Reviewerly/exHarmony huggingface dataset:
Boolean Connexive Logic and Content Relationship

Abstract We present here some Boolean connexive logics (BCLs) that are intended to be connexive counterparts of selected Epstein’s content relationship logics (CRLs). The main motivation for analyzing such logics is to explain the notion of connexivity by means of the notion of content relationship. The article consists of two parts. In the first one, we focus on the syntactic analysis by means of axiomatic systems. The starting point for our syntactic considerations will be the smallest BCL and the smallest CRL. In the first part, we also identify axioms of Epstein’s logics that, together with the connexive principles, lead to contradiction. Moreover, we present some principles that will be equivalent to the connexive theses, but not to the content connexive theses we will propose. In the second part, we focus on the semantic analysis provided by relating- and set-assignment models. We define sound and complete relating semantics for all tested systems. We also indicate alternative relating models for the smallest BCL, which are not alternative models of the connexive counterparts of the considered CRLs. We provide a set-assignment semantics for some BCLs, giving thus a natural formalization of the content relationship understood either as content sharing or as content inclusion.
Dissimilarity analysis based on diffusion maps

Compositional measurements from species assemblages define a high dimensional dataspace in which the data can form complex structures, termed manifolds. Comparing assemblages in this dataspace is difficult because the data is often sparse relative to its dimensionality and the complex structure of the manifold introduces bias and error in measurements of distance. Here, we apply diffusion maps, a manifold learning method, to find and characterize manifolds in high‐dimensional compositional data. We show that diffusion maps embed the data in reduced dimensions in which the Euclidean distance between data points approximates the distance between them along the manifold. This is especially useful when species turnover is high, as it provides a way to measure meaningful distances between assemblages even when they harbor disjoint sets of species. We anticipate diffusion maps will therefore be particularly useful for characterizing community change over large spatial and temporal scales.
Postdigital Process Theology

Process theology is a school of religious thought that has been particularly influential in American progressive Protestant theology since the 1960s. Process theology draws on Alfred North Whitehead’s process philosophy, which was an early attempt to give a metaphysical account that incorporated quantum theory and evolutionary theory. It emphasizes God as continually becoming rather than having eternal and changeless existence. Process theology has long had a leading role in science-religion dialogues and has provided an impetus for progressive Christian engagement with environmental, economic, and social justice issues. The relational nature of process theology allows engagement with new theories as they emerge, including postdigital theory.
Integrating Visual and Semantic Similarity Using Hierarchies for Image Retrieval

Most of the research in content-based image retrieval (CBIR) focus on developing robust feature representations that can effectively retrieve instances from a database of images that are visually similar to a query. However, the retrieved images sometimes contain results that are not semantically related to the query. To address this, we propose a method for CBIR that captures both visual and semantic similarity using a visual hierarchy. The hierarchy is constructed by merging classes with overlapping features in the latent space of a deep neural network trained for classification, assuming that overlapping classes share high visual and semantic similarities. Finally, the constructed hierarchy is integrated into the distance calculation metric for similarity search. Experiments on standard datasets: CUB-200-2011 and CIFAR100, and a real-life use case using diatom microscopy images show that our method achieves superior performance compared to the existing methods on image retrieval.
How good are virtual hands? Influences of input modality on motor tasks in virtual reality

Hand-tracking enables controller-free interaction with virtual environments, which can make virtual reality (VR) experiences more natural and immersive. As naturalness hinges on both technological and human influence factors, fine-tuning the former while assessing the latter can be used to increase overall experience. This paper investigates a reach-grab-place task inside VR using two input modalities (hand-tracking vs. handheld-controller). Subjects (N = 33) compared the two input methods available on a consumer grade VR headset for their effects on objective user performance and subjective experience of the perceived sense of presence, cognitive workload, and ease-of-use. We found that virtual hands (with hand-tracking) did not influence the subjective feelings of perceived presence, naturalness, & engagement; neither did it inspire the overall ease-of-use while performing the task. In fact, subjects completed the task faster and felt a lower mental workload and higher overall usability with handheld-controllers. The result found that in this particular case, hand-tracking did not improve the psychological and emotional determinants of immersive VR experiences. The study helps expand on our understanding of the two input modalities in terms of their viability for naturalistic experiences in VR akin to real-world scenarios.
BEVSeg: Geometry and Data-Driven Based Multi-view Segmentation in Bird’s-Eye-View

