Reviewerly/exHarmony huggingface dataset:
Integrating AI into CCTV Systems: A Comprehensive Evaluation of Smart Video Surveillance in Community Space

This article presents an AI-enabled Smart Video Surveillance (SVS) designed to enhance safety in community spaces such as educational and recreational areas, and small businesses. The proposed system innovatively integrates with existing CCTV and wired camera networks, simplifying its adoption across various community cases to leverage recent AI advancements. Our SVS system, focusing on privacy, uses metadata instead of pixel data for activity recognition, aligning with ethical standards. It features cloud-based infrastructure and a mobile app for real-time, privacy-conscious alerts in communities. This article notably pioneers a comprehensive real-world evaluation of the SVS system, covering AI-driven visual processing, statistical analysis, database management, cloud communication, and user notifications. It's also the first to assess an end-to-end anomaly detection system's performance, vital for identifying potential public safety incidents. For our evaluation, we implemented the system in a community college, serving as an ideal model to exemplify the proposed system's capabilities. Our findings in this setting demonstrate the system's robustness, with throughput, latency, and scalability effectively managing 16 CCTV cameras. The system maintained a consistent 16.5 frames per second (FPS) over a 21-hour operation. The average end-to-end latency for detecting behavioral anomalies and alerting users was 26.76 seconds.
Learning Pseudo-Labeler beyond Noun Concepts for Open-Vocabulary Object Detection

Open-vocabulary object detection (OVOD) has recently gained significant attention as a crucial step toward achieving human-like visual intelligence. Existing OVOD methods extend target vocabulary from pre-defined categories to open-world by transferring knowledge of arbitrary concepts from vision-language pre-training models to the detectors. While previous methods have shown remarkable successes, they suffer from indirect supervision or limited transferable concepts. In this paper, we propose a simple yet effective method to directly learn region-text alignment for arbitrary concepts. Specifically, the proposed method aims to learn arbitrary image-to-text mapping for pseudo-labeling of arbitrary concepts, named Pseudo-Labeling for Arbitrary Concepts (PLAC). The proposed method shows competitive performance on the standard OVOD benchmark for noun concepts and a large improvement on referring expression comprehension benchmark for arbitrary concepts.
Mitigating Data Injection Attacks on Federated Learning

Federated learning is a technique that allows multiple entities to collaboratively train models using their data without compromising data privacy. However, despite its advantages, federated learning can be susceptible to false data injection attacks. In these scenarios, a malicious entity with control over specific agents in the network can manipulate the learning process, leading to a suboptimal model. Consequently, addressing these data injection attacks presents a significant research challenge in federated learning systems. In this paper, we propose a novel technique to detect and mitigate data injection attacks on federated learning systems. Our mitigation method is a local scheme, performed during a single instance of training by the coordinating node, allowing the mitigation during the convergence of the algorithm. Whenever an agent is suspected to be an attacker, its data will be ignored for a certain period, this decision will often be re-evaluated. We prove that with probability 1, after a finite time, all attackers will be ignored while the probability of ignoring a trustful agent becomes 0, provided that there is a majority of truthful agents. Simulations show that when the coordinating node detects and isolates all the attackers, the model recovers and converges to the truthful model.
PaSCo: Urban 3D Panoptic Scene Completion with Uncertainty Awareness

We propose the task of Panoptic Scene Completion (PSC) which extends the recently popular Semantic Scene Completion (SSC) task with instance-level information to produce a richer understanding of the 3D scene. Our PSC proposal utilizes a hybrid mask-based technique on the non-empty voxels from sparse multi-scale completions. Whereas the SSC literature overlooks uncertainty which is critical for robotics applications, we instead propose an efficient ensembling to estimate both voxel-wise and instance-wise uncertainties along PSC. This is achieved by building on a multi-input multi-output (MIMO) strategy, while improving performance and yielding better uncertainty for little additional compute. Additionally, we introduce a technique to aggregate permutation-invariant mask predictions. Our experiments demonstrate that our method surpasses all baselines in both Panoptic Scene Completion and uncertainty estimation on three large-scale autonomous driving datasets. Our code and data are available at https://astra-vision.github.io/PaSCo .
Can we truly transfer an actor's genuine happiness to avatars? An investigation into virtual, real, posed and spontaneous faces

