Reviewerly/exHarmony huggingface dataset:
Self-Supervised Monocular Depth Estimation With Isometric-Self-Sample-Based Learning

Managing the dynamic regions in the photometric loss formulation has been a main issue for handling the self-supervised depth estimation problem. Most previous methods have alleviated this issue by removing the dynamic regions in the photometric loss formulation based on the masks estimated from another module, making it difficult to fully utilize the training images. In this letter, to handle this problem, we propose an isometric self-sample-based learning (ISSL) method to fully utilize the training images in a simple yet effective way. The proposed method provides additional supervision during training using self-generated images that comply with pure static scene assumption. Specifically, the isometric self-sample generator synthesizes self-samples for each training image by applying random rigid transformations on the estimated depth. Thus both the generated self-samples and the corresponding training image always follow the static scene relation. Our method can serve as a plug-and-play module for two existing models without any architectural modifications. It provides additional supervision during training phase only. Thus, there is no additional overhead on base model parameters and computation during inference phase. These properties fit well with models oriented to real-time applications. We show that plugging our ISSL module into two existing models consistently improves the performance by a large margin. In addition, it also boosts the depth accuracy over different types of scene, i.e., outdoor scenes (KITTI and Make3D) and indoor scene (NYUv2), validating its high effectiveness.
Exploring the Role of Language Families for Building Indic Speech Synthesisers

Building end-to-end speech synthesisers for Indian languages is challenging, given the lack of adequate clean training data and multiple grapheme representations across languages. This work explores the importance of training multilingual and multi-speaker text-to-speech (TTS) systems based on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">language families</i> . The objective is to exploit the phonotactic properties of language families, where small amounts of accurately transcribed data across languages can be pooled together to train TTS systems. These systems can then be adapted to new languages belonging to the same family in extremely low-resource scenarios. TTS systems are trained separately for Indo-Aryan and Dravidian language families, and their performance is compared to that of a combined Indo-Aryan+Dravidian voice. We also investigate the amount of training data required for a language in a multilingual setting. Same-family and cross-family synthesis and adaptation to unseen languages are analysed. The analyses show that language family-wise training of Indic systems is the way forward for the Indian subcontinent, where a large number of languages are spoken.
How Stable Are Transferability Metrics Evaluations?

Transferability metrics is a maturing field with increasing interest, which aims at providing heuristics for selecting the most suitable source models to transfer to a given target dataset, without fine-tuning them all. However, existing works rely on custom experimental setups which differ across papers, leading to inconsistent conclusions about which transferability metrics work best. In this paper we conduct a large-scale study by systematically constructing a broad range of 715k experimental setup variations. We discover that even small variations to an experimental setup lead to different conclusions about the superiority of a transferability metric over another. Then we propose better evaluations by aggregating across many experiments, enabling to reach more stable conclusions. As a result, we reveal the superiority of LogME at selecting good source datasets to transfer from in a semantic segmentation scenario, $$\mathcal {N}$$ LEEP at selecting good source architectures in an image classification scenario, and GBC at determining which target task benefits most from a given source model. Yet, no single transferability metric works best in all scenarios.
Acquiring a Dynamic Light Field through a Single-Shot Coded Image

We propose a method for compressively acquiring a dynamic light field (a 5-D volume) through a single-shot coded image (a 2-D measurement). We designed an imaging model that synchronously applies aperture coding and pixel-wise exposure coding within a single exposure time. This coding scheme enables us to effectively embed the original information into a single observed image. The observed image is then fed to a convolutional neural network (CNN) for light-field reconstruction, which is jointly trained with the camera-side coding patterns. We also developed a hardware prototype to capture a real 3-D scene moving over time. We succeeded in acquiring a dynamic light field with 5x5 viewpoints over 4 temporal sub-frames (100 views in total)from a single observed image. Repeating capture and reconstruction processes over time, we can acquire a dynamic light field at 4x the frame rate of the camera. To our knowledge, our method is the first to achieve a finer temporal resolution than the camera itself in compressive light-field acquisition. Our software is available from our project webpage. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://www.fujii.nuee.nagoya-u.ac.jp/Research/CompCam2
Efficient Nonprofiled Side-Channel Attack Using Multi-Output Classification Neural Network

