Reviewerly/exHarmony huggingface dataset:
Deep hard modality alignment for visible thermal person re-identification

Visible Thermal Person Re-Identification (VTReID) is essentially a cross-modality problem and widely encountered in real night-time surveillance scenarios, which is still in need of vigorous performance improvement. In this work, we design a simple but effective Hard Modality Alignment Network (HMAN) framework to learn modality-robust features. Since current VTReID works do not consider the cross-modality discrepancy imbalance, their models are likely to suffer from the selective alignment behavior. To solve this problem, we propose a novel Hard Modality Alignment (HMA) loss to simultaneously balance and reduce the modality discrepancies. Specifically, we mine the hard feature subspace with large modality discrepancies and abandon the easy feature subspace with small modality discrepancies to make the modality distributions more distinguishable. For mitigating the discrepancy imbalance, we pay more attention on reducing the modality discrepancies of the hard feature subspace than that of the easy feature subspace. Furthermore, we propose to jointly relieve the modality heterogeneity of global and local visual semantics to further boost the cross-modality retrieval performance. This paper experimentally demonstrates the effectiveness of the proposed method, achieving superior performance over the state-of-the-art methods on RegDB and SYSU-MM01 datasets.
Multi Receptive Field Network for Semantic Segmentation

Semantic segmentation is one of the key tasks in computer vision, which is to assign a category label to each pixel in an image. Despite significant progress achieved recently, most existing methods still suffer from two challenging issues: l)the size of objects and stuff in an image can be very diverse, demanding for incorporating multi-scale features into the fully convolutional networks (FCNs); 2) the pixels close to or at the boundaries of object/stuff are hard to classify due to the intrinsic weakness of convolutional networks. To address the first issue, we propose a new Multi-Receptive Field Module (MRFM), explicitly taking multi-scale features into account. For the second issue, we design an edge-aware loss which is effective in distinguishing the boundaries of object/stuff. With these two designs, our Multi Receptive Field Network achieves new state-of-the-art results on two widely-used semantic segmentation benchmark datasets. Specifically, we achieve a mean IoU of 83.0% on the Cityscapes dataset and 88.4% mean IoU on the Pascal VOC2012 dataset.
Breast Cancer Histopathology Image Classification and Localization using Multiple Instance Learning

Breast cancer has the highest mortality among cancers in women. Computer-aided pathology to analyze microscopic histopathology images for diagnosis with an increasing number of breast cancer patients can bring the cost and delays of diagnosis down. Deep learning in histopathology has attracted attention over the last decade of achieving state-of-the-art performance in classification and localization tasks. The convolutional neural network, a deep learning framework, provides remarkable results in tissue images analysis, but lacks in providing interpretation and reasoning behind the decisions. We aim to provide a better interpretation of classification results by providing localization on microscopic histopathology images. We frame the image classification problem as weakly supervised multiple instance learning problem where an image is collection of patches i.e., instances. Attention-based multiple instance learning (A-MIL) learns attention on the patches from the image to localize the malignant and normal regions in an image and use them to classify the image. We present classification and localization results on two publicly available BreakHIS and BACH dataset. The classification and visualization results are compared with other recent techniques. The proposed method achieves better localization results without compromising classification accuracy.
Targeted Poisoning Attacks on Social Recommender Systems

With the popularity of online social networks, social recommendations that rely on ones social connections to make personalized recommendations have become possible. This introduces vulnerabilities for an adversarial party to compromise the recommendations for users by utilizing their social connections. In this paper, we propose the targeted poisoning attack on the factorization-based social recommender system in which the attacker aims to promote an item to a group of target users by injecting fake ratings and social connections. We formulate the optimal poisoning attack as a bi-level program and develop an efficient algorithm to find the optimal attacking strategy. We then evaluate the proposed attacking strategy on real-world dataset and demonstrate that the social recommender system is sensitive to the targeted poisoning attack. We find that users in the social recommender system can be attacked even if they do not have direct social connections with the attacker.
South‐South and triangular cooperation in ICT4D

Abstract This paper addresses cooperation between countries in the Global South based on information and communications technologies (ICTs) and also triangular cooperation including partners in the developed countries. It is argued that now is an important time for research and action in this space and in information and communications technologies for development (ICT4D) more generally. Some opportunities are identified, selecting mainly from papers in the IFIP WG9.4 conference in Dar‐es‐Salaam, Tanzania, in May 2019. The paper has a strong critical dimension, asking questions such as who is driving ICT4D projects, who benefits from the work, and are the less advantaged adequately included in the process. The paper ends with five broad principles on which further ICT4D research could build.
The CADE-27 Automated theorem proving System Competition – CASC-27

