Reviewerly/exHarmony huggingface dataset:
Multi-frame Super-resolution via Generative Image Model

Recent start-of-the-art video super-resolution (VSR) network has achieved huge success on popular video datasets. However, these methods may fail in real-word video data, since low-resolution (LR) images are generated by bicubic interpolation on corresponding high-resolution (HR) images, which are not subjected to the real-word degrading model. Besides, the training dataset may lack sub-pixel motion between frames, which is crucial for image detail restoration. To address this issue, a novel generative model is proposed for constructing low-resolution image sequences with sub-pixel motion information and various blur kernel. This data augmentation is an effective way to improve the performance of dealing with spatial-temporal information. Based on this strategy, the super-resolution results are more reliable and robust. Extensive experiment shows our proposal helps the VSR network make full use of sub-pixel information to reconstruct a reliable high-resolution image with rich details.
BGTracker: Cross-Task Bidirectional Guidance Strategy for Multiple Object Tracking

Recent works have shown that the joint-detection-and-embedding (JDE) paradigm has significantly enhanced the performance of multiple object tracking by simultaneously learning detection and re-identification features. These methods always utilize a weight-shared backbone network and two non-interactive branches for different tasks. This non-interactive multi-task learning strategy cannot make full use of geometric and semantic information between detection and re-identification tasks. And in the JDE paradigm, there exists a feature misalignment between detection and re-identification due to their different optimization directions. In this paper, BGTracker is proposed as a novel online tracking framework with a cross-task bidirectional guidance strategy between detection and re-identification. Firstly, we propose a Channel-based Decoupling module and Cross-direction Transformer to alleviate feature misalignment, which can obtain task-aligned embeddings and discriminative representations at the feature level. Then, we propose the bidirectional guidance strategy to link the two tasks by the prediction map's statistical information. In this strategy, two designed feature transformations are employed to utilize the advantages of each task for complementing each other at the task level. Finally, extensive experiments demonstrate that the proposed BGTracker outperforms various existing methods on the MOTChallenge benchmarks.
Efficient Difference Analysis of Guaranteeable Requirements for Fault-tolerant Self-adaptation

Modern systems suffer system faults in both hardware and software. Requirement degradation is a widely applied redundant-design approach to deal with system fault by degrading or disabling (non-critical) requirements. However, due to the environment's unpredictability, it is unrealistic for engineers to prepare such redundant designs for unforeseen and unknown faults in advance. Requirement-aware self-adaptive systems have been investigated to address this challenge. Specifically, such a system can autonomously analyze which requirements are guaranteeable so that the requirement degradation can be determined automatically to satisfy the maximal requirements. The previous work has proposed an algorithm to recognize the guaranteeable requirements by analyzing a two-player game between the environment and the system. However, such an algorithm is not designed for the system fault, and the entire game must be re-analyzed from scratch, making it inefficient for runtime adaptation. In this paper, we propose an efficient difference analysis algorithm that reuses the previous analysis result to reduce the runtime computational overhead. Specifically, it carefully specifies which part of the result is reusable and only re-analyzes the part that may be affected. We evaluated the algorithm's effectiveness with four case studies, and the results indicate that an average of 46.5% computational time is reduced.
InDecGAN: Learning to Generate Complex Images From Captions Via Independent Object-Level Decomposition and Enhancement

Text-to-image synthesis is a challenging problem, in which a complex scene contains diverse objects of various sizes and sub-images of objects belonging to the same class have diverse forms from different perspectives. Thus, synthesis models have difficulty in capturing varied objects in the complex scene. To alleviate these problems, we devise an independent object-level decomposing and enhancing generative adversarial networks, denoted as InDecGAN, to synthesize complex images and capture varied objects in a complex scene. Specifically, InDecGAN fully utilizes the independent object-level information, bounding boxes and high-resolution images of objects in training, by employing independent object-level pathways to synthesize varied objects. The independent object-level pathway integrates an independent object-level adversarial loss and the bounding box information to learn the visual features of objects independently, then, the main pathway exploits the features provided by the object-level pathway to compose the full scene and synthesize images. In addition, we analyze the generalization properties of the proposed InDecGAN and demonstrate the improvement from the perspective of the model architecture. Moreover, extensive experiments conducted on a widely used dataset are presented to demonstrate that the proposed model with an independent object-level pathway produces synthesized images of significantly improved quality.
RML22: Realistic Dataset Generation for Wireless Modulation Classification

