Reviewerly/exHarmony huggingface dataset:
Accurate and Privacy-Preserving Person Localization Using Federated-Learning and the Camera Surveillance Systems of Public Places

In this paper we propose an accurate and privacy-preserving scheme that enables a law enforcement agency to locate persons of interest using the camera surveillance systems of public places. Comparing to the existing schemes that measure the Euclidean distance to locate persons using their embedding vectors storing facial features, we use a more accurate approach by training a machine learning model. Moreover, to avoid leaking sensitive information by sharing the images of the public places’ visitors to train the model, we use a federated learning technique to compute the model in a privacy-preserving way. The model is designed in such a way that makes executing it over encrypted data efficient. Specifically, the model is executed by three parties as follows. Each public place computes an embedding vector for each visitor’s image and inputs it to a neural network and encrypts the output using a modified inner product encryption scheme and sends the ciphertext to a cloud server. The law enforcement agency does the same steps on the images of persons of interest. Finally, the server uses these ciphertexts to evaluate the last layer of the model by computing the inner product of the two vectors over encrypted data. The cryptosystem enables the server to compute the inner product of two vectors using their ciphertexts without being able to learn the vectors. We have modified an encryption cryptosystem that is designed for a single public place and a single law enforcement agency to make it more efficient in our application that has multiple public places. To evaluate our scheme, we have conducted extensive experiments and the results confirm that our model is accurate in locating persons of interest with low communication and computation overhead. A formal proof and analysis are used to demonstrate the ability of our scheme to preserve privacy.
Mutuality as a Foundation for Co-designing Health Futures

This paper explores how the notion of mutuality can be an ethical driver for engaging participation in the co-design of future health practices and spaces. The context is the design and delivery of a co-design workshop to understand the future practices and ideal spaces for a university health education and research precinct. We report on how mutuality was employed as a framework across four distinct parts of an online co-design workshop. First, to develop the participatory toolkit; second, to engage the interdisciplinary facilitation team; third, to engage participants in envisioning future practices to take place in the precinct; and finally, to link the translation of future practices and values to spatial principles to drive architectural planning processes.
The Future of Money or Quest for Good Money

We live in the fourth industrial revolution, based on the Internet and digital technology. Our society is in the process of digital transformation (DX). Naturally the future of money depends on this process. It is quite clear that we need to reach a social consensus on how to collect digital information, how to use it socially, and where to store it. It is time to share information and statistics socially, to allow the agents (the government, the company, the individual researcher, etc.) to use them openly and to store them safely. In the fourth industrial revolution, data play a central role, and all infrastructures and analytical tools are focused to facilitate data analysis. The future role of money will find its place in the digital society accordingly.
AspIOC: Aspect-Enhanced Deep Neural Network for Actionable Indicator of Compromise Recognition

A crucial component of unstructured threat information is the Indicator of Compromise (IOC), which includes malicious IP addresses and domain names. Because non-malicious IP addresses and domain names exist in the threat intelligence texts, the extracted IOCs are often blended with benign entities. Therefore, the current IOC extraction methods are limited in accuracy when determining whether an entity is malicious. In this paper, the problem of IOC recognition is defined as the issue of aspect-level text polarity classification and an aspect-enhanced deep network model for IOC recognition (AspIOC) is presented. While proposing a pre-training model, the network combines IOC contextual characteristics with IOC character features. We collect about 100,000 samples and construct a dataset using an open-source web platform. The experimental results demonstrate that the accuracy and F1 of the proposed IOC discovery method are 99.92%. Our model is better than the most advanced methods currently in use and satisfies industry standards for IOC recognition.
Monocular Depth Estimation for Equirectangular Videos

Depth estimation from panoramic imagery has received minimal attention in contrast to standard perspective imagery, which constitutes the majority of the literature on the key research topic. The vast - and frequently complete - field of view provided by such panoramic photographs makes them appealing for a variety of applications, including robots, autonomous vehicles, and virtual reality. Consumer-level camera systems capable of capturing such images are likewise growing more affordable, and may be desirable complements to autonomous systems' sensor packages. They do, however, introduce significant distortions and violate some assumptions regarding perspective view images. Additionally, many state-of-the-art algorithms are not designed for its projection model, and their depth estimation performance tends to degrade when being applied to panoramic imagery. This paper presents a novel technique for adapting view synthesis-based depth estimation models to omnidirectional vision. Specifically, we: 1) integrate a “virtual” spherical camera model into the training pipeline, facilitating the model training, 2) exploit spherical convolutional layers to perform convolution operations on equirectangular images, handling the severe distortion, and 3) propose an optical flow-based masking scheme to mitigate the effect of unwanted pixels during training. Our qualitative and quantitative results demonstrate that these simple yet efficient designs result in significantly improved depth estimations when compared to previous approaches.
Weight minimization of a hollow shaft using non-traditional optimization

