Reviewerly/exHarmony huggingface dataset:
Self-supervised temporal event segmentation inspired by cognitive theories

We consider the event segmentation problem, i.e., how to mark the event's temporal boundaries and spatially locate them in the image. We draw heavily from cognitive science research to define the problem of event segmentation and to design highly effective computer vision algorithms for spatio-temporal segmentation of events in videos. These approaches do not require any annotated data. They can process streaming video data while learning robust representations for segmenting events. First, we introduce the Event Segmentation Theory (EST) model based on the perceptual prediction model to compute the event boundaries proposed by Zacks et al. (Radvansky and Zacks, 2014). Then we present our computer vision solutions based on the perceptual prediction model from EST in three progressive versions: temporal segmentation using perceptual prediction framework, temporal segmentation along with event working models based on attention maps, and finally spatial and temporal localization of events. As in the EST, a perceptual processing unit (a CNN stack) sends the extracted features from the current frame (conditioned on the working event model) to a prediction unit (LSTM), which predicts future perceptual features. A mismatch between the predicted features and the actual, observed features generates a prediction error signal. The prediction error triggers a gating mechanism to update the working event model (hidden states in the LSTM) with a new model and mark an event boundary. Extensive experiments demonstrate that the predictive learning approach can learn robust event representations from only a single-pass through an unlabeled streaming video. We show state-of-the-art performance in unsupervised temporal segmentation and spatial-temporal action localization while offering competitive performance with fully supervised baselines that require extensive amounts of annotation. Additionally, we show that our framework can process streaming video data of extremely long duration, close to real-time processing.
Reasoning Method Based on Intervals with Symmetric Truncated Normal Density

Error parameters are inevitable in systems. In formal verification, previous reasoning methods seldom considered the probability information of errors. In this article, errors are described as symmetric truncated normal intervals consisting of the intervals and symmetric truncated normal probability density. Furthermore, we also rigorously prove lemmas and a theorem to partially simplify the calculation process of truncated normal intervals and independently verify the formulas of variance and expectation of symmetric truncated interval given by some scholars. The mathematical derivation process or verification codes are provided for most of the key formulas in this article. Hence, we propose a new reasoning method that combines the probability information of errors with the previous statistical reasoning methods. Finally, an engineering example of the reasoning verification of train acceleration is provided. After simulating the large-scale cases, it is shown that the simulation results are consistent with the theoretical reasoning results. This method needs more calculation, while it is more effective in detecting non-error’s fault factors than other error reasoning methods.
Pre-model Interpretability and Explainability

This chapter discusses various ways of using pre-modeling explainability: a set of techniques aimed at gaining insights into a dataset to help build more effective models. Since any machine learning model is built from the data, understanding the content on which the model is based is imperative for explainability and interpretability. Many of these techniques that summarize, visualize, and explore data have existed for a long time. There have been some recent additions to improve the methods, especially with respect to scaling and performance. We will present exploratory data analysis with some well-known univariate and multivariate techniques to visualize the data. Time series data needs different transformations, visualizations and analysis than structured data. Some of the common time series visualizations and data exploration techniques will be discussed next. Similarly, exploratory data analysis on unstructured data such as text needs special handling as compared to structured data. This will entail a discussion on some of the well-known EDA techniques common to many NLP tasks. We will then discuss some of the feature engineering techniques that help us get more insights for explainability. For our discussions, we will be using the diabetes dataset for structured data: LitCovid (for NLP data), and Mauna Loa CO2 dataset (for time series data) and Fashion-MNIST (for computer vision).
Using Transfer Learning in Building Federated Learning Models on Edge Devices

Today&#x2019;s state-of-the-art machine learning techniques, such as deep networks, are typically trained using cloud platforms, leveraging the elastic scalability of the cloud. For such processing, data from various sources need to be transferred to a cloud server. While this works well for some application domains, it is not suitable for all due to privacy and network overhead concerns. Federated learning is a machine learning setting designed for learning from distributed data over edge devices. In federated learning, data does not leave users&#x2019; devices. Instead, each user trains a local model with their data and shares the model updates with a federated server to aggregate and build a global model. Such training respects users&#x2019; privacy while reducing the network overhead for transferring data to a central server. Despite all advantages, each user usually has limited data that might not be enough to train a well-performing local model. In this work, to solve this challenge while keeping the client data private, we combine transfer learning with federated learning, where we train a base model with a public dataset. The base model is passed to the federated users to be fine-tuned for the target task. We have built a federated image classification model for the CIFAR-100 dataset using transfer learning and have studied how different parameters affect the model accuracy. Using this process, we achieve a model accuracy similar to a model trained in a central fashion while keeping the data private.
An Integrated LSTM-HeteroRGNN Model for Interpretable Opioid Overdose Risk Prediction

