Reviewerly/exHarmony huggingface dataset:
A large-scale comparison of Python code in Jupyter notebooks and scripts

In recent years, Jupyter notebooks have grown in popularity in several domains of software engineering, such as data science, machine learning, and computer science education. Their popularity has to do with their rich features for presenting and visualizing data, however, recent studies show that notebooks also share a lot of drawbacks: high number of code clones, low reproducibility, etc. In this work, we carry out a comparison between Python code written in Jupyter Notebooks and in traditional Python scripts. We compare the code from two perspectives: structural and stylistic. In the first part of the analysis, we report the difference in the number of lines, the usage of functions, as well as various complexity metrics. In the second part, we show the difference in the number of stylistic issues and provide an extensive overview of the 15 most frequent stylistic issues in the studied mediums. Overall, we demonstrate that notebooks are characterized by the lower code complexity, however, their code could be perceived as more entangled than in the scripts. As for the style, notebooks tend to have 1.4 times more stylistic issues, but at the same time, some of them are caused by specific coding practices in notebooks and should be considered as false positives. With this research, we want to pave the way to studying specific problems of notebooks that should be addressed by the development of notebook-specific tools, and provide various insights that can be useful in this regard.
Definition of Centroid Method as Defuzzification

Summary In this study, using the Mizar system [1], [2], we reuse formalization e orts in fuzzy sets described in [5] and [6]. This time the centroid method which is one of the fuzzy inference processes is formulated [10]. It is the most popular of all defuzzied methods ([11], [13], [7]) – here, defuzzified crisp value is obtained from domain of membership function as weighted average [8]. Since the integral is used in centroid method, the integrability and bounded properties of membership functions are also mentioned to fill the formalization gaps present in the Mizar Mathematical Library, as in the case of another fuzzy operators [4]. In this paper, the properties of piecewise linear functions consisting of two straight lines are mainly described.
A Comparative Study of Privacy-Preserving Homomorphic Encryption Techniques in Cloud Computing

In cloud computing, a third party hosts a client's data, which raises privacy and security concerns. To maintain privacy, data should be encrypted by cryptographic techniques. However, encrypting the data makes it unsuitable for indexing and fast processing, as data needs to be decrypted to plain text before it can be further processed. Homomorphic encryption helps to overcome this shortcoming by allowing users to perform operations on encrypted data without decryption. Many academics have attempted to address the issue of data security, but none have addressed the issue of data privacy in cloud computing as thoroughly as this study has. This paper discusses the challenges involved in maintaining the privacy of cloud-based data and the techniques used to address these challenges. It was identified that homomorphic encryption is the best solution of all. This work also identified and compared the various homomorphic encryption schemes which are capable of ensuring the privacy of data in cloud storage and ways to implement them through libraries.
Stereo Depth from Events Cameras: Concentrate and Focus on the Future

Neuromorphic cameras or event cameras mimic human vision by reporting changes in the intensity in a scene, instead of reporting the whole scene at once in a form of an image frame as performed by conventional cameras. Events are streamed data that are often dense when either the scene changes or the camera moves rapidly. The rapid movement causes the events to be overridden or missed when creating a tensor for the machine to learn on. To alleviate the event missing or overriding issue, we propose to learn to concentrate on the dense events to produce a compact event representation with high details for depth estimation. Specifically, we learn a model with events from both past and future but infer only with past data with the predicted future. We initially estimate depth in an event-only setting but also propose to further incorporate images and events by a hier-archical event and intensity combination network for better depth estimation. By experiments in challenging real-world scenarios, we validate that our method outperforms prior arts even with low computational cost. Code is available at: https://github.com/yonseivnl/se-cff.
ViTAS: Vision Transformer Architecture Search

Vision transformers (ViTs) inherited the success of NLP but their structures have not been sufficiently investigated and optimized for visual tasks. One of the simplest solutions is to directly search the optimal one via the widely used neural architecture search (NAS) in CNNs. However, we empirically find this straightforward adaptation would encounter catastrophic failures and be frustratingly unstable for the training of superformer. In this paper, we argue that since ViTs mainly operate on token embeddings with little inductive bias, imbalance of channels for different architectures would worsen the weight-sharing assumption and cause the training instability as a result. Therefore, we develop a new cyclic weight-sharing mechanism for token embeddings of the ViTs, which enables each channel could more evenly contribute to all candidate architectures. Besides, we also propose identity shifting to alleviate the many-to-one issue in superformer and leverage weak augmentation and regularization techniques for more steady training empirically. Based on these, our proposed method, ViTAS, has achieved significant superiority in both DeiT- and Twins-based ViTs. For example, with only 1.4G FLOPs budget, our searched architecture achieves $$3.3\%$$ higher accuracy than the baseline DeiT on ImageNet-1k dataset. With 3.0G FLOPs, our results achieve $$82.0\%$$ accuracy on ImageNet-1k, and $$45.9\%$$ mAP on COCO2017, which is $$2.4\%$$ superior than other ViTs.
Efficient and Secure Certificateless Signcryption Without Pairing for Edge Computing-Based Internet of Vehicles

