Reviewerly/exHarmony huggingface dataset:
Censored and Fair Universal Representations using Generative Adversarial Models

We present a data-driven framework for learning \textit{censored and fair universal representations} (CFUR) that ensure statistical fairness guarantees for all downstream learning tasks that may not be known \textit{a priori}. Our framework leverages recent advancements in adversarial learning to allow a data holder to learn censored and fair representations that decouple a set of sensitive attributes from the rest of the dataset. The resulting problem of finding the optimal randomizing mechanism with specific fairness/censoring guarantees is formulated as a constrained minimax game between an encoder and an adversary where the constraint ensures a measure of usefulness (utility) of the representation. We show that for appropriately chosen adversarial loss functions, our framework enables defining demographic parity for fair representations and also clarifies {the optimal adversarial strategy against strong information-theoretic adversaries}. We evaluate the performance of our proposed framework on multi-dimensional Gaussian mixture models and publicly datasets including the UCI Census, GENKI, Human Activity Recognition (HAR), and the UTKFace. Our experimental results show that multiple sensitive features can be effectively censored while ensuring accuracy for several \textit{a priori} unknown downstream tasks. Finally, our results also make precise the tradeoff between censoring and fidelity for the representation as well as the fairness-utility tradeoffs for downstream tasks.
WaterNet: An adaptive matching pipeline for segmenting water with volatile appearance

We develop a novel network to segment water with significant appearance variation in videos. Unlike existing state-of-the-art video segmentation approaches that use a pre-trained feature recognition network and several previous frames to guide segmentation, we accommodate the object’s appearance variation by considering features observed from the current frame. When dealing with segmentation of objects such as water, whose appearance is non-uniform and changing dynamically, our pipeline can produce more reliable and accurate segmentation results than existing algorithms.
Inferring Knowledge from Textual Data by Natural Deduction

In this paper, we introduce the system for inferring implicit computable knowledge from textual data by natural deduction. Our background system is Transparent Intensional Logic (TIL) with its procedural semantics that assigns abstract procedures known as TIL constructions to terms of natural language as their context-invariant meanings. The input data for our method are produced by the so-called Normal Translation Algorithm (NTA). The algorithm processes natural-language texts and produces TIL constructions. In this way we have obtained a large corpus of TIL meaning procedures. These procedures are furthermore processed by our algorithms for type checking and context recognition so that the rules of natural deduction for inferring computable knowledge can be afterwards applied.
QoE Oriented Adaptive Streaming Method for 360° Virtual Reality Videos

Recently streaming virtual reality (VR) video has become increasingly accessible to a growing number of consumers following the rapid development of network capacity and widespread high specification devices. Various solutions have been proposed including adaptive streaming methods for VR videos. However, the high bandwidth requirement of VR videos brings new challenges in designing proper adaptive streaming methods with respect to VR characteristics in terms of both coding and transmission. Streaming VR videos differ from traditional 2D videos as their users' perceptual Quality of Experience (QoE) is more sensitive to video stalls, quality variations, and as a matter of novelty, viewport dependent. The viewport movement is associated with new issues in adaptive streaming scheme design. In order to adapt to the fast viewport movement and restrict time constraints in VR videos, a tile-based VR video format adjustment is adopted in our research. In this paper, we present Q360AS, a novel adaptive streaming method for 360° VR videos that aims at maximizing the overall QoE during the transmission in environments with varying network conditions. The proposed method shows a notable increase in real-time QoE in comparison to existing adaptive VR video streaming methods.
Computing Concreteness Ratings of Russian and English Most Frequent Words: Contrastive Approach

The article presents results of exploring differences of perceiving words as concrete or abstract by Russians and Americans. The research was mostly motivated by the extensive theoretical demand for understanding the notion of concreteness / abstractness but limited research implemented on the matter discussed in academia. Semantic differential measurement technique and online survey of over 500 respondents were used to compile concreteness / abstractness ratings of 500 most frequent Russian nouns and contrast them with concreteness / abstractness ratings of American English equivalents. To reveal the causes of differences in American / Russian equivalents’ ratings we also performed Corpus analysis of these words and demonstrated that distributional patterns and frequency of collocations in the Corpus impact concreteness / abstractness.
Joint Relational Dependency Learning for Sequential Recommendation