"A look is worth a thousand words" is a popular phrase. And why is a simple look enough to portray our feelings about something or someone? Behind this question are the theoretical foundations of the field of psychology regarding social cognition and the studies of psychologist Paul Ekman. Facial expressions, as a form of non-verbal communication, are the primary way to transmit emotions between human beings. The set of movements and expressions of facial muscles that convey some emotional state of the individual to their observers are targets of studies in many areas. Our research aims to evaluate Ekman's action units (AU) in datasets of real human faces, posed and spontaneous, and virtual human faces resulting from transferring real faces into Computer Graphics (CG) faces. In addition, we also conducted a case study with specific movie characters, such as She-Hulk and Genius. We intend to find differences and similarities in facial expressions between real and CG datasets, posed and spontaneous faces, and also to consider the actors' genders in the videos. This investigation can help several areas of knowledge, whether using real or virtual human beings, in education, health, entertainment, games, security, and even legal matters. Our results indicate that AU intensities are greater for posed than spontaneous datasets, regardless of gender. Furthermore, there is a smoothing of intensity up to 80% for AU6 and 45% for AU12 when a real face is transformed into CG.
Aligning and Prompting Everything All at Once for Universal Visual Perception

Vision foundation models have been explored recently to build general-purpose vision systems. However, predominant paradigms, driven by casting instance-level tasks as an object-word alignment, bring heavy cross-modality interaction, which is not effective in prompting object detection and visual grounding. Another line of work that focuses on pixel-level tasks often encounters a large annotation gap of things and stuff, and suffers from mutual interference between foreground-object and background-class segmentation. In stark contrast to the prevailing methods, we present APE, a universal visual perception model for aligning and prompting everything all at once in an image to perform diverse tasks, i.e., detection, segmentation, and grounding, as an instance-level sentence-object matching paradigm. Specifically, APE advances the convergence of detection and grounding by reformulating language-guided grounding as open-vocabulary detection, which efficiently scales up model prompting to thousands of category vocabularies and region descriptions while maintaining the effectiveness of cross-modality fusion. To bridge the granularity gap of different pixel-level tasks, APE equalizes semantic and panoptic segmentation to proxy instance learning by considering any isolated regions as individual instances. APE aligns vision and language representation on broad data with natural and challenging characteristics all at once without task-specific fine-tuning. The extensive experiments on over 160 datasets demonstrate that, with only one-suit of weights, APE outperforms (or is on par with) the state-of-the-art models, proving that an effective yet universal perception for anything aligning and prompting is indeed feasible. Codes and trained models are released at https://github.com/shenyunhang/APE.
LiPoSeg: A Lightweight Encoder-Decoder Network for LiDAR-based Road-Object Semantic Segmentation

LiDAR point cloud segmentation is one of the most challenging tasks in autonomous driving systems, as it requires a cutting-edge perception method that should be accurate, low-cost, and real-time. In the last decade, several deep encoder-decoder neural networks like PointSeg were proposed for semantic segmentation of point clouds with noticeable achievements, but they still posed some shortcomings of high complexity and limited compatibility with resource-constrained devices. With a keen awareness of these limitations, this paper introduces LiPoSeg, an intricately designed network inspired by PointSeg. LiPoSeg significantly reduces the network size while maintaining almost no accuracy degradation. In particular, two vital innovations characterize the LiPoSeg architecture: (i) a grouped enlargement layer with grouped convolution layers to replace regular convolution layers for complexity reduction while maintaining a good segmentation accuracy and (ii) a novel region-wise attention mechanism enables to capture locally meaningful features. The combination of these two innovations results in our proposed network that is smaller than PointSeg in terms of the network size (a.k.a., the total number of learnable parameters). Remarkably, this reduction is achieved with only decrease of segmentation accuracy when evaluated on the WPI and Panda datasets for LiDAR-based road-object semantic segmentation.
Peirce, Poe, and Protoplasm

