Reviewerly/exHarmony huggingface dataset:
Parallel Learning between Science for AI and AI for Science: A Brief Overview and Perspective

This paper gives a brief overview of how AI and Science promote each other. First, the main algorithms inspired by mathematics, physics, and biology during the last 80~ years are summarized. Second, on the counter-direction, AI methods that help to solve hard problems in recent years are introduced. Perspectives and challenges are also discussed. Third, the Parallel Learning is introduced for the purpose of a general framework to assist the development of both Science for AI and AI for Science.
Mining Weak Relations Between Reviews for Opinion Spam Detection

Online reviews play a significant role in purchase decisions of consumers by providing feedback information from buyers of products. In order to mislead consumers, opinion spammers are hired to write fake reviews to promote or demote specific products for illegitimate benefits. Existing methods for spam review detection mainly focused on designing manual features, which highly rely on expert knowledge. Although recent works utilized deep learning methods to automatically learn the semantics of reviews through the inherent user-review-product strong relation, they fail to capture the weak relations between reviews at the content, sentiment and temporal levels, which provides various semantic information to expose fake reviews. Moreover, the imbalanced class distribution in spam detection issues makes this work even more challenging. To address the above problems, we propose a novel Weak-Strong Unified Network (WSUN) for opinion spam detection. Multi-level weak relation graphs are constructed to reveal the abnormal behavioral patterns of spammers, which aggregates the semantics of strong relations by graph convolutional networks and extracts comprehensive review representation by utilizing relation-level attention mechanism. In addition, a graph-based over-sampling method is devised to mitigate the impact of imbalanced class distribution. Extensive experimental results on real-world datasets show that our model is more effective than the state-of-the-art methods.
Enhanced Multi-Domain Dialogue State Tracker With Second-Order Slot Interactions

Dialogue state tracking (DST) is often used to track the system's understanding of the user goal in task-oriented dialogue systems. Existing DST methods mainly fall into two categories according to their adopted model structure: non-hierarchical and hierarchical models. The former takes the whole dialogue history as inputs during each conversation round, while the latter leverages both an utterance encoder and a dialogue encoder to efficiently model the long-term dialogue dependency. However, few of them exploit the second-order slot interaction, which refers to the pair-wise semantic relationships between different slots. As a result, these methods fall short in the context understanding throughout conversations, leading to sub-optimal performance. Towards this end, in this paper, we present a novel hierarchy-based DST framework equipped with a well-designed value copy mechanism. In particular, to model the second-order slot interaction, we firstly encode the utterance via a state reuse module to yield slot-sensitive context representation. We then selectively and effectively copy the filled values from other slots to attain more accurate state tracking. In order to evaluate the effectiveness of the proposed method, we perform extensive experiments on the widely adopted benchmark dataset MultiWOZ2.1. Our experimental results demonstrate the superiority in context understanding, as well as the strong generalization capability under a zero-shot setting compared with several DST baselines.
Early Detection of Volcanic Eruption through Artificial Intelligence on board

In this manuscript we propose a warning system based on Machine Learning (ML) techniques to allow early detection of volcanic eruptions. The use of Artificial Intelligence (AI)-based algorithms and the early detection purpose can be exploited together to carry out the specific detection and inform the Civil Protection and the authorities in charge to guarantee immediate intervention for the protection of people. Indeed, the main goal presented in this work is to conceptualize and realize a system to be mounted on-board of satellites with the intent of producing swift alerts useful for decision makers. In the initial phase the proposed ML model is trained with SO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> data acquired by Sentine1-5P. The final scope is to define an on-board system similar to Phi-Sat 1, with its own sensor and its own systems. Our feasibility study has shown that it is possible to classify and identify volcanic eruptions in advance with an accuracy of 80% and 70% respectively. Work is in progress to improve these results.
A Survey on XAI for Cyber Physical Systems in Medicine

The growing number of machine learning-based Cyber-Physical Systems (CPSs) and their ability to adapt and to learn is gaining research interest in several biomedical applications. The use of learning capabilities allows CPSs to interact and analyse their environment, learn from patterns, and perform highly complex prediction tasks. However, while on the one side the use of machine learning acts as a flywheel to the diffusion of those systems, on the other side exposes them to the problem of transparency and interpretability that affect any machine-learning-based systems. This, in critical fields like medicine, is just as important as models’ performances, in order to understand their behaviour, errors, and to garner user trust. In this paper we investigate the role of state-of-the-art explainable AI techniques in the field of cyber-physical systems.
Ambient Assisted Living using Non-intrusive Smart Sensing and IoT for Gait Rehabilitation

