Reviewerly/exHarmony huggingface dataset:
Generative Adversarial Training Data Adaptation for Very Low-Resource Automatic Speech Recognition

It is important to transcribe and archive speech data of endangered languages for preserving heritages of verbal culture and automatic speech recognition (ASR) is a powerful tool to facilitate this process. However, since endangered languages do not generally have large corpora with many speakers, the performance of ASR models trained on them are considerably poor in general. Nevertheless, we are often left with a lot of recordings of spontaneous speech data that have to be transcribed. In this work, for mitigating this speaker sparsity problem, we propose to convert the whole training speech data and make it sound like the test speaker in order to develop a highly accurate ASR system for this speaker. For this purpose, we utilize a CycleGAN-based non-parallel voice conversion technology to forge a labeled training data that is close to the test speaker's speech. We evaluated this speaker adaptation approach on two low-resource corpora, namely, Ainu and Mboshi. We obtained 35-60% relative improvement in phone error rate on the Ainu corpus, and 40% relative improvement was attained on the Mboshi corpus. This approach outperformed two conventional methods namely unsupervised adaptation and multilingual training with these two corpora.
Precise Task Formalization Matters in Winograd Schema Evaluations

Performance on the Winograd Schema Challenge (WSC), a respected English commonsense reasoning benchmark, recently rocketed from chance accuracy to 89% on the SuperGLUE leaderboard, with relatively little corroborating evidence of a correspondingly large improvement in reasoning ability. We hypothesize that much of this improvement comes from recent changes in task formalization—the combination of input specification, loss function, and reuse of pretrained parameters—by users of the dataset, rather than improvements in the pretrained model’s reasoning ability. We perform an ablation on two Winograd Schema datasets that interpolates between the formalizations used before and after this surge, and find (i) framing the task as multiple choice improves performance dramatically and (ii)several additional techniques, including the reuse of a pretrained language modeling head, can mitigate the model’s extreme sensitivity to hyperparameters. We urge future benchmark creators to impose additional structure to minimize the impact of formalization decisions on reported results.
Filtering Noisy Dialogue Corpora by Connectivity and Content Relatedness

Large-scale dialogue datasets have recently become available for training neural dialogue agents. However, these datasets have been reported to contain a non-negligible number of unacceptable utterance pairs. In this paper, we propose a method for scoring the quality of utterance pairs in terms of their connectivity and relatedness. The proposed scoring method is designed based on findings widely shared in the dialogue and linguistics research communities. We demonstrate that it has a relatively good correlation with the human judgment of dialogue quality. Furthermore, the method is applied to filter out potentially unacceptable utterance pairs from a large-scale noisy dialogue corpus to ensure its quality. We experimentally confirm that training data filtered by the proposed method improves the quality of neural dialogue agents in response generation.
Your Classifier can Secretly Suffice Multi-Source Domain Adaptation

Multi-Source Domain Adaptation (MSDA) deals with the transfer of task knowledge from multiple labeled source domains to an unlabeled target domain, under a domain-shift. Existing methods aim to minimize this domain-shift using auxiliary distribution alignment objectives. In this work, we present a different perspective to MSDA wherein deep models are observed to implicitly align the domains under label supervision. Thus, we aim to utilize implicit alignment without additional training objectives to perform adaptation. To this end, we use pseudo-labeled target samples and enforce a classifier agreement on the pseudo-labels, a process called Self-supervised Implicit Alignment (SImpAl). We find that SImpAl readily works even under category-shift among the source domains. Further, we propose classifier agreement as a cue to determine the training convergence, resulting in a simple training algorithm. We provide a thorough evaluation of our approach on five benchmarks, along with detailed insights into each component of our approach. Â© 2020 Neural information processing systems foundation. All rights reserved.
Using Curvilinear Features in Focus for Registering a Single Image to a 3D Object

In the context of 2D/3D registration, this paper introduces an approach that allows for matching features detected in two different modalities, photographs, and 3D models, by using a common 2D representation. More precisely, 2D images are matched with a set of depth images representing the 3D model. After introducing the concept of Curvilinear Saliency, which is related to curvature estimation, we propose a new ridge and valley detector for depth images rendered from 3D models. A variant of this detector is adapted to photographs, first by considering multi-scale features and second by integrating the focus curve principle. Finally, a registration algorithm determines the correct view of the 3D model and, thus, the pose of the photograph. This approach relies on the Histogram of Curvilinear Saliency (HCS), an adaptation of the Histogram of Oriented Gradients (HOG) to the proposed features in 2D and 3D. The presented results highlight both the quality of the features detected in terms of repeatability and the interest of the approach for registration and pose estimation.
High-Level Task-Driven Single Image Deraining: Segmentation in Rainy Days

