Reviewerly/exHarmony huggingface dataset:
Deep Learning-based Anomaly Detection for Compressors Using Audio Data

SUMMARY & CONCLUSIONSBy pressurizing natural gas in pipelines, the compression system interlocks upstream gas production and downstream consumer use. Considering the installation cost of $1 to $2 million US dollars for a compressor, the failure of the component can be costly. Therefore, the anomaly detection for the compression system is essential. In this paper, a deep learning-based anomaly detection method is proposed to identify the failure of midstream compressors using audio sensor data. Firstly, short-term Fourier transform (STFT), Mel-frequency cepstral coefficients (MFCC), and spectral centroid (SC) features are computed using the input audio signals. Secondly, deep learning-based feature extraction is applied to create high-level features. Finally, a principal component analysis step and a support vector machine are applied to classify normal and anomaly audio signals.The proposed method was evaluated using two datasets with a total of 10196 audio signals collected from a compressor. The experimental results demonstrate that MFCC features are better than STFT features for anomaly detection and the combined deep MFCC features and SC features can achieve the best normal and anomaly signal classification performance, 100% for both datasets, using the proposed method.
Intergenerational Making with Young Children

As the Maker Movement has continued to expand, there has been a complementary expansion in the spaces and contexts for engaging in Making. Most pertinent to this paper is the growing body of research on intergenerational making experiences. A hallmark of intergenerational making is its ability to promote both play and collaboration as participants learn from one another. However, the prevailing work on intergenerational making seldom includes children less than seven years old. We build on this body of work by designing and studying an intergenerational making experiences where most of the children were between 2 and 7 years old. Five parents and their family members (ages 2-10) participated in a one-week program on designing games using digital fabrication and household craft materials. Consistent with prior work, and building on designs for intergenerational making experiences, we discuss several interactions that highlight play, joint creation, and connectedness. We consider the benefits and challenges of these types of interactions and present suggestions on the design of intergenerational making experiences that include young children.
A Comprehensive Survey on Content-Based Image Retrieval Using Machine Learning

In computer vision systems, however, retrieving a necessary image for a normal user is difficult. Over the last two decades, several studies have been conducted to improve the efficiency of automated image annotation, which has mainly concentrated on content-based image retrieval (CBIR) and attempts to identify specific images from a large dataset that are close to a query image. Various hybrid feature descriptors based on image cues such color, texture, shape, and so on that depict images were examined. For more than a decade, machine learning has been gaining traction as a viable alternative to hand-designed feature engineering. Feature extraction techniques and mathematical models are used to improve the performance and complexity of the image retrieval process. Furthermore, using the existing AlexNet convolutional neural network classifier, to increase the accuracy and performance of retrieval of Corel and Wang Datasets. Present state-of-the-art methods are described from different viewpoints for a deeper understanding of the development. Over the last period, the survey paper has given a comprehensive overview of hybrid feature extraction methods and machine learning-based enhancements for CBIR. In this paper, we hope to include a succinct overview of recent developments in CBIR and hybrid feature extraction methods, as well as machine learning-based enhancements for content-based image retrieval.
PAIR: Phone as an Augmented Immersive Reality Controller

Immersive head-mounted augmented reality allows users to overlay 3D digital content on a user's view of the world. Current-generation devices primarily support interaction modalities such as gesture, gaze and voice, which are readily available to most users yet lack precision and tactility, rendering them fatiguing for extended interactions. We propose using smartphones, which are also readily available, as companion devices complementing existing AR interaction modalities. We leverage user familiarity with smartphone interactions, coupled with their support for precise, tactile touch input, to unlock a broad range of interaction techniques and applications - for instance, turning the phone into an interior design palette, touch-enabled catapult or AR-rendered sword. We describe a prototype implementation of our interaction techniques using an off-the-shelf AR headset and smartphone, demonstrate applications, and report on the results of a positional accuracy study.
Reference stack for Steganographic applications

Steganography is the practice of concealing a secret message within a nonsecret message or data. There are many techniques which have been proposed in the literature and most of them are specific to the cover medium - for example, a technique (like F5) that works with a lossy cover medium (JPEG) will not work with a lossless cover medium (PNG). The steganography domain is strewn with many such techniques, but a standardized reference model does not exist which can be referred to by a novice in order to correctly implement a steganographic application. Such a reference model would assist developers and fellow researchers to implement the various aspects of steganography correctly and in the right order. Additionally, if the developer wants, they can replace a particular technique/component of the steganography stack with another technique which provides a similar functionality without worrying about the rest of the implementation. This is much like using UDP instead of TCP for the OSI network stack without breaking the overall functionality of networking. In this paper, the authors propose a layered reference model for steganographic applications thus providing a streamlined method of applying steganographic techniques.
Escaping From Consensus: Instantly Redactable Blockchain Protocols in Permissionless Setting

