Reviewerly/exHarmony huggingface dataset:
AMSC: Adaptive Multi-channel Graph Convolutional Network-Enhanced Web Services Classification

With the development of Service Oriented Architecture (SOA), the number of Web services on the Internet is also growing rapidly. Classifying Web services accurately and efficiently is helpful to improve the quality of services discovery and promote the efficiency of service composition. However, existing deep learning-based Web services classification methods, such as graph convolutional networks (GCNs), are incapable of adaptively learning the correlation between service topology structure and service node features concurrently, resulting in unsatisfactory classification performance. To address this problem, this paper proposes an adaptive multi-channel GCN-enhanced Web services classification method. In this method, we first extract specific and shared embedding, in the Web API node isomorphic network, from the node features, topology, and combination of Web service nodes. Then, an attention mechanism is used to learn the importance weight of each embedding. By doing this, we adaptively integrate these weights to ensure the consistency and difference of each learned embedding. Finally, experimental results on real datasets from Programmable Web show that compared with LINE, Node2vec, Deep-walk, GCN, and GAT, the method proposed in this paper has an average improvement in accuracy of 19.81&#x0025;, 19.35&#x0025;, 19.16&#x0025;, 11.56&#x0025;, and 7.75&#x0025; respectively.
Automated Line Segmentation for Gurmukhi Text Recognition System

In a Text Recognition system, segmentation is a critical and difficult phase. When words in the target language cover more than one zone, such as in Gurmukhi, it becomes more challenging. Moreover, automated systems are becoming more popular in this modern age due to their ability to save time and labour. This research also looks at the automation process for text recognition systems, in which lines are segmented automatically without the need for external assistance, particularly in the Gurmukhi language. Earlier, only a few solutions were developed, but they were not evaluated on huge datasets, which does not reflect the efficiency of the existing systems. So, in order to produce efficient results, an automated line segmentation strategy &#x2018;AutoLineSeg&#x2019; is proposed in this work, which uses the projection profile method along with some pre-processing. To offer reliable data for segmentation, this proposed system additionally does skew correction. The testing of the system is done on the dataset with 1000 images of typewritten Gurmukhi samples. The proposed method&#x0027;s accuracy is calculated, and the findings reveal that the system obtained around 91&#x0025; accuracy, which is sufficient for a text recognition system.
AutoTR: Efficient Reformatting Text Spread out in a DOM Tree for Text-Analytic Applications

We present an efficient algorithm to automatically reformat text contained in multiple nodes that are spread out in a DOM tree of an HTML file converted from a PDF document. Reformatting text on the fly is needed for hierarchical reading and other text-analytic applications. A naive approach would traverse the DOM tree multiple times, failing to meet the requirement of real-time reformatting. Our algorithm meets the real-time requirement by indexing text nodes and sentences with a pair of tag holders inserted into each text node to allow fast reformatting.
Proposed Methodology for Sentiment Analysis of Social Media Data Focusing on the Sentiment Analysis in Political Domain

As we know that Social media has become an important part of today&#x0027;s generation. People try to post their daily routine on social media (Facebook, twitter etc.). Thus, results produced from mining the social media data are very effective in understanding current trends. As we all know these trends are very helpful for different kind of business, launching new products etc. But there is one another field where social media plays a big role that is in understanding social and political dynamic. This proposal paper is based on understanding this political dynamic through mining the social media data and producing trends which can even be used for future plans.
Click Prediction Based on Feature Fusion and Attention Mechanism

The application of deep learning in recommendation systems is mainly to capture high-level feature combinations, which can better discover the impact of cross-features on user clicks than traditional recommendation models. Differently, traditional factorization machine models work better on the low-level features. The combination of these two models has good memory and generalization capabilities for recommendation systems. However, due to the different effects of low-level features and high-level features on the user click behaviors, a straightforward combination of these two models is incompetent to predict user clicks. To address this issue, we propose ADAFM, a feature fusion model based on the attention mechanism to select and emphasize the important features. Experimental results show that our model outperforms the baseline models in terms of better ranking ability.
An Emotion Guidance Decision-Making Model Based on Dialogue Context

