Reviewerly/exHarmony huggingface dataset:
Visually Exploring the Carbon Emissions in China from a Multi-Temporal Scale Perspective: A Case Study on the Province-Level CO<sub>2</sub> Emissions

Global warming has become one of the most serious challenges that all countries in the world are facing together. One important factor which results in this challenge is the emissions of carbon, e.g., carbon dioxide (CO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> ). As one of the major countries which has the largest carbon emissions, China has been positively seeking effective ways to solve this worldwide problem. Motivated by this, research on carbon emissions has been a main focus in various domains. Among the existing research, however, few has been conducted specifically from a multi-temporal scale perspective. Therefore, in this paper, we aim to explore the carbon emissions from a multi-temporal scale using the Continuous Triangular Model (CTM) by taking China as an example. Specifically, the data of province-level CO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> emissions are adopted for case study, and the explorations are achieved mainly through visualizations. The visualizations are the various CTM diagrams derived based on two indices (i.e., the total CO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> emissions per year and the growth rate of the total CO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> emissions per year, respectively) at two spatial levels (i.e., province-level and region-level, respectively). For each CTM diagram, corresponding information at all temporal scales is included. The case study demonstrates that several meaningful information can be found through visual explorations, which might be useful to related departments for decision making.
LC-GAN: Improving Adversarial Robustness of Face Recognition Systems on Edge Devices

Deep-learning-based (DL-based) face recognition has become an important application in the Internet of Things (IoT) environment. However, recent studies demonstrate that elaborate adversarial examples can mislead the results of DL-based face recognition on mobile and edge devices. Such vulnerability threats the robustness of face recognition systems and causes security issues. Generative adversarial defense methods can reform adversarial examples before input into the face recognition model to improve the accuracy under adversarial attacks. Unfortunately, the existing generative adversarial defense methods cannot completely remove the misleading features of adversarial examples due to the lack of robust encoding ability. In this article, we propose a local consistency generative adversarial network (LC-GAN) framework by adding the constraint of local consistency to force the encoder to mine consistent features in each local area, achieving robust encoding ability consequently. The framework includes three main novel designs. First, we present a patch-wise contrastive learning-based refinement stage with local consistency loss to encode robust identity features from nonsalient areas that are undamaged by adversarial attacks. Second, we use a powerful expert network to guide the training of LC-GAN for eliminating adversarial identity features. Third, we design a multilevel identity loss to enhance the identity preservation ability by unifying the local and global identity features. Experimental results on four widely used face data sets show that LC-GAN outperforms other generative adversarial defense methods.
A Coverless video steganography: A Survey

In the traditional methods of information hiding, confidential information is embedded in the carriers by modifying carriers’ parameter characteristics, that will certainly leave traces of adjustments on the carriers. These adjustments are usually too small to be discerned by the human eye, but they can be discovered by some steganalysis algorithms and therefore, safety cannot be guaranteed using these methods. This challenge can be solved completely through a new technique called coverless steganography. This method does not need to employ a cover for the purpose of hiding, as well as no traces of modifications occur in the cover by hiding the secret message. In this method, multiple covers are chosen so that these covers contain the characteristics of the message to be hidden, then they are considered as container covers for the secret message and are sent to the recipient for the purpose of extracting the secret message. Therefore, coverless steganography methods have higher security than traditional methods of information hiding. In this study, more than 30 papers are included to provide an extensive explanation of this field as well as cover the main aspects of a coverless information hiding method to hide the confidential information in the video cover.
Online Adaptive Metrics for Model Evaluation on Non-representative Offline Test Data

A major challenge encountered in the offline evaluation of machine learning models before being released to production is the discrepancy between the distributions of the offline test data and of the online data, due to, e.g., biased sampling scheme, data aging issues and occurrence(s) of regime shift. Consequently, the offline evaluation metrics often do not reflect the actual performance of the model online. In this paper, we propose online adaptive metrics, a computationally efficient method which re-weights the offline metrics based on calculating the joint distributions of the model hypotheses over the offline test data VS. the online data. It provides offline metrics which estimate the production performance of the model by taking into account the test data biases. The proposed method is demonstrated by real life examples on text classification and a commercial natural language understanding system. We show that the online adaptive metrics can provide accurate predictions of online recall and precision even with a small test dataset.
Multiscale Graph Scattering Transform

