Reviewerly/exHarmony huggingface dataset:
Machine Learning Approaches for Load Balancing in Cloud Computing Services

As the demand for cloud services increases, optimization of resources becomes essential. Static algorithms are no longer sufficient to solve cloud-related challenges such as imbalanced workload distribution in Virtual Machines or improper resource allocation to cloud users. Thus, the need to explore other rich approaches can greatly improve cloud applications' performance and tackle the above challenges. This research investigates the latest Machine Learning approaches that can tackle the above challenges in cloud environment. A comparison of these approaches included highlighting their strengths and weaknesses to induce a research gap useful for upcoming researchers in the field.
Towards Discovery and Attribution of Open-world GAN Generated Images

With the recent progress in Generative Adversarial Networks (GANs), it is imperative for media and visual forensics to develop detectors which can identify and attribute images to the model generating them. Existing works have shown to attribute images to their corresponding GAN sources with high accuracy. However, these works are limited to a closed set scenario, failing to generalize to GANs unseen during train time and are therefore, not scalable with a steady influx of new GANs. We present an iterative algorithm for discovering images generated from previously unseen GANs by exploiting the fact that all GANs leave distinct fingerprints on their generated images. Our algorithm consists of multiple components including network training, out-of-distribution detection, clustering, merge and refine steps. Through extensive experiments, we show that our algorithm discovers unseen GANs with high accuracy and also generalizes to GANs trained on unseen real datasets. We additionally apply our algorithm to attribution and discovery of GANs in an online fashion as well as to the more standard task of real/fake detection. Our experiments demonstrate the effectiveness of our approach to discover new GANs and can be used in an open-world setup.
First U-Pb dating of fossilized soft tissue using a new approach to paleontological chronometry

Abstract Previous U-Pb dating of fossils has had only limited success because of low uranium content and abundance of common Pb as well as element mobility during late diagenesis. We report the first accurate U-Pb dating of fossilized soft tissue from a Pliocene phosphatized bivalve mold using laser ablation–inductively coupled mass spectrometry (LA-ICPMS). The fossilized soft tissue yields a diagenetic U-Pb age of 3.16 ± 0.08 Ma, which is consistent with its late Pliocene stratigraphy and similar to the oldest U-Pb age measured on accompanying shark teeth. Phosphate extraclasts give a distinctly older age of 5.1 ± 1.7 Ma, indicating that they are likely detrital and may have furnished P, promoting phosphatization of the mold. The U-Pb ages reported here along with stratigraphic constraints suggest that diagenesis occurred shortly after the death of the bivalve and that the U-Pb system in the bivalve mold remained closed until the present. Shark teeth collected from the same horizon show variable resetting due to late diagenesis. Data were acquired as line scans in order to exploit the maximum Pb/U variation and were regressed as counts, rather than ratios, in three-dimensional space using a Bayesian statistical method.
Automatic segmentation of medical images using a novel Harris Hawk optimization method and an active contour model

Medical image segmentation is a key step to assist diagnosis of several diseases, and accuracy of a segmentation method is important for further treatments of different diseases. Different medical imaging modalities have different challenges such as intensity inhomogeneity, noise, low contrast, and ill-defined boundaries, which make automated segmentation a difficult task. To handle these issues, we propose a new fully automated method for medical image segmentation, which utilizes the advantages of thresholding and an active contour model. In this study, a Harris Hawks optimizer is applied to determine the optimal thresholding value, which is used to obtain the initial contour for segmentation. The obtained contour is further refined by using a spatially varying Gaussian kernel in the active contour model. The proposed method is then validated using a standard skin dataset (ISBI 2016), which consists of variable-sized lesions and different challenging artifacts, and a standard cardiac magnetic resonance dataset (ACDC, MICCAI 2017) with a wide spectrum of normal hearts, congenital heart diseases, and cardiac dysfunction. Experimental results show that the proposed method can effectively segment the region of interest and produce superior segmentation results for skin (overall Dice Score 0.90) and cardiac dataset (overall Dice Score 0.93), as compared to other state-of-the-art algorithms.
Chasing Luck: Data-driven Prediction, Faith, Hunch, and Cultural Norms in Rural Betting Practices