Perception and awareness of the surroundings are significant for autonomous vehicle navigation. To drive safely, autonomous systems must be able to extract spatial information and understand the semantic meaning of the environment. We propose a novel network architecture BEVSeg to generate the perception and semantic information by incorporating geometry-based and data-driven techniques into two respective modules. Specifically, the geometry-based aligned BEV domain data augmentation module addresses overfitting and misalignment issues by augmenting the coherent BEV feature map, aligning the augmented object and segmentation ground truths, and aligning augmented BEV feature map and its augmented ground truths. The data-driven hierarchy double-branch spatial attention module addresses the inflexibility of the BEV feature generation by learning multi-scale BEV features flexibly via the enlarged feature receptive field and learned interest regions. Experimental results on the nuScenes benchmark dataset demonstrate BEVSeg achieves state-of-the-art results with a higher mIoU of 3.6% than the baseline. Code and models will be released.
Mixing Domains for Smartly Picking and Using Limited Datasets in Industrial Object Detection

Object detection is a popular computer vision task that is performed by autonomous industrial robots. However, training a detection model requires a large annotated image dataset that belongs to the camera domain of the robot (the test domain). Acquiring such data in a similar domain or rendering photo-realistic images from a realistic virtual environment composed of accurate 3D models and using powerful hardware, can be expensive, time-consuming, and requires specialized expertise. This article focuses on investigating the growth of average precision (AP) in object detection as we progressively train and test our models using various combinations of acquired and rendered datasets from different domains: real and synthetic. By analyzing the results on industrial load carrier box detection, we discovered that a hybrid dataset comprising 20–30% of images similar to the test domain leads to achieving nearly maximum detection accuracy.
Hands, Objects, Action! Egocentric 2D Hand-Based Action Recognition

Action recognition is at the core of egocentric camera-based assistive technologies, as it enables automatic and continuous monitoring of Activities of Daily Living (ADLs) without any conscious effort on the part of the user. This study explores the feasibility of using 2D hand and object pose information for egocentric action recognition. While current literature focuses on 3D hand pose information, our work shows that using 2D skeleton data is a promising approach for hand-based action classification and potentially allows for reduced computational power. The study implements a state-of-the-art transformer-based method to recognise actions. Our approach achieves an accuracy of 95% in validation and 88% in test subsets on the publicly available benchmark, outperforming other existing solutions by 9% and proving that the presented technique offers a successful alternative to 3D-based approaches. Finally, the ablation study shows the significance of each network input and explores potential ways to improve the presented methodology in future research.
SIFT-Guided Saliency-Based Augmentation for Weed Detection in Grassland Images: Fusing Classic Computer Vision with Deep Learning

Weed detection is a challenging case within object detection as the weed targets do not generally strike out from the background in terms of color. This paper investigates how the density of structural features can be used to assist the training process of a Deep-Learning-based object detector. SIFT keypoint density is used to create overlay masks to augment images, emphasizing low-density areas—typically corresponding to weed plants. Our method is shown to improve detection $$mAP_{.5:.05:.95}$$ on the YOLOR-CSP detector by up to 0.0215.
Performance and Failure Cause Estimation for Machine Learning Systems in the Wild

Machine learning systems at the edge may fail as the real world data can be noisy and have different distribution from the training dataset which the machine learning systems were developed on. However, it is very difficult to detect the system failures and identify root cause of the failures for systems on the edge devices due to many factors such as privacy concerns, regulations, constrained computation resources and expensive error labeling. In this work, we propose a flexible and general framework, PERF, to estimate the performance of a machine learning system deployed at the edge device and identify the root cause of failure if it fails. PERFis similar yet different from the classic teacher-student paradigm. Within PERF, a larger performance estimation model $$\mathcal{P}\mathcal{E}$$ is deployed along with the smaller target system $$\mathcal {T}$$ to be evaluated on the same edge device. While the device is idle, $$\mathcal{P}\mathcal{E}$$ can be activated and predicts $$\mathcal {T}$$ ’s performance and the failure causes from $$\mathcal {T}$$ ’s internal and outputs features on the device without human intervention. The privacy risk can be avoided as the evaluation is done on the edge device without sending any user data to the backend cloud. We validated PERFon two exemplar tasks and showed promising results.
Of Mice and Pose: 2D Mouse Pose Estimation from Unlabelled Data and Synthetic Prior

Numerous fields, such as ecology, biology, and neuroscience, use animal recordings to track and measure animal behaviour. Over time, a significant volume of such data has been produced, but some computer vision techniques cannot explore it due to the lack of annotations. To address this, we propose an approach for estimating 2D mouse body pose from unlabelled images using a synthetically generated empirical pose prior. Our proposal is based on a recent self-supervised method for estimating 2D human pose that uses single images and a set of unpaired typical 2D poses within a GAN framework. We adapt this method to the limb structure of the mouse and generate the empirical prior of 2D poses from a synthetic 3D mouse model, thereby avoiding manual annotation. In experiments on a new mouse video dataset, we evaluate the performance of the approach by comparing pose predictions to a manually obtained ground truth. We also compare predictions with those from a supervised state-of-the-art method for animal pose estimation. The latter evaluation indicates promising results despite the lack of paired training data. Finally, qualitative results using a dataset of horse images show the potential of the setting to adapt to other animal species.
Quantum correlation swapping between qutrit states

