Reviewerly/exHarmony huggingface dataset:
A Framework for Substructural Type Systems

Abstract Mechanisation of programming language research is of growing interest, and the act of mechanising type systems and their metatheory is generally becoming easier as new techniques are invented. However, state-of-the-art techniques mostly rely on structurality of the type system — that weakening, contraction, and exchange are admissible and variables can be used unrestrictedly once assumed. Linear logic, and many related subsequent systems, provide motivations for breaking some of these assumptions. We present a framework for mechanising the metatheory of certain substructural type systems, in a style resembling mechanised metatheory of structural type systems. The framework covers a wide range of simply typed syntaxes with semiring usage annotations, via a metasyntax of typing rules. The metasyntax for the premises of a typing rule is related to bunched logic, featuring both sharing and separating conjunction, roughly corresponding to the additive and multiplicative features of linear logic. We use the uniformity of syntaxes to derive type system-generic renaming, substitution, and a form of linearity checking.
Improving the Interpretability of GradCAMs in Deep Classification Networks

Deep classification networks play an important role as backbone networks in industrial AI applications. These applications are often cost or safety critical; explainability of the AI results is a highly demanded feature. We introduce CAM fostering, a method to improve the explainability of classification nets based on local layers such as convolutional or pooling layers. Several CAM interpretability measures are defined and used as additional loss terms. Even though the method requires second-order derivatives, it is demonstrated that deep nets can be trained on large datasets without frozen parameters. The training parameters can be chosen such that the accuracy degradation remains decent in favor of the CAM interpretability improvement. We conclude by comparing the results of different training parameter configurations.
Image Classification based on CNN: Models and Modules

With the recent development of deep learning techniques, deep learning methods are widely used in image classification tasks, especially for those based on convolutional neural networks (CNN). In this paper, a general overview on the image classification tasks will be presented. Besides, the differences and contributions to essential progress in the image classification tasks of the deep learning models including LeNet, AlexNet, Inception, VggNet and ResNet are introduced. This paper will also explain in detail, how different units in these CNN models, other than the convolutional layer, including pooling, activation, and dropout functionalize to support better results for these models. These results offer a guideline for deeply understanding the utility of CNN units.
Texture and Materials Image Classification Based on Wavelet Pooling Layer in CNN

Convolutional Neural Networks (CNNs) have recently been proposed as a solution in texture and material classification in computer vision. However, inside CNNs, the internal layers of pooling often cause a loss of information and, therefore, is detrimental to learning the architecture. Moreover, when considering images with repetitive and essential patterns, the loss of this information affects the performance of subsequent stages, such as feature extraction and analysis. In this paper, to solve this problem, we propose a classification system with a new pooling method called Discrete Wavelet Transform Pooling (DWTP). This method is based on the image decomposition into sub-bands, in which the first level sub-band is considered as its output. The objective is to obtain approximation and detail information. As a result, this information can be concatenated in different combinations. In addition, wavelet pooling uses wavelets to reduce the size of the feature map. Combining these methods provides acceptable classification performance for three databases (CIFAR-10, DTD, and FMD). We argue that this helps eliminate overfitting and that the learning graphs reflect that the datasets show learning generalization. Therefore, our results indicate that our method based on wavelet analysis is feasible for texture and material classification. Moreover, in some cases, it outperforms traditional methods.
The USV Path Planning Based on an Improved DQN Algorithm

Research on the path planning algorithm of USV (unmanned surface vessel) has received widespread attention. Among them, the algorithm based on the combination of deep learning and reinforcement learning achieves better performance. However, in the complex water environment, this algorithm is prone to overestimate the action value, which will lead to path planning deviation. In this paper, we propose a Double DQN algorithm to optimize obstacle avoidance and path planning, which based on deep Q network (DQN) algorithm. The Double DQN algorithm will decouple the target Q value’s action selection and action evaluation, and index the action value corresponding to the maximum Q value through the current network, then input the selected action value into the target network to calculate the target Q value. As a result, the overestimation is reduced, and path deviations are corrected. The simulation results show that the Double DQN algorithm has a good performance in path planning compared with the DQN algorithm, and indicate the Double DQN algorithm can effectively process complex environmental information and make optimal path planning.
Why KDAC? A general activation function for knowledge discovery