A shaft is used to transmit power from a device that produces power to a gadget that absorbs energy. The strength of the hollow shaft is greater than the equal weight stable shaft. Hollow shafts have a greater polar second of inertia, which permits them to transmit greater torque than stable shafts. Non-Traditional Optimization strategies are efficaciously used to resolve many engineering problems. This paper uses ten non-traditional optimization techniques to discover the weight minimization of a hollow shaft. The comparative outcomes display that the Particle Swarm Optimization outperforms different techniques.
Exploring Functionality and Efficiency of Feature Model Product Configuration Solutions

Variability-intensive systems are software systems in which variability management is a core activity. Examples of variability-intensive systems are the web content management system Drupal, the Linux kernel, and the Linux Debian distributions. Feature models have been considered valuable tools for modeling variability-intensive systems for more than 30 years, and their automated analysis is a thriving, motivating, and active research area. In 2010, Benavides et al. published the survey results of the first 20 years of Automated Analysis of Feature Model AAFM solutions. At that time, mainly sequential computing solutions exist. The product configuration of feature models represents a relevant operation demanding efficient automated solutions, which are now possible for assisting the feature model product configuration, such as minimal conflict detection, diagnosis, and product completion. The two main goals of this article are the following: First, to review the fundaments of product configuration of feature models. Second, to assess the functionality and computing performance of commonly used AAFM solutions for minimal conflict detection, minimal diagnosis, and the minimal completion of partial product configuration and the approaches. This article summarizes research opportunities for developing new and more efficient solutions for conflict detection, diagnosis, and product completion of large-scale configurations.
3D3M: 3D Modulated Morphable Model for Monocular Face Reconstruction

3D face reconstruction from a single image is a vital task in various multimedia applications. A key challenge for 3D face shape reconstruction is to build the correct dense face correspondence between the monocular input face and the deformable mesh. Most existing methods rely on shape labels fitted by traditional methods or strong priors such as multi-view geometry consistency. In contrast, we propose an innovative 3D Modulated Morphable Model (3D3M) to learn the dense shape correspondence from monocular images in a self-supervised manner. Specifically, given a batch of input faces, 3D3M encodes their 3DMM attributes (shape, texture, lighting, etc.) and then randomly shuffles the 3DMM attributes to generate the attribute-changed faces. The attribute-changed faces can be encoded and rendered back in a cycle-consistent manner, which enables us to utilize the self-supervised consistencies in dense mesh vertices and reconstructed pixels. The dense shape and pixel correspondence enable us to adopt a series of self-supervised constraints to fit the 3D face model accurately and learn the per-vertex correctives end-to-end. 3D3M builds excellent high-quality 3D face reconstruction results from monocular images. Both quantitative and qualitative experimental results have verified the superiority of 3D3M over prior arts on 3D face reconstruction and face alignment.
FedVar : Federated Learning Algorithm with Weight Variation in Clients

Among the studies to solve statistical-related problems in federated learning (FL), there are many studies to improve the non-independent and identically distributed (Non-IID) problem of user data. This problem occurs because each local device collects data from different clients, so the size and characteristics of the data are different. When the data distribution of local devices is IID, it does not negatively affect learning, but when non-IID, it does not reach the general cloud computing performance. In this paper, we propose FedVar, an algorithm that uses the weight standard deviation for each client, to improve the situation in which the distribution of data is non-IID, so FL learning is not done properly. In addition, the performance is verified by comparing the acuity and loss of the proposed algorithm with other existing FL algorithms including FedAvg.
DNN Machine Translation for Indian Languages

