Reviewerly/exHarmony huggingface dataset:
A Hybrid CNN-Based Review Helpfulness Filtering Model for Improving E-Commerce Recommendation Service

As the e-commerce market grows worldwide, personalized recommendation services have become essential to users’ personalized items or services. They can decrease the cost of user information exploration and have a positive impact on corporate sales growth. Recently, many studies have been actively conducted using reviews written by users to address traditional recommender system research problems. However, reviews can include content that is not conducive to purchasing decisions, such as advertising, false reviews, or fake reviews. Using such reviews to provide recommendation services can lower the recommendation performance as well as a trust in the company. This study proposes a novel review of the helpfulness-based recommendation methodology (RHRM) framework to support users’ purchasing decisions in personalized recommendation services. The core of our framework is a review semantics extractor and a user/item recommendation generator. The review semantics extractor learns reviews representations in a convolutional neural network and bidirectional long short-term memory hybrid neural network for review helpfulness classification. The user/item recommendation generator models the user’s preference on items based on their past interactions. Here, past interactions indicate only records in which the user-written reviews of items are helpful. Since many reviews do not have helpfulness scores, we first propose a helpfulness classification model to reflect the review helpfulness that significantly impacts users’ purchasing decisions in personalized recommendation services. The helpfulness classification model is trained about limited reviews utilizing helpfulness scores. Several experiments with the Amazon dataset show that if review helpfulness information is used in the recommender system, performance such as the accuracy of personalized recommendation service can be further improved, thereby enhancing user satisfaction and further increasing trust in the company.
MMPoint-GNN: Graph Neural Network with Dynamic Edges for Human Activity Recognition through a Millimeter-Wave Radar

Human activity recognition has a wide range of application prospects and research significance in intelligent monitoring, assisted driving and human-computer interaction, such as intelligent monitoring of the elderly living alone, warning of dangerous behaviors of drivers and development of somatosensory games. Traditionally, human activity recognition is realized by cameras or wearable devices. However, in privacy-sensitive areas such as wards and cars, users may not be willing to share too many private videos. In this paper, we use millimeterwave radar to collect point clouds of human activities, design a novel graph neural network MMPoint-GNN with dynamic edges for the first time to process sparse point clouds, and combine it with Bidirectional LSTM to build a human activity recognition framework. We transform the logic operation into a differentiable function by edge selection network, and achieve the dynamic edge selection in MMPoint-GNN. Finally, we evaluate our method by comparing it with other methods on MMActivity dataset and MMGesture dataset. The results show that MMPoint-GNN outperforms all other baselines. The code is available at https://github.com/gongpx20069/mmRadar_for_HAR_VS
ObjectAug: Object-level Data Augmentation for Semantic Image Segmentation

Effective training of deep neural networks (DNNs) usually requires labeling a large dataset, which is time and labor intensive. Recently, various data augmentation strategies like regional dropout and mix strategies have been proposed, which are effective as the augmented dataset can guide the model to attend on less discriminative parts. However, these strategies operate only at the image level, where the objects and the background are coupled. Thus, the boundaries are not well augmented due to the fixed semantic scenario. In this paper, we propose ObjectAug to perform object-level augmentation for semantic image segmentation. Our method first decouples the image into individual objects and the background using semantic labels. Second, each object is augmented individually with commonly used augmentation methods (e.g., scaling, shifting, and rotation). Third, the pixel artifacts brought by object augmentation are further restored using image inpainting. Finally, the augmented objects and background are assembled as an augmented image. In this way, the boundaries can be fully explored in the various semantic scenarios. In addition, ObjectAug can support category-aware augmentation that gives various possibilities to objects in each category, and can be easily combined with existing image-level augmentation methods to further boost the performance. Comprehensive experiments are conducted on both natural image and medical image datasets. Experiment results demonstrate that our ObjectAug can effectively improve segmentation performance.
Recognizing human activities in Industry 4.0 scenarios through an analysis-modeling- recognition algorithm and context labels

