Reviewerly/exHarmony huggingface dataset:
High-accuracy multi-camera reconstruction enhanced by adaptive point cloud correction algorithm

Multi-camera schemes can effectively increase the perception range of vision systems compared to single-camera schemes and are common in many optical applications. Unavoidable errors emerge in the global multi-camera calibration process, however, such as manufacturing error of the optical devices and computational error from marker detection algorithms, which drive down the accuracy of the camera system correlation. This paper discusses the causes of global calibration errors in detail. A four-camera vision system was built to obtain the visual information of targets including static objects and a dynamic concrete-filled steel tubular (CFST) specimen. Local calibration and global calibration were applied successively to realize multi-camera correlation, followed by filtering and stitching operations to acquire filtered global point clouds. A point cloud correction algorithm is designed accordingly to optimize the stitched point cloud structures and further improve the accuracy of the reconstructed surfaces. Based on the density features of the targets themselves (rather than standard calibration markers), the proposed point cloud correction algorithm is effective for various targets and adaptive under dynamic conditions. The point clouds and corresponding reconstructed models are shown to be more accurate after the proposed enhancement process. The point cloud correction algorithm also has strong adaptability to different static targets with complex surfaces and performs well under uncertain geometric changes and vibration. The results presented here provide both theoretical and practical support for advancements in multi-vision applications such as optical measurement, real-time target tracking, quality monitoring, and surface data acquisition.
Deep learning approach to peripheral leukocyte recognition

Microscopic examination of peripheral blood plays an important role in the field of diagnosis and control of major diseases. Peripheral leukocyte recognition by manual requires medical technicians to observe blood smears through light microscopy, using their experience and expertise to discriminate and analyze different cells, which is time-consuming, labor-intensive and subjective. The traditional systems based on feature engineering often need to ensure successful segmentation and then manually extract certain quantitative and qualitative features for recognition but still remaining a limitation of poor robustness. The classification pipeline based on convolutional neural network is of automatic feature extraction and free of segmentation but hard to deal with multiple object recognition. In this paper, we take leukocyte recognition as object detection task and apply two remarkable object detection approaches, Single Shot Multibox Detector and An Incremental Improvement Version of You Only Look Once. To improve recognition performance, some key factors involving these object detection approaches are explored and the detection models are generated using the train set of 14,700 annotated images. Finally, we evaluate these detection models on test sets consisting of 1,120 annotated images and 7,868 labeled single object images corresponding to 11 categories of peripheral leukocytes, respectively. A best mean average precision of 93.10% and mean accuracy of 90.09% are achieved while the inference time is 53 ms per image on a NVIDIA GTX1080Ti GPU.
Hate Speech Detection is Not as Easy as You May Think

Hate speech is an important problem that is seriously affecting the dynamics and usefulness of online social communities. Large scale social platforms are currently investing important resources into automatically detecting and classifying hateful content, without much success. On the other hand, the results reported by state-of-the-art systems indicate that supervised approaches achieve almost perfect performance but only within specific datasets. In this work, we analyze this apparent contradiction between existing literature and actual applications. We study closely the experimental methodology used in prior work and their generalizability to other datasets. Our findings evidence methodological issues, as well as an important dataset bias. As a consequence, performance claims of the current state-of-the-art have become significantly overestimated. The problems that we have found are mostly related to data overfitting and sampling issues. We discuss the implications for current research and re-conduct experiments to give a more accurate picture of the current state-of-the art methods.
Evaluation of temperature-based machine learning and empirical models for predicting daily global solar radiation

Accurate global solar radiation data are fundamental information for the allocation and design of solar energy systems. The current study compared different machine learning and empirical models for global solar radiation prediction only using air temperature as inputs. Four machine learning models, e.g., hybrid mind evolutionary algorithm and artificial neural network model, original artificial neural network, random forests and wavelet neural network, as well as four empirical temperature-based models (Hargreaves-Samani model, Bristow-Campbell model, Jahani model, and Fan model) were applied for prediction of daily global solar radiation in temperate continental regions of China. The results indicated the hybrid mind evolutionary algorithm and artificial neural network model provided better estimations, compared with the existing machine learning and empirical models. Thus, the temperature-based hybrid model is highly recommended to predict global solar radiation in temperate continental regions of China when only air temperature data are available. Combining the hybrid model with future air temperature forecasts, we can get the accurate information of future solar radiation, which is of great importance to management and operation of solar energy systems.
Deep Learning for Signal Authentication and Security in Massive Internet-of-Things Systems