Blockchain technologies have drawn a lot of attentions, and its immutability is paramount to applications requiring persistent records. However, tremendous real-world incidents have exposed the harm of strict immutability, such as the illicit data stored on Bitcoin and the loss of millions of dollars in vulnerable smart contracts. Moreover, “Right to be Forgotten” has been imposed in new General Data Protection Regulation (GDPR) of European Union, which is incompatible with blockchain's immutability. Therefore, it is imperative to design efficient redactable blockchain in a controlled way. In this paper, we present a generic design of redactable blockchain protocols in the permissionless setting, applied to both proof-of-stake and proof-of-work blockchains. Our protocol can (1) maintain the same adversary bound requirement as the underlying blockchain, (2) support various network environments, (3) offer public verifiability for any redaction, and (4) achieve instant redaction, even only within one slot in the best case, which is desirable for redacting harmful data. Furthermore, we define the first ideal protocol of redactable blockchain and conduct security analysis following the language of universal composition. Finally, we develop a proof-of-concept implementation showing that the overhead remains minimal for both online and re-spawning nodes, which demonstrates the high efficiency of our design.
On the Potential of Zines as a Medium for Visualization

Zines are a form of small-circulation self-produced publication often akin to a magazine. This free-form medium has a long history and has been used as means for personal or intimate expression, as a way for marginalized people to describe issues that are important to them, and as a venue for graphical experimentation. It would seem then that zines would make an ideal vehicle for the recent interest in applying feminist or humanist ideas to visualization. Yet, there has been little work combining visualization and zines. In this paper we explore the potential of this intersection by analyzing examples of zines that use data graphics and by describing the pedagogical value that they can have in a visualization classroom. In doing so, we argue that there are plentiful opportunities for visualization research and practice in this rich intersectional-medium.
Relative estimates of software development effort: Are they more accurate or less time-consuming to produce than absolute estimates, and to what extent are they person-independent?

Estimates of software development effort may be given as judgments of relationships between the use of efforts on different tasks-that is, as relative estimates. The use of relative estimates has increased with the introduction of story points in agile software development contexts. This study examines to what extent relative estimates are likely to be more accurate or less time-consuming to produce than absolute software development effort estimates and to what extent relative estimates can be considered developer-independent. We conducted two experiments. In the first experiment, we collected estimates from 102 professional software developers estimating the same tasks and randomly allocated to providing relative estimates in story points or absolute estimates in work-hours. In the second experiment, we collected the actual efforts of 20 professional software developers completing the same 5 programming tasks and used these to analyze the variance in relative efforts. The results from the first experiment indicates that the relative estimates were less accurate than the absolute estimates, and that the time consumed completing the estimation work was higher for those using relative estimation, even when only considering developers with extensive prior experience in story point–based estimation for both tasks. The second experiment revealed that the relative effort was far from developer-independent, especially for the least productive developers. This suggests that relative estimates to a large extent are developer-dependent. Although there may be good reasons for the continued use of relative estimates, we interpret our results as not supporting that the use of relative estimates is connected with higher estimation accuracy or less time consumed on producing the estimates. Neither do our results support a high degree of developer-independence in relative estimates.
Multi-stage programming with generative and analytical macros

In metaprogramming, code generation and code analysis are complementary. Traditionally, principled metaprogramming extensions for programming languages, like MetaML and BER MetaOCaml, offer strong foundations for code generation but lack equivalent support for code analysis. Similarly, existing macro systems are biased towards the code generation aspect.
Learning any memory-less discrete semantics for dynamical systems represented by logic programs

