Reviewerly/exHarmony huggingface dataset:
A Local-Global Self-attention Interaction Network for RGB-D Cross-Modal Person Re-identification

RGB-D cross-modal person re-identification (Re-ID) task aims to match the person images between the RGB and depth modalities. This task is rather challenging for the tremendous discrepancy between these two modalities in addition to common issues such as lighting conditions, human posture, camera angle, etc. Nowadays only few types of research focus on this task, and existing Re-ID methods tend to learn homogeneous structural relationships in an image, which have limited discriminability and weak robustness to noisy images. In this paper, we propose A Local-Global Interaction Network dedicated to processing cross-modal problems. The network can constrain the center distance between two modals, and improve the intra-class cross-modality similarity. Besides, it can also learn the local and global features of different modalities to enrich the features extracted from different modes. We validate the effectiveness of our approach on public benchmark datasets. Experimental results demonstrate our method outperforms other state-of-the-arts in terms of visual quality and quantitative measurement.
Modality-Oriented Graph Learning for OCM

Beyond the fashion compatibility modeling, introduced in Chapter 2, which only considers the visual and textual modalities, as well as only the intramodal compatibility, this chapter presents the modality-oriented graph learning for fashion compatibility modeling, whereby both the intramodal and intermodal compatibilities between fashion items are incorporated for propagating over the entire graph.
Generalized surface reconstruction and fringe analysis through phase measuring deflectometry

A deflectometry simulation system for measuring and generating the surface profiles of freeform optical elements was designed. Unlike alternative optical metrology methods, deflectometry systems utilize the principle of pixel to pixel point mapping to measure a specular optical surface. The in-lab set up uses readily available materials such as an LCD monitor, a CMOS camera, and other basic lab items such as optical posts and post holders. A software that enlists the usage of phase unwrapping in order to derive the incident and reflected light vectors from a surface under test. These vectors provide slope information which can then be integrated into a surface reconstruction. This allows for a non-contact surface reconstruction method as well as a simulation to help calculate the best respective placements of monitor, camera, and test surface. This type of system is useful in a measuring more challenging optical surfaces such as free forms and convex optical surfaces. Disclosures of the system and distance sensing lasers could enable a very user friendly and intuitive handler experience for creating the surface reconstruction profiles with much more reliable system geometry information and reduction of excess light and scattering noise. Multi rotation stages can also be used for adjusting tip and tilt angles of test surfaces.
Knowledge Learning Without Forgetting for the Detection of Alzheimer’s Disease

Alzheimer’s disease (AD) is an extremely damaging, slow-progressing neurological disease that causes tremendous inconvenience to patients’ lives. Numerous medical professionals have researched that timely diagnosis and early therapy of AD when it is in its early stages could slow down the progression of AD and even be cured. Therefore, early diagnosis of AD is in urgent need of significant advancement. Nevertheless, there are problems such as brain images with many similar features that are difficult to extract and classify, and insufficient data for training. In this paper, a transfer learning-based knowledge learning without forgetting method we proposed for AD detection, which can preserve the learned knowledge during the transfer process so that it will not be excessively forgotten and this method achieve promising outcomes. The classification accuracy of our method based on resnet50 and resnet18 on the ADNI dataset reached 96.15% and 96.39%, compared to training directly on resnet50 and resnet18, our method increased the classification accuracy by 2.16% and 3.61%, which achieved remarkable results and contributes greatly to the development of AD detection.
Content-Aware Differential Privacy with Conditional Invertible Neural Networks

Differential privacy (DP) has arisen as the gold standard in protecting an individual’s privacy in datasets by adding calibrated noise to each data sample. While the application to categorical data is straightforward, its usability in the context of images has been limited. Contrary to categorical data the meaning of an image is inherent in the spatial correlation of neighboring pixels making the simple application of noise infeasible. Invertible Neural Networks (INN) have shown excellent generative performance while still providing the ability to quantify the exact likelihood. Their principle is based on transforming a complicated distribution into a simple one e.g. an image into a spherical Gaussian. We hypothesize that adding noise to the latent space of an INN can enable differentially private image modification. Manipulation of the latent space leads to a modified image while preserving important details. Further, by conditioning the INN on meta-data provided with the dataset we aim at leaving dimensions important for downstream tasks like classification untouched while altering other parts that potentially contain identifying information. We term our method content-aware differential privacy (CADP). We conduct experiments on publicly available benchmarking datasets as well as dedicated medical ones. In addition, we show the generalizability of our method to categorical data. The source code is publicly available at https://github.com/Cardio-AI/CADP .
Design of a Web Crawler for Water Quality Monitoring Data and Data Visualization

