Reviewerly/exHarmony huggingface dataset:
A Bias-Reducing Loss Function for CT Image Denoising

There is growing interest in the use of deep neural network (DNN) based image denoising to reduce patient’s X-ray dosage in medical computed tomography (CT). An effective denoiser must remove noise while maintaining the texture and detail. Commonly used mean squared error (MSE) loss functions in the DNN training weight errors due to bias and variance equally. However, the error due to bias is often more egregious since it results in loss of image texture and detail. In this paper, we present a novel approach to designing a loss function that penalizes variance and bias differently. Our proposed bias-reducing loss function allows us to train a DNN denoiser so that the amount of texture and detail retained can be controlled through a user adjustable parameter. Our experiments verify that the proposed loss function enhances the texture and detail in denoised images with only a slight increase in the MSE.
Blockchain-based Secure Platform for Coalition Loyalty Program Management

In this paper, we propose a novel blockchain-based platform for the coalition loyalty program management. The platform allows the customers to freely exchange loyalty points from different existing blockchain-based loyalty programs by utilizing the sidechain technology. Moreover, by adopting the Proof-of-Stake consensus mechanism, we can further increase customer engagement by allowing the customers to participate in the consensus process to earn additional tokens. However, this might lead to situations where the customers centralize all tokens to a single chain/loyalty program if the chain offers more rewards for consensus participation. Through security and performance analyses, we show that such centralization of stakes poses a threat to the security and performance of the platform. Therefore, we develop a non-cooperative game model to analyze the rational behavior of the users. We reveal that the consensus participation rewards govern the user behavior and the decentralization of the system. Numerical experiments confirm our analytical results and show that the ratios between the consensus rewards have a significant impact on the system's security and performance.
Crank: An Open-Source Software for Nonparallel Voice Conversion Based on Vector-Quantized Variational Autoencoder

In this paper, we present an open-source software for developing a nonparallel voice conversion (VC) system named crank. Although we have released an open-source VC software based on the Gaussian mixture model named sprocket in the last VC Challenge, it is not straightforward to apply any speech corpus because it is necessary to prepare parallel utterances of source and target speakers to model a statistical conversion function. To address this issue, in this study, we developed a new open-source VC software that enables users to model the conversion function by using only a nonparallel speech corpus. For implementing the VC software, we used a vector-quantized variational autoencoder (VQVAE). To rapidly examine the effectiveness of recent technologies developed in this research field, crank also supports several representative works for autoencoder-based VC methods such as the use of hierarchical architectures, cyclic architectures, generative adversarial networks, speaker adversarial training, and neural vocoders. Moreover, it is possible to automatically estimate objective measures such as mel-cepstrum distortion and pseudo mean opinion score based on MOSNet. In this paper, we describe representative functions developed in crank and make brief comparisons by objective evaluations.
What’s in a name? – gender classification of names with character based machine learning models

Gender information is no longer a mandatory input when registering for an account at many leading Internet companies. However, prediction of demographic information such as gender and age remains an important task, especially in intervention of unintentional gender/age bias in recommender systems. Therefore it is necessary to infer the gender of those users who did not to provide this information during registration. We consider the problem of predicting the gender of registered users based on their declared name. By analyzing the first names of 100M+ users, we found that genders can be very effectively classified using the composition of the name strings. We propose a number of character based machine learning models, and demonstrate that our models are able to infer the gender of users with much higher accuracy than baseline models. Moreover, we show that using the last names in addition to the first names improves classification performance further.
Improved the detection and classification of breast cancer using hyper parameter tuning

The early diagnosis of any disease can be curable with a small amount of human effort. Most people will not be able to detect their illness before it becomes chronic. Breast cancer has been one of the diseases that can be treated until it spreads to all parts of the body when the disease is diagnosed at an earlier stage.Breast cancer is causing an increase in death rates all over the world. The medical practitioner can diagnose diseases incorrectly due to misinterpretation. In both developed and emerging countries, breast cancer is one of the leading causes of death for women. Breast cancer can be detected and classified early on in its progression, allowing patients to get adequate treatment. For detecting and classifying breast cancer in breast cytology videos, deep learning frameworks are suggested. The suggested architecture uses the Convolutional layer and the pooling layer to remove features. The data would be classified using the dense layer.The Convolutional Neural Network and DenseNet are used to extract image features that are then fed into a fully connected layer for the classification of malignant and benign cellsFor image classification of breast cancer, the models are trained, validated, and tested.The distinction is based on CNN and DenseNet's different batch sizes and learning rates. To see how well the proposed CNN and DenseNet programs work, they are compared. In terms of accuracy, the suggested system will outperform CNN and DenseNet in the diagnosis and classification of breast tumors from histological photographs. Hyper parameter tuning is used to measure the efficiency of CNN and DenseNet.
Attention-Oriented Action Recognition for Real- Time Human-Robot Interaction