The CADE ATP System Competition (CASC) is the annual evaluation of fully automatic, classical logic Automated Theorem Proving (ATP) systems. CASC-27 was the twenty-fourth competition in the CASC series. Twenty-five ATP systems and system variants competed in the various competition divisions. This paper presents an outline of the competition design, and a commentated summary of the results.
TOPSIS inspired cost-efficient concurrent workflow scheduling algorithm in cloud

Scheduling is a decision-making mechanism that enables the sharing of resources among several activities by determining their execution order on the set of available resources. In distributed systems, it is a great challenge to schedule multiple workflows submitted at different times. In particular, concurrent workflow scheduling with time constraints makes the problem more complex in the cloud due to the dynamics of the cloud such as elasticity, non-homogeneous resource types, various pricing schemes, and virtualization. A well-managed deadline workflow scheduling is required to improve end-user satisfaction and system performance. In the meantime, the intrinsic uncertainty in the cloud increases the difficulties of scheduling problems. Therefore, it is a great challenge to improve system performance and optimize several scheduling criteria simultaneously. To address the above issues, a novel concurrent workflow scheduling method for heterogeneous distributed environments based on the new Multi-Criteria Decision Making (MCDM) method i.e., TOPSIS (Technique of Order Preference by Similarity to Ideal Solution) is presented. A weighted sum of execution time, cost and communication time are used to find out the optimal resource among the existing resources as per the workflow task requirements. The proposed method minimizes the makespan and execution cost of the workflow and improves the resource efficiency under uncertain environment. The performance of the proposed work is compared with the state-of-the-art algorithms such as Cloud-based Workflow Scheduling Algorithm (CWSA), Earliest Finish Time-Maximum Effective Reduction (EFT-MER) and Heterogeneous Earliest-Finish-Time (HEFT) algorithms based on deadline constraint and resource utilization. Our experimental results demonstrate that the proposed T-CCWSA outperforms current state-of-the-art heuristics with the criteria of achieving the deadline constraint, minimizing the cost of execution and resource efficiency.
Deep Feature Extraction for Panoramic Image Stitching

Image stitching is an important task in image processing and computer vision. Image stitching is the process of combining multiple photographic images with overlapping fields of view to produce a segmented panorama, resolution image. It is widely used in object reconstruction, panoramic creating. In this paper, we present an approach based on deep learning for image stitching, which are applied to generate high resolution panoramic image supporting for virtual tour interaction. Different from most existing image matching methods, the proposed method extracts image features using deep learning approach. Our approach directly estimates locations of features between pairwise constraint of images by maximizing an image- patch similarity metric between images. A large dataset high resolution images and videos from natural tourism scenes were collected for training and evaluation. Experimental results illustrated that the deep feature approach outperforms.
PororoGAN

Generating a sequence of images from a multi-sentence paragraph is a recently proposed task called Story-Visualization. In this task, how to keep the global consistency across dynamic scenes and characters in the story flow is the distinct difference from other single-image works, which is also a significant challenge. However, the visual quality and semantic relevance of existing results are not satisfying when handling datasets with high semantic complexity, such as Pororo-SV cartoon dataset. To address this issue, we propose a new story visualization model named PororoGAN, which jointly considers story-to-image-sequence, sentence-to-image and word-to-image-patch alignment. In particular, we introduce ASE (aligned sentence encoder) and AWE (attentional word encoder) to improve global and local relevance, respectively. Additionally, we add an image patches discriminator to improve the reality of results. Both quantitative and qualitative studies show that PororoGAN outperforms the state-of-the-art models.
Pooling Approach for Task Allocation in the Blockchain Based Decentralized Storage Network

Blockchain technology has provided a solid system to develop incentivization algorithms using the smart contract. Blockchain applies the distributed ledger to store transaction histories, and the information is stored across a network of computers instead of on a single server. This facilitates the development of a new set of applications such as distributed file storage systems where users can rent out their storage in return for a premium. The distributed file storage systems provide more privacy and security compared to the centralized storage models as there is no need to have a trusted party. New schemes have been developed for distributed file storage systems on top of the blockchain platform, however, the problem of task/service allocation in these models have not been studied before. In this paper, we study the task/service allocation in the distributed file storage systems considering the challenge of computation cost. First, we formalize the problem of task/service allocation in a decentralized storage network, and then we discuss different approaches to allocate storage tasks to storage servers in an efficient manner. Moreover, we study the benefits of the cooperation (a.k.a pooling) in the storage and retrieval markets of distributed storage networks. The evaluation results show the benefit of our proposed pooling based approach in storage and retrieval markets.
Hardness-Aware Deep Metric Learning