Opioid overdose (OD) has become a leading cause of accidental death in the United States, and overdose deaths reached a record high during the COVID-19 pandemic. Combating the opioid crisis requires targeting high-need populations by identifying individuals at risk of OD. While deep learning emerges as a powerful method for building predictive models using large scale electronic health records (EHR), it is challenged by the complex intrinsic relationships among EHR data. Further, its utility is limited by the lack of clinically meaningful explainability, which is necessary for making informed clinical or policy decisions using such models. In this paper, we present LIGHTED, an integrated deep learning model combining long short term memory (LSTM) and graph neural networks (GNN) to predict patients’ OD risk. The LIGHTED model can incorporate the temporal effects of disease progression and the knowledge learned from interactions among clinical features. We evaluated the model using Cerner’s HealthFacts database with over 5 million patients. Our experiments demonstrated that the model outperforms traditional machine learning methods and other deep learning models. We also proposed a novel interpretability method by exploiting embeddings provided by GNNs and clustering patients and EHR features respectively, then conducted qualitative feature cluster analysis for clinical interpretations. Our study shows that LIGHTED can take advantage of longitudinal EHR data and the intrinsic graph structure of EHRs among patients to provide effective and interpretable OD risk predictions that may potentially improve clinical decision support.
Text generation by probabilistic suffix tree language model

During last decade, language modeling has been dominated by neural structures; RNN, LSTM or Transformer. These neural language models provide excellent performance to the detriment of very high computational cost. This work investigates the use of probabilistic language model that requires much less computational cost. In particular, we are interested in variable-order Markov model that can be efficiently implemented on a probabilistic suffix tree (PST) structure. The PST construction is cheap and can be easily scaled to very large dataset. Experimental results show that this model can be used to generated realistic sentences.
Design and Realization of Basketball Tactics Computer Aided Teaching System

With the development and popularization of computer technology and the improvement of education quality, this computer-assisted teaching method has been widely used in basketball and other classroom teaching. The combination of computer and basketball tactics teaching also brings a kind of fresh air to basketball tactics teaching workers. The interaction between the two makes this kind of computer-assisted teaching play an important role in basketball tactics teaching. In the current basketball tactical teaching, computer-aided technology has not only become an advanced method and teaching tool for comprehensively promoting the development of quality education, but also provides us with a wealth of expressive power, strong inclusive beauty and good mutual control powerful computer basketball tactics teaching mode. This paper studies the design and implementation of basketball tactics computer-assisted teaching system, based on the relevant literature, it has a simple understanding of basketball tactics computer-aided teaching system, and then based on the understanding of the materials, the design of basketball tactics computer-aided teaching system a simple analysis was carried out, mainly aiming at the teaching requirements that the designed system must meet, and then the system was designed according to the system analysis, and finally the teaching system designed in this article was tested, and the test results were obtained, although the basketball tactics computer-assisted teaching system still there are shortcomings, but generally speaking, the number of people with better evaluations of the performance of the teaching system accounted for more than 32%, and generally more than 30%.
“Did you know that David Beckham speaks nine languages?”: AI-supported production process for enhanced personalization of audio-visual content

The introduction of artificial intelligence (AI) into the media production process has contributed to the automation of selected tasks and stronger hybridization of man and machine in the process; however, the AI-supported production process has expanded from the traditional, three-stage model by a new phase of consumer evaluation and feedback collection, analysis, and application. This has opened a way for far-reaching content personalization and thus offers a new type of media experience. Powering the production process with a constant stream of consumer data has also affected the process itself and changed its nature from linear to cyclical.
Intelligent Water Drop Algorithm based Web Page Recommendation Using Linear Regression Learning