Graph scattering transform (GST) is mathematically-designed graph convolutional model that iteratively applies graph filter banks to achieve comprehensive feature extraction from graph signals. While GST performs excessive decomposition of graph signals in the graph spectral domain, it does not explicitly achieve multiresolution in the graph vertex domain, causing potential failure in handling graphs with hierarchical structures. To address the limitation, this work proposes novel multiscale graph scattering transform (MGST) to achieve hierarchical representations along both graph vertex and spectral domains. With recursive partitioning a graph structure, we yield multiple subgraphs at various scales and then perform scattering frequency decomposition on each subgraph. MGST finally obtains a series of representations and each of them corresponds to a specific graph vertex-spectral subband, achieving multiresolution along both graph vertex and spectral domains. In the experiments, we validate the superior empirical performances of MGST and visualize each graph vertex-spectral subband.
String Test Data Generation for Java Programs

Appropriate string test data generation is important for program testing. Complex string APIs combinations are commonly used to handle string parameters. However, the complex combinations make it difficult to express comprehensive string related constraints and generate suitable string data to trigger bugs and cover more branches. In this paper, we propose a novel approach to characterize the input strings and their operations (API invocations) with the regular expressions for string test data generation, with insight that they support rich syntax and can express the semantics of various string APIs combinations. We build a set of mapping rules that map 48 string APIs in Java to regular expressions, and design an inference algorithm to generate regular expressions for the complex string APIs combinations. With these regular expressions, more effective string data can be generated in an efficient way. Experiments on multi-type programs from assignments, LeetCode platform and open source community show that our approach can increase the branch coverage (17%) and find more bugs (+81) than the existing work. For the basic library JDK, 17 defects have been found, of which 14 are confirmed by the JDK developers and 3 are fixed in new version.
Design and Analysis of Truss Aerial Transportation System (TATS): The Lightweight Bar Spherical Joint Mechanism

In aerial cooperative transportation missions, it has been recognized that for small-sized but heavy payloads, the cable-suspended framework is a preferred manner. However, to maintain proper safe flight distances, cables always stay inclined, which implies that horizontal force components have to be generated by UAVs, and only partial thrust forces are used for gravity compensation. To overcome this drawback, in this paper, a new cooperative transportation system named Truss Aerial Transportation System (TATS) is proposed, where those horizontal forces can be internally compensated by the bar spherical joint structure. In the TATS, rigid bars can powerfully sustain the desired distances among UAVs for safe flight, resulting in a more compact and effective transportation system. Thanks to the structural advantage of the truss, the rigid bars can be made lightweight so as to minimize their induced gravity burden. The construction method of the proposed TATS is presented. The improvement in energy efficiency is analyzed and compared with the cable-suspended framework. Furthermore, the robustness property of a TATS configuration is evaluated by computing the margin capacity. Finally, a load test experiment is conducted on our made prototype, the results of which show the effectiveness and feasibility of the proposed TATS.
CNN-based Bronze Inscriptions Character Recognition

Bronze inscriptions characters are always recognized manually by paleographic professionals, which is a very hard and time-consuming task. Automatic computer recognition can be very helpful to this problem. Bronze inscriptions character recognition by computer is a classic image classification task which is the speciality of convolutional neural network (CNN). In this paper, a targeted model was designed and tested based on CNN-related technologies. Specifically, we explored many advanced CNN models and modified them to accommodate our needs as our base models to recognize bronze inscriptions characters. Then we brought in attention mechanism for this model by adopting spatial transformer network (STN) which was a learnable component to perform spatial transformation to attention the important regions in images. Furthermore, a robust loss function was introduced to implement implicit semantic data augmentation (ISDA) to help regularize the whole model and improve the final performance. A new large bronze inscriptions character dataset was used in our experiments helping our model get an excellent performance. A lot of experiments with detailed analysis were conducted and the final 91.21% accuracy showed the feasibility and effectiveness of our work.
Improving code autocompletion with transfer learning

Software language models have achieved promising results predicting code completion usages, and several industry studies have described successful IDE integration. Recently, accuracy in autocompletion prediction improved 12.8%[2] from training on a real-world dataset collected from programmers' IDE activities. But what if the number of examples of IDE autocompletion in the target programming language is inadequate for model training? In this paper, we highlight practical reasons for this inadequacy, and make a call to action in using transfer learning to overcome the issue.
Comparing the Robustness of Classical and Deep Learning Techniques for Text Classification