Deraining driven by semantic segmentation task is very important for autonomous driving because rain streaks and raindrops on the car window will seriously degrade the segmentation accuracy. As a pre-processing step of semantic segmentation network, a deraining network should be capable of not only removing rain in images but also preserving semantic-aware details of derained images. However, most of the state-of-the-art deraining approaches are only optimized for high PSNR and SSIM metrics without considering objective effect for high-level vision tasks. Not only that, there is no suitable dataset for such tasks. In this paper, we first design a new deraining network that contains a semantic refinement residual network (SRRN) and a novel two-stage segmentation aware joint training method. Precisely, our training method is composed of the traditional deraining training and the semantic refinement joint training. Hence, we synthesize a new segmentation-annotated rain dataset called Raindrop-Cityscapes with rain streaks and raindrops which makes it possible to test deraining and segmentation results jointly. Our experiments on our synthetic dataset and real-world dataset show the effectiveness of our approach, which outperforms state-of-the-art methods and achieves visually better reconstruction results and sufficiently good performance on semantic segmentation task.
On the Sub-layer Functionalities of Transformer Decoder

There have been significant efforts to interpret the encoder of Transformer-based encoderdecoder architectures for neural machine translation (NMT); meanwhile, the decoder remains largely unexamined despite its critical role.During translation, the decoder must predict output tokens by considering both the source-language text from the encoder and the target-language prefix produced in previous steps.In this work, we study how Transformerbased decoders leverage information from the source and target languages -developing a universal probe task to assess how information is propagated through each module of each decoder layer.We perform extensive experiments on three major translation datasets (WMT En-De, En-Fr, and En-Zh).Our analysis provides insight on when and where decoders leverage different sources.Based on these insights, we demonstrate that the residual feed-forward module in each Transformer decoder layer can be dropped with minimal loss of performance -a significant reduction in computation and number of parameters, and consequently a significant boost to both training and inference speed.
Duality-Induced Regularizer for Tensor Factorization Based Knowledge Graph Completion

Tensor factorization based models have shown great power in knowledge graph completion (KGC). However, their performance usually suffers from the overfitting problem seriously. This motivates various regularizers---such as the squared Frobenius norm and tensor nuclear norm regularizers---while the limited applicability significantly limits their practical usage. To address this challenge, we propose a novel regularizer---namely, DUality-induced RegulArizer (DURA)---which is not only effective in improving the performance of existing models but widely applicable to various methods. The major novelty of DURA is based on the observation that, for an existing tensor factorization based KGC model (primal), there is often another distance based KGC model (dual) closely associated with it. Experiments show that DURA yields consistent and significant improvements on benchmarks.
Learning to Cluster Under Domain Shift

While unsupervised domain adaptation methods based on deep architectures have achieved remarkable success in many computer vision tasks, they rely on a strong assumption, i.e. labeled source data must be available. In this work we overcome this assumption and we address the problem of transferring knowledge from a source to a target domain when both source and target data have no annotations. Inspired by recent works on deep clustering, our approach leverages information from data gathered from multiple source domains to build a domain-agnostic clustering model which is then refined at inference time when target data become available. Specifically, at training time we propose to optimize a novel information-theoretic loss which, coupled with domain-alignment layers, ensures that our model learns to correctly discover semantic labels while discarding domain-specific features. Importantly, our architecture design ensures that at inference time the resulting source model can be effectively adapted to the target domain without having access to source data, thanks to feature alignment and self-supervision. We evaluate the proposed approach in a variety of settings (Code available at https://github.com/willi-menapace/acids-clustering-domain-shift ), considering several domain adaptation benchmarks and we show that our method is able to automatically discover relevant semantic information even in presence of few target samples and yields state-of-the-art results on multiple domain adaptation benchmarks.
Society-oriented applications development: investigating users' values from bangladeshi agriculture mobile applications

