Reviewerly/exHarmony huggingface dataset:
Sign Language to Text Conversion using Deep Learning

Signs and gestures are a way of communication that, instead of spoken words, makes use of movements and gestures made by hands along with changes in facial expression and changes in bodily movements. There are many distinct sign languages throughout the world, just like there are many different spoken languages. Signs and gestures are primarily used by people who cannot hear and speak and use this to express their emotions and interact with each otherIn ancient times, gestures and signs were the only way of interacting with each other, since there were no spoken languages and was considered an obvious and natural way of communicating. However, as the time passed, the utilization of sign language started becoming more prevalent only for the people who are deaf or have any hearing impairments.Currently, over 80 million people in the world use gestures made by hands and symbols, which is a small part of the 780 crores people living on Earth as of 2020. These people depend greatly on sign languages to learn, access services and be a part of the communities. However, there is a lack of support in terms of providing basic services because of the fewer sign interpreters available.To overcome the challenges and issues created due to the dearth of information and knowledge relating to sign languages and to provide services that people deserve, there is a need to spread the use of sign languages among the general public. However, this involves a lot of effort and does not provide the results that are equivalent to the labour done. Detecting the correct signs in images can be a complex process and can include a variety of pre-processing techniques to be performed. In this project, various images containing the signs of the American-Sign-Language have been processed and utilised for implementing a CNN model to classify these images as their alphabetical equivalent.
FAM-Graph: Graph Analytics on Disaggregated Memory

Disaggregated memory is being proposed as a way to provide efficient memory scaling for data intensive applications. High performance interconnect technologies, such as CXL, make disaggregated, fabric-attached-memory (FAM) a viable secondary tier of memory. Previous work on remote memory relies on extending kernel level paging to utilize FAM as an additional storage tier after local memory. These approaches have the advantage of exposing remote memory in application transparent ways that do not require code changes, but they incur large overheads due to the mismatch between the abstraction of a flat virtual address space and the reality of the tiered nature of FAM. In this paper, we present an alternative approach to remote memory based on application-specific objects. We design FAM-Graph - a semi-external graph processing system that leverages application-level properties, such as read only edge data, to efficiently tier data between local and remote memory, and prefetch remote data for local computation. Using several graph algorithms and datasets, we demonstrate that FAM-Graph achieves end-to-end performance within factors of 1–6× of Galois, the state of the art shared memory graph processing system, while using up to 20× less local memory. When Galois is used in conjunction with an OS-level FAM solution, we show that FAM-Graph achieves better end-to-end performance by up to 9× when both systems are configured with the same amount of local memory.
Redactable Blockchain-Enabled Hierarchical Access Control Framework for Data Sharing in Electronic Medical Records

The application of blockchain to data sharing in an untrusted environment has received widespread industry attention in recent years. However, the tamper-proof property of blockchain brings protection for sharing data and also leads to a new limitation for the deletion of malicious data. Nowadays, many methods based on redactable blockchain with a chameleon hash is proposed to attempt to solve the above problem, but loss of effective access control to the modifiers can easily lead to breaking the positive characteristics of the blockchain. To address the aforementioned problem, a hierarchical access control redactable blockchain model for data sharing through attribute-based encryption and a chameleon hash is introduced. Under this model, the data owner can specify who can modify their data by setting an access policy and authenticating the modifier with a digital signature. The data owner’s full control over rights is guaranteed, while the modifier’s behavior is regulated. In addition, the hierarchical access control decentralized technique solves the overpowerful problem of center authority and single-point-of-failure problems in the existing works. The security analysis indicates that the proposed scheme achieves indistinguishable chosen plaintext attack security as well as collision resistance security. Performance analysis shows that the proposed scheme is more complete and has better efficiency compared with other schemes.
Evaluating Scale Attention Network for Automatic Brain Tumor Segmentation with Large Multi-parametric MRI Database

