Reviewerly/exHarmony huggingface dataset:
Listen, Attend, Spell and Adapt: Speaker Adapted Sequence-to-Sequence ASR

Sequence-to-sequence (seq2seq) based ASR systems have shown state-of-the-art performances while having clear advantages in terms of simplicity. However, comparisons are mostly done on speaker independent (SI) ASR systems, though speaker adapted conventional systems are commonly used in practice for improving robustness to speaker and environment variations. In this paper, we apply speaker adaptation to seq2seq models with the goal of matching the performance of conventional ASR adaptation. Specifically, we investigate Kullback-Leibler divergence (KLD) as well as Linear Hidden Network (LHN) based adaptation for seq2seq ASR, using different amounts (up to 20 hours) of adaptation data per speaker. Our SI models are trained on large amounts of dictation data and achieve state-of-the-art results. We obtained 25% relative word error rate (WER) improvement with KLD adaptation of the seq2seq model vs. 18.7% gain from acoustic model adaptation in the conventional system. We also show that the WER of the seq2seq model decreases log-linearly with the amount of adaptation data. Finally, we analyze adaptation based on the minimum WER criterion and adapting the language model (LM) for score fusion with the speaker adapted seq2seq model, which result in further improvements of the seq2seq system performance.
Features that Matter: Feature Selection for On-line Stalling Prediction in Encrypted Video Streaming

Despite the vast literature and major developments in HTTP adaptive video streaming (HAS) technology, stalling events due to re-buffering still are by far the worst Quality of Experience (QoE) degradation, and thus, represent a major issue for ISPs. In this paper, we address the problem of real-time QoE monitoring of HAS, focusing on the detection of re-buffering events and QoE-relevant metrics, for the particular case of YouTube. Given the wide adoption of end-to-end encryption, we resort to machine learning models to predict these metrics directly from the analysis of the encrypted traffic. The salient feature of our approach ViCrypt is its ability to perform QoE predictions in real-time, during the course of an ongoing YouTube streaming session, relying on constant memory, stream-like inputs continuously extracted from the encrypted stream of packets. We show through empirical evaluations that ViCrypt can predict the occurrence of re-buffering events with a time granularity as small as one second with very high accuracy. By aggregating independent predictions, ViCrypt is able to accurately estimate per-video-session QoE-relevant metrics such as initial playback delay, number of re-buffering events and stalling ratio. In this situation, we investigate if a decent prediction performance can also be reached by selecting reduced feature sets based on the relevance of the features. Moreover, we explore the potential of including recurrent features, namely, stalling predictions from past time slots, to improve the prediction performance.
Exploring the Needs of Victims of Cyber-dependent and Cyber-enabled Crimes

The central question of this article is: To what extent and how do the needs of victims of cybercrime deviate from the needs of victims of traditional offline offenses? In total, 19 victims and 22 experts were interviewed. Our study shows that victims of online offenses have the same needs as victims of traditional offenses: ranging from emotional or financial needs to information needs. However, victims' needs are often not met. Victims state that in most cases, they are not acknowledged as a victim, that no statement is recorded by the police, or no investigation is started.
Engaging Identity, Assets, and Constraints in Designing for Resilience

We contribute to the growing conversation on assets-based approaches to design in Computer-Supported Cooperative Work (CSCW) and Human-Computer Interaction (HCI) with a qualitative study of resilience. Our study is situated within a community health infrastructure in a rural county in southwest Kenya, where health organizations pay community health workers' salaries via digital payments, backdropped by ongoing issues with missing and delayed payments. Through the lens of intersectionality, we examine how community health workers of diverse backgrounds and contracted status respond to the mandated use of digital payment methods and long payment delays. We highlight how resilience in this context is situated in workers' intersecting socioeconomic and professional identities, which shape the assets and constraints that workers engage with in efforts to be resilient. We leverage our findings to discuss how assets-based approaches to design can be further operationalized and used to sustainably support resilience.
A new clustering method based on morphological operations

With the booming development of data science, many clustering methods have been proposed. All clustering methods have inherent merits and deficiencies. Therefore, they are only capable of clustering some specific types of data robustly. In addition, the accuracies of the clustering methods rely heavily on the characteristics of the data. In this paper, we propose a new clustering method based on the morphological operations. The morphological dilation is used to connect the data points based on their adjacency and form different connected domains. The iteration of the morphological dilation process stops when the number of connected domains equals the number of the clusters or when the maximum number of iteration is reached. The morphological labeling is then used to label the connected domains. The Euclidean distance between each data point and the points in each labeled connected domain is calculated. For each data point, there is a labeled connected domain that contains a point that yields the smallest Euclidean distance. The data point is assigned with the same labeling number as the labeled connected domain. We evaluate and compare the proposed method with state of the art clustering methods with different types of data. Experimental results are favorable.
Optimized Method for Computing Odd-Degree Isogenies on Edwards Curves