Analysis of website visitor for improving the performance of user experience on web portal is primary requirement in this online world. Hence number of web-based feature was used for the study of user behavior and recommend page accordingly. This research work presents the utilization of weblog feature for analysis and pattern learning. Proposed model extract association rule forms the weblog with text pattern format on the web page. Here we have considered the input data set from a live website’s weblogs called as project tunnel.com. Association rule and text URL patterns were used for learning multi-Regression probability generation. Once the regression value is set than Intelligent Water Drop Algorithm will be applied for predicting or recommending the next page as per user previous set of pages, where IWD algorithm increases the accuracy of prediction by utilizing the regression values in fitness function. Live websites weblogs were analyzed for the experiment and comparison of proposed WPPIWD (Web Page Prediction Intelligent Water Drop) has improved the work efficiency when compared to other existing methods.
The Architecture Design of Intelligent Campus System Based on Network Information Security

In the process of cultivating the quantity and quality of modern talents, in order to effectively compete for talents, we can transform the essence of education competition into the structure competition of the intelligent campus network system for the economic and social development of our country. Under the typical computer network development background of the third civilization of information technology, the typical development characteristic of education is to be able to integrate the school’s information management and personal progress with network technology into the advanced systematic development framework. The key point of intelligent campus system architecture design is not to take physical and mechanical energy as the main body, but to take more advanced intelligent development as the main body, and hope to improve the core level of education information, and promote the continuous evolution of education reform process.
Optical Steganography using Phase Encoding of Coherent States in a Noisy Thermal Channel

We demonstrate steganography through an optical communication channel by encrypting the information in the phase of the coherent state, where the phase distribution emulates a thermal state, making the communication both secure and secret.
Scalability and Fabrication Tolerance Analysis of Reconfigurable Unitary Optical Converter

We carry out numerical analysis and physical simulation of scalability and fabrication tolerance for programmable unitary converter, verifying that MSE of around - 20 dB can be achieved for matrix order of 20 and phase-shifter stage of 11 with tolerant fabrication errors.
Analyzing Time Complexity of Practical Learned Image Compression Models

We have witnessed the rapid development of learned image compression (LIC). The latest LIC models have outperformed almost all traditional image compression standards in terms of rate-distortion (RD) performance. However, the time complexity of LIC model is still underdiscovered, limiting the practical applications in industry. Even with the acceleration of GPU, LIC models still struggle with long coding time, especially on the decoder side. In this paper, we analyze and test a few prevailing and representative LIC models, and compare their complexity with traditional codecs including H.265/HEVC intra and H.266/VVC intra. We provide a comprehensive analysis on every module in the LIC models, and investigate how bitrate changes affect coding time. We observe that the time complexity bottleneck mainly exists in entropy coding and context modelling. Although this paper pay more attention to experimental statistics, our analysis reveals some insights for further acceleration of LIC model, such as model modification for parallel computing, model pruning and a more parallel context model.
Supervised evolutionary learning: Use of gradient histogram &amp; particle swarm algorithm to detection &amp; tracking pedestrian in sequence of infrared images

Recently, tracking and pedestrian detection from various images have become one of the major issues in the field of image processing and statistical identification. In this regard, using evolutionary learning-based approaches to improve performance in different contexts can greatly influence the appropriate response. There are problems with pedestrian tracking/identification, such as low accuracy for detection, high processing time, and uncertainty in response to answers. Researchers are looking for new processing models that can accurately monitor one's position on the move. In this study, a hybrid algorithm for the automatic detection of pedestrian position is presented. It is worth noting that this method, contrary to the analysis of visible images, examines pedestrians' thermal and infrared components while walking and combines a neural network with maximum learning capability, wavelet kernel (Wavelet transform), and particle swarm optimization (PSO) to find parameters of learner model. Gradient histograms have a high effect on extracting features in infrared images. As well, the neural network algorithm can achieve its goal (pedestrian detection and tracking) by maximizing learning. The proposed method, despite the possibility of maximum learning, has a high speed in education, and results of various data sets in this field have been analyzed. The result indicates a negligible error in observing the infrared sequence of pedestrian movements, and it is suggested to use neural networks because of their precision and trying to boost the selection of their hyperparameters based on evolutionary algorithms.
Attention-Based Sub-Word Network for Multilingual Short Text Classification

