Reviewerly/exHarmony huggingface dataset:
Training Super-Resolution Network with Difficulty-Based Adaptive Sampling

The performance of super-resolution (SR) networks is highly dependent on the quality and size of the training data. However, research on better use of the available SR dataset remains unexplored. In this work, we propose Difficulty-based Adaptive Sampling (DAS) strategy to fill this gap. Specifically, to further exploit the input samples, DAS first uses a Calculating and Sorting module (CS module) to calculate the upsampling difficulty of the input samples and sorts them. The CS module makes DAS be efficient with an online form and using a fast method to calculate the difficulty degrees of samples. Then it uses a Sampler module to select appropriate samples. Finally, with a Recorder module to record the training states, DAS can dynamically select the samples suitable for different training stages. Extensive experiments demonstrate that DAS selecting the appropriate samples for each iteration can effectively improve the performance of SR networks.
Source-Free Unsupervised Cross-Domain Pedestrian Detection via Pseudo Label Mining and Screening

Although current cross-domain pedestrian detection frame-works have obtained certain positive results, the performance is still source data dependent, which is cumbersome and im-practical in practical applications. To address this issue, we propose a source-free unsupervised pedestrian detection with pseudo label mining and screening. First, a modified CSP de-tector with DropBlock and three detection heads is presented. Then, a multi-expert method is proposed to fuse pseudo la-bels from three detection heads. Finally, a clustering-based self-supervised learning is adopted to categorize pseudo la-bels into positive and negative classes, which forms a set of clusters via similarity of pseudo labels and give classification results based on two confidence scores of each label from the detector backbone and multi-expert fusion. Experimental re-sults on three benchmark datasets show that the proposed approach can achieve state-of-the-art performance and be even comparable with other latest works using source data.
Multi-View Deep Matrix Factorization with Consensual Solution from Multiple Paths

Multi-view data often contain redundant information that cannot be simply spliced. Many existing methods for processing them by assigning weights to each view cannot capture features dynamically. Therefore, we propose a multi-view deep matrix factorization method via neural networks that captures semantic hierarchical information of the data and dynamically produces a consistent representation using the complementarity of multi-view features. Due to the usefulness of deep matrix factorization, the generated representation is easily interpretable. The proposed method yields a harmonized representation directly from multi-view data without an extra weight learning process. In addition, we use a multi-path network to search for a consensual solution and obtain an optimal result. Additional feature optimization is used to enhance the discriminative characterization of the representation matrix. Finally, experiments on four real-world datasets show that the proposed method is superior to state-of-the-arts.
PhraseGAN: Phrase-Boost Generative Adversarial Network for Text-to-Image Generation

A phrase contains an object-orienting noun and some attribution-associating words. Therefore, focusing on phrases could better generate images with the objects and their tightly relevant characteristics. We propose a Phrase-boost Gener-ative Adversarial Network (PhraseGAN) with threefold im-provement for scene level text-to-image generation. First, we propose a Transformer-based encoder to encode the in-put words and sentences and encode related words and their targeting nouns into phrases by text correlation analysis. Sec-ond, we utilize Graph Convolution Networks to measure fine-grained text-image similarity, which could gain constraints on relative positions between different objects. Finally, we de-sign a phrase-region discriminator to discriminate the qual-ity of the generated objects and the consistency between the phrases and their corresponding objects. Experimental results on the Microsoft COCO dataset demonstrate that PhraseGAN can generate better images from texts than state-of-the-art methods.
A Service-based Information System for AI-supported Health Informatics

Artificial Intelligence (AI) and Big Data Analysis are both trending research areas that can be applied to any application domain to increase efficiency, quality, or bridge supply shortages. An example is the analysis of medical information in health informatics and its subdomains bioinformatics and medical informatics. It aims to explore medical information like, e.g., patient data or clinical studies in order to support medical doctors, healthcare professionals, and ultimately all patients. The increasing level of digitization of people's lives and global pandemics boost the relevance of this research area even further. This paper analyzes a medical information use case in order to extend and improve an existing Information System for AI-based Visual Big Data Analysis with a special focus on user empowerment. In more detail, this paper extends a software architecture, introduces a reference implementation, and refines the underlying reference model AI2VIS4BigData. It focuses on supporting both, expert users with profound technical knowledge and end users without that expertise. The resulting concepts and implementations are consequently evaluated with both target groups: A cognitive walkthrough with a group of AI and Big Data Analysis experts confirms the system's usability and a survey with medical doctors and health domain experts (potential end users) positively evaluates the developed concepts.
A Novel Approach for Semi-Supervised Network Traffic Classification

