Reviewerly/exHarmony huggingface dataset:
SpaceEdit: Learning a Unified Editing Space for Open-Domain Image Color Editing

Recently, large pretrained models (e.g., BERT, Style-GAN, CLIP) show great knowledge transfer and generalization capability on various downstream tasks within their domains. Inspired by these efforts, in this paper we propose a unified model for open-domain image editing focusing on color and tone adjustment of open-domain images while keeping their original content and structure. Our model learns a unified editing space that is more semantic, intu-itive, and easy to manipulate than the operation space (e.g., contrast, brightness, color curve) used in many existing photo editing softwares. Our model belongs to the image-to-image translation framework which consists of an image encoder and decoder, and is trained on pairs of before-and-after edited images to produce multimodal outputs. We show that by inverting image pairs into latent codes of the learned editing space, our model can be leveraged for vari-ous downstream editing tasks such as language-guided image editing, personalized editing, editing-style clustering, retrieval, etc. We extensively study the unique properties of the editing space in experiments and demonstrate superior performance on the aforementioned tasks <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Code and supplementary material can be found at the project page https://jshi31.github.io/SpaceEdit.
LiDARCap: Long-range Markerless 3D Human Motion Capture with LiDAR Point Clouds

Existing motion capture datasets are largely short-range and cannot yet fit the need of long-range applications. We propose LiDARHuman26M, a new human motion capture dataset captured by LiDAR at a much longer range to overcome this limitation. Our dataset also includes the ground truth human motions acquired by the IMU system and the synchronous RGB images. We further present a strong base-line method, LiDARCap, for LiDAR point cloud human motion capture. Specifically, we first utilize <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$PointNet++$</tex> to encode features of points and then employ the inverse kinematics solver and SMPL optimizer to regress the pose through aggregating the temporally encoded features hierarchically. Quantitative and qualitative experiments show that our method outperforms the techniques based only on RGB images. Ablation experiments demonstrate that our dataset is challenging and worthy of further research. Finally, the experiments on the KITTI Dataset and the Waymo Open Dataset show that our method can be generalized to different LiDAR sensor settings.
Mr.BiQ: Post-Training Non-Uniform Quantization based on Minimizing the Reconstruction Error

Post-training quantization compresses a neural network within few hours with only a small unlabeled calibration set. However, so far it has been only discussed and empirically demonstrated in the context of uniform quantization on convolutional neural networks. We thus propose a new posttraining non-uniform quantization method, called Mr.BiQ, allowing low bit-width quantization even on Transformer models. In particular, we leverage multi-level binarization for weights while allowing activations to be represented as various data formats (e.g., INT8, bfloat16, binary-coding, and FP32). Unlike conventional methods which optimize full-precision weights first, then decompose the weights into quantization parameters, Mr.BiQ recognizes the quantization parameters (i.e., scaling factors and bit-code) as directly and jointly learnable parameters during the optimization. To verify the superiority of the proposed quantization scheme, we test Mr.BiQ on various models including convolutional neural networks and Transformer models. According to experimental results, Mr.BiQ shows significant improvement in terms of accuracy when the bit-width of weights is equal to 2: up to 5.35 p.p. improvement in CNNs, up to 4.23 p.p. improvement in Vision Transformers, and up to 3.37 point improvement in Transformers for NLP.
Open-World Instance Segmentation: Exploiting Pseudo Ground Truth From Learned Pairwise Affinity

Open-world instance segmentation is the task of grouping pixels into object instances without any pre-determined taxonomy. This is challenging, as state-of-the-art methods rely on explicit class semantics obtained from large labeled datasets, and out-of-domain evaluation performance drops significantly. Here we propose a novel approach for mask proposals, Generic Grouping Networks (GGNs), constructed without semantic supervision. Our approach combines a local measure of pixel affinity with instance-level mask supervision, producing a training regimen designed to make the model as generic as the data diversity allows. We introduce a method for predicting Pairwise Affinities (PA), a learned local relationship between pairs of pixels. PA generalizes very well to unseen categories. From PA we construct a large set of pseudo-ground-truth instance masks; combined with human-annotated instance masks we train GGNs and significantly outperform the SOTA on open-world instance segmentation on various benchmarks including COCO, LVIS, ADE20K, and UVO.
End-Side Gesture Recognition Method for UAV Control

