Reviewerly/exHarmony huggingface dataset:
The limits of distribution-free conditional predictive inference

Abstract We consider the problem of distribution-free predictive inference, with the goal of producing predictive coverage guarantees that hold conditionally rather than marginally. Existing methods such as conformal prediction offer marginal coverage guarantees, where predictive coverage holds on average over all possible test points, but this is not sufficient for many practical applications where we would like to know that our predictions are valid for a given individual, not merely on average over a population. On the other hand, exact conditional inference guarantees are known to be impossible without imposing assumptions on the underlying distribution. In this work, we aim to explore the space in between these two and examine what types of relaxations of the conditional coverage property would alleviate some of the practical concerns with marginal coverage guarantees while still being possible to achieve in a distribution-free setting.
A Novel Chaos-Based Color Image Encryption Scheme Using Bit-Level Permutation

To ensure the security of digital images during transmission and storage, an efficient and secure chaos-based color image encryption scheme using bit-level permutation is proposed. Our proposed image encryption algorithm belongs to symmetric cryptography. Here, we process three color components simultaneously instead of individually, and consider the correlation between them. We propose a novel bit-level permutation algorithm that contains three parts: a plain-image related rows and columns substitution, a pixel-level roll shift part, and a bit-level cyclic shift part. In the plain-related rows and columns substitution part, we involve the plain-image information to generate a control sequence by using a skew tent system. This process ensures that the correlation between three color components can be totally broken, and our cryptosystem has enough plain-image sensitivity to resist the differential attack. In the pixel-level roll shift part and bit-level cyclic shift part, we have a fully bit-level permutation controlled by two sequences using a Rucklidge system. The simulation and some common security analyses are given. Test results show that our proposed scheme has good security performance and a speed advantage compared to other works.
Traceability of Ready-to-Wear Clothing through Blockchain Technology

Traceability and monitoring of industrial processes are becoming more important to assure the value of final products. Blockchain technology emerged as part of a movement linked to criptocurrencies and the Internet of Things, providing nice-to-have features such as traceability, authenticity and security to sectors willing to use this technology. In the retail industry, blockchain offers users the possibility to monitor details about time and place of elaboration, the origin of raw materials, the quality of materials involved in the manufacturing processes, information on the people or companies that work on it, etc. It allows to control and monitor textile articles, from their production or importing initial steps, up to their acquisition by the end consumer, using the blockchain as a means of tracking and identification during the whole process. This technology can also be used by the apparel industry in general and, more specifically, for ready-to-wear clothing, for tracing suppliers and customers along the entire logistics chain. The goal of this paper is to introduce the more recent traceability schemes for the apparel industry together with the proposal of a framework for ready-to-wear clothing which allows to ensure the transparency in the supply chain, clothing authenticity, reliability and integrity, and validity of the retail final products, and of the elements that compose the whole supply chain. In order to illustrate the proposal, a case study on a women’s shirt from an apparel and fashion company, where a private and open blockchain is used for tracing the product, is included. Blockchain actors are proposed for each product stage.
Generation-Level Parallelism for Evolutionary Computation: A Pipeline-Based Parallel Particle Swarm Optimization

Due to the population-based and iterative-based characteristics of evolutionary computation (EC) algorithms, parallel techniques have been widely used to speed up the EC algorithms. However, the parallelism usually performs in the population level where multiple populations (or subpopulations) run in parallel or in the individual level where the individuals are distributed to multiple resources. That is, different populations or different individuals can be executed simultaneously to reduce running time. However, the research into generation-level parallelism for EC algorithms has seldom been reported. In this article, we propose a new paradigm of the parallel EC algorithm by making the first attempt to parallelize the algorithm in the generation level. This idea is inspired by the industrial pipeline technique. Specifically, a kind of EC algorithm called local version particle swarm optimization (PSO) is adopted to implement a pipeline-based parallel PSO (PPPSO, i.e., P3SO). Due to the generation-level parallelism in P3SO, when some particles still perform their evolutionary operations in the current generation, some other particles can simultaneously go to the next generation to carry out the new evolutionary operations, or even go to further next generation(s). The experimental results show that the problem-solving ability of P3SO is not affected while the evolutionary speed has been substantially accelerated in a significant fashion. Therefore, generation-level parallelism is possible in EC algorithms and may have significant potential applications in time-consumption optimization problems.
Connecting the Dots: A Knowledgeable Path Generator for Commonsense Question Answering