Learning from interpretation transition (LFIT) automatically constructs a model of the dynamics of a system from the observation of its state transitions. So far the systems that LFIT handled were mainly restricted to synchronous deterministic dynamics. However, other dynamics exist in the field of logical modeling, in particular the asynchronous semantics which is widely used to model biological systems. In this paper, we propose a modeling of discrete memory-less multi-valued dynamic systems as logic programs in which a rule represents what can occur rather than what will occur. This modeling allows us to represent non-determinism and to propose an extension of LFIT to learn regardless of the update schemes, allowing to capture a large range of semantics. We also propose a second algorithm which is able to learn a whole system dynamics, including its semantics, in the form of a single propositional logic program with constraints. We show through theoretical results the correctness of our approaches. Practical evaluation is performed on benchmarks from biological literature.
Toward an idiomatic framework for cognitive robotics

Inspired by the "cognitive hourglass" model presented by the researchers behind the cognitive architecture called Sigma, we propose a framework for developing cognitive architectures for cognitive robotics. The main purpose of the proposed framework is to ease development of cognitive architectures by encouraging cooperation and re-use of existing results. This is done by proposing a framework dividing development of cognitive architectures into a series of layers that can be considered partly in isolation, some of which directly relate to other research fields. Finally, we introduce and review some topics essential for the proposed framework. We also outline a set of applications.
A Novel Bird Detection and Identification based on DPU processor on PYNQ FPGA

In this paper, deep learning bird identification is proposed and implemented on PYNQ FPGA with SoC architecture. The new detection method can be divided into moving object detection, and neural network processor architecture. The moving object detection is based on the principle of frame difference to obtain the image label. The recorded frames after being processed through morphology, fuzzy and binarization result in the moving object detected with its size and position within the image. The confirmed moving object is pushed through a deep-learning processor unit (DPU) for classification, resulting in the type of the bird. The results of the experiment show that the proposed method can reach 84.3% accuracy with 126.8 GOP/s/W power efficiency, which is very suitable for low power surveillance experiments in forests or outdoor environments.
Zen and the art of model adaptation: Low-utility-cost attack mitigations in collaborative machine learning

Abstract In this study, we aim to bridge the gap between the theoretical understanding of attacks against collaborative machine learning workflows and their practical ramifications by considering the effects of model architecture, learning setting and hyperparameters on the resilience against attacks. We refer to such mitigations as model adaptation . Through extensive experimentation on both, benchmark and real-life datasets, we establish a more practical threat model for collaborative learning scenarios. In particular, we evaluate the impact of model adaptation by implementing a range of attacks belonging to the broader categories of model inversion and membership inference. Our experiments yield two noteworthy outcomes: they demonstrate the difficulty of actually conducting successful attacks under realistic settings when model adaptation is employed and they highlight the challenge inherent in successfully combining model adaptation and formal privacy-preserving techniques to retain the optimal balance between model utility and attack resilience.
Network Traffic Shaping for Enhancing Privacy in IoT Systems

Motivated by traffic analysis attacks based on the packet sizes and timing information in the Internet of Things (IoT) networks, we establish a rigorous event-level differential privacy (DP) model on infinite packet streams. We propose a traffic shaper satisfying a first-come-first-served queuing discipline that outputs traffic dependent on the input using a DP mechanism. We show that in special cases the proposed mechanism recovers existing shapers which standardize the output independently from the input. To find the optimal shapers for given levels of privacy and transmission efficiency, we formulate the constrained problem of minimizing the expected delay per packet and propose using the expected queue size across time as a proxy. We further show that the constrained minimization is a convex program. We demonstrate the effect of shapers on both synthetic data and packet traces from actual IoT devices. The experimental results reveal inherent privacy-overhead tradeoffs: more shaping overhead provides better privacy protection. Under the same privacy level, there is a tradeoff between dummy traffic and delay. When shaping heavier or less bursty traffic, all shapers become more overhead-efficient. We also show that increased traffic from more IoT devices makes guaranteeing event-level privacy easier. The DP shaper offers tunable privacy that is invariant with the change in the input traffic distribution and has an advantage in handling burstiness over traffic-independent shapers. This approach accommodates heterogeneous network conditions and user demands in privacy and overhead.
SHealS and HealS: Isogeny-Based PKEs from a Key Validation Method for SIDH

In 2016, Galbraith et al. presented an adaptive attack on the SIDH key exchange protocol. In SIKE, one applies a variant of the Fujisaki-Okamoto transform to force Bob to reveal his encryption key to Alice, which Alice then uses to re-encrypt Bob's ciphertext and verify its validity. Therefore, Bob can not reuse his encryption keys. There have been two other proposed countermeasures enabling static-static private keys: k-SIDH and its variant by Jao and Urbanik. These countermeasures are relatively expensive since they consist in running multiple parallel instances of SIDH.
Failure Modes of Domain Generalization Algorithms