Secure signal authentication is arguably one of the most challenging problems in the Internet of Things (IoT), due to the large-scale nature of the system and its susceptibility to man-in-the-middle and data-injection attacks. In this paper, a novel watermarking algorithm is proposed for dynamic authentication of IoT signals to detect cyber-attacks. The proposed watermarking algorithm, based on a deep learning long short-term memory structure, enables the IoT devices (IoTDs) to extract a set of stochastic features from their generated signal and dynamically watermark these features into the signal. This method enables the IoT gateway, which collects signals from the IoTDs, to effectively authenticate the reliability of the signals. Moreover, in massive IoT scenarios, since the gateway cannot authenticate all of the IoTDs simultaneously due to computational limitations, a game-theoretic framework is proposed to improve the gateway's decision making process by predicting vulnerable IoTDs. The mixed-strategy Nash equilibrium (MSNE) for this game is derived, and the uniqueness of the expected utility at the equilibrium is proven. In the massive IoT system, due to the large set of available actions for the gateway, the MSNE is shown to be analytically challenging to derive, and thus, a learning algorithm that converges to the MSNE is proposed. Moreover, in order to handle incomplete information scenarios, in which the gateway cannot access the state of the unauthenticated IoTDs, a deep reinforcement learning algorithm is proposed to dynamically predict the state of unauthenticated IoTDs and allow the gateway to decide on which IoTDs to authenticate. Simulation results show that with an attack detection delay of under 1 s, the messages can be transmitted from IoTDs with an almost 100% reliability. The results also show that by optimally predicting the set of vulnerable IoTDs, the proposed deep reinforcement learning algorithm reduces the number of compromised IoTDs by up to 30%, compared to an equal probability baseline.
AE2-Nets: Autoencoder in Autoencoder Networks

Learning on data represented with multiple views (e.g., multiple types of descriptors or modalities) is a rapidly growing direction in machine learning and computer vision. Although effectiveness achieved, most existing algorithms usually focus on classification or clustering tasks. Differently, in this paper, we focus on unsupervised representation learning and propose a novel framework termed Autoencoder in Autoencoder Networks (AE <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -Nets), which integrates information from heterogeneous sources into an intact representation by the nested autoencoder framework. The proposed method has the following merits: (1) our model jointly performs view-specific representation learning (with the inner autoencoder networks) and multi-view information encoding (with the outer autoencoder networks) in a unified framework; (2) due to the degradation process from the latent representation to each single view, our model flexibly balances the complementarity and consistence among multiple views. The proposed model is efficiently solved by the alternating direction method (ADM), and demonstrates the effectiveness compared with state-of-the-art algorithms.
MoEL: Mixture of Empathetic Listeners

Previous research on empathetic dialogue systems has mostly focused on generating responses given certain emotions.However, being empathetic not only requires the ability of generating emotional responses, but more importantly, requires the understanding of user emotions and replying appropriately.In this paper, we propose a novel end-toend approach for modeling empathy in dialogue systems: Mixture of Empathetic Listeners (MoEL).Our model first captures the user emotions and outputs an emotion distribution.Based on this, MoEL will softly combine the output states of the appropriate Listener(s), which are each optimized to react to certain emotions, and generate an empathetic response.Human evaluations on empatheticdialogues (Rashkin et al., 2018) dataset confirm that MoEL outperforms multitask training baseline in terms of empathy, relevance, and fluency.Furthermore, the case study on generated responses of different Listeners shows high interpretability of our model.
Deep learning to filter SMS Spam

The popularity of short message service (SMS) has been growing over the last decade. For businesses, these text messages are more effective than even emails. This is because while 98% of mobile users read their SMS by the end of the day, about 80% of the emails remain unopened. The popularity of SMS has also given rise to SMS Spam, which refers to any irrelevant text messages delivered using mobile networks. They are severely annoying to users. Most existing research that has attempted to filter SMS Spam has relied on manually identified features. Extending the current literature, this paper uses deep learning to classify Spam and Not-Spam text messages. Specifically, Convolutional Neural Network and Long Short-Term Memory models were employed. The proposed models were based on text data only, and self-extracted the feature set. On a benchmark dataset consisting of 747 Spam and 4,827 Not-Spam text messages, a remarkable accuracy of 99.44% was achieved.
The Book of Why: The New Science of Cause and Effect

Judea Pearl is on a mission to change the way we interpret data. An eminent professor of computer science, Pearl has documented his research and opinions in scholarly books and papers. Now, he has ...
THINGS: A database of 1,854 object concepts and more than 26,000 naturalistic object images

