Reviewerly/exHarmony huggingface dataset:
EDICT: Exact Diffusion Inversion via Coupled Transformations

Finding an initial noise vector that produces an input image when fed into the diffusion process (known as inversion) is an important problem in denoising diffusion models (DDMs), with applications for real image editing. The state-of-the-art approach for real image editing with inversion uses denoising diffusion implicit models (DDIMs) to deterministically noise the image to the intermediate state along the path that the denoising would follow given the original conditioning. However, DDIM inversion for real images is unstable as it relies on local linearization assumptions, which result in the propagation of errors, leading to incorrect image reconstruction and loss of content. To alleviate these problems, we propose Exact Diffusion Inversion via Coupled Transformations (EDICT), an inversion method that draws inspiration from affine coupling layers. EDICT enables mathematically exact inversion of real and model-generated images by maintaining two coupled noise vectors which are used to invert each other in an alternating fashion. Using Stable Diffusion, a state-of-the-art latent diffusion model, we demonstrate that EDICT successfully reconstructs real images with high fidelity. On complex image datasets like MS-COCO, EDICT reconstruction significantly outperforms DDIM, improving the mean square error of reconstruction by a factor of two. Using noise vectors inverted from real images, EDICT enables a wide range of image edits--from local and global semantic edits to image stylization--while maintaining fidelity to the original image structure. EDICT requires no model training/finetuning, prompt tuning, or extra data and can be combined with any pretrained DDM. Code is available at https://github.com/salesforce/EDICT.
Quantum algorithms and the power of forgetting

The so-called welded tree problem provides an example of a black-box problem that can be solved exponentially faster by a quantum walk than by any classical algorithm. Given the name of a special ENTRANCE vertex, a quantum walk can find another distinguished EXIT vertex using polynomially many queries, though without finding any particular path from ENTRANCE to EXIT. It has been an open problem for twenty years whether there is an efficient quantum algorithm for finding such a path, or if the path-finding problem is hard even for quantum computers. We show that a natural class of efficient quantum algorithms provably cannot find a path from ENTRANCE to EXIT. Specifically, we consider algorithms that, within each branch of their superposition, always store a set of vertex labels that form a connected subgraph including the ENTRANCE, and that only provide these vertex labels as inputs to the oracle. While this does not rule out the possibility of a quantum algorithm that efficiently finds a path, it is unclear how an algorithm could benefit by deviating from this behavior. Our no-go result suggests that, for some problems, quantum algorithms must necessarily forget the path they take to reach a solution in order to outperform classical computation.
Person Image Synthesis via Denoising Diffusion Model

The pose-guided person image generation task requires synthesizing photorealistic images of humans in arbitrary poses. The existing approaches use generative adversarial networks that do not necessarily maintain realistic textures or need dense correspondences that struggle to handle complex deformations and severe occlusions. In this work, we show how denoising diffusion models can be applied for high-fidelity person image synthesis with strong sample diversity and enhanced mode coverage of the learnt data distribution. Our proposed Person Image Diffusion Model (PIDM) disintegrates the complex transfer problem into a series of simpler forward-backward denoising steps. This helps in learning plausible source-to-target transformation trajectories that result in faithful textures and undistorted appearance details. We introduce a 'texture diffusion module' based on cross-attention to accurately model the correspondences between appearance and pose information available in source and target images. Further, we propose 'disentangled classifier-free guidance' to ensure close resemblance between the conditional inputs and the synthesized output in terms of both pose and appearance information. Our extensive results on two large-scale benchmarks and a user study demonstrate the photorealism of our proposed approach under challenging scenarios. We also show how our generated images can help in downstream tasks. Our code and models will be publicly released.
AeDet: Azimuth-invariant Multi-view 3D Object Detection

Recent LSS-based multi-view 3D object detection has made tremendous progress, by processing the features in Brid-Eye-View (BEV) via the convolutional detector. However, the typical convolution ignores the radial symmetry of the BEV features and increases the difficulty of the detector optimization. To preserve the inherent property of the BEV features and ease the optimization, we propose an azimuth-equivariant convolution (AeConv) and an azimuth-equivariant anchor. The sampling grid of AeConv is always in the radial direction, thus it can learn azimuth-invariant BEV features. The proposed anchor enables the detection head to learn predicting azimuth-irrelevant targets. In addition, we introduce a camera-decoupled virtual depth to unify the depth prediction for the images with different camera intrinsic parameters. The resultant detector is dubbed Azimuth-equivariant Detector (AeDet). Extensive experiments are conducted on nuScenes, and AeDet achieves a 62.0% NDS, surpassing the recent multi-view 3D object detectors such as PETRv2 and BEVDepth by a large margin. Project page: https://fcjian.github.io/aedet.
A finite and fast time-optimal trajectory planning based on velocity sinking method for robots along specified path