Limited consideration of users' values in mobile applications (apps) can lead to user disappointments and negative socio-economic consequences. Therefore, it is important to consider values in app development to avoid such adverse effects and to secure the optimum use of apps. With this aim, we conducted a case study to identify the users' desired values that are either reflected or missing in the existing Bangladeshi agriculture mobile apps. We manually analyzed 1522 reviews from 29 existing Bangladeshi agriculture apps in Google Play by following a widely used human values theory, Schwartz's theory of basic human values. Our results show that users of the selected apps have twenty one (21) desired individual values where eleven (11) values are reflected in the apps and ten (10) values are missing. This research provides a basis for the developers to design apps that consider users' values. It also provides a direction on which values they should address while developing apps. Moreover, repeating this research in different domains or societies would result in society-oriented apps that are more sensitive to users' values.
Testing differential privacy with dual interpreters

Applying differential privacy at scale requires convenient ways to check that programs computing with sensitive data appropriately preserve privacy. We propose here a fully automated framework for testing differential privacy, adapting a well-known “pointwise” technique from informal proofs of differential privacy. Our framework, called DPCheck, requires no programmer annotations, handles all previously verified or tested algorithms, and is the first fully automated framework to distinguish correct and buggy implementations of PrivTree, a probabilistically terminating algorithm that has not previously been mechanically checked. We analyze the probability of DPCheck mistakenly accepting a non-private program and prove that, theoretically, the probability of false acceptance can be made exponentially small by suitable choice of test size. We demonstrate DPCheck’s utility empirically by implementing all benchmark algorithms from prior work on mechanical verification of differential privacy, plus several others and their incorrect variants, and show DPCheck accepts the correct implementations and rejects the incorrect variants. We also demonstrate how DPCheck can be deployed in a practical workflow to test differentially privacy for the 2020 US Census Disclosure Avoidance System (DAS).
CompCertELF: verified separate compilation of C programs into ELF object files

We present CompCertELF, the first extension to CompCert that supports verified compilation from C programs all the way to a standard binary file format, i.e., the ELF object format. Previous work on Stack-Aware CompCert provides a verified compilation chain from C programs to assembly programs with a realistic machine memory model. We build CompCertELF by modifying and extending this compilation chain with a verified assembler which further transforms assembly programs into ELF object files. CompCert supports large-scale verification via verified separate compilation: C modules can be written and compiled separately, and then linked together to get a target program that refines the semantics of the program linked from the source modules. However, verified separate compilation in CompCert only works for compilation to assembly programs, not to object files. For the latter, the main difficulty is to bridge the two different views of linking: one for CompCert's programs that allows arbitrary shuffling of global definitions by linking and the other for object files that treats blocks of encoded definitions as indivisible units. We propose a lightweight approach that solves the above problem without any modification to CompCert's framework for verified separate compilation: by introducing a notion of syntactical equivalence between programs and proving the commutativity between syntactical equivalence and the two different kinds of linking, we are able to transit from the more abstract linking operation in CompCert to the more concrete one for ELF object files. By applying this approach to CompCertELF, we obtain the first compiler that supports verified separate compilation of C programs into ELF object files.
Custom Instruction Support for Modular Defense Against Side-Channel and Fault Attacks

The design of software countermeasures against active and passive adversaries is a challenging problem that has been addressed by many authors in recent years. The proposed solutions adopt a theoretical foundation (such as a leakage model) but often do not offer concrete reference implementations to validate the foundation. Contributing to the experimental dimension of this body of work, we propose a customized processor called SKIVA that supports experiments with the design of countermeasures against a broad range of implementation attacks. Based on bitslice programming and recent advances in the literature, SKIVA offers a flexible and modular combination of countermeasures against power-based and timing-based side-channel leakage and fault injection. Multiple configurations of side-channel protection and fault protection enable the programmer to select the desired number of shares and the desired redundancy level for each slice. Recurring and security-sensitive operations are supported in hardware through custom instruction-set extensions. The new instructions support bitslicing, secret-share generation, redundant logic computation, and fault detection. We demonstrate and analyze multiple versions of AES from a side-channel analysis and a fault-injection perspective, in addition to providing a detailed performance evaluation of the protected designs. To our knowledge, this is the first validated end-to-end implementation of a modular bitslice-oriented countermeasure.
THE EFFECT OF HERRINGBONE TECHNIQUE ON THE STUDENTS’ ABILITY IN READING COMPREHENSION ON RECOUNT TEXT