The emergence of the vehicular ad-hoc network (VANETs) could meet the need for an efficient and safe transportation. Vehicles wirelessly interact with nearby road-side units (RSUs) and send the data to the cloud servers (CS), but this approach has limitations (i.e., network congestion and privacy leakage). Edge computing can effectively solve problems of network congestion, feedback delay and privacy leakage during transmission caused by CS. Thus, this paper proposes a certificateless aggregate signcryption with online/offline encryption (CLOASC) based on edge computing (EDC). Correspondingly, certificateless signcryption (CLSC), online/offline encryption (OOE) and aggregate signcryption technology solve the key escrow problem, shorten the encryption time and realize the quick verification of multiple users, respectively. Furthermore, it is worth mentioning that the scheme we propose does not have any bilinear pairings or scalar multiplications in the online encryption stage, which greatly reduces the computational cost in the online stage. In the random oracle model, our scheme was proved to be existential unforgeability under adaptive chosen message attacks (EUF-ACM) and adaptive indistinguishability under chosen-ciphertext attack (IND-CCA2) security. Simulation results show that our scheme is computationally and communication based lightweight as compared to the related.
Adaptive Ensemble Methods for Tampering Detection in Automotive Aftertreatment Systems

Control and diagnostic processes in modern vehicles incorporate nowadays a wide set of functionalities to preserve the vehicle's health.Automotive vehicles contain embedded systems that must perform a diverse palette of tasks, ranging from less critical tasks (e.g, audio/video media control), to crucial ones, such as controlling the engine, fuel consumption, or the aftertreatment system.This paper identifies and addresses one emerging threat, namely, automotive tampering.Tampering denotes a procedure that changes the behavior of the system to gain financial or functional advantages, without damaging the system and without triggering the built-in safety features of the vehicle.Numerous studies show a growing number of tampered vehicles worldwide and considering that tampered vehicles contribute to air and atmosphere pollution, tampering remains a serious environmental threat.This paper proposes two ensemblebased approaches for tampering detection, both using Long Short-Term Memory neural network predictors, together with Cumulative Sum and Histogram distance-based detectors.Additionally, an Adaptive Majority Weighted Voting fusion methodology is proposed, that considers the historical decisions of the detectors.Experimental results are based on three unique datasets that incorporate a multitude of tampering scenarios.The results prove the efficiency of the proposed ensembles, with a 0% false alert rate and up to 100% detection rate, even when dealing with intelligent tamperers, and even in comparison with modern tampering detection solutions.Moreover, this paper offers resource consumption and scalability measurements on a reference embedded system, further demonstrating the integrability of the proposed techniques in a real embedded environment.
Category-Aware Transformer Network for Better Human-Object Interaction Detection

Human-Object Interactions (HOI) detection, which aims to localize a human and a relevant object while recognizing their interaction, is crucial for understanding a still image. Recently, tranformer-based models have significantly advanced the progress of HOI detection. However, the capability of these models has not been fully explored since the Object Query of the model is always simply initialized as just zeros, which would affect the performance. In this paper, we try to study the issue of promoting transformer-based HOI detectors by initializing the Object Query with category-aware semantic information. To this end, we innovatively propose the Category-Aware Transformer Network (CATN). Specifically, the Object Query would be initialized via category priors represented by an external object detection model to yield a better performance. Moreover, such category priors can be further used for enhancing the representation ability of features via the attention mechanism. We have firstly verified our idea via the Oracle experiment by initializing the Object Query with the groundtruth category information. And then extensive experiments have been conducted to show that a HOI detection model equipped with our idea outperforms the baseline by a large margin to achieve a new state-of-the-art result.
T-PRM: Temporal Probabilistic Roadmap for Path Planning in Dynamic Environments