Application of Deep learning (DL) to modulation classification has shown significant performance improvements. The focus has been model centric, where newer architectures are attempted on benchmark dataset RADIOML.2016.10A (RML16). RML16 is a high impact effort that laid the foundation for generating a synthetic dataset for applying DL models to wireless problems. This encouraged development of newer architectures to RML16. We use a data centric DL approach where focus moves from model architectures to data quality. RML16 has shortcomings such as errors and ad-hoc choices of parameters. We build upon RML16 and provide realistic and correct methodology of generating dataset. A new benchmark dataset RML22 is generated. Going forward, we envision researchers to improve model quality on RML22. We attempt to improve data quality by studying the impact of information sources. Further, the choices of artifacts and signal model parameterization are analyzed carefully. The Python source code used to generate RML22 is shared to enable researchers to further improve dataset quality.
Memory Bandwidth Efficient Design for Super-Resolution Accelerators With Structure Adaptive Fusion and Channel-Aware Addressing

State-of-the-art (SOTA) super-resolution (SR) models can generate high-quality images. However, they require a large external memory bandwidth, making it impossible to implement these models on hardware. Although some work has presented different kinds of layer fusion to reduce memory traffic, they can only work on simple model architectures and only consider the feature extraction part. To solve the above issues, this article proposes structure adaptive fusion (SAF) for the feature extraction part to avoid intermediate feature map I/O. This method selects the repetitive structure as the fusion unit and fuses multiple ones to meet buffer size and memory bandwidth constraints, which can deal with different SR models. In addition, we also propose channel-aware addressing for the upscale part to avoid off-chip data transfers. The proposed methods achieve over 90% of memory traffic reduction in all tested SOTA models. Compared to the SOTA fusion method, our approach requires a 52% smaller buffer size and up to 61% lower memory bandwidth for the same number of fusion layers.
New Techniques for Static Symmetry Breaking in Many-Sorted Finite Model Finding

Symmetry in finite model finding problems of many-sorted first-order logic (MSFOL) can be exploited to reduce the number of interpretations considered during search, thereby improving solver performance for tools such as the Alloy Analyzer.We present a framework to soundly compose static symmetry breaking schemes for many-sorted finite model finding.Then, we introduce and prove the correctness of three static symmetry breaking schemes for MSFOL: 1) one for functions with distinct sorts in the domain and range; 2) one for functions where the range sort appears in the domain; and 3) one for predicates.We provide a novel presentation of sort inference in the context of symmetry breaking that yields a new mathematical link between sorts and symmetries.We empirically investigate how our symmetry breaking approaches affect solving performance.
Pipeline Signed Japanese Translation Using PBSMT and Transformer in a Low-Resource Setting

We propose a novel pipeline method for translating signed Japanese sentences into written Japanese. Sign languages often suppress functional words such as particles, and most words are not morphologically inflected as they are in spoken languages. Our method explicitly compares and contrasts the two languages and divides the translation process into two tasks: first, it translates glosses into lemmatized Japanese words or phrases, followed by complementing particles and conjugating predicates such as verbs, auxiliary verbs, and adjectives. Our method is especially effective when the size of the parallel corpus is very limited and costly to obtain, but there are plenty of monolingual corpora for the target. Specifically, our method first uses phrase-based statistical machine translation (PBSMT) to map sign glosses to corresponding Japanese words or phrases, and then employs a transformer-based neural machine translation (NMT) model trained with a monolingual corpus to refine the output in the first translation. Experimental results show that our pipeline method outperforms direct PBSMT and competitive NMT models with data augmentation, including back-translation and transfer learning in a low-resource setting with a corpus size on the order of 104 words.
New Design of Blockwise Interleaved Ideal Low-Rank Parity-Check Codes for Fast Post-Quantum Cryptography

In this letter, a new design of blockwise interleaved ideal low-rank parity-check (BII-LRPC) codes is proposed for a fast cryptographic application. We show that the proposed ideal LRPC codes can be used as a key encapsulation mechanism (KEM) for post-quantum cryptography (PQC). The simulation results show that the KEM constructed from the proposed BII-LRPC codes is faster by 30-70% than the ROLLO-I algorithm, an existing LRPC-based cryptosystem.
Quantum Program Synthesis Through Operator Learning and Selection

Programming for quantum computers is complicated and time consuming, because quantum operations are counterintuitive and their combined effects are difficult to understand.Existing tools allow automatic synthesis of quantum programs, which releases the burden of handwriting.However, many existing systems arrange predetermined operators in successive manner to gradually reduce the gap with requirements; these methods are quick but often produce lengthy programs, and they are difficult to adopt for new operators.Other systems depend on stochastic or heuristic search; they identify near-optimal programs for certain cases, but it is not easy to tune the algorithms for a wide range of cases.We propose a system that produces compact programs for most cases and easily evolves with new operators.The system automatically learns the roles of available operators by composing various possible programs.Based on the knowledge, it selects a subset of operators most appropriate for requirements and uses them to compose a program.The learning is geared toward concise programs; thus, the system tends to produce programs with the fewest operators possible.We implemented the system and evaluated it by synthesizing over 400 programs.In comparison with a state-ofthe-art system, the proposed system produced programs with approximately 40-times fewer operators at the cost of increased synthesis time from seconds to minutes.We also observed that the system successfully adopted new operators by learning their differences from existing operators and utilizing them in right places.We believe that the system provides a basis of utilizing machine learning for quantum program synthesis.
EPPFM: Efficient and Privacy-Preserving Querying of Electronic Medical Records With Forward Privacy in Multiuser Setting