For developing and using various features provided by the internet by all the sections of the society, it is important to make the technology accessible irrespective of the language. So, the translation between the languages becomes important criterion. Previously, the machine translation has been used to translate between different languages, which eased the communication between people from different linguistic backgrounds. But in the last couple of years, the advancement and enhancement of Deep Neural Network (DNN) allowed us to rewrite our approach for machine translation completely. This paper discusses the process and the methodology to build the Machine translation system using Deep Neural Network (DNN) for translation between different Indian languages, using the combination of two ideas, i.e., Recurrent Neural Networks and Encoding.
Reversible data hiding scheme based on image partitioning and histogram shifting

This paper proposes a new reversible data hiding scheme based on image partitioning and histogram shifting. The existing histogram shifting reversible data hiding scheme has been presented based on finding the values of the peak point and the zero point and then moving the histogram to modify the relevant pixel values to embed information. Although this scheme has several advantages, its embedding capacity is greatly restricted, and its image quality is not very good. Aiming at this vulnerability, a new method is proposed: first dividing the image into blocks, then sorting the embedding order of the sub-images according to specific rules, and finally, embedding information according to the sorted sub-image order. The sorting rule is an ascending order based on the number of invalid pixel values that need to be modified. Experimental results show that the image embedding capacity is significantly improved with guaranteed quality. And when the amount of payload to be embedded is less than the entire payload, this scheme improves image quality by reducing unnecessary pixel value shifts.
Buzz! Deepening Human Connection to Plants Through Technology

In imagining a future where climate change forces more intimate relationships between humans and nature, social robots can be introduced to revolutionize the way humans understand and communicate with plants. Through the Double Diamond design method with plant owners, we uncovered dif-ferent perspectives of the plant caretaking process and designed a social agent that aids in plant caretaking while fostering a positive human-robot interaction. Then, based on our takeaways from the design process, we crafted a story of an individual who interacts with a future version of our robot who overcomes the language barrier between plants and humans.
SQL-Rank++: A Novel Listwise Approach for Collaborative Ranking with Implicit Feedback

Collaborative ranking with implicit feedback such as users' clicks is an important recommendation problem in various real-world applications. Most existing approaches are developed based on some pointwise or pairwise preference assumptions, although the listwise assumption is widely accepted as a better alternative due to its consistency with the final delivery result. In this paper, we first identify two fundamental limitations of the most current collaborative listwise approaches, in which their modeling is based on the Plackett-Luce probability. They are too strict and too weak relative preference comparison between the items with the same feedback and between the items with different feedback, respectively. As a response, we propose a novel and improved listwise approach called SQL-Rank++, which is able to learn the user preferences more accurately by leveraging some specifically constructed auxiliary lists, including some positive lists and some negative lists. Specifically, the positive lists have as much semantic consistency as the original list as possible, while the negative lists are the opposite. To construct these auxiliary lists, we design a self-based sampling strategy and a user similarity-based one. Finally, we have four variants of our SQL-Rank++ with different combinations of the auxiliary lists. We then conduct extensive experiments on four public datasets, and find that our SQL-Rank++ achieves very promising performance in comparison with several pointwise, pairwise and listwise approaches. We also study the influence of the two sampling strategies and the key components in our SQL-Rank++.
Super-resolution reconstruction of remote sensing images based on convolutional neural networks

Aiming at the problem that the current remote sensing image super-resolution reconstruction algorithm extracts insufficient image information and lacks high-frequency information such as edges and textures, this paper proposes a remote sensing image super-resolution reconstruction algorithm based on multi-scale extraction and attention mechanism. The algorithm uses the Inception-ResNet module to fuse information extracted at different levels to obtain richer image features; introduces an attention mechanism to obtain high-frequency information and enriches texture details; uses a residual network to supplement image information and alleviate network gradient problems. Experiments show that the peak signal-to-noise ratio and structural similarity of the proposed algorithm are better than those of SRCNN, VDSR, SRGAN, and other comparison algorithms, and the reconstruction effect has more detailed information and clearer edge texture than the comparison algorithms.
Fast Hierarchical Learning for Few-Shot Object Detection

Transfer learning based approaches have recently achieved promising results on the few-shot detection task. These approaches however suffer from “catastrophic forgetting” issue due to finetuning of base detector, leading to sub-optimal performance on the base classes. Furthermore, the slow convergence rate of stochastic gradient descent (SGD) results in high latency and consequently restricts real-time applications. We tackle the aforementioned issues in this work. We pose few-shot detection as a hierarchical learning problem, where the novel classes are treated as the child classes of existing base classes and the background class. The detection heads for the novel classes are then trained using a specialized optimization strategy, leading to significantly lower training times compared to SGD. Our approach obtains competitive novel class performance on few-shot MS-COCO benchmark, while completely retaining the performance of the initial model on the base classes. We further demonstrate the application of our approach to a new class-refined few-shot detection task.
Sign Language Techno Arm

