Reviewerly/exHarmony huggingface dataset:
Smoke removal and image enhancement of laparoscopic images by an artificial multi-exposure image fusion method

In laparoscopic surgery, image quality is often degraded by surgical smoke or by side effects of the illumination system, such as reflections, specularities, and non-uniform illumination. The degraded images complicate the work of the surgeons and may lead to errors in image-guided surgery. Existing enhancement algorithms mainly focus on enhancing global image contrast, overlooking local contrast. Here, we propose a new Patch Adaptive Structure Decomposition utilizing the Multi-Exposure Fusion technique to enhance the local contrast of laparoscopic images for better visualization. The set of under-exposure level images is obtained from a single input blurred image by using gamma correction. Spatial linear saturation is applied to enhance image contrast and to adjust the image saturation. The Multi-Exposure Fusion (MEF) is used on a series of multi-exposure images to obtain a single clear and smoke-free fused image. MEF is applied by using adaptive structure decomposition on all image patches. Image entropy based on the texture energy is used to calculate image energy strength. The texture entropy energy determined the patch size that is useful in the decomposition of image structure. The proposed method effectively eliminate smoke and enhance the degraded laparoscopic images. The qualitative results showed that the visual quality of the resultant images is improved and smoke-free. Furthermore, the quantitative scores computed of the metrics: FADE, Blur, JNBM, and Edge Intensity are significantly improved as compared to other existing methods.
Bayesian Maximal Information Coefficient (BMIC) to reason novel trends in large datasets

The Bayesian network (BN) is a probability inference model to describe the explicit relationship between cause and effect, which may be examined in the complex system of rice price with data uncertainty. However, discovering the optimized structure from a super-exponential number of graphs in the search space is an NP-hard problem. In this paper, Bayesian Maximal Information Coefficient (BMIC) is proposed to uncover the causal correlations from a large data set in a random system by integrating probabilistic graphical model (PGM) and maximal information coefficient (MIC) with Bayesian linear regression (BLR). First, MIC is to capture the strong dependence between predictor variables and a target variable to reduce the number of variables for the BN structural learning of PGM. Second, BLR is responsible for assigning orientation in a graph resulting from a posterior probability distribution. It conforms to what BN needs to acquire a conditional probability distribution when given the parents for each node by the Bayes' Theorem. Third, the Bayesian information criterion (BIC) is treated as an indicator to determine the well-explained model with its data to ensure correctness. The score shows that the proposed BMIC obtains the highest score compared to the two traditional learning algorithms. Finally, the proposed BMIC is applied to discover the causal correlations from the large data set on Thai rice price by identifying the causal changes in the paddy price of Jasmine rice. The results of the experiments show that the proposed BMIC returns directional relationships with clues to identify the cause(s) and effect(s) of paddy price with a better heuristic search.
Hyperparameter Tuning

Hyperparameter tuning is considered one of the most important steps in the machine learning pipeline and can turn, what may be viewed as, an “unsuccessful” model into a solid business solution by finding the right combination of input values. In this chapter, the theoretical foundations behind different traditional approaches to optimizing hyperparameters, such as Manual Search, Grid Search, and Random Search, will be laid out in addition to outlining more advanced and informed search techniques, for instance, Bayesian Optimization and Genetic Algorithms. Alongside theoretical explanations, a practical example including Python code as well as the latest research applications of hyperparameter tuning in tourism literature will also be provided.
Improving Bag-of-Deep-Visual-Words Model via Combining Deep Features With Feature Difference Vectors

Bag-of-Deep-Visual-Words (BoDVW) model has shown its advantage over Convolutional Neural Network (CNN) model in image classification tasks with a small number of training samples. An essential step in BoDVW model is to extract deep features by using an off-the-shelf CNN model as a feature extractor. Two deep feature extraction methods have been raised in recent years. The first method densely samples multi-scale image patches and then converts them into deep features via a deep-level fully-connected layer. The second method uses the output of a deep-level convolutional layer or pooling layer as the source of deep features. By contrast, the second method is much more efficient. However, it performs worse than the first method in classification accuracy. The reason is that deep features extracted by the second method are yielded in receptive fields of a single size. To make BoDVW model have high feature extraction efficiency and high classification accuracy, we propose enhancing deep features extracted by the second method at low added computation costs by supplementing the information obtained from receptive fields of different sizes. Concretely, we raise a novel feature named “feature difference (FD) vector” in this article. It can roughly preserve the information of multiple deep features extracted by the convolutional layers of different receptive field sizes. Each deep feature is enhanced by combining an FD vector to form a combined feature. The image representation vector of an image is generated using the combined features extracted from it. Our experimental results on three public datasets (15-Scenes, TF-Flowers, and NWPU-RESISC45) show that our method can avoid the high computation costs of the first method and achieve comparable results to the first method, which exhibits the effectiveness of our method.
Towards End-to-End Integration of Dialog History for Improved Spoken Language Understanding