With the application of the Internet of Things (IoT) and cloud computing, the eHealthcare industry has developed markedly, attracting many patients to seek medical treatment in an eHealthcare system. However, for patients who first register in the system, due to lack of experience, an important aspect is to choose appropriate medical services. Considering the sensitivity of health care data and the semi-honest nature of the cloud server, it is a good solution to use searchable encryption (SE) to obtain some historical electronic medical records (EMRs) that are consistent with the patient's symptom keyword combination and have high service scores for reference. However, existing SE schemes still have issues meeting the requirements of the eHealthcare system for flexible authorization and revocation, efficiency, and forward privacy. To resolve these issues, we propose two efficient and privacy-preserving electronic medical records query schemes with forward privacy in a multiuser setting (EPPFM). First, we present the basic scheme EPPFM-I to achieve a multiuser multikeyword exact match query under linear search complexity. In EPPFM-I, we also use the pseudorandom function (PRF) to perform the function of forward privacy. Then, we use a bucket structure to construct the improved scheme EPPFM-II, which has a faster-than-linear search complexity. Finally, we use detailed security analysis and extensive simulations to show the security and efficiency of the proposed schemes, respectively.
Enhancing S-Box Nonlinearity in AES for Improved Security Using Key-Dependent Dynamic S-Box

The Advanced Encryption Standard (AES) is the most widely used symmetric and secure encryption algorithm for commercial and research purposes. The core of AES is its substitution box (S-box), which primarily provides nonlinearity, confusion, and diffusion. The improvements to nonlinearity in S-box is one of the methods used by researchers to enhance security in AES. In this paper, to increase the nonlinearity of the S-box, AES using a key-dependent dynamic S-box is implemented and compared with the AES using a static S-box. The algorithm proposed in this paper draws observation from results of security parameters like hamming distance, strict avalanche criteria (SAC), and balanced output while comparing key-dependent S-box and static AES S-box. Among 40 experimental results, the key-dependent dynamic S-box performs 22.5% times better for hamming distance observation drawn is that the hamming distance is more in the dynamic AES than static AES. The average bit difference of 10 rounds of dynamic AES is around 65%, which is a good avalanche criterion.
Reinforcement learning based distantly supervised biomedical named entity recognition

Data used by current Biomedical named entity recognition (BioNER) systems has mostly been manually labelled for supervision. However, it might be difficult to find large amounts of annotated data, especially in fields with a high level of specialization, such as biomedical, bioinformatics, and so on. When dictionaries and ontologies are available, which are domain-specific knowledge resources, automatically tagged distantly supervised biomedical training data can be developed. However, any such distantly supervised NER result is normally noisy. The prevalence of false positives and false negatives with this type of autonomously generated data is the main problem that directly affects efficiency. This research investigates distant supervision to detect false positive occurrences in BioNER task. A reinforcement learning technique is employed that is modelled as a graphical processing unit (GPU) accelerated Markov decision process (MDP) with a neural network policy. To deal with false negative cases, we employ a partial annotation conditional random field (CRF) technique. Results on two benchmark datasets show a cutting-edge methodology that can enhance the functionality of the neural NER system. It goes on to show how the proposed approach cuts down on human annotated data for BioNER tasks in Natural Language Processing (NLP).
Feature optimization in CNN using MROA for disease classification

Electronic Medical Records (EMR) carry important information about a patient’s journey. The past decade shows substantial use of Natural Language Processing (NLP)-based Information Retrieval (IR) techniques to extract insights such as symptoms, diseases, and tests from these unstructured records. The state-of-the-art shows that convolutional neural networks (CNN) make a significant contribution to the disease classification task.A significant improvement in precise knowledge mining is possible with precise feature extraction. Feature selection addresses undesirable, unneeded, or irrelevant features. This article proposes a Modified Rider Optimization Algorithm (MROA) to choose important features by selecting optimal weights from a pool of randomly generated weights based on high accuracy and less training time in the CNN algorithm. A modified approach is trained on 114 N2C2 patients’ records to extract symptoms, disease, and tests are performed on them to perform disease classification tasks. The proposed approach is found to be accurate, with 97.77% accuracy in the disease classification and treatment prediction task from EMR.
A cryptographic security framework for hybrid <scp>Cloud‐Internet of Things</scp> network

