Reviewerly/exHarmony huggingface dataset:
Deep Learning on Mobile Devices Through Neural Processing Units and Edge Computing

Deep Neural Network (DNN) is becoming adopted for video analytics on mobile devices. To reduce the delay of running DNNs, many mobile devices are equipped with Neural Processing Units (NPU). However, due to the resource limitations of NPU, these DNNs have to be compressed to increase the processing speed at the cost of accuracy. To address the low accuracy problem, we propose a Confidence Based Offloading (CBO) framework for deep learning video analytics. The major challenge is to determine when to return the NPU classification result based on the confidence level of running the DNN, and when to offload the video frames to the server for further processing to increase the accuracy. We first identify the problem of using existing confidence scores to make offloading decisions, and propose confidence score calibration techniques to improve the performance. Then, we formulate the CBO problem where the goal is to maximize accuracy under some time constraint, and propose an adaptive solution that determines which frames to offload at what resolution based on the confidence score and the network condition. Through real implementations and extensive evaluations, we demonstrate that the proposed solution can significantly outperform other approaches.
Disentangled Graph Neural Networks for Session-Based Recommendation

Session-based recommendation (SBR) has drawn increasingly research attention in recent years, due to its great practical value by only exploiting the limited user behavior history in the current session. The key of SBR is to accurately infer the anonymous user purpose in a session which is typically represented as session embedding, and then match it with the item embeddings for the next item prediction. Existing methods typically learn the session embedding at the item level, namely, aggregating the embeddings of items with or without assigned attention weights to items. However, they ignore the fact that a user's intent on adopting an item is driven by certain factors of the item (e.g., the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">leading actors</i> of an movie). In other words, they have not explored finer-granularity interests of users at the factor level to generate the session embedding, leading to sub-optimal performance. To address the problem, we propose a novel method called Disentangled Graph Neural Network (Disen-GNN) to capture the session purpose with the consideration of factor-level attention on each item. Specifically, we first employ the disentangled learning technique to cast item embeddings into the embeddings of multiple factors, and then use the gated graph neural network (GGNN) to learn the embedding factor-wisely based on the item adjacent similarity matrix computed for each factor. Moreover, the distance correlation is adopted to enhance the independence between each pair of factors. After representing each item with independent factors, an attention mechanism is designed to learn user intent to different factors of each item in the session. The session embedding is then generated by aggregating the item embeddings with attention weights of each item's factors. To this end, our model takes user intents at the factor level into account to infer the user purpose in a session. Extensive experiments on three benchmark datasets demonstrate the superiority of our method over existing methods.
A Modular U-Net for Automated Segmentation of X-Ray Tomography Images in Composite Materials

X-Ray Computed Tomography (XCT) techniques have evolved to a point that high-resolution data can be acquired so fast that classic segmentation methods are prohibitively cumbersome, demanding automated data pipelines capable of dealing with non-trivial 3D images. Meanwhile, deep learning has demonstrated success in many image processing tasks, including materials science applications, showing a promising alternative for a human-free segmentation pipeline. However, the rapidly increasing number of available architectures can be a serious drag to the wide adoption of this type of models by the end user. In this paper a modular interpretation of U-Net (Modular U-Net) is proposed with a parametrized architecture that can be easily tuned to optimize it. As an example, the model is trained to segment 3D tomography images of a three-phased glass fiber-reinforced Polyamide 66. We compare 2D and 3D versions of our model, finding that the former is slightly better than the latter. We observe that human-comparable results can be achievied even with only 13 annotated slices and using a shallow U-Net yields better results than a deeper one. As a consequence, neural networks show indeed a promising venue to automate XCT data processing pipelines needing no human, adhoc intervention.
Improved vehicle detection systems with double-layer LSTM modules

Abstract The vision-based smart driving technologies for road safety are the popular research topics in computer vision. The precise moving object detection with continuously tracking capability is one of the most important vision-based technologies nowadays. In this paper, we propose an improved object detection system, which combines a typical object detector and long short-term memory (LSTM) modules, to further improve the detection performance for smart driving. First, starting from a selected object detector, we combine all vehicle classes and bypassing low-level features to improve its detection performance. After the spatial association of the detected objects, the outputs of the improved object detector are then fed into the proposed double-layer LSTM (dLSTM) modules to successfully improve the detection performance of the vehicles in various conditions, including the newly-appeared, the detected and the gradually-disappearing vehicles. With stage-by-stage evaluations, the experimental results show that the proposed vehicle detection system with dLSTM modules can precisely detect the vehicles without increasing computations.
Colon Cancer Classification of Histopathological Images Using Data Augmentation

