Reviewerly/exHarmony huggingface dataset:
Polysemy Deciphering Network for Robust Human–Object Interaction Detection

Human–Object Interaction (HOI) detection is important to human-centric scene understanding tasks. Existing works tend to assume that the same verb has similar visual characteristics in different HOI categories, an approach that ignores the diverse semantic meanings of the verb. To address this issue, in this paper, we propose a novel Polysemy Deciphering Network (PD-Net) that decodes the visual polysemy of verbs for HOI detection in three distinct ways. First, we refine features for HOI detection to be polysemy-aware through the use of two novel modules: namely, Language Prior-guided Channel Attention (LPCA) and Language Prior-based Feature Augmentation (LPFA). LPCA highlights important elements in human and object appearance features for each HOI category to be identified; moreover, LPFA augments human pose and spatial features for HOI detection using language priors, enabling the verb classifiers to receive language hints that reduce intra-class variation for the same verb. Second, we introduce a novel Polysemy-Aware Modal Fusion module, which guides PD-Net to make decisions based on feature types deemed more important according to the language priors. Third, we propose to relieve the verb polysemy problem through sharing verb classifiers for semantically similar HOI categories. Furthermore, to expedite research on the verb polysemy problem, we build a new benchmark dataset named HOI-VerbPolysemy (HOI-VP), which includes common verbs (predicates) that have diverse semantic meanings in the real world. Finally, through deciphering the visual polysemy of verbs, our approach is demonstrated to outperform state-of-the-art methods by significant margins on the HICO-DET, V-COCO, and HOI-VP databases. Code and data in this paper are available at https://github.com/MuchHair/PD-Net .
Security and Privacy Issues in Deep Learning: A Brief Review

Nowadays, deep learning is becoming increasingly important in our daily life. The appearance of deep learning in many applications in life relates to prediction and classification such as self-driving, product recommendation, advertisements and healthcare. Therefore, if a deep learning model causes false predictions and misclassification, it can do great harm. This is basically a crucial issue in the deep learning model. In addition, deep learning models use large amounts of data in the training/learning phases, which contain sensitive information. Therefore, when deep learning models are used in real-world applications, it is required to protect the privacy information used in the model. In this article, we carry out a brief review of the threats and defenses methods on security issues for the deep learning models and the privacy of the data used in such models while maintaining their performance and accuracy. Finally, we discuss current challenges and future developments.
PCANet: Pyramid convolutional attention network for semantic segmentation

Pyramid Convolutional Attention Network is proposed to efficiently capture long-range dependency and fuse features from different levels for benefitting semantic segmentation problems. In this paper, we focus on how to extract more representative features for segmentation object recognition and design a decoder to recover details in a more efficient way. Inspired by atrous sampling and attention mechanism, we propose Pyramid Atrous Attention module to capture long-range dependency for learning richer contextual features. We also find that features of different levels have diverse representation so we design Convolutional Attention Refinement module to provide global context for low-level features and local details for high-level features. By combining with these two efficient module, we construct our Pyramid Convolutional Attention Network (PCANet), which achieves state-of-the-art results on Pascal VOC 2012 and Cityscapes benchmark.
Secure Communication: Using Parallel Synchronization Technique On Novel Fractional Order Chaotic System

In this paper a novel 4-D fractional order chaotic system has been introduced. It has been synchronized in dislocated phase and anti-phase synchronization with its parallel systems by constructing non linear control inputs. The synchronization technique has been illustrated with help of an example in the field of secure communication.
PEFS: AI-Driven Prediction Based Energy-Aware Fault-Tolerant Scheduling Scheme for Cloud Data Center

Cloud data centers (CDCs) have become increasingly popular and widespread in recent years with the growing popularity of cloud computing and high-performance computing. Due to the multi-step computation of data streams and heterogeneous task dependencies, task failure frequently occurs, resulting in poor user experience and additional energy consumption. To reduce task execution failure as well as energy consumption, we propose a novel AI-driven energy-aware proactive fault-tolerant scheduling scheme for CDCs in this paper. First, a prediction model based on the machine learning approach is trained to classify the arriving tasks into “failure-prone tasks” and “non-failure-prone tasks” according to the predicted failure rate. Then, two efficient scheduling mechanisms are proposed to allocate two types of tasks to the most appropriate hosts in a CDC. The vector reconstruction method is developed to construct super tasks from failure-prone tasks and separately schedule these super tasks and non-failure-prone tasks to the most suitable physical host. All the tasks are scheduled in an earliest-deadline-first manner. Our evaluation results show that the proposed scheme can intelligently predict task failure and achieves better fault tolerance and reduces total energy consumption better than the existing schemes.
A fuzzy cognitive map approach to predict the hazardous effects of malathion to environment (air, water and soil)