ABSTRACT: This article interprets a series of aborted transcriptions of "The Raven" undertaken by Charles Sanders Peirce and named by him "Art Chirography" as a species of re-mediation of Poe's poem. Attending to Peirce's knowledge of Poe's works (real and faked), the relation between sound and inscription, the power of handwriting as media phenomenon in the nineteenth century, and Peirce's evolutionary metaphysics, the essay concludes that Peirce's "art chirography" is an attempt to image the play of chance and habit-taking that lies at the heart of both Poe's poem and Peirce's metaphysics.
Retracted: Comparative Analysis of Machine Learning Methods for Breast Cancer Classification in Genetic Sequences

[This retracts the article DOI: 10.1155/2022/7199290.].
Retracted: Design and Manufacture of Data Gloves for Rehabilitation Training and Gesture Recognition Based on Flexible Sensors

[This retracts the article DOI: 10.1155/2021/6359403.].
Retracted: Design of Intelligent Nursing Decision Support System Based on Multiattribute Decision Model

[This retracts the article DOI: 10.1155/2022/1088393.].
Retracted: Construction and Signal Feature Processing of Gold Nanobiosensors Based on the Internet of Things

[This retracts the article DOI: 10.1155/2022/1432266.].
Research on Health Status Evaluation of Measurement and Control Devices by Fusing Chip and Board Information

Measurement and control devices are important secondary equipment in substations, and their health status can provide guidance for equipment condition maintenance and fault disposal, which is essential to ensure safe and stable operation of substations. However, the existing secondary equipment assessment focuses on equipment or board-level information, lacking an overall consideration of chip-board-device. This paper establishes a device health evaluation system covering chip-board information of the core modules of the measurement and control device and proposes a fuzzy comprehensive rating method. Specifically, firstly, the typical structure of the measurement and control device is introduced; secondly, the device health evaluation index including chip-board information of the measurement and control device is constructed; thirdly, the fuzzy comprehensive evaluation method for the measurement and control device is proposed based on the normal-type affiliation function; finally, the effectiveness of the method is verified by combining with examples.
Towards Privacy-first Manufacturing Data Exchange Platform

Reducing their operating costs and optimizing manufacturing processes are main challenges for manufacturers that need no-doubt help from machine suppliers-OEMs. However, like 64% of Business entities, they do not intend to collaborate as long as their confidential data can be seen by anyone. Until now, some solutions on market using technologies like Confidential Computing, Differential Privacy, Multi Party Computation cannot completely fit to industrial requirements, data are sometimes partially encrypted or using trust execution environment (a bunker) to analyse in clear format. For this reason, until now, no secure computation and no solution for privacy-preserving data analysis are yet completely satisfactory (in terms of privacy and security constraints) and moreover they are often tested for different applications and on different datasets. Fully Homomorphic Encryption (FHE) technology is going to change the game. FHE allows service providers to work directly on encrypted data without ever decrypting it, which offers a privacy data protection for both customers and OEMs. In the collaboration with Siemens France, we provide FHE-based manufacturing data exchange space which is a part of RaiseSens© Data eXchange Platform (RS-DXP). In this respect, API-driven RS-DXP architecture allows the practical and easy integration of FHE techniques combined with optimisation engine, and non-moving data techniques applied in lightweight yet real-world manufacturing applications and deploying them in Cloud computing environment to offer a solution at low software engineering cost. This will pave the way for a wide deployment, boosting data-enabled manufacturing services.
Improving Single Positive Multi-label Classification via Knowledge-based Label-weighted Large Loss Rejection