Health monitoring of users in medical centers, nursing homes, physiotherapy clinics or other healthcare centers, is an important and recurring task. In addition, these facilities require a structured, organized system with access to all users’ medical history. The design of a system that can measure individuals’ physiological health characteristics (PHC) may also be applied to physical rehabilitation. In this context, this study presents a possible solution using a non-intrusive smart sensing system as part of an IoT ecosystem, that aims to solve all the issues stated. This IoT system uses a smart carpet, SensFloor, to measure several physical indicators, such as users’ position and gait characteristics. The produced data by the system is stored in a real-time cloud database, thus physical therapists and patients are able to access it. Also, several dashboards are produced in real-time, to provide further analysis on users’ gait and PHCs, so that action can be taken to improve the training plan with personalized exercises.
Complementary role of conversational agents in e-health services

In recent years, business environments are undergoing disruptive changes across sectors [1]. Globalization and technological advances, such as artificial intelligence and the internet of things, have completely redesigned business activities, bringing to light an ever-increasing interest and attention towards the customer [2], especially in healthcare sector. In this context, researchers is paying more and more attention to the introduction of new technologies capable of meeting the patients’ needs [3, 4] and the Covid-19 pandemic has contributed and still contributes to accelerate this phenomenon [5]. Therefore, emerging technologies (i.e., AI-enabled solutions, service robots, conversational agents) are proving to be effective partners in improving medical care and quality of life [6]. Conversational agents, often identified in other ways as “chatbots”, are AI-enabled service robots based on the use of text [7] and capable of interpreting natural language and ensuring automation of responses by emulating human behavior [8, 9, 10]. Their introduction is linked to help institutions and doctors in the management of their patients [11, 12], at the same time maintaining the negligible incremental costs thanks to their virtual aspect [13–14]. However, while the utilization of these tools has significantly increased during the pandemic [15, 16, 17], it is unclear what benefits they bring to service delivery. In order to identify their contributions, there is a need to find out which activities can be supported by conversational agents.This paper takes a grounded approach [18] to achieve contextual understanding design and to effectively interpret the context and meanings related to conversational agents in healthcare interactions. The study context concerns six chatbots adopted in the healthcare sector through semi-structured interviews conducted in the health ecosystem. Secondary data relating to these tools under consideration are also used to complete the picture on them. Observation, interviewing and archival documents [19] could be used in qualitative research to make comparisons and obtain enriched results due to the opportunity to bridge the weaknesses of one source by compensating it with the strengths of others. Conversational agents automate customer interactions with smart meaningful interactions powered by Artificial Intelligence, making support, information provision and contextual understanding scalable. They help doctors to conduct the conversations that matter with their patients. In this context, conversational agents play a critical role in making relevant healthcare information accessible to the right stakeholders at the right time, defining an ever-present accessible solution for patients’ needs. In summary, conversational agents cannot replace the role of doctors but help them to manage patients. By conveying constant presence and fast information, they help doctors to build close relationships and trust with patients.
Physics Informed Intrinsic Rewards in Reinforcement Learning

Model-free algorithms in Reinforcement Learning (RL) are known to be a powerful learning tool and have performed well in solving complex issues. However, RL training results are often poor when the reward function is sparse or misleading in short term. In this paper, we propose a physics informed intrinsic reward function to assist the agent to overcome this difficulty. We evaluate the proposed intrinsic reward method on different types of actor-critic (AC) algorithms. The experimental results show noticeable improvement.
Highlight Removal with Orthogonal Decomposition

In this paper, based on orthogonal decomposition, we present a robust and effective method for highlight removal. First, we obtain the reflectance image of an image through orthogonal decomposition. Then, the reflectance image is used to cluster the image. According to the clustering results, illumination chromaticity is estimated. Finally, we separate diffuse and specular reflections per pixel according to the distance from each pixel chromaticity to illumination chromaticity, where the diffuse reflection image is the highlight removal image. According to our extensive experimental results, the proposed method outperforms all the existing state-of-the-art (SOTA) methods according to the Peak-Signal-to-Noise-Ratio (PSNR) and the Structural Similarity (SSIM) Index score.
Breast Density Analysis on Mammograms: Application of Machine Learning with Textural Features

Breast cancer (BC) is the world’s most prevalent cancer in female population, with 2.3 million new cases diagnosed worldwide in 2020. The great efforts made to set screening campaigns, early detection programs, and increasingly targeted treatments led to a significant improvement in patients’ survival. Full-Field Digital Mammograph (FFDM) is considered the gold standard method for the early diagnosis of BC. From several previous studies it has emerged that breast density (BD) is a risk factor in the development of BC, affecting the periodicity of screening plans present today at an international level. In this study, the focus is the development of mammographic image processing techniques that allows the extraction of indicators derived from textural patterns of the mammary parenchyma indicative of BD risk factor. Different Machine Learning (ML) techniques have been employed to classify breasts based on the values of the tissue density. Textural features were extracted only from breast parenchyma with which to train classifiers thanks to aid of ML algorithms. The preliminary results are in line with those obtained in the literature, reaching an accuracy of 93.55% for a binary classification between dense and no-dense tissues.
An Integrated Collaborative Filtering Framework With Location-Aware Graph Embedding in Intelligent Internet of Things Systems

