Reviewerly/exHarmony huggingface dataset:
Risks and Opportunities for Reforming Construction with Blockchain: Bibliometric Study

Emerging technologies are always treated with caution, and Blockchain is no exception to this rule. The AECO (Architecture, Engineering, Construction, and Operations) sector is considered conservative when it comes to technology adoption, which is often positive in an industry that deals with a vital commodity that could harm humans or result in catastrophe. However, excessive caution also stifles innovation, as shown in the industry’s low R&amp;D expenditure when compared to other industrial activities. In recent years, the AECO sector has benefited from the adoption of new information management tools and systems that allow professionals to develop, share and store construction data more effectively. These tools have successfully tackled many of the traditional interoperability issues that have affected the industry, but several challenges remain unsolved. Indeed, effective real-world communication depends, not only on the compatibility between data formats and systems, but also on issues such as privacy, transparency, and trust. Blockchain has been adopted in different activities as a tool to address these issues, but its impact in the construction industry remains scarce. This paper presents a bibliometric study for the available literature on Blockchain implementation in the AECO sector. In addition to the bibliometric review, content analysis for the literature retrieved is presented to provide a clear vision of the current directions regarding Blockchain technology adoption. The purpose of this article is to evaluate the maturity of Blockchain in Construction literature using statistics based on the available bibliometrics in addition to content analysis. The main results show that although the number of articles about the use of Blockchain in Construction has increased, no studies that present ready to use solutions were found. Instead, the covered studies discuss the technical capabilities of the technology and suggest possible fields of implementation, such as smart contracts and automated payments. Study limitations include the limited amount of literature that can be found on major indexing services, which cover a relatively short reference period.
HealthAssistantBot: A Personal Health Assistant for the Italian Language

In this article, we present HealthAssistantBot, an intelligent virtual assistant able to talk with patients in order to understand their symptomatology, suggest doctors, and monitor treatments and health parameters. In a simple way, by exploiting a natural language-based interaction, the system allows the user to create her health profile, to describe her symptoms, to search for doctors or to simply remember a treatment to follow. Specifically, our methodology exploits machine learning techniques to process users symptoms and to automatically infer her diseases. Next, the information obtained is used by our recommendation algorithm to identify the nearest doctor who can best treat the user's condition, considering the community data. In the experimental session we evaluated our HealthAssistantBot with both an offline and online evaluation. In the first case, we assessed the performance of our internal components, while in the second one we carried out a study involving 102 subjects who interacted with the conversational agent in a daily use scenario. Results are encouraging and showed the effectiveness of the strategy in supporting the patients in taking care of their health.
History for Visual Dialog: Do we really need it?

Visual Dialog involves "understanding" the dialog history (what has been discussed previously) and the current question (what is asked), in addition to grounding information in the image, to generate the correct response.In this paper, we show that co-attention models which explicitly encode dialog history outperform models that don't, achieving state-ofthe-art performance (72 % NDCG on val set).However, we also expose shortcomings of the crowd-sourcing dataset collection procedure by showing that history is indeed only required for a small amount of the data and that the current evaluation metric encourages generic replies.To that end, we propose a challenging subset (VisDialConv) of the VisDial val set and provide a benchmark of 63% NDCG.
Making of Night Vision: Object Detection Under Low-Illumination

Object detection has so far achieved great success.However, almost all of current state-ofthe-art methods focus on images with normal illumination, while object detection under low-illumination is often ignored.In this paper, we have extensively investigated several important issues related to the challenge low-illumination detection task, such as the importance of illumination on detection, the applicabilities of illumination enhancement on low-illumination object detection task, and the influences of illumination balanced dataset and model's parameters initialization, etc.We further have proposed a Night Vision Detector (NVD) with specifically designed feature pyramid network and context fusion network for object detection under low-illuminance.Through conducting comprehensive experiments on a public real low-illuminance scene dataset ExDARK and a selected normal-illumination counterpart COCO*, we on one hand have reached some valuable conclusions for reference, on the other hand, have found specific solutions for low-illumination object detection.Our strategy improves detection performance by 0.5%∼2.8%higher than basic model on all standard COCO evaluation criterions.Our work can be taken as effective baseline and shed light to future studies on low-illumination detection.
Human Posture Recognition Using a Hybrid of Fuzzy Logic and Machine Learning Approaches