Malathion is an organophosphorus insecticide and pesticide commonly used in crops and residential applications. The negative effects of Malathion on human health and ecosystems are of great concern. In this work, a mathematical model pivot on Fuzzy Cognitive Map (FCM) is used to analyse the causes and hazardous effects of Malathion to the environmental components (air, water and soil). Based on expert’s opinion the possible factors that cause damage to health and ecosystems due to Malathion is identified, which serve as the input to the FCM. The FCM mathematically establishes the causal relation between these factors. The mathematical simulation is done by Python Programming. This approach can be used to study the interdependencies between the adverse effects of any pesticide in human health and environment due to prolonged exposure.
A Smartphone Enabled Approach to Manage COVID-19 Lockdown and Economic Crisis

The emergence of novel COVID-19 causes an over-load in health system and high mortality rate. The key priority is to contain the epidemic and prevent the infection rate. In this context, many countries are now in some degree of lockdown to ensure extreme social distancing of entire population and hence slowing down the epidemic spread. Furthermore, authorities use case quarantine strategy and manual second/third contact-tracing to contain the COVID-19 disease. However, manual contact-tracing is time-consuming and labor-intensive task which tremendously over-load public health systems. In this paper, we developed a smartphone-based approach to automatically and widely trace the contacts for confirmed COVID-19 cases. Particularly, contact-tracing approach creates a list of individuals in the vicinity and notifying contacts or officials of confirmed COVID-19 cases. This approach is not only providing awareness to individuals they are in the proximity to the infected area, but also tracks the incidental contacts that the COVID-19 carrier might not recall. Thereafter, we developed a dashboard to provide a plan for policymakers on how lockdown/mass quarantine can be safely lifted, and hence tackling the economic crisis. The dashboard used to predict the level of lockdown area based on collected positions and distance measurements of the registered users in the vicinity. The prediction model uses k-means algorithm as an unsupervised machine learning technique for lockdown management.
The Impact of Executive Verbal Communication on the Convergence of Investors’ Opinions

This paper studies the influence of executive verbal communication on the convergence of investors’ opinions, defined as reduction of differences in investors’ valuations. Building on the corporate...
ISSAFE: Improving Semantic Segmentation in Accidents by Fusing Event-based Data

Ensuring the safety of all traffic participants is a prerequisite for bringing intelligent vehicles closer to practical applications. The assistance system should not only achieve high accuracy under normal conditions, but obtain robust perception against extreme situations. However, traffic accidents that involve object collisions, deformations, overturns, etc., yet unseen in most training sets, will largely harm the performance of existing semantic segmentation models. To tackle this issue, we present a rarely addressed task regarding semantic segmentation in accidental scenarios, along with an accident dataset DADA-seg. It contains 313 various accident sequences with 40 frames each, of which the time windows are located before and during a traffic accident. Every 11th frame is manually annotated for benchmarking the segmentation performance. Furthermore, we propose a novel event-based multi-modal segmentation architecture ISSAFE. Our experiments indicate that event-based data can provide complementary information to stabilize semantic segmentation under adverse conditions by preserving fine-grain motion of fast-moving foreground (crash objects) in accidents. Our approach achieves +8.2% mIoU performance gain on the proposed evaluation set, exceeding more than 10 state-of-the-art segmentation methods. The proposed ISSAFE architecture is demonstrated to be consistently effective for models learned on multiple source databases including Cityscapes, KITTI-360, BDD and ApolloScape.
Detecting Group Shilling Attacks in Online Recommender Systems Based on Bisecting K-Means Clustering

Existing shilling attack detection approaches focus mainly on identifying individual attackers in online recommender systems and rarely address the detection of group shilling attacks in which a group of attackers colludes to bias the output of an online recommender system by injecting fake profiles. In this article, we propose a group shilling attack detection method based on the bisecting K-means clustering algorithm. First, we extract the rating track of each item and divide the rating tracks to generate candidate groups according to a fixed time interval. Second, we propose item attention degree and user activity to calculate the suspicious degrees of candidate groups. Finally, we employ the bisecting K-means algorithm to cluster the candidate groups according to their suspicious degrees and obtain the attack groups. The results of experiments on the Netflix and Amazon data sets indicate that the proposed method outperforms the baseline methods.
Introduction to solid state physics

Solid-state physics – and, by extension, condensed-matter physics – is one of the most active areas of physics today. Hence, any author trying to write an introductory textbook covering its basics ...
A human-centred approach to smart housing