Automatic segmentation of brain tumors is an essential but challenging step for extracting quantitative imaging biomarkers for accurate tumor detection, diagnosis, prognosis, treatment planning and assessment. This is the 10th year of Brain Tumor Segmentation (BraTS) Challenge that utilizes multi-institutional multi-parametric magnetic resonance imaging (mpMRI) scans for tasks: 1) evaluation the state-of-the-art methods for the segmentation of intrinsically heterogeneous brain glioblastoma sub-regions in mpMRI scans; and 2) the evaluation of classification methods to predict the MGMT promoter methylation status at pre-operative baseline scans. We participated the image segmentation task by applying a fully automated segmentation framework that we previously developed in BraTS 2020. This framework, named as scale-attention network, incorporates a dynamic scale attention mechanism to integrate low-level details with high-level feature maps at different scales. Our framework was trained using the 1251 challenge training cases provided by BraTS 2021, and achieved an average Dice Similarity Coefficient (DSC) of 0.9277, 0.8851 and 0.8754, as well as $$95\%$$ Hausdorff distance (in millimeter) of 4.2242, 15.3981 and 11.6925 on 570 testing cases for whole tumor, tumor core and enhanced tumor, respectively, which ranked itself as the second place in the brain tumor segmentation task of RSNA-ASNR-MICCAI BraTS 2021 Challenge (id: deepX).
User Modeling and Profiling in Information Systems

User modeling or user profiling is fundamental to manage information overload issues in many adaptive and personalized systems (e.g., recommender systems, personalized search engines, adaptive user interfaces). Although there are some literature review papers that provide an overview of existing studies in user modeling and their usage, there is currently a lack of bibliometric studies that can provide a systematic and quantitative overview of this research area. Therefore, this paper aims to complete the existing literature in this research area through a bibliometric study based on 52,027 relevant publications extracted from Scopus, a world-leading publisher-independent global citation database. The analyses enabled us to identify the most relevant publications, sources of publications, authors, institutions, countries, and their collaboration. We also identify and classify the twelve most important associated topics, along with their subtopics and their trends. Some identified weak signals in topic trend analysis also provide good ideas of potential future research directions.
GSMFlow: Generation Shifts Mitigating Flow for Generalized Zero-Shot Learning

Generalized Zero-Shot Learning (GZSL) aims to recognize images not only for seen classes but also for unseen ones by transferring semantic-visual relationships from the seen to the unseen classes. It is an intuitive solution to take the advantage of generative models to hallucinate realistic unseen samples based on the knowledge learned from the seen classes. However, due to the generation shifts, the synthesized samples by most existing methods may drift from the real distribution of the unseen data. To address this issue, we propose a novel flow-based generative framework that consists of multiple conditional affine coupling layers for learning unseen data generation. Specifically, we investigate and address three essential problems that trigger the generation shifts, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">semantic inconsistency</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">variance collapse</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">structure disorder</i> . First, to improve the reflection of the semantic information in the generated samples, we proactively embed the semantic information into the transformation in each conditional affine coupling layer. Second, to promote the intrinsic feature variance of the unseen classes, we introduce a boundary sample mining strategy with entropy maximization to discover ambiguous visual variants of semantic prototypes and hereby calibrate the decision boundary of the classifiers. Third, a relative positioning strategy is proposed to revise the attribute embeddings, guiding which to fully preserve the inter-class geometric structure and further avoid structure disorder in the semantic space. Extensive experimental results on four GZSL benchmark datasets demonstrate that GSMFlow achieves the state-of-the-art performance on GZSL.
Free Speech, False Polarization, and the Paradox of Tolerance

Guoping Zhao observes similarities between the techniques employed, in her words, "to restrict liberty and freedom in China and…to pursue social justice in the US." 1 In both China and the states, she notes how dissent, simple questioning, and opposing prevailing views are treated with intolerance: they are "chilled out," delegitimated, and otherwise marked as unwanted.While the government leads this charge in China (as Zhao notes), cross-sections of the public prevail within an emergent "cancel culture" in the states, described in an open letter in Harper's as holding "a new set of moral attitudes and political commitments that
Large-Scale Machine Learning Cluster Scheduling via Multi-Agent Graph Reinforcement Learning

Efficient scheduling of distributed deep learning (DL) jobs in large GPU clusters is crucial for resource efficiency and job performance. While server sharing among jobs improves resource utilization, interference among co-located DL jobs occurs due to resource contention. Interference-aware job placement has been studied, with white-box approaches based on explicit interference modeling and black-box schedulers with reinforcement learning. In today’s clusters containing thousands of GPU servers, running a single scheduler to manage all arrival jobs in a timely and effective manner is challenging, due to the large workload scale. We adopt multiple schedulers in a large-scale cluster/data center, and propose a multi-agent reinforcement learning (MARL) scheduling framework to cooperatively learn fine-grained job placement policies, towards the objective of minimizing job completion time (JCT). To achieve topology-aware placements, our proposed framework uses hierarchical graph neural networks to encode the data center topology and server architecture. In view of a common lack of precise reward samples corresponding to different placements, a job interference model is further devised to predict interference levels in face of various co-locations, for training of the MARL schedulers. Testbed and trace-driven evaluations show that our scheduler framework outperforms representative scheduling schemes by more than 20% in terms of average JCT, and is adaptive to various machine learning cluster topologies.
Extraction of entity relations from Chinese medical literature based on multi-scale CRNN