An autonomous assistive robot needs to recognize the body-limb posture of the person being assisted while he/she is lying in a bed to provide care services such as helping change the posture of the person or carrying him/her from the bed to a wheelchair. This paper presents a data-efficient classification of human postures when lying in a bed using a hybrid fuzzy logic and machine learning approach. The classifier was trained using a relatively small dataset containing 19,800 annotated depth images collected using Kinect from 32 test subjects lying in bed. An overall accuracy of 97.1% was achieved on the dataset. Furthermore, the image dataset including depth and red-green-blue (RGB) images, is available to the research community with the publication of this paper, with the hope that it can benefit other researchers.
Nationwide Results of COVID-19 Contact Tracing in South Korea: Individual Participant Data From an Epidemiological Survey

Background Evidence regarding the effectiveness of contact tracing of COVID-19 and the related social distancing is limited and inconclusive. Objective This study aims to investigate the epidemiological characteristics of SARS-CoV-2 transmission in South Korea and evaluate whether a social distancing campaign is effective in mitigating the spread of COVID-19. Methods We used contract tracing data to investigate the epidemic characteristics of SARS-CoV-2 transmission in South Korea and evaluate whether a social distancing campaign was effective in mitigating the spread of COVID-19. We calculated the mortality rate for COVID-19 by infection type (cluster vs noncluster) and tested whether new confirmed COVID-19 trends changed after a social distancing campaign. Results There were 2537 patients with confirmed COVID-19 who completed the epidemiologic survey: 1305 (51.4%) cluster cases and 1232 (48.6%) noncluster cases. The mortality rate was significantly higher in cluster cases linked to medical facilities (11/143, 7.70% vs 5/1232, 0.41%; adjusted percentage difference 7.99%; 95% CI 5.83 to 10.14) and long-term care facilities (19/221, 8.60% vs 5/1232, 0.41%; adjusted percentage difference 7.56%; 95% CI 5.66 to 9.47) than in noncluster cases. The change in trends of newly confirmed COVID-19 cases before and after the social distancing campaign was significantly negative in the entire cohort (adjusted trend difference –2.28; 95% CI –3.88 to –0.68) and the cluster infection group (adjusted trend difference –0.96; 95% CI –1.83 to –0.09). Conclusions In a nationwide contact tracing study in South Korea, COVID-19 linked to medical and long-term care facilities significantly increased the risk of mortality compared to noncluster COVID-19. A social distancing campaign decreased the spread of COVID-19 in South Korea and differentially affected cluster infections of SARS-CoV-2.
A new hybrid text encryption approach over mobile ad hoc network

Data exchange has been rapidly increased recently by increasing the use of mobile networks. Sharing information (text, image, audio and video) over unsecured mobile network channels is liable for attacking and stealing. Encryption techniques are the most suitable methods to protect information from hackers. Hill cipher algorithm is one of symmetric techniques, it has a simple structure and fast computations, but weak security because sender and receiver need to use and share the same private key within a non-secure channel. Therefore, a novel hybrid encryption approach between elliptic curve cryptosystem and hill cipher (ECCHC) is proposed in this paper to convert Hill Cipher from symmetric technique (private key) to asymmetric one (public key) and increase its security and efficiency and resist the hackers. Thus, no need to share the secret key between sender and receiver and both can generate it from the private and public keys. Therefore, the proposed approach presents a new contribution by its ability to encrypt every character in the 128 ASCII table by using its ASCII value direct without needing to assign a numerical value for each character. The main advantages of the proposed method are represented in the computation simplicity, security efficiency and faster computation.
Seismic signal augmentation to improve generalization of deep neural networks

Deep learning has emerged as an effective approach for seismic data processing in general, and for earthquake monitoring in particular. The ability of deep learning models to generalize beyond the training and validation data is important for comprehensive earthquake monitoring; this ability furthermore depends on the availability of a sufficiently large and complete training dataset. However, this requirement can prove challenging to meet due to significant effort and time for data collection and labeling. Data augmentation provides an efficient and effective approach for increasing the dimension of training samples and improving generalization to unseen samples. In this paper, we present augmentation methods appropriate for seismic waveforms and demonstrate their ability to reduce bias and increase performance. These augmentation methods can be applied to a wide range of deep learning applications designed for seismic data.
Weakly Supervised Multiple Instance Learning Histopathological Tumor Segmentation

Histopathological image segmentation is a challenging and important topic in medical imaging with tremendous potential impact in clinical practice. State of the art methods rely on hand-crafted annotations which hinder clinical translation since histology suffers from significant variations between cancer phenotypes. In this paper, we propose a weakly supervised framework for whole slide imaging segmentation that relies on standard clinical annotations, available in most medical systems. In particular, we exploit a multiple instance learning scheme for training models. The proposed framework has been evaluated on multi-locations and multi-centric public data from The Cancer Genome Atlas and the PatchCamelyon dataset. Promising results when compared with experts’ annotations demonstrate the potentials of the presented approach. The complete framework, including 6481 generated tumor maps and data processing, is available at https://github.com/marvinler/tcga_segmentation.
Open Domain Question Answering based on Text Enhanced Knowledge Graph with Hyperedge Infusion

