Reviewerly/exHarmony huggingface dataset:
Improving Automatic Speech Recognition and Speech Translation via Word Embedding Prediction

In this article, we target speech translation (ST). We propose lightweight approaches that generally improve either ASR or end-to-end ST models. We leverage continuous representations of words, known as word embeddings, to improve ASR in cascaded systems as well as end-to-end ST models. The benefit of using word embedding is that word embedding can be obtained easily by training on pure textual data, which alleviates data scarcity issue. Also, word embedding provides additional contextual information to speech models. We motivate to distill the knowledge from word embedding into speech models. In ASR, we use word embeddings as a regularizer to reduce the WER, and further propose a novel decoding method to fuse the semantic relations among words for further improvement. In the end-to-end ST model, we propose leveraging word embeddings as an intermediate representation to enhance translation performance. Our analysis shows that it is possible to map speech signals to semantic space, which motivates future work on applying the proposed methods in spoken language processing tasks.
Virtual machines migration game approach for multi‐tier application in infrastructure as a service cloud computing

Virtual machines migration is an essential feature of virtualisation technology which brings many advantages in cloud computing management process such as proactive fault tolerance, load balancing, and power management. There have been many virtual migration algorithms that have been developed but it is hard to find one that best suits all applications as well as ensures the desire of all stakeholders. The optimal virtual machines migration of cloud computing is usually NP-hard or NP-complete. In addition, determining when virtual machines migrate is a challenge because of the difficulty of fault detection in cloud computing. In this study, the authors propose a virtual machines migration game approach for multi-tier application in infrastructure-as-a-service cloud computing where they first proposed fault detection model based on Takagi–Sugeno fuzzy system with metric's physical machines and then optimal or near optimal migration solution is approximated based on Nash equilibrium. The results of experiment demonstrate the efficiency of their proposal. The proposed virtual machines migration algorithm has been benchmarked in order to highlight their strength and feasibility.
Interval Type-2 Fuzzy C-Means Data Placement Optimization in Scientific Cloud Workflow applications

Scientific workflows stand as practical solutions useful for maintaining data intensive applications representation and execution purposes, which entail not only powerful computing resources, but also massive storage. With the emergence of cloud environment, which enhanced the execution of such applications, the study of workflow placement strategies, as targeted to effectively reduce data movements across data centers, has grown into a highly challenging objective. Given the fact that the workflow execution process is implemented in conformity with a task-execution order, and that each task may deal with either a single or multi-dataset, within a unique data center, various data partitioning or clustering methods have been devised in a bid to retrieve the most optimally effective workflow datasets’ distribution among data centers with the aim of remarkably reducing the datasets’ movements. In this work, a fuzzy data-dependencies based partitioning layer is implemented. More specifically, a dynamic massive data placement strategy is advanced through application of an Interval Type-2 Fuzzy C-Means technique. The latter is opted for as a means whereby the cluster related data centers can be rendered more consistent, thereby, making datasets rather closely associated in terms of related dependency, which helps in remarkably influencing the amounts of transferred data. The proposed strategy is evaluated by means of a simulation technique, using both random and real-world scientific workflows. The performed experiments appear to reveal well that our suggested strategy proves to outperform noticeably the relevant state-of-the-art methods, in that it noticeably helps in reducing the number of data movements across data centers.
Identification of Integrated Rating Mechanisms as Optimization Problem

A problem of identification of an integrated ranking mechanism is considered. We present a perspective approach to the task of identification of an integrated ranking mechanism - we show that representation of any such mechanism in one-hot encoded form allows us to formulate such a task as an optimization problem that may be solved with a wide variety of optimization techniques. The approach suggested is illustrated by several examples of identification or approximation of learning sets generated by several Boolean functions.
KRIPKE COMPLETENESS OF STRICTLY POSITIVE MODAL LOGICS OVER MEET-SEMILATTICES WITH OPERATORS

Our concern is the completeness problem for spi-logics, that is, sets of implications between strictly positive formulas built from propositional variables, conjunction and modal diamond operators. Originated in logic, algebra and computer science, spi-logics have two natural semantics: meet-semilattices with monotone operators providing Birkhoff-style calculi, and first-order relational structures (aka Kripke frames) often used as the intended structures in applications. Here we lay foundations for a completeness theory that aims to answer the question whether the two semantics define the same consequence relations for a given spi-logic.
Entropic Regularization of Markov Decision Processes