It is undeniable that high-quality data plays a crucial role in achieving good outcomes. However, obtaining such a dataset is always challenging, particularly in multi-label classification, where a fully labeled dataset is required in traditional approaches. This challenge has led to the emergence of several effective learning techniques called single positive multi-label learning (SPML), which utilize multi-label training images annotated with only one single positive label. In our work, we propose an effective method that improves the cutting-edge BoostLU baseline by leveraging efficient label-weighted loss and prior knowledge based regularization strategies. Firstly, we present a novel approach for reweighting the contribution of each label to the total loss, giving higher weight to the true positive label while eliminating unreliable pseudo negative labels by assigning them zero weight. This is achieved through the exploration of large per-label losses. Additionally, we introduce an auxiliary loss to regulate the expected value of positive labels, encouraging the model to predict a reasonable number of positive labels per image based on prior knowledge about the dataset. Experimental results across several benchmark datasets showcase the superior performance of our method compared to both the baseline and other state-of-the-art methods.
A Y-shaped network based single-shot absolute phase recovery method for fringe projection profilometry

Abstract Fringe projection profilometry (FPP) is a widely used non-contact 3D measurement method. Though maturing in the last decade, it remains a significant challenge when facing the phase unwrapping of measured object surfaces in a single-shot measurement setting. With the rapid development of deep learning techniques, the adoption of a data-driven approach is gaining popularity in the field of optical metrology. This study proposes a new absolute phase recovery method based on the devised single-stage deep learning network. The aim is to ensure high-quality absolute phase recovery from a single-shot fringe projection measurement. Unlike most existing approaches, where the numerators and denominators of the wrapped phases and the fringe orders are predicted in various stages, the proposed method acquires the wrapped phases and the corresponding fringe orders within a single network, i.e. it can predict both wrapped phases and the corresponding fringe orders directly and simultaneously from the single fringe pattern projected in the single-shot mode based on a unified Y-shaped network. Experiments on benchmark datasets and models have demonstrated the effectiveness and efficiency of the technique, especially in terms of high-quality recovery of absolute phase information by using the lightweight single-stage network, and enabling the FPP-based phase 3D measurements in an online manner.
INFORMATION AND COMMUNICATION TECHNOLOGY (ICT) FRAMEWORK FOR YOUTH EMPOWERMENTS IN NIGERIA

The rate of unemployment among Nigerian youths is alarming and this unfortunate situation has led many youths to involve in various forms of crimes such as armed robbery, banditry, kidnapping, human trafficking, insurgency, to mention but a few. Though, governments at all levels are trying to minimize unemployment scenario through different interventions, such as poverty alleviation, agriculture anchor programme, trainings of youths on different technical programmes and so on. This paper proposes an ICT based framework for youth empowerment in Nigeria so as to complements the existing efforts on the joblessness. The paper x-rayed concept of ICT, youth empowerments and unemployment. It was also explained in the paper the causes of unemployment in Nigeria and governments efforts to reduce unemployment to barest minimum. The paper provides some recommendations, such as, provision of interest-free loans by federal government to youths who are willing to stand on their own for self-reliant and employer of labour in the nearest future.
Mineral Mapping through Advanced Airborne Hyperspectral Remote Sensing Techniques

