Reviewerly/exHarmony huggingface dataset:
A Novel Image Classification Method Based on Residual Network, Inception, and Proposed Activation Function

In deeper layers, ResNet heavily depends on skip connections and Relu. Although skip connections have demonstrated their usefulness in networks, a major issue arises when the dimensions between layers are not consistent. In such cases, it is necessary to use techniques such as zero-padding or projection to match the dimensions between layers. These adjustments increase the complexity of the network architecture, resulting in an increase in parameter number and a rise in computational costs. Another problem is the vanishing gradient caused by utilizing Relu. In our model, after making appropriate adjustments to the inception blocks, we replace the deeper layers of ResNet with modified inception blocks and Relu with our non-monotonic activation function (NMAF). To reduce parameter number, we use symmetric factorization and 1×1 convolutions. Utilizing these two techniques contributed to reducing the parameter number by around 6 M parameters, which has helped reduce the run time by 30 s/epoch. Unlike Relu, NMAF addresses the deactivation problem of the non-positive number by activating the negative values and outputting small negative numbers instead of zero in Relu, which helped in enhancing the convergence speed and increasing the accuracy by 5%, 15%, and 5% for the non-noisy datasets, and 5%, 6%, 21% for non-noisy datasets.
Controlling fracture propagation using deep reinforcement learning

Mechanical discontinuity embedded in a material plays an essential role in determining the bulk mechanical, physical, and chemical properties. The ability to control mechanical discontinuity is relevant for industries dependent on natural, synthetic and composite materials, e.g. construction, aerospace, oil and gas, ceramics, metal, and geothermal industries, to name a few. The paper is a proof-of-concept development of a reinforcement learning framework to control the propagation of mechanical discontinuity. The reinforcement learning framework is coupled with an OpenAI-Gym-based environment that uses the mechanistic equation governing the propagation of mechanical discontinuity. Learning agent does not explicitly know about the underlying physics of propagation of discontinuity; nonetheless, the learning agent can infer the control strategy by continuously interacting the environment. The design of Markov decision process, which includes state, action and reward, is crucial for robust control. The deep deterministic policy gradient (DDPG) algorithm is implemented for learning continuous actions. It is also observed that the training efficiency is strongly determined by the formulation of reward function. The reward function that forces the learning agent to stay on the shortest linear path between crack tip and goal point performs much better than the reward function that aims to reach closest to the goal point in minimum number of steps. After close to 500 training episodes, the reinforcement learning framework successfully controlled the propagation of discontinuity in a material despite the complexity of the propagation pathway determined by multiple goal points.
<i>iVisit-Communicate</i> for AEC Education: Using Virtual Humans to Practice Communication Skills in 360-Degree Virtual Field Trips

To prepare graduates for the dynamic and complex working environment in the architecture, engineering, and construction (AEC) industry, educational institutions have distinctly emphasized the significance of communication skills. Field trips are a widely utilized strategy to provide students with a situated learning setting where students can communicate with professionals onsite. However, several challenges limit the application of this strategy, and virtual field trips become a potential and promising supplement or alternative approach. This study discusses the pedagogical use of a virtual field trip experience augmented with conversational virtual humans—iVisit-Communicate—to produce realistic field trips for students to practice their communication skills. This virtual experience is supported by immersive computing (i.e., 360-degree virtual space) and conversational virtual human technologies. iVisit-Communicate simulates two types of communication to provide student-professional verbal communication opportunities: guided scenario and conversational scenario. The paper provides a detailed description of the technical and pedagogical development of iVisit-Communicate components followed by a student-centered experiment, focusing on measures such as system usability, sense of presence, and communication skills of students. The contribution of this study is to provide an in-depth understanding of the significance of integrating communication opportunities in virtual field trips and illustrate how such on-the-site opportunities successfully facilitate students' communication practices.
Coffee, Tea, Robots?

We present an ethnographic observational study of six robot cafes in Japan to understand how service robots are performatively staged and presented to the public. We particularly attend to the diverse ways in which the physical setting and ambience of the cafes, the verbal characterization of and staff behaviors toward robots, explicit and implicit instructions on appropriate interactions with robots, and handling of robot malfunctions constitute robots as socially acceptable and useful in daily life. Such scaffolding enables robots to provide material and affective services to cafe visitors, and visitors to explore various interaction possibilities with robots. Our work contributes to the critical study of the ongoing construction of "robot cultures" in Japan, and calls attention to public interactions with robots and the importance of contextual staging beyond individual robot features in human-robot interaction design.
IRE: Improved Image Super-Resolution Based on Real-ESRGAN