Despite the notable progress made in action recognition tasks, not much work has been done in action recognition specifically for human-robot interaction. In this paper, we deeply explore the characteristics of the action recognition task in interaction scenes and propose an attention-oriented multi-level network framework to meet the need for real-time interaction. Specifically, a Pre-Attention network is employed to roughly focus on the interactor in the scene at low resolution firstly and then perform fine-grained pose estimation at high resolution. The other compact CNN receives the extracted skeleton sequence as input for action recognition, utilizing attention-like mechanisms to capture local spatial-temporal patterns and global semantic information effectively. To evaluate our approach, we construct a new action dataset specially for the recognition task in interaction scenes. Experimental results on our dataset and high efficiency (112 fps at 640 × 480 RGBD) on the mobile computing platform (Nvidia Jetson AGX Xavier) demonstrate excellent applicability of our method on action recognition in real-time human-robot interaction.
Defences Against web Application Attacks and Detecting Phishing Links Using Machine Learning

In recent years web applications that are hacked every day estimated to be 30 000, and in most cases, web developers or website owners do not even have enough knowledge about what is happening on their sites. Web hackers can use many attacks to gain entry or compromise legitimate web applications, they can also deceive people by using phishing sites to collect their sensitive and private information. In response to this, the need is raised to take proper measures to understand the risks and be aware of the vulnerabilities that may affect the website and hence the normal business flow. In the scope of this study, mitigations against the most common web application attacks are set, and the web administrator is provided with ways to detect phishing links which is a social engineering attack, the study also demonstrates the generation of web application logs that simplifies the process of analyzing the actions of abnormal users to show when behavior is out of bounds, out of scope, or against the rules. The methods of mitigation are accomplished by secure coding techniques and the methods for phishing link detection are performed by various machine learning algorithms and deep learning techniques. The developed application has been tested and evaluated against various attack scenarios, the outcomes obtained from the test process showed that the website had successfully mitigated these dangerous web application attacks, and for the detection of phishing links part, a comparison is made between different algorithms to find the best one, and the outcome of the best model gave 98% accuracy.
Similarity Judgment Within and Across Categories: A Comprehensive Model Comparison

Abstract Similarity is one of the most important relations humans perceive, arguably subserving category learning and categorization, generalization and discrimination, judgment and decision making, and other cognitive functions. Researchers have proposed a wide range of representations and metrics that could be at play in similarity judgment, yet have not comprehensively compared the power of these representations and metrics for predicting similarity within and across different semantic categories. We performed such a comparison by pairing nine prominent vector semantic representations with seven established similarity metrics that could operate on these representations, as well as supervised methods for dimensional weighting in the similarity function. This approach yields a factorial model structure with 126 distinct representation‐metric pairs, which we tested on a novel dataset of similarity judgments between pairs of cohyponymic words in eight categories. We found that cosine similarity and Pearson correlation were the overall best performing unweighted similarity functions, and that word vectors derived from free association norms often outperformed word vectors derived from text (including those specialized for similarity). Importantly, models that used human similarity judgments to learn category‐specific weights on dimensions yielded substantially better predictions than all unweighted approaches across all types of similarity functions and representations, although dimension weights did not generalize well across semantic categories, suggesting strong category context effects in similarity judgment. We discuss implications of these results for cognitive modeling and natural language processing, as well as for theories of the representations and metrics involved in similarity.
Exploring the Design Space of Immersive Social Fitness Games: The ImSoFit Games Model