Classification of colon cancer has great importance in medical diagnosis. An accurate and real-time investigation provides medical experts to review typical treatment timely. Histopathological inspection is a commonly used method to diagnose colon cancer. Presently, diagnosis methods depend on self-made features which take a long inspection period and require an expert medical professional. This paper proposes four convolutional neural network (CNN), namely, baseline CNN, two-block CNN, three-block CNN, and three-block CNN with data augmentation respectively, to classify colon tissue histopatho-logical images. The purpose of data augmentation is to check the efficacy of the proposed network compared to other existing methods. The histopathological images are given as input to four CNN. Three-block CNN with data augmentation achieved an accuracy of 99.40% shows that the proposed approach outper-forms other existing methods. The performance of the proposed network leads to an approach for precise cancer diagnosis.
Medical Code Prediction from Discharge Summary: Document to Sequence BERT using Sequence Attention

Clinical notes are unstructured text generated by clinicians during patient encounters. Clinical notes are usually accompanied by a set of metadata codes from the international classification of diseases (ICD). ICD code is an important code used in various operations, including insurance, reimbursement, medical diagnosis, etc. Therefore, it is important to classify ICD codes quickly and accurately. However, annotating these codes is costly and time-consuming. So we propose a model based on bidirectional encoder representations from transformers (BERT) using the sequence attention method for automatic ICD code assignment. We evaluate our approach on the medical information mart for intensive care III (MIMIC-III) benchmark dataset. Our model achieved performance of macro-averaged F1: 0.62898 and micro-averaged F1: 0.68555 and is performing better than a performance of the state-of-the-art model using the MIMIC-III dataset. The contribution of this study proposes a method of using BERT that can be applied to documents and a sequence attention method that can capture important sequence information appearing in documents.
TriboOnto: A Strategic Domain Ontology Model for Conceptualization of Tribology as a Principal Domain

For many years, scholars have been interested in representation of knowledge and question creation. Ontologies have become increasingly important as the requirement for data to be described in a way that is understandable by people and computers develops. Tribology is an area where there are presently no ontologies, hence it was decided to construct one. Significant information and semantic linkages have been identified after a thorough examination of the topic. The ontology was visualised in Protégé then in OWL format. The suggested ontology was found to have an effective reuse ratio of 0.944.
Memory-Oriented Unpaired Learning for Single Remote Sensing Image Dehazing

Remote sensing image dehazing (RSID) is an extremely challenging problem due to the irregular and nonuniform distribution of haze. The existing RSID methods achieve excellent performance using deep learning; however, relying on paired synthetic data is limited to their generality in various haze distribution. In this letter, we present a memory-oriented generative adversarial network (MO-GAN), which tries to capture the desired hazy features in an unpaired learning manner toward single RSID. For better extracting the haze-relevant features, a novel multistage attentive-recurrent memory module is designed to guide an autoencoder neural network, which can record the various appearances of haze distribution at different stages. To well differentiate fake images from real ones, a dual region discriminator is constructed to handle spatially varying haze densities in global and local regions. Extensive experiments demonstrate that our designed MO-GAN outperforms the recent comparing approaches on the various frequently used datasets, especially in real world nonuniform haze conditions. The source code is released in <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/cxtalk/MO-GAN</uri> .
Pervasive Augmented Reality—Technology and Ethics

In the foreseeable future, mobile and wearable computing technology with an augmented reality (AR) interface can provide an omnipresent, environmentally adaptive, and everyday reality augmentation. This new pervasive AR technology will lead to a continuous moderation of experienced reality with the potential to support better and faster decision-making, the exploration of new information, and novel ways of communication, interaction, and collaboration. However, pervasive AR technology will also have undesired consequences, e.g., in the areas of privacy, commercial exploitation, distractions, digital inequality, and our perception of what is true and real. Little is known about how severe these effects will be when AR has become pervasive and how they can be prevented or mitigated. We draw on current developments in research and the market, sketch a near-time future of pervasive AR technology, identify ethical considerations, and discuss the development of pervasive AR systems.
Bayesian Quantum Neural Networks