Image degradation technique has been the focus of current research in image superresolution(SR).The High-order Deterioration Model(HDM) of Real-ESRGAN is more effective than the conventional bicubic kernel interpolation in simulating the degradation of real-world images.However, the images reconstructed by Real-ESRGAN suffer from two notable weaknesses.Firstly, the rebuilt image is overly smooth and suffers from substantial texture information loss, making it worse than classical models like SRGAN and ESRGAN.Moreover, despite the reconstructed images having a better visualization effect, they are entirely different from the original image, violating the principle of image reconstruction.In order to address these two issues, an improved model built on Real-ESRGAN (IRE) is presented in this work.We remove the first-order degradation modeling of HDM and keep its second-order degradation modeling to lessen the degree of visual deterioration.Then we utilized PatchGAN as the fundamental structure of the discriminator, added a channel attention mechanism to the dense block of the generator, and increased the texture detail in the reconstructed images.Finally, we replaced the L1 loss function with the SmoothL1 loss function to improve the convergence speed with better model performance.The ablation study shows that the average training time was decreased by about 28% when employing PatchGAN rather than U-net as the discriminator.In addition, the ablation study presents that the RankIQA measures assessed by IRE on Set5, Set14, and DIV2K100 were decreased by 15.20%, 20.46%, and 10.27%, respectively, and the PI measures considered by IRE+ on BSD100, Urban100 were decreased by 15.76%, 8.67%, respectively, compared to Real-ESRGAN.Also, the comparison experiment illustrates that the NIQE measures assessed by our suggested model on Set5, RealSR-Canon, and RealSR-Nikon were decreased by 12.31%, 3%, and 2.41%, respectively, compared to Real-ESRGAN.
Affective Image Captioning for Visual Artworks Using Emotion-Based Cross-Attention Mechanisms

Within the museum community, the automatic generation of artwork description is expected to accelerate the improvement of accessibility for visually impaired visitors.Captions that describe artworks should be based on emotions because art is inseparable from viewers' emotional reactions.By contrast, artworks typically do not have unique interpretations; thus, it is difficult for systems to reflect the specified emotions in captions precisely.Most existing methods attempt to leverage predicted emotion labels from images to generate emotion-oriented captions; however, they do not allow users to specify arbitrary emotions.We propose an affective visual encoder, which integrates emotion attributes and cross-modal joint features of images into visual information over all encoder blocks.Moreover, we introduce affective tokens that fuse grid-and region-based image features to cover both contextual and object-level information.We validated our method on the ArtEmis dataset, and the results demonstrated that our method outperformed baseline methods on all metrics in the emotion-conditioned task.
Location Privacy-Aware Task Offloading in Mobile Edge Computing

In mobile edge computing (MEC), users can offload tasks to nearby MEC servers to reduce computation cost. Considering that the size of offloaded tasks could disclose user location information, several location privacy-preserving task offloading mechanisms have been proposed under the single-server scenario. However, to the best of our knowledge, none of them could provide a strict privacy protection guarantee or be applicable to the multi-server scenario where the user's location can be inferred more accurately if servers collude with each other. In this paper, we propose a novel location privacy-aware task offloading framework (LPA-Offload) for both single-server and multi-server scenarios, which provides strict and provable location privacy protection while achieving efficient task offloading. Specifically, we propose a location perturbation mechanism that allows each user to perturb its real location within a rational perturbation region and provides a differential privacy guarantee. To make a satisfactory offloading strategy, we propose a perturbation region determination mechanism and an offloading strategy generation mechanism that adaptively select a proper perturbation region according to the customized privacy factor, and then generate an optimal offloading strategy based on the perturbed location within the decided region. The determination of the perturbation region could achieve personalized privacy requirements while reducing computation cost. LPA-Offload is proved to satisfy <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$(\epsilon ,\delta )$</tex-math></inline-formula> -differential privacy, and the experiments demonstrate the effectiveness of our framework.
A Hybrid CNN-LSTM Model With Word-Emoji Embedding For Improving The Twitter Sentiment Analysis on Indonesia's PPKM Policy

