Reviewerly/exHarmony huggingface dataset:
A non-asymptotic approach for model selection via penalization in high-dimensional mixture of experts models

Mixture of experts (MoE) are a popular class of statistical and machine learning models that have gained attention over the years due to their flexibility and efficiency. In this work, we consider Gaussian-gated localized MoE (GLoME) and block-diagonal covariance localized MoE (BLoME) regression models to present nonlinear relationships in heterogeneous data with potential hidden graph-structured interactions between high-dimensional predictors. These models pose difficult statistical estimation and model selection questions, both from a computational and theoretical perspective. This paper is devoted to the study of the problem of model selection among a collection of GLoME or BLoME models characterized by the number of mixture components, the complexity of Gaussian mean experts, and the hidden block-diagonal structures of the covariance matrices, in a penalized maximum likelihood estimation framework. In particular, we establish non-asymptotic risk bounds that take the form of weak oracle inequalities, provided that lower bounds for the penalties hold. The good empirical behavior of our models is then demonstrated on synthetic and real datasets.
Compression Coding Method Using Internal Restructuring of Information Space

The subject of the study in this article is data transmission processes of the video information resource in the information communication systems of the air segment under the conditions of errors in the data transmission channel. The purpose of the article is the development of the method of compression coding in order to ensure an increase in the level of reliability of video information resources under the conditions of errors in communication channels. The following tasks are identified: to develop a method of compression coding using structural decomposition of statistical space; analyze the effectiveness of the developed method from the standpoint of ensuring the required level of reliability. The following results are obtained: the developed method of encoding video information allows increasing the level of reliability in the conditions of the transmission of video information resources in the information communication systems of the air segment due to the localization of the action of errors.
Blockchain Potentials in the Maritime Sector: A Survey

Digitalization is becoming more omnipresent in the maritime sector, and actors are increasingly using technological solutions to gain competitiveness and efficacy. Generally, entities use distinct heterogeneous systems, making it challenging to integrate them into a commonly advantageous single and secure interconnected system. While maritime actors show interest in Blockchain, given that its features align with the industry’s needs, they remain reluctant to allocate the needed resources to explore the technology’s revolutionizing potentials. In this paper, we present insights related to Blockchain applications in the maritime sector and the technology’s capabilities in revolutionizing and accelerating the global digitization of the shipping industry. We explore Blockchain literature and bring forward the advantages of its innovative applications in the sector.
Software Defect Prediction based on Machine Learning and Deep Learning

Software system quality can be enhanced by reducing the possible software defects in the system, this can be achieved by regular monitoring of the system for any defect alerts. Identifying the defects with the features is difficult and this system is researched less in the literature. The automated tool will be useful for maintaining high quality of the system. The defects may create a huge loss to enterprises, thus detecting early and accurately is mandatory for the system quality and helps in Software development life cycle (SDLC). The proposed work introduced machine learning algorithm Naïve Bayes and deep learning algorithm Long Short Term Memory(LSTM) and Deep Neural Network (DNN). The dataset considered for the proposed study is PROMISE dataset as a binary prediction. As the software defect prediction is binary, the classification model is opted for this study. Thus NB on ML model is used and DNN, LSTM are compared with their accuracy. Experimental study showed that DNN algorithm outperform in accurate detection of software defects.
Hierarchical K-Means Clustering Method for Friend Recommendation System

Friend suggestion is one of the most popular social networking sites since it helps users connect with others who are similar or known to them. The notion of friend recommendation originated on social media platforms such as Twitter and Facebook, suggesting individuals use the friends-of-friends mechanism. It may be claimed that users do not form friends randomly but rather with their friends' friends. Existing techniques have a limited suggestion scope and are inefficient. Therefore, a novel buddy recommendation model that overcomes the shortcomings of the present system has been proposed. To create a more accurate buddy recommendation system, collaborative filtering is utilized to analyze users' similar and dissimilar data, and a recommendation system that provides user-to-user recommendations based on their comparable choices, activities, and preferences is created. Location-based buddy recommendation systems are gaining popularity because they connect the actual world to the digital realm and provide a complete picture of users' preferences or interests. This recommendation method will broaden the reach of recommendations from one user to another who has similar interests and is located in the same area.
Image encryption using fractional integral transforms: Vulnerabilities, threats, and future scope