The astounding acceleration in Artificial Intelligence and Quantum Computing advances naturally gives rise to a line of research, which unrolls the potential advantages of quantum computing on classical Machine Learning tasks, known as Quantum Machine Learning or Quantum Machine Intelligence. The typical objectives are either (1) exploring the potential quantum advantages on classical learning tasks or (2) levering well-established classical ML algorithms to tackle quantum-related problems on Noisy Intermediate-Scale Quantum (NISQ) devices. Along the second research direction, we study Quantum Neural Networks (QNNs) to accomplish the purpose of Bayesian learning. By observing a wide range of studies on QNNs, in which the sole training method is based on frequentist training, we find that Bayesian learning benefits QNNs from two aspects. First, Bayesian-trained models enjoy a high level of generalization due to the prior and posterior distribution usage compared to frequentist training, which will be justified by this paper’s theoretical study of model capacity. Second, Bayesian Inference offers epistemic uncertainty estimation, which merits the decision-making process. It is worth mentioning that frequentist-trained QNNs generally lack this desirable property. Under the Bayesian training procedure, our derived models can be considered a new class of QNNs (called BayesianQNNs) which possesses both desirable properties of Bayesian Inference while maintaining comparable predictive performance as frequentist counterparts. The proposed Bayesian Quantum Neural Networks is justified by empirical evidence from numerical experiments.
TSGB: Target-Selective Gradient Backprop for Probing CNN Visual Saliency

The explanation for deep neural networks has drawn extensive attention in the deep learning community over the past few years. In this work, we study the visual saliency, a.k.a. visual explanation, to interpret convolutional neural networks. Compared to iteration based saliency methods, single backward pass based saliency methods benefit from faster speed, and they are widely used in downstream visual tasks. Thus, we focus on single backward pass based methods. However, existing methods in this category struggle to successfully produce fine-grained saliency maps concentrating on specific target classes. That said, producing faithful saliency maps satisfying both target-selectiveness and fine-grainedness using a single backward pass is a challenging problem in the field. To mitigate this problem, we revisit the gradient flow inside the network, and find that the entangled semantics and original weights may disturb the propagation of target-relevant saliency. Inspired by those observations, we propose a novel visual saliency method, termed Target-Selective Gradient Backprop (TSGB), which leverages rectification operations to effectively emphasize target classes and further efficiently propagate the saliency to the image space, thereby generating target-selective and fine-grained saliency maps. The proposed TSGB consists of two components, namely, TSGB-Conv and TSGB-FC, which rectify the gradients for convolutional layers and fully-connected layers, respectively. Extensive qualitative and quantitative experiments on the ImageNet and Pascal VOC datasets show that the proposed method achieves more accurate and reliable results than the other competitive methods. Code is available at https://github.com/123fxdx/CNNvisualizationTSGB.
<i>We’re Not Gonna Break It!</i> Consistency-Preserving Operators for Efficient Product Line Configuration

When configuring a software product line, finding a good trade-off between multiple orthogonal quality concerns is a challenging multi-objective optimisation problem. State-of-the-art solutions based on search-based techniques create invalid configurations in intermediate steps, requiring additional repair actions that reduce the efficiency of the search. In this work, we introduce <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">consistency-preserving configuration operators</i> (CPCOs)—genetic operators that maintain valid configurations throughout the entire search. CPCOs bundle coherent sets of changes: the activation or deactivation of a particular feature together with other (de)activations that are needed to preserve validity. In our evaluation, our instantiation of the IBEA algorithm with CPCOs outperforms two state-of-the-art tools for optimal product line configuration in terms of both speed and solution quality. The improvements are especially pronounced in large product lines with thousands of features.
Cryptocurrency exchanges: Predicting which markets will remain active

Abstract About 99% of cryptocurrency trades occur on organized exchanges with many investors subsequently keeping their digital assets in accounts with cryptocurrency markets. This generates exposure to the risk of exchange closures. We construct a database containing eight key characteristics on 238 cryptocurrency exchanges and employ machine learning techniques to predict whether a cryptocurrency market will remain active or whether it will go out of business. Both in‐sample and out‐of‐sample measures of forecasting performance are computed and ranked for four popular machine learning algorithms. Although all four models produce satisfactory classification accuracy, our best model is a random forest classifier. It reaches accuracy of 90.4% on training data and 86.1% on a test dataset. From the list of predictors, we find that exchange lifetime, transacted volume, and cyber‐security measures such as security audit, cold storage, and bug bounty programs rank high in terms of feature importance across multiple algorithms. On the other hand, whether an exchange has previously experienced a security breach does not rank highly according to its contribution to classification accuracy.
A Phase-Type Expansion Approach for the Performability of Composite Web Services

The purpose of web service composition is to satisfy complex requirements by constructing a composite service through loosely coupling web services over the Internet. Services available for composition may experience changes in performance and reliability at any time. Therefore, it is desirable to estimate performance and reliability of the composite service based on the data of atomic services such that it is checked up before service deployment whether the service-level agreement is satisfied. In this article, a phase-type expansion approach to establish an expanded homogeneous continuous time Markov chain of composite web services is proposed. By explicitly including failure states and restart states into the model, both performance and reliability can be computed for the composite service via phase-type fitting method based on the observations on execution times of atomic services. Experimental results based on real-world web services are provided to demonstrate the efficacy of the proposed approach.
Meta-path Enhanced Lightweight Graph Neural Network for Social Recommendation