This paper presents a hardness-aware deep metric learning (HDML) framework for image clustering and retrieval. Most existing deep metric learning methods employ the hard negative mining strategy to alleviate the lack of informative samples for training. However, this mining strategy only utilizes a subset of training data, which may not be enough to characterize the global geometry of the embedding space comprehensively. To address this problem, we perform linear interpolation on embeddings to adaptively manipulate their hardness levels and generate corresponding label-preserving synthetics for recycled training so that information buried in all samples can be fully exploited and the metric is always challenged with proper difficulty. As a single synthetic for each sample may still not be enough to describe the unobserved distributions of the training data which is crucial for the generalization performance, we further extend HDML to generate multiple synthetics for each sample. We propose a randomly hardness-aware deep metric learning (HDML-R) method and an adaptively hardness-aware deep metric learning (HDML-A) method to sample multiple random and adaptive directions, respectively, for hardness-aware synthesis. Since the generated multiple synthetics might not all be useful and adaptive, we propose a synthetic selection method with three criteria for the selection of qualified synthetics that are beneficial to the training of the metric. Extensive experimental results on the widely used CUB-200-2011, Cars196, Stanford Online Products, In-Shop Clothes Retrieval, and VehicleID datasets demonstrate the effectiveness of the proposed framework.
Detection of Cyber Grooming in Online Conversation

In this paper we will focus on the detection of sexual predators in online chat conversations. We use 3 different approaches (message-based, author-based and conversation-based) combined with 5 different classification algorithms and 2 different features sets. The best results were obtained using either the author-based approach with the Neural Network classifier on the TF-IDF feature set, or the conversation-based approach using the Ridge or the Naïve Bayes classifier on the TF-IDF feature set. In this paper, for the first time, we looked at how quick a predator could be detected, and found that in most cases 26-161 messages of a conversation were sufficient. This constitutes only a small fraction of the full conversations, showing that we can have an early detection system of sexual predators instead of knowing in retrospect that a child was the victim of a sexual predator.
Vision and Sensor-Based Human Activity Recognition

Human action or activity or behavior analysis, recognition, and understanding are very important research areas in the field of computer vison, internet of things (IoT) sensor-based analysis, human-computer interaction (HCI), affective computing, intelligent system, healthcare facilities, and so on. There is much importance in human action recognition. This chapter introduces the core aspects of human action or activity recognition (HAR). These are split into two different domains: computer vision-based action/activity recognition, action localization, etc.; and wearable IoT sensor-based HAR. Though cameras are also sensors that provide vision-based information, the author puts camera-based or vision-based methods in another category. The chapter introduces core challenges and strategies to move forward.
The Role of Attributes in Product Quality Comparisons

In online shopping quality is a key consideration when purchasing an item. Since customers cannot physically touch or try out an item before buying it, they must assess its quality from information gathered online. In a typical eCommerce setting, the customer is presented with seller-generated content from the product catalog, such as an image of the product, a textual description, and lists or comparisons of attributes. In addition to catalog attributes, customers often have access to customer-generated content such as reviews and product questions and answers. In a crowdsourced study, we asked crowd workers to compare product pairs from kitchen, electronics, home, beauty and office categories. In a side-by-side comparison, we asked them to choose the product that is higher quality, and further to identify the attributes that contributed to their judgment, where the attributes were both seller-generated and customer-generated. We find that customers tend to perceive more expensive items as higher quality but that their purchase decisions are uncorrelated with quality, suggesting that customers seek a trade-off between price and quality when making purchase decisions. Crowd workers placed a higher value on attributes derived from customer-generated content such as reviews than on catalog attributes. Among the catalog attributes, brand, item material and pack size were most often selected. Finally, attributes with a low correlation with perceived quality are nonetheless useful in predicting purchases in a machine-learned system.
RotInvMTL: Rotation Invariant MultiNet on Fisheye Images for Autonomous Driving Applications