HCI research predominantly uses scientific rationality to explain users’ behaviors, decisions, and interactions with statistical models based and data-driven systems. However, such interactions are often more diverse in real life, and may straddle beyond scientific and economic rationality. Building on a ten-month ethnography at seven Bangladeshi villages, we explore the social and cultural factors that influence the online betting practices among the villagers. We describe how bets harmonize with users’ faith, hunch, and cultural practices, along with statistical recommendations. Drawing on a rich body of social science work on gambling, we contribute to the HCI scholarship in rationality, justification, and postcolonial computing. Finally, we present such betting as an under-appreciated site for HCI that contradicts with the ideological hegemony of statistical rationality, and recommend a smooth integration of AI system with the “other” rationalities of the Global South.
Graph-based semi-supervised learning via improving the quality of the graph dynamically

Graph-based semi-supervised learning (GSSL) is an important paradigm among semi-supervised learning approaches and includes the two processes of graph construction and label inference. In most traditional GSSL methods, the two processes are completed independently. Once the graph is constructed, the result of label inference cannot be changed. Therefore, the quality of the graph directly determines the GSSL's performance. Most traditional graph construction methods make certain assumptions about the data distribution, resulting in the quality of the graph heavily depends on the correctness of these assumptions. Therefore, it is difficult to handle complex and various data distribution for traditional graph construction methods. To overcome such issues, this paper proposes a framework named Graph-based Semi-supervised Learning via Improving the Quality of the Graph Dynamically. In it, the graph construction based on the weighted fusion of multiple clustering results and the label inference are integrated into a unified framework to achieve their mutual guidance and dynamic improvement. Moreover, the proposed framework is a general framework, and most existing GSSL methods can be embedded into it so as to improve their performance. Finally, the working mechanism, the effectiveness in improving the performance of GSSL methods and the advantage compared with other GSSL methods based on dynamic graph construction methods of the proposal are verified through systematic experiments.
Privacy Enhancing Techniques in the Internet of Things Using Data Anonymisation

Abstract The Internet of Things (IoT) and Industrial 4.0 bring enormous potential benefits by enabling highly customised services and applications, which create huge volume and variety of data. However, preserving the privacy in IoT and Industrial 4.0 against re-identification attacks is very challenging. In this work, we considered three main data types generated in IoT: context data , continuous data , and media data . We first proposed a stream data anonymisation method based on k -anonymity for data collected by IoT devices; and then privacy enhancing techniques for both continuous data and media data were proposed for different IoT scenarios. The experiment results show that the proposed techniques can well preserve privacy without significantly affecting the utility of the data.
Knowledge Distillation Beyond Model Compression

Knowledge distillation (KD) is commonly deemed as an effective model compression technique in which a compact model (student) is trained under the supervision of a larger pretrained model or an ensemble of models (teacher). Various techniques have been proposed since the original formulation, which mimic different aspects of the teacher such as the representation space, decision boundary, or intra-data relationship. Some methods replace the one-way knowledge distillation from a static teacher with collaborative learning between a cohort of students. Despite the recent advances, a clear understanding of where knowledge resides in a deep neural network and an optimal method for capturing knowledge from teacher and transferring it to student remains an open question. In this study, we provide an extensive study on nine different KD methods which covers a broad spectrum of approaches to capture and transfer knowledge. We demonstrate the versatility of the KD framework on different datasets and network architectures under varying capacity gaps between the teacher and student. The study provides intuition for the effects of mimicking different aspects of the teacher and derives insights from the performance of the different distillation approaches to guide the design of more effective KD methods. Furthermore, our study shows the effectiveness of the KD framework in learning efficiently under varying severity levels of label noise and class imbalance, consistently providing generalization gains over standard training. We emphasize that the efficacy of KD goes much beyond a model compression technique and it should be considered as a general-purpose training paradigm which offers more robustness to common challenges in the real-world datasets compared to the standard training procedure.
Two-Stage Adaptive Object Scene Flow Using Hybrid CNN-CRF Model

Scene flow estimation based on stereo sequences is a comprehensive task relevant to disparity and optical flow. Some existing methods are time-consuming and often fail in the presence of reflective surfaces. In this paper, we propose a two-stage adaptive object scene flow estimation method using a hybrid CNN-CRF model (ACOSF), which benefits from high-quality features and the structured modelling capability. Meanwhile, in order to balance the computational efficiency and accuracy, we employ adaptive iteration for energy function optimization, which is flexible and efficient for various scenes. Besides, we utilize high-quality pixel selection to reduce the computation time with only a slight decrease in accuracy. Our method achieves competitive results with the state-of-the-art, which ranks second on the challenging KITTI 2015 scene flow benchmark.
ActID: An efficient framework for activity sensor based user identification