Differential deep learning analysis (DDLA) is the first deep-learning-based nonprofiled side-channel attack (SCA) on embedded systems. However, DDLA requires many training processes to distinguish the correct key. In this letter, we introduce a nonprofiled SCA technique using multi-output classification to mitigate the aforementioned issue. Specifically, a multi-output multilayer perceptron and a multi-output convolutional neural network are introduced against various SCA protected schemes, such as masking, noise generation, and trace de-synchronization countermeasures. The experimental results on different power side channel datasets have clarified that our model performs the attack up to 9–30 times faster than DDLA in the case of masking and de-synchronization countermeasures, respectively. In addition, regarding combined masking and noise generation countermeasure, our proposed model achieves a higher success rate of at least 20% in the cases of the standard deviation equal to 1.0 and 1.5.
An Analytic Study on Clustering-Based Pseudo-labels for Self-supervised Deep Speaker Verification

One of the most widely used self-supervised methods to train a speaker verification system is to generate the pseudo-labels using unsupervised clustering algorithms and train the speaker embedding network using the pseudo-labels in a discriminative fashion. Although the pseudo-label-based self-supervised speaker embedding extraction scheme have shown impressive performance, not much exploration was done regarding the pseudo-label generation process. In this paper, we have conducted a set of experiments using several clustering algorithms to analyze the impact of different clustering configurations for the pseudo-label-based self-supervised speaker verification system training strategy. From the experimental results, we observe that the performance of the self-supervised speaker embedding system heavily depends on the accuracy of the pseudo-labels, and the performance can be severely degraded when overfitting to the inaccurately generated pseudo-labels.
Offloaded Receding Horizon Planning for Environments with Variable Communication Delays

The ultimate goal for autonomous robots is quick and safe navigation in a variety of environments and under realistic conditions. Consider a robot tasked to quickly and safely navigate a cluttered environment such as a heavy forested area. One common feature of outdoor motion planning is the presence of a base station that can communicate with, plan and control the motion of this robot. This base station can offer several advantages over local motion planning, such as information about the environment that is not locally avail-able to the robot and superior computational resources. With such considerations, we propose a novel networked receding horizon planning method to navigate cluttered environments. Our proposed approach has the following capabilities: (i) it generates a sequence of waypoints optimized over a future time horizon considering knowledge about the lower level controller and the dynamics of the robot and (ii) it detects and adapts to communication delays to maintain safety. The proposed scheme is validated with simulations of a UAV flying in a cluttered forested environment under different communication losses with a base station.
Dual-Contrastive for Federated Social Recommendation

Existing federated social recommendation systems mainly focus on protecting users' data privacy, and the hetero-geneity of data distribution among users is less mentioned. A key reason for the poor performance of federated social recommendation systems compared to traditional ones is the heterogeneity of the local data in the federated setting. Also, it severely limits the development of federated social recommendation systems. This paper proposes a federated social recommendation framework based on Contrastive Learning. We use contrastive learning to minimize the distance between a user and his trusted users on the user level's feature space and maximize the consistency between local and global item embeddings for item embedding. Corrections for updates to user embeddings and item embeddings alleviate the performance impact of heterogeneous data distributions. Experiments on three datasets show that our approach significantly improves performance over existing methods.
An efficient iot based healthcare data management and transfer using blockchain technology

In recent times, technological advancements have resulted in dramatic change in the health sector. The Internet of Things, Cloud Computing, Block chain, lab-on-a-chip, non-invasive and percutaneous operations, and other innovations have made treating a variety of ailments much easier. Both research and the healthcare business have benefited greatly from these emerging innovations. Miniaturized healthcare sensors driven by IoT can be used for clinical tests and self-health tracking. They assist professionals in remote regions who weren't in direct communication with clients with immediate diagnostic and treatment recommendations. Controlling access mechanisms and uneven security policies were a hurdle in meeting the security standards of these data. A blockchain-based intelligent contract and an enterprise-distributed record architecture can be used to track the patient's condition. This permits patients' medical information, and an irreversible and lengthy history log, to be retrieved worldwide at whatever time. The suggested system gives greater monitoring, increased connectivity, and higher data security if compared to standard patient monitoring devices.
RGB-Multispectral Matching: Dataset, Learning Methodology, Evaluation