In this paper, we present an efficient method to compute arbitrary odd-degree isogenies on Edwards curves. By using the w-coordinate, we optimized the isogeny formula on Edwards curves by Moody and Shumow. We demonstrate that Edwards curves have an additional benefit when recovering the coefficient of the image curve during isogeny computation. For $$\ell $$-degree isogeny where $$\ell =2s+1$$, our isogeny formula on Edwards curves outperforms Montgomery curves when $$s \ge 2$$. To better represent the performance improvements when w-coordinate is used, we implement CSIDH using our isogeny formula. Our implementation is about 20% faster than the previous implementation. The result of our work opens the door for the usage of Edwards curves in isogeny-based cryptography, especially for CSIDH which requires higher degree isogenies.
Sounds interesting: can sonification help us design new proteins?

Introduction: The practice of turning scientific data into music, a practice known as sonification, is a growing field. Driven by analogies between the hierarchical structures of proteins and many forms of music, multiple attempts of mapping proteins to music have been made. Previous works have either worked at a low level, mapping amino acid to notes, or at a higher level, using the overall structure as a basis for composition.Areas covered: We report a comprehensive mapping strategy that encompasses the encoding of the geometry of proteins, in addition to the amino acid sequence and secondary structure information. This leads to a piece of music that is both more complete and closely linked to the original protein. By using this mapping, we can invert the process and map music to proteins, retrieving not only the amino acid sequence but also the secondary structure and folding from musical data.Expert opinion: We can train a machine learning model on ‘protein music’ to generate new music that can be translated to new proteins. By selecting proper datasets and conditioning parameters on the generative model, we could tune de novo proteins with high level parameters to achieve certain protein design features.
Coherent Gradients: An Approach to Understanding Generalization in Gradient Descent-based Optimization

An open question in the Deep Learning community is why neural networks trained with Gradient Descent generalize well on real datasets even though they are capable of fitting random data. We propose an approach to answering this question based on a hypothesis about the dynamics of gradient descent that we call Coherent Gradients: Gradients from similar examples are similar and so the overall gradient is stronger in certain directions where these reinforce each other. Thus changes to the network parameters during training are biased towards those that (locally) simultaneously benefit many examples when such similarity exists. We support this hypothesis with heuristic arguments and perturbative experiments and outline how this can explain several common empirical observations about Deep Learning. Furthermore, our analysis is not just descriptive, but prescriptive. It suggests a natural modification to gradient descent that can greatly reduce overfitting.
Exploration in Reinforcement Learning with Deep Covering Options

While many option discovery methods have been proposed to accelerate exploration in reinforcement learning, they are often heuristic. Recently, covering options was proposed to discover a set of options that provably reduce the upper bound of the environment's cover time, a measure of the difficulty of exploration. Covering options are computed using the eigenvectors of the graph Laplacian, but they are constrained to tabular tasks and are not applicable to tasks with large or continuous state-spaces. We introduce deep covering options, an online method that extends covering options to large state spaces, automatically discovering task-agnostic options that encourage exploration. We evaluate our method in several challenging sparse-reward domains and we show that our approach identifies less explored regions of the state-space and successfully generates options to visit these regions, substantially improving both the exploration and the total accumulated reward.
RIDE: Rewarding Impact-Driven Exploration for Procedurally-Generated Environments

Exploration in sparse reward environments remains one of the key challenges of model-free reinforcement learning (RL). Instead of solely relying on extrinsic rewards provided by the environment, many state-of-the-art methods use intrinsic rewards to encourage the agent to explore the environment. However, we show that existing methods fall short in procedurally-generated environments where an agent is unlikely to ever visit the same state more than once. We propose a novel type of intrinsic exploration bonus which rewards the agent for actions that change the agent's learned state representation. We evaluate our method on multiple challenging procedurally-generated tasks in MiniGrid, as well as on tasks used in prior curiosity-driven exploration work. Our experiments demonstrate that our approach is more sample efficient than existing exploration methods, particularly for procedurally-generated MiniGrid environments. Furthermore, we analyze the learned behavior as well as the intrinsic reward received by our agent. In contrast to previous approaches, our intrinsic reward does not diminish during the course of training and it rewards the agent substantially more for interacting with objects that it can control.
Blockchain Dividing Based on Node Community Clustering in Intelligent Manufacturing CPS