Time-optimal trajectory planning is used in many areas of industrial robots and has a wide range of applications. This paper presents a finite and fast computation method for calculating time-optimal robot trajectories along specified geometric paths, namely the velocity sinking method. The velocity sinking method differs from those proposed in the existing literature which solve uncertain optimization problem. Firstly, the maximum velocity in multiple state constraints is calculated using a path parameterization method. Then, the pseudo maximum velocity trajectory is calculated using a numerical integration method. Finally, the time-optimal trajectory planning under path constraints is determined using an iterative velocity sinking algorithm. The experiment demonstrates the effectiveness and efficiency of this method in industrial robotic settings.
PREDICTIVE MODELLING FOR ENVIRONMENTAL TELEMETRY DATA AND DAMAGE PREDICTION

Big data is being produced by the IoT more and more.Realizing this hinges on being able to extract relevant information from huge data.Big data may be utilized to optimize processes.This may be done using neural networks and machine learning, which also makes it possible to create intelligent apps that exploit this data.Sensory boxes were used to collect data from rooms, and many LSTM-based neural networks were employed to forecast the sensor values in the future.Calculations were made to determine the absolute mean error and standard deviation of the forecasts.A standard deviation-adjusted absolute mean figure was used to gauge how long it took to create a forecast.The performance and prediction accuracy of the LSTM models were then assessed.Smart healthcare platforms can continually monitor the health and activities of patients and the elderly, as well as the security and safety of the environment, with the use of environmental sensors and wearable technology.When combined with a person's medical history, this constant flow of data can provide tailored diagnosis and treatment and automate crucial duties like medical data archiving and assessing the efficacy of medical therapies.Recent developments in big data processing and artificial intelligence (AI) technology have made it possible to construct extremely dependable, precise, and strong infrastructures for data capture and processing.The main aim of the author is to predict the damage happening in the sensors that are used to detect the environmental data and also the damage prediction of the sensors using random forest as the technique for the prediction.
PROGRAMMABLE MECHATRONIC SYSTEM USED TO IMPLEMENT COMPLEX 3D MOTION CYCLOGRAMS

The paper presents a complex mechatronic equipment which can perform predefined sequential motion cycles.The equipment comprises two parts: an automation panel and a pneumatic assembly.The Master Control Unit of the automation panel is programmed to run a specific software application that ensures the proper fulfilment of the motion cycles.The MCU is connected to a Radio Frequency transceiver and could be easily connected to IoT and/or LAN using a wired network controller.The input data has the following sources: the remote RF sensors' data, the position of the proximity sensors (placed on the pneumatic cylinders), the numerical codes transmitted by the IR remote control and/or by IoT connection and the current motion cycle's settings.The output data consists of digital output signals (send both to the solenoid valves for actuators through relays and to the status lamps) and text messages written onto a LCD.
Retracted: Biomechanical Research on Special Ability of Long Jump Take-Off Muscle Based on Multisource Information Fusion

[This retracts the article DOI: 10.1155/2022/2556087.].
Retracted: Design of Enterprise Financial Information Management System Based on Blockchain Technology

Management System Based on Blockchain Technology" [1] due to concerns that the peer review process has been compromised.Following an investigation conducted by the Hindawi Research Integrity team [2], signifcant concerns were identifed with the peer reviewers assigned to this article; the investigation has concluded that the peer review process was compromised.We therefore can no longer trust the peer review process, and
Hybrid-attention based Feature-reconstructive Adversarial Hashing Networks for Cross-modal Retrieval

With the massive growth of data of various modal types, people no longer use a single modal retrieval method, but a cross-modal retrieval method when performing retrieval tasks. Such methods often need to store data efficiently while maintaining the characteristics of fast query. Because the hashing learning method can represent the original high-dimensional data through a simple and compact binary hash code, which can greatly compress the data size and facilitate data storage and mutual retrieval, the cross-modal hashing retrieval has gradually become a hot topic in recent years. However, how to bridge the gap between modalities to improve the retrieval performance further is still a challenging problem. In order to solve this problem, we propose a Hybrid-attention based Feature-reconstructive Adversarial Hashing (HFAH) networks for cross-modal retrieval. First, a label semantic guidance module is introduced to guide the extraction process of text features and image features through the learning of labels, so as to fully maintain the semantic similarity between different modal data. Then, the hybrid-attention module is introduced to make the extracted data contain richer semantic information. Finally, the feature reconstruction network is used to make the relevant degree between similar cross-modal data pairs higher than that between dissimilar data pairs. Related experiments on two benchmark datasets confirm to us that HFAH performs better than several existing cross- modal retrieval methods.
Uji Kemampuan Adsorpsi Komposit Keratin Bulu Itik-Polietilena Terhadap Ion Besi(III)