Traditional heavy and complex control equipment such as remote controllers (RCs) and ground stations (GSs) are not able to meet the fast and flexible control requirements of unmanned aerial vehicles (UAVs) in complex environments. This article thus proposes a gesture recognition method for the control of UAVs. To achieve the goal of gestural manipulation of UAVs, this method can accurately recognize a variety of gestures and convert the gestures into corresponding UAV control commands. First, the wireless data glove incorporates flex sensors, and inertial sensors are used to gather gesture datasets. The trained neural network model is then deployed on the data glove based on an STM32 microcontroller for real-time gesture recognition, in which the backpropagation (BP) network is used for static gesture recognition and the bidirectional gated recurrent unit (Bi-GRU) network is used for dynamic gesture recognition. Finally, the gesture is converted into the control command and sent to the aircraft terminal to control the UAV. Using the UAV simulation test on the simulation platform, the recognition accuracy of the basic UAV control commands corresponding to ten static gestures is found to be 100%, and the mean recognition accuracy of the UAV mode-switching commands corresponding to five dynamic gestures is 98.4% indicating that the gesture recognition effect of the system is perfect. Mission testing performed in the scene constructed in the simulation environment demonstrates that the UAV can respond rapidly to gestures, and the method proposed in this article can achieve real-time and stable control of the UAV on the end side.
Noise-induced phase error comparison in multi-frequency phase-shifting profilometry based on few fringes

For fringe projection profilometry (FPP), the combination of phase-shifting profilometry (PSP) and multi-frequency temporal phase unwrapping (MFTPU) is a phase extraction scheme with high accuracy and robustness, which is widely used in three dimensional (3D) measurement equipment. To make it suitable for real-time or dynamic objects 3D measurement, reducing the number of fringes is an effective method. To this end, this paper introduces four phase extraction algorithms based on few fringes. We introduce the unit phase accuracy, and analyze the phase extraction accuracy and reliability of the above four algorithms. Experiments are carried out to confirm the theoretical analysis conclusions. Our theoretical and experimental results include that the wrapped phase error variance is inversely proportional to the number of phase-shifting steps, and that the phase unwrapping reliability is proportional to the wrapped phase accuracy and inversely proportional to the frequency ratio between fringe sequences. Compared with the existing four standard multi-frequency phase-shifting methods, the measurement efficiency of the four non-standard multi-frequency phase-shifting methods supplemented in this paper are improved by 16.7%, 37.5%, 22.2% and 41.7%, respectively. However, the phase unwrapping reliability decreases. The balance between phase extraction accuracy and efficiency needs to be determined according to the actual application. This paper can provide a reference for the selection of phase extraction algorithm.
Tight Bounds for Quantum State Certification with Incoherent Measurements

We consider the problem of quantum state certification, where we are given the description of a mixed state $\sigma\in\mathbb{C}^{d\times d},n$ copies of a mixed state $\rho\in\mathbb{C}^{d\times d}$, and $\varepsilon\gt 0$, and we are asked to determine whether $\rho=\sigma$ or whether $\|\rho-\sigma\|_{1}\gt \varepsilon$. When $\sigma$ is the maximally mixed state $\frac{1}{d}I_{d}$, this is known as mixedness testing. We focus on algorithms which use incoherent measurements, i.e. which only measure one copy of $\rho$ at a time. Unlike those that use entangled, multi-copy measurements, these can be implemented without persistent quantum memory and thus represent a large class of protocols that can be run on current or near-term devices. For mixedness testing, there is a folklore algorithm which uses incoherent measurements and only needs $O(d^{3/2}/\varepsilon^{2})$ copies. The algorithm is non-adaptive, that is, its measurements are fixed ahead of time, and is known to be optimal for non-adaptive algorithms. However, when the algorithm can make arbitrary incoherent measurements, the best known lower bound is only $\Omega(d^{4/3}/\varepsilon^{2})$ [5], and it has been an outstanding open problem to close this polynomial gap. In this work: •We settle the copy complexity of mixedness testing with incoherent measurements and show that $\Omega(d^{3/2}/\varepsilon^{2})$ copies are necessary. This fully resolves open questions of [15] and [5].•We show that the instance-optimal bounds for state certification to general $\sigma$ first derived in [7] for non-adaptive measurements also hold for arbitrary incoherent measurements. Qualitatively, our results say that adaptivity does not help at all for these problems. Our results are based on new techniques that allow us to reduce the problem to understanding the concentration of certain matrix martingales, which we believe may be of independent interest.
Transfer-based adaptive tree for multimodal sentiment analysis based on user latent aspects