Commonsense question answering (QA) requires background knowledge which is not explicitly stated in a given context.Prior works use commonsense knowledge graphs (KGs) to obtain this knowledge for reasoning.However, relying entirely on these KGs may not suffice, considering their limited coverage and the contextual dependence of their knowledge.In this paper, we augment a general commonsense QA framework with a knowledgeable path generator.By extrapolating over existing paths in a KG with a state-of-the-art language model, our generator learns to connect a pair of entities in text with a dynamic, and potentially novel, multi-hop relational path.Such paths can provide structured evidence for solving commonsense questions without finetuning the path generator.Experiments on two datasets show the superiority of our method over previous works which fully rely on knowledge from KGs (with up to 6% improvement in accuracy), across various amounts of training data.Further evaluation suggests that the generated paths are typically interpretable, novel, and relevant to the task.
RBPR: A hybrid model for the new user cold start problem in recommender systems

The recommender systems aim to predict potential demands of users by analyzing their preferences and provide personalized recommendation services. User preferences can be inferred from explicit or implicit feedback data. Most existing collaborative filtering (CF) methods rely heavily on explicit feedback data. However, these methods perform poorly when rating data is sparse. In this paper, we deal with the extreme case of sparse data, i.e., the new user cold start problem. In order to overcome this problem, we propose a novel CF ranking model, which combines a rating-oriented approach of Probabilistic Matrix Factorization (PMF) and a pairwise ranking-oriented approach of Bayesian Personalized Ranking (BPR) together. Therefore, our proposed model makes full use of the explicit and implicit feedback data. Experiments on the constructed new user cold start datasets based on four public datasets demonstrate the effectiveness of the proposed model for cold start recommendation. Code for the proposed method is available in https://gitee.com/xia_zhaoqiang/recomender-systems-rbpr.
One SPRING to Rule Them Both: Symmetric AMR Semantic Parsing and Generation without a Complex Pipeline

In Text-to-AMR parsing, current state-of-the-art semantic parsers use cumbersome pipelines integrating several different modules or components, and exploit graph recategorization, i.e., a set of content-specific heuristics that are developed on the basis of the training set. However, the generalizability of graph recategorization in an out-of-distribution setting is unclear. In contrast, state-of-the-art AMR-to-Text generation, which can be seen as the inverse to parsing, is based on simpler seq2seq. In this paper, we cast Text-to-AMR and AMR-to-Text as a symmetric transduction task and show that by devising a careful graph linearization and extending a pretrained encoder-decoder model, it is possible to obtain state-of-the-art performances in both tasks using the very same seq2seq approach, i.e., SPRING (Symmetric PaRsIng aNd Generation). Our model does not require complex pipelines, nor heuristics built on heavy assumptions. In fact, we drop the need for graph recategorization, showing that this technique is actually harmful outside of the standard benchmark. Finally, we outperform the previous state of the art on the English AMR 2.0 dataset by a large margin: on Text-to-AMR we obtain an improvement of 3.6 Smatch points, while on AMR-to-Text we outperform the state of the art by 11.2 BLEU points. We release the software at github.com/SapienzaNLP/spring.
3DIoUMatch: Leveraging IoU Prediction for Semi-Supervised 3D Object Detection

3D object detection is an important yet demanding task that heavily relies on difficult to obtain 3D annotations. To reduce the required amount of supervision, we propose 3DIoUMatch, a novel semi-supervised method for 3D object detection applicable to both indoor and outdoor scenes. We leverage a teacher-student mutual learning framework to propagate information from the labeled to the unlabeled train set in the form of pseudo-labels. However, due to the high task complexity, we observe that the pseudo-labels suffer from significant noise and are thus not directly usable. To that end, we introduce a confidence-based filtering mechanism, inspired by FixMatch. We set confidence thresholds based upon the predicted objectness and class probability to filter low-quality pseudo-labels. While effective, we observe that these two measures do not sufficiently capture localization quality. We therefore propose to use the estimated 3D IoU as a localization metric and set category-aware self-adjusted thresholds to filter poorly localized proposals. We adopt VoteNet as our backbone detector on indoor datasets while we use PV-RCNN on the autonomous driving dataset, KITTI. Our method consistently improves state-of-the-art methods on both ScanNet and SUN-RGBD benchmarks by significant margins under all label ratios (including fully labeled setting). For example, when training using only 10% labeled data on ScanNet, 3DIoUMatch achieves 7.7 absolute improvement on mAP@0.25 and 8.5 absolute improvement on mAP@0.5 upon the prior art. On KITTI, we are the first to demonstrate semi-supervised 3D object detection and our method surpasses a fully supervised baseline from 1.8% to 7.6% under different label ratio and categories.
Feature Enhancement Network for Object Detection in Optical Remote Sensing Images