Abstract In this study, the qubit quantum correlation swapping (QCS) is generalized to the qutrit case. To be concrete, two qutrit Werner states are taken as the research objects and the complete set of qutrit Bell states are utilized as the intermediate two-qutrit measure to implement QCS. The popular quantum correlation measure, i.e. quantum discord (Ollivier and Zurek 2001 Phys. Rev. Lett. 88 017901), is employed to characterize and quantify quantum correlations in the concerned states. The properties of the variance and threshold of quantum correlations occurring in the swapping process are revealed, and two distinct QCS phenomena are thus observed. Moreover, it is found that the maximal amount of quantum correlations in the final state can not exceed the minimal one of these in the initial states.
On the Cryptanalysis of a Simplified AES Using a Hybrid Binary Grey Wolf Optimization

Cryptosystem cryptanalysis is regarded as an NP-Hard task in modern cryptography. Due to block ciphers that are part of a modern cipher and have nonlinearity and low autocorrelation in their structure, traditional techniques and brute-force attacks suffer from breaking the key presented in traditional techniques, and brute-force attacks against modern cipher S-AES (simplified-advanced encryption standard) are complex. Thus, developing robust and reliable optimization with high searching capability is essential. Motivated by this, this paper attempts to present a novel binary hybridization algorithm based on the mathematical procedures of the grey wolf optimizer (GWO) and particle swarm optimization (PSO), named BPSOGWO, to deal with the cryptanalysis of (S-AES). The proposed BPSOGWO employs a known plaintext attack that requires only one pair of plaintext–ciphertext pairs instead of other strategies that require more pairs (i.e., it reduces the number of messages needed in an attack, and secret information such as plaintext-ciphertext pairs cannot be obtained easily). The comprehensive and statistical results indicate that the BPSOGWO is more accurate and provides superior results compared to other peers, where it improved the cryptanalysis accurateness of S-AES by 82.5%, 84.79%, and 79.6% compared to PSO, GA, and ACO, respectively. Furthermore, the proposed BPSOGWO retrieves the optimal key with a significant reduction in search space compared to a brute-force attack. Experiments show that combining the suggested fitness function with HPSOGWO resulted in a 109-fold reduction in the search space. In cryptanalysis, this is a significant factor. The results prove that BPSOGWO is a promising and effective alternative to attack the key employed in the S-AES cipher.
Using LSTM neural networks for cross‐lingual phonetic speech segmentation with an iterative correction procedure

Abstract This article describes experiments on speech segmentation using long short‐term memory recurrent neural networks. The main part of the paper deals with multi‐lingual and cross‐lingual segmentation, that is, it is performed on a language different from the one on which the model was trained. The experimental data involves large Czech, English, German, and Russian speech corpora designated for speech synthesis. For optimal multi‐lingual modeling, a compact phonetic alphabet was proposed by sharing and clustering phones of particular languages. Many experiments were performed exploring various experimental conditions and data combinations. We proposed a simple procedure that iteratively adapts the inaccurate default model to the new voice/language. The segmentation accuracy was evaluated by comparison with reference segmentation created by a well‐tuned hidden Markov model‐based framework with additional manual corrections. The resulting segmentation was also employed in a unit selection text‐to‐speech system. The generated speech quality was compared with the reference segmentation by a preference listening test.
Joint Impact of Agents and Services in Enhancing Software Requirements Engineering

Requirements engineering (RE) is a significant aspect of system development stages in generating reliable software (SW). Despite RE’s decisive impact on project success, SW systems still fail since there is a perplexity in sorting out requirements correctly. Researchers have tried several paradigms to deal with the specified challenges, such as agent-oriented RE (AORE), model-based RE, and service-oriented RE (SORE). By investigating the limitations of the independent use of these paradigms, this research sets an objective that proposes a framework which integrates the two paradigms (agent and service) on top of social media to enhance the SW RE processes. Thus, the research addresses challenges in gathering adequate requirements, detecting alignment between business requirements and SW products, prioritizing requirements, and recommending innovative ideas. The research has mainly adopted an empirical research methodology for SW engineering. Accordingly, two distinct expert groups have been formed based on their previous experience in AORE and SORE, respectively. The experts have been selected from enterprises and academic institutions, and they participated in our case study. After performing the necessary assessment based on specified criteria, those experts in the first group have reported that CASCRE (Collaboration of Agents and Services for Crowd-based Requirements Engineering) with a score of 93.7% is found to be better than that of AORE with a score of 88.7%. Moreover, experts in the second group have declared that CASCRE, with a score of 92.3%, is better than SORE, with a score of 83.7%. In both cases, improvements have been observed, which reveals that the synergy of the CASCRE features has a better impact on the RE process than utilizing individual approaches. Moreover, in order to demonstrate the applicability of CASCRE, feedback has been gathered from a focused crowd of local pharmaceuticals using a mini-prototype. Accordingly, 250 requirements related comments have been gathered from the discussion forum, and 1400 keywords were generated. Then, after performing a sentiment analysis using NLP algorithms, the result was demonstrated to experts. Therefore, 93% of gurus strongly agreed on the applicability of CASCRE in real projects.
Bug report priority prediction using social and technical features