We address the problem of registering synchronized color (RGB) and multi-spectral (MS) images featuring very different resolution by solving stereo matching correspondences. Purposely, we introduce a novel RGB-MS dataset framing 13 different scenes in indoor environments and providing a total of 34 image pairs annotated with semi-dense, high-resolution ground-truth labels in the form of disparity maps. To tackle the task, we propose a deep learning architecture trained in a self-supervised manner by exploiting a further RGB camera, required only during training data acquisition. In this setup, we can conveniently learn cross-modal matching in the absence of ground-truth labels by distilling knowledge from an easier RGB-RGB matching task based on a collection of about 11K unlabeled image triplets. Experiments show that the proposed pipeline sets a good performance bar (1.16 pixels average registration error) for future research on this novel, challenging task.
Multi-priors Guided Dehazing Network Based on Knowledge Distillation

Single image dehazing is a key prerequisite of high-level computer vision tasks since degraded images seriously affect the recognition ability of computers. Traditional prior-based methods conduct favorable dehazing effect but tend to cause artifacts and color distortions due to inaccurate parameter estimations. By contrast, recent learning-based methods can provide better color fidelity via the supervised training of synthetic images. But unfortunately, these methods always acquire under-dehazed results due to the domain differences between synthetic hazy images and their real-world ones. To combine the merits of these two categories, we propose a multi-priors guided dehazing network (MGDNet) based on knowledge distillation. Specifically, we adopt the dehazed images of dark channel prior and non-local dehazing prior as fake ground truths, and use them to pretrain two teacher networks. Then we build a student network based on encoder-decoder structure, and set up both feature-level and pixel-level knowledge distillation losses to guide the training process of the student network. Experimental results on some real-world datasets widely used in recent works demonstrate that our MGDNet can generate visually appealing images with more discriminative textures and vivid color when compared with the state-of-the-arts.
Forest GUMP: A Tool for Explanation

Abstract In this paper, we present Forest GUMP (for Generalized, Unifying Merge Process) a tool for providing tangible experience with three concepts of explanation. Besides the well-known model explanation and outcome explanation , Forest GUMP also supports class characterization , i.e., the precise characterization of all samples with the same classification. Key technology to achieve these results is algebraic aggregation, i.e., the transformation of a Random Forest into a semantically equivalent, concise white-box representation in terms of Algebraic Decision Diagrams (ADDs). The paper sketches the method and illustrates the use of Forest GUMP along an illustrative example taken from the literature. This way readers should acquire an intuition about the tool, and the way how it should be used to increase the understanding not only of the considered dataset, but also of the character of Random Forests and the ADD technology, here enriched to comprise infeasible path elimination.
Collaborative Software Visualization for Program Comprehension

In the context of program comprehension, learning and working in teams, e.g., via pair programming, shared documentation, and discussions, can facilitate the comprehension tasks. So far, team collaboration is a relatively unexplored aspect in software visualizations, in particular approaches which are designed and explored to enable collaborative program comprehension.In this paper, we introduce our collaboratively usable software visualization environment for program comprehension. Related approaches are often limited to single-user modes, therefore neglect the advantages of multi-user collaboration, or allow only the use of a single type of device. Our approach addresses this topic and allows users to collaboratively explore software visualizations in a device-heterogeneous environment. User events, e.g., sharable pop-up information windows, are synchronized between each session participant, regardless of the employed device.To the best of our knowledge, this is one of the first approaches that combines on-screen, virtual reality, and augmented reality modes in a single web-based SV tool for program comprehension. We conducted a user study to collect initial results regarding the perceived usefulness and enjoyment of co-explored software cities In that study, 20 participants collaboratively solved program comprehension tasks while using each mode consecutively. The results indicate that the majority of participants find our approach useful and enjoyable, with AR being the least favored mode. We provide each participant’s video recording, the study’s raw results, Jupyter Notebooks, and all steps to reproduce our evaluation as supplementary package. Furthermore, a live demo of our tool is available online. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> We invite other researchers to extend our open-source software and jointly research this novel approach.Video URL: https://youtu.be/MYAkRMWLVD8
Motion Planning and Model Predictive Control for Automated Tractor-Trailer Hitching Maneuver

In recent years significant progress has been made in optimization-based planning and control for automated vehicle operation. For heavy-duty vehicles, the research focus has been on platooning and control of articulated vehicles especially when cruising on the highway. This paper proposes an integrated system using a motion planning algorithm and a real-time reference tracking controller, tailored to the task of automated tractor-trailer hitching which is a critical maneuver in heavy-duty vehicle operations, due to requiring a very high precision. The motion planner is based on a bi-directional A-search guided tree algorithm and the tracking controller is im-plemented using nonlinear model predictive control. To validate the proposed approach, we present results from hardware-in-the-loop simulations on a dSPACE Scalexio real-time computing unit and extensive Monte Carlo closed-loop simulations.
Rigorous Analysis of Data Orthogonalization for Self-Organizing Maps in Machine Learning Cyber Intrusion Detection for LoRa Sensors