Sequential recommendation leverages the temporal information of users’ transactions as transition dependencies for better inferring user preference, which has become increasingly popular in academic research and practical applications. Short-term transition dependencies contain the information of partial item orders, while long-term transition dependencies infer long-range user preference, the two dependencies are mutually restrictive and complementary. Although some work investigates unifying both long-term and short-term dependencies for better performance, they still neglect the fact that short-term interactions are multi-folds, which are either individual-level interactions or union-level interactions. Existing sequential recommendations mainly focus on user’s individual (i.e., individual-level) interactions but ignore the important collective influence at union-level. Since union-level interactions can reflect that human decisions are made based on multiple items he/she has already interacted, ignoring such interactions can result in the disability of capturing the collective influence between items. To alleviate this issue, we proposed a Joint Relational Dependency learning (JRD-L) for sequential recommendation that exploits both long-term and short-term preferences at individual-level and union-level. Specifically, JRD-L combines long-term user preferences with short-term interests by measuring short-term pair relations at individual-level and union-level. Moreover, JRD-L can alleviate the sparsity problem of union-level interactions by adding more descriptive details to each item, which is carried by individual-level relations. Extensive numerical experiments demonstrate JRD-L outperforms state-of-the-art baselines for the sequential recommendation.
Key Recovery from Gram–Schmidt Norm Leakage in Hash-and-Sign Signatures over NTRU Lattices

In this paper, we initiate the study of side-channel leakage in hash-and-sign lattice-based signatures, with particular emphasis on the two efficient implementations of the original GPV lattice-trapdoor paradigm for signatures, namely NIST second-round candidate Falcon and its simpler predecessor DLP. Both of these schemes implement the GPV signature scheme over NTRU lattices, achieving great speed-ups over the general lattice case. Our results are mainly threefold. First, we identify a specific source of side-channel leakage in most implementations of those schemes, namely, the one-dimensional Gaussian sampling steps within lattice Gaussian sampling. It turns out that the implementations of these steps often leak the Gram–Schmidt norms of the secret lattice basis. Second, we elucidate the link between this leakage and the secret key, by showing that the entire secret key can be efficiently reconstructed solely from those Gram–Schmidt norms. The result makes heavy use of the algebraic structure of the corresponding schemes, which work over a power-of-two cyclotomic field. Third, we concretely demonstrate the side-channel attack against DLP (but not Falcon due to the different structures of the two schemes). The challenge is that timing information only provides an approximation of the Gram–Schmidt norms, so our algebraic recovery technique needs to be combined with pruned tree search in order to apply it to approximate values. Experimentally, we show that around $$2^{35}$$ DLP traces are enough to reconstruct the entire key with good probability.
An Empirical Analysis of Information Encoded in Disentangled Neural Speaker Representations

The primary characteristic of robust speaker representations is that they are invariant to factors of variability not related to speaker identity. Disentanglement of speaker representations is one of the techniques used to improve robustness of speaker representations to both intrinsic factors that are acquired during speech production (e.g., emotion, lexical content) and extrinsic factors that are acquired during signal capture (e.g., channel, noise). Disentanglement in neural speaker representations can be achieved either in a supervised fashion with annotations of the nuisance factors (factors not related to speaker identity) or in an unsupervised fashion without labels of the factors to be removed. In either case it is important to understand the extent to which the various factors of variability are entangled in the representations. In this work, we examine speaker representations with and without unsupervised disentanglement for the amount of information they capture related to a suite of factors. Using classification experiments we provide empirical evidence that disentanglement reduces the information with respect to nuisance factors from speaker representations, while retaining speaker information. This is further validated by speaker verification experiments on the VOiCES corpus in several challenging acoustic conditions. We also show improved robustness in speaker verification tasks using data augmentation during training of disentangled speaker embeddings. Finally, based on our findings, we provide insights into the factors that can be effectively separated using the unsupervised disentanglement technique and discuss potential future directions.
Network protocol recognition based on convolutional neural network

How to correctly acquire the appropriate features is a primary problem in network protocol recognition field. Aiming to avoid the trouble of artificially extracting features in traditional methods and improve recognition accuracy, a network protocol recognition method based on Convolutional Neural Network (CNN) is proposed. The method utilizes deep learning technique, and it processes network flows automatically. Firstly, normalization is performed on the intercepted network flows and they are mapped into two-dimensional matrix which will be used as the input of CNN. Then, an improved classification model named PtrCNN is built, which can automatically extract the appropriate features of network protocols. Finally, the classification model is trained to recognize the network protocols. The proposed approach is compared with several machine learning methods. Experimental results show that the tailored CNN can not only improve protocol recognition accuracy but also ensure the fast convergence of classification model and reduce the classification time.
Object-centric Forward Modeling for Model Predictive Control.