Reliable Internet of Things (IoT) service discovery is a significant task in intelligent service-oriented IoT systems. Collaborative filtering (CF) turns out to be an effective solution to IoT service discovery. However, the traditional CF framework is facing the following challenges: inefficiency in learning the high-order interactions between users and IoT services and ineffectively making use of geographical location information. Moreover, deploying a CF framework in real-world distributed IoT systems poses another significant challenge in terms of reliability assurance and privacy protection. For bridging this gap, we propose an innovative integrated collaborative filtering framework (ICF) with incorporating the location-aware quality of IoT services into a heterogeneous graph embedding model. Meanwhile, a Geohash-based privacy-preservation mechanism is introduced to encoding the location information into a short string for protecting the sensitive location information. The proposed ICF framework is an integrated architecture combining advanced graph embedding learning and heterogeneous side information. And a joint objective optimization function is designed in the graph embedding learning to qualify automatic IoT service features. In this way, the hidden user-service interaction information and location-aware quality semantic features can be explored in an effective and reliable way. Furthermore, the learned location-aware quality embedding vector is incorporated to discover the reliable services among all IoT services with the mini-batch online clustering-based CF algorithm. The experimental findings illustrate the significant efficacy and reliability of our proposed ICF framework in large scale IoT service discovery scenarios.
Augmented Reality for assembly operation training: does immersion affect the recall performance?

This study aims at comparing three assembly training applications based on different XR technologies characterized by different degrees of immersion (i.e., an MR application based on Hololens 2, a desktop AR application and a digital handbook visualized on a monitor). A total of 54 subjects, recruited among students and personnel of Università Politecnica delle Marche, have been involved. They were assigned to 3 groups age and gender matching. Each group is asked to complete the training related to the assembly of a Lego commercial set (i.e., LEGO 10593), using one of the three considered applications. Results allows us to observe the effects of the immersion on the recall performances, assessed in terms of recall completion time, assembly mistakes, picking mistakes and sequence mistakes.
Realtime Hand-Gesture Recognition Based on Novel Charge Variation Sensor and IMU

Wearable hand gesture devices are of growing interest to enhance human-machine interfaces as well as digital communication between humans. Although many solutions have been proposed using various sensing technologies, the development of a truly wearable, low power, reliable and unobtrusive device is still a challenge. This work proposes a new device based on a combination of a novel electrostatic sensing circuit fused with a six degree of freedom Inertial Measurement Unit (IMU), to achieve a wearable bracelet capable of active gesture detection for over 11 hours on one battery charge. Due to the proposed tiny machine learning model, gestures are detected in real time by an onboard ARM Cortex M33 microcontroller with an accuracy of up to 87%. The IMU gesture recognition is enhanced by the electrostatic sensing bracelet, increasing accuracy by more than 10% with respect to IMU only estimations.
Boost the Performance of Model Training with the Ray Framework for Emerging AI Applications

Ray is a high-performance distributed execution framework aimed at large-scale machine learning and reinforcement learning applications. We propose a method to alleviate the heavy burden of parameter servers in Ray, leading to a reduction of 50% of training time with almost the same model accuracy.
Constrained differentiable cross-entropy method for safe model-based reinforcement learning

Reinforcement learning agents must explore their environments to learn optimal policies through trial and error. Due to challenges in simulating the complexities of the real world, there is a growing trend of training reinforcement learning (RL) agents directly in the real world instead of mostly or entirely in simulation. Safety concerns are paramount when training RL agents directly in the real world. This paper proposes MPC-CDCEM, a model-based reinforcement algorithm (RL) that allows the agent to safely interact with the environment and explore without additional assumptions on system dynamics. The algorithm uses a Model Predictive Control (MPC) framework with a differentiable cross-entropy optimizer, which induces a differentiable policy that considers the constraints while addressing the objective mismatch problem in model-based RL algorithms. We evaluate our algorithm in Safety Gym environments and on a practical building energy optimization problem. In addition, we showed that in both experiments, our algorithms have the lowest number of constraint violations and achieve comparable rewards compared to baseline constrained RL algorithms.
TEA-bot