With the enormous usage of digital media in almost every sphere from education to entertainment, the security of sensitive information has been a concern. As images are the most frequently used means to convey information, the issue related to the privacy preservation needs to be addressed in each of the application domains. There are various security methods proposed by researchers from time to time. This paper presents a review of various image encryption schemes based on fractional integral transform. As the fractional integral transforms have evolved through their applications from optical signal processing to digital signal and digital image processing over the decades. In this article, we have adopted an architecture and corresponding domain-based taxonomy to classify various existing schemes in the literature. The schemes are classified according to the implementation platform, that may be an optical setup comprising of the spatial modulators, lenses, and charge-coupled devices or it can be a mathematical modeling of such transforms. Various schemes are classified according to the methodology adopted in each of them and a comparative analysis is also presented in tabular form. Based on the observations, the work is converged into a summary of various challenges and some constructive guidelines are provided for consideration in future works. Such a narrative review of encryption algorithm based on various architectural schematics in fractional integral transforms has not been presented before at one place.
An Exploration into the Benefits of the CLIP model for Lifelog Retrieval

In this paper, we attempt to fine-tune the CLIP (Contrastive Language-Image Pre-Training) model on the Lifelog Question Answering dataset (LLQA) to investigate retrieval performance of the fine-tuned model over the zero-shot baseline model. We train the model adopting a weight space ensembling approach using a modified loss function to take into account the differences in our dataset (LLQA) when compared with the dataset the CLIP model was originally pretrained on. We further evaluate our fine-tuned model using visual as well as multimodal queries on multiple retrieval tasks, demonstrating improved performance over the zero-shot baseline model.
Inferential Visual Question Generation

The task of Visual Question Generation (VQG) aims to generate natural language questions for images. Many methods regard it as a reverse Visual Question Answering (VQA) task. They trained a data-driven generator on VQA datasets, which is hard to obtain questions that can challenge robots and humans. Other methods rely heavily on elaborate but expensive artificial preprocessing to generate. To overcome these limitations, we propose a method to generate inferential questions from the image with noisy captions. Our method first introduces a core scene graph generation module, which can align text features and salient visual features to the initial scene graph. It constructs a special core scene graph with expanded linkage outwards from the high-confidence nodes hop by hop. Next, a question generation module uses the core scene graph as a basis to instantiate the function templates, resulting in questions with varying inferential paths. Experiments show that the visual questions generated by our method are controllable in both content and difficulty, and demonstrate clear inferential properties. In addition, since the salient region, captions, and function templates can be replaced by human-customized ones, our method has strong scalability and potential for more interactive applications. Finally, we use our method to automatically build a new dataset, InVQA, containing about 120k images and 480k question-answer pairs, to facilitate the development of more versatile VQA models.
Zero-shot Video Classification with Appropriate Web and Task Knowledge Transfer

Zero-shot video classification (ZSVC) that aims to recognize video classes that have never been seen during model training, has become a thriving research direction. ZSVC is achieved by building mappings between visual and semantic embeddings. Recently, ZSVC has been achieved by automatically mining the underlying objects in videos as attributes and incorporating external commonsense knowledge. However, the object mined from seen categories can not generalized to unseen ones. Besides, the category-object relationships are usually extracted from commonsense knowledge or word embedding, which is not consistent with video modality. To tackle these issues, we propose to mine associated objects and category-object relationships for each category from retrieved web images. The associated objects of all categories are employed as generic attributes and the mined category-object relationships could narrow the modality inconsistency for better knowledge transfer. Another issue of existing ZSVC methods is that the model sufficiently trained with labeled seen categories may not generalize well to distinct unseen categories. To encourage a more reliable transfer, we propose Task Similarity aware Representation Learning (TSRL). In TSRL, the similarity between seen categories and the unseen ones is estimated and used to regularize the model in an appropriate way. We construct a model for ZSVC based on the constructed attributes, the mined category-object relationships and the proposed TSRL. Experimental results on four public datasets, i.e., FCVID, UCF101, HMDB51 and Olympic Sports, show that our model performs favorably against state-of-the-art methods. Our codes are publicly available at https://github.com/junbaoZHUO/TSRL.
Investigation of Energy Cost of Data Compression Algorithms in WSN for IoT Applications