Deep learning oriented named entity recognition (DNER) has gradually become the paradigm of knowledge discovery, which greatly promotes domain intelligence. However, the activation function of DNER fails to treat gradient vanishing, no negative output or non-differentiable existence, which may impede the exploration of knowledge due to the omission and incomplete representation of the latent semantic. To break through the dilemma, we present a novel activation function termed KDAC. Detailly, KDAC is an aggregation function with multiple conversion modes. The backbone is the interaction between exponent and linearity, and the both ends are extended through adaptive linear divergence, which can surmount the gradient vanishing and no negative output. Crucially, the non-differentiable points can be alerted and eliminated by an approximate smoothing algorithm. KDAC has a series of brilliant properties, such as nonlinear, stable near-linear transformation and derivative, as well as dynamic style, etc. We perform experiments based on BERT-BiLSTM-CNN-CRF model on six benchmark datasets containing different domain knowledge, such as Weibo, Clinical, E-commerce, Resume, HAZOP and People's daily. The evaluation results show that KDAC is advanced and effective, and can provide more generalized activation to stimulate the performance of DNER. We hope that KDAC can be exploited as a promising activation function to devote itself to the construction of knowledge.
Cross-Language Binary-Source Code Matching with Intermediate Representations

Binary- source code matching plays an important role in many security and software engineering related tasks such as malware detection, reverse engineering and vulnerability assessment. Currently, several approaches have been proposed for binary-source code matching by jointly learning the embeddings of binary code and source code in a common vector space. Despite much effort, existing approaches target on matching the binary code and source code written in a single programming language. However, in practice, software applications are often written in different programming languages to cater for different requirements and computing platforms. Matching binary and source code across programming languages introduces additional challenges when maintaining multi-language and multi-platform applications. To this end, this paper formulates the problem of cross-language binary-source code matching, and develops a new dataset for this new problem. We present a novel approach XLIR, which is a Transformer-based neural network by learning the intermediate representations for both binary and source code. To validate the effectiveness of XLIR, comprehensive experiments are conducted on two tasks of cross-language binary-source code matching, and cross-language source-source code matching, on top of our curated dataset. Experimental results and analysis show that our proposed XLIR with intermediate representations significantly outperforms other state-of-the-art models in both of the two tasks.
Development of Smart Waste Bin Segregation using Image Processing

In the Philippines, solid waste management is still a significant problem. Improper waste disposal causes serious health problems and environmental risks such as contamination of the water systems, floods, ground and air pollution, and diseases. Unfortunately, most people mistakenly believe that not segregating waste is acceptable. This study aims to develop and design a Smart Waste Bin Segregation using Image Processing and assist waste segregation through waste identification and segregation built on machine learning capable of navigating the one-time path set by the user. In particular, create an intelligent waste bin segregation prototype using image processing with three classifications. These classifications are the biodegradable, non-bio-degradable, and unknown intended to segregate solid waste into its respective bins and conduct accuracy tests using appropriate statistical tools. This device is designed for school use and may also be used in other establishments if more waste is trained, alleviate the waste segregation problem and help build an eco-friendlier society without compromising health and hygiene. The proponents successfully materialized the device, and function tests yielded an overall result of 97.33% accuracy.
Self-organizing migrating algorithm: review, improvements and comparison

Abstract The self-organizing migrating algorithm (SOMA) is a population-based meta-heuristic that belongs to swarm intelligence. In the last 20 years, we can observe two main streams in the publications. First, novel approaches contributing to the improvement of its performance. Second, solving the various optimization problems. Despite the different approaches and applications, there exists no work summarizing them. Therefore, this work reviews the research papers dealing with the principles and application of the SOMA. The second goal of this work is to provide additional information about the performance of the SOMA. This work presents the comparison of the selected algorithms. The experimental results indicate that the best-performing SOMAs provide competitive results comparing the recently published algorithms.
Implementing Ethical Governors in BDI

Increasingly, BDI agents are being used not just for basic decision-making, but for more abstract ethical decisions. Several authors have built ad-hoc extensions of BDI systems that provide varying levels of sophistication. In this paper, we introduce a general-purpose approach for implementing ethical governors in BDI systems. With this we aim to provide a broad, flexible and consistent framework for implementing increasingly complex ethical reasoning. Our approach is based on a set of domain-independent abstract agents (evidential reasoner, arbiter and execution agent) that together represent an ethical governor. We discuss the implementation of these abstract agents in the Jason agent programming language and demonstrate how they can be used in practice by instantiating agents in two different case studies, one using utilitarianism and the other deontic logic for reasoning about ethical decisions.
Coherent perfect absorption in Tavis-Cummings models