Sampling-based motion planners are widely used in robotics due to their simplicity, flexibility and computational efficiency. However, in their most basic form, these algorithms operate under the assumption of static scenes and lack the ability to avoid collisions with dynamic (i.e. moving) obstacles. This raises safety concerns, limiting the range of possible applications of mobile robots in the real world. Motivated by these challenges, in this work we present Temporal-PRM, a novel sampling-based path-planning algorithm that performs obstacle avoidance in dynamic environments. The proposed approach extends the original Probabilistic Roadmap (PRM) with the notion of time, generating an augmented graph-like structure that can be efficiently queried using a time-aware variant of the A* search algorithm, also introduced in this paper. Our design maintains all the properties of PRM, such as the ability to perform multiple queries and to find smooth paths, while circumventing its downside by enabling collision avoidance in highly dynamic scenes with a minor increase in the computational cost. Through a series of challenging experiments in highly cluttered and dynamic environments, we demonstrate that the proposed path planner outperforms other state-of-the-art sampling-based solvers. Moreover, we show that our algorithm can run onboard a flying robot, performing obstacle avoidance in real time.
Management of an Academic HPC &amp; Research Computing Facility: The ULHPC Experience 2.0

With the advent of the technological revolution and the digital transformation that made all scientific disciplines becoming computational, the need for High Performance Computing (HPC) has become and a strategic and critical asset to leverage new research and business in all domains requiring computing and storage performance. Since 2007, the University of Luxembourg operates a large academic HPC facility which remains the reference implementation within the country. This paper provides a general description of the current platform implementation as well as its operational management choices which have been adapted to the integration of a new liquid-cooled supercomputer, named Aion, released in 2021. The administration of a HPC facility to provide state-of-art computing systems, storage and software is indeed a complex and dynamic enterprise with the soul purpose to offer an enhanced user experience for intensive research computing and large-scale analytic workflows. Most design choices and feedback described in this work have been motivated by several years of experience in addressing in a flexible and convenient way the heterogeneous needs inherent to an academic environment towards research excellence. The different layers and stacks used within the operated facilities are reviewed, in particular with regards the user software management, or the adaptation of the Slurm Resource and Job Management System (RJMS) configuration with novel incentives mechanisms. In practice, the described and implemented environment brought concrete and measurable improvements with regards the platform utilization (+12,64%), jobs efficiency (average Wall-time Request Accuracy improved by 110,81%), the management and funding (increased by 10%). Thorough performance evaluation of the facility is also presented in this paper through reference benchmarks such as HPL , HPCG , Graph500 , IOR or IO500. It reveals sustainable and scalable performance comparable to the most powerful supercomputers in the world, including for energy-efficient metrics (for instance, 5,19 GFlops/W (resp. 6,14 MTEPS/W) were demonstrated for full HPL (resp. Graph500) runs across all Aion nodes).
FairGRAPE: Fairness-Aware GRAdient Pruning mEthod for Face Attribute Classification

Existing pruning techniques preserve deep neural networks' overall ability to make correct predictions but could also amplify hidden biases during the compression process. We propose a novel pruning method, Fairness-aware GRAdient Pruning mEthod (FairGRAPE), that minimizes the disproportionate impacts of pruning on different sub-groups. Our method calculates the per-group importance of each model weight and selects a subset of weights that maintain the relative between-group total importance in pruning. The proposed method then prunes network edges with small importance values and repeats the procedure by updating importance values. We demonstrate the effectiveness of our method on four different datasets, FairFace, UTKFace, CelebA, and ImageNet, for the tasks of face attribute classification where our method reduces the disparity in performance degradation by up to 90% compared to the state-of-the-art pruning algorithms. Our method is substantially more effective in a setting with a high pruning rate (99%). The code and dataset used in the experiments are available at https://github.com/Bernardo1998/FairGRAPE
TokenMix: Rethinking Image Mixing for Data Augmentation in Vision Transformers

CutMix is a popular augmentation technique commonly used for training modern convolutional and transformer vision networks. It was originally designed to encourage Convolution Neural Networks (CNNs) to focus more on an image’s global context instead of local information, which greatly improves the performance of CNNs. However, we found it to have limited benefits for transformer-based architectures that naturally have a global receptive field. In this paper, we propose a novel data augmentation technique TokenMix to improve the performance of vision transformers. TokenMix mixes two images at token level via partitioning the mixing region into multiple separated parts. Besides, we show that the mixed learning target in CutMix, a linear combination of a pair of the ground truth labels, might be inaccurate and sometimes counter-intuitive. To obtain a more suitable target, we propose to assign the target score according to the content-based neural activation maps of the two images from a pre-trained teacher model, which does not need to have high performance. With plenty of experiments on various vision transformer architectures, we show that our proposed TokenMix helps vision transformers focus on the foreground area to infer the classes and enhances their robustness to occlusion, with consistent performance gains. Notably, we improve DeiT-T/S/B with +1% ImageNet top-1 accuracy. Besides, TokenMix enjoys longer training, which achieves 81.2% top-1 accuracy on ImageNet with DeiT-S trained for 400 epochs.
Big graphs