Dialog history plays an important role in spoken language understanding (SLU) performance in a dialog system. For end-to-end (E2E) SLU, previous work has used dialog history in text form, which makes the model dependent on a cascaded automatic speech recognizer (ASR). This rescinds the benefits of an E2E system which is intended to be compact and robust to ASR errors. In this paper, we propose a hierarchical conversation model that is capable of directly using dialog history in speech form, making it fully E2E. We also distill semantic knowledge from the available gold conversation transcripts by jointly training a similar text-based conversation model with an explicit tying of acoustic and semantic embeddings. We also propose a novel technique that we call DropFrame to deal with the long training time incurred by adding dialog history in an E2E manner. On the HarperValleyBank dialog dataset, our E2E history integration outperforms a history independent baseline by 7.7% absolute F1 score on the task of dialog action recognition. Our model performs competitively with the state-of-the-art history based cascaded baseline, but uses 48% fewer parameters. In the absence of gold transcripts to fine-tune an ASR model, our model outperforms this baseline by a significant margin of 10% absolute F1 score.
Improved Local Binary Pattern for Face Recognition

This work gives rise to novel LBP variant so-called Improved LBP (ILBP) for Face Recognition (FR), to overcome the LBP limitation. LBP unable to produce the balanced transformed image for histogram extraction by utilizing only the neighbors gray pixel comparison with the center pixel. ILBP launches robust methodology in 2 steps. First there is computation of gray level difference between neighbors and its mean and second negative threshold is used for comparison with gray level difference. By utilizing mean and negative threshold there is evolution of ILBP transformed image which is more balanced than LBP. As 2 negative thresholds are used therefore 2 ILBP transformed images are produced. The histograms (region wise) of ILBP transformed image is so impressive that it beats histograms of LBP, HELBP, MBP and HOG. ILBP achieves best recognition rates of 99.58% and 90.85% on ORL and GT datasets, which is much higher than the 4 compared descriptors. Several literature based methods are also outclassed by ILBP. PCA and SVMs are used for compression.
Toward Hardware Security Property Generation at Scale

Using formal methods requires first developing the properties to be verified, which is a difficult and time-consuming task. This article describes our research on automating the property generation process for information-flow properties that are critical to the security of hardware designs.
Recursive state estimation for multi‐rate time‐varying systems with multiplicative noises: Dealing with sensor resolutions

Abstract In this article, the recursive state estimation problem is investigated for a class of multi‐rate systems with multiplicative noises where the measurement outputs are collected from sensors with certain resolutions. Due to the existence of the sensor resolution, the actual measurement output of the sensor might deviate from its true value and such a deviation, if not adequately taken into account, would lead to serious degradation of the estimation performance, and we are therefore motivated to develop an effective state estimation algorithm that is insensitive to the sensor‐resolution‐induced measurement distortions. The aim of the considered estimation problem is to design a state estimator such that an upper bound on the estimation error covariance is first guaranteed and then minimized by properly choosing the estimator gain. Moreover, a simulation example with application background on moving target tracking problem is presented to verify the validity of the developed recursive state estimation algorithm.
The Rank-Based Cryptography Library

Rank-based cryptography provides cryptosystems that aim to be secure against both classical and quantum computers. In the past few years, the interest for code-based cryptography in the rank metric setting has tremendously increased notably since the beginning of the NIST post-quantum cryptography standardization process. This paper introduces RBC a library dedicated to Rank-Based Cryptography and details its design and architecture. The performances of RBC are illustrated against comparable state of the art librairies. RBC greatly outperforms those libraries as it is 2 to 5 times faster than NTL and 40 to 138 times faster than mp $$\mathbb {F}_q$$ on the multiplication and inversion over $$\mathbb {F}_{q^m}^n$$ which are the most critical operations when it comes to rank-based cryptography performances. In addition, the performances of ROLLO and RQC two rank-based cryptosystems provided by the library are reported for two platforms: a desktop computer equipped with an Intel Skylake-X CPU and an ARM Cortex-M4 microcontroller.
Topology-Abstraction-Based Protection Scheme in Quantum Key Distribution Networks with Partially Trusted Relays