This research is a study of improving students’ ability in reading comprehension. The aim was to find out whether the use of Herringbone technique would bring a significant difference in teaching reading comprehension through Recount text, compared with the result before the students being treated. The study was conducted in the tenth grade students of SMA N 4 Pematangsiantar. The research method used was the experimental design, where the Experimental group was the PMIA 3 class, and the PMIA 2 class as the control class. Both of the classes had 34 students in each of it. The data were obtained through pre-test, treatment and post test. The pre-test was held to know the students’ basic ability or score before treatment was given. Meanwhile, the post test was held to know the students’ achievement and significant effect after the students were taught using Herringbone technique. Finding of the research showed that both groups had a significant improvement in their reading ability after being treated and passed the KKM. The Control group, which the pre-test score was 60, made an improvement shown in their post-test score in amount of 70,44. The experimental group, the group which the researcher treated the Herringbone technique to them, had a higher improvement than the control group. The average score of pre-test in this group was about 54,41 where then being improved up to 78,97. The score of t-test (3,981) was higher than t-table (1,668) at the level of significance 5% for two tailed test, so Null Hypothesis is rejected and Alternative Hypothesis is accepted.
Policing cybercrime

Law enforcement faces significant challenges when investigating cybercrime, both by individual officers and organizations. Perceptions of cybercrime and its perpetrators by law enforcement officers varies depending on several factors such as the rank of the officer or the type of crime. Due to officer discretion in the field, these perceptions may influence the response to cybercrime complaints. Another concern for agencies is the exposure of officers to trauma. Some cybercrime investigations, like Internet crimes against children, may result in exposure to traumatic stimulus, much like “traditional” violent crimes. Organizations also face the challenge of enforcing cyber-specific legislation or must interpret existing law in its application to cybercrime while dealing with jurisdictional issues. Despite advances in the field of digital forensics, additional basic training for line officers is still needed. This chapter will address both the human and organizational factors of law enforcement response to cybercrime.
Pitch control of wind turbine based on deep neural network

Abstract This paper analyzed the input and output data of wind farm based on deep neural network, developed intelligent model, and realized the predictive modeling of important parameter variables and control of wind turbine. By establishing the Deep Extreme Learning Machine(DELM), the higher-order nonlinear model is simplified. In this structure, unsupervised hierarchical ELM is conducted for feature extraction, and the features of the lower layer are transferred to the higher layer through layer by layer coding to form a relatively complete feature representation. Finally, the Extreme Learning Machine (ELM) is used to complete the mapping of feature representation to target output to minimize the loss of information in the transmission process. The target output is used as reference data for Pitch control of wind turbine, which is proposed by using a radial basis function (REF) neutral network. Simulation results from GH-Bladed show that proposed control algorithm can mitigate the loads effectively. The algorithm provides a practical reference for the design of wind turbine controller.
Can I Still Trust You?: Understanding the Impact of Distribution Shifts on Algorithmic Recourses.

As predictive models are being increasingly deployed to make a variety of consequential decisions ranging from hiring decisions to loan approvals, there is growing emphasis on designing algorithms that can provide reliable recourses to affected individuals. In this work, we assess the reliability of algorithmic recourses through the lens of distribution shifts i.e., we study if the recourses generated by state-of-the-art algorithms are robust to distribution shifts. To the best of our knowledge, this work makes the first attempt at addressing this critical question. We experiment with multiple synthetic and real world datasets capturing different kinds of distribution shifts including temporal shifts, geospatial shifts, and shifts due to data corrections. Our results demonstrate that all the aforementioned distribution shifts could potentially invalidate the recourses generated by state-of-the-art algorithms. Our theoretical results establish a lower bound on the probability of recourse invalidation due to distribution shifts, and show the existence of a tradeoff between this invalidation probability and typical notions of cost minimized by modern recourse generation algorithms. Our findings not only expose fundamental flaws in recourse finding strategies but also pave new way for rethinking the design and development of recourse generation algorithms.
Video Super Resolution by Neural Network: A Theoretical Aspect

The paper explores literature on the video super resolution by neural network, with all essential basics related to it. The extensive applicability and need of enhanced resolution becomes attraction for researchers. The limitations of traditional methods gives rise to the new generation of neural network based super resolution. The neural networks are well known for parallel and fast computation of data. But embedding a neural network with challenging super resolution era has come up with benefits as well as drawbacks. Still the researchers are working on the challenges like, limited practical feasibility and utility, accuracy at the time cost, complexity, etc. This paper is useful for new researchers to get information about the basics of super resolution and neural network, relative study of learning processes, comparative summarization of neural network architecture used for resolution improvement.