Research on the adsorption of iron(III) ion using an adsorbent from duck feather-polyethylene composite has been studied. This study aimed to determine the optimum pH conditions and the optimum contact time required in the iron(III) ion adsorption process. Optimum pH and contact time are then measured using AAS to determine the amount of iron(III) ion adsorbed with the duck feather-polyethylene composite adsorbent. The determination of the optimum pH was carried out with pH variations which are 2, 3, 4, 5, 6, 7, and 8, while determination of the optimum contact time was carried out with contact time variations which are 40, 60, 80, 100, 120, 140, and 160 minutes. The result of this research showed that optimum conditions for iron(III) ion adsorption at pH 6 and contact time at 80 minutes, which each adsorption capacity are 68.33% and 79.78%.
Ising model selection using ℓ <sub>1</sub>-regularized linear regression: a statistical mechanics analysis*

Abstract We theoretically analyze the typical learning performance of ℓ 1 -regularized linear regression ( ℓ 1 -LinR) for Ising model selection using the replica method from statistical mechanics. For typical random regular graphs in the paramagnetic phase, an accurate estimate of the typical sample complexity of ℓ 1 -LinR is obtained. Remarkably, despite the model misspecification, ℓ 1 -LinR is model selection consistent with the same order of sample complexity as ℓ 1 -regularized logistic regression ( ℓ 1 -LogR), i.e. <?CDATA $M=\mathcal{O}\left(\mathrm{log}\,N\right)$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mi>M</mml:mi> <mml:mo>=</mml:mo> <mml:mi mathvariant="script">O</mml:mi> <mml:mfenced close=")" open="("> <mml:mrow> <mml:mi>log</mml:mi> <mml:mspace width="0.17em" /> <mml:mi>N</mml:mi> </mml:mrow> </mml:mfenced> </mml:math> , where N is the number of variables of the Ising model. Moreover, we provide an efficient method to accurately predict the non-asymptotic behavior of ℓ 1 -LinR for moderate M , N , such as precision and recall. Simulations show a fairly good agreement between theoretical predictions and experimental results, even for graphs with many loops, which supports our findings. Although this paper mainly focuses on ℓ 1 -LinR, our method is readily applicable for precisely characterizing the typical learning performances of a wide class of ℓ 1 -regularized M -estimators including ℓ 1 -LogR and interaction screening.
TOWARDS CROSS-ATTENTION PRE-TRAINING IN NEURAL MACHINE TRANSLATION

The advent of pre-train techniques and large language models has significantly leveraged the performance of many natural language processing (NLP) tasks.However, pre-trained language models for neural machine translation remain a challenge as little information about the interaction of the language pair is learned.In this paper, we explore several studies trying to define a training scheme to pre-train the cross-attention module between the encoder and the decoder by using the large-scale monolingual corpora independently.The experiments show promising results, proving the effectiveness of using pre-trained language models in neural machine translation.
ENHANCING OWL ONTOLOGIES MATCHING BASED ON SEMANTIC SIMILARITY MEASUREMENT

Recently, Web Ontology Language (OWL) has become a widely-used language for providing a source of precisely defined concepts.The number of OWL documents, increasing with the growth of the Semantic Web, leads to the heterogeneous problem.The same concepts may be defined differently, using different terms and positions in the documental structure.Therefore, identifying the element similarity in different ontologies becomes crucial for the success of web mining and information integration systems.In this paper, we propose a new semantic similarity measure for comparing elements in different OWL ontologies.This measure is designed to enable the extraction of information encoded in OWL element descriptions and to take into account the element relationships with its ancestors, brothers, and children.We evaluate the proposed metrics in the context of matching two OWL documents to determine the number of matches between them.The experimental results show better accuracy over other approaches.
Syntax-Enhanced Neural Machine Translation with Graph Encoder

Neural Machine Translation (NMT) is a new paradigm in machine translation (MT) powered by recent advances in sequence to sequence learning frameworks. With the advance of NeuralNetworks, NMT has become the most promising MT approach in recent years.Despite the apparent success, NMT still suffers from one significant drawback in integrating syntactic knowledge into neural networks.This paper proposes an extension of the NMT model to incorporate additional syntactic information from constituency trees.We represent the constituency trees under graph forms encoded by a graph encoder to enhance the attention layer, which allows the decoder to focus on both sequential and graph representation at each decoding step.The experiments show promising results of the proposed method on English-Vietnamese datasets, proving the effectiveness of our syntax-enhanced NMT method.
Accurate Prediction of Electric Fields of Nanoparticles with Deep Learning Methods