Smart buildings are complex systems, yet architecture, engineering, and construction (AEC) professionals often perform their work without considering the human factors of building occupants. Traditionally, the AEC industry has employed a linear design and delivery approach. As buildings become smarter, the AEC industry must adapt. To maximize human well-being and the operational performance of smart buildings, an iterative, human-centred approach must be employed. The omission of human factors in the design and delivery of smart building systems risks misalignment between occupant-user needs and the AEC industry's perception of occupant-user needs. This research proposes a human-centred approach to smart housing. The study employed a multi-phase, mixed-methods research design. Data were collected from 309 high performance housing units in the United States. Longitudinal energy use data, occupant surveys, and semi-structured interviews are the primary data inputs. Affinity diagramming was leveraged to categorize the qualitative data. The output of the affinity diagramming analysis and energy analysis led to the development of data-driven Personas that communicate smart housing user needs. While these data were gathered in the United States, researchers, practitioners, and policy-makers can leverage the human-centred approach presented in this paper toward the design of other human-centred buildings and infrastructure.
The synergy of double attention: Combine sentence-level and word-level attention for image captioning

The existing attention models of image captioning typically extract only word-level attention information, i.e., the attention mechanism extracts local attention information from the image to generate the current word, and lacks accurate image global information guidance. In this paper, we first propose an image captioning approach based on self-attention. Sentence-level attention information is extracted from the image through self-attention mechanism to represent the global image information needed to generate sentences. Furthermore, we propose a double attention model which combines sentence-level attention model with word-level attention model to generate more accurate captions. We implement supervision and optimization in the intermediate stage of the model to solve information interference problems. In addition, we perform two-stage training with reinforcement learning to optimize the evaluation metric of the model. Finally, we evaluated our model on three standard datasets, i.e., Flickr8k, Flickr30k and MSCOCO. Experimental results show that our double attention model can generate more accurate and richer captions, and outperforms many state-of-the-art image captioning approaches in various evaluation metrics.
Identifying Sepsis Subphenotypes via Time-Aware Multi-Modal Auto-Encoder

Sepsis is a heterogeneous clinical syndrome that is the leading cause of mortality in hospital intensive care units (ICUs). Identification of sepsis subphenotypes may allow for more precise treatments and lead to more targeted clinical interventions. Recently, sepsis subtyping on electronic health records (EHRs) has attracted interest from healthcare researchers. However, most sepsis subtyping studies ignore the temporality of EHR data and suffer from missing values. In this paper, we propose a new sepsis subtyping framework to address the two issues. Our subtyping framework consists of a novel Time-Aware Multi-modal auto-Encoder (TAME) model which introduces time-aware attention mechanism and incorporates multi-modal inputs (e.g., demographics, diagnoses, medications, lab tests and vital signs) to impute missing values, a dynamic time wrapping (DTW) method to measure patients' temporal similarity based on the imputed EHR data, and a weighted k-means algorithm to cluster patients. Comprehensive experiments on real-world datasets show TAME outperforms the baselines on imputation accuracy. After analyzing TAME-imputed EHR data, we identify four novel subphenotypes of sepsis patients, paving the way for improved personalization of sepsis management.
Anonymous Authentication on Trust in Blockchain-Based Mobile Crowdsourcing

Mobile crowdsourcing (MCS) has become an effective data collection method due to its mobility, low cost, and flexibility. However, since centralized MCS confronts severe security and privacy risks in reality, many researchers are devoted to building a decentralized MCS system based on blockchain. Despite the effectiveness of these schemes, they fail to offer anonymous authentication on the trust of MCS nodes, although privacy is a main concern in MCS and trust plays an important role in a series of MCS activities, such as worker selection and truth discovery. Nevertheless, anonymous authentication on trust is not a trivial issue since trust evaluation usually conflicts with anonymity, which is a necessary privacy requirement in an open MCS environment. To tackle this problem, we leverage Intel software guard extension (SGX) and propose a scheme to anonymously authenticate trust with trustworthy trust evaluation in a blockchain-based MCS system. The scheme employs an SGX-enabled cloud server to periodically alter user public/private key pairs and mix newly altered keys among a number of faked keys in order to ensure unlinkability. Besides, we consider the unique features of MCS and work out a novel trust evaluation method by aggregating both subjective feedback and objective behaviors. Finally, we conduct several analyses and experiments to illustrate its security and efficiency.
The Dark Side of Artificial Intelligence in Retail Innovation

Learning Outcomes This chapter attempts to provide answers to the following questions: What is artificial intelligence (AI)? Moreover, what is AI-based retail innovation? How does AI work? What are the applications of AI in retail services innovation? What are the ethical aspects, considerations and issues regarding the employment of AI in retail?
Aerial Manipulator With Rolling Base for Inspection of Pipe Arrays