Automatic and robust object detection in remote sensing images is of vital significance in real-world applications such as land resource management and disaster rescue. However, poor performance arises when the state-of-the-art natural image detection algorithms are directly applied to remote sensing images, which largely results from the variations in object scale, aspect ratio, indistinguishable object appearances, and complex background scenario. In this paper, we propose a novel Feature Enhancement Network (FENet) for object detection in optical remote sensing images, which consists of a Dual Attention Feature Enhancement (DAFE) module and a Context Feature Enhancement (CFE) module. Specifically, the DAFE module is introduced to highlight the network to focus on the distinctive features of the objects of interest and suppress useless ones by jointly recalibrating the spatial and channel feature responses. The CFE module is designed to capture global context cues and selectively strengthen class-aware features by leveraging image-level contextual information that indicates the presence or absence of the object classes. To this end, we employ a context encoding loss to regularize the model training which promotes the object detector to understand the scene better and narrows the probable object categories in prediction. We achieve our proposed FENet by unifying DAFE and CFE into the framework of Faster R-CNN. In the experiments, we evaluate our proposed method on two large-scale remote sensing image object detection datasets including DIOR and DOTA and demonstrate its effectiveness compared with the baseline methods.
Integrating digital pathology into clinical practice

The field of anatomic pathology has been evolving in the last few decades and the advancements have been largely fostered by innovative technology. Immunohistochemistry enabled a paradigm shift in discovery and diagnostic evaluation, followed by booming genomic advancements which allowed for submicroscopic pathologic characterization, and now the field of digital pathology coupled with machine learning and big data acquisition is paving the way to revolutionize the pathology medical domain. Whole slide imaging (WSI) is a disruptive technology where glass slides are digitized to produce on-screen whole slide images. Specifically, in the past decade, there have been significant advances in digital pathology systems that have allowed this technology to promote integration into clinical practice. Whole slide images (WSI), or digital slides, can be viewed and navigated comparable to glass slides on a microscope, as digital files. Whole slide imaging has increased in adoption among pathologists, pathology departments, and scientists for clinical, educational, and research initiatives. Integration of digital pathology systems requires a coordinated effort with numerous stakeholders, not only within the pathology department, but across the entire enterprise. Each pathology department has distinct needs, use cases and blueprints, however the framework components and variables for successful clinical integration can be generalized across any organization seeking to undergo a digital transformation at any scale. This article will review those components and considerations for integrating digital pathology systems into clinical practice.
End-to-End Video Object Detection with Spatial-Temporal Transformers

Recently, DETR and Deformable DETR have been proposed to eliminate the need for many hand-designed components in object detection while demonstrating good performance as previous complex hand-crafted detectors. However, their performance on Video Object Detection (VOD) has not been well explored. In this paper, we present TransVOD, an end-to-end video object detection model based on a spatial-temporal Transformer architecture. The goal of this paper is to streamline the pipeline of VOD, effectively removing the need for many hand-crafted components for feature aggregation, e.g., optical flow, recurrent neural networks, relation networks. Besides, benefited from the object query design in DETR, our method does not need complicated post-processing methods such as Seq-NMS or Tubelet rescoring, which keeps the pipeline simple and clean. In particular, we present temporal Transformer to aggregate both the spatial object queries and the feature memories of each frame. Our temporal Transformer consists of three components: Temporal Deformable Transformer Encoder (TDTE) to encode the multiple frame spatial details, Temporal Query Encoder (TQE) to fuse object queries, and Temporal Deformable Transformer Decoder (TDTD) to obtain current frame detection results. These designs boost the strong baseline deformable DETR by a significant margin (3%-4% mAP) on the ImageNet VID dataset. TransVOD yields comparable results performance on the benchmark of ImageNet VID. We hope our TransVOD can provide a new perspective for video object detection.
Cross-Layer Device Authentication With Quantum Encryption for 5G Enabled IIoT in Industry 4.0

Industrial Internet of Things (IIoT), a core enabler of Industry 4.0, is evolving rapidly to tackle the challenges imposed by explosive real-time manufacturing data in the context of Internet and telecommunication industry. 5G technology is the key to addressing such challenges. This is done by bypassing upper authentication protocols and supporting small data transmission during initial access, which, however, causes serious security breaches in IIoT device authentication. To solve this, in this article propose a secure cross-layer authentication framework based on quantum walk on circles. The system performs random hash coding on multidomain physical-layer resources to encode and decode device identifiers securely, while using a quantum walk based privacy-preserving protocol to maintain code privacy at arbitrary high level, being controlled by the number of occupied physical resources. The upper bound of decoding errors is derived and a nonconvex integer programming problem of minimizing the bound is formulated to characterize the security performance. The space of one-time keys for encryption is also derived that show how high privacy and scalability advantage is maintained against classical and quantum computers. Finally, we derive novel expressions of failure probability of this new authentication system and numerically show that our scheme can bring ultrahigh level of security and privacy protection with low latency despite attack.
QuestEval: Summarization Asks for Fact-based Evaluation