Emotional dialogues are mainly for small chats, companionship, emotional comfort and other scenarios. In most scenarios, we hope to guide the user&#x0027;s emotions in a positive direction in the dialogue. However, the lack of appropriate emotion guidance mechanisms may lead to unexpected emotions in dialogue response, which may hurt the user&#x0027;s feelings and reduce the user&#x0027;s willingness to chat. Current dialogue models have achieved great empirical success in emotional expression and controllability of responses, but there is not yet an effort in user emotion guidance decision-making. In this paper, inspired by time-series models and the dynamic characteristics of emotional changes in dialogue, we propose an emotion guidance decision-making model with an emotion guidance decision-making cell to decide a proper emotion for the current response according to the specified sentiment. Experimental results of automatic and human evaluations demonstrate that the proposed emotion guidance decision-making model can effectively decide the emotions that are consistent with the target sentiment.
Adversarial Training Negatively Affects Fairness

With the increasing presence of deep learning models, many applications have had significant improvements; however, they face a new vulnerability known as adversarial examples. Adversarial examples can mislead deep learning models to predict the wrong classes without human actors noticing. Recently, many works have tried to improve adversarial examples to make them stronger and more effective. However, although some researchers have invented mechanisms to defend deep learning models against adversarial examples, those mechanisms may negatively affect different measures of fairness, which are critical in practice. This work mathematically defines four fairness scores to show that training adversarially robust models can harm fairness scores. Furthermore, we empirically show that adversarial training, one of the most potent defensive mechanisms against adversarial examples, can harm them.
Winograd-CNN Accelerator Compatible with Sparse and Non-sparse Models

The inference speed of convolutional neural networks is a key factor for their application in the industry. The two research directions of non-sparse model acceleration and sparse model acceleration are now studied independently. In this paper, we propose a hybrid granular pruning method and a fixed-length binary pruning index scheme, based on which a compatible acceleration architecture is further designed. The hybrid granular pruning method is realized by executing fine-grained pruning after coarse-grained pruning. The binary pruning index scheme ensures a simple fixed-length binary mask to index valid weight positions no matter the model is pruned or not, thus makes it possible for an accelerator compatible with both non-sparse models without pruning and sparse models with hybrid granular pruning. An acceleration architecture is further designed, using a new data layout, a buffer-pipeline mechanism for the data buffering, and converting the Winograd algorithm into matrix operations with parallelism carried out from three aspects. The experimental results suggest that, our proposed acceleration solution shows not only better acceleration performance but also excellent compatibility, and it can be directly applied for the acceleration of models at different pruning levels without recompilation.
Passenger Payment Willingness Prediction by Static and Dynamic Multi-dimensional Ticket Attributes Fusion

Ticket pricing is always a challenging problem for world-wide airline companies when balancing their revenues and sales, where tickets are often discounted to adapt to a marketable price level. In this paper, we transform the problem of modeling Passenger Payment Willingness (PPW) into a top- <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$K$</tex> recommendation problem, where a list of ticket discounted ratios is recommended by fully considering ticket discount histories of peer airline companies and multi-dimensional ticket attributes, i.e., passenger purchasing capability. We propose a novel deep model, called “NCL”, which integrates N-Beats, a Graph Convolutional Neural Network (GCN) and an LSTM together to model temporal variations of ticket discounts and complex relationships among multi-dimensional ticket attributes. Specifically, first, the ticket discount historical sequence is integrated by N-Beats. Then, multi-dimensional ticket attributes are divided into dynamic and static categories, where an attribute graph of static attributes is constructed, and a GCN is leveraged to extract features from it. After, LSTM is used to perform temporal feature fusion on the dynamic attributes. Finally, NCL integrates features from all the above and predicts future ticket discounts. Experiments confirm that the prediction accuracy of NCL is more than 60% in terms of ACC@1.
Exploring Neighborhood Service and Development Strategies by Multi-dimensional Sentiment Analysis of Online Restaurant Review

With the rise of the Internet economy, cyber data have interacted with the physical world. One of the directions is to merge attribute and qualitative data with location information. In this paper, we apply the sentiment analysis of online restaurant reviews based on the public data from Meituan and Weibo, the two most influential online platforms in China, to study the restaurant facility in the neighborhood from the perspective of users, and look for neighborhood services&#x0027; management and development strategies in the big data background. This paper takes Shanghai as an example, establishing a multi-dimensional evaluation model to study Shanghai&#x0027;s restaurant facilities from seven dimensions, which are obtained and categorized from a bottom-up method. Different from the &#x201C;quantity&#x201D; analysis in previous studies of neighborhood facilities, this paper pays more attention to the &#x201C;quality&#x201D; analysis of neighborhood facilities from the perspective of residents. Besides, this paper also enriches the empirical study of bidirectional LSTM sentiment analysis, which will promote the development of restaurant business and neighborhood services, and contribute to both the economic benefits of enterprises and refined governance of cities.
JointCS: Joint Search for Deep Model Compression and Segmentation on Heterogeneous IoT Devices