Multimodal sentiment analysis benefits various applications, such as human–computer interaction and recommendation systems. It infers the user-based sentiments using modals, enriched by neutrality and ambivalence empirical. Although researchers affirm the association between cognitive cues and emotional manifestations, most current multimodal sentiment analytical approaches disregard user-specific aspects. To tackle this issue, we devise a novel method to perform multimodal sentiment prediction using cognitive cues. Our cognitive-aware framework constructs an adaptive tree by hierarchically dividing users and then trains the Long short-term memory submodels, utilizing an attention-based fusion to transfer cognitive-oriented knowledge within the tree. Subsequently, the framework consumes the conclusive agglomerative knowledge from the adaptive tree to predict final sentiments. We also develop a dynamic dropout method to regularize data of pertinent groups during hierarchical training. The empirical results on real-world datasets determine that our proposed model for sentiment prediction can surpass trending baselines. Moreover, compared to other ensemble approaches, the proposed transfer-based algorithm can better utilize the latent cues and foster the prediction outcomes, benefitting from multiple categorization strategies. Based on the given extrinsic and intrinsic analysis results, we note that compared to other theoretical-based techniques, the proposed hierarchical categorization model can better group the users within the adaptive tree.
Neighborhood representative for improving outlier detectors

Over the decades, traditional outlier detectors have ignored the group-level factor when calculating outlier scores for objects in data by evaluating only the object-level factor, failing to capture the collective outliers. To mitigate this issue, we present a framework called neighborhood representative (NR), which empowers all the existing outlier detectors to efficiently detect outliers, including collective outliers, while maintaining their computational integrity. It achieves this by selecting representative objects, scoring these objects, then applies the score of the representative objects to its collective objects. Without altering existing detectors, NR is compatible with existing detectors, while improving performance on eleven real world datasets with +8% (0.72 to 0.78 AUC) on average relative to twelve state-of-the-art outlier detectors. The implementation of NR can be found via www.OutlierNet.com for reproducibility. Index Terms—Outlier detection, Preprocessing, Neighborhood representative, K nearest neighbors.
Attentive deep neural networks for legal document retrieval

Legal text retrieval serves as a key component in a wide range of legal text processing tasks such as legal question answering, legal case entailment, and statute law retrieval. The performance of legal text retrieval depends, to a large extent, on the representation of text, both query and legal documents. Based on good representations, a legal text retrieval model can effectively match the query to its relevant documents. Because legal documents often contain long articles and only some parts are relevant to queries, it is quite a challenge for existing models to represent such documents. In this paper, we study the use of attentive neural network-based text representation for statute law document retrieval. We propose a general approach using deep neural networks with attention mechanisms. Based on it, we develop two hierarchical architectures with sparse attention to represent long sentences and articles, and we name them Attentive CNN and Paraformer. The methods are evaluated on datasets of different sizes and characteristics in English, Japanese, and Vietnamese. Experimental results show that: i) Attentive neural methods substantially outperform non-neural methods in terms of retrieval performance across datasets and languages; ii) Pretrained transformer-based models achieve better accuracy on small datasets at the cost of high computational complexity while lighter weight Attentive CNN achieves better accuracy on large datasets; and iii) Our proposed Paraformer outperforms state-of-the-art methods on COLIEE dataset, achieving the highest recall and F2 scores in the top-N retrieval task.
The Readiness of Lasem Batik Small and Medium Enterprises to Join the Metaverse

Today’s business competitiveness necessitates the capacity of all company players, particularly small and medium enterprises (SMEs), to enter a broader market through information technology. However, the Lasem Batik SMEs have endured a great deal of turmoil during the COVID-19 pandemic. Marketing has been conducted through physical and internet channels, but the results have not been maximized. The purpose of this research was to consider the possibilities of Lasem Batik SMEs adopting metaverse technology as a marketing medium to enhance sales. The investigation was conducted on 40 Lasem Batik SMEs who met the requirements of using online media to sell their products, having a medium-sized firm, and displaying marketing that has reached the provincial level. The findings of this study are as follows: (1) The majority of participants stated that the metaverse is a virtual 3D space. This understanding is deepened by discussions about virtual 3D spaces that combine VR and AR, which today is often referred to as the metaverse. (2) Batik business owners hope that by using the metaverse, they will be able to obtain many benefits, especially related to market expansion. (3) Lasem Batik SMEs show great interest in expanding their marketing channels to a wider area; Lasem Batik entrepreneurs also accept the challenge of studying the metaverse with new knowledge and techniques they have never considered. (4) Overall, 75% of participants were ready to use the metaverse, and 25% still required guidance. (5) Local communities, universities, and large corporations provide great support for the use of the metaverse. (6) The commercial success of Lasem Batik SMEs is defined by product quality; ongoing online and offline advertising; originality and innovation; and the capacity to capitalize on possibilities, retain local wisdom, and preserve strong customer connections. The main conclusion is that the readiness of batik entrepreneurs to use the metaverse is highly dependent on the support of various parties. A strong desire to progress and develop one’s business is the main factor determining one’s intention to use the metaverse. As a result of the research, a prototype of a metaverse platform for Lasem Batik exhibitions has been developed. SMEs can use the room template provided by the platform and join other SMEs to hold a metaverse exhibition to attract global customers. These results can be connected to create a metaverse exhibition to attract global customers.
Optimal Solar Farm Site Selection in the George Town Conurbation Using GIS-Based Multi-Criteria Decision Making (MCDM) and NASA POWER Data