We theoretically study the conditions under which two laser fields can undergo Coherent Perfect Absorption (CPA) when shined on a single-mode bi-directional optical cavity coupled with two two-level quantum emitters (natural atoms, artificial atoms, quantum dots, qubits, etc.). In addition to being indirectly coupled through the cavity-mediated field, in our Tavis-Cummings model, the two quantum emitters (QEs) are allowed to interact directly via the dipole-dipole interaction (DDI). Under the mean-field approximation and low-excitation assumption, in this work, we particularly focus on the impact of DDI on the existence of CPA in the presence of decoherence mechanisms (spontaneous emission from the QEs and the leakage of photons from the cavity walls). We also present a dressed-state analysis of the problem to discuss the underlying physics related to the allowed polariton state transitions in the Jaynes-Tavis-Cummings ladder. As a key result, we find that in the strong-coupling regime of cavity quantum electrodynamics, the strong DDI and the emitter-cavity detuning can act together to achieve the CPA at two laser frequencies tunable by the inter-atomic separation which are not possible to attain with a single QE in the presence of detuning. Our CPA results are potentially applicable in building quantum memories that are an essential component in long-distance quantum networking.
Multi-Dimensional Model Compression of Vision Transformer

Vision transformers (ViT) have recently attracted considerable attentions, but the huge computational cost remains an issue for practical deployment. Previous ViT pruning methods tend to prune the model along one dimension solely, which may suffer from excessive reduction and lead to sub-optimal model quality. In contrast, we advocate a multi-dimensional ViT compression paradigm, and propose to harness the redundancy reduction from attention head, neuron and sequence dimensions jointly. We firstly propose a statistical dependence based pruning criterion that is generalizable to different dimensions for identifying deleterious components. Moreover, we cast the multi-dimensional compression as an optimization, learning the optimal pruning policy across the three dimensions that maximizes the compressed model's accuracy under a computational budget. The problem is solved by our adapted Gaussian process search with expected improvement. Experimental results show that our method effectively reduces the computational cost of various ViT models. For example, our method reduces 40% FLOPs without top-1 accuracy loss for DeiT and T2T-ViT models, outperforming previous state-of-the-arts.
Federated Learning With Erroneous Communication Links

In this letter, we consider the federated learning (FL) problem in the presence of communication errors. We model the link between the devices and the central node (CN) by a packet erasure channel, where the local parameters from devices are either erased or received correctly by CN with probability <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\epsilon $ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1-\epsilon $ </tex-math></inline-formula> , respectively. We proved that the FL algorithm in the presence of communication errors, where the CN uses the past local update if the fresh one is not received from a device, converges to the same global parameter as that the FL algorithm converges to without any communication error. We provide several simulation results to validate our theoretical analysis. We also show that when the dataset is uniformly distributed among devices, the FL algorithm that only uses fresh updates and discards missing updates might converge faster than the FL algorithm that uses past local updates.
Deep Learning-Based Object Detection in Diverse Weather Conditions

The number of different types of composite images has grown very rapidly in current years, making Object Detection, an extremely critical task that requires a deeper understanding of various deep learning strategies that help to detect objects with higher accuracy in less amount of time. A brief description of object detection strategies under various weather conditions is discussed in this paper with their advantages and disadvantages. So, to overcome this transfer learning has been used and implementation has been done with two Pretrained Models i.e., YOLO and Resnet50 with denoising which detects the object under different weather conditions like sunny, snowy, rainy, and hazy weather. And comparison has been made between the two models. The objects are detected from the images under different conditions where Resnet50 is identified to be the best Model.
Chaos-Based Uncompressed Frame Level Video Encryption

In recent years, video encryption based on chaotic maps have emerged as an important research problem. Properties like high randomness, ergodicity, sensitivity to controlling parameters, and initial states make chaotic map-based video encryption more popular than the conventional encryption techniques. In this paper, we propose a compression-independent method for video encryption at frame level where a new confusion technique, based on sort index permutation, is introduced. During shuffling, a new sequence is generated for every video frame under consideration. Generated pseudorandom chaotic sequence depends on initial values, controlling parameters, and the video frames. We have used a logistic-sine-coupling map (LSCM) for generating highly pseudorandom sequence. Block-level diffusion has been employed for scrambling the frames. The proposed framework is more robust against known and chosen-plaintext attacks as we have incorporated the size and sum of all pixel values for generating random chaotic sequence. Experimental results clearly demonstrate that our method is effective in terms of security, efficiency, and robustness compared to some of the existing approaches.
Machine Learning, IoT, and Blockchain Integration for Improving Process Management Application Security