Activity recognition technologies only present a good performance in controlled conditions, where a limited number of actions are allowed. On the contrary, industrial applications are scenarios with real and uncontrolled conditions where thousands of different activities (such as transporting or manufacturing craft products), with an incredible variability, may be developed. In this context, new and enhanced human activity recognition technologies are needed. Therefore, in this paper, a new activity recognition technology, focused on Industry 4.0 scenarios, is proposed. The proposed mechanism consists of different steps, including a first analysis phase where physical signals are processed using moving averages, filters and signal processing techniques, and an atomic recognition step where Dynamic Time Warping technologies and k-nearest neighbors solutions are integrated; a second phase where activities are modeled using generalized Markov models and context labels are recognized using a multi-layer perceptron; and a third step where activities are recognized using the previously created Markov models and context information, formatted as labels. The proposed solution achieves the best recognition rate of 87% which demonstrates the efficacy of the described method. Compared to the state-of-the-art solutions, an improvement up to 10% is reported.
Poisson Multi-Bernoulli Approximations for Multiple Extended Object Filtering

The Poisson multi-Bernoulli mixture (PMBM) is a multiobject conjugate prior for the closed-form Bayes random finite set filter. The extended object PMBM filter provides a closed-form solution for multiple extended object filtering with standard models. This article considers computationally lighter alternatives to the extended object PMBM filter by propagating a Poisson multi-Bernoulli (PMB) density through the filtering recursion. A new local hypothesis representation is presented, where each measurement creates a new Bernoulli component. This facilitates the developments of methods for efficiently approximating the PMBM posterior density after the update step as a PMB. Based on the new hypothesis representation, two approximation methods are presented: one is based on the track-oriented multi-Bernoulli (MB) approximation, and the other is based on the variational MB approximation via Kullback–Leibler divergence minimization. The performance of the proposed PMB filters with gamma Gaussian inverse-Wishart implementations are evaluated in a simulation study.
Understanding and compensating for noise on IBM quantum computers

Quantum algorithms offer efficient solutions to computational problems that are expensive to solve classically. Publicly available quantum computers, such as those provided by IBM, can now be used to run small quantum circuits that execute quantum algorithms. However, these quantum computers are highly prone to noise. Here, we introduce important concepts of quantum circuit noise and connectivity that must be addressed to obtain reliable results on quantum computers. We utilize several examples to show how noise scales with circuit depth. We present Simon's algorithm, a quantum algorithm for solving a computational problem of the same name, explain how to implement it in IBM's Qiskit platform, and compare the results of running it both on a noiseless simulator and on physical hardware subject to noise. We discuss the impact of Qiskit's transpiler, which adapts ideal quantum circuits for physical hardware with limited connectivity between qubits. We show that even circuits of only a few qubits can have their success rate significantly reduced by quantum noise unless specific measures are taken to minimize its impact.
Cybersecurity through the lens of Digital Identity and Data Protection: Issues and Trends

The use of a secure and robust digital identification system that is capable of protecting privacy is an essential, reliable and user-friendly element for a strong cyber resilience strategy and is a source of new business opportunities and applications for banks, private sector with a return on their investment. The march towards Digital Identity is well underway therefore, focus should be on both adoption and adaption of the new structures and regulations. These are needed to govern the associated services and transactions as well as establishing laws that enforce penalties for violations. There is no doubt then that more and more entities and institutions would move to the cloud. Security challenges affecting the cloud may not be new but the mode of addressing them would be different. The authors develop a Data Colouring technique for securing data processed or stored on both cloud and non-cloud platforms. The technique combines Public Key Infrastructure (PKI), concatenated fingerprints and digital watermarking. Using this technique, data can be secured at creation or during storage and remains secure during processing.
Recommendation on Live-Streaming Platforms: Dynamic Availability and Repeat Consumption

Live-streaming platforms broadcast user-generated video in real-time. Recommendation on these platforms shares similarities with traditional settings, such as a large volume of heterogeneous content and highly skewed interaction distributions. However, several challenges must be overcome to adapt recommendation algorithms to live-streaming platforms: first, content availability is dynamic which restricts users to choose from only a subset of items at any given time; during training and inference we must carefully handle this factor in order to properly account for such signals, where ‘non-interactions’ reflect availability as much as implicit preference. Streamers are also fundamentally different from ‘items’ in traditional settings: repeat consumption of specific channels plays a significant role, though the content itself is fundamentally ephemeral.
High dynamic range imaging via gradient-aware context aggregation network