Many countries are paying more and more attention to the protection of water resources at present, and how to protect water resources has received extensive attention from society.Water quality monitoring is the key work to water resources protection.How to efficiently collect and analyze water quality monitoring data is an important aspect of water resources protection.In this paper, python programming tools and regular expressions were used to design a web crawler for the acquisition of water quality monitoring data from Global Freshwater Quality Database (GEMStat) sites, and the multi-thread parallelism was added to improve the efficiency in the process of downloading and parsing.In order to analyze and process the crawled water quality data, Pandas and Pyecharts are used to visualize the water quality data to show the intrinsic correlation and spatiotemporal relationship of the data.
An Architecture for Capturing and Presenting Learning Outcomes using Augmented Reality Enhanced Analytics

Augmented Reality (AR) applications have the capabilities to collect a range of sensor data that is relevant to educators. We propose a platform for using AR to enhance learning analytics by 1) Capturing information from a range of sources including directly from sensors but also from interactions and outcomes within the AR experience. 2) Deriving metrics from this sensory data to provide five categories of measure representing the quality of the learning experience: namely (a) Learning Analytics, (b) Interaction Analytics, (c) Spatial Analytics, (d) Sensory Analytics and (d) Emotion Analytics. 3) Presenting real-time information as analytics to teachers directly as an information overlay using an AR view within the classroom and customised to each student. 4) Closing the student-teacher-student feedback loop so that analytics information feeds directly into teaching in addition to assessment after the event. We evaluate the feasibility of this architecture by 1) Providing a proof-of-concept demonstration showing that the required data can be collected on the targeted platforms. 2) Identifying relevant educational metrics and relating these to the sensor data being collected. 3) Creating educational augmented reality applications and validating these with learners. 4) Identifying teacher requirements with respect to the use of analytics dashboards. The proposed architecture ensures that teachers can differentiate teaching support for each student based on individual needs across the range of learning needs.
mPoW: How to Make Proof of Work Meaningful

Proof of Work (PoW), which is a consensus algorithm for blockchain, entails a large number of meaningless hash calculations and wastage of electric power and computational resources. In 2021, it is estimated that the PoW of Bitcoin consumes as much electricity as Pakistan's annual power consumption (91TWh). This is a serious problem against sustainable development goals. To solve this problem, this study proposes Meaningful-PoW (mPoW), which involves a meaningful calculation, namely the application of a genetic algorithm (GA) to PoW. Specifically, by using the intermediate values that are periodically generated through GA calculations as an input to the Hashcash used in Bitcoin, it is possible to make this scheme a meaningful calculation (GA optimization problem) while maintaining the properties required for PoW. Furthermore, by applying a device-binding technology, mPoW can be ASIC resistant without the requirement of a large memory. Thus, we show that mPoW can reduce the excessive consumption of both power and computational resources.
Integrated Image Recommendation Based on Category and Color Utilizing CNN and VGG-16

In earlier days, image retrieval is based on text whereas due to the improvement in technology, and this paper aims at deep learning (DL) techniques to produce integrated image recommendations. As the recommendation system has become inevitable in e-commerce sites, the proposed work is based on recommending the matching color of the same category product. Initially, the preprocessing of the dataset is carried out by applying lemmatization and stop words removal then by applying term frequency-inverse document frequency (TF-IDF), the top 15 frequent categories of the product are identified which is considered for further processing. Then, the Web scraping technique is applied to retrieve the images from the product URL. To identify the color of the product, the color histogram approach is incorporated. The fetched images are fed into the DL techniques of convolutional neural network (CNN) and VGG-16 to recommend the matching color of the same category product. The evaluation metrics of precision, recall, F1score, and accuracy are applied to evaluate the implemented model. From the experimented results, it is justified that the VGG-16 technique scores better than CNN.
A Brief Discussion on the Reform of Mathematics Teaching in Artificial Intelligence Majors - Taking Matrix Computation and Optimization as Examples

The Artificial Intelligence program is an engineering application program but involves deeper mathematical theory. Matrix Computation and Optimization belong to the core theoretical courses of AI majors. This paper discusses the inner connection between the two courses and the current teaching problems. In order to enable students to connect theory with practice, this paper proposes the organic integration of the two courses and proposes classroom teaching reform methods and practical teaching reform methods for the integrated course in order to improve the teaching quality of the classroom, mobilize students’ classroom enthusiasm, and cultivate students’ innovative thinking and teamwork ability. These methods are also inspiring for the teaching of other courses in artificial intelligence.
Online Algorithms for Finding Distinct Substrings with Length and Multiple Prefix and Suffix Conditions