Big data is typically characterized with 4V's: Volume, Velocity, Variety and Veracity. When it comes to big graphs, these challenges become even more staggering. Each and every of the 4V's raises new questions, from theory to systems and practice. Is it possible to parallelize sequential graph algorithms and guarantee the correctness of the parallelized computations? Given a computational problem, does there exist a parallel algorithm for it that guarantees to reduce parallel runtime when more machines are used? Is there a systematic method for developing incremental algorithms with effectiveness guarantees in response to frequent updates? Is it possible to write queries across relational databases and semistructured graphs in SQL? Can we unify logic rules and machine learning, to improve the quality of graph-structured data, and deduce associations between entities? This paper aims to incite interest and curiosity in these topics. It raises as many questions as it answers.
Quantum Computing and Quantum Machine Learning Classification – A Survey

The rapid development of machine learning technology leads to make the devices in the industries working autonomously. However, the growth of sensors in the industries leads to produce vast data that is utilized by machine learning algorithms to improve the autonomous devices’ performances. However, classical ML algorithms and hardware systems cannot process large data to meet real-time problems. Hence, the researchers have developed Quantum Computing hardware systems and Quantum Machine learning algorithms to speed up the process. This research work presented the review of quantum computing mechanisms and QML algorithms that are applied to classify the images. This work demonstrated the performance comparison of various QML algorithms. It showed that the images are classified using various QML algorithms faster than classical ML algorithms in terms of time.
TransZero++: Cross Attribute-Guided Transformer for Zero-Shot Learning

Zero-shot learning (ZSL) tackles the novel class recognition problem by transferring semantic knowledge from seen classes to unseen ones. Semantic knowledge is typically represented by attribute descriptions shared between different classes, which act as strong priors for localizing object attributes that represent discriminative region features, enabling significant and sufficient visual-semantic interaction for advancing ZSL. Existing attention-based models have struggled to learn inferior region features in a single image by solely using unidirectional attention, which ignore the transferable and discriminative attribute localization of visual features for representing the key semantic knowledge for effective knowledge transfer in ZSL. In this paper, we propose a cross attribute-guided Transformer network, termed TransZero++, to refine visual features and learn accurate attribute localization for key semantic knowledge representations in ZSL. Specifically, TransZero++ employs an attribute → visual Transformer sub-net (AVT) and a visual → attribute Transformer sub-net (VAT) to learn attribute-based visual features and visual-based attribute features, respectively. By further introducing feature-level and prediction-level semantical collaborative losses, the two attribute-guided transformers teach each other to learn semantic-augmented visual embeddings for key semantic knowledge representations via semantical collaborative learning. Finally, the semantic-augmented visual embeddings learned by AVT and VAT are fused to conduct desirable visual-semantic interaction cooperated with class semantic vectors for ZSL classification. Extensive experiments show that TransZero++ achieves the new state-of-the-art results on three golden ZSL benchmarks and on the large-scale ImageNet dataset. The project website is available at: https://shiming-chen.github.io/TransZero-pp/TransZero-pp.html.
Cerberus Transformer: Joint Semantic, Affordance and Attribute Parsing

Multi-task indoor scene understanding is widely considered as an intriguing formulation, as the affinity of different tasks may lead to improved performance. In this paper, we tackle the new problem of Joint semantic, affordance and attribute parsing. However, successfully resolving it requires a model to capture long-range dependency, learn from weakly aligned data and properly balance sub-tasks during training. To this end, we propose an attention-based architecture named Cerberus and a tailored training framework. Our method effectively addresses aforementioned challenges and achieves state-of-the-art performance on all three tasks. Moreover, an in-depth analysis shows concept affinity consistent with human cognition, which inspires us to explore the possibility of weakly supervised learning. Surprisingly, Cerberus achieves strong results using only 0.1%–1% annotation. Visualizations further confirm that this success is credited to common attention maps across tasks. Code and models can be accessed at https://github.com/OPEN-AIR-SUN/Cerberus.