An optimal feedback controller for a given Markov decision process (MDP) can in principle be synthesized by value or policy iteration. However, if the system dynamics and the reward function are unknown, a learning agent must discover an optimal controller via direct interaction with the environment. Such interactive data gathering commonly leads to divergence towards dangerous or uninformative regions of the state space unless additional regularization measures are taken. Prior works proposed bounding the information loss measured by the Kullback-Leibler (KL) divergence at every policy improvement step to eliminate instability in the learning dynamics. In this paper, we consider a broader family of $f$-divergences, and more concretely $\alpha$-divergences, which inherit the beneficial property of providing the policy improvement step in closed form at the same time yielding a corresponding dual objective for policy evaluation. Such entropic proximal policy optimization view gives a unified perspective on compatible actor-critic architectures. In particular, common least-squares value function estimation coupled with advantage-weighted maximum likelihood policy improvement is shown to correspond to the Pearson $\chi^2$-divergence penalty. Other actor-critic pairs arise for various choices of the penalty-generating function $f$. On a concrete instantiation of our framework with the $\alpha$-divergence, we carry out asymptotic analysis of the solutions for different values of $\alpha$ and demonstrate the effects of the divergence function choice on common standard reinforcement learning problems.
Sign Language Interpreter System: An alternative system for machine learning

Losing the ability to speak exerts psychological and social impacts on the affected people due to the lack of proper communication. Thus, Sign Language (SL) is considered a boon to people with hearing and speech impairment. SL has developed as a handy mean of communication that form the core of local deaf cultures. It is a visual-spatial language based on positional and visual components, such as the shape of fingers and hands, their location and orientation as well as arm and body movements. The problem is that SL is not understood by everyone, forming a communication gap between the mute and the able people. Multiple and systematic scholarly interventions that vary according to context have been implemented to overcome disability-related difficulties. Sign language recognition (SLR) systems based on sensory gloves are significant innovations that aim to procure data on the shape or movement of the human hand to bridge this communication gap, as the proposed system. The proposed model is a glove equipped with five flex sensors, interfacing with a control unit fixed on the arm, translating American Sign Language (ASL) and Arabic Sign Language (ArSL) to both text and speech, displayed on a simple Graphical User Interface (GUI). The proposed system aims to provide an affordable and user friendly SL translator system, working on the basis of Machine Learning (ML). However, it adapts to each person's hand instead of using a generic data set. The system achieved 95% recognition rate with static gestures and up to 88% with dynamic gestures.
hActNET: An Improved Neural Network based Method in Recognizing Human Activities

Human activity recognition (HAR) is considered as one of the most difficult and challenging issues now a days. Many experiments are now in progress regarding this problem. Among many human activities, mostly six are considered for research in this area. This activity recognition issue can be measured with the help of smartphones and smartphone sensors, along with the connection of Internet of Things (IoT) devices. In this research, an improved deep learning scheme is proposed for the recognition of human activities. A customized Neural Network (NN) model was designed and tested for the research. The proposed model obtained 96.47% accuracy on the HAR with smartphones dataset that is better than most other analyzed models. Sensors such as accelerometer, gyroscope are focused on the data analysis portion of this research work. This article will give a clear idea of the dataset, Machine Learning algorithms, and the effect of the proposed algorithm.
FENCE: Feasible Evasion Attacks on Neural Networks in Constrained Environments

As advances in Deep Neural Networks (DNNs) demonstrate unprecedented levels of performance in many critical applications, their vulnerability to attacks is still an open question. We consider evasion attacks at testing time against Deep Learning in constrained environments, in which dependencies between features need to be satisfied. These situations may arise naturally in tabular data or may be the result of feature engineering in specific application domains, such as threat detection in cyber security. We propose a general iterative gradient-based framework called FENCE for crafting evasion attacks that take into consideration the specifics of constrained domains and application requirements. We apply it against Feed-Forward Neural Networks trained for two cyber security applications: network traffic botnet classification and malicious domain classification, to generate feasible adversarial examples. We extensively evaluate the success rate and performance of our attacks, compare their improvement over several baselines, and analyze factors that impact the attack success rate, including the optimization objective and the data imbalance. We show that with minimal effort (e.g., generating 12 additional network connections), an attacker can change the model’s prediction from the Malicious class to Benign and evade the classifier. We show that models trained on datasets with higher imbalance are more vulnerable to our FENCE attacks. Finally, we demonstrate the potential of performing adversarial training in constrained domains to increase the model resilience against these evasion attacks.
Deciding memory safety for single-pass heap-manipulating programs