Entity relation extraction technology can be used to extract entities and relations from medical literature, and automatically establish professional mapping knowledge domains. The classical text classification model, convolutional neural networks for sentence classification (TEXTCNN), has been shown to have good classification performance, but also has a long-distance dependency problem, which is a common problem of convolutional neural networks (CNNs). Recurrent neural networks (RNN) address the long-distance dependency problem but cannot capture text features at a specific scale in the text.To solve these problems, this study sought to establish a model with a multi-scale convolutional recurrent neural network for Sentence Classification (TEXTCRNN) to address the deficiencies in the 2 neural network structures. In entity relation extraction, the entity pair is generally composed of a subject and an object, but as the subject in the entity pair of medical literature is always omitted, it is difficult to use this coding method to obtain general entity position information. Thus, we proposed a new coding method to obtain entity position information to re-establish the relationship between subject and object and complete the entity relation extraction.By comparing the benchmark neural network model and 2 typical multi-scale TEXTCRNN models, the TEXTCRNN [bidirectional long- and short-term memory (BiLSTM)] and TEXTCRNN [double-layer stacking gated recurrent unit (GRU)], the results showed that the multi-scale CRNN model had the best F1 value performance, and the TEXTCRNN (double-layer stacking GRU) was more capable of entity relation classification when the same entity word did not belong to the same entity relation.The experimental results of the entity relation extraction from Pharmacopoeia of the People's Republic of China-Guidelines for Clinical Drug Use-Volume of Chemical Drugs and Biological Products showed that entity relation extraction could effectively proceed using the new labeling method. Additionally, compared to typical neural network models, including the TEXTCNN, GRU, and BiLSTM, the multi-scale convolutional recurrent neural network structure had advantages across several evaluation indicators.
Complex Adaptation in Audio Feedback Networks for the Synthesis of Music and Sounds

Abstract This article presents recent outcomes of the author's research on musical complex adaptive systems (CASs). The first part focuses on the concepts of adaptation and complexity within the framework of CASs and suggests a rigorous placing of the concepts within the musical domain. This analysis involves a distinction of the notions of context and information between the engineering field of information theory and the philosophical one of radical constructivism. I conclude this section by showing that, in this approach, information and context are mutually determining. Then, I introduce a technique related to the notion of evolvability in biology and genetic algorithms and that has significantly increased the complexity and long-term variety in music systems during autonomous evolutions. This technique distributes adaptation across higher levels and allows the system to reorganize the relationships among its agents and their structure circularly while interpreting and constructing its context. To conclude, an autonomous live performance piece from 2019–2020, “Constructing Realities (Homage to Heinz von Foerster),” which implements the theories mentioned above, is described, showing DSP processes and techniques that relate to evolvability, autopoiesis, fitness, and complexity through agent-based modeling. This article is accompanied by a companion article discussing the technical aspects of information processing algorithms, which are an essential part for the implementation of music CASs: “Time-Domain Adaptive Algorithms for Low- and High-Level Audio Information Processing.”
Reintroducing Straight-Through Estimators as Principled Methods for Stochastic Binary Networks

Training neural networks with binary weights and activations is a challenging problem due to the lack of gradients and difficulty of optimization over discrete weights. Many successful experimental results have been achieved with empirical straight-through (ST) approaches, proposing a variety of ad-hoc rules for propagating gradients through non-differentiable activations and updating discrete weights. At the same time, ST methods can be truly derived as estimators in the stochastic binary network (SBN) model with Bernoulli weights. We advance these derivations to a more complete and systematic study. We analyze properties, estimation accuracy, obtain different forms of correct ST estimators for activations and weights, explain existing empirical approaches and their shortcomings, explain how latent weights arise from the mirror descent method when optimizing over probabilities. This allows to reintroduce ST methods, long known empirically, as sound approximations, apply them with clarity and develop further improvements.
A circle chaos random search strategy particle swarm optimization with its application