Deep learning algorithms achieve exceptional accuracies in various tasks. Despite this success, those models are known to be prone to errors, i.e. low in robustness, due to differences between training and production environment. One might assume that more model complexity translates directly to more robustness. Therefore, we compare simple, classical models (logistic regression, support vector machine) with complex deep learning techniques (convolutional neural networks, transformers) to provide novel insights into the robustness of machine learning systems. In our approach, we assess the robustness by developing and applying three realistic perturbations, mimicking scanning, typing, and speech recognition errors occurring in inputs for text classification tasks. Hence, we performed a thorough study analyzing the impact of different perturbations with variable strengths on character and word level. A noteworthy finding is that algorithms with low complexity can achieve high robustness. Additionally, we demonstrate that augmented training regarding a specific perturbation can strengthen the chosen models' robustness against other perturbations without reducing their accuracy. Our results can impact the selection of machine learning models and provide a guideline on how to examine the robustness of text classification methods for real-world applications. Moreover, our implementation is publicly available, which contributes to the development of more robust machine learning systems.
SCN-DRL: Scheduler for large-scale Critical Notification applications based on Deep Reinforcement Learning

A multi-objective Scheduling approach for large-scale microservice Critical Notification system applications (SCN-DRL) based on Deep Reinforcement Learning is presented. This paper addresses optimization for three objectives: cloud service cost, cloud resource utilization, and system notification deadline. Recent advances in the deep reinforcement learning field inspired us to research how to build a solution that learns to manage the cloud container’s resources directly from experience. SCN-DRL is an example solution that translates the containerized microservice cloud cluster scheduling problem into a learning problem. To the best of our knowledge, this is the first work dealing with multi-optimization objectives for critical microservice notification applications using deep reinforcement learning, where the notification load is variable and depends on the results of the other previously prior processed microservice. Our initial results show that SCNDRL performs better than state-of-the-art heuristics and adapts to different workload conditions without a noticeable drop in performance.
Global software engineering in a global classroom

Due to globalization, many software projects have become large-scale and distributed tasks that require software engineers to learn and apply techniques for distributed requirements analysis, modeling, development, and deployment. Globally-distributed projects require special skills in communication across different locations and time zones in all stages of the project. There has been advancement in teaching these concepts at universities, but adapting global software engineering in a curriculum is still in infancy.
Overview of Electromagnetic Representation and Interpretation of Sign Language Semantics

Sign language, as the main means of communication used by hearing-impaired people, is an indispensable part for them to better integrate into a harmonious society. At the same time, the all-round development of today's society urgently needs to build a bridge for the hearing impaired to communicate with the society, create a barrier-free environment, and increase the degree of socialization of the hearing impaired. All these factors promote the demand for direct human-computer interaction in sign language. This paper aims to explore the electromagnetic representation and interpretation of sign language semantics, hoping to provide a new way to improve the level of human-computer interaction and care for the hearing impaired.
Benchmarking Omni-Vision Representation Through the Lens of Visual Realms

Though impressive performance has been achieved in specific visual realms (e.g. faces, dogs, and places), an omni-vision representation generalizing to many natural visual domains is highly desirable. But, existing benchmarks are biased and inefficient to evaluate the omni-vision representation—these benchmarks either only include several specific realms, or cover most realms at the expense of subsuming numerous datasets that have extensive realm overlapping. In this paper, we propose Omni-Realm Benchmark (OmniBenchmark). It includes 21 realm-wise datasets with 7,372 concepts and 1,074,346 images. Without semantic overlapping, these datasets cover most visual realms comprehensively and meanwhile efficiently. In addition, we propose a new supervised contrastive learning framework, namely Relational Contrastive learning (ReCo), for a better omni-vision representation. Beyond pulling two instances from the same concept closer—the typical supervised contrastive learning framework—ReCo also pulls two instances from the same semantic realm closer, encoding the semantic relation between concepts, facilitating omni-vision representation learning. We benchmark ReCo and other advances in omni-vision representation studies that are different in architectures (from CNNs to transformers) and in learning paradigms (from supervised learning to self-supervised learning) on OmniBenchmark. We illustrate the superior of ReCo to other supervised contrastive learning methods, and reveal multiple practical observations to facilitate future research. The code and models are available at https://zhangyuanhan-ai.github.io/OmniBenchmark .
Chaotic Ant Lion Optimization Algorithm