We present an approach to learn an object-centric forward model, and show that this allows us to plan for sequences of actions to achieve distant desired goals. We propose to model a scene as a collection of objects, each with an explicit spatial location and implicit visual feature, and learn to model the effects of actions using random interaction data. Our model allows capturing the robot-object and object-object interactions, and leads to more sample-efficient and accurate predictions. We show that this learned model can be leveraged to search for action sequences that lead to desired goal configurations, and that in conjunction with a learned correction module, this allows for robust closed loop execution. We present experiments both in simulation and the real world, and show that our approach improves over alternate implicit or pixel-space forward models. Please see our project page (this https URL) for result videos.
Information security through controlled quantum teleportation networks

Information security is the backbone of current intelligent systems, such as the Internet of Things (IoT), smart grids, and Machine-to-Machine(M2M) communication. The increasing threat of information security requires new models to ensure the safe transmission of information through such systems. Recently, quantum systems have drawn much attention since they are expected to have a significant impact on the research in information security. This paper proposes a quantum teleportation scheme based on controlled multi-users to ensure the secure information transmission among users. Quantum teleportation is an original key element in a variety of quantum information tasks as well as quantum-based technologies, which plays a pivotal role in the current progress of quantum computing and communication. In the proposed scheme, the sender transmits the information to the receiver under the control of a third user or controller. Here, we show that the efficiency of the proposed scheme depends on the properties of the transmission channel and the honesty of the controller. Compared with various teleportation scheme presented recently in the literature, the most important difference in the proposed scheme is the possibility of suspicion about the honesty of the controller and, consequently, taking proper precautions.
Two Birds, One Stone: A Simple, Unified Model for Text Generation from Structured and Unstructured Data

A number of researchers have recently questioned the necessity of increasingly complex neural network (NN) architectures.In particular, several recent papers have shown that simpler, properly tuned models are at least competitive across several NLP tasks.In this work, we show that this is also the case for text generation from structured and unstructured data.We consider neural tableto-text generation and neural question generation (NQG) tasks for text generation from structured and unstructured data, respectively.Table -to-text generation aims to generate a description based on a given table, and NQG is the task of generating a question from a given passage where the generated question can be answered by a certain sub-span of the passage using NN models.Experimental results demonstrate that a basic attentionbased seq2seq model trained with the exponential moving average technique achieves the state of the art in both tasks.Code is available at https://github.com/h-shahidi/2birds-gen.
The True Sample Complexity of Identifying Good Arms.

We consider two multi-armed bandit problems with $n$ arms: (i) given an $\epsilon > 0$, identify an arm with mean that is within $\epsilon$ of the largest mean and (ii) given a threshold $\mu_0$ and integer $k$, identify $k$ arms with means larger than $\mu_0$. Existing lower bounds and algorithms for the PAC framework suggest that both of these problems require $\Omega(n)$ samples. However, we argue that these definitions not only conflict with how these algorithms are used in practice, but also that these results disagree with intuition that says (i) requires only $\Theta(\frac{n}{m})$ samples where $m = |\{ i : \mu_i > \max_{i \in [n]} \mu_i - \epsilon\}|$ and (ii) requires $\Theta(\frac{n}{m}k)$ samples where $m = |\{ i : \mu_i > \mu_0 \}|$. We provide definitions that formalize these intuitions, obtain lower bounds that match the above sample complexities, and develop explicit, practical algorithms that achieve nearly matching upper bounds.
A Performance-Based Start State Curriculum Framework for Reinforcement Learning

Sparse reward problems present a challenge for reinforcement learning (RL) agents. Previous work has shown that choosing start states according to a curriculum can significantly improve the learning performance. We observe that many existing curriculum generation algorithms rely on two key components: Performance measure estimation and a start selection policy. Therefore, we propose a unifying framework for performance-based start state curricula in RL, which allows to analyze and compare the performance influence of the two key components. Furthermore, a new start state selection policy using spatial performance measure gradients is introduced. We conduct extensive empirical evaluations to compare performance-based start state curricula and investigate the influence of performance measure model choice and estimation. Benchmarking on difficult robotic navigation tasks and a high-dimensional robotic manipulation task, we demonstrate state-of-the-art performance of our novel spatial gradient curriculum.
Local Binary Pattern Based ELM for Face Identification