Let two static sequences of strings P and S, representing prefix and suffix conditions respectively, be given as input for preprocessing. For the query, let two positive integers $$k_1$$ and $$k_2$$ be given, as well as a string T given in an online manner, such that $$T_i$$ represents the length-i prefix of T for $$1 \le i \le |T|$$ . In this paper we are interested in computing the set $$ ans_i $$ of distinct substrings w of $$T_i$$ such that $$k_1 \le |w| \le k_2$$ , and w contains some $$p \in P$$ as a prefix and some $$s \in S$$ as a suffix. More specifically, the counting problem is to output $$| ans_i |$$ , whereas the reporting problem is to output all elements of $$ ans_i $$ , for each iteration i. Let $$\sigma $$ denote the alphabet size, and for a sequence of strings A, $$\Vert A\Vert =\sum _{u\in A}|u|$$ . Then, we show that after $$O((\Vert P\Vert +\Vert S\Vert )\log \sigma )$$ -time preprocessing, the solutions for the counting and reporting problems for each iteration up to i can be output in $$O(|T_i| \log \sigma )$$ and $$O(|T_i| \log \sigma + | ans_i |)$$ total time. The preprocessing time can be reduced to $$O(\Vert P\Vert +\Vert S\Vert )$$ for integer alphabets of size polynomial with regard to $$\Vert P\Vert +\Vert S\Vert $$ . Our algorithms have possible applications to network traffic classification.
A BiLSTM-CRF Based Approach to Word Segmentation in Chinese

This paper proposes a approach for word segmentation in Chinese. The word segmentation model in this paper combines Bi-directional Long Short-Term Memory (BiLSTM) and Conditional Random Fields (CRF), and proposes a four-state word segmentation model of DSZM, so that the model can not only consider the correlation between the front and rear of the sequence like CRF, but also have the feature extraction and fitting capabilities of BiLSTM.
Bayesian Training in Photonic Neural Meshes

Neural networks based on reconfigurable photonic integrated chips (RPICs) can offer zero-latency processing, marginal power consumption and operational flexibility. On the other hand, they are subject to, performance affecting, operational/fabrication deviations in their building blocks. Here, we present a Bayesian learning framework that when combined with device characterization, can dramatically decrease power consumption beyond 74% and significantly simplify the driving circuitry.
Multi-View RGB-D Based 3D Point Cloud Face Model Reconstruction System

This paper proposes a novel multi-view 3D point cloud face model reconstruction system which can well capture local asymmetry features to deal with the occlusion problem often happening in single-view-based approaches. With the help of asymmetries between facial landmarks to register 3D point clouds, this system can significantly improve the reconstruction rate even though the offsets or transformations between head poses are large. Moreover, its accuracy can be further improved via a face segmentation method to exclude non-facial elements and filter out facial outlier. Experimental results show the average distance between could points is 0.17mm which outperforms than other SoTA techniques, and 98.2 percent accuracy on face recognition is achieved.
Learned and Native Concepts in Latent Representations of Terrain Images

Research into generative representations of complex data is a rapidly expanding field in machine learning. In this work we propose and evaluate a process of production and analysis of informative low-dimensional latent representations of real-world images with neural network models of unsupervised generative learning. A model of convolutional autoencoder based on VGG-16 architecture was used to produce low-dimensional generative representations of two datasets of aerial images and the characteristics of distributions of several classes of images of terrain studied. An analysis of latent distributions of terrain classes demonstrated a landscape of compact density structures for most studied classes with good separation of concept regions. The results of this work can be used in developing methods of effective learning in problems and environments with a deficit of labeled data based on the concept-sensitive structure in the latent representations that emerges in the process of unsupervised generative self-learning.
Teaching History during Covid-19

It is here reported the experience of a first classroom of an Italian Liceo during the semi-lockdown at school (students in weekly rotation), then full. Creating a participatory environment was the aim, to give all students the opportunity to acquire concrete knowledge and to develop the 21st century skills, starting from the Learning ones. So, cooperative peers in flipped classroom, were invited to: read and see teacher’s selected sources (also for 3D visors); write an online document (Word), answering to questions of their peer; create a digital storytelling of the topic to orally expose online, collaborating through social media (WhatsApp) and collaborative online tools; finally, answer to a quiz (Quizziz). The obtained results concern: the engagement of the classroom, which fosters all students’ essential understanding and knowledge; inclusion of weaknesses, through their active role in cooperation; inclusion of mobile-phones as democratic tool; ubiquity of learning, which reduces the distance between online and in-presence schooling; above all, fostering the achievement of the 21st century skills, particularly learning-to-learn. For all these reasons, what lockdown teaches will remain as a valid didactical lesson.
Multi-Agent Reinforcement Learning-Implementation of Hide and Seek