Social information is widely used in recommender systems to alleviate data sparsity. Since users play a central role in both user-user social graphs and user-item interaction graphs, many previous social recommender systems model the information diffusion process in both graphs to obtain high-order information. We argue that this approach does not explicitly encode high-order connectivity, resulting in potential collaborative signals between user and item not being captured. Moreover, direct modeling of explicit interactions may introduce noises into the model and we expect users to pay more attention to reliable links. In this work, we propose a new recommendation framework named Meta-path Enhanced Lightweight Graph Neural Network (ME-LGNN), which fuses social graphs and interaction graphs into a unified heterogeneous graph to encode high-order collaborative signals explicitly. We consider using a lightweight GCN to model collaborative signals. To enable users to capture reliable information more efficiently, we design meta-paths to further enhance the embedding learning by calculating meta-path dependency probabilities. Empirically, we conduct extensive experiments on three public datasets to demonstrate the effectiveness of our model.
Children’s Participation in the Design of Smart Solutions: A Literature Review

Smart solutions are widespread and diversified. Smart cities and smart objects are example of smart solutions. Their design usually follows certain patterns so that they can detect events and react accordingly. As future citizens, children are expected to interact with them in their daily lives. It is thus crucial to provide children with the tools for understanding, creating, and possibly programming them—in short, designing them. This paper presents a literature review of workshops involving children in designing smart solutions. The review coded a total of 25 papers that met the inclusion criteria. The discussion draws a research agenda using the workshops organized by the authors, the Roobopoli workshops for smart cities and the SNaP workshops for smart objects, as reference. Whereas smart cities and smart objects are usually addressed separately, this paper binds and compares them in order to investigate what, in different settings, enables children to be part of the design of smart solutions.
Thinking with care in human–computer interaction

In this article, human–computer interaction (HCI) is explored as a design-oriented practice nurturing the becoming of what is not-yet in future-oriented and speculative manners. Such approaches have evolved over time and now the field seems ready to take leaps targeting social and culturally infused contexts, such as those suggested by critical design, design things, adversarial design, making futures, pluriversal design and critical fabulations. It is in this respect that feminist theories, methods and imaginaries are rendered important. Feminist theory is in this article considered an important companion and part of the practical tool-kit necessary for generative, speculative and ethical approaches within the field of HCI. How to think with care is explored as a meta-design strategy directed and informed by feminist onto-epistemologies – a strategy intended to ‘seed’ speculative and social justice-oriented design endeavours through generative figurations and critical dilemmas to foster abilities and sensibilities for dealing with difference differently. What is advanced is the need for meta-design space in HCI, in this article referred to as a contact zone, a feminist figuration with the intention to open up for design explorations with ethical imperatives. Four other interrelated feminist figurations are also loosely explored in order to frame how thinking with care in HCI could be advanced further, i.e. diffractive thinking, intra-activism, becoming-with and response-ability. By considering serious feminist accounts of situated knowledges and touching visions, it is argued that feminist thinking is well on its way to offering real alternatives of great importance for HCI.
Design and Implementation of 2D Convolution on x86/x64 Processors

In this paper, a new method for accelerating the 2D direct Convolution operation on x86/x64 processors is presented. It includes efficient vectorization by using SIMD intrinsics, bit-twiddling optimizations, the optimization of the division operation, multi-threading using OpenMP, register blocking and the shortest possible bit-width value of the intermediate results. The proposed method, which is provided as open-source, is general and can be applied to other processor families too, e.g., Arm. The proposed method has been evaluated on two different multi-core Intel CPUs, by using twenty different image sizes, 8-bit integer computations and the most commonly used kernel sizes (3x3, 5x5, 7x7, 9x9). It achieves from <inline-formula><tex-math notation="LaTeX">$2.8\times$</tex-math></inline-formula> to <inline-formula><tex-math notation="LaTeX">$40\times$</tex-math></inline-formula> speedup over the Intel IPP library (OpenCV GaussianBlur and Filter2D routines), from <inline-formula><tex-math notation="LaTeX">$105 \times$</tex-math></inline-formula> to <inline-formula><tex-math notation="LaTeX">$400 \times$</tex-math></inline-formula> speedup over the gemm-based convolution method (by using Intel MKL int8 matrix multiplication routine), and from <inline-formula><tex-math notation="LaTeX">$8.5\times$</tex-math></inline-formula> to <inline-formula><tex-math notation="LaTeX">$618\times$</tex-math></inline-formula> speedup over the vslsConvExec Intel MKL direct convolution routine. The proposed method is superior as it achieves far fewer arithmetical and load/store instructions.