Reviewerly/exHarmony huggingface dataset:
Efficient People Counting in Thermal Images: The Benchmark of Resource-Constrained Hardware

The monitoring of presence is a timely topic in intelligent building management systems. Nowadays, most rooms, halls, and auditoriums use a simple binary presence detector that is used to control the operation of HVAC systems. This strategy is not optimal and leads to significant amounts of energy being wasted due to inadequate control of the system. Therefore, knowing the exact person count facilitates better adjustment to current needs and cost reduction. The vision-based people-counting is a well-known area of computer vision research. In addition, with rapid development in the artificial intelligence and IoT sectors, power-limited and resource-constrained devices like single-board computers or microcontrollers are able to run even such sophisticated algorithms as neural networks. This capability not only ensures the tiny size and power effectiveness of the device but also, by definition, preserves privacy by limiting or completely eliminating the transfer of data to the cloud. In this paper, we describe the method for efficient occupancy estimation based on low-resolution thermal images. This approach uses a U-Net-like convolutional neural network that is capable of estimating the number of people in the sensor’s field of view. Although the architecture was optimized and quantized to fit the limited microcontroller’s memory, the metrics obtained by the algorithm outperform the other state-of-the-art solutions. Additionally, the algorithm was deployed on a range of embedded devices to perform a set of benchmarks. The tests carried out on embedded processors allowed the comparison of a wide range of chips and proved that people counting can be efficiently executed on resource-limited hardware while maintaining low power consumption.
A Dynamic-Efficient Structure for Secure and Verifiable Location-Based Skyline Queries

In a broad range of commercial and government applications, supporting secure location-based query services over outsourced cloud-based services particularly for data update on encrypted datasets remains challenging in practice. Compounding the challenge is the need to ensure update and query efficiency, dataset confidentiality (including against potentially malicious cloud service providers) and query authenticity. Thus in this paper, we propose DynPilot, a novel solution for privacy-preserving verifiable location-based skyline queries over dynamic and encrypted data(sets). The key challenge is how to devise a ciphertext-based authenticated data structure (ADS) that not only protects the confidentiality of the dataset (including the verification phase), but also the effective maintenance of such a dataset. Moreover, to motivate the cloud into actively updating ADS, the digest of the raw dataset is stored in the blockchain due to its immutability and consensus mechanism where update cost is also considered. Therefore, we present a novel ADS (hereafter referred to as <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</u> ynamic-Efficient <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</u> ecure and <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V</u> erifiable <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</u> ree (DSV-tree)), designed to be dynamic and support secure and verifiable skyline queries. Meanwhile, DynPilot also achieves forward privacy using a novel fuzzy update strategy. To further improve the efficiency of queries, an optimized version (i.e., DSV*-tree) is also developed based on the idea of the multi-level index structure. Finally, we analyze the security and complexity of our approach, and the empirical evaluations demonstrate the utility of our approach.
The First Challenge on Moving Object Detection and Tracking in Satellite Videos: Methods and Results

In this paper, we briefly summarize the first challenge on moving object detection and tracking in satellite videos (SatVideoDT). This challenge has three tracks related to satellite video analysis, including moving object detection (Track 1), single object tracking (Track 2), and multiple-object tracking (Track 3). 123, 89, and 70 participants successfully registered, while 37, 42, and 29 teams submitted their final results on the test datasets for Tracks 1-3, respectively. The top-performing methods and their results in each track are described with details. This challenge establishes a new benchmark for satellite video analysis.
Twitter User Representation Using Weakly Supervised Graph Embedding

Social media platforms provide convenient means for users to participate in multiple online activities on various contents and create fast widespread interactions. However, this rapidly growing access has also increased the diverse information, and characterizing user types to understand people's lifestyle decisions shared in social media is challenging. In this paper, we propose a weakly supervised graph embedding based framework for understanding user types. We evaluate the user embedding learned using weak supervision over well-being related tweets from Twitter, focusing on 'Yoga', 'Keto diet'. Experiments on real-world datasets demonstrate that the proposed framework outperforms the baselines for detecting user types. Finally, we illustrate data analysis on different types of users (e.g., practitioner vs. promotional) from our dataset. While we focus on lifestyle-related tweets (i.e., yoga, keto), our method for constructing user representation readily generalizes to other domains.
Vision Transformer Based Model for Describing a Set of Images as a Story