Multi-agent reinforcement learning (MRL) is the study of several agents (learning and acting part of the problem) living together in an environment to achieve the end goal. Using a reinforcement algorithm, the aim is to optimize the future rewards by predicting the appropriate action of the agent for reaching goal state. We have applied this theory to implement a simple game of hide and seek, where a team of seekers (multiple agents) would train themselves to seek a team of hiders and vice versa. The objective of the research paper is to study various reinforcement theories and algorithms like Temporal Difference Learning, Q-Learning, SARSA and select/choose an appropriate algorithm to implement a game of hide and seek. This paper also explains how we implemented the hide and seek game, with multiple agents and the insights that we came up with after observing the various episodes of the game in the environment.
Multi-objective Detection and Tracking Algorithm for Unmanned Street View Based on Improved YOLOv5 and DeepSORT under Complex Scenes

With the continuous deepening of research in the field of computer vision, related technologies have gradually been applied in the field of unmanned driving. This paper first briefly introduces the principles and algorithm implementation ideas of algorithms for multi-target detection and tracking in unmanned street scenes in complex scenes. Based on analyzing and comparing the advantages and disadvantages of these algorithms, we propose our improved YOLOV5 and the target detection and tracking algorithm of DeepSORT. Moreover, the theoretical analysis and implementation details of the theory’s image enhancement, target detection and tracking are discussed. Finally, the algorithm is tested. Test results show that our algorithm based on improved YOLOV5 and DeepSORT performs better than other algorithms in most quantitative evaluation metrics.
Secure Access Policy (SAP): Invisibly Executing Speculative Unsafe Accesses in an Isolated Environment

In recent years, speculative execution attacks bring severe threats to hardware security. An attacker tempts the victim process to read secret data during speculative execution and recovers it via micro-architectural covert channels. Most defense strategies in the literature adopt a blocking approach, in which the execution of malicious speculative instructions is withheld until they become safe and, thus, excessive performance overhead is inevitable. Although several non-blocking approaches have also been proposed, extra hardware components are introduced to buffer data of speculative cache accesses, bringing on significant hardware costs. In this paper, we propose the Secure Access Policy (SAP) to enable the non-blocking execution of malicious speculative instructions in an isolated environment, which reuses the existing Line Fill Buffer and Translation Lookaside Buffer, without introducing any new hardware component. The isolated environment, named Safe Shelter area (SS area), maintains the data accessed by malicious speculative instructions and those data cannot be transmitted to the outside. An improved taint tracking technique is introduced to effectively reduce the number of accesses to the SS area. In addition, an extended cache coherence mechanism and a process-bound technique are also proposed to guarantee the validity and security of SS area’s data. We evaluate SAP on SPEC2006 and PARSEC3.0 workloads. The evaluation results show that SAP can effectively defend those attacks leveraging speculative executions and cache covert channels, and outperforms existing approaches in performance overhead, 2.35% and 3.21% in the Spectre and Futuristic defense models, respectively.
An authorization server development based on an open source library at plant machinery manufacturers

Providing access to protected resources with necessary transfer personal data to a third-party is a problem in many plant machinery engineering companies. Using off-brand services is not always reliable and often quite expensive. Creating your own authorization server will help to avoid such problems. This article helps to solve issue connecting with secure access to your data. After reading the presented material, you can independently organize a protected work with personal data. In the course of work an authorization server was developed that allows to obtain necessary information, using only an access token. It provides with minimal protection when working with personal data. All data is stored in a database on the server, which simplifies the work with them. There is an opportunity to perform standard operations on them: view, create, edit and delete. Despite the fact, that it has basic functionality that can be expanded or adapted to the needs of a particular company.
An Approach Towards Data Privacy Issues in Distributed Cyber Physical System

A cyber physical system (CPS) comprises countless geologically scattered substances, and conveyed information sharing is important to accomplish network-wide objectives. A significant concern is raised when disseminated data is shared as private data of the authorized entities might be leaked to the unofficial entities. To deploy a certain cyber physical system, we need to address priority as it has become a high priority issue. Most privacy protection methods being used only pay attention to cyberspace and ignore the real world so they alone are not sufficient to secure the cyber physical system privacy. In this paper, we sum up the ongoing examinations on the most proficient method to create control-hypothetical ways to deal with supplementing existing protection safeguarding procedures and guarantee CPS security.