Quantum key distribution (QKD) can protect the exchange process of confidential information between communicating parties. By using the basic principles of quantum mechanics and combined with “one-time pad” cipher encryption, information can be unconditionally secure. The BB84 protocol first describes the method of transmitting information by photon polarization state, and it expounds the transmission process of services between trusted relays. However, due to the defects of real experimental devices, there are security vulnerabilities in QKD in a real system. The birth of measurement-device-independent quantum key distribution (MDI-QKD) protocol solves the problem, providing immunity to hacker attacks at the end of the detector. It can enable both sides of the transmission service to establish a connection and generate secret keys through an untrusted relay node to ensure information security. However, the types and properties of link nodes in quantum key distribution network (QKDN) based on partially trusted relay are more complex, which can easily result in network fault. Therefore, how to prevent the impact of failure on QKDN has become an urgent problem. In this paper, we propose a protection scheme for QKDN with partially trusted relays. The method deals with trusted and untrusted relays differently and constructs the working and protection paths of the secret key for each service. It reduces resource conflict between the protection and working paths by establishing a key protection threshold, which realizes the resource trade-off between the two factors. Simulation results show that the scheme provides effective protection to the services, and it improves the stability and reliability of QKDN based on partially trusted relay.
Improving Security for Users of Decentralized Exchanges Through Multiparty Computation

Decentralized cryptocurrency exchanges offer compelling security benefits over centralized exchanges: users control their funds and avoid the risk of an exchange hack or malicious operator. However, because user assets are fully accessible by a secret key, decentralized exchanges pose significant internal security risks for trading firms and automated trading systems, where a compromised system can result in total loss of funds. Centralized exchanges mitigate this risk through API key based security policies that allow professional users to give individual traders or automated systems specific and customizable access rights such as trading or withdrawal limits. Such policies, however, are not compatible with decentralized exchanges, where all exchange operations require a signature generated by the owner's secret key. This paper introduces a protocol based upon multiparty computation that allows for the creation of API keys and security policies that can be applied to any existing decentralized exchange. Our protocol works with both ECDSA and EdDSA signature schemes and prioritizes efficient computation and communication. We have deployed this protocol on Nash exchange, as well as around several Ethereum-based automated market maker smart contracts, where it secures the trading accounts and wallets of thousands of users.
Research on Improved Yolo on Garbage Classification Task

With the development of society, more and more waste is produced, which is in urgent need of waste classification treatment. In view of this, this paper proposes a target detection model Yolov3 ++ for 10 types of recyclable garbage, which is suitable for garbage classification. First in this paper, the backbone of YOLOv3 is optimized, the input image size from 608 to 618 to increase the scale of characteristics, change the RELU activation function to Leaky RELU prevent data explosion phenomenon, to reduce the Darknet 52 layer model on branch processing optimization for 43 layer, so as to reduce the amount of calculation and the number, use the K - mean average clustering algorithm is introduced to improve accuracy of the model, the output characteristics of figure size from 38 * 38 and 76 x 76 optimization for 19 lines and the characteristics of the 38 * 38 figure scale to further improve the suitability of the model. Then the transfer learning method is used to introduce the parameters of Image Net after the pre-training model to further improve the accuracy of the detection model. Therefore, the target detection model Yolov3 ++ proposed in this paper is suitable for the garbage detection task.
Automatic Classification and Recognition Method based on Partially-Connected Differentiable Architecture Search for ISAC Systems

Automatic classification and recognition (ACR) is considered one of promising techniques in integrated sensing and communication (ISAC) systems. Existing ACR method uses Wigner-Ville Distribution (WVD) time frequency analysis (TFA) of modulation signal to obtain its WVD images which can be recognized by convolution neural network (CNN). In this paper, we proposed an improved ACR method using partially-connected differentiable architecture search (PC-DARTS). The search space is relaxed to a continuous space, and then the performance of the validation set of the architecture is optimized by gradient descent, so as to find the optimal CNN structure for signal classification and recognition. Experimental results show that the proposed method can achieve better performance with lower complexity than the other deep learning-based methods.
A Survey on Underwater Object Detection

Underwater object detection is a challenging area of research because of unclear images. Underwater object detection covers the detection of fish, planktons, submerged ships, pipelines, debris, etc. The work reviews different art of studies where it considers either object detection or object localization. Here we have reviewed the existing work into two categories learning-based approach and non-learning-based approach. Learning-based approaches are capable of classifying objects but other approaches mostly localize objects or detect the shape of the objects. The deep learning-based technique is mostly used in optical images but in sonar images mostly segmentation, clustering, edge detection is used. In this chapter, it is tried to address the key issues of different literature's also made a summary of some of the literature with their findings and our findings. The paper gives an idea about the detection of small objects. Also, it covers the literature that deals with shadow and objects related confusion.
Optimizing and Extending Serverless Platforms: A Survey