Visual Story-Telling is the process of forming a multi sentence story from a set of images. Appropriately including visual variation and contextual information captured inside the input images is one of the most challenging aspects of visual storytelling. Consequently, stories developed from a set of images often lack cohesiveness, relevance, and semantic relationship. In this paper, we propose a novel Vision Transformer Based Model for describing a set of images as a story. The proposed method extracts the distinct features of the input images using a Vision Transformer (ViT). Firstly, input images are divided into 16 $$\times $$ 16 patches and bundled into a linear projection of flattened patches. The transformation from a single image to multiple image patches captures the visual variety of the input visual patterns. These features are used as input to a Bidirectional-LSTM which is part of the sequence encoder. This captures the past and future image context of all image patches. Then, an attention mechanism is implemented and used to increase the discriminatory capacity of the data fed into the language model, i.e. a Mogrifier-LSTM. The performance of our proposed model is evaluated using the Visual Story-Telling dataset (VIST), and the results show that our model outperforms the current state of the art models.
MaxFEM: Mining Maximal Frequent Episodes in Complex Event Sequences

For the analysis of discrete sequences, frequent episode mining (FEM) is a key technique. The goal is to enumerate all subsequences of symbols or events that are appearing at least some minimum number of times. In the last decades, several efficient episode mining algorithms were designed. Nonetheless, a major issue is that they often yield a huge number of frequent episodes, which is inconvenient for users. As a solution, this paper presents an efficient algorithm called MaxFEM (Maximal Frequent Episode Miner) to identify only the maximal frequent episodes of a complex sequence. A major benefit is to reduce the set of frequent episodes presented to the user. MaxFEM includes many strategies to improve its performance. The evaluation of MaxFEM on real datasets confirms that it has excellent performance.
FaST

In software projects, applications are often monitored by systems that automatically identify crashes, collect their information into reports, and submit them to developers. Especially in popular applications, such systems tend to generate a large number of crash reports in which a significant portion of them are duplicate. Due to this high submission volume, in practice, the crash report deduplication is supported by devising automatic systems whose efficiency is a critical constraint. In this paper, we focus on improving deduplication system throughput by speeding up the stack trace comparison. In contrast to the state-of-the-art techniques, we propose FaST, a novel sequence alignment method that computes the similarity score between two stack traces in linear time. Our method independently aligns identical frames in two stack traces by means of a simple alignment heuristic. We evaluate FaST and five competing methods on four datasets from open-source projects using ranking and binary metrics. Despite its simplicity, FaST consistently achieves state-of-the-art performance regarding all metrics considered. Moreover, our experiments confirm that FaST is substantially more efficient than methods based on optimal sequence alignment.
The eNaira - Opportunities and challenges

Emerging technology activities are evolving and turning our world into a digital world. The digital world offers many digital inventions such as digital currency, smart devices, social media, digital devices, and the list continues. Digital or electronic currency initiatives such as eNaira are fast becoming a norm across countries as means of payment for goods and services. But many are concerned and worried about eNaira, lack of understanding of the opportunities and challenges of eNaira, and limited academic literature. eNaira is a digital currency or electronic currency which is accessible through the Internet without physical print. The introduced eNaira was rolled out on 25 October 2021, and the announcement gained a mixed reaction from the public as a result of the opportunities and challenges that come with it. This paper deployed a literature review approach to provide a comprehensive analysis of eNaira, the opportunities and challenges of eNaira for Nigerians and businesses (individuals and organizations). The paper found that eNaira presents some opportunities for the central bank of Nigeria (CBN) (apex bank) and Nigerians (customers). It assists Nigerians (customers) to access their money quickly, and CBN can monitor and control transactions and many more. Also, the study discovered that eNaira faces trust and many other challenges, but the existing challenges must be addressed for the opportunities to be derived. This paper contributes to academic knowledge by providing comprehensive information on eNaira, and discovering the opportunities and challenges of eNaira.
Self-Explanation in Systems of Systems

Technical systems have reached a complexity rendering their behaviour difficult to comprehend or seemingly non-deterministic. Thus, self-explaining digital systems would be a strong support for tasks like debugging, diagnosis of failures, reliably operating the system or optimization. To be useful, self-explanation must be efficiently computable in a technical system and must be understandable to the addressee. The addressee might be another technical system at the same or another system layer or a human.We provide a conceptual framework for self-explanation including formalization of the inherent concepts of explanation, understandability etc. We instantiate these generic concepts on the example of Mealy machine models of embedded systems and illustrate their use via example from autonomous driving.
Nezha: Exploiting Concurrency for Transaction Processing in DAG-based Blockchains