The blockchain technology becomes a key facilitator for Intelligent Manufacturing as it enables intelligent nodes to participate in global manufacturing networks with secure ledgers and smart contracts features. However, Traditional centralized storage cannot meet performance and security requirements and fully distributed storage consumes a large amount of computing, storage and network resources, which is inefficient and difficult to implement. In this paper, we propose a clustering strategy on node community clustering by constructing a trust model based on the decentralization of blockchain technology. We introduce a multi-chain storage structure. Our experiments show that the proposed strategy reduces data synchronization time and storage space, improves system performance by enabling efficient parallel processing.
Mapping long-term coral reef ecosystems regime shifts: A small island developing state case study

Coral reefs are among the most fragile ecosystems that provide essential services to local Small Island Developing States (SIDS) communities. As such, exploring the characteristics and interactions shaping regime shifts of coral reefs is of paramount importance in managing system pressures; enhancing resilience; aiding their regeneration and recovery process; and restoring habitat complexity. However, understanding the dynamics of coral reef ecosystems regime shift requires employing an approach capable of dealing with systems being affected by multiple climatic and socio-economic non-climatic pressures as well as an effective treatment of systemic embedded uncertainties. This study applies Fuzzy Cognitive Mapping (FCM) in a participatory stepwise and systematic procedure to reflect dynamic casualties and temporal changes of coral reef ecosystem regime change over a long-time perspective. This mapping technique allows conceptualising dynamic models to represent causalities and modelling input values to simulate fluctuations within a complex temporal system. Port Resolution on Tanna Island in Vanuatu was selected as the case study region representative of Pacific-SIDS geography and human communities. As an initial outcome and an indicator of multidisciplinary of this study, twenty-seven principal influential factors and their corresponding causal relationships were identified. Subsequently, the coral reef regime shift was analysed under four main plausible scenarios representing major climatic and non-climatic trajectories. The results indicate that climate change factors play pivotal roles in the regime shift of the coral reef ecosystem globally. At the focal scale of this study, the tourism industry and coral fisheries are the most vulnerable services provided by coral reefs. As such, coupled local management interventions and global efforts in mitigating the adverse impacts of climate change is likely to yield better coral reef ecosystem services at a local community level.
Rethinking epistemic logic with belief bases

We introduce a new semantics for a family of logics of explicit and implicit belief based on the concept of multi-agent belief base. Differently from standard semantics for epistemic logic in which the notions of possible world and doxastic/epistemic alternative are primitive, in our semantics they are non-primitive but are computed from the concept of belief base. We provide complete axiomatizations and prove decidability for our logics via finite model arguments. Furthermore, we provide polynomial embeddings of our logics into Fagin & Halpern's logic of general awareness and establish complexity results via the embeddings. We also present variants of the logics incorporating different forms of epistemic introspection for explicit and/or implicit belief and provide complexity results for some of these variants. Finally, we present a number of dynamic extensions of the static framework by informative actions of both public and private type, including public announcement, belief base expansion and forgetting. We illustrate the application potential of the logical framework with the aid of a concrete example taken from the domain of conversational agents.
GSA-LA: gravitational search algorithm based on learning automata

Regardless of the performance of gravitational search algorithm (GSA), it is nearly incapable of avoiding local optima in high-dimension problems. To improve the accuracy of GSA, it is necessary to fine tune its parameters. This study introduces a gravitational search algorithm based on learning automata (GSA-LA) for optimisation of continuous problems. Gravitational constant G(t) is a significant parameter that is used to adjust the accuracy of the search. In this work, learning capability is utilised to select G(t) based on spontaneous reactions. To measure the performance of the introduced algorithm, numerical analysis is conducted on several well-designed test functions, and the results are compared with the original GSA and other evolutionary-based algorithms. Simulation results demonstrate that the learning automata-based gravitational search algorithm is more efficient in finding optimum solutions and outperforms the existing algorithms.
High Accuracy and High Fidelity Extraction of Neural Networks