Serverless Computing is a new cloud computing paradigm wherein people in academia and industry are actively proposing either interesting improvements or building excellent applications on top of it. AWS, Google Cloud, Microsoft Azure, and IBM are popular samples of public clouds that offer Function-as-a-Service on top of their Serverless Computing platforms. Although this paradigm has had numerous advantages for software developers and programmers, it has introduced new challenges to cloud providers. Factors like fine-grained pricing and pay-as-you-go manner, eliminating the responsibility of resource management on the developer side, promises of elasticity and highly-available service, fault tolerance, auto-scaling, and being able to run embarrassingly parallel jobs make it a suitable platform for developers. On the other hand, efficient resource management, offering low-latency service, and providing proper security/isolation have been partly the main challenges introduced on the cloud provider side. This paper presents a literature review on today's Serverless platform optimizations and extensions that people have proposed and implemented to further capitalize the Serverless infras-tructure. In the end, we will provide the current Serverless paradigm's limitations and a few future directions and research opportunities regarding Serverless Computing.
Cross‐Situational Word Learning With Multimodal Neural Networks

In order to learn the mappings from words to referents, children must integrate co-occurrence information across individually ambiguous pairs of scenes and utterances, a challenge known as cross-situational word learning. In machine learning, recent multimodal neural networks have been shown to learn meaningful visual-linguistic mappings from cross-situational data, as needed to solve problems such as image captioning and visual question answering. These networks are potentially appealing as cognitive models because they can learn from raw visual and linguistic stimuli, something previous cognitive models have not addressed. In this paper, we examine whether recent machine learning approaches can help explain various behavioral phenomena from the psychological literature on cross-situational word learning. We consider two variants of a multimodal neural network architecture and look at seven different phenomena associated with cross-situational word learning and word learning more generally. Our results show that these networks can learn word-referent mappings from a single epoch of training, mimicking the amount of training commonly found in cross-situational word learning experiments. Additionally, these networks capture some, but not all of the phenomena we studied, with all of the failures related to reasoning via mutual exclusivity. These results provide insight into the kinds of phenomena that arise naturally from relatively generic neural network learning algorithms, and which word learning phenomena require additional inductive biases.
Traditional Machine Learning Methods for Side-Channel Analysis

Traditional machine learning techniques (excluding deep learning) include a range of approaches, such as supervised, semi-supervised, and unsupervised modeling methods, often coupled with data augmentation and dimensionality reduction. The aim of this chapter is to provide an overview of the application of traditional machine learning methods in the field of side-channel analysis. The chapter encompasses the common methods used in side-channel attacks, a historical overview of the use of machine learning methods in side-channel analysis, and a brief description of various machine learning approaches that have been used in related studies. Both machine learning methods and side-channel specific methods such as Principal Component Analysis, Linear Discriminant Analysis, Template Attacks, Random Forests, Multilayer Perceptron and many others are compared and the current status of their use in side-channel analysis is presented. Several research avenues are still incomplete and the chapter points out some of the open questions.
Flexible Vision-Based Auto-docking Control System for Unmanned Ground Vehicles Equipped with Differential Chassis

This paper proposes configurable, parametrically flexible, vision-based automatic docking control system for unmanned surface vehicles (mobile robots) equipped with a differential chassis. The proposed system uses only fiducial localization source to find and attach the robot to docking station. It exploits peculiarities of multiple tags setups to obtain final push-in docking motion straight with high geometrical precision. The trajectories with inverse curvatures are implemented to decrease the number of docking attempts with respect to the actual orientation of the charging slot.
Cumulant GAN

In this article, we propose a novel loss function for training generative adversarial networks (GANs) aiming toward deeper theoretical understanding as well as improved stability and performance for the underlying optimization problem. The new loss function is based on cumulant generating functions (CGFs) giving rise to Cumulant GAN. Relying on a recently derived variational formula, we show that the corresponding optimization problem is equivalent to Rényi divergence minimization, thus offering a (partially) unified perspective of GAN losses: the Rényi family encompasses Kullback-Leibler divergence (KLD), reverse KLD, Hellinger distance, and χ2 -divergence. Wasserstein GAN is also a member of cumulant GAN. In terms of stability, we rigorously prove the linear convergence of cumulant GAN to the Nash equilibrium for a linear discriminator, Gaussian distributions, and the standard gradient descent ascent algorithm. Finally, we experimentally demonstrate that image generation is more robust relative to Wasserstein GAN and it is substantially improved in terms of both inception score (IS) and Fréchet inception distance (FID) when both weaker and stronger discriminators are considered.