This paper considers the inspection by contact of long arrays of pipe structures in hard-to-reach places, typical of chemical plants or oil and gas industries, presenting the design of a hybrid rolling-aerial platform capable of landing and moving along the pipes without wasting energy in the propellers during the inspection. The presented robot overcomes the limitation in terms of operation time and positioning accuracy in the application of flying robots to industrial inspection and maintenance tasks. The robot consists of a hexa-rotor platform integrating a rolling base with velocity and direction control, and a 5-DOF (degree of freedom) robotic arm supported by a 1-DOF linear guide system that facilitates the deployment of the arm in the array of pipes to inspect their contour once the platform has landed. Given a set of points to be inspected in different arrays of pipes, the path of the multirotor and the rolling platform is planned with a hybrid RRT* (Rapidly-exploring Random Tree) based algorithm that minimizes the energy consumption. The performance of the system is evaluated in an illustrative outdoor scenario with two arrays of pipes, using a laser tracking system to measure the position of the robot from the ground control station.
Acoustic-decoy: Detection of adversarial examples through audio modification on speech recognition system

Deep neural networks (DNNs) display good performance in the domains of recognition and prediction, such as on tasks of image recognition, speech recognition, video recognition, and pattern analysis. However, adversarial examples, created by inserting a small amount of noise into the original samples, can be a serious threat because they can cause misclassification by the DNN. Adversarial examples have been studied primarily in the context of images, but their effect in the audio context is now drawing considerable interest as well. For example, by adding a small distortion to an original audio sample, imperceptible to humans, an audio adversarial example can be created that humans hear as error-free but that causes misunderstanding by a machine. Therefore, it is necessary to create a method of defense for resisting audio adversarial examples. In this paper, we propose an acoustic-decoy method for detecting audio adversarial examples. Its key feature is that it adds well-formalized distortions using audio modification that are sufficient to change the classification result of an adversarial example but do not affect the classification result of an original sample. Experimental results show that the proposed scheme can detect adversarial examples by reducing the similarity rate for an adversarial example to 6.21%, 1.27%, and 0.66% using low-pass filtering (with 12 dB roll-off), 8-bit reduction, and audio silence removal techniques, respectively. It can detect an audio adversarial example with a success rate of 97% by performing a comparison with the initial audio sample.
Multi-stage fuzzy swarm intelligence for automatic hepatic lesion segmentation from CT scans

Segmentation of liver and hepatic lesions using computed tomography (CT) is a critical and challenging task for doctors to accurately identify liver abnormalities and to reduce the risk of liver surgery. This study proposed a novel dynamic approach to improve the fuzzy c-means (FCM) clustering algorithm for automatic localization and segmentation of liver and hepatic lesions from CT scans. More specifically, we developed a powerful optimization approach in terms of accuracy, speed, and optimal convergence based on fast-FCM, chaos theory, and bio-inspired ant lion optimizer (ALO), named (CALOFCM), for automatic liver and hepatic lesion segmentation. We employed ALO to guide the FCM to determine the optimal cluster centroids for segmentation processes. We used chaos theory to improve the performance of ALO in terms of convergence speed and local minima avoidance. In addition, chaos theory-based ALO prevented the FCM from getting stuck in local minima and increased computational performance, thus increasing stability, reducing sensitivity in the iterative process, and allowing the best centroids to be used by FCM. We validated the proposed approach on a group of patients with abdominal liver CT images, and the results showed good detection and segmentation performance compared with other popular techniques. This new hybrid approach allowed for the clinical diagnosis of hepatic lesions earlier and more systematically, thereby helping medical experts in their decision-making.
Generating design knowledge for blockchain-based access control to personal health records

Abstract In the course of digitization in healthcare, personal health records (PHRs) are handled as a key solution. Despite the indisputable benefits, the adoption of PHRs is hampered by data security and data privacy concerns. Blockchain technology offers promising potential to address these issues by enabling secure transactions of sensitive data. With regards to PHRs, the blockchain can be used to manage the access to health-related data. Besides existing generic PHR architectures, we systematically identified issues for the healthcare sector that need to be considered for the development of a PHR. We subsequently derived eight meta-requirements that were consolidated into three design principles. Within a 1-year design science research project, we developed the blockchain-secured PHR prototype, OSHealthRec, and evaluated the system in four evaluation cycles. The findings of our research are twofold. On the one hand, we contribute to the design knowledge base by presenting three design principles. On the other hand, we present the development of a real, operational blockchain-secured PHR and the findings from its continuous evaluation, which may serve as useful advice for further solutions.