The exponential growth in remote sensing, coupled with advancements in integrated circuits (IC) design and fabrication technology for communication, has prompted the progress of Wireless Sensor Networks (WSN). WSN comprises of sensor nodes and hubs fit for detecting, processing, and communicating remotely. Sensor nodes have limited resources such as memory, energy and computation capabilities restricting their ability to process large volume of data that is generated. Compressing the data before transmission will help alleviate the problem. Many data compression methods have been proposed but mainly for image processing and a vast majority of them are not pertinent on sensor nodes because of memory impediment, energy utilization and handling speed. To overcome this issue, authors in this research have chosen Run Length Encoding (RLE) and Adaptive Huffman Encoding (AHE) data compression techniques as they can be executed on sensor nodes. Both RLE and AHE are capable of balancing compression ratio and energy utilization. In this paper, a hybrid method comprising RLE and AHE, named as H-RLEAHE, is proposed and further investigated for sensor nodes. In order to verify the efficacy of the data compression algorithms, simulations were run, and the results compared with the compression techniques employing RLE, AHE, H-RLEAHE, and without the use of any compression approach for five distinct scenarios. The results demonstrate the RLE's efficiency, as it surpasses alternative data compression methods in terms of energy efficiency, network speed, packet delivery rate, and residual energy throughout all iterations.
Construction of Teaching and Education Quality Improvement Model Based on Distributed Information Fusion

Following the growth of educational informatization, the integrated adoption of the Internet in classroom teaching has facilitated the change of instructional pattern from the conventional instructional pattern to a novel information-based instructional pattern. Based on the background of “Internet+,” a variety of new information-based classroom teaching models have emerged. However, in practice, there are still many deficiencies due to the immature application of the model. This paper proposes to use the design-based research paradigm to construct and optimize the teaching mode, and to clarify the design principles of the teaching mode. This paper analyses and designs the English lesson instruction pattern in the distributed information convergence condition from five dimensions: theoretical foundation, online instructional setting, instructional targets, instructional events, and instructional assessment, which construct an instructional pattern. It adopts the method of combining quantitative research with qualitative research and combines the experimental method, questionnaire survey method, classroom observation method, and interview method to assist the research. Through the investigation of 122 valid data, it is shown that the overall amount of classroom codes before the experiment is 57, and the overall amount of classroom codes after the experiment is 150. There is a big difference between the two. It validates the effectiveness of the English lesson instruction pattern in distributed information integration terms in improving students’ English achievement, cooperative learning ability, information literacy, and problem-solving ability.
Deep learning accelerates whole slide imaging for next-generation digital pathology applications

Deep learning demonstrates the ability to significantly increase the scanning speed of whole slide imaging in histology. This transformative solution can be used to further accelerate the adoption of digital pathology.
Fall detection using accelerometer-based smartphones: Where do we go from here?

According to World Health Organization statistics, falls are the second leading cause of unintentional injury deaths worldwide. With older people being particularly vulnerable, detecting, and reporting falls have been the focus of numerous health technology studies. We screened 267 studies and selected 15 that detailed pervasive fall detection and alerting apps that used smartphone accelerometers. The fall datasets used for the analyses included between 4 and 38 participants and contained data from young and old subjects, with the recorded falls performed exclusively by young subjects. Threshold-based detection was implemented in six cases, while machine learning approaches were implemented in the other nine, including decision trees, k-nearest neighbors, boosting, and neural networks. All methods could ultimately achieve real-time detection, with reported sensitivities ranging from 60.4 to 99.3% and specificities from 74.6 to 100.0%. However, the studies had limitations in their experimental set-ups or considered a restricted scope of daily activities-not always representative of daily life-with which to define falls during the development of their algorithms. Finally, the studies omitted some aspects of data science methodology, such as proper test sets for results evaluation, putting into question whether reported results would correspond to real-world performance. The two primary outcomes of our review are: a ranking of selected articles based on bias risk and a set of 12 impactful and actionable recommendations for future work in fall detection.
A Novel Mixed-Attribute Fusion-Based Naive Bayesian Classifier