The incompleteness of knowledge base (KB) is a vital factor limiting the performance of question answering (QA).This paper proposes a novel QA method by leveraging text information to enhance the incomplete KB.The model enriches the entity representation through semantic information contained in the text, and employs graph convolutional networks to update the entity status.Furthermore, to exploit the latent structural information of text, we treat the text as hyperedges connecting entities among it to complement the deficient relations in KB, and hypergraph convolutional networks are further applied to reason on the hypergraph-formed text.Extensive experiments on the WebQuestionsSP benchmark with different KB settings prove the effectiveness of our model.
Improving Compositional Generalization in Semantic Parsing

Generalization of models to out-ofdistribution (OOD) data has captured tremendous attention recently.Specifically, compositional generalization, i.e., whether a model generalizes to new structures built of components observed during training, has sparked substantial interest.In this work, we investigate compositional generalization in semantic parsing, a natural test-bed for compositional generalization, as output programs are constructed from sub-components.We analyze a wide variety of models and propose multiple extensions to the attention module of the semantic parser, aiming to improve compositional generalization.We find that the following factors improve compositional generalization: (a) using contextual representations, such as ELMO and BERT, (b) informing the decoder what input tokens have previously been attended to, (c) training the decoder attention to agree with pre-computed token alignments, and (d) downsampling examples corresponding to frequent program templates.While we substantially reduce the gap between in-distribution and OOD generalization, performance on OOD compositions is still substantially lower.
Patient Monitoring by Abnormal Human Activity Recognition Based on CNN Architecture

Human action recognition has emerged as a challenging research domain for video understanding and analysis. Subsequently, extensive research has been conducted to achieve the improved performance for recognition of human actions. Human activity recognition has various real time applications, such as patient monitoring in which patients are being monitored among a group of normal people and then identified based on their abnormal activities. Our goal is to render a multi class abnormal action detection in individuals as well as in groups from video sequences to differentiate multiple abnormal human actions. In this paper, You Look only Once (YOLO) network is utilized as a backbone CNN model. For training the CNN model, we constructed a large dataset of patient videos by labeling each frame with a set of patient actions and the patient’s positions. We retrained the back-bone CNN model with 23,040 labeled images of patient’s actions for 32 epochs. Across each frame, the proposed model allocated a unique confidence score and action label for video sequences by finding the recurrent action label. The present study shows that the accuracy of abnormal action recognition is 96.8%. Our proposed approach differentiated abnormal actions with improved F1-Score of 89.2% which is higher than state-of-the-art techniques. The results indicate that the proposed framework can be beneficial to hospitals and elder care homes for patient monitoring.
Multi-Stage Feature Fusion Network for Video Super-Resolution

Video super-resolution (VSR) is to restore a photo-realistic high-resolution (HR) frame from both its corresponding low-resolution (LR) frame (reference frame) and multiple neighboring frames (supporting frames). An important step in VSR is to fuse the feature of the reference frame with the features of the supporting frames. The major issue with existing VSR methods is that the fusion is conducted in a one-stage manner, and the fused feature may deviate greatly from the visual information in the original LR reference frame. In this paper, we propose an end-to-end Multi-Stage Feature Fusion Network that fuses the temporally aligned features of the supporting frames and the spatial feature of the original reference frame at different stages of a feed-forward neural network architecture. In our network, the Temporal Alignment Branch is designed as an inter-frame temporal alignment module used to mitigate the misalignment between the supporting frames and the reference frame. Specifically, we apply the multi-scale dilated deformable convolution as the basic operation to generate temporally aligned features of the supporting frames. Afterwards, the Modulative Feature Fusion Branch, the other branch of our network accepts the temporally aligned feature map as a conditional input and modulates the feature of the reference frame at different stages of the branch backbone. This enables the feature of the reference frame to be referenced at each stage of the feature fusion process, leading to an enhanced feature from LR to HR. Experimental results on several benchmark datasets demonstrate that our proposed method can achieve state-of-the-art performance on VSR task.
NTT Multiplication for NTT-unfriendly Rings