Many countries are committed to boosting renewable energy in their national energy mix by 2030 through the support and incentives for solar energy harnessing. However, the observed solar data limitation may result in ineffective decision making, regarding solar farm locations. Therefore, the aim of this study is to utilise GIS-based multi criteria decision making (MCDM) and NASA POWER data to identify the optimal locations for solar farm installations, with the George Town Conurbation as a case study. Although NASA POWER is tailored for the application, at least, on the regional level, the information it provided on the solar radiation and the maximum and minimum temperatures are deemed useful for the initial solar mapping attempt at the local level, especially in the absence or lack of local data. The performance of the GIS-based MCDM model is categorized as good in identifying solar farms. There are no significant differences in the area under the curve (AUC) values between the map of the NASA POWER data and ground-measured data. This indicates the potential of using the NASA POWER data for generating the much-needed initial insights for the local optimal solar farm site selection. The stakeholders can benefit from the suitability map generated to effectively target the locations that have the highest potential to generate solar energy efficiently and sustainably.
SDSM: Secure Data Sharing for Multilevel Partnerships in IoT Based Supply Chain

Symmetric encryption algorithms enable rapid encryption of data in IoT based supply chains, which helps to alleviate the concerns of supply chain participants about privacy disclosure when sharing data. However, in supply chain management where multilevel partnerships exist universally, a pure symmetric encryption scheme cannot provide efficient data sharing and fine-grained access control. To overcome these problems, this paper proposes a secure data sharing scheme (SDSM) for IoT based supply chains by combining blockchain and ciphertext-based attribute cryptography. This scheme supports the enforcement of fine-grained access control for different levels of partnerships. In addition, to identify partnerships, we propose a metric based on the historical transaction facts on the blockchain, where the level of partnerships among participants is automatically calculated by smart contracts. Finally, we introduce personalized attributes of participants in the ciphertext-based attribute encryption algorithm to support the construction of access policies that include partnerships, allowing for more fine-grained access control. Security analyses and simulation experiments show that our proposed scheme is secure, effective, and practical.
A Comparative Analysis of Machine Learning Algorithms for Classification Purpose

A few of the popular data-mining techniques are clustering, classification, and association. The classification process simplifies the process of identifying and accessing data. Classification of data is crucial for risk management, compliance, and data security. Classifying data facilitates its search-ability and traceability by categorising the information. Each data mining model has a distinct level of information. The success of a model is solely determined by the datasets being used, as there is no such thing as an excellent or a poor model. As a part of this study, we examine how accurate different classification algorithms are on diverse datasets. On five different datasets, four classification models are compared: Decision tree, SVM, Naive Bayesian, and K-nearest neighbor. The Naive Bayesian algorithm is proven to be the most effective among other algorithms.
Big Data as a Service and Application for Indian Banking Sector

The financial world, money markets, and the growth of an economy are all powered by banks. In India's financial sector, where cashless and paperless transactions are expanding quickly, bank fraud is also rising quickly, and fraudsters are now turning to cutting-edge techniques. Fighting financial crimes has always been a priority for the banking industry. Banks’ fraud losses will diminish when they are able use analytics and emerging technologies for better fraud prevention. Data has evolved into more than simply an IT resource; it is a vital feature of the banking industry's digital transformation in the present world. The majority of contemporary risk monitoring systems, which are based on data science discipline such as big data, allow for surveillance and offer Early Warning Signals (EWS) about any kind of worsening. The study of this paper signifies that how big data as a service and application proven as a strong and innovative instrument that assists Indian banking sector not only in identifying security issues and fraudulent behavior, but also in their early prevention. The experimental results of bibliometric study conducted on Scopus and Web of Science databases using in Vos Viewer software, clearly discovered big data technology is a crucial approach to deploy in Indian banking system. Findings of this paper will contribute to knowledge and practice of researchers working in this filed by increasing their understanding related to numerous big data technologies and techniques deployment for handling banking financial risks and its role in operational banking workflow management.