Reviewerly/exHarmony huggingface dataset:
Exploring Cross-Modality Commonalities via Dual-Stream Multi-Branch Network for Infrared-Visible Person Re-Identification

Infrared-Visible person Re-IDentification (IV-ReID) is an emerging subject, which has important research significance for nighttime monitoring.Existing works focus on reducing cross-modality discrepancies, but the cross-modality discrepancy cannot be completely eliminated.Therefore, we concentrate on excavating cross-modality commonalities to handle the task.Since similar features between two modalities are possessed of cross-modality commonalities, our goal is to find more similar features in infrared and visible images.A novel Dual-stream Multi-layer Corresponding Fusion Network(DMCF) is proposed to explore more similar features between two modalities in this paper.It mainly contains three aspects.1) We explore more similar features between two modalities by learning low-level features, Meanwhile, we also propose a method that the same level features between two modalities are correspondingly fused to reduce cross-modality discrepancies.2) We adopt different Multi-granularity dividing methods for multi-layer features, so that it can improve the ability of the model to perceive feature details. 3) We separately calculate the loss for different-layer features.Therefore, we learn different weighting factors for the loss of different hierarchical features through Multi-task Learning, so that each branch can be fully optimized.Extensive experiments on two datasets demonstrate the superior performance compared to the state-of-the-arts.
EFFICIENT INFORMATION MAINTENANCE USING COMPUTATIONAL INTELLIGENCE IN THE MULTI-CLOUD ARCHITECTURE

The multi cloud architecture improvises the constancy of the system associated with the cloud computing as they offer benefits such as the reducing the severity of the vendor lock in, which is the major impediment to the cloud service adoption. However the information maintenance in the multi cloud architecture is a very tedious and challenging as it inherits multitudes of complexity due to various services rendered, interfaces and technologies used. As storage is the fundamental usage of the cloud computing, the failures in the information maintenance (storage, securing, retrieval) in the traditional cloud computing has led to the development of different techniques that are highly reliable and flexible. The paper also scope in developing one such adaptable and highly secure information maintenance using the Cryptography integrated Computational intelligence (CICI) for application deployment in the multi cloud architecture. The proposed method is validated using the network simulator –Two, to verify the performance augmentation of the multi-cloud architecture in its information maintenance.
The Vid3oC and IntVID Datasets for Video Super Resolution and Quality Mapping

The current rapid advancements of computational hardware has opened the door for deep networks to be applied for real-time video processing, even on consumer devices. Appealing tasks include video super-resolution, compression artifact removal, and quality enhancement. These problems require high-quality datasets that can be applied for training and benchmarking. In this work, we therefore introduce two video datasets, aimed for a variety of tasks. First, we propose the Vid3oC dataset, containing 82 simultaneous recordings of 3 camera sensors. It is recorded with a multi-camera rig, including a high-quality DSLR camera, a high-end smartphone, and a stereo camera sensor. Second, we introduce the IntVID dataset, containing over 150 high-quality videos crawled from the internet. The datasets were employed for the AIM 2019 challenges for video super-resolution and quality mapping.
HoneyDetails: A prototype for ensuring patient’s information privacy and thwarting electronic health record threats based on decoys

Advancements in electronic health record system allow patients to store and selectively share their medical records as needed with doctors. However, privacy concerns represent one of the major threats facing the electronic health record system. For instance, a cybercriminal may use a brute-force attack to authenticate into a patient’s account to steal the patient’s personal, medical or genetic details. This threat is amplified given that an individual’s genetic content is connected to their family, thus leading to security risks for their family members as well. Several cases of patient’s data theft have been reported where cybercriminals authenticated into the patient’s account, stole the patient’s medical data and assumed the identity of the patients. In some cases, the stolen data were used to access the patient’s accounts on other platforms and in other cases, to make fraudulent health insurance claims. Several measures have been suggested to address the security issues in electronic health record systems. Nevertheless, we emphasize that current measures proffer security in the short-term. This work studies the feasibility of using a decoy-based system named HoneyDetails in the security of the electronic health record system. HoneyDetails will serve fictitious medical data to the adversary during his hacking attempt to steal the patient’s data. However, the adversary will remain oblivious to the deceit due to the realistic structure of the data. Our findings indicate that the proposed system may serve as a potential measure for safeguarding against patient’s information theft.
Uncertain motion tracking based on convolutional net with semantics estimation and region proposals