Feature computation of multilingual text is an important semantic processing task in the field of natural language processing (NLP). In the actual production environment, the state-of-the-art models cannot analyze short texts mixed with multi-languages correctly. To tackle these problems, we propose a sub-word embedding network with multilingual features for short text understanding to capture the most important semantic information in a multilingual short sentence. In this work, our method utilizes a coupling coefficient calculation-based model that generates the sub-words of the input sentence. By sharing sub-word features, the feature space of multilingual mixed-word is constructed. The method that can extract the most significant information in a sentence without ignoring other relevant information. While the model is structurally simple, it can easily be trained end-to-end and scales to a large amount of training data. The experimental results on the Multilingual Short Text (MST), THUCNews and AGNews datasets show that our method outperforms most of the existing methods.
Assessment of Cyber Security Maturity for Critical Domains in Republic of Moldova

In this paper we present the evaluation results based on the model for cyber security maturity assessment upon the cyber security program development for critical infrastructures (CI) in Republic of Moldova [1]. During last year the healthcare domains has become a target of strategic interest amongst cyber criminals. These events can lead not only to security breaches, but also to safety events that affect human lives. The findings confirm the recommendations of previous case studies on evaluation of cyber security requirements for healthcare inclusive nuclear and radiological domain. Therefore, we classify the results based on the dimensions of the recently developed Model for Cyber Security Maturity Assessment in CI [1]. The proposed model focuses on four primary dimensions: policies and administration, education and training, work environment, cyber risk management. It has been developed with the goal to help decision makers analyze the efficiency of information systems used for cyber risk management. The model is multidimensional and can also show the actual state of cyber security maturity in an organization, fact which was also proven by external reviews. The experimental test of this analysis confirm the applicability of the Model for organizations at different stages of cyber security development, as well as from different domains.
Self-Supervised Learning of Depth From Sequence of Images

The Estimation of distance between objects in a relation to camera is still a challenging problem of many robotics applications, such as self-driving cars, 3D scene reconstruction, and robot grasping. Estimation of depth at which different objects are present in the scene are previously approached using many supervised based learning methods. Our Self-supervised learning based architecture has demonstrated great potential in estimating depth from Monocular images. SFM (Structure from Motion) and depth animation based on stereo images, basically rely on the feature matching of different views of the same scene. Capturing depth related data from a single photograph would be a challenging task (ill-posed problem). In this work, We tried to present a self-supervised learning based monocular depth estimation method. Further, we also addressed some of the common issues caused because of dynamic objects and occlusions. We have evaluated the results of the monocular sequence based method with stereo image based method and we observed that with no additional contribution the model performs poorly and addition of masking, min re-projection loss improves the performance of the model.
Disentangling Reconstruction Network for Unsupervised Cross-Domain Person Re-Identification

Unsupervised cross-domain Person Re-Identification (Re-ID) suffers from severe domain gap issue. While different works address this issue, bridging domain gap with high-level representation is hard as it comprises of entangled information including identity, background, occlusion, and other domain-specific variations. In this paper, we propose a disentangled reconstruction method to address the domain-shift problem for Re-ID in an unsupervised manner. To this end, we have two major contributions. First, we propose to disentangle identity-relevant and identity-irrelevant features from person images. Second, in the target domain, we explicitly consider the camera style transfer images as a data augmentation to address intra-domain discrepancy and to learn the camera invariant features. Experimental results on the challenging benchmarks of Market-1501 and DukeMTMC-reID demonstrate that our proposed method achieves competitive performance.
Image-based Content Recommendation System with CNN

Online shopping is a recent trend all over the world. These stores depend on search engines, which take a keyword from user and returns similar keywords. However, search engines are expanded to take an image and returns similar images. The technique behind these search engines is known as recommender systems. They typically appear on shopping sites as a “you may also like” segment. If the user watches YouTube this section shows users’ favorite videos, the user browses Amazon items this section displays users’ preferred products that are matching.The proposed framework presents a recommender system, which takes input as an image attempts to recognize the facts about items to recommend similar items. Initially, this research work utilizes a basic neural network model to classify an input image as one of the items’ categories. Then, a CNN model is used to identify the closer images. Finally, transfer learning model is used to improve accuracy and speed. The proposed dataset is prepared by labelling and identifying preferred portion of an image. Lastly, this research study recommend items with higher similarity score more than 0.5 and suggest precise on-line shopping support.