Identification is the core of any authentication protocol design as the purpose of the authentication is to verify the user’s identity. The efficient establishment and verification of identity remain a big challenge. Recently, biometrics-based identification algorithms gained popularity as a means of identifying individuals using their unique biological characteristics. In this paper, we propose a novel and efficient identification framework, ActID, which can identify a user based on his/her hand motion while walking. ActID not only selects a set of high-quality features based on Optimal Feature Evaluation and Selection and Correlation-based Feature Selection algorithms but also includes a novel sliding window based voting classifier. Therefore, it achieves several important design goals for gait authentication based on resource-constrained devices, including lightweight and real-time classification, high identification accuracy, a minimum number of sensors, and a minimum amount of data collected. Performance evaluation shows that ActID is cost-effective and easily deployable, satisfies real-time requirements, and achieves a high identification accuracy of 100%.
Exploratory Data Analysis of Human Activity Recognition Based on Smart Phone

In a smart urban environment, providing accurate information about human activities is an important task.It is a trend to implement human activity recognition (HAR) algorithms and applications on smart phones, including health monitoring, self-management system, health tracking and so on.However, human activity recognition(HAR) is very complex, and it is important to use the best technology and machine learning to understand human activities.One of the main problems of the existing HAR strategies is that the classification accuracy is relatively low, and in order to improve the accuracy, it needs high computational overhead.The purpose of this paper is to use an exploratory data analysis method to deal with HAR, after the implementation of different data mining techniques, the results of dimensionality reduction and visualization are obtained.The HAR method based on smart phone and EDA proposed in this paper is a high precision method.Compared with other classifiers, its accuracy is 96.56%.This article will discuss computational prediction activities and the computational limitations of using exploratory data to analyze (EDA) on 564 feature data frames.The experimental results show that HAR-based exploratory data analysis is a common sensory signal processing technology.The GridSearchCV and LinearSVC algorithm can provide accurate automatic human activity recognition (HAR), for elderly and disabled patients in need of continuous care, and it is a decision-making tool to support sports coaches to make plans.
What Makes a Well-Documented Notebook? A Case Study of Data Scientists’ Documentation Practices in Kaggle

Many data scientists use computational notebooks to test and present their work, as a notebook can weave code and documentation together (computational narrative), and support rapid iteration on code experiments. However, it is not easy to write good documentation in a data science notebook, partially because there is a lack of a corpus of well-documented notebooks as exemplars for data scientists to follow. To cope with this challenge, this work looks at Kaggle — a large online community for data scientists to host and participate in machine learning competitions — and considers highly-voted Kaggle notebooks as a proxy for well-documented notebooks. Through a qualitative analysis at both the notebook level and the markdown-cell level, we find these notebooks are indeed well documented in reference to previous literature. Our analysis also reveals nine categories of content that data scientists write in their documentation cells, and these documentation cells often interplay with different stages of the data science lifecycle. We conclude the paper with design implications and future research directions.
Mitigation of Cybersickness in Immersive 360°Videos

We investigate the mitigation of cybersickness (CS) in 360° videos, a phenomenon caused by the visually induced impression of ego-motion while being physically at rest. We evaluate the effectiveness of scene modulations to reduce motion in the peripheral visual field by deliberately blurring or opaque occluding eccentric view areas of up to ten degrees. Our results indicate that both methods effectively reduce CS in pre-recorded 360° video with the dynamic opaque occlusion method yielding best results.
Exploiting Operation Importance for Differentiable Neural Architecture Search

Recently, differentiable neural architecture search (NAS) methods have made significant progress in reducing the computational costs of NASs. Existing methods search for the best architecture by choosing candidate operations with higher architecture weights. However, architecture weights cannot accurately reflect the importance of each operation, that is, the operation with the highest weight might not be related to the best performance. To circumvent this deficiency, we propose a novel indicator that can fully represent the operation importance and, thus, serve as an effective metric to guide the model search. Based on this indicator, we further develop a NAS scheme for "exploiting operation importance for effective NAS" (EoiNAS). More precisely, we propose a high-order Markov chain-based strategy to slim the search space to further improve search efficiency and accuracy. To evaluate the effectiveness of the proposed EoiNAS, we applied our method to two tasks: image classification and semantic segmentation. Extensive experiments on both tasks provided strong evidence that our method is capable of discovering high-performance architectures while guaranteeing the requisite efficiency during searching.
Quantum Interfaces to the Nanoscale

