Reviewerly/exHarmony huggingface dataset:
“Garbage in, garbage out” revisited: What do machine learning application papers report about human-labeled training data?

Abstract Supervised machine learning, in which models are automatically derived from labeled training data, is only as good as the quality of that data. This study builds on prior work that investigated to what extent “best practices” around labeling training data were followed in applied ML publications within a single domain (social media platforms). In this paper, we expand by studying publications that apply supervised ML in a far broader spectrum of disciplines, focusing on human-labeled data. We report to what extent a random sample of ML application papers across disciplines give specific details about whether best practices were followed, while acknowledging that a greater range of application fields necessarily produces greater diversity of labeling and annotation methods. Because much of machine learning research and education only focuses on what is done once a “ground truth” or “gold standard” of training data is available, it is especially relevant to discuss issues around the equally important aspect of whether such data is reliable in the first place. This determination becomes increasingly complex when applied to a variety of specialized fields, as labeling can range from a task requiring little-to-no background knowledge to one that must be performed by someone with career expertise.
ROSEFusion

Online reconstruction based on RGB-D sequences has thus far been restrained to relatively slow camera motions (&lt;1m/s). Under very fast camera motion (e.g., 3m/s), the reconstruction can easily crumble even for the state-of-the-art methods. Fast motion brings two challenges to depth fusion: 1) the high nonlinearity of camera pose optimization due to large inter-frame rotations and 2) the lack of reliably trackable features due to motion blur. We propose to tackle the difficulties of fast-motion camera tracking in the absence of inertial measurements using random optimization, in particular, the Particle Filter Optimization (PFO). To surmount the computation-intensive particle sampling and update in standard PFO, we propose to accelerate the randomized search via updating a particle swarm template (PST). PST is a set of particles pre-sampled uniformly within the unit sphere in the 6D space of camera pose. Through moving and rescaling the pre-sampled PST guided by swarm intelligence, our method is able to drive tens of thousands of particles to locate and cover a good local optimum extremely fast and robustly. The particles, representing candidate poses, are evaluated with a fitness function defined based on depth-model conformance. Therefore, our method, being depth-only and correspondence-free, mitigates the motion blur impediment as (ToF-based) depths are often resilient to motion blur. Thanks to the efficient template-based particle set evolution and the effective fitness function, our method attains good quality pose tracking under fast camera motion (up to 4m/s) in a realtime framerate without including loop closure or global pose optimization. Through extensive evaluations on public datasets of RGB-D sequences, especially on a newly proposed benchmark of fast camera motion, we demonstrate the significant advantage of our method over the state of the arts.
Classification of Contaminated Insulators Using k-Nearest Neighbors Based on Computer Vision

Contamination on insulators may increase the surface conductivity of the insulator, and as a consequence, electrical discharges occur more frequently, which can lead to interruptions in a power supply. To maintain reliability in an electrical distribution power system, components that have lost their insulating properties must be replaced. Identifying the components that need maintenance is a difficult task as there are several levels of contamination that are hard to notice during inspections. To improve the quality of inspections, this paper proposes using k-nearest neighbors (k-NN) to classify the levels of insulator contamination based on images of insulators at various levels of contamination simulated in the laboratory. Computer vision features such as mean, variance, asymmetry, kurtosis, energy, and entropy are used for training the k-NN. To assess the robustness of the proposed approach, a statistical analysis and a comparative assessment with well-consolidated algorithms such as decision tree, ensemble subspace, and support vector machine models are presented. The k-NN showed up to 85.17% accuracy using the k-fold cross-validation method, with an average accuracy higher than 82% for the multi-classification of contamination of insulators, being superior to the compared models.
UAV Path Planning Based on Improved A<math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"><mo>∗</mo></math>and DWA Algorithms

This work proposes a path planning algorithm based on A <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"><mo>∗</mo></math> and DWA to achieve global path optimization while satisfying security and speed requirements for unmanned aerial vehicles (UAV). The algorithm first preprocesses the map for irregular obstacles encountered by a UAV in flight, including grid preprocessing for arc-shaped obstacles and convex preprocessing for concave obstacles. Further, the standard A <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"><mo>∗</mo></math> algorithm is improved based on UAV’s flight environment information and motion constraints. Further, the DWA algorithm’s limitations regarding local optimization and long planning time are mitigated by adaptively adjusting the evaluation function according to the UAV’s safety threshold, obstacles, and environment information. As a result, the global optimal path evaluation subfunction is constructed. Finally, the key points of the global path are selected as the subtarget points of the local path planning. Under the premise of the optimal path, the UAV real-time path’s efficiency and safety are effectively improved. The experimental results demonstrate that the path planning based on improved A <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"><mo>∗</mo></math> and DWA algorithms shortens the path length, reduces the planning time, improves the UAV path smoothness, and enhances the safety of UAV path obstacle avoidance.
Cryptanalyzing Two Image Encryption Algorithms Based on a First-Order Time-Delay System