In real world, most of the tracking methods suffer from uncertain motion, which may make the tracker failure because of a local search window with the motion smooth assumption. To address this problem, a novel tracking framework based on convolutional net with semantics estimation and region proposals is proposed. Firstly, we present a semantics object proposals generation strategy, including category-level semantics proposals, one-object-level semantics estimation and semantics-contextual proposals generation, to obtain a few of high-quality object-oriented proposals covering uncertain motion. Secondly, combining the globally sparse semantics region proposals prediction and correlation filter prediction, a hybrid semantics tracking algorithm is proposed, which obtains a coarse object location by the decision of multiple response maps. Finally, we learn and train independent correlation filter to estimate the scale of target for a higher tracking accuracy. Extensive experiments on two visual tracking benchmarks and results demonstrate our method achieves state-of-the-art performance.
Parallel hardware implementation of the brain storm optimization algorithm using FPGAs

Brain Storm Optimization (BSO) is a metaheuristic algorithm that has been gaining attention in solving engineering problems. The algorithm emulates the human brainstorming procedure by initializing a population and optimizing it over several generations. The algorithm enjoys intrinsic parallelism that enables the development of high-speed hardware implementations. However, investigations on accelerating the BSO are yet limited in the literature. In this paper, we present a parallel BSO processor under Field Programmable Gate Arrays (FPGAs). The development includes sequentially modeling the algorithm, deriving parallel versions, targeting a rich set of benchmark evaluation functions, and performing thorough validations. The results confirm the achievement of appealing performance characteristics that significantly outperform software implementations in terms of execution speed. The paper includes thorough analysis, evaluation, and sets the ground for future works.
Batch Mode Active Learning on the Riemannian Manifold for Automated Scoring of Nuclear Pleomorphism in Breast Cancer

Breast cancer is the most prevalent invasive type of cancer among women. The mortality rate of the disease can be reduced considerably through timely prognosis and felicitous treatment planning, by utilizing the computer aided detection and diagnosis techniques. With the advent of whole slide image (WSI) scanners for digitizing the histopathological tissue samples, there is a drastic increase in the availability of digital histopathological images. However, these samples are often unlabeled and hence they need labeling to be done through manual annotations by domain experts and experienced pathologists. But this annotation process required for acquiring high quality large labeled training set for nuclear atypia scoring is a tedious, expensive and time consuming job. Active learning techniques have achieved widespread acceptance in reducing this human effort in annotating the data samples. In this paper, we explore the possibilities of active learning on nuclear pleomorphism scoring over a non-Euclidean framework, the Riemannian manifold. Active learning technique adopted for the cancer grading is in the batch-mode framework, that adaptively identifies the apt batch size along with the batch of instances to be queried, following a submodular optimization framework. Samples for annotation are selected considering the diversity and redundancy between the pair of samples, based on the kernelized Riemannian distance measures such as log-Euclidean metrics and the two Bregman divergences - Stein and Jeffrey divergences. Results of the adaptive Batch Mode Active Learning on the Riemannian metric show a superior performance when compared with the state-of-the-art techniques for breast cancer nuclear pleomorphism scoring, as it makes use of the information from the unlabeled samples.
Decision Boundary of Deep Neural Networks

One crucial aspect that yet remains fairly unknown while can inform us about the behavior of deep neural networks is their decision boundaries. Trust can be improved once we understand how and why deep models carve out a particular form of decision boundary and thus make particular decisions. Robustness against adversarial examples is directly related to the decision boundary as adversarial examples are basically 'missed out' by the decision boundary between two classes. Investigating the decision boundary of deep neural networks, nevertheless, faces tremendous challenges. First, how we can generate instances near the decision boundary that are similar to real samples? Second, how we can leverage near decision boundary instances to characterize the behaviour of deep neural networks? Motivated to solve these challenges, we focus on investigating the decision boundary of deep neural network classifiers. In particular, we propose a novel approach to generate instances near decision boundary of pre-trained DNNs and then leverage these instances to characterize the behaviour of deep models.
Adversarial Dual Network Learning With Randomized Image Transform for Restoring Attacked Images

We develop a new method for defending deep neural networks against attacks using adversarial dual network learning with randomized nonlinear image transform.We introduce a randomized nonlinear transform to disturb and partially destroy the sophisticated pattern of attack noise.We then design a generative cleaning network to recover the original image content damaged by this nonlinear transform and remove residual attack noise.We also construct a detector network which serves as the dual network for the target classifier to be defended, being able to detect patterns of attack noise.The generative cleaning network and detector network are jointly trained using adversarial learning, fighting against each other to minimize both perceptual loss and adversarial loss.Our extensive experimental results demonstrate that our approach improves the state-of-art by large margins in both white-box and black-box attacks.It significantly improves the classification accuracy for white-box attacks upon the second best method by more than 30% on the SVHN dataset and more than 14% on the challenging CIFAR-10 dataset.
SHE: Sentiment Hashtag Embedding Through Multitask Learning