We investigate the decidability of automatic program verification for programs that manipulate heaps, and in particular, decision procedures for proving memory safety for them. We extend recent work that identified a decidable subclass of uninterpreted programs to a class of alias-aware programs that can update maps. We apply this theory to develop verification algorithms for memory safety--- determining if a heap-manipulating program that allocates and frees memory locations and manipulates heap pointers does not dereference an unallocated memory location. We show that this problem is decidable when the initial allocated heap forms a forest data-structure and when programs are streaming-coherent, which intuitively restricts programs to make a single pass over a data-structure. Our experimental evaluation on a set of library routines that manipulate forest data-structures shows that common single-pass algorithms on data-structures often fall in the decidable class, and that our decision procedure is efficient in verifying them.
Breaking the Communication-Privacy-Accuracy Trilemma

Two major challenges in distributed learning and estimation are 1) preserving the privacy of the local samples; and 2) communicating them efficiently to a central server, while achieving high accuracy for the end-to-end task. While there has been significant interest in addressing each of these challenges separately in the recent literature, treatments that simultaneously address both challenges are still largely missing. In this paper, we develop novel encoding and decoding mechanisms that simultaneously achieve optimal privacy and communication efficiency in various canonical settings. In particular, we consider the problems of mean estimation and frequency estimation under <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\varepsilon $ </tex-math></inline-formula> -local differential privacy and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$b$ </tex-math></inline-formula> -bit communication constraints. For mean estimation, we propose the SQKR mechanism, a scheme based on Kashin’s representation and random sampling, with order-optimal estimation error under both constraints. We further apply SQKR to distributed SGD and obtain a communication efficient and (locally) differentially private distributed SGD protocol. For frequency estimation, we present the RHR mechanism, a scheme that leverages the recursive structure of Walsh-Hadamard matrices and achieves order-optimal estimation error for all privacy levels and communication budgets. As a by-product, we also construct a distribution estimation mechanism that is rate-optimal for all privacy regimes and communication constraints, extending recent work that is limited to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$b=1$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\varepsilon =O(1)$ </tex-math></inline-formula> . Our results demonstrate that intelligent encoding under joint privacy and communication constraints can yield a performance that matches the optimal accuracy achievable under either constraint alone. In other words, the optimal performance is determined by the more stringent of the two constraints, and the less stringent constraint can be satisfied for free.
Telugu handwritten character recognition using deep residual learning

Present years are the exciting times for recognition of handwritten characters in the fields of Image Processing, Pattern Recognition, and Computer Vision. Recognizing handwritten characters using deep convolutional neural networks is a new era. There are various techniques available for handwritten recognition of characters, depending on hand-designed features. The proposed work is based on a systematic method to recognize both offline and online Telugu handwritten characters with residual learning framework called ResNet. A residual learning network is a concept of deeper neural networks where the training of the data is more effective. ResNet enables building very deep networks by addressing the vanishing gradient problem that occurs in deep convolutional neural networks. This paper deals in developing a fast, reliable Telugu handwritten ResNet for both online and offline character recognition and also improves the classification performance. The model is evaluated with IIITS-Telugu Handwriting Database; HP Labs database (Telugu) India and achieved very promising results. The Proposed residual net (ResNet-50) achieves 2.37% error on the ResNet-18 & 34 test set.
Flexible coinductive logic programming

Abstract Recursive definitions of predicates are usually interpreted either inductively or coinductively. Recently, a more powerful approach has been proposed, called flexible coinduction , to express a variety of intermediate interpretations, necessary in some cases to get the correct meaning. We provide a detailed formal account of an extension of logic programming supporting flexible coinduction. Syntactically, programs are enriched by coclauses , clauses with a special meaning used to tune the interpretation of predicates. As usual, the declarative semantics can be expressed as a fixed point which, however, is not necessarily the least, nor the greatest one, but is determined by the coclauses. Correspondingly, the operational semantics is a combination of standard SLD resolution and coSLD resolution. We prove that the operational semantics is sound and complete with respect to declarative semantics restricted to finite comodels.
Statistical-Query Lower Bounds via Functional Gradients