The design space of social exergames remains narrow despite the many benefits of playing and exercising together. Towards opening this design space, we followed a Research through Design (RtD) approach focused on exergames that can be fun and immersive social training experiences. Through embodied sketching activities with designers and 10 pairs of players, we explored future games for the ExerCube, an immersive exergame platform. Our work contributes with forms of intermediate-level knowledge: a design space model (the Immersive Social Fitness—ImSoFit—Games model); and a novel design vocabulary including new bodily orientations in co-located physical interaction. We illustrate their use and value scrutinizing three of our games and applying three analytical lenses to 1) understand how design choices impact how players move together; 2) evaluate design expectations and analyze players' behavior in relation to design choices; and 3) potentially extend the design space of immersive co-located social fitness games.
Learning a Tree of Neural Nets

Much of the success of deep learning is due to choosing good neural net architectures and being able to train them effectively. A type of architecture that has been long sought is one that combines decision trees and neural nets. This is straightforward if the tree makes soft decisions (i.e., an input instance follows all paths in the tree with different probabilities), because the model is differentiable. However, the optimization is much harder if the tree makes hard decisions, but this produces an architecture that is much faster at inference, since an instance follows a single path in the tree. We show that it is possible to train such architectures, with guaranteed monotonic decrease of the loss, and demonstrate it by learning trees with linear decision nodes and deep nets at the leaves. The resulting architecture improves state-of-the-art deep nets, by achieving comparable or lower classification error but with fewer parameters and faster inference time. In particular, we show that, rather than improving a ResNet by making it deeper, it is better to construct a tree of small ResNets. The resulting tree-net hybrid is also more interpretable.
A Model-Based Chatbot Generation Approach to Converse with Open Data Sources

The Open Data movement promotes the free distribution of data. More and more companies and governmental organizations are making their data available online following the Open Data philosophy, resulting in a growing market of technologies and services to help publish and consume data. One of the emergent ways to publish such data is via Web APIs, which offer a powerful means to reuse this data and integrate it with other services. Socrata, CKAN or OData are examples of popular specifications for publishing data via Web APIs. Nevertheless, querying and integrating these Web APIs is time-consuming and requires technical skills that limit the benefits of Open Data movement for the regular citizen. In other contexts, chatbot applications are being increasingly adopted as a direct communication channel between companies and end-users. We believe the same could be true for Open Data as a way to bridge the gap between citizens and Open Data sources. This paper describes an approach to automatically derive full-fledged chatbots from API-based Open Data sources. Our process relies on a model-based intermediate representation (via UML class diagrams and profiles) to facilitate the customization of the chatbot to be generated.
Word-level human interpretable scoring mechanism for novel text detection using Tsetlin Machines

Abstract Recent research in novelty detection focuses mainly on document-level classification, employing deep neural networks (DNN). However, the black-box nature of DNNs makes it difficult to extract an exact explanation of why a document is considered novel. In addition, dealing with novelty at the word level is crucial to provide a more fine-grained analysis than what is available at the document level. In this work, we propose a Tsetlin Machine (TM)-based architecture for scoring individual words according to their contribution to novelty. Our approach encodes a description of the novel documents using the linguistic patterns captured by TM clauses. We then adapt this description to measure how much a word contributes to making documents novel. Our experimental results demonstrate how our approach breaks down novelty into interpretable phrases, successfully measuring novelty.
Towards Accountability: Providing Intelligible Explanations in Autonomous Driving

The safe deployment of autonomous vehicles (AV s) in real world scenarios requires that AV s are accountable. One way of ensuring accountability is through the provision of explanations for what the vehicles have ‘seen’, done and might do in a given scenario. Intelligible explanations can help developers and regulators to assess AV s' behaviour, and in turn, uphold accountability. In this paper, we propose an interpretable (tree-based) and user-centric approach for explaining autonomous driving behaviours. In a user study, we examined different explanation types instigated by investigatory queries. We conducted an experiment to identify scenarios that require explanations and the corresponding appropriate explanation types for such scenarios. Our findings show that an explanation type matters mostly in emergency and collision driving conditions. Also, providing intelligible explanations (especially contrastive types) with causal attributions can improve accountability in autonomous driving. The proposed interpretable approach can help realise such intelligible explanations with causal attributions.
Academic Naming: Changing Patterns of Noun Use in Research Writing