An increased number of spectral channels in the visible to near-infrared region (VNIR) and short-wave infrared region (SWIR) in hyperspectral sensors help in the identification of distinct variations in minerals. Airborne Visible Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) hyperspectral sensor with 425 spectral bands is useful for mineral Constrained Energy Minimization (CEM), Spectral Angle Mapper (SAM), and Adaptive Coherence Estimator (ACE) advanced tools utilized to detect target minerals in the study area for mineral exploration. Minimum Noise Fraction (MNF), ACE, SAM and CEM advanced hyperspectral remote sensing techniques were applied to the AVIRIS-NG airborne data to map the kaolinite, dolomite, chlorite and hematite. In the MNF images, some areas are prominently highlighted and identified as areas of interest. CEM algorithm has classified the four minerals better than the ACE and SAM. The potential zones identified from the remote sensing data were verified, and abandoned old mines are present in three locations out of four. These locations are picked up well in different MNF images. In the mapped area, carbonate is present in the form of altered minerals in metabasalt, and iron leached surface indicates hematite mineral. The remote sensing outputs well also supported by the petrographic studies. The approach used in this study has great implications for mineral mapping.
Creating and manipulating 3D paths with mixed reality spatial interfaces

Mixed reality offers unique opportunities to situate complex tasks within spatial environments. One such task is the creation and manipulation of intricate, three-dimensional paths, which remains a crucial challenge in many fields, including animation, architecture, and robotics. This paper presents an investigation into the possibilities of spatially situated path creation using new virtual and augmented reality technologies and examines how these technologies can be leveraged to afford more intuitive and natural path creation. We present a formative study ( n = 20) evaluating an initial path planning interface situated in the context of augmented reality and human-robot interaction. Based on the findings of this study, we detail the development of two novel techniques for spatially situated path planning and manipulation that afford intuitive, expressive path creation at varying scales. We describe a comprehensive user study ( n = 36) investigating the effectiveness, learnability, and efficiency of both techniques when paired with a range of canonical placement strategies. The results of this study confirm the usability of these interaction metaphors and provide further insight into how spatial interaction can be discreetly leveraged to enable interaction at scale. Overall, this work contributes to the development of 3DUIs that expand the possibilities for situating path-driven tasks in spatial environments.
The Six Ways to Build Trust and Reduce Privacy Concern in a Central Bank Digital Currency (CBDC)

Central Bank Digital Currencies (CBDCs) have been implemented by only a handful of countries, but they are being explored by many more. CBDCs are digital currencies issued and backed by a central bank. Consumer trust can encourage or discourage the adoption of this currency, which is also a payment system and a technology. This research attempts to understand consumer trust in CBDCs so that the development and adoption stages are more effective and satisfying for all the stakeholders.
Ways of expressing emotional reactions as a communicative practice of social media users

Introduction. The current martial law in Ukraine for 1.5 years now requires a study of indirect ways of communication between Ukrainians in the absence of a permanent telephone connection, interruptions in Internet connection, and, accordingly, an urgent need to use the possibilities of online communication with a delayed response. Relevance and purpose. One of the simplest and most convenient communication practices in social networks and messengers is to mark an emotional reaction, which can be used even in the absence of an Internet connection in a delayed mode. Therefore, it is necessary to study the question of what purpose and how consciously Ukrainian social media users use this communication practice. In view of this, the purpose of our article is to find out the peculiarities of using graphic images to indicate emotional reactions of social media users as a communicative practice, in particular in the current conditions of the full-scale invasion of Russia. Methodology. To achieve this goal, we used the following methods: searching for scientific sources on the topic of sign-visual communication and analyzing them, describing ways to indicate emotional reactions of users of social networks and messengers, a survey by questioning the target audience (Ukrainians aged 16 and older, divided into 6 age groups who use social networks and messengers) using a Google form that allowed them to choose three answers and/or add their own answer. Results. The study found that the purpose of using likes for more than 85% of Ukrainian users is to support the author of the post (joy, sympathy, etc.). At the same time, almost 60% of respondents approach the process of marking a post deliberately and consciously. Conclusions. The novelty of the study is that for the first time an attempt was made to establish the purpose and nature of the use of likes as a communication practice by Ukrainian users of social networks and messengers. The potential significance of the research results lies in the need to conduct educational work on the use of various graphic signs to denote the entire range of emojis within a particular social network or messenger. Prospects for further research are to study other communication practices of Ukrainians within social networks and messengers.