The Naive Bayesian classifier (NBC) is a well-known classification model that has a simple structure, low training complexity, excellent scalability, and good classification performances. However, the NBC has two key limitations: (1) it is built upon the strong assumption that condition attributes are independent, which often does not hold in real-life, and (2) the NBC does not handle continuous attributes well. To overcome these limitations, this paper presents a novel approach for NBC construction, called mixed-attribute fusion-based NBC (MAF-NBC). It alleviates the two aforementioned limitations by relying on a mixed-attribute fusion mechanism with an improved autoencoder neural network for NBC construction. MAF-NBC transforms the original mixed attributes of a data set into a series of encoded attributes with maximum independence as a pre-processing step. To guarantee the generation of useful encoded attributes, an efficient objective function is designed to optimize the weights of the autoencoder neural network by considering both the encoding error and the attribute’s dependence. A series of persuasive experiments was conducted to validate the feasibility, rationality, and effectiveness of the designed MAF-NBC approach. Results demonstrate that MAF-NBC has superior classification performance than eight state-of-the-art Bayesian algorithms, namely the discretization-based NBC (Dis-NBC), flexible naive Bayes (FNB), tree-augmented naive (TAN) Bayes, averaged one-dependent estimator (AODE), hidden naive Bayes (HNB), deep feature weighting for NBC (DFW-NBC), correlation-based feature weighting filter for NBC (CFW-NBC), and independent component analysis-based NBC (ICA-NBC).
Energy-Constrained D2D Assisted Federated Learning in Edge Computing

The surging of deep learning brings new vigor and vitality to shape the prospect of intelligent Internet of Things (IoT), and edge intelligence arises to provision real-time deep neural network (DNN) inference services for mobile users. To perform efficient and effective DNN model training in edge environments while preserving training data security and privacy of IoT devices, federated learning has been envisioned as an ideal learning paradigm for this purpose. In this paper we study energy-aware DNN model training in an edge environment. We first formulate a novel energy-aware, device-to-device (D2D) assisted federated learning problem with the aim to minimize the global loss of a training DNN model, subject to bandwidth capacity on an edge server and the energy capacity on each IoT device. We then devise an efficient heuristic algorithm for the problem. The crux of the proposed algorithm is to explore the energy usage of neighboring devices of each device for its local model uploading, by reducing the problem to a series of maximum weight matching problems in corresponding auxiliary graphs. We finally evaluate the performance of the proposed algorithm through experimental simulations. Experimental results show that the proposed algorithm is promising.
Learning a Prototype Discriminator With RBF for Multimodal Image Synthesis

Multimodal image synthesis has emerged as a viable solution to the modality missing challenge. Most existing approaches employ softmax-based classifiers to provide modal constraints for the generated models. These methods, however, focus on learning to distinguish inter-domain differences while failing to build intra-domain compactness, resulting in inferior synthetic results. To provide sufficient domain-specific constraint, we hereby introduce a novel prototype discriminator for generative adversarial network (PT-GAN) to effectively estimate the missing or noisy modalities. Different from most previous works, we introduce the Radial Basis Function (RBF) network, endowing the discriminator with domain-specific prototypes, to improve the optimization of generative model. Since the prototype learning extracts more discriminative representation of each domain, and emphasizes intra-domain compactness, it reduces the sensitivity of discriminator to pixel changes in generated images. To address this dilemma, we further propose a reconstructive regularization term which connects the discriminator with the generator, thus enhancing its pixel detectability. To this end, the proposed PT-GAN provides not only consistent domain-specific constraints, but also reasonable uncertainty estimation of generated images with the RBF distance. Experimental results show that our method outperforms the state-of-the-art techniques. The source code will be available at: https://github.com/zhiweibi/PT-GAN.
Developments in associative theory: A tribute to the contributions of Robert A. Rescorla.

The field of associative learning theory was forever changed by the contributions of Robert A. Rescorla. He created an organizational structure that gave us a framework for thinking about the key questions surrounding learning theory: what are the conditions that produce learning?, what is the content of that learning?, and how is that learning expressed in performance? He gave us beautifully sophisticated experimental designs that tackled deep theoretical problems in experimentally clever and elegant ways. And he left us with a collection of work that fundamentally altered the way we as a field think about basic learning processes. Few scientists have impacted their field in the way that Rescorla impacted animal learning theory. In this paper, we introduce this special issue (Developments in Associative Theory: A Tribute to Robert A. Rescorla) by considering some of the many ways in which Rescorla's empirical and theoretical contributions impacted learning theory over his almost 50-year career. We conclude by identifying multiple fundamental issues we think he would have found especially fruitful to pursue as we continue to move forward. (PsycInfo Database Record (c) 2022 APA, all rights reserved).