In this article, a novel unsupervised machine learning (ML) algorithm is presented for the expeditious radio frequency (RF) fingerprinting of long range (LoRa)-modulated chirps. Identification based on the received signal strength indicator (RSSI) alone is unlikely to yield a robust means of authentication for critical infrastructure deployments. This is especially true for LoRa, a low-power and LoRa wireless Internet-of-Things (IoT) air-interface technology, where modulated chirps have constant envelope power and correlated in-phase/quadrature (I/Q) samples when the chirps are directly extracted. This makes traditional cyber intrusion detection techniques via a convolutional neural network (CNN) impractical. Moreover, we also prove that such correlation leads to an orthogonally inseparable dataset, due to which classification becomes intractable. Therefore, we propose an efficient way to produce self-organizing maps (SOMs) of LoRa transmitters (TXs) and a potential rogue node prior to CNN classification. This approach offers SOM orthogonalization, thus minimizing the mean square error (MSE) within the CNN using our specially constituted SOM engine for precisely profiling each LoRa TX. This method demonstrates cent-percent success in recognizing each LoRa TX as either being a legitimate device or a rogue.
Automatic Detection of Machine Generated Texts: Need More Tokens

Current advances in text generation using neural approaches make it possible to create texts hardly distinguishable from human texts. A survey to improve the efficiency of automatic discriminators to detect machine-generated text could be useful in revealing features directly affecting the quality of detection. Recently, many works have appeared in the natural language processing (NLP) and machine learning (ML) communities to create accurate detectors for the English language. Despite the importance of this problem, all the works that exist for Russian rely only on short sequence length. In this work, we argue that context length matters. First, we present novel open dataset for Russian language with long texts for the task of machinegenerated text detection. We describe the collection, generative models selection, and sampling process in detail and present exploratory analysis of the quality of various discriminators. Second, we conduct a set of learning experiments to build accurate machine-generated text detectors for both English and Russian languages. In addition, we conduct a comparative analysis of the quality of discriminators when training a multi-task model.
Communication-Efficient and Attack-Resistant Federated Edge Learning with Dataset Distillation

Federated Edge Learning considers a large amount of distributed edge nodes collectively train a global gradient-based model for edge computing in the Artificial Internet of Things, which significantly promotes the development of cloud computing. However, current federated learning algorithms take tens of communication rounds transmitting unwieldy model weights under ideal circumstances and hundreds when data is poorly distributed. This drawback directly results in expensive communication overhead for edge devices. Inspired by recent work on dataset distillation and distributed one-shot learning, we propose Distilled One-Shot Federated Learning (DOSFL) to significantly reduce the communication cost while achieving comparable performance. In just one round, each client distills their private dataset, sends the synthetic data to the server, and collectively trains a global model. The distilled data look like noise and are only useful to the specific model weights, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.,</i> become useless after the model updates. With this weight-less and gradient-less design, the total communication cost of DOSFL is up to three orders of magnitude less than FedAvg while preserving up to 99% performance of centralized training on both vision and language tasks with different models including CNN, LSTM, Transformer, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">etc</i> . We demonstrate that an eavesdropping attacker cannot properly train a good model using the leaked distilled data, without knowing the initial model weights. DOSFL serves as an inexpensive method to quickly converge on a performant pre-trained model with less than 0.1% communication cost of traditional methods.
A Feature-based Stochastic Morphological Analyzer for Filipino Affixed Words

This paper papers presents a featured-based stochastic stemming methods for obtaining affixes in Filipino language. The method aims to introduce a statistical stemming approach that is based on the morphological attributes of Filipino words. Various Filipino word forms from different types of sources were obtained and test for affix removal system. The stemmer initially performs lexicon check from the created lexis which is comprises of common based words and various categorical language forms. Feature examinations are executed to check the data entry's structure. These includes affix removal, word assimilation, partial duplication, derivational words, and inflectional words. A KSTEM assimilatory method from Hybrid Stemming Algorithm are utilized to support derivational and inflectional conditions. From the created stochastic featured-based template algorithm, the entries were analyzed and perform the final phase of the stemming process. An average of 92.46 percent was obtained using the test data and stemming technique.