Recent studies have shown the importance of utilizing hashtags for sentiment analysis task on social media data. However, as the hashtag generation process is less restrictive, it throws several challenges, such as hashtag normalization, topic modeling, and semantic similarity. Recently, researchers have tried to address the above-mentioned challenges through representation learning. However, most of the studies on hashtag embedding try to capture the semantic distribution of hashtags and often fail to capture the sentiment polarity. Furthermore, generating a task-specific hashtag embedding can distort its semantic representation, which is undesirable for sentiment representation of hashtag. Therefore, this article proposes a semisupervised sentiment hashtag embedding (SHE) model, which is capable of preserving both semantic as well as sentiment distribution of the hashtags. In particular, SHE leverages a multitask learning approach using an autoencoder and a convolutional neural network-based classifier. To assess the efficacy of hashtag embedding, we compare the performance of SHE against suitable baselines for three different tasks, namely, hashtag sentiment classification, tweet sentiment classification, and retrieval of semantically similar hashtags. It is evident from various experimental results that SHE outperforms the majority of the baselines with significant margins.
Multi-Modal Sentiment Classification With Independent and Interactive Knowledge via Semi-Supervised Learning

Multi-modal sentiment analysis extends conventional text-based definition of sentiment analysis to a multi-modal setup where multiple relevant modalities are leveraged to perform sentiment analysis.In real applications, however, acquiring annotated multi-modal data is normally labor expensive and time-consuming.In this paper, we aim to reduce the annotation effort for multi-modal sentiment classification via semi-supervised learning.The key idea is to leverage the semi-supervised variational autoencoders to mine more information from unlabeled data for multi-modal sentiment analysis.Specifically, the mined information includes both the independent knowledge within single modality and the interactive knowledge among different modalities.Empirical evaluation demonstrates the great effectiveness of the proposed semi-supervised approach to multi-modal sentiment classification.
Service selection and workflow composition in modern business processes

Enterprises heavily rely on the state-of-the-art information technologies user behaviors and social system to sustain business competitiveness. Service-Oriented Architecture (SoA) and Cloud allow enterprises to utilize dynamic distributed services across enterprise boundaries and even individuals. Based on the needs of society, enterprise can spin off a virtual enterprise for new businesses where the demanded manufacturing resources are obtained through service selection and workflow composition over the Internet. An increasing number of Internet of Things (IoT) services make enterprises facing the challenge to select preferable services for their business workflows. We improve workflow compositions methods at three major aspects: (1) we present an integrated approach for service selection and composition to meet the needs of users and social system. (2) We extend Service Workflow Specification (SWSpec) as a formal requirement specification to determine a design space of services. (3) We introduce the Fuzzy Analytic Hierarchy Process (F-AHP) to evaluate non-functional specifications quantitatively with less uncertainty. Appropriate rankings can be assigned to available services based on non-functional requirements to determine the best-suited services with less uncertainty.
A New Formulation for Hand–Eye Calibrations as Point-Set Matching

Conventional methods formulate the calibration problem between a robot hand and a camera, also known as hand-eye-calibration problem, as AX = XB. However, in practice, these methods have limited accuracy, as will be shown in our experimental results. In this article, we formulated the hand-eye calibration problem as a point-set-matching problem and proposed a new approach to solve this problem. The proposed approach is particularly suitable for robotic applications and offers good accuracy. We obtain a solution for the said problem using the gradient-descent (GD) technique on the special Euclidean group SE(3). We call this approach GD-SE(3). To prove the validity of the proposed approach and to demonstrate its advantages, experimental results are provided, where we compare the performance of GD-SE(3) with both conventional solutions for the hand-eye calibration problem as well as those based on point-set matching. The results show that the accuracy of GD-SE(3) is comparable with those based on other point-set-matching algorithms. Yet, it outperforms conventional formulations based on AX = XB while offering a more suitable approach for the hand-eye calibration problem of robot manipulators.
Real-time glove and android application for visual and audible Arabic sign language translation