In this paper we explore the ways academics name processes as things and how these practices have changed over the past fifty years. Focusing on nominalization, noun-noun sequences, and acronyms, we document an increase in these features across a corpus of 2.2 million words within a consistent set of journals from four disciplines. Our results show that nominalizations and acronyms have increased in all four fields, particularly in applied linguistics and sociology, and that while noun-noun sequences have fallen in electrical engineering, they have risen in the other disciplines, especially sociology. We also suggest that noun-noun phrases have increasingly come to name methodological approaches, rather than concepts or objects, and we seek to account for these changes. We observe that these increases in naming are related to the need for succinctness in modern research writing and the advantages of endowing named objects with a real existence which can then be credited with explanatory authority. We question, however, the appropriacy of these practices for interpretation in the social sciences.
Excluded volume and its relation to the theory of fiber-fiber contacts

Contemporary understanding of fiber-fiber contacts is vital in diversifying the applications of fiber networks. Various properties of fiber networks rely on the architecture, which essentially requires encapsulating realistic features arising from excluded volume effects. Herein, an architectural-based interfiber spacing model of ‘generalized’ fiber networks has been proposed by combining the geometrical probability, stochastic, and excluded volume approaches. The interfiber spacing model features the classical work of excluded volume approach of Onsager, superimposed on Komori and Makishima's theory of fiber-fiber contacts. The significance of the interfiber spacing model relies on its ability to systematically demystify the anomalies in Onsager's work for random networks composed of exceedingly low-aspect-ratio fibers. Nevertheless, a good agreement between the theory and experiments justified that Onsager's excluded volume approach is sufficiently robust to predict the interfiber spacing of moderate to high-aspect-ratio fiber networks.
Cost-and-Delay Aware Dynamic Resource Allocation in Federated Vehicular Clouds

Modern vehicular applications are resource-intensive and have different Quality of Service (QoS) requirements. In this direction, Vehicular Cloud Computing (VCC) has emerged as a new paradigm to host such applications in the cloud. In this paper, we extend the VCC paradigm to include federation, wherein the data centers from multiple cloud providers are pooled in to support applications that can not be supported in a single provider. We address the problem of dynamic resource allocation in a federated vehicular cloud. We propose a dynamic resource pricing model for sharing resources between the cloud providers, based on which we formulate resource allocation as an optimization problem. The objective is to minimize the cost of the vehicular service provider while meeting the delay bounds of different vehicular applications, for which a cost and delay-aware, polynomial-time algorithm is proposed. We evaluate the proposed algorithm under different scenarios to show that exploiting federation brings great benefits for the service providers and end-users alike. The proposed algorithm is seen to meet the delay requirements with a significant cost reduction to the provider up to 50% compared to baseline methods. We also show that the proposed algorithm can serve more than 90% of requests with lower cost and fewer VM migrations. It reduces the number of VM migrations by 72%.
Double-DCCCAE: Estimation of Body Gestures From Speech Waveform

This paper presents an approach for body-motion estimation from audio-speech waveform, where context information in both input and output streams is taken in to account without using recurrent models. Previous works commonly use multiple frames of input to estimate one frame of motion data, where the temporal information of the generated motion is little considered. To resolve the problems, we extend our previous work and propose a system, double deep canonical-correlation-constrained autoencoder (D-DCCCAE), which encodes each of speech and motion segments into fixed-length embedded features that are well correlated with the segments of the other modality. The learnt motion embedded feature is estimated from the learnt speech-embedded feature through a simple neural network and further decoded back to the sequential motion. The proposed pair of embedded features showed higher correlation than spectral features with motion data, and our model was more preferred than the baseline model (BA) in terms of human-likeness and comparable in terms of similar appropriateness.
Brightening the Optical Flow through Posit Arithmetic

As new technologies are invented, their commercial viability needs to be carefully examined along with their technical merits and demerits. The posit <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TM</sup> data format, proposed as a drop-in replacement for IEEE 754 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TM</sup> float format, is one such invention that requires extensive theoretical and experimental study to identify products that can benefit from the advantages of posits for specific market segments. In this paper, we present an extensive empirical study of posit-based arithmetic vis-à-vis IEEE 754 compliant arithmetic for the optical flow estimation method called Lucas-Kanade (LuKa). First, we use SoftPosit and SoftFloat format emulators to perform an empirical error analysis of the LuKa method. Our study shows that the average error in LuKa with SoftPosit is an order of magnitude lower than LuKa with SoftFloat. We then present the integration of the hardware implementation of a posit adder and multiplier in a RISC-V open-source platform. We make several recommendations, along with the analysis of LuKa in the RISC-V context, for future generation platforms incorporating posit arithmetic units.