The Ant Lion Optimization (ALO) algorithm is a relative recent metaheuristic, designed on the concept of predator ant lions. The basic concepts revolve around hunting prey such as the random walk of ants, building traps, entrapment of ants in traps, catching preys, and re-building traps. The proposed algorithm focus on the stochasticity of the algorithm, with the embedding of chaos maps as pseudo-random number generation. The uniqueness of this approach is to evaluate the behavior of ALO, which employs minimal tuning parameters, and to observe the effectiveness of chaotic systems in mostly self-tuning algorithms. The experimentations was conducted on standard benchmark unimodal and multimodal problems and the results compared with the canonical version of ALO and other published algorithms. Based on the results comparisons, the Chaotic Ant Lion Optimization (CALO) performed significantly better than ALO and most compared algorithms.
An Area-Efficient Large Integer NTT-Multiplier Using Discrete Twiddle Factor Approach

Number theoretic transform (NTT) method shows great advantages in speed and efficiency for applications such as homomorphic encryption. However, the twiddle factor data consumes a lot of memories. In this brief, we propose a novel data compression method, together with the corresponding data storage scheme and addressing algorithm. Furthermore, we design a 768k-bit multiplier with a full pipeline structure. Our proposed compression method has achieved a compression rate of 98.8% for the twiddle factor data. Compared with the state-of-the-art FPGA implementations, our design shows up to 44.2% improvement in terms of area-efficiency.
Real-Time Hybrid Mapping of Populated Indoor Scenes using a Low-Cost Monocular UAV

Unmanned aerial vehicles (UAVs) have been used for many applications in recent years, from urban search and rescue, to agricultural surveying, to autonomous underground mine exploration. However, deploying UAVs in tight, indoor spaces, especially close to humans, remains a challenge. One solution, when limited payload is required, is to use micro-UAVs, which pose less risk to humans and typically cost less to replace after a crash. However, micro-UAVs can only carry a limited sensor suite, e.g. a monocular camera instead of a stereo pair or LiDAR, complicating tasks like dense mapping and markerless multi-person 3D human pose estimation, which are needed to operate in tight environments around people. Monocular approaches to such tasks exist, and dense monocular mapping approaches have been successfully deployed for UAV applications. However, despite many recent works on both marker-based and markerless multi-UAV single-person motion capture, markerless single-camera multi-person 3D human pose estimation remains a much earlier-stage technology, and we are not aware of existing attempts to deploy it in an aerial context. In this paper, we present what is thus, to our knowledge, the first system to perform simultaneous mapping and multi-person 3D human pose estimation from a monocular camera mounted on a single UAV. In particular, we show how to loosely couple state-of-the-art monocular depth estimation and monocular 3D human pose estimation approaches to reconstruct a hybrid map of a populated indoor scene in real time. We validate our component-level design choices via extensive experiments on the large-scale ScanNet and GTA-IM datasets. To evaluate our system-level performance, we also construct a new Oxford Hybrid Mapping dataset of populated indoor scenes.
Clustering Method for Touristic Photographic Spots Recommendation

Tourism and photography have become very complementary, and tourists are constantly seeking the best spots to capture pictures and memorize their vacations. However, the search for the best and unforgettable photographic spots is difficult and time-consuming for tourists, especially when visiting new regions. In this paper, we propose a method for discovering tourist photo spots from geotagged photos using clustering algorithms. The clusters are characterized to determine the type of photos such as selfies or panoramic. We compare our approach to the most used clustering algorithms namely K-Means and DBSCAN. The approach is simulated and experimentally evaluated on a real photographic dataset of the French capital Paris. Our approach identifies the best-known, quirky and thematic spots in the reference websites.
Meta-free few-shot learning via representation learning with weight averaging

Recent studies on few-shot classification using transfer learning pose challenges to the effectiveness and efficiency of episodic meta-learning algorithms. Transfer learning approaches are a natural alternative, but they are restricted to few-shot classification. Moreover, little attention has been on the development of probabilistic models with well-calibrated uncertainty from few-shot samples, except for some Bayesian episodic learning algorithms. To tackle the aforementioned issues, we propose a new transfer learning method to obtain accurate and reliable models for few-shot regression and classification. The resulting method does not require episodic meta-learning and is called meta-free representation learning (MFRL). MFRL first finds low-rank representation generalizing well on meta-test tasks. Given the learned representation, probabilistic linear models are fine-tuned with few-shot samples to obtain models with well-calibrated uncertainty. The proposed method not only achieves the highest accuracy on a wide range of few-shot learning benchmark datasets but also correctly quantifies the prediction uncertainty. In addition, weight averaging and temperature scaling are effective in improving the accuracy and reliability of few-shot learning in existing meta-learning algorithms with a wide range of learning paradigms and model architectures.