widely known thanks to Bitcoin and the cryptocurrencies. In this chapter, we analyze different aspects that relate to the application of blockchain with techniques commonly used in the field of cybersecurity. Beginning by introducing the use of blockchain technology as a secure infrastructure, the document delves into how blockchain can be useful to achieve several security requirements, common to most applications. The document has been focused on some specific cybersecurity disciplines to maintain simplicity: backup and recovery, threat intelligence and content delivery networks. As illustrated, some projects and initiatives are in the process of joining these two fields to provide solutions to existing problems.
Dependency-Based Relative Positional Encoding for Transformer NMT

In this paper, we propose a novel model for Transformer neural machine translation that incorporates syntactic distances between two source words into the relative position representations of a selfattention mechanism.In particular, the proposed model encodes pair-wise relative depths on a source dependency tree, which are the differences between the depths of two source words, in the encoder's selfattention.Experiments show that our proposed model achieved a 0.5 point gain in BLEU on the Asian Scientific Paper Excerpt Corpus Japanese-to-English translation task.
Development, Validation and Testing of the Bayesian Network of Educational Institutions Financing

In this paper, we propose a methodology for using static Bayesian networks (BN) to simulate the processes of educational institutions financing and their impact on Gross Domestic Product (GDP). The methods for constructing the BN structure, their parametric learning, validation, sensitivity analysis and scenario analysis "What-if" are considered.
3D Vision System for a Robotic Arm Based on Equal Baseline Camera Array

Abstract This paper presents a lightweight 3D vision system called Equal Baseline Camera Array (EBCA). EBCA can work in different light conditions and it can be applied for measuring large range of distances. The system is a useful alternative to other known distance measuring devices such as structured-light 3D scanners, time-of-flight cameras, Light Detection and Ranging (LIDAR) devices and structure from motion techniques. EBCA can be mounted on a robotic arm without putting significant load on its construction. EBCA consists of a central camera and a ring of side cameras. The system uses stereo matching algorithms to acquire disparity maps and depth maps similarly as in case of using stereo cameras. This paper introduces methods of adapting stereo matching algorithms designed for stereo cameras to EBCA. The paper also presents the analysis of local, semi-global and global stereo matching algorithms in the context of the EBCA usage. Experiments show that, on average, results obtained from EBCA contain 37.49% less errors than the results acquired from a single stereo camera used in the same conditions.