We propose TEA-bot (Thermography Enabled Autonomous Robot) to address the problem of labor-intensive manual inspections of thermal leaks in Heating Ventilation and Air Conditioning (HVAC) systems while avoiding installation of sensor networks, which can be challenging and expensive for existing buildings. TEA-bot is an Unmanned Ground Vehicle (UGV) designed to navigate in ceilings using visual-based Simultaneously Localization And Mapping (SLAM) while detecting thermal leaks from HVAC systems using Convolutional Neural Networks (CNN). TEA-bot uses inexpensive 3D printed parts as the bones, a single-board computer (SBC) as the brain, an RGB-D camera as the eyes, and a thermal camera as the leak detector. We build an in-lab ceiling environment as a true-to-size testing site with five types of common leaks in HVAC systems (e.g., improper connection) to test the feasibility and effectiveness of TEA-bot. Results show that TEA-bot can 1) generate high-resolution ceiling maps in the format of point clouds for existing buildings without Building Information Models (BIMs), with an average error of 1.56 inches in duct length measurements; 2) identify leak types with a precision of 86.60% using the thermal camera images; and 3) register leaks onto the point cloud maps, providing a holistic and intuitive view of leak locations in an unknown ceiling environment.
Real-Time Dog Detection and Alert System using Tensorflow Lite Embedded on Edge Device

Wild and domestic animals constitute a massive part of the animal kingdom. There are approximately 8.7 million different species of animal available. Still, many species are being discovered. Cats and dogs are the most common animal in the society where people live and work. Human beings are also social animals who know society's living norms. However, animals like dogs and cats are not aware of the rules and norms to be followed in society. Hence it is necessary to keep track of the animals in the restricted areas to avoid sudden death, which happens accidentally. So, this paper proposed a real-time dog detection model with email notifications using Embedded ML running on Raspberry Pi for the University campus. Machine learning models like SSD, YOLO v3, and YOLO v4 are used to detect the dog and send the notification to the concerned authority. It has been found that the YOLO v3 model outperforms all other models with the highest accuracy of 94%, whereas SSD and YOLO v4 Achieved an accuracy of 89% and 63%, respectively.
Storage Essentiality in Solar PV Incorporated Grid Connected System Estimating its Lifespan

The presentation of a sunlight based photovoltaic framework with and without a battery energy stockpiling gadget is investigated in this examination (BESS). The predominance of the capacity based framework is explored and assessed, with the battery's normal life term. At the point when the innovation is put through a lot of hardship, the examination will be fittingly checked. The dynamic force, responsive force, current and voltage at the reason behind normal coupling (PCC) are thought about between the sun powered PV-based organization and the sun based PV with energy stockpiling framework during a flaw. At long last, the battery's normal life expectancy has been determined utilizing a stochastic method. In the MATLAB/SIMULINK climate, the whole framework is displayed, approved, and surveyed.
Serverless Implementation of Data Wizard Application using Azure Kubernetes Service and Docker

Several providers in the cloud provide server-less solutions that claim to be highly available, scalable, and low-maintenance. To that end, serverless computing using Azure Kubernetes Service (AKS) and Dockers is promoted as a feasible choice for pay-per-use Web API, data processing, and backend services. They offer elastic and automated scalability, in contrast to VMs, by automatically provisioning and allocating resources. We provide our findings from analyzing the Data Wizard application running on AKS and as Function(s)-as-a-Service, a serverless solution (FaaS). We compare these rollouts by moving data between generations of Azure to track latencies and costs. When VMs are prepared for the anticipated load, and test scenarios are within the FaaS hardware constraints, we discovered no major changes in response time between deployments. FaaS easily manages larger data migration volumes, but VMs require more man hours to get the same outcomes. The benefits of serverless computing, as we've seen them, make it easy to decide between serverless and virtual machines for a Data Wizard (DW) programme.
Towards a Strong Containment for Efficient Matching of Expressive Graph Patterns

We consider conditional graph patterns (CGPs) that allow querying data graphs with complex features like quantification, negation and predicates. To overcome the prohibitive cost of subgraph isomorphism, we consider matching of CGPs under simulation semantics which can be conducted in quadratic time. In emerging applications that deal with large data graphs, one would like to reduce more this matching time. We consider the optimization problem that checks whether match results of some pattern P1, over any data graph, can be extracted from those of another pattern P2. This allows an efficient matching of P1 using only match results of P2 which may be much smaller than the underlying data graph. We show that when patterns are very simple then the traditional containment can be used to meet our optimization goal. However, in case of complex pattern features, the containment semantics does not suffice since containment between two patterns does not imply a possible extraction between their match results. Hence, we propose an enhanced semantics, called strong containment, that consists in checking match results extraction between two patterns. We show that strong containment can be decided in cubic time for CGPs by providing such an algorithm. This new semantics can be applied in many emerging applications such as view-based answering, query optimization and caching systems maintenance.