In this paper, we show how multiplication for polynomial rings used in the NIST PQC finalists Saber and NTRU can be efficiently implemented using the Number-theoretic transform (NTT). We obtain superior performance compared to the previous state of the art implementations using Toom–Cook multiplication on both NIST’s primary software optimization targets AVX2 and Cortex-M4. Interestingly, these two platforms require different approaches: On the Cortex-M4, we use 32-bit NTT-based polynomial multiplication, while on Intel we use two 16-bit NTT-based polynomial multiplications and combine the products using the Chinese Remainder Theorem (CRT).For Saber, the performance gain is particularly pronounced. On Cortex-M4, the Saber NTT-based matrix-vector multiplication is 61% faster than the Toom–Cook multiplication resulting in 22% fewer cycles for Saber encapsulation. For NTRU, the speed-up is less impressive, but still NTT-based multiplication performs better than Toom–Cook for all parameter sets on Cortex-M4. The NTT-based polynomial multiplication for NTRU-HRSS is 10% faster than Toom–Cook which results in a 6% cost reduction for encapsulation. On AVX2, we obtain speed-ups for three out of four NTRU parameter sets.As a further illustration, we also include code for AVX2 and Cortex-M4 for the Chinese Association for Cryptologic Research competition award winner LAC (also a NIST round 2 candidate) which outperforms existing code.
FairFL: A Fair Federated Learning Approach to Reducing Demographic Bias in Privacy-Sensitive Classification Models

The recent advance of the federated learning (FL) has brought new opportunities for privacy-aware distributed machine learning (ML) applications to train a powerful ML model without accessing the private training data of the participants. In this paper, we focus on addressing a novel fair classification problem in FL where the model trained by FL displays discriminatory bias towards particular demographic groups. Addressing the fairness issue in a FL framework posts three critical challenges: fairness and performance trade-offs, restricted information, and constrained coordination. To address these challenges, we develop FairFL, a fair federated learning framework dedicated to reducing the bias in privacy-sensitive ML applications. It consists of a principled deep multi-agent reinforcement learning framework and a secure information aggregation protocol that optimizes both the accuracy and the fairness of the learned model while respecting the strict privacy constraints of the clients. Evaluation results on real-world applications showed that FairFL can achieve significant performance gains in both fairness and accuracy of the learned model compared to state-of-the-art baselines.
Tokenized: The Law of Non-Fungible Tokens and Unique Digital Property

Markets for unique digital property—digital equivalents of rare artworks, collectible trading cards, and other assets that gain value from scarcity—have exploded in the past several months. At root is the next iteration of blockchain technology, unique digital assets called non-fungible tokens. Unlike Bitcoin, where one coin is the same as another, NFTs are unique, each with different attributes. An NFT that represented ownership of Boardwalk would be quite different from one that represented Baltic Avenue. NFTs have grown from a few early breakout successes to a rapidly developing market for unique digital treasures. The attraction to buyers is that unlike digital assets like e-books or licensed movies, NFTs can be bought, sold, displayed, gifted, or even destroyed just like personal property. Yet law has not kept pace with demand for unique digital property. In particular, the rules designed for the 2000s internet focused on expanding intellectual property licenses and online contracts to the point that we are mere users, not owners, of digital assets. This article proposes a clear path for the evolution of the legal underpinnings of NFTs. It argues that NFTs are personal property, not contracts (despite the “smart contracts” popular nomenclature) or pure intellectual property licenses (despite the currently governing law of digital assets like e-books). Because transactions in NFTs are in the form of a sale, the law of sales of personal property should apply. And finally, the article notes that NFTs will serve as a powerful grounding example of digital personal property, a legal form of ownership that is both sorely needed and has not yet been clearly established online. That example will ground others, and permit law to again characterize those who buy scarce and valuable digital assets as true owners rather than mere users.
Finding Concept-specific Biases in Form–Meaning Associations

This work presents an information-theoretic operationalisation of cross-linguistic nonarbitrariness.It is not a new idea that there are small, cross-linguistic associations between the forms and meanings of words.For instance, it has been claimed (Blasi et al., 2016) that the word for TONGUE is more likely than chance to contain the phone [l].By controlling for the influence of language family and geographic proximity within a very large concept-aligned, cross-lingual lexicon, we extend methods previously used to detect within language non-arbitrariness (Pimentel et al., 2019) to measure cross-linguistic associations.We find that there is a significant effect of non-arbitrariness, but it is unsurprisingly small (less than 0.5% on average according to our information-theoretic estimate).We also provide a concept-level analysis which shows that a quarter of the concepts considered in our work exhibit a significant level of cross-linguistic non-arbitrariness.In sum, the paper provides new methods to detect cross-linguistic associations at scale, and confirms their effects are minor.