In recent years, the use of a large number of object concepts and naturalistic object images has been growing strongly in cognitive neuroscience research. Classical databases of object concepts are based mostly on a manually curated set of concepts. Further, databases of naturalistic object images typically consist of single images of objects cropped from their background, or a large number of naturalistic images of varying quality, requiring elaborate manual image curation. Here we provide a set of 1,854 diverse object concepts sampled systematically from concrete picturable and nameable nouns in the American English language. Using these object concepts, we conducted a large-scale web image search to compile a database of 26,107 high-quality naturalistic images of those objects, with 12 or more object images per concept and all images cropped to square size. Using crowdsourcing, we provide higher-level category membership for the 27 most common categories and validate them by relating them to representations in a semantic embedding derived from large text corpora. Finally, by feeding images through a deep convolutional neural network, we demonstrate that they exhibit high selectivity for different object concepts, while at the same time preserving variability of different object images within each concept. Together, the THINGS database provides a rich resource of object concepts and object images and offers a tool for both systematic and large-scale naturalistic research in the fields of psychology, neuroscience, and computer science.
PointCloud Saliency Maps

3D point-cloud recognition with PointNet and its variants has received remarkable progress. A missing ingredient, however, is the ability to automatically evaluate pointwise importance w.r.t. classification performance, which is usually reflected by a saliency map. A saliency map is an important tool as it allows one to perform further processes on point-cloud data. In this paper, we propose a novel way of characterizing critical points and segments to build point-cloud saliency maps. Our method assigns each point a score reflecting its contribution to the model-recognition loss. The saliency map explicitly explains which points are the key for model recognition. Furthermore, aggregations of highly-scored points indicate important segments/subsets in a point-cloud. Our motivation for constructing a saliency map is by point dropping, which is a non-differentiable operator. To overcome this issue, we approximate point-dropping with a differentiable procedure of shifting points towards the cloud centroid. Consequently, each saliency score can be efficiently measured by the corresponding gradient of the loss w.r.t the point under the spherical coordinates. Extensive evaluations on several state-of-the-art point-cloud recognition models, including PointNet, PointNet++ and DGCNN, demonstrate the veracity and generality of our proposed saliency map. Code for experiments is released on https://github.com/tianzheng4/ Point-Cloud-Saliency-Maps.
FLchain: Federated Learning via MEC-enabled Blockchain Network

In this paper, we propose blockchain network based architecture called “FLchain” for enhancing security of Federated Learning (FL). We leverage the concept of channels for learning multiple global models on FLchain. Local model parameters for each global iteration are stored as a block on the channel-specific ledger. We introduce the notion of “the global model state trie” which is stored and updated on the blockchain network based on the aggregation of local model updates collected from mobile devices. Qualitative evaluation shows that FLchain is more robust than traditional FL schemes as it ensures provenance and maintains auditable aspects of FL model in an immutable manner.
Unpaired Image Captioning via Scene Graph Alignments

Most of current image captioning models heavily rely on paired image-caption datasets. However, getting large scale image-caption paired data is labor-intensive and time-consuming. In this paper, we present a scene graph-based approach for unpaired image captioning. Our framework comprises an image scene graph generator, a sentence scene graph generator, a scene graph encoder, and a sentence decoder. Specifically, we first train the scene graph encoder and the sentence decoder on the text modality. To align the scene graphs between images and sentences, we propose an unsupervised feature alignment method that maps the scene graph features from the image to the sentence modality. Experimental results show that our proposed model can generate quite promising results without using any image-caption training pairs, outperforming existing methods by a wide margin.
Blockchain-Based Distributed Trust and Reputation Management Systems: A Survey

Distributed Ledger Technologies (DLTs), like Blockchain, are characterized by features such as transparency, traceability, and security by design. These features make the adoption of Blockchain attractive to enhance information security, privacy, and trustworthiness in very different contexts. This paper provides a comprehensive survey and aims at analyzing and assessing the use of Blockchain in the context of Distributed Trust and Reputation Management Systems (DTRMS). The analysis includes academic research as well as initiatives undertaken in the business domain. The paper defines two taxonomies for both Blockchain and DTRMS and applies a Formal Concept Analysis. Such an approach allowed us to identify the most recurrent and stable features in the current scientific landscape and several important implications among the two taxonomies. The results of the analysis have revealed significant trends and emerging practices in the current implementations that have been distilled into recommendations to guide Blockchain's adoption in DTRMS systems.
SSE-PT: Sequential Recommendation Via Personalized Transformer