Security is a key problem for the transmission, interchange and storage process of multimedia systems and applications. In 2018, M. Li et al. proposed in-depth security analysis on an image encryption algorithm based on a first-order time-delay system (IEATD) and gave a specific chosen-plaintext attack on it. Moreover, an enhanced version called as IEACD was designed to fix the reported security defects. This paper analyzes the essential structures of the two algorithms and evaluates their real security performances: 1) no efficient nonlinear operations are adopted to assure the sensibility of keystream; 2) the equivalent secret key of IEATD can be efficiently recovered from one known plain-image and the corresponding cipher-image; 3) IEACD can still be efficiently cracked with a chosen-plaintext attack. Both rigorous theoretical analyses and detailed experimental results are provided to demonstrate effectiveness of the advanced cryptanalytic methods.
Exploring Sequence Feature Alignment for Domain Adaptive Detection Transformers

Detection transformers have recently shown promising object detection results and attracted increasing attention. However, how to develop effective domain adaptation techniques to improve its cross-domain performance remains unexplored and unclear. In this paper, we delve into this topic and empirically find that direct feature distribution alignment on the CNN backbone only brings limited improvements, as it does not guarantee domain-invariant sequence features in the transformer for prediction. To address this issue, we propose a novel Sequence Feature Alignment (SFA) method that is specially designed for the adaptation of detection transformers. Technically, SFA consists of a domain query-based feature alignment (DQFA) module and a token-wise feature alignment (TDA) module. In DQFA, a novel domain query is used to aggregate and align global context from the token sequence of both domains. DQFA reduces the domain discrepancy in global feature representations and object relations when deploying in the transformer encoder and decoder, respectively. Meanwhile, TDA aligns token features in the sequence from both domains, which reduces the domain gaps in local and instance-level feature representations in the transformer encoder and decoder, respectively. Besides, a novel bipartite matching consistency loss is proposed to enhance the feature discriminability for robust object detection. Experiments on three challenging benchmarks show that SFA outperforms state-of-the-art domain adaptive object detection methods. Code has been made available at: https://github.com/encounter1997/SFA.
DAWN

Training machine learning (ML) models is expensive in terms of computational power, amounts of labeled data and human expertise. Thus, ML models constitute business value for their owners. Embedding digital watermarks during model training allows a model owner to later identify their models in case of theft or misuse. However, model functionality can also be stolen via model extraction, where an adversary trains a surrogate model using results returned from a prediction API of the original model. Recent work has shown that model extraction is a realistic threat. Existing watermarking schemes are ineffective against model extraction since it is the adversary who trains the surrogate model. In this paper, we introduce DAWN (Dynamic Adversarial Watermarking of Neural Networks), the first approach to use watermarking to deter model extraction theft. Unlike prior watermarking schemes, DAWN does not impose changes to the training process but operates at the prediction API of the protected model, by dynamically changing the responses for a small subset of queries (e.g., 0.5%) from API clients. This set is a watermark that will be embedded in case a client uses its queries to train a surrogate model. We show that DAWN is resilient against two state-of-the-art model extraction attacks, effectively watermarking all extracted surrogate models, allowing model owners to reliably demonstrate ownership (with confidence greater than 1-2-64), incurring negligible loss of prediction accuracy (0.03-0.5%).
Metaverse: the future of immersive training

Purpose This paper aims to review and explore the adoption of Metaverse as a training ecosystem. The paper focuses on the current debate on the relevance of Metaverse for training. Design/methodology/approach This paper reviews the recent articles and interviews and then critically discusses the future of training in Metaverse. Findings Recent reports, studies and developments show that immersive training holds much potential. The future for training in Metaverse seems to be bright with benefits for both employers and employees. Originality/value This viewpoint paper is easy to read and comprehensive.
Activation Modulation and Recalibration Scheme for Weakly Supervised Semantic Segmentation