A Directed Acyclic Graph (DAG)-based blockchain with its inherent parallel structure can potentially significantly improve the throughput performance over conventional blockchains. Such a performance improvement can be further enhanced through concurrent transaction processing. This, however, brings new challenges in concurrency control design in that there is an increased number of concurrent reads and writes to the same address in a DAG-based blockchain, which leads to a considerable rise of potential conflicts. Therefore, one critical problem is how to effectively and efficiently detect and order conflicting transactions. In this work, for the first time, we aim to improve system throughput and processing latency by exploring the address dependencies among different transactions. We propose NEZHA, an efficient concurrency control scheme for DAG-based blockchains. Specifically, NEZHA intelligently constructs an address-based conflict graph (ACG) while incorporating address dependencies as edges to capture all conflicting transactions. To generate a total order between transactions, we propose a hierarchical sorting (HS) algorithm to derive sorting ranks of addresses based on the ACG and sort transactions on each address. Extensive experiments demonstrate that, even under high data contention, NEZHA can increase the throughput over the conventional conflict graph scheme by up to 8 ×, while decreasing the transaction processing latency up to 10 ×.
Self-Supervised Learning for Semi-Supervised Temporal Language Grounding

Given a text description, Temporal Language Grounding (TLG) aims to localize temporal boundaries of the segments that contain the specified semantics in an untrimmed video. TLG is inherently a challenging task, as it requires comprehensive understanding of both sentence semantics and video contents. Previous works either tackle this task in a fully-supervised setting that requires a large amount of temporal annotations or in a weakly-supervised setting that usually cannot achieve satisfactory performance. Since manual annotations are expensive, to cope with limited annotations, we tackle TLG in a semi-supervised way by incorporating self-supervised learning, and propose <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</b> elf- <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</b> upervised <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</b> emi- <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</b> upervised <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</b> emporal <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</b> anguage <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</b> rounding (S <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{4}$</tex-math></inline-formula> TLG). S <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{4}$</tex-math></inline-formula> TLG consists of two parts: (1) A pseudo label generation module that adaptively produces instant pseudo labels for unlabeled samples based on predictions from a teacher model; (2) A self-supervised feature learning module with inter-modal and intra-modal contrastive losses to learn video feature representations under the constraints of video content consistency and video-text alignment. We conduct extensive experiments on the ActivityNet-CD-OOD and Charades-CD-OOD datasets. The results demonstrate that our proposed S <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{4}$</tex-math></inline-formula> TLG can achieve competitive performance compared to fully-supervised state-of-the-art methods while only requiring a small portion of temporal annotations.
Unraveling Social Perceptions &amp; Behaviors towards Migrants on Twitter

We draw insights from the social psychology literature to identify two facets of Twitter deliberations about migrants, i.e., perceptions about migrants and behaviors towards migrants. Our theoretical anchoring helped us in identifying two prevailing perceptions (i.e., sympathy and antipathy) and two dominant behaviors (i.e., solidarity and animosity) of social media users towards migrants. We have employed unsupervised and supervised approaches to identify these perceptions and behaviors. In the domain of applied NLP, our study offers a nuanced understanding of migrant-related Twitter deliberations. Our proposed transformer-based model, i.e., BERT + CNN, has reported an F1-score of 0.76 and outperformed other models. Additionally, we argue that tweets conveying antipathy or animosity can be broadly considered hate speech towards migrants, but they are not the same. Thus, our approach has fine-tuned the binary hate speech detection task by highlighting the granular differences between perceptual and behavioral aspects of hate speeches.
Bicriteria Oversampling for Imbalanced Data Classification

The paper proposes bicriteria oversampling strategy for mining imbalanced data. We use two specialized criteria for oversampling -classification potential and distance from the borderline between minority and majority instances. The potential is to be maximized and the distance minimized. The required number of synthetic examples is selected from the non-dominated set of examples produced by the evolutionary algorithm. At the final step the balanced set of examples is used by GEP classifier. Computational experiment confirmed that the approach assures high quality performance.
FloatingFusion: Depth from ToF and Image-Stabilized Stereo Cameras

High-accuracy per-pixel depth is vital for computational photography, so smartphones now have multimodal camera systems with time-of-flight (ToF) depth sensors and multiple color cameras. However, producing accurate high-resolution depth is still challenging due to the low resolution and limited active illumination power of ToF sensors. Fusing RGB stereo and ToF information is a promising direction to overcome these issues, but a key problem remains: to provide high-quality 2D RGB images, the main color sensor's lens is optically stabilized, resulting in an unknown pose for the floating lens that breaks the geometric relationships between the multimodal image sensors. Leveraging ToF depth estimates and a wide-angle RGB camera, we design an automatic calibration technique based on dense 2D/3D matching that can estimate camera extrinsic, intrinsic, and distortion parameters of a stabilized main RGB sensor from a single snapshot. This lets us fuse stereo and ToF cues via a correlation volume. For fusion, we apply deep learning via a real-world training dataset with depth supervision estimated by a neural reconstruction method. For evaluation, we acquire a test dataset using a commercial high-power depth camera and show that our approach achieves higher accuracy than existing baselines.
Context and Intention aware 3D Human Body Motion Prediction using an Attention Deep Learning model in Handover Tasks