Temporal information is crucial for recommendation problems because user preferences are naturally dynamic in the real world. Recent advances in deep learning, especially the discovery of various attention mechanisms and newer architectures in addition to widely used RNN and CNN in natural language processing, have allowed for better use of the temporal ordering of items that each user has engaged with. In particular, the SASRec model, inspired by the popular Transformer model in natural languages processing, has achieved state-of-the-art results. However, SASRec, just like the original Transformer model, is inherently an un-personalized model and does not include personalized user embeddings. To overcome this limitation, we propose a Personalized Transformer (SSE-PT) model, outperforming SASRec by almost 5% in terms of [email protected] on 5 real-world datasets. Furthermore, after examining some random users’ engagement history, we find our model not only more interpretable but also able to focus on recent engagement patterns for each user. Moreover, our SSE-PT model with a slight modification, which we call SSE-PT++, can handle extremely long sequences and outperform SASRec in ranking results with comparable training speed, striking a balance between performance and speed requirements. Our novel application of the Stochastic Shared Embeddings (SSE) regularization is essential to the success of personalization. Code and data are open-sourced at https://github.com/wuliwei9278/SSE-PT.
Blockchain and agricultural supply chains traceability: research trends and future challenges

Blockchain is today one of the most interesting and debated research topics. Blockchain technology was implemented for the first time in the financial sector a few years ago. However, it is currently used in many other areas, such as: healthcare, smart cities, smart contracts, energy markets, government sector. The success of this technology mainly lies in the following properties: reliability, transparency, immutability. In this study, we collect and analyze the main contributions in the literature about the application of blockchain in the agricultural sector, focusing on food traceability issues. Considering the quick growth of this technology and the high number of published documents in recent months, there is a need to catalog the different methodologies, proposed by the various scholars. Our aim is to detect the current research trends and possible future challenges. In the agricultural context, the need for an adequate traceability system is motivated by several bad habits and problems, such as the wide use of pesticides and fertilizers in fruits and vegetables, which are extremely harmful for human health. Moreover, in the last few years, the consumers’ attention about the quality of agricultural products has considerably increased. The present study shows that the blockchain technology is still in its early stage. Although there are several proposals in the literature, still a limited number of applications have been put into use in the real context. From the point of view of scientific research, only some countries are investing in this technology: China and United States are among the most active, but Italy is also very involved in this phenomenon. Overall, the blockchain technology appears very promising, but still many efforts are needed to reach the maturation stage.
Adoption of blockchain technology in various realms: Opportunities and challenges

Abstract Our society is becoming “smarter” and more interconnected, as evidenced by technological trends such as smart farming, smart healthcare, supply chain and logistics, and smart city and nation. Security and privacy are two major key considerations in these smart applications, such as blockchain‐based security solutions for Industry 4.0‐based applications (the focus of this study). In this study, we present a systematic review of existing blockchain‐based solutions, particularly for Industry 4.0‐based applications. We present the merits and demerits of various state‐of‐the‐art approaches. We also highlight the challenges of the blockchain with the scope of interoperability and governance so that this gives a clear understanding to researchers and professionals.
Mobile Learning in Higher Education: Unavoidable Alternative during COVID-19

Covid-19 is an acute respiratory disease caused by Coronavirus Sars-Cov-2. Declared recently as pandemic disease, Covid-19 has affected educational systems worldwide. Many countries around the world have closed educational institutions to reduce the spread of this pandemic. Hence, Education in high schools is facing unprecedented challenges. This paper reflects on the role of mobile learning as remote teaching strategies sustaining student-centered learning. The use of mobile learning allows learning anytime, anyplace, and anywhere. Mobile Learning is an unavoidable alternative during COVID-19.
A robust meaningful image encryption scheme based on block compressive sensing and SVD embedding

In this paper, an efficient and robust meaningful image encryption (MIE) scheme is developed by combining block compressive sensing (BCS) and singular value decomposition (SVD) embedding. This work devotes to the balanced performance of security, compression, robustness and running efficiency. First, the plain image is divided equally and sparsely represented in discrete cosine transform (DCT) domain, and the coefficient vectors are confused using the coefficient random permutation (CRP) strategy and encrypted into a secret image by compressive sensing. Next, SVD embedding is employed to embed the secret image into a carrier image to create the final meaningful cipher image. In pursuit of superior security, the hyper-chaotic Lorenz system is utilized to generate the updated secret code streams for encryption and embedding with assistance from the counter mode. This scheme is suitable for processing the medium and large images in parallel. Additionally, it exhibits superior robustness and efficiency compared with existing related schemes. Simulation results and comprehensive performance analyses are presented to demonstrate the effectiveness, secrecy and robustness of the proposed scheme.