Obtaining a high dynamic range (HDR) image from multiple low dynamic range images with different exposures is an important step in various computer vision tasks. One of the ongoing challenges in the field is to generate HDR images without ghosting artifacts. Motivated by an observation that such artifacts are particularly noticeable in the gradient domain, in this paper, we propose an HDR imaging approach that aggregates the information from multiple LDR images with guidance from image gradient domain. The proposed method generates artifact-free images by integrating the image gradient information and the image context information in the pixel domain. The context information in a large area helps to reconstruct the contents contaminated by saturation and misalignments. Specifically, an additional gradient stream and the supervision in the gradient domain are applied to incorporate the gradient information in HDR imaging. To use the context information captured from a large area while preserving spatial resolution, we adopt dilated convolutions to extract multi-scale features with rich context information. Moreover, we build a new dataset containing 40 groups of real-world images from diverse scenes with ground truth to validate the proposed model. The samples in the proposed dataset include more challenging moving objects inducing misalignments. Extensive experimental results demonstrate that our proposed model outperforms previous methods on different datasets in terms of both quantitative measure and visual perception quality.
Abusive language detection in youtube comments leveraging replies as conversational context

Nowadays, social media experience an increase in hostility, which leads to many people suffering from online abusive behavior and harassment. We introduce a new publicly available annotated dataset for abusive language detection in short texts. The dataset includes comments from YouTube, along with contextual information: replies, video, video title, and the original description. The comments in the dataset are labeled as abusive or not and are classified by topic: politics, religion, and other. In particular, we discuss our refined annotation guidelines for such classification. We report a number of strong baselines on this dataset for the tasks of abusive language detection and topic classification, using a number of classifiers and text representations. We show that taking into account the conversational context, namely, replies, greatly improves the classification results as compared with using only linguistic features of the comments. We also study how the classification accuracy depends on the topic of the comment.
Exploiting sample correlation for crowd counting with multi-expert network

Crowd counting is a difficult task because of the diversity of scenes. Most of the existing crowd counting methods adopt complex structures with massive backbones to enhance the generalization ability. Unfortunately, the performance of existing methods on large-scale data sets is not satisfactory. In order to handle various scenarios with less complex network, we explored how to efficiently use the multi-expert model for crowd counting tasks. We mainly focus on how to train more efficient expert networks and how to choose the most suitable expert. Specifically, we propose a task-driven similarity metric based on sample’s mutual enhancement, referred as co-fine-tune similarity, which can find a more efficient subset of data for training the expert network. Similar samples are considered as a cluster which is used to obtain parameters of an expert. Besides, to make better use of the proposed method, we design a simple network called FPN with Deconvolution Counting Network, which is a more suitable base model for the multi-expert counting network. Experimental results show that multiple experts FDC (MFDC) achieves the best performance on four public data sets, including the large scale NWPU-Crowd data set. Furthermore, the MFDC trained on an extensive dense crowd data set can generalize well on the other data sets without extra training or fine-tuning. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
What We Speculate About When We Speculate About Sustainable HCI

Fears of climate change and the escalating impacts of environmental damage are growing, and recent papers in the area of Sustainable HCI have called for urgent, non-linear solutions to these problems. Speculative design, along with related approaches including design fiction, have been taken up as means of navigating the "wicked problems" that structure contemporary nature/society relations. We conduct a survey of speculative design papers published in ACM venues between 2008 and 2021, assessing fundamental questions such as who is involved in the process, how is sustainability framed, and how is speculation used. Our evaluation of this body of work yielded mixed results; we find both promising trends as well as notable and problematic limitations in how the HCI community is taking up speculative practice in this domain. We build upon this evaluation to offer four provocations to designers seeking to use speculative practice in support of sustainability goals.
A Simple Baseline for Weakly-Supervised Scene Graph Generation

We investigate the weakly-supervised scene graph generation, which is a challenging task since no correspondence of label and object is provided. The previous work regards such correspondence as a latent variable which is iteratively updated via nested optimization of the scene graph generation objective. However, we further reduce the complexity by decoupling it into an efficient first-order graph matching module optimized via contrastive learning to obtain such correspondence, which is used to train a standard scene graph generation model. The extensive experiments show that such a simple pipeline can significantly surpass the previous state-of-the-art by more than 30% on the Visual Genome dataset, both in terms of graph matching accuracy and scene graph quality. We believe this work serves as a strong baseline for future research. Code is available at https://github.com/jshi31/WS-SGG.
Data-based ensemble approach for semi-supervised anomaly detection in machine tool condition monitoring