We give the first statistical-query lower bounds for agnostically learning any non-polynomial activation with respect to Gaussian marginals (e.g., ReLU, sigmoid, sign). For the specific problem of ReLU regression (equivalently, agnostically learning a ReLU), we show that any statistical-query algorithm with tolerance $n^{-(1/\epsilon)^b}$ must use at least $2^{n^c} \epsilon$ queries for some constant $b, c > 0$, where $n$ is the dimension and $\epsilon$ is the accuracy parameter. Our results rule out general (as opposed to correlational) SQ learning algorithms, which is unusual for real-valued learning problems. Our techniques involve a gradient boosting procedure for amplifying recent lower bounds due to Diakonikolas et al. (COLT 2020) and Goel et al. (ICML 2020) on the SQ dimension of functions computed by two-layer neural networks. The crucial new ingredient is the use of a nonstandard convex functional during the boosting procedure. This also yields a best-possible reduction between two commonly studied models of learning: agnostic learning and probabilistic concepts.
Autonomous quantum state transfer by dissipation engineering

Quantum state transfer from an information-carrying qubit to a receiving qubit is ubiquitous for quantum information technology. In a closed quantum system, this task requires precisely-timed control of coherent qubit-qubit interactions that are intrinsically reciprocal. Here, breaking reciprocity by tailoring dissipation in an open system, we show that it is possible to autonomously transfer a quantum state between stationary qubits without time-dependent control. We present the general requirements for this directional transfer process, and show that the minimum system dimension for transferring one qubit of information is 3 $\times$ 2 (between one physical qutrit and one physical qubit), plus one auxiliary reservoir. We propose realistic implementations in present-day superconducting circuit QED experiments, and further propose schemes compatible with long-distance state transfer using impedance-matched dissipation engineering.
A novel symmetric image cryptosystem resistant to noise perturbation based on S8 elliptic curve S-boxes and chaotic maps

The recent decade has seen a tremendous escalation of multimedia and its applications. These modern applications demand diverse security requirements and innovative security platforms. In this manuscript, we proposed an algorithm for image encryption applications. The core structure of this algorithm relies on confusion and diffusion operations. The confusion is mainly done through the application of the elliptic curve and S8 symmetric group. The proposed work incorporates three distinct chaotic maps. A detailed investigation is presented to analyze the behavior of chaos for secure communication. The chaotic sequences are then accordingly applied to the proposed algorithm. The modular approach followed in the design framework and integration of chaotic maps into the system makes the algorithm viable for a variety of image encryption applications. The resiliency of the algorithm can further be enhanced by increasing the number of rounds and S-boxes deployed. The statistical findings and simulation results imply that the algorithm is resistant to various attacks. Moreover, the algorithm satisfies all major performance and quality metrics. The encryption scheme can also resist channel noise as well as noise-induced by a malicious user. The decryption is successfully done for noisy data with minor distortions. The overall results determine that the proposed algorithm contains good cryptographic properties and low computational complexity makes it viable to low profile applications.
Capturing Relevant Context for Visual Tracking

Studies have shown that contextual information can promote the robustness of trackers. However, trackers based on convolutional neural networks (CNNs) only capture local features, which limits their performance. We propose a novel relevant context block (RCB), which employs graph convolutional networks to capture the relevant context. In particular, it selects the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> largest contributors as nodes for each query position (unit) that contain meaningful and discriminative contextual information and updates the nodes by aggregating the differences between the query position and its contributors. This operation can be easily incorporated into the existing networks and can be easily end-to-end trained using a standard backpropagation algorithm. To verify the effectiveness of RCB, we apply it to two trackers, SiamFC and GlobalTrack, respectively, and the two improved trackers are referred to as Siam-RCB and GlobalTrack-RCB. Extensive experiments on OTB, VOT, UAV123, LaSOT, TrackingNet, OxUvA, and VOT2018LT show the superiority of our method. For example, our Siam-RCB outperforms SiamFC by a very large margin (up to 11.2% in the success score and 7.8% in the precision score) on the OTB-100 benchmark.