This work explores how contextual information and human intention affect the motion prediction of humans during a handover operation with a social robot. By classifying human intention in four different classes, we developed a model able to generate a different motion for each intention class. Furthermore, the model uses a multi-headed attention architecture to add contextual information to the pipeline, such as the position of the robot end effector (REE) or the position of obstacles in the interaction scene. We generate predictions up to two and half seconds in the future given an input sequence of one second containing the previous motion of the human. The results show an improvement of the prediction accuracy, both for the full skeleton prediction and the human hand used for the delivery. The model also allows to generate different sequences with the desired human intention.
CITS-MEW: Multi-Party Entangled Watermark in Cooperative Intelligent Transportation System

Federated learning is good for building better cooperative intelligent transportation system (C-ITS). Intellectual property protection in C-ITS brings many benefits to all vehicles. Although the protection of model intellectual property by watermark has received much research attention, the existing works only deploy watermark in centralized models. Due to the difference of watermark distribution among vehicles, the global model accuracy of watermark in federated learning is significantly reduced or the local watermark is invalid. To solve these problems, we propose a multi-party entangled watermark algorithm in federated learning. Specifically, in the local training, we propose a watermark enhancement algorithm, which solves the problem of local watermark failure. Then, in the global aggregation, we propose an entanglement aggregation algorithm, which solves the problem of a great loss of global model accuracy. We conduct extensive experiments on public datasets to show the superiority of our proposal. The results show that our scheme can obtain more than 16% and 31% advantages in model accuracy and watermark success rate, respectively, compared with existing watermark schemes in federated learning.
Design and Validation of a Compact Bi-Prism- Based Single-Bilateral-Telecentric-Camera Stereo-DIC System

Bi-prism-based single-bilateral-telecentric-camera stereo-digital image correlation (SBTC stereo-DIC) is a recently developed variant of single-camera stereo-DIC technique, featuring attractive advantages of easy implementation, calibration-free imaging model, and high accuracy. However, the effects of key structural parameters (e.g., the prism angle and the location of the bi-prism) on the metrological performance of the system, e.g., field of view (FOV), system compactness, working distance and measurement precision, and how to design a compact bi-prism-based SBTC stereo-DIC system, remain unclear. To address these problems, we first investigated the influence of the key geometric parameter, the horizontal distance between the bi-prism and the bilateral telecentric lens (BTL), on the metrological performance of bi-prism-based SBTC stereo-DIC in terms of system compactness, FOV, and measurement precision. Based on these findings, a bi-prism adapter was designed and printed by a 3-D printer that can be directly attached to the BTL as a spectroscope for single-camera pseudo-stereo imaging. The metrological performance of the established bi-prism-based single-BTL stereo-DIC systems was experimentally examined by the FOV validation test, cylinder translation, and low-carbon steel tensile tests. The results of this work provide practical guidance on constructing a compact bi-prism-based single-BTL stereo-DIC system.
Developing Language Skills for 4-5 Years Old Children at RA Muslimat NU 1 Tulus Rejo Kec. Pekalongan Through the Development of Ice Cream Stick Media

sticks are learning media that can stimulate the teaching and learning process. The use of ice cream stick media at RA Muslimat NU 1 Tulus Rejo Kec. Pekalongan has not been used in learning, besides that the media material for ice cream sticks is very easy to get and cheap. The language ability of children aged 4-5 years is the lowest level of achievement compared to other abilities. development of ice cream stick media at RA Muslimat NU 1 Tulus Rejo with the following steps: 1) Ice cream sticks were developed into letter sticks. One side is written with letters, one side is also written with letters, 2) At first the child reads the letters on one side, 3) If correct, the child may read the letters on the other side. 4) If the child wants to learn to read, the game is reversed, the child reads one side of the letter first and then opens the side with the letter on the other side, 5) If the child answers correctly, he/she is given a picture of a smile, and if he/she answers incorrectly, he/she is given a picture of crying. The use of ice cream stick media for children aged 4-5 years at RA Muslimat NU 1 Tulus Rejo can improve children's language skills.&#x0D;