Network traffic classification is the basic block for network management services such as network security and QoS control. When facing with the emergence of stronger encryption protocols like TLS 3.0, we need more advanced technologies, such as classical machine learning and deep learning methods to solve the problems. Every moment there are thousands of applications are sending the packets in the network, it's difficult for us to label all the sessions, we only have few labelled samples to train our classifiers. In this paper, we propose a semi supervised network traffic classification method. The basic idea is to use the samples segmented by encoder-decoder networks and unlabeled data to improve the performance of the classifier trained based on a small number of labeled samples. To explore the correlation of the continuously arriving packets, we segmented the sequence of packets' features parsed from raw traffic files (PCAPs), and using the segmentations to identify the traffic types. Then we adopt an Adversarial Auto-encoder to perform semi-supervised phase. The experimental study on VPN-nonVPN datasets shows that the proposed method is effective compared with the baseline method when there are relatively few available labeled training data.
Modality-Specific Multimodal Global Enhanced Network for Text-Based Visual Question Answering

Text-based visual question answering (T-VQA) aims to answer questions about images by comprehending both detected objects and OCR(optical character recognition) tokens. Most existing methods fail to eliminate the noisy and redundant detected objects, and ignore the modality-specific information. To address these concerns, we propose multimodal global enhanced network (MGEN) for T-VQA. In MGEN the multi-modal global enhanced OCR graph focus on modeling the spatial relationships between OCR tokens rather than objects with noise and redundancy. Then, we introduce the multi-modal global enhanced transformer module, which is formed using the proposed attention mechanism, to reflect the specificity in the various modalities. The preceding two modules can leverage global features, implying that not only will the model's attention be directed to critical parts, but the noise can also be further reduced. Extensive experiments demonstrate the effectiveness and superiority of the proposed MGEN against the state-of-the-art methods.
Fusing Acoustic and Text Emotional Features for Expressive Speech Synthesis

Prominent methods based on Tacotron2 and advanced models have improved the quality of synthesized speech. However, most data-driven Text- To-Speech (TTS) synthesis methods only aim to achieve reasonable neutral prosody, so the synthesized speech is less expressive. In this paper, a method was proposed which fuses acoustic and text emotional features to produce more vivid and realistic speech. Specifically, to obtain acoustic features, two acoustic encoders are leveraged to extract utterance-level and phoneme-level vectors from the target speech, respectively. To obtain the objective sentiment features of the text, the sentiment analysis model is exploited to extract the sentiment vector from the text and expand it. The expanded vector is feature- fused with the output vector of the acoustic model. The experimental results on the LJSpeech dataset show that the naturalness and expressiveness of the MOS score are 3.63 and 3.45, respectively, and the similarity of the SMOS score is 4.14.
Machine‐as‐a‐Service: Blockchain‐based management and maintenance of industrial appliances

Abstract Machine‐as‐a‐Service (MaaS) is an emerging service model for industrial appliances. With MaaS, machines are rented instead of being acquired, and their lifecycle is handled by an ecosystem of specialized actors, such as different independent maintenance companies certified for interventions on specific hardware. As the number of actors, clients, and providers involved in a MaaS ecosystem grows, maintaining mutual trust relationships between all involved parties and orchestrating MaaS operations in centralized fashion quickly becomes intractable. We present a blockchain‐based approach to providing MaaS in industrial settings where rented machines are equipped with IoT sensors, and where MaaS operations are orchestrated in a transparent, decentralized, and scalable way using a collection of smart contracts deployed over an infrastructure combining the Ethereum and InterPlanetary File System decentralized services. We detail the operations of MaaS, such as the lifecycle of management operations, and report on the performance of a prototype implementation deployed in the cloud.
CONCERTS: Coverage Competency-Based Target Search for Heterogeneous Robot Teams