The policy of limiting community mobilization is implemented to reduce the daily rate of COVID-19. However, a high-accuracy sentiment analysis model can determine public sentiment toward such policies. Our research aims to improve the accuracy of the LSTM model on sentiment analysis of the Jakarta community towards PPKM using Indonesian language Tweets with emoji embedding. The first stage is modeling using the hybrid CNN-LSTM model. It is a combination between CNN and LSTM. The CNN model cites word embedding and emoji embedding features that reflect the dependence on temporary short-term sentiment. At the same time, LSTM builds long-term sentiment relationships between words and emojis. Next, the model evaluation uses Accuracy, Loss, the receiver operating curve (ROC), the precision and recall curve, and the area under curve (AUC) value to see the performance of the designed model. Based on the results of the tests, we conclude that the CNN-LSTM Hybrid Model performs better with the words+emoji dataset. The ROC AUC is 0.966, while the precision-recall curve AUC is 0.957.
Inspiration Transfer for Intelligent Design: A Generative Adversarial Network With Fashion Attributes Disentanglement

Traditional fashion design is heavily dependent on the talent and inspiration of designers. However, for inexperienced designers or common consumers, it is difficult to complete the design of ready-to-wear clothes. In this paper, we introduce a novel fashion attributes disentanglement generative adversarial network (FadGAN) to assist users in automatically accomplishing the design process. Different from the typical style transfer methods, which are limited to perform coarse-level style transfer, our FadGAN works on learning the accurate mapping which can preserve the identity of one fashion item, as well as learning the texture of another fashion item. Specifically, given two different fashion items, a fashion item disentanglement encoder first disentangles the features of the two inputs into structure codes and texture codes, respectively. Subsequently, a texture swap module is proposed to learn mixed texture codes to eliminate the differences of features from different fashion items. The mixed texture codes and the structure codes are then fed into a generator for new fashion item generation. Experimental results demonstrate that our model achieves superior results in comparison to several state-of-the-art methods with a 0.006-0.446 improvement on the mask-LPIPS, a 3.976-6.724 improvement on CDH, a 9.266-13.127 improvement on CIEDE, and a 0.145-0.325 improvement on HISTO.
MAP Inference in Probabilistic Answer Set Programs

Abstract Reasoning with uncertain data is a central task in artificial intelligence. In some cases, the goal is to find the most likely assignment to a subset of random variables, named query variables, while some other variables are observed. This task is called Maximum a Posteriori (MAP). When the set of query variables is the complement of the observed variables, the task goes under the name of Most Probable Explanation (MPE). In this paper, we introduce the definitions of cautious and brave MAP and MPE tasks in the context of Probabilistic Answer Set Programming under the credal semantics and provide an algorithm to solve them. Empirical results show that the brave version of both tasks is usually faster to compute. On the brave MPE task, the adoption of a state-of-the-art ASP solver makes the computation much faster than a naive approach based on the enumeration of all the worlds.
Approximate Inference in Probabilistic Answer Set Programming for Statistical Probabilities

Abstract “Type 1” statements were introduced by Halpern in 1990 with the goal to represent statistical information about a domain of interest. These are of the form “x% of the elements share the same property”. The recently proposed language PASTA (Probabilistic Answer set programming for STAtistical probabilities) extends Probabilistic Logic Programs under the Distribution Semantics and allows the definition of this type of statements. To perform exact inference, PASTA programs are converted into probabilistic answer set programs under the Credal Semantics. However, this algorithm is infeasible for scenarios when more than a few random variables are involved. Here, we propose several algorithms to perform both conditional and unconditional approximate inference in PASTA programs and test them on different benchmarks. The results show that approximate algorithms scale to hundreds of variables and thus can manage real world domains.
Expressivity of Variational Quantum Machine Learning on the Boolean Cube

Categorical data plays an important part in machine learning research and appears in a variety of applications. Models that can express large classes of real-valued functions on the Boolean cube are useful for problems involving discrete-valued data types, including those which are not Boolean. To this date, the commonly used schemes for embedding classical data into variational quantum machine learning models encode continuous values. Here we investigate quantum embeddings for encoding Boolean-valued data into parameterized quantum circuits used for machine learning tasks. We narrow down representability conditions for functions on the $n$-dimensional Boolean cube with respect to previously known results, using two quantum embeddings: a phase embedding and an embedding based on quantum random access codes. We show that for any real-valued function on the $n$-dimensional Boolean cube, there exists a variational linear quantum model based on a phase embedding using $n$ qubits that can represent it and an ensemble of such models using $d < n$ qubits that can express any function with degree at most $d$. Additionally, we prove that variational linear quantum models that use the quantum random access code embedding can express functions on the Boolean cube with degree $ d\leq \lceil\frac{n}{3}\rceil$ using $\lceil\frac{n}{3}\rceil$ qubits, and that an ensemble of such models can represent any function on the Boolean cube with degree $ d\leq \lceil\frac{n}{3}\rceil$. Furthermore, we discuss the potential benefits of each embedding and the impact of serial repetitions. Lastly, we demonstrate the use of the embeddings presented by performing numerical simulations and experiments on IBM quantum processors using the Qiskit machine learning framework.
On robustness of neural ODEs image classifiers