Deep neural networks (DNNs) play an important role in a variety of intelligent applications (e.g. image classification and target recognition), yet at the cost of heavy computation burden, that makes DNNs difficult to deploy on resource-constrained IoT devices. To solve this problem, there are two categories of model computation adjustment methods: model compression and model segmentation. However, model compression mainly reduces resource consumption at the cost of accuracy while model segmentation reduces resource consumption according to the cost of communication latency. In this paper, we propose Joint Search for Model Compression and Segmentation (JointCS) that highlights the following aspects: 1) we integrate both model compression and model segmentation under an automatic and progressive framework, it simplifies model to fit the different IoT resource requirements. JointCS achieves a series slim models that outperform better both in accuracy and latency. 2) we train a network architecture-aware latency predictor to fast measure the latency of the slimed model on heterogeneous IoT devices. 3) we introduce a search algorithm to select the optimal state in progressively joint search. Finally, we evaluate the performance of our proposed method for image classification on CIFAR datasets comparing with the state-of-the-art approach, the inference time of the proposed method has inference speedup of 12.2 % −30.9 % under the same accuracy.
Measuring the Effectiveness of Investment in Security Treatments through a Security ROI Model

With the fast expansion of the digital economy and more adoption of data driven decision making business management process in most industries, there is an urgent need for a quantitative model abstract enough to capture the complexity of threat and security management, and practical enough to measure the effectiveness of any type of investment in security treatments towards establishing a truly tangible business return on investment. In this paper we have developed a Security ROI (Return on Investment) model which is a function of three independent variables: (a) severity of security threat, (b) the probability of threat realization, and (c) the investment on security treatments. We have demonstrated the entire process of how the model is developed. Also, through a few examples that reflect some typical real-world investment on security treatments approaches, we have applied the Security ROI model to analyze the value of security treatments on threats and tangible revenue impacts in each of these scenarios to show the usability and suitability of the model.
Iterative Ensemble Transductive Learning for Microscopy Image Analysis

In automatic histopathology and microscopy image analysis, due to high patient-level variability, the model trained based on the images from a set of patients may not perform well on the images from another set of patients. To overcome this issue, motivated by transductive learning and ensemble learning, we propose an iterative framework to train ensemble transductive models using pseudo-labels of test data. In each iteration, a number of individual models are first trained by combining the training set with part of randomly selected test data which have high prediction confidence, and then ensembled to predict the labels of test set for the next iteration. In this way, the latent information in test set would be exposed to the model such that the model can directly learn from the test data. Experimental evaluation on the white blood cancer microscopic image set and the breast histopathology image set shows that the proposed approach significantly outperforms the traditional ensemble models.
FNCS: Federated Learning Strategy Based on Cosine Similarity under Resource Constraints

Federated learning has been widely applied in healthcare services and real-time object tracking. However, limited by communication resources, such as the server bandwidth, and the impact of client data heterogeneity, the convergence rates and accuracy of federated learning significantly drop. Hence, this study proposes a novel federated normalization learning strategy based on cosine similarity (FNCS). Starting from a new perspective of the relationship between local and global updates of the model, FNCS selects valuable clients to upload updates using cosine similarity. The regularization term is then inserted in the last layer of clients by utilizing cosine distance-based update divergence. Numerous experiments are conducted in PyTorch for accelerated validation. Results show that the high accuracy experiments are conducted on the complex CelebA dataset, and the communication rounds of FNCS are improved by 44.71&#x0025; and 41.98&#x0025; compared with FedAvg and FedProx, respectively.
Accelerating Workload Processing with MPI for Porter’s Stemming Algorithm

Stemming is the process of reducing a word to the root of words known as a lemma. For example, a stemming algorithm reduces the words "likes" and "likely" to the stem "like". Stemming is a part of linguistic studies and is used in information retrieval and extraction; and plays a critical role in artificial intelligence (AI), especially in natural language processing (NLP). Computers are now equipped with multicore processors and many of the software solutions to a diversity of problems do not take advantage of all these cores to process information and accelerate the processing of workloads. This research presents an effort to accelerate workload processing through the use of Message Passing Interface (MPI) for Porter&#x2019;s stemming algorithm.
Privacy Modelling in Contact Tracing