This paper proposes CONCERTS: Coverage competency-based target search, a failure-resilient path-planning algorithm for heterogeneous robot teams performing target searches for static targets in indoor and outdoor environments. This work aims to improve search completion time for realistic scenarios such as search and rescue or surveillance, while maintaining the computational speed required to perform online re-planning in scenarios when teammates fail. To provide high-quality candidate paths to an information-theoretic utility function, we split the sample generation process into two steps, namely Heterogeneous Clustering (H-Clustering) and multiple Traveling Salesman Problems (TSP). The H-Clustering step generates plans that maximize the coverage potential of each team member, while the TSP step creates optimal sample paths. In situations without prior target information, we compare our method against a state-of-the-art algorithm for multi-robot Coverage Path Planning and show a 9% advantage in total mission time. Additionally, we perform experiments to demonstrate that our algorithm can take advantage of prior target information when it is available. The proposed method provides resilience in the event of single or multiple teammate failure by recomputing global team plans online. Finally, we present simulations and deploy real hardware for search to show that the generated plans are sufficient for executing realistic missions.
Visual Pretraining via Contrastive Predictive Model for Pixel-Based Reinforcement Learning

In an attempt to overcome the limitations of reward-driven representation learning in vision-based reinforcement learning (RL), an unsupervised learning framework referred to as the visual pretraining via contrastive predictive model (VPCPM) is proposed to learn the representations detached from the policy learning. Our method enables the convolutional encoder to perceive the underlying dynamics through a pair of forward and inverse models under the supervision of the contrastive loss, thus resulting in better representations. In experiments with a diverse set of vision control tasks, by initializing the encoders with VPCPM, the performance of state-of-the-art vision-based RL algorithms is significantly boosted, with 44% and 10% improvement for RAD and DrQ at 100 steps, respectively. In comparison to the prior unsupervised methods, the performance of VPCPM matches or outperforms all the baselines. We further demonstrate that the learned representations successfully generalize to the new tasks that share a similar observation and action space.
COVID-19 Impact: A Case Study at the School of Agricultural Engineering and Environment of the Universitat Politècnica de València

To study the first impact of the COVID-19 crisis on the results obtained by students belonging to the School of Agricultural Engineering and Environment at the Universitat Politècnica de València (Spain), the average of the marks corresponding to three academic years (2016–2019) was compared to those obtained in 2019–2020 for a total of four bachelor’s degrees and two semesters. Our results suggest a positive effect on the marks obtained during the activation of emergency remote teaching during the spring semester of 2019–2020 in three out of the four degrees, with these differences being significant for the whole study. Moreover, just at the end of that period, instructors and students were surveyed regarding teaching methodologies, evaluation modalities, and difficulties found throughout the process of adapting to distance teaching. Our results allow us to sensibly think about that exceptional situation in order to propose a set of counterweighting measures which could improve the implementation of distance learning in engineering colleges.
Comparison of Graph Fitting and Sparse Deep Learning Model for Robot Pose Estimation

The paper presents a simple, yet robust computer vision system for robot arm tracking with the use of RGB-D cameras. Tracking means to measure in real time the robot state given by three angles and with known restrictions about the robot geometry. The tracking system consists of two parts: image preprocessing and machine learning. In the machine learning part, we compare two approaches: fitting the robot pose to the point cloud and fitting the convolutional neural network model to the sparse 3D depth images. The advantage of the presented approach is direct use of the point cloud transformed to the sparse image in the network input and use of sparse convolutional and pooling layers (sparse CNN). The experiments confirm that the robot tracking is performed in real time and with an accuracy comparable to the accuracy of the depth sensor.
Internet Scamming and the Techniques of Neutralization: Parents’ Excuses and Justifications for Children’s Involvement in Online Dating Fraud in Nigeria

Abstract Techniques of neutralization have largely been used to explain the triadic relationship between cybercrimes, offenders and victims, while the possible involvement of the offenders’ social networks as accomplices has been ignored. Interviewing parents, a qualitative study of their children’s romance scam involvement was conducted in Nigeria, exploring the parents’ disposition towards their children’s delinquent behaviour. In all, 52 interviews were conducted, and a thematic analysis of the narratives was carried out. Findings indicated that parents’ initial opposition to cybercrimes and support for enforcing laws against perpetrators changed towards accepting their children’s involvement in romance fraud. Parents adopted their children’s neutralization techniques in accepting their deviance. These findings have important implications in understanding the depth of Internet scams and parents’ social expectations in controlling their children’s behaviours.
Wireless communication based cloud network architecture using AI assisted with IoT for FinTech application