Scalable quantum information systems would store, manipulate, and transmit quantum information locally and across a quantum network, but no single qubit technology is currently robust enough to perform all necessary tasks. Defect centers in solid-state materials have emerged as potential intermediaries between other physical manifestations of qubits, such as superconducting qubits and photonic qubits, to leverage their complementary advantages. It remains an open question, however, how to design and to control quantum interfaces to defect centers. Such interfaces would enable quantum information to be moved seamlessly between different physical systems. Understanding and constructing the required interfaces would, therefore, unlock the next big steps in quantum computing, sensing, and communications. In this Perspective, we highlight promising coupling mechanisms, including dipole-, phonon-, and magnon-mediated interactions, and discuss how contributions from nanotechnologists will be paramount in realizing quantum information processors in the near-term.
Streaming Simultaneous Speech Translation with Augmented Memory Transformer

Transformer-based models have achieved state-of-the-art performance on speech translation tasks. However, the model architecture is not efficient enough for streaming scenarios since self-attention is computed over an entire input sequence and the computational cost grows quadratically with the length of the input sequence. Nevertheless, most of the previous work on simultaneous speech translation, the task of generating translations from partial audio input, ignores the time spent in generating the translation when analyzing the latency. With this assumption, a system may have good latency quality trade-offs but be inapplicable in real-time scenarios. In this paper, we focus on the task of streaming simultaneous speech translation, where the systems are not only capable of translating with partial input but are also able to handle very long or continuous input. We propose an end-to-end transformer-based sequence-to-sequence model, equipped with an augmented memory transformer encoder, which has shown great success on the streaming automatic speech recognition task with hybrid or transducer-based models. We conduct an empirical evaluation of the proposed model on segment, context and memory sizes and we compare our approach to a transformer with a unidirectional mask. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Siamese networks for large-scale author identification

Authorship attribution is the process of identifying the author of a text. Approaches to tackling it have been conventionally divided into classification-based ones, which work well for small numbers of candidate authors, and similarity-based methods, which are applicable for larger numbers of authors or for authors beyond the training set; these existing similarity-based methods have only embodied static notions of similarity. Deep learning methods, which blur the boundaries between classification-based and similarity-based approaches, are promising in terms of ability to learn a notion of similarity, but have previously only been used in a conventional small-closed-class classification setup. Siamese networks have been used to develop learned notions of similarity in one-shot image tasks, and also for tasks of mostly semantic relatedness in NLP. We examine their application to the stylistic task of authorship attribution on datasets with large numbers of authors, looking at multiple energy functions and neural network architectures, and show that they can substantially outperform previous approaches.
Expressivity in Interaction: a Framework for Design

Expressivity is frequently recurring as a term in HCI, but it is often approached from different perspectives. Affective computing prompted research into emotional expressivity, and with technology becoming more ubiquitous and tangible, the opportunities for expressive behaviors towards systems as well as the expressivity of systems increases. By analyzing exemplar research-through-design cases and a literature survey on the use of expressivity in interaction, we discuss how different perspectives and concepts contribute to understand expressivity in interaction. We integrate these perspectives and make them operational for interaction design by creating a framework including design considerations such as freedom of interaction, action-perception loops, multimodality, subtlety, ambiguity, skill development and temporal form. The framework is a result of a mixed-method approach including a review of existing definitions and scholarly artefacts, and a systematic literature review to identify design cases including an analysis of these design cases. We finally illustrate how the framework has been used to inform the design of a shape-changing soft-robotic interface. As a result, we contribute an integrated framework on how to design for expressivity in interaction.
Dual-copying mechanism and dynamic emotion dictionary for generating emotional responses

Generating emotional responses plays an important role in human–computer conversational system. Adopting emotional information to the generation process improves user’s satisfaction and makes the generated responses more human-like. Furthermore using fixed and unrelated emotion dictionary limits the overall performance of recent models. In order to generate and utilize the emotional words related to our datasets and sensitive to semantic importance, we propose a dynamic emotion dictionary module to generate a proper emotional expression. Considering the response sometimes focuses on the certain words of the input text, we also introduce dual-copying mechanism for taking advantages of not only emotion dictionary but also words appearing in the input text. Experimental results demonstrate that our model outperforms strong alternatives in several metrics, e.g. BLEU, embedding similarity, Rouge-L and the quality of human evaluation.