The application of machine learning (ML) procedures is enormously scaling up owing to the quick digitization and advancement of innovative know-hows like the Internet of Things (IoT) and blockchain technology (BT). In the present digital period, it is discovered that ML techniques are employed in the fields of industries, health care, IoT, engineering, process management, finance, etc. Smart Process Applications (SPA) are founded on the numerous requirements for forecasting the consistency and eminence of the application. Several ML techniques are being evaluated in this respect. This study, therefore, employed the combination of BT and ML to protect network communications and manage datasets to overwhelm the counterfeit dataset. To bring about and evaluate the gathered dataset, big data procedures were employed. Likewise, the fault diagnosis forecast aspect was evaluated on the predictive ML approach proposed which is the improved ensemble learning (IEL) classification ML technique. The system was implemented using the traditional ensemble learning (TEL) and the improved ensemble learning (IEL), and performance matrices like accuracy, precision, sensitivity, and false-positive rate (FPR) were used to evaluate the system performance. In conclusion, the two classification ML techniques (TEL and IEL) were compared using the performance metrics, and it was discovered that the IEL outperformed the TEL with an accuracy of 98.3%, precision of 97.5%, sensitivity of 100%, and FPR of 4.8%.
Uncertainty Quantification using Variational Inference for Biomedical Image Segmentation

Deep learning motivated by convolutional neural networks has been highly successful in a range of medical imaging problems like image classification, image segmentation, image synthesis etc. However for validation and interpretability, not only do we need the predictions made by the model but also how confident it is while making those predictions. This is important in safety critical applications for the people to accept it. In this work, we used an encoder decoder architecture based on variational inference techniques for segmenting brain tumour images. We evaluate our work on the publicly available BRATS dataset using Dice Similarity Coefficient (DSC) and Intersection Over Union (IOU) as the evaluation metrics. Our model is able to segment brain tumours while taking into account both aleatoric uncertainty and epistemic uncertainty in a principled bayesian manner.
Evaluating the Impact of Possible Dependencies on Architecture-Level Maintainability

Dependencies among software entities are the foundation for much of the research on software architecture analysis and architecture analysis tools. Dynamically typed languages, such as Python, JavaScript and Ruby, tolerate the lack of explicit type references, making certain dependencies indiscernible by a purely syntactic analysis of source code. We call these <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">possible dependencies</i> , in contrast with the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">explicit dependencies</i> that are directly manifested in source code. We find that existing architecture analysis tools have not taken possible dependencies into consideration. An important question therefore is: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">to what extent will these missing possible dependencies impact architecture analysis?</i> To answer this question, we conducted a study of 499 open-source Python projects, employing type inference techniques and type hint practices to discern possible dependencies. We investigated the consequences of possible dependencies in three software maintenance contexts, including capturing co-change relations recorded in revision history, measuring architectural maintainability, and detecting architecture anti-patterns that violate design principles and impact maintainability. Our study revealed that the impact of possible dependencies on architecture-level maintainability is substantial—higher than that of explicit dependencies. Our findings suggest that architecture analysis and tools should take into account, assess, and highlight the impacts of possible dependencies caused by dynamic typing.
Combining Geometric Semantic GP with Gradient-Descent Optimization

Geometric semantic genetic programming (GSGP) is a well-known variant of genetic programming (GP) where recombination and mutation operators have a clear semantic effect. Both kind of operators have randomly selected parameters that are not optimized by the search process. In this paper we combine GSGP with a well-known gradient-based optimizer, Adam, in order to leverage the ability of GP to operate structural changes of the individuals with the ability of gradient-based methods to optimize the parameters of a given structure. Two methods, named HYB-GSGP and HeH-GSGP, are defined and compared with GSGP on a large set of regression problems, showing that the use of Adam can improve the performance on the test set. The idea of merging evolutionary computation and gradient-based optimization is a promising way of combining two methods with very different – and complementary – strengths.
Establishing Strong Baselines For TripClick Health Retrieval

We present strong Transformer-based re-ranking and dense retrieval baselines for the recently released TripClick health ad-hoc retrieval collection. We improve the – originally too noisy – training data with a simple negative sampling policy. We achieve large gains over BM25 in the re-ranking task of TripClick, which were not achieved with the original baselines. Furthermore, we study the impact of different domain-specific pre-trained models on TripClick. Finally, we show that dense retrieval outperforms BM25 by considerable margins, even with simple training procedures.