Financial Technology has revolutionized the delivery and usage of the autonomous operations and processes to improve the financial services. Banks and Financial institutions play a crucial role in economic development of country albeit slow to embrace IoT technology. Integrating Artificial Intelligence in IoT enhances IoT performance by analysing the information and extracting knowledge therefore making them more intelligent. so this paper enables the cloud based wireless communication network architecture integrated with IoT in Financial Technology (FinTech) where the data have been collected by cloud based server incorporated with secure transaction based authentication protocol (STAP) to identify the malicious users and fraudulent in bank transactions. The collected data from cloud server has been classified based on deep Q- learning (DQL) which uses training and testing by neural networks. By enhancing the authentication protocol during the transactions will improve the security of the banking networks and by classifying the cloud data it will identify whether there are any unauthenticated users proceed with the transaction without knowledge of the banking sector. The experimental analysis was carried out based on both secure transactions based authentication protocol and deep Q-learning through the parameters in terms of authentication protocol, security rate of the network, average end-to-end delay and cloud data based classification for secure bank transactions.
A GIS Open-Data Co-Simulation Platform for Photovoltaic Integration in Residential Urban Areas

The rising awareness of environmental issues and the increase of renewable energy sources (RES) has led to a shift in energy production toward RES, such as photovoltaic (PV) systems, and toward a distributed generation (DG) model of energy production that requires systems in which energy is generated, stored, and consumed locally. In this work, we present a methodology that integrates geographic information system (GIS)-based PV potential assessment procedures with models for the estimation of both energy generation and consumption profiles. In particular, we have created an innovative infrastructure that co-simulates PV integration on building rooftops together with an analysis of households’ electricity demand. Our model relies on high spatiotemporal resolution and considers both shadowing effects and real-sky conditions for solar radiation estimation. It integrates methodologies to estimate energy demand with a high temporal resolution, accounting for realistic populations with realistic consumption profiles. Such a solution enables concrete recommendations to be drawn in order to promote an understanding of urban energy systems and the integration of RES in the context of future smart cities. The proposed methodology is tested and validated within the municipality of Turin, Italy. For the whole municipality, we estimate both the electricity absorbed from the residential sector (simulating a realistic population) and the electrical energy that could be produced by installing PV systems on buildings’ rooftops (considering two different scenarios, with the former using only the rooftops of residential buildings and the latter using all available rooftops). The capabilities of the platform are explored through an in-depth analysis of the obtained results. Generated power and energy profiles are presented, emphasizing the flexibility of the resolution of the spatial and temporal results. Additional energy indicators are presented for the self-consumption of produced energy and the avoidance of CO2 emissions.
Rich learning representations for human activity recognition: How to empower deep feature learning for biological time series

Deep learning versus feature engineering has drawn significant attention specifically for applications where expertly crafted features have been used for decades. Human activity recognition is no exception where statistical and motion specific features have shown potential in detecting falls and other daily activities across a wide range of datasets. This paper provides an in-depth study and comparison of two fundamentally different approaches to HAR while introducing a novel way to harness the spectral properties of biological time series in addition to temporal features. A research group at Stanford recently proposed subject agnostic features as state-of-the-art when applied to a large dataset with many participants of different ages. In this paper, we demonstrate that implicit feature learning in the latent spaces of deep learning algorithms can be powerful alternatives to using finely tuned domain-specific features for HAR. In fact, when using a spectrotemporal representation of the raw sensor data in the form of spectrograms, a standard convolutional neural network without any prior conditioning on the features, statistically significantly outperforms the prior state-of-the-art using subject agnostic features in all the different partitions of the dataset with a significant 29.8% reduction in the overall average error rate.
Explore versus store: Children strategically trade off reliance on exploration versus working memory during a complex task

During complex tasks, we use working memory to actively maintain goal sets and direct attention toward goal-relevant information in the environment. However, working memory is severely limited, and storing information in working memory is cognitively effortful. Previous work by Kibbe and Kowler [2011, Journal of Vision, 11(3), Article 14] showed that adults strategically modulate reliance on working memory during complex, goal-oriented tasks, varying the amount of information they store in working memory depending both on the cognitive demands of the task and on the ease with which task-relevant information can be accessed from the environment. We asked whether children, whose working memory and executive functions are undergoing significant developmental change, also use working memory strategically during complex tasks. Forty-six 8-10-year-old children searched through arrays of hidden objects to find three that belonged to a given category defined over the objects' features. We manipulated the cognitive demands of the task by increasing the complexity of the category. We manipulated the exploration costs of the task by varying the rate at which task-relevant information could be accessed. We measured children's search patterns to gain insights into how the children used working memory during the task. We found that as the cognitive demands of the task increased, children stored less information in working memory, relying more on exploration. When exploration was costlier, children explored less, storing more in working memory. These results suggest that developing children, like adults, make strategic decisions about when to explore versus when to store during a complex, goal-oriented task.