As a popular optimization, particle swarm optimization has attracted many researcher's attention and also been successfully applied to solve many real world problems. As adopting the strategy of history experience, the convergence rate of PSO is faster than other evolutionary algorithms. But the individuals of PSO are easily be trapped into local optimum, which causes the algorithm could not guaranty confidence of the final results. In this paper, for further enhance the global search ability of PSO, based on comparing different chaos based random strategies, we propose a circle chaos based mutation strategy into PSO algorithm. First, compared with other operators, since the circle chaos based operator could generate more even results, we propose the circle operator based random strategy to balance the global and local search of our PSO algorithm. Second, for attracting individuals to precisely search in a promising region, a new historic experience based elite array is proposed to lead the search direction of population. Then the algorithm could achieve more precise results. For further proving the effectiveness of our algorithm, we apply the algorithm into PID fuzzy logic controller design. Compared with other EA algorithms, the experimental results demonstrate our methods achieve better performance on convergence rate and precise of results.
ESBMC-CHERI: towards verification of C programs for CHERI platforms with ESBMC

This paper presents ESBMC-CHERI -- the first bounded model checker capable of formally verifying C programs for CHERI-enabled platforms. CHERI provides run-time protection for the memory-unsafe programming languages such as C/C++ at the hardware level. At the same time, it introduces new semantics to C programs, making some safe C programs cause hardware exceptions on CHERI-extended platforms. Hence, it is crucial to detect memory safety violations and compatibility issues ahead of compilation. However, there are no current verification tools for reasoning over CHERI-C programs. We demonstrate the work undertaken towards implementing support for CHERI-C in our state-of-the-art bounded model checker ESBMC and the plans for future work and extensive evaluation of ESBMC-CHERI. The ESBMC-CHERI demonstration and the source code are available at https://github.com/esbmc/esbmc/tree/cheri-clang.
Application of Deep Learning Based on Garbage Image Classification

In recent years, with the rapid development of economy, the country's various construction is thriving, and remarkable achievements. At the same time, resources and the environment have been seriously damaged. This phenomenon is directly related to the irrationality of garbage classification and delivery, and the contradiction between the two is becoming increasingly acute as people strongly reflect the problem of environmental pollution but do nothing about it. This paper designs a garbage image classification system based on deep learning, the main research content is to compare multiple deep learning neural network models, find the optimal classifier, develop web applications and deploy neural networks, which includes image data acquisition, image pre-processing, and comparison of VGG16, Inception, and Resnet neural network model accuracy.
Interval Valued Intuitionistic Fuzzy Line Graphs

In the field of graph theory, an intuitionistic fuzzy set becomes a useful tool to handle problems related to uncertainty and impreciseness. We introduced the interval-valued intuitionistic fuzzy line graphs (IVIFLG) and explored the results related to IVIFLG.Some propositions and theorems related to IVIFLG are proposed and proved, which are originated from intuitionistic fuzzy graphs (IVIG). Furthermore, Isomorphism between two IVIFLGs toward their IVIFGs was determined and verified.
Hosted Cuckoo Optimization Algorithm with Stacked Autoencoder-Enabled Sarcasm Detection in Online Social Networks

Sarcasm detection has received considerable interest in online social media networks due to the dramatic expansion in Internet usage. Sarcasm is a linguistic expression of dislikes or negative emotions by using overstated language constructs. Recently, detecting sarcastic posts on social networking platforms has gained popularity, especially since sarcastic comments in the form of tweets typically involve positive words that describe undesirable or negative characteristics. Simultaneously, the emergence of machine learning (ML) algorithms has made it easier to design efficacious sarcasm detection techniques. This study introduces a new Hosted Cuckoo Optimization Algorithm with Stacked Autoencoder-Enabled Sarcasm Detection and Classification (HCOA-SACDC) model. The presented HCOA-SACDC model predominantly focuses on the detection and classification of sarcasm in the OSN environment. To achieve this, the HCOA-SACDC model pre-processes input data to make them compatible for further processing. Furthermore, the term frequency-inverse document frequency (TF-IDF) model is employed for the useful extraction of features. Moreover, the stacked autoencoder (SAE) model is utilized for the recognition and categorization of sarcasm. Since the parameters related to the SAE model considerably affect the overall classification performance, the HCO algorithm is exploited to fine-tune the parameters involved in the SAE, showing the novelty of the work. A comprehensive experimental analysis of a benchmark dataset is performed to highlight the superior outcomes of the HCOA-SACDC model. The simulation results indicate that the HCOA-SACDC model accomplished enhanced performance over other techniques.