This work proposes a novel system to aid in communicating with those having vocal and hearing disabilities. However, certain people with special needs are unable to communicate their feelings due to mutism. They learn sign language as a means of communicating because it is more convenient for them. What if a deaf person is trying to communicate with someone who doesn't understand sign languages?The majority of people on the planet are unable to comprehend sign language. The Techno-ARM enters the picture at this stage. A Techno-ARM, which is essentially a pair of gloves with sensors, is required for the individual who is unable to speak. By wearing these gloves, a person's gestures are translated to text depending on the preferences of the audience with whom they are conversing. They also use computer vision to assist in the conversion of American Sign Language (ASL) into text and speech. The message for the mute person's sign language will be shown by an application. Additionally, voice is used to read the text shown to the listener aloud. It is much easier for mute people to converse naturally in the world with the general public with these pair of Techno-Arms and the use of Computer Vision.
Research and Practice on the Whole Process Management and Sharing Technology System of Multi-source Heterogeneous Spatio-temporal Data

Natural resource management undertakes the investigation and monitoring of land, minerals, forests, grasslands, wetlands, water, oceans and other natural resources, unified right confirmation registration as well as the establishment of spatial planning system, which forms a lot of geographic information spatio-temporal data. Based on the whole life cycle management of geographic information data, relying on the pilot project of electronic document management, the construction of demonstration digital archives, and other engineering projects, this paper investigates the data acquisition technology, data format conversion technology, data packaging and transmission technology, data display and sharing and utilization technology, etc. Through a series of technical and interface specifications, it breaks the barriers between systems, realizes the full life cycle management of all kinds of spatio-temporal data generation, capturing, processing, packaging, classification, archiving, preservation, sharing and utilization, and provides the more efficient, accurate and comprehensive technical support for natural resources management.
HEMTH: Small Depth Multilevel Thresholding for a Homomorphically Encrypted Image

One of the image segmentation techniques, multilevel thresholding, is widely used in many computer vision applications because of its low computational complexity and efficient data representation. When it is used in cyber-physical systems and internet-of-things, a special technique is required to protect the sensitive information in an image. This paper proposes a novel homomorphic encryption (HE)-based multilevel thresholding method. To implement a comparison operation in the HE domain, which is not a basic homomorphic operation, a numerical method is adopted. Our proposed method executes comparison operations in parallel to perform more iterations and increase accuracy. When the number of iterations in the numerical comparison operation is (5, 3), the proposed three-level thresholding method shows an average peak signal-to-noise ratio of 28 dB compared to a conventional non-HE-based method and takes 3 minutes on a PC.
A Semi-Supervised Learning Approach for Traditional Chinese Scene Text Detection

With the advancement of multimedia technology, the information in surrounding environment has becoming accessible. In particular, automatic scene text detection is essential for subsequent text recognition, understanding and analysis. However, most existing methods are primarily designed for English, while those for other languages are scarce. In this paper we present a traditional Chinese scene text detector, built upon a robust object detector trained with labeled and unlabeled data via semi-supervised learning. Moreover, we expand the limited labeled data by data synthesis and a data augmentation method. We demonstrate the effectiveness of the proposed method through extensive experiments, and examine the design choices in developing a practical system that can instantly and accurately detect traditional Chinese texts in complex scenes.
Leveraging Relational Information for Learning Weakly Disentangled Representations

Disentanglement is a difficult property to enforce in neural representations. This might be due, in part, to a formalization of the disentanglement problem that focuses too heavily on separating relevant factors of variation of the data in single isolated dimensions of the neural representation. We argue that such a definition might be too restrictive and not necessarily beneficial in terms of downstream tasks. In this work, we present an alternative view over learning (weakly) disentangled representations, which leverages concepts from relational learning. We identify the regions of the latent space that correspond to specific instances of generative factors, and we learn the relationships among these regions in order to perform controlled changes to the latent codes. We also introduce a compound generative model that implements such a weak disentanglement approach. Our experiments shows that the learned representations can separate the relevant factors of variation in the data, while preserving the information needed for effectively generating high quality data samples.