In a model extraction attack, an adversary steals a copy of a remotely deployed machine learning model, given oracle prediction access. We taxonomize model extraction attacks around two objectives: *accuracy*, i.e., performing well on the underlying learning task, and *fidelity*, i.e., matching the predictions of the remote victim classifier on any input. To extract a high-accuracy model, we develop a learning-based attack exploiting the victim to supervise the training of an extracted model. Through analytical and empirical arguments, we then explain the inherent limitations that prevent any learning-based strategy from extracting a truly high-fidelity model---i.e., extracting a functionally-equivalent model whose predictions are identical to those of the victim model on all possible inputs. Addressing these limitations, we expand on prior work to develop the first practical functionally-equivalent extraction attack for direct extraction (i.e., without training) of a model's weights. We perform experiments both on academic datasets and a state-of-the-art image classifier trained with 1 billion proprietary images. In addition to broadening the scope of model extraction research, our work demonstrates the practicality of model extraction attacks against production-grade systems.
Unbounded Dynamic Predicate Compositions in ABE from Standard Assumptions

At Eurocrypt’19, Attrapadung presented several transformations that dynamically compose a set of attribute-based encryption (ABE) schemes for simpler predicates into a new ABE scheme for more expressive predicates. Due to the powerful unbounded and modular nature of his compositions, many new ABE schemes can be obtained in a systematic manner. However, his approach heavily relies on q-type assumptions, which are not standard. Devising such powerful compositions from standard assumptions was left as an important open problem. In this paper, we present a new framework for constructing ABE schemes that allow unbounded and dynamic predicate compositions among them, and show that the adaptive security of these composed ABE will be preserved by relying only on the standard matrix Diffie-Hellman (MDDH) assumption. This thus resolves the open problem posed by Attrapadung. As for applications, we obtain various ABEs that are the first such instantiations of their kinds from standard assumptions. These include the following adaptively secure large-universe ABEs for Boolean formulae under MDDH: At the core of our framework lies a new partially symmetric design of the core 1-key 1-ciphertext oracle component called Key Encoding Indistinguishability, which exploits the symmetry so as to obtain compositions.
Lightweight Super-Resolution Using Deep Neural Learning

There is a gap between recent development of 4K display technologies and the short storage of 4K contents. Super-Resolution (SR) serves as a bridge to harmonize the need and demand. Recently, Convolutional Neural Network (CNN) based networks have demonstrated great property in image SR. However, most existing methods require large model capacity and consume expensive computation for high performance. Besides, most methods keep the upscaling part relatively simple compared with the feature extraction part. For feature fusion, some methods directly concatenate the features of multi-levels, which is suboptimal due to ignoring the importance of different features. In this work, we propose a recursive multi-stage upscaling network (RMUN) with multiple sub-upscaling modules (SUMs) and a discriminative self-ensemble module (SEM). Specifically, we extract local hierarchical features by using a novel feature extraction module (FEM) which is recursive to reduce the number of parameters. Then, we construct multiple sub-upscaling modules to produce various high-resolution features in forward propagation. This strategy enhances the upscaling part and provides multiple error feedback routes. Furthermore, we employ an SEM for global hierarchical feature recalibration, which can selectively emphasize informative features and surpass less useful ones. Extensive quantitative and qualitative evaluations on benchmark datasets show that our proposed method performs comparable with the state-of-the-art methods in terms of the balance of model size and model performance.
BAE: BERT-based Adversarial Examples for Text Classification

Modern text classification models are susceptible to adversarial examples, perturbed versions of the original text indiscernible by humans which get misclassified by the model. Recent works in NLP use rule-based synonym replacement strategies to generate adversarial examples. These strategies can lead to out-of-context and unnaturally complex token replacements, which are easily identifiable by humans. We present BAE, a black box attack for generating adversarial examples using contextual perturbations from a BERT masked language model. BAE replaces and inserts tokens in the original text by masking a portion of the text and leveraging the BERT-MLM to generate alternatives for the masked tokens. Through automatic and human evaluations, we show that BAE performs a stronger attack, in addition to generating adversarial examples with improved grammaticality and semantic coherence as compared to prior work.
Temporal-mode continuous-variable three-dimensional cluster state for topologically protected measurement-based quantum computation

Measurement-based quantum computation with continuous variables in an optical setup shows the great promise towards implementation of large-scale quantum computation, where the time-domain multiplexing approach enables us to generate the large-scale cluster state used to perform measurement-based quantum computation. To make effective use of the advantage of the time-domain multiplexing approach, in this paper, we propose the method to generate the large-scale 3-dimensional cluster state which is a platform for topologically protected measurement-based quantum computation. Our method combines a time-domain multiplexing approach with a divide-and-conquer approach, and has the two advantages for implementing large-scale quantum computation. First, the squeezing level for verification of the entanglement of the 3-dimensional cluster states is experimentally feasible. The second advantage is the robustness against analog errors derived from the finite squeezing of continuous variables during topologically-protected measurement-based quantum computation. Therefore, our method is a promising approach to implement large-scale quantum computation with continuous variables.