Neural Ordinary Differential Equations (Neural ODEs), as a family of novel deep models, delicately link conventional neural networks and dynamical systems, which bridges the gap between theory and practice. However, they have not made substantial progress on activation functions, and ReLU is always utilized by default. Moreover, the dynamical behavior existing in them becomes more unclear and complicated as training progresses. Fortunately, existing studies have shown that activation functions are essential for Neural ODEs in governing intrinsic dynamics. Motivated by a family of weight functions used to enhance the stability of dynamical systems, we introduce a new activation function named half-Swish to match Neural ODEs. Besides, we explore the effect of evolution time and batch size on Neural ODEs, respectively. Experiments show that our model consistently outperforms Neural ODEs with basic activation functions on robustness both against stochastic noise images and adversarial examples across Fashion-MNIST, CIFAR-10, and CIFAR-100 datasets, which strongly validates the applicability of half-Swish and suggests that half-Swish function plays a positive role in regularizing the dynamic behavior to enhance stability. Meanwhile, our work theoretically provides a prospective framework to choose appropriate activation functions to match neural differential equations.
A supervised data augmentation strategy based on random combinations of key features

Data augmentation strategies have always been important in machine learning techniques and play a unique role in model performance optimization processes. Therefore, in recent years, these techniques have become popular in the artificial intelligence field. In this paper, a new data augmentation strategy is proposed based on the interpretation algorithm of deep convolutional neural networks, i.e., constructing new training samples by deeply exploiting key features extracted from interpretable networks to achieve sample augmentation. Thus, a novel supervised data augmentation approach known as Supervised Data Augmentation–Key Feature Extraction (SDA-KFE) was proposed. By introducing the Neural Network Interpreter-Segmentation Recognition and Interpretation (NNI-SRI) algorithm, an augmentation strategy is proposed that can balance the high accuracy and high robustness of the final model while ensuring a large amount of data augmentation. The advantages of the SDA-KFE algorithm are mainly reflected in the following aspects. First, it is easy to implement. This algorithm is implemented based on the lightweight NNI-SRI algorithm, which lays the foundation for the implementation of SDA-KFE so that it can be easily implemented on convolutional neural networks. Second, this model, which is widely applicable, can be applied to almost any deep convolutional network. Through research and experiments on this proposed algorithm, SDA-KFE can be applied in graphical image binary classification and multiclassification models. Third, SDA-KFE can rapidly construct data samples with diverse variations. Under the premise of determining the classification labels of the generated samples, the distribution of the feature unit composition of the samples can be controlled. Compared with traditional data augmentation methods, SDA-KFE can control the direction of the model performance, i.e., the balance between the pursuit of high accuracy and robust performance of the model. Therefore, the novel supervised augmentation approach proposed in this paper is relevant for optimizing deep convolutional neural networks, solving model overfitting, augmenting data types, etc. The data augmentation algorithm proposed in this paper can be regarded as a useful supplement to traditional data augmentation methods, such as horizontal or vertical image flipping, cropping, color transformation, extension and rotation.
Contrastive Learning Based Graph Convolution Network for Social Recommendation

Exploiting social networks is expected to enhance the performance of recommender systems when interaction information is sparse. Existing social recommendation models focus on modeling multi-graph structures and then aggregating the information from these multiple graphs to learn potential user preferences. However, these methods often employ complex models and redundant parameters to get a slight performance improvement. Contrastive learning has been widely researched as an effective paradigm in the area of recommendation. Most existing contrastive learning-based models usually focus on constructing multi-graph structures to perform graph augmentation for contrastive learning. However, the effect of graph augmentation on contrastive learning is inconclusive. In view of these challenges, in this work, we propose a contrastive learning based graph convolution network for social recommendation (CLSR), which integrates information from both the social graph and the interaction graph. First, we propose a fusion-simplified method to combine the social graph and the interaction graph. Technically, on the basis of exploring users’ interests by interaction graph, we further exploit social connections to alleviate data sparsity. By combining the user embeddings learned through two graphs in a certain proportion, we can obtain user representation at a finer granularity. Meanwhile, we introduce a contrastive learning framework for multi-graph network modeling, where we explore the feasibility of constructing positive and negative samples of contrastive learning by conducting data augmentation on embedding representations. Extensive experiments verify the superiority of CLSR’s contrastive learning framework and fusion-simplified method of integrating social relations.