Abstract The most cutting‐edge innovation in recent times, cloud technology, changed the whole computer paradigm from less capable, specific, user‐isolated offline systems to potent multi‐server interactive systems. Despite being a huge benefit, controlling the security and accessibility of data stored in the cloud is a difficult task. Hence, this article presents a four‐phased security paradigm for securing data generated by internet of things (IoT) devices and transmitted to fog servers. In the article, we outline a comprehensive, cutting‐edge architecture for safeguarding and protecting the information that IoT devices produce and transmit to the cloud. The suggested architecture blends the advantages of an innovative mutual authentication algorithm, a novel key distribution algorithm, and a novel encryption hybrid algorithm for maximum effectiveness and increased security. The findings demonstrate that the suggested protocol outperforms previous techniques in terms of time and resource consumption and throughput. In contrast to previous protocols, the suggested approach reduces encrypting time by 28% and decryption time by about 32%, while the volume of encrypted messages produced stays consistent. There has also been a 28% increase in encryption throughput. Proposed work demonstrates minimal resource utilization, good performance and a higher level of security robustness.
The Grammar of Logical Meaning in Korean: clause complexing

This chapter focuses on grammatical resources for construing logical relations between clauses. The chapter begins by clarifying the distinction between a clause simplex and a clause complex and the distinction between a clause complex and a verbal group complex. Subsequently the basic oppositions between parataxis and hypotaxis on the one hand and between projection and expansion on the other are introduced and relevant resources are then presented, section by section, in more detail. A sample analysis of a longer clause complex rounds off the discussion.
EVALUATION OF WEB SERVICES COMPOSITION PROBLEM IN MULTI-CLOUD ENVIRONMENT

The development of Web services as a distribution, service consumption, and discovery architecture was successful. The components of SOA are widely used in e-business, process control, multimedia services, and several other disciplines. A QoS is still helpful for connecting non-functional concepts to Web services, and it is next used as a key point of differentiation for the different Service provider. In a multi-cloud environment (MCE), each atomic web service published by any cloud provider with the same functionality has a different price and quality of service (QoS). Service discovery and composition are the key challenges for web services development. The challenges in the composition of services distributed in multi-cloud environments include increased cost and a reduction in its speed due to the increasing number of services, providers, and clouds. Consequently, to overcome these challenges, the QoS-aware multi-cloud web service composition is presented in this work a Hybrid Firefly and Bee Colony Optimization Algorithm is introduced for the Multi-cloud service composition problem of NP-Hard in a multi- cloud environment. Keywords: Services Composition, Firefly, Bee Colony Algorithm, QoS Metrics.
Multiplexing methods for scaling up photonic logic

Existing photonic matrix processers are too small to tackle relevant problems. Here, I review our group’s recent work on scaling up analog photonic platforms. This work includes iterative advances to old approaches (accurate methods to calibrate MZI meshes), experimental demonstrations of recent proposals (a VCSEL array-based coherent detection ONN and a single-shot ONN based on reconfigurable free-space optical fan-out and weighting), and entirely new architectures (WDM-powered and RF-photonic fiber circuits for edge computing). The lessons learned from studying this diverse array of approaches helps inform the future development of photonic hardware for computation.
Camera calibration as machine learning problem using dense phase shifting pattern, checkerboards, and different cameras

Non-contact measurements using digital cameras require a reliable camera calibration typically based on a pinhole camera model with a few lower-order distortions (in OpenCV typically up to 14 parameters). The assessment of the calibration quality is typically done with the re-projection error (RPE). We propose a different quality measure, the forward propagation error (FPE) that determines the deviation in real world coordinates using parameters from the camera calibration. In addition, we introduce a machine learning-inspired method for a more reliable camera calibration. We explore the quality of our camera calibration using RPE, FPE, and a machine learning method by a series of checkerboard (using spares points) or phase shifting patterns (dense points), different camera types, and different camera models by comparing results from simulations and experiments. The machine learning inspired method helps to identify outliers which can easily be removed from the calibration process ensuring a reliable camera calibration. Our investigation shows the better the camera the better the camera calibration. We found that the 5 parameter OpenCV model was sufficient for our camera calibration. In addition, the 5 parameter model and the dense phase shifting pattern were precise and accurate and only limited by our target 8k monitor with about 0.09 mm pixel pitch in terms of FPE. We found already a good calibration using about 20 different poses and a checkerboard pattern by a correct generation of poses. The checkerboard pattern shows good results, and can easily be interpreted with the FPE.