Summary Software stakeholders report bugs in issue tracking system (ITS) with manually labeled priorities. However, the lack of knowledge and standard for prioritization may cause stakeholders to mislabel the priorities. In response, priority predictors are actively developed to support them. Prior studies trained machine learners based on textual similarity, categorical, and numeric technical features of bug reports. Most models were validated by time‐insensitive approaches, and they were producing suboptimal results for practical usage. While they ignored the social aspects of ITS, the technical aspects were also limited in surface features of bug reports. To better model the bug report, we extract their topic and most similar code structures. Since ITS bridges users and developers as the main contributors, we also integrate their experience, sentiment, and socio‐technical features to construct a new dataset. Then, we perform two‐classed and multiclassed bug priority prediction based on the dataset. We also introduce adversarial training using generated training data with random word swap and random word deletion. We validate our model in within‐project, cross‐project, and time‐wise scenarios, and it outperforms the two baselines by up to 15% in area under curve‐receiver operating characteristics (AUC‐ROC) and 19% in Matthews correlation coefficient (MCC). We reveal involving contributor (i.e., assignee and reporter) features such as sentiment that could boost prediction performance. Finally, we test statistically the mean and distribution of the features that reflect the differences in social and technical aspects (e.g., quality of communication and resource distribution) between high and low priority reports. In conclusion, we suggest that researchers should consider both social and technical aspects of ITS in bug report priority prediction and introduce adversarial training to boost model performance.
A Novel and Secure Fake-Modulus Based Rabin-Ӡ Cryptosystem

Electronic commerce (E-commerce) transactions require secure communication to protect sensitive information such as credit card numbers, personal identification, and financial data from unauthorized access and fraud. Encryption using public key cryptography is essential to ensure secure electronic commerce transactions. RSA and Rabin cryptosystem algorithms are widely used public key cryptography techniques, and their security is based on the assumption that it is computationally infeasible to factorize the product of two large prime numbers into its constituent primes. However, existing variants of RSA and Rabin cryptosystems suffer from issues like high computational complexity, low speed, and vulnerability to factorization attacks. To overcome the issue, this article proposes a new method that introduces the concept of fake-modulus during encryption. The proposed method aims to increase the security of the Rabin cryptosystem by introducing a fake-modulus during encryption, which is used to confuse attackers who attempt to factorize the public key. The fake-modulus is added to the original modulus during encryption, and the attacker is unable to distinguish between the two. As a result, the attacker is unable to factorize the public key and cannot access the sensitive information transmitted during electronic commerce transactions. The proposed method’s performance is evaluated using qualitative and quantitative measures. Qualitative measures such as visual analysis and histogram analysis are used to evaluate the proposed system’s quality. To quantify the performance of the proposed method, the entropy of a number of occurrences for the pixels of cipher text and differential analysis of plaintext and cipher text is used. When the proposed method’s complexity is compared to a recent variant of the Rabin cryptosystem, it can be seen that it is more complex to break the proposed method—represented as O(ɲ× τ) which is higher than Rabin-P (O(ɲ)) algorithms.
Efficient and Controllable Model Compression through Sequential Knowledge Distillation and Pruning

Efficient model deployment is a key focus in deep learning. This has led to the exploration of methods such as knowledge distillation and network pruning to compress models and increase their performance. In this study, we investigate the potential synergy between knowledge distillation and network pruning to achieve optimal model efficiency and improved generalization. We introduce an innovative framework for model compression that combines knowledge distillation, pruning, and fine-tuning to achieve enhanced compression while providing control over the degree of compactness. Our research is conducted on popular datasets, CIFAR-10 and CIFAR-100, employing diverse model architectures, including ResNet, DenseNet, and EfficientNet. We could calibrate the amount of compression achieved. This allows us to produce models with different degrees of compression while still being just as accurate, or even better. Notably, we demonstrate its efficacy by producing two compressed variants of ResNet 101: ResNet 50 and ResNet 18. Our results reveal intriguing findings. In most cases, the pruned and distilled student models exhibit comparable or superior accuracy to the distilled student models while utilizing significantly fewer parameters.