Image-level weakly supervised semantic segmentation (WSSS) is a fundamental yet challenging computer vision task facilitating scene understanding and automatic driving. Most existing methods resort to classification-based Class Activation Maps (CAMs) to play as the initial pseudo labels, which tend to focus on the discriminative image regions and lack customized characteristics for the segmentation task. To alleviate this issue, we propose a novel activation modulation and recalibration (AMR) scheme, which leverages a spotlight branch and a compensation branch to obtain weighted CAMs that can provide recalibration supervision and task-specific concepts. Specifically, an attention modulation module (AMM) is employed to rearrange the distribution of feature importance from the channel-spatial sequential perspective, which helps to explicitly model channel-wise interdependencies and spatial encodings to adaptively modulate segmentation-oriented activation responses. Furthermore, we introduce a cross pseudo supervision for dual branches, which can be regarded as a semantic similar regularization to mutually refine two branches. Extensive experiments show that AMR establishes a new state-of-the-art performance on the PASCAL VOC 2012 dataset, surpassing not only current methods trained with the image-level of supervision but also some methods relying on stronger supervision, such as saliency label. Experiments also reveal that our scheme is plug-and-play and can be incorporated with other approaches to boost their performance. Our code is available at: https://github.com/jieqin-ai/AMR.
NTTRU: Truly Fast NTRU Using NTT

We present NTTRU – an IND-CCA2 secure NTRU-based key encapsulation scheme that uses the number theoretic transform (NTT) over the cyclotomic ring Z7681[X]/(X768−X384+1) and produces public keys and ciphertexts of approximately 1.25 KB at the 128-bit security level. The number of cycles on a Skylake CPU of our constant-time AVX2 implementation of the scheme for key generation, encapsulation and decapsulation is approximately 6.4K, 6.1K, and 7.9K, which is more than 30X, 5X, and 8X faster than these respective procedures in the NTRU schemes that were submitted to the NIST post-quantum standardization process. These running times are also, by a large margin, smaller than those for all the other schemes in the NIST process as well as the KEMs based on elliptic curve Diffie-Hellman. We additionally give a simple transformation that allows one to provably deal with small decryption errors in OW-CPA encryption schemes (such as NTRU) when using them to construct an IND-CCA2 key encapsulation.
OneRel: Joint Entity and Relation Extraction with One Module in One Step

Joint entity and relation extraction is an essential task in natural language processing and knowledge graph construction. Existing approaches usually decompose the joint extraction task into several basic modules or processing steps to make it easy to conduct. However, such a paradigm ignores the fact that the three elements of a triple are interdependent and indivisible. Therefore, previous joint methods suffer from the problems of cascading errors and redundant information. To address these issues, in this paper, we propose a novel joint entity and relation extraction model, named OneRel, which casts joint extraction as a fine-grained triple classification problem. Specifically, our model consists of a scoring-based classifier and a relation-specific horns tagging strategy. The former evaluates whether a token pair and a relation belong to a factual triple. The latter ensures a simple but effective decoding process. Extensive experimental results on two widely used datasets demonstrate that the proposed method performs better than the state-of-the-art baselines, and delivers consistent performance gain on complex scenarios of various overlapping patterns and multiple triples.
Cloud ArcGIS Online as an innovative tool for developing geoinformation competence with future geography teachers

The article dwells upon the scientifically relevant problem of using cloud-based GIS-technologies when training future geography teachers (based on ArcGIS Online application). The authors outline the basic principles for implementing ArcGIS Online in the educational process (interdisciplinary integration, the sequence of individualization in training, communicability, distance education and regional studies), and provide an example of an interactive map created with the help of the specified cloud GIS, since this kind of map is the most popular a form of research by geography students. In the article it is noted that integration of ArcGIS Online into the educational process allows the teacher to follow a clear pedagogical strategy, taking into account possible variants of its use (demonstration, direct mastering of GIS in a computer class and independent work in an individual mode). Considering cloud GIS as a new stage in the development of geoinformational education, the authors emphasize their key benefits (round-the-clock access, work with GIS package in the cloud, the ability to use other maps as well as the creation of their own maps and web-applications) and disadvantages (monetization of services, underestimation of the GIS role in the curriculum of the higher school, the lack of Ukrainian content, etc.).
Materials and devices for immersive virtual reality

The metaverse may change the way we live and interact with one another, and its potential applications range from entertainment to health care. Extended reality is the main technology to realize the highly realistic, interactive and immersive metaverse experience, and wearable electronic devices and materials are at its core.
The Impact of Leadership on Boosting Employee Creativity: The Role of Knowledge Sharing as a Mediator

In this study, we examined the role that knowledge sharing plays in mediating the relationship between the employee trust in leadership and employee innovation in the service sector in the United Arab Emirates (UAE). We included 346 people employed in the service industry. According to the study’s findings, having faith in one’s leader has a beneficial and discernibly positive impact on the degree to which employees share their knowledge and innovate. Knowledge sharing has a positive and substantial effect on the creativity of employees, and vice versa. According to the study’s findings, the openness of leaders partially mediates the willingness of employees to try new things and share information, which is a consequence of the trust that employees have in their leaders and their willingness to experiment with new concepts.
Metaverse Meets Branding: Examining Consumer Responses to Immersive Brand Experiences