Summarization evaluation remains an openresearch problem: current metrics such as ROUGE are known to be limited and to correlate poorly with human judgments.To alleviate this issue, recent work has proposed evaluation metrics which rely on question answering models to assess whether a summary contains all the relevant information in its source document.Though promising, the proposed approaches have so far failed to correlate better than ROUGE with human judgments.In this paper, we extend previous approaches and propose a unified framework, named QU E S TEV A L.In contrast to established metrics such as ROUGE or BERTScore, QU E S TEV A L does not require any groundtruth reference.Nonetheless, QU E S TEV A L substantially improves the correlation with human judgments over four evaluation dimensions (consistency, coherence, fluency, and relevance), as shown in extensive experiments.We make code and models available. 1
Deep Learning-Based Object Detection and Scene Perception under Bad Weather Conditions

Large cities’ expanding populations are causing traffic congestion. The maintenance of the city’s road network necessitates ongoing monitoring, growth, and modernization. An intelligent vehicle detection solution is necessary to address road traffic concerns with the advancement of automatic cars. The identification and tracking vehicles on roads and highways are part of intelligent traffic monitoring while driving. In this paper, we have presented how You Only Look Once (YOLO) v5 model may be used to identify cars, traffic lights, and pedestrians in various weather situations, allowing for real-time identification in a typical vehicular environment. In an ordinary or autonomous environment, object detection may be affected by bad weather conditions. Bad weather may make driving dangerous in various ways, whether due to freezing roadways or the illusion of low fog. In this study, we used YOLOv5 model to recognize objects from street-level recordings for rainy and regular weather scenarios on 11 distinct classes of vehicles (car, truck, bike), pedestrians, and traffic signals (red, green, yellow). We utilized freely available Roboflow datasets to train the proposed system. Furthermore, we used real video sequences of road traffic to evaluate the proposed system’s performance. The study results revealed that the suggested approach could recognize cars, trucks, and other roadside items in various circumstances with acceptable results.
Music Recommendation via Hypergraph Embedding

In recent years, we have witnessed an ever wider spread of multimedia streaming platforms (e.g., Netflix, Spotify, and Amazon). Hence, it has become more and more essential to provide such systems with advanced recommendation facilities, in order to support users in browsing these massive collections of multimedia data according to their preferences and needs. In this context, the modeling of entities and their complex relationships (e.g., users listening to topic-based songs or authors creating different releases of their lyrics) represents the key challenge to improve the recommendation and maximize the users' satisfaction. To this end, this is the first study to leverage the high representative power of hypergraph data structures in combination with modern graph machine learning techniques in the context of music recommendation. Specifically, we propose hypergraph embeddings for music recommendation (HEMR), a novel framework for song recommendation based on hypergraph embedding. The hypergraph data model allows us to represent seamlessly all the possible and complex interactions between users and songs with the related characteristics; meanwhile, embedding techniques provide a powerful way to infer the user-song similarities by vector mapping. We have experimented the effectiveness and efficiency of our approach with respect to the state-of-the-art most recent music recommender systems, exploiting the Million Song dataset. The results show that HEMR significantly outperforms other state-of-the-art techniques, especially in scenarios where the cold-start problem arises, thus making our system a suitable solution to embed within a music streaming platform.
Augmented Reality in Cultural Heritage: An Overview of the Last Decade of Applications

Augmented reality is a mature technology that uses the real world as a substrate and extends it by overlaying computer-generated information. It has been applied to several domains. In particular, the technology was proven to be useful for the management and preservation of Cultural Heritage. This study provides an overview of the last decade of the use of augmented reality in cultural heritage through a detailed review of the scientific papers in the field. We analyzed the applications published on Scopus and Clarivate Web of Science databases over a period of 9 years (2012–2021). Bibliometric data consisted of 1201 documents, and their analysis was performed using various tools, including ScientoPy, VOS Viewer, and Microsoft Excel. The results revealed eight trending topics of applying augmented reality technology to cultural heritage: 3D reconstruction of cultural artifacts, digital heritage, virtual museums, user experience, education, tourism, intangible cultural heritage, and gamification. Each topic is discussed in detail in the article sections, providing insight into existing applications and research trends for each application field.