Researchers can develop new systems to capture, analyze, recognize, memorize and interpret hand gestures with machine learning and sensors. Acoustic communication is a way to convey human opinions, feelings, messages, and information. Deaf and mute individuals communicate using sign language that is not understandable by everyone. Unfortunately, they face extreme difficulty in conveying their messages to others. To facilitate the communication between deaf/mute individuals and normal people, we propose a real-time prototype using a customized glove equipped with five flex and one-accelerometer sensors. These sensors are able to detect the bindings of the fingers and the movements of the hand. In addition, we developed an android mobile application to recognize the captured Arabic Sign Language (ArSL) gestures and translate them into displayed texts and audible sounds. The developed prototype is accurate, low cost and fast in response.
Financial Big Data Analysis and Early Warning Platform: A Case Study

In order to keep the bottom line of systemic financial risks and prevent the mitigation of major risks, this work focuses on the investigation of multi-source heterogeneous data fusion algorithms and cleaning technologies to establish a suitable style for data analysis and big data computation frame.In this paper, according to the above method, we provide the basis for early analysis of economic security.Utilizing the big data analysis, an emerging information technology method, we can be able to explore new risk early-warning methods, build a risk monitoring and early-warning platform and achieve scientific economic decision-making, so that the sources of economic risk in national economic security can be traced.
A Prototype of Android-Based Emergency Management Application

There are so many disasters which are by nature, non-nature, and even social disaster make us increase our cautious. From now on, an emergency can be reported by calling 112 command centers. Emergency reporting being developed by many researchers like in Surabaya city, Depok, that implementing a panic button to acquire less time. At this research, we develop an emergency management prototype app which pinpoints victim location using network-based or GPS, and also measure the distance from helper and victim with the help of Google Distance Matrix API. The emergency report can be done with 3 ways: with a button inside the app, power button, and voice command. The result from performance testing shows that application runs with the average time needed to report an emergency until it gets to helper 6.96 seconds if using a button in-app, 21.08 seconds if using a power button, and 20.33 seconds if using voice command. We suggest for future research that application should be developed for another platform, integration to the authorities, and increase victim's location accuracy.
Human Annotated Dialogues Dataset for Natural Conversational Agents

Conversational agents are gaining huge popularity in industrial applications such as digital assistants, chatbots, and particularly systems for natural language understanding (NLU). However, a major drawback is the unavailability of a common metric to evaluate the replies against human judgement for conversational agents. In this paper, we develop a benchmark dataset with human annotations and diverse replies that can be used to develop such metric for conversational agents. The paper introduces a high-quality human annotated movie dialogue dataset, HUMOD, that is developed from the Cornell movie dialogues dataset. This new dataset comprises 28,500 human responses from 9500 multi-turn dialogue history-reply pairs. Human responses include: (i) ratings of the dialogue reply in relevance to the dialogue history; and (ii) unique dialogue replies for each dialogue history from the users. Such unique dialogue replies enable researchers in evaluating their models against six unique human responses for each given history. Detailed analysis on how dialogues are structured and human perception on dialogue score in comparison with existing models are also presented.
A novel graph-based clustering method using noise cutting

Recently, many methods have appeared in the field of cluster analysis. Most existing clustering algorithms have considerable limitations in dealing with local and nonlinear data patterns. Algorithms based on graphs provide good results for this problem. However, some widely used graph-based clustering methods, such as spectral clustering algorithms, are sensitive to noise and outliers. In this paper, a cut-point clustering algorithm (CutPC) based on a natural neighbor graph is proposed. The CutPC method performs noise cutting when a cut-point value is above the critical value. Normally, the method can automatically identify clusters with arbitrary shapes and detect outliers without any prior knowledge or preparatory parameter settings. The user can also adjust a coefficient to adapt clustering solutions for particular problems better. Experimental results on various synthetic and real-world datasets demonstrate the obvious superiority of CutPC compared with k-means, DBSCAN, DPC, SC, and DCore.
Personality, emotion and individual differences in response to online fraud

The growth in social communication facilitated by technology mean that online scams represent a growing societal issue, with perpetrators successfully persuading people to make fraudulent payments or download malicious attachments. Incidents of online scams have continued to increase across the world, aided by technology that allows fraudsters to mimic communications so as to appear to come from legitimate sources. Much of the previous literature has focused on dispositional factors, such as personality, alongside experiential factors, such as knowledge of security policy; there is limited examination of the context or state induced factors, such as emotional state of the individual, and, importantly, how that may impact upon their decision making process. This paper reviews and applies the literature exploring the role of emotions in decision making to understand how individuals may be susceptible to scams in online contexts. The mood maintenance/mood-repair view is proposed as an appropriate theoretical foundation for research in this area; it provides the focus on the interaction between the individuals current emotional state and the emotional appeals embedded in any message, and what the likely response will be.