The metaverse connects the physical and digital worlds to improve the consumer experience, but little is known about how consumers respond to branded virtual worlds in real life. This study proposes a model to investigate the influence of metaverse experiential value on consumer’s brand perception and behavioral responses in the virtual and real world. The covariance-based SEM analysis was conducted based on data from 702 Thai users of Asia’s largest metaverse platform. Results revealed that all three dimensions of value indirectly affect consumer-brand engagement (CBE) through brand image and virtual purchase intention. Hedonic value had no direct effect on consumer responses, but on symbolic and utilitarian value which in turn influences CBE and virtual purchase respectively. As one of the first empirical studies on metaverse brand experience, this study contributed by incorporating multidimensional and interrelated experiential value and examining the mediating role of brand image and virtual purchase. The findings suggest brand design the branded virtual environment to facilitate consumer learning and virtual ownership, and align virtual merchandise and store ambiance with the real world to unify the brand image in both worlds.
Dynamic Game Balancing: an Evaluation of User Satisfaction

User satisfaction in computer games seems to be influenced by game balance, the level of challenge faced by the user. This work presents an evaluation, performed by human players, of dynamic game balancing approaches. The results indicate that adaptive approaches are more effective. This paper also enumerates some issues encountered in evaluating users' satisfaction, in the context of games, and depicts some learned lessons.
Word2Vec model for sentiment analysis of product reviews in Indonesian language

&lt;span lang="EN-US"&gt;Online product reviews have become a source of greatly valuable information for consumers in making purchase decisions and producers to improve their product and marketing strategies. However, it becomes more and more difficult for people to understand and evaluate what the general opinion about a particular product in manual way since the number of reviews available increases. Hence, the automatic way is preferred. One of the most popular techniques is using machine learning approach such as Support Vector Machine (SVM). In this study, we explore the use of Word2Vec model as features in the SVM based sentiment analysis of product reviews in Indonesian language. The experiment result show that SVM can performs well on the sentiment classification task using any model used. However, the Word2vec model has the lowest accuracy (only 0.70), compared to other baseline method including Bag of Words model using Binary TF, Raw TF, and TF.IDF. This is because only small dataset used to train the Word2Vec model. Word2Vec need large examples to learn the word representation and place similar words into closer position.&lt;/span&gt;
Deterministic limit of temporal difference reinforcement learning for stochastic games

Reinforcement learning in multiagent systems has been studied in the fields of economic game theory, artificial intelligence, and statistical physics by developing an analytical understanding of the learning dynamics (often in relation to the replicator dynamics of evolutionary game theory). However, the majority of these analytical studies focuses on repeated normal form games, which only have a single environmental state. Environmental dynamics, i.e., changes in the state of an environment affecting the agents' payoffs has received less attention, lacking a universal method to obtain deterministic equations from established multistate reinforcement learning algorithms. In this work we present a methodological extension, separating the interaction from the adaptation timescale, to derive the deterministic limit of a general class of reinforcement learning algorithms, called temporal difference learning. This form of learning is equipped to function in more realistic multistate environments by using the estimated value of future environmental states to adapt the agent's behavior. We demonstrate the potential of our method with the three well-established learning algorithms Q learning, SARSA learning, and actor-critic learning. Illustrations of their dynamics on two multiagent, multistate environments reveal a wide range of different dynamical regimes, such as convergence to fixed points, limit cycles, and even deterministic chaos.
SuperVAE: Superpixelwise Variational Autoencoder for Salient Object Detection

Image saliency detection has recently witnessed rapid progress due to deep neural networks. However, there still exist many important problems in the existing deep learning based methods. Pixel-wise convolutional neural network (CNN) methods suffer from blurry boundaries due to the convolutional and pooling operations. While region-based deep learning methods lack spatial consistency since they deal with each region independently. In this paper, we propose a novel salient object detection framework using a superpixelwise variational autoencoder (SuperVAE) network. We first use VAE to model the image background and then separate salient objects from the background through the reconstruction residuals. To better capture semantic and spatial contexts information, we also propose a perceptual loss to take advantage from deep pre-trained CNNs to train our SuperVAE network. Without the supervision of mask-level annotated data, our method generates high quality saliency results which can better preserve object boundaries and maintain the spatial consistency. Extensive experiments on five wildly-used benchmark datasets show that the proposed method achieves superior or competitive performance compared to other algorithms including the very recent state-of-the-art supervised methods.