&lt;p&gt;Three different deep learning models were designed in this paper, to predict the electric fields of single nanoparticles, dimers, and nanoparticle arrays. For single nanoparticles, the prediction error was 4.4%, respectively. For dimers with strong couplings, a sample self-normalization method was proposed, and the error was reduced by an order of magnitude compared with traditional methods. For nanoparticle arrays, the error was reduced from 28.8% to 5.6% compared with previous work. Numerical tests proved the validity of the proposed deep learning models, which have potential applications in the design of nanostructures.&lt;/p&gt;
5G Network Simulation in Smart Cities using Neural Network Algorithm

Abstract: The speed of internet has increased dramatically with the introduction of 4G and 5G promises an even greater transmission rate with coverage outdoors and indoors in smart cities. This indicates that the introduction of 5G might result in replacing the Wi-Fi that is being currently used for applications such as geo-location using continuous radio coverage there by initiating the involvement of IoT in all devices that are used. The introduction of WiFi 6 is already underway for applicationsthat work with IoT, smart city applications will still require 5G to provide internet services using Big Data to reduce the requirement of mobile networks and additional private network infrastructure. However, as the network access begins to expand, it also introduces the risk of cyber security with the enhanced connectivity in the networking. Additional digital targets will be given to the cyber attackers and independent services will also be sharing access channel infrastructure between mobile and wireless network. In order to address these issues, we have introduced a random neural network blockchain technology that can be used to strengthen cybersecurity in many applications. Here the identity of the user is maintained as a secret while the information is codified using neural weights. However, when a cyber security breach occurs, the attacker will be easily tracked by mining the confidential identity. Thus a reliable anddecentralized means of authentication method is proposed in this work. The results thus obtained are validated and shows that the introduction of the random neural network using blockchain improves connectivity, decentralized user access and cybersecurity resilience
Multi-key Fully Homomorphic Encryption without CRS from RLWE

&lt;p&gt;Fully Homomorphic Encryption (FHE) is a powerful encryption system in cloud computing that allows homomorphic computations on encrypted data without decrypting them. Multi-key fully homomorphic encryption (MFHE), as an extension to FHE, allows homomorphic computations on ciphertexts encrypted under different keys. However, most MFHE schemes require a Common Random\slash Reference String (CRS), while the few that do not are based on the Learning With Errors (LWE) problem, which means that they can only deal with single bit plaintext. Consequently, MFHE schemes based on the Ring Learning With Errors (RLWE) problem are more desirable, as they can handle polynomial plaintext. Requiring the CRS seems to weaken the semantic definition of MFHE, where all users generate their own keys independently.&lt;/p&gt; &lt;p&gt;In this paper, we study the RLWE-based MFHE in the CRS model and propose the first RLWE-based MFHE without CRS. To this end, we remove the CRS by designing a new relinearization algorithm. Like previous MFHE schemes, our RLWE-based MFHE without CRS has a simple 1-round threshold decryption, which implies a $3$-round secure MPC protocol in the plain model from the RLWE assumption.&lt;/p&gt;
Sign Language Recognition System Using Machine Learning

Abstract: Voice and language is the main thing that people understand one with the other. We can understand thoughts through hearing each other. Even nowadays we can give commands using voice recognition. But what if one hears absolutely nothing and eventually can't speak. Sign language is the main communication tool for hearing impaired and mute people and also to ensure their independent lives, automatic sign language interpretation is an extensive area of research. With use of image processing and artificial intelligence, many techniques and algorithms have been developed in this area. Every sign language recognition system is trained to recognize characters and convert them into the desired pattern. The goal of the proposed system to provide speech to infants, in this document ambidextrous Indian Sign Language is captured as a series of images and is processed using Python and then converted to speech and text.
Sign Language Detection Using CNN

Abstract: Hand signs are a viable type of human-to-human correspondence that has various potential applications. Being a characteristic method for collaboration, they are generally utilized for correspondence purposes by discourse impeded individuals around the world. As a matter of fact, around one percent of the Indian populace has a place with this class. This is the key motivation behind why it would meaningfully affect these people to integrate a structure that would figure out Indian Gesture based communication. In this paper, we present a method that utilizes the Pack of Visual Words model (BOVW) to perceive Indian communication via gestures letter sets (A-Z) and digits (0-9) in a live video transfer and result the anticipated marks as text as well as discourse. Division is done in view of skin tone as well as foundation deduction. In this paper we are going to use convolutional neural network for sign language detection.