Domain generalization algorithms use training data from multiple domains to learn models that generalize well to unseen domains. While recently proposed benchmarks demon-strate that most of the existing algorithms do not outperform simple baselines, the established evaluation methods fail to expose the impact of various factors that contribute to the poor performance. In this paper we propose an evaluation framework for domain generalization algorithms that allows decomposition of the error into components capturing distinct aspects of generalization. Inspired by the prevalence of algorithms based on the idea of domain-invariant representation learning, we extend the evaluation framework to capture various types of failures in achieving invariance. We show that the largest contributor to the generalization error varies across methods, datasets, regularization strengths and even training lengths. We observe two problems associated with the strategy of learning domain-invariant representations. On Colored MNIST, most domain generalization algorithms fail because they reach domain-invariance only on the training domains. On Camelyon-17, domain-invariance degrades the quality of representations on unseen domains. We hypothesize that focusing instead on tuning the classifier on top of a rich representation can be a promising direction.
A Novel Fault Diagnosis method for Rotating Machinery of Imbalanced Data

In this paper, a novel classification approach for imbalanced data with high-dimensional and intra-class imbalance is proposed, and they applied to fault diagnosis of rotating machinery. It is noted that the most of existed work on imbalanced learning focus on the inter-class imbalance, and ignore the intra-class imbalance. To solve the classification of imbalanced data with high-dimensional and intra-class imbalance, we proposed an integrated data-based and feature-based algorithm, which combines hybrid feature dimensionality reduction with a varied density based safe level synthetic minority oversampling technique (VDB-SLSMOTE), transforming the imbalanced data into balanced data. The balanced data is classified by random forest, and the final experimental result verified the effectiveness of the algorithm.
HoloKeys: Interactive Piano Education Using Augmented Reality and IoT

The rise of online learning poses unique challenges in music education, where live demonstration and musical synchronization are critical for student success. We present HoloKeys, a music education interface which allows instructors to play remotely located pianos using an augmented reality headset and wifi-enabled microcontrollers. This approach allows students to receive distance education which is more direct, immersive, and comprehensive than conventional video conferencing allows for. HoloKeys enables remote students to observe live instructional demonstration on a physical keyboard in their immediate environment just as they would in traditional settings. HoloKeys consists of two separate components: an augmented reality user interface and a piano playing apparatus. Our system aims to extend online music education beyond desktop platforms into the physical world, thereby addressing crucial obstacles encountered by educators and students transitioning into online education.
Challenges and Countermeasures of Translation Teaching in the Era of Artificial Intelligence

Abstract The development of artificial intelligence has greatly improved the quality of machine translation (MT), bringing great changes to translation business and translation teaching in universities. At present, teachers and students of translation study do not have enough knowledge about the latest development of MT, and rarely integrate MT into translation practice. Outdated teaching methods and poor use of translation technologies fail to cultivate talents needed by the translation industry. The teaching of translation should make corresponding changes according to changes of practice in language service by updating teaching methods, incorporating MT and related translation tools and technologies into teaching, establishing a diversified translation evaluation system so as to further improve students’ post-translation editing ability and language competence.
Passive Data Use for Ethical Digital Public Health Surveillance in a Postpandemic World

There is a fundamental need to establish the most ethical and effective way of tracking disease in the postpandemic era. The ubiquity of mobile phones is generating large amounts of passive data (collected without active user participation) that can be used as a tool for tracking disease. Although discussions of pragmatism or economic issues tend to guide public health decisions, ethical issues are the foremost public concern. Thus, officials must look to history and current moral frameworks to avoid past mistakes and ethical pitfalls. Past pandemics demonstrate that the aftermath is the most effective time to make health policy decisions. However, an ethical discussion of passive data use for digital public health surveillance has yet to be attempted, and little has been done to determine the best method to do so. Therefore, we aim to highlight four potential areas of ethical opportunity and challenge: (1) informed consent, (2) privacy, (3) equity, and (4) ownership.
Comparison of Backpropagation and Kalman Filter-based Training for Neural Networks

This work describes and compares the backpropagation algorithm with the extended Kalman filter (EKF), a second-order training method which can be applied to the problem of learning neural network parameters and is known to converge in only a few iterations. The algorithms are compared with respect to their effectiveness and speed of convergence using simulated data for both, a regression and a classification task.