Contact tracing is a particularly important part of health care and is often overlooked or forgotten up until right when it is needed the most. With the wave of technological achievements in the last decade, a digital perspective for aid in contact tracing was a natural development from traditional contact tracing. When COVID-19 was categorized as a pandemic, the need for modernized contact tracing solutions became apparent, and highly sought after. Solutions using the Bluetooth protocol and/or Global Positioning System data (GPS) were hastily made available to the public in nations all over the world. These solutions quickly became criticized by privacy experts as being potential tools for tracking.
Accelerating Model Validation by Reconstructing Image Sets Using GAN

In recent years, the field of image recognition has been advancing rapidly, and various methods have been developed. However, it is necessary to perform many validations because the appropriate network structure differs depending on the type of image. In many examples, models have been validated using all the original image dataset, which requires a lot of effort. In this study, we used CGAN to reconstruct a small image dataset and succeeded in significantly reducing the model validation time.
Enhancing the Resolution of Satellite Imagery Using a Generative Model

Recent breakthroughs in deep learning algorithms introduced the image super-resolution technique that maps the low-resolution image to generate a high-resolution image. These techniques increase various surveillance applications by providing finer spatial details than data from original sensors. Satellite images obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) observation offer essential information about the earth&#x2019;s landscape, ocean, and ecosystem, contributing to monitoring various applications in the scientific field. The spatial resolution of satellite images has a significant impact on image accuracy. This paper focuses on improving image resolution by training a convolutional neural network to produce super-resolution images from low-resolution images. We present an implementation of Super Resolution Generative Adversarial Network (SRGAN), a GAN-based approach that uses a perceptual loss function that includes an adversarial loss and a content loss. Using a discriminator network that is designed for discerning between super-resolved images and original photo-realistic images, the adversarial loss drives the solution of this architecture to natural images. Moreover, the content loss is driven by perceptual similarity rather than pixel space similarity. We used this architecture to satellite images collected from NASA MODIS devices and found satisfactory results. Our key finding is that our system&#x2019;s result can now be used to improve a variety of low-resolution images.
Edge Intelligence-Based UAV Human Target Recognition with Improved YOLOV5 Algorithm

Edge intelligent computing is often combined with the tasks of Unmanned Aerial Vehicle (UAV). Most applications of UAV require the location and identity information of human targets. Traditional human target detection and face recognition algorithms require a lot of time in edge intelligent computing. In addition, the final recognition result is not accurate enough. To this end, this paper proposes an improved YOLOV5-Cozy target detection algorithm to detect human targets and faces in a fast and accurate way. The algorithm uses MobileNetV3, depthwise separable convolution and shuffleconv to improve the original YOLOV5 (You Only Look Once version 5) algorithm. Compared with the original algorithm, the improved algorithm has an average detection accuracy drop of less than 5&#x0025;, and its calculation time has been reduced by 20&#x0025;. In terms of face recognition, we use the FaceNet algorithm with the addition of a face augmentation module. The module enhances the face recognition ability of the FaceNet algorithm in different environments, and the accuracy of face recognition has increased by 20&#x0025;. We implement the proposed algorithm on the embedded computer Jetson Agx Xavier for UAV edge intelligent computing, and the average FPS achieves at about 23.6.
See where you are: Somatic meditation and contemplative photography

This article shares contemplative explorations of somatic and visual perception through guided movement meditations and photography practice, with roots in Body-Mind Centering ® and the Shambhala Buddhist tradition. These are hybrid practices that I have developed through designing and teaching an online course in Somatic Meditation and Photography. Body-Mind Centering and contemplative photography share some common approaches and values, including inquiry into the nature of perception and the movement of attention, interest in being present to what is unfolding in the present moment and perception as a full-bodied felt sense experience. Contemplative arts and somatic practices offer ways of engaging with intention and choice, to work with the mind and senses, to lightly direct our attention and awareness, which can guide us to moments of landing in and with the present moment as it emerges. I offer three somatic meditations that lead into photography practice for the reader to experience.
Analysis of the content of science books for the first grades of the primary stage in the light of science standards for the next generation

The study aimed to determine the degree of availability of science standards for the next generation in science books for the first grades of the primary stage in the Kingdom of Saudi Arabia. To achieve this, the researcher prepared a list of science standards for the next generation that must be available in science books for the first grades of the primary stage (first-second-third), and a content analysis card was prepared for the three books. a set of results were reached, the most important of which is: The science standards for the next generation were included in the three science books for the first grades of the primary school, at a rate of (100%) and with a great degree of availability. The difference between these books came in the number of repetitions of the criteria in each book. Where these standards came in science books for the first grade of primary school by (59) recurrence, and in science books for the second primary grade by (75) recurrence, and in science books for the third primary grade by (91) recurrence.