Data-based methods are capable to monitor machine components. Approaches for semi-supervised anomaly detection are trained using sensor data that describe the normal state of machine components. Thus, such approaches are interesting for industrial practice, since sensor data do not have to be labeled in a time-consuming and costly way. In this work, an ensemble approach for semi-supervised anomaly detection is used to detect anomalies. It is shown that the ensemble approach is suitable for condition monitoring of ball screws. For the evaluation of the approach, a data set of a regular test cycle of a ball screw from automotive industry is used.
Improving Cost Learning for JPEG Steganography by Exploiting JPEG Domain Knowledge

Although significant progress has been achieved recently in automatic learning of steganographic cost, the existing methods designed for spatial images cannot be directly applied to JPEG images which are more common media in daily life. The difficulties of migration are mainly caused by the characteristics of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$8\times 8$ </tex-math></inline-formula> DCT mode structure. To address the issue, in this paper we extend an existing automatic cost learning scheme to JPEG, where the proposed scheme called JEC-RL (JPEG Embedding Cost with Reinforcement Learning) is explicitly designed to tailor the JPEG DCT structure. It works with the embedding action sampling mechanism under reinforcement learning, where a policy network learns the optimal embedding policies via maximizing the rewards provided by an environment network. Following a domain-transition design paradigm, the policy network is composed of three modules, i.e., pixel-level texture complexity evaluation module, DCT feature extraction module, and mode-wise rearrangement module. These modules operate in serial, gradually extracting useful features from a decompressed JPEG image and converting them into embedding policies for DCT elements, while considering JPEG characteristics including inter-block and intra-block correlations simultaneously. The environment network is designed in a gradient-oriented way to provide stable reward values by using a wide architecture equipped with a fixed preprocessing layer with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$8\times 8$ </tex-math></inline-formula> DCT basis filters. Extensive experiments and ablation studies demonstrate that the proposed method can achieve good security performance for JPEG images against both advanced feature-based and modern CNN-based steganalyzers.
Adversarial Robustness for Unsupervised Domain Adaptation

Extensive Unsupervised Domain Adaptation (UDA) studies have shown great success in practice by learning transferable representations across a labeled source domain and an unlabeled target domain with deep models. However, current work focuses on improving the generalization ability of UDA models on clean examples without considering the adversarial robustness, which is crucial in real-world applications. Conventional adversarial training methods are not suitable for the adversarial robustness on the unlabeled target domain of UDA since they train models with adversarial examples generated by the supervised loss function. In this work, we propose to leverage intermediate representations learned by robust ImageNet models to improve the robustness of UDA models. Our method works by aligning the features of the UDA model with the robust features learned by ImageNet pre-trained models along with domain adaptation training. It utilizes both labeled and unlabeled domains and instills robustness without any adversarial intervention or label requirement during domain adaptation training. Our experimental results show that our method significantly improves adversarial robustness compared to the baseline while keeping clean accuracy on various UDA benchmarks.
Attention-TCN-BiGRU: An Air Target Combat Intention Recognition Model

The prerequisite for victory in war is the rapid and accurate identification of the tactical intention of the target on the battlefield. The efficiency of manual recognition of the combat intention of air targets is becoming less and less effective with the advent of information warfare. Moreover, if the traditional method of combat intention of air targets is based only on data from a single moment in time, the characteristic information on the time-series data is difficult to capture effectively. In this context, we design a new deep learning method attention mechanism with temporal convolutional network and bidirectional gated recurrent unit (Attention-TCN-BiGRU) to improve the recognition of the combat intent of air targets. Specifically, suitable characteristics are selected based on the combat mission and air posture to construct a characteristic set of air target intentions and encode them into temporal characteristics. Each characteristic in the characteristic set is given an appropriate weight through the attention mechanism. In addition, temporal convolutional network (TCN) is used to mine the data for latent characteristics and bidirectional gated recurrent unit (BiGRU) is used to capture long-term dependencies in the data. Experiments comparing with other methods and ablation demonstrate that Attention-TCN-BiGRU outperforms state-of-the-art methods in terms of accuracy in recognizing target intent in the air.
Capacitive Sensing Based On-board Hand Gesture Recognition with TinyML

Although hand gesture recognition has been widely explored with sensing modalities like IMU, electromyography and camera, it is still a challenge of those modalities to provide a compact, power-efficient on-board inferencing solution. In this work, we present a capacitive-sensing wristband surrounded by four single-end electrodes for on-board hand gesture recognition. By deploying a single convolutional hidden layer as the classifier at the sensing edge, the wristband can recognize seven hand gestures from a single user with an accuracy of 96.4%.