Precise understanding of the scene around the car is of the utmost importance to achieve autonomous driving. Convolutional neural networks (CNNs) have been widely used for road scene understanding in the last few years with great success. Surround view (SV) systems with fisheye cameras have been in production in various cars and trucks for close to a decade. However, there are very few CNNs that are employed directly on SV systems due to the fisheye nature of its cameras. Typically, correction of fisheye distortion is applied to the data before it is processed by the CNNs, thereby increasing the system complexity and also reducing the field of view (FOV). In this paper, we propose RotInvMTL: a multi-task network (MTL) to perform joint semantic segmentation, boundary prediction, and object detection directly on raw fisheye images. We propose a rotation invariant object detection decoder that adapts to fisheye distortion and show that it outperforms YOLOv2 by 9% mAP. By combining the MTL outputs, an accurate foot-point information and a rough instance level segmentation may be obtained, both of which are critical for automotive applications. In conclusion, RotInvMTL is an efficient network that performs well for autonomous driving applications.
Token Standard for Heterogeneous Assets Digitization into Commodity

The paper describes the transfer of exchange investment mechanisms to the cryptocurrency and tokenized assets market. We propose an architecture of a new token standard, which allows dividing different assets into a commodity. The implementation of the prototype on the Ethereum platform is provided. An example of the token application on the banking system is presented.
SAR image despeckling using plug‐and‐play ADMM

The presence of speckle, a granular structure, seriously affects the visual interpretability of the synthetic aperture radar (SAR) images. Recently, model-based reconstruction techniques that use alternating direction method of multipliers (ADMM) have been widely utilised for denoising problems. Owing to the modular structure of ADMM, it is very simple to implement and also enables one to plug in any off-the-shelf denoising algorithm. However, the major limitations of these methods are mostly they have been established for Gaussian noise and the selection of a regulariser for a specific forward model is unknown. In this study, the despeckling of SAR images is addressed in the presence of multiplicative noise such as Rayleigh and Poisson. A mean filter is introduced for images corrupted by Rayleigh-based speckle, which transforms it to Nakagami distributed speckle, to improve the performance. Maximum a posteriori-based estimation involving Nakagami and Poisson distributions are applied to plug-and-play ADMM (PnP ADMM) framework, which provides enhanced despeckling ability along with the preservation of important details. The convergence analysis of the proposed PnP ADMM for multiplicative noise is also provided. The simulation studies are carried out taking several parameters for visual quality and statistical assessment to justify the effectiveness of the proposed method.
Computing Time-Varying ML-Weighted Pseudoinverse by the Zhang Neural Networks

The Zhang neural network (ZNN), a recurrent neural network, proposed in 2001, is particularly effective in solving time-varying problems. It has shown high efficiency and excellent performance in various applications. The weighted pseudoinverse is a useful tool in solving and analyzing the constrained least-squares problems. In this paper, we propose a ZNN model for computing the weighted pseudoinverse of a time-varying matrix. We show that our model converges globally and exponentially to the solution and our system is robust at the presence of small errors. A Matlab Simulink implementation of our model is presented. Our convergence analysis is verified by our experiments on testing matrices. A comparison study shows that our model has superior performance over the conventional gradient-based neural networks.
Attribute susceptibility and entropy based data anonymization to improve users community privacy and utility in publishing data

Abstract User attributes affect community (i.e., a group of people with some common properties/attributes) privacy in users’ data publishing because some attributes may expose multiple users’ identities and their associated sensitive information during published data analysis. User attributes such as gender, age, and race, may allow an adversary to form users’ communities based on their values, and launch sensitive information inference attack subsequently. As a result, explicit disclosure of private information of a specific users’ community can occur from the privacy preserved published data. Each item of user attributes impacts users’ community privacy differently, and some types of attributes are highly susceptible. More susceptible types of attributes enable multiple users’ unique identifications and sensitive information inferences more easily, and their presence in published data increases users’ community privacy risks. Most of the existing privacy models ignore the impact of susceptible attributes on user’s community privacy and they mainly focus on preserving the individual privacy in the released data. This paper presents a novel data anonymization algorithm that significantly improves users’ community privacy without sacrificing the guarantees on anonymous data utility in publishing data. The proposed algorithm quantifies the susceptibility of each attribute present in user’s dataset to effectively preserve users’ community privacy. Data generalization is performed adaptively by considering both user attributes’ susceptibility and entropy simultaneously. The proposed algorithm controls over-generalization of the data to enhance anonymous data utility for the legitimate information consumers. Due to the widespread applications of social networks (SNs), we focused on the SN users’ community privacy preserved and utility enhanced anonymous data publishing. The simulation results obtained from extensive experiments, and comparisons with the existing algorithms show the effectiveness of the proposed algorithm and verify the aforementioned claims.