In this chapter, local binary pattern (LBP) based extreme learning machine (ELM) is presented for identification of high-dimensional face images of different resolution. In this scheme, LBP is used for the representation of micro-regions of face image in the form of feature vectors which are further concatenated to be utilized as face descriptors for representation of face image. The obtained face descriptors are classified using ELM which is a simple and fast learning algorithm. Extensive simulations on AT&T and Yale databases of face images illustrate the effectiveness of the proposed scheme.
Map-based uncertainty analysis for exploration using basin modeling and machine learning techniques applied to the Levant Basin petroleum systems, Eastern Mediterranean

The Levant Basin of the East-Mediterranean region contains a biogenic petroleum system and possibly an underlying thermogenic system. The application of a new map-based uncertainty and sensitivity analysis that integrates a large set of geological parameters demonstrates that a connection between an Upper Cretaceous thermogenic petroleum system and the shallower Oligo-Miocene reservoirs is highly possible in the southern part of the basin (offshore Israel and southern Lebanon) where hydrocarbon fluids are generated from the Campanian source rock and charge the overlying Oligo-Miocene reservoir interval through faults. Estimates of volumes of expelled hydrocarbons combined with regional analysis of the Eocene top seal integrity indicates that the most prospective zones correspond to the southern offshore Lebanon and offshore Israel. In the northern part of the Levant Basin (offshore central and northern Lebanon), petroleum fluids potentially generated by the Campanian source rocks remain trapped under the Eocene seal or they migrate towards the margins of the basin. Our approach is based on applications of machine learning techniques and robust statistical analyses to petroleum systems modeling, focusing on spatial uncertainty analysis. It may be used on similar frontier or emerging hydrocarbon provinces to help de-risking the petroleum systems.
Competencies for Educators in Delivering Digital Accessibility in Higher Education

The aim of this paper is to critically review the capabilities of the European Framework for the Digital Competence of Educators (DigCompEdu) and the UNESCO ICT Competency Framework for in delivering greater accessibility for students with disabilities in a Higher Education landscape undergoing Digital Transformation. These frameworks describe what it means for educators to be digitally competent. However are there other competencies required to deliver Digital Accessibility in education. The particular focus of this paper is the role of the teachers in delivering Digital Accessibility in higher education. What should be expected of them and what are the required competencies to meet these expectations? Is it fair for example to expect teachers to cross boundaries where the effectiveness of general accessibility strategies such as UDL end for particular groups of students for example blind students in STEM subjects and where there is a need for individualised accommodations?
Lateral logging sounding and lateral logging complex effective inverse problem solving method

Summary Using the example of lateral logging sounding the possibility of assessing the influence of the adjacent beds and boundary effects on the real vertical resolution of the inverse problem solution is shown. An example of such solution to the inverse problem of the “BKZ+BK” complex for a real well material in a sedimentary rocks is given. The conclusions are made that: for the correct use of the algorithm for solving the inverse problem, it is necessary to have an estimate of the vertical resolution of the electrometry method as a whole; a real assessment of the influence of boundary effects on the measurement of the apparent resistance of the probes of the complex allows us to evaluate the effectiveness of the method; the use of the proposed residual functional allows us to change the effect of probes of different lengths on the final result and thereby allows to achieve satisfactory accuracy even when studying the geoelectric parameters of formations whose thickness is less than the length of the largest probes of the complex. The results of the work were introduced into the production of a number of commercial geophysical organizations.
Real‐time implementation and performance evaluation of speech classifiers in speech analysis‐synthesis

In this work, six voiced/unvoiced speech classifiers based on the autocorrelation function (ACF), average magnitude difference function (AMDF), cepstrum, weighted ACF (WACF), zero crossing rate and energy of the signal (ZCR-E), and neural networks (NNs) have been simulated and implemented in real time using the TMS320C6713 DSP starter kit. These speech classifiers have been integrated into a linear-predictive-coding-based speech analysis-synthesis system and their performance has been compared in terms of the percentage of the voiced/unvoiced classification accuracy, speech quality, and computation time. The results of the percentage of the voiced/unvoiced classification accuracy and speech quality show that the NN-based speech classifier performs better than the ACF-, AMDF-, cepstrum-, WACF- and ZCR-E-based speech classifiers for both clean and noisy environments. The computation time results show that the AMDF-based speech classifier is computationally simple, and thus its computation time is less than that of other speech classifiers, while that of the NN-based speech classifier is greater compared with other classifiers.