Reviewerly/exHarmony huggingface dataset:
Anamorphic Encryption: Private Communication Against a Dictator

Cryptosystems have been developed over the years under the typical prevalent setting which assumes that the receiver’s key is kept secure from the adversary, and that the choice of the message to be sent is freely performed by the sender and is kept secure from the adversary as well. Under these fundamental and basic operational assumptions, modern Cryptography has flourished over the last half a century or so, with amazing achievements: New systems (including public-key Cryptography), beautiful and useful models (including security definitions such as semantic security), and new primitives (such as zero-knowledge proofs) have been developed. Furthermore, these fundamental achievements have been translated into actual working systems, and span many of the daily human activities over the Internet. However, in recent years, there is an overgrowing pressure from many governments to allow the government itself access to keys and messages of encryption systems (under various names: escrow encryption, emergency access, communication decency acts, etc.). Numerous non-direct arguments against such policies have been raised, such as “the bad guys can utilize other encryption system” so all other cryptosystems have to be declared illegal, or that “allowing the government access is an ill-advised policy since it creates a natural weak systems security point, which may attract others (to masquerade as the government).” It has remained a fundamental open issue, though, to show directly that the above mentioned efforts by a government (called here “a dictator” for brevity) which mandate breaking of the basic operational assumption (and disallowing other cryptosystems), is, in fact, a futile exercise. This is a direct technical point which needs to be made and has not been made to date. In this work, as a technical demonstration of the futility of the dictator’s demands, we invent the notion of “Anamorphic Encryption” which shows that even if the dictator gets the keys and the messages used in the system (before anything is sent) and no other system is allowed, there is a covert way within the context of well established public-key cryptosystems for an entity to immediately (with no latency) send piggybacked secure messages which are, in spite of the stringent dictator conditions, hidden from the dictator itself! We feel that this may be an important direct technical argument against the nature of governments’ attempts to police the use of strong cryptographic systems, and we hope to stimulate further works in this direction.
Distance learning in primary school during the COVID-19 pandemic: results of the “SMART KIDS” experiment

The paper analyzes the results of the introduction of the distance learning form (DLF) using electronic educational resources (EER) and the teacher’s virtual classroom in primary school. The experiment took place within the framework of the “Smart Kids” All-Ukrainian project during the long quarantine caused by the COVID-19 pandemic. The educational process took place both synchronously and asynchronously. The present paper substantiates the model of organization of distance learning of primary school students using EER and outlines its three main components: the organization of learning; conducting online classes (explaining new material or practicing skills by students) and monitoring the quality of students’ independent performance of tasks. The results of the experiment prove that it is necessary to provide teachers and students with computer equipment, Internet access, digital resources for teaching and assessment to implement DLF. It has been established that EER in distance learning can be used both on a regular basis – in each class, and periodically – to explain new material or train skills; the quality of tasks performed by students can be monitored in the virtual office of the teacher and shape an individual trajectory of students’ development. The teachers identified the following main problems of DLF implementation: internet interruptions; problems with providing new computer equipment to students and some teachers; lack of state aid in providing EER to all participants in the educational process; limited access to students’ computers during complete isolation due to online work of parents. Despite the outlined problems, the quality of distance learning of primary school students during the pandemic using EER was positively and highly assessed by teachers.
Sentence part-enhanced BERT with respect to downstream tasks

Abstract Bidirectional encoder representations from transformers (BERT) have achieved great success in many natural language processing tasks. However, BERT generally takes the embedding of the first token to represent sentence meaning in the tasks such as sentiment analysis and textual similarity, which does not properly treat different sentence parts. Different sentence parts have different levels of importance for different downstream tasks. For example, main parts (subject, predicate, and object) play crucial roles in textual similarity calculation, while secondary parts (adverbial and complement) are more important than the main parts in sentiment analysis. To this end, we propose a sentence part-enhanced BERT (SpeBERT) model that uses sentence parts with respect to downstream tasks to enhance sentence representations. Specifically, we encode sentence parts based on dependency parsing and downstream tasks, and extract embeddings through a pooling operation. Furthermore, we design several fusion strategies to incorporate different embeddings. We evaluate the proposed SpeBERT model on two downstream tasks, sentiment classification, and semantic textual similarity, with six benchmark datasets. The experimental results show that our model achieves better performance than competitor models.
Semantic Compression Embedding for Generative Zero-Shot Learning

Generative methods have been successfully applied in zero-shot learning (ZSL) by learning an implicit mapping to alleviate the visual-semantic domain gaps and synthesizing unseen samples to handle the data imbalance between seen and unseen classes. However, existing generative methods simply use visual features extracted by the pre-trained CNN backbone. These visual features lack attribute-level semantic information. Consequently, seen classes are indistinguishable, and the knowledge transfer from seen to unseen classes is limited. To tackle this issue, we propose a novel Semantic Compression Embedding Guided Generation (SC-EGG) model, which cascades a semantic compression embedding network (SCEN) and an embedding guided generative network (EGGN). The SCEN extracts a group of attribute-level local features for each sample and further compresses them into the new low-dimension visual feature. Thus, a dense-semantic visual space is obtained. The EGGN learns a mapping from the class-level semantic space to the dense-semantic visual space, thus improving the discriminability of the synthesized dense-semantic unseen visual features. Extensive experiments on three benchmark datasets, i.e., CUB, SUN and AWA2, demonstrate the signiﬁcant performance gains of SC-EGG over current state-of-the-art methods and its baselines.
RePFormer: Refinement Pyramid Transformer for Robust Facial Landmark Detection

This paper presents a Refinement Pyramid Transformer (RePFormer) for robust facial landmark detection. Most facial landmark detectors focus on learning representative image features. However, these CNN-based feature representations are not robust enough to handle complex real-world scenarios due to ignoring the internal structure of landmarks, as well as the relations between landmarks and context. In this work, we formulate the facial landmark detection task as refining landmark queries along pyramid memories. Specifically, a pyramid transformer head (PTH) is introduced to build both homologous relations among landmarks and heterologous relations between landmarks and cross-scale contexts. Besides, a dynamic landmark refinement (DLR) module is designed to decompose the landmark regression into an end-to-end refinement procedure, where the dynamically aggregated queries are transformed to residual coordinates predictions. Extensive experimental results on four facial landmark detection benchmarks and their various subsets demonstrate the superior performance and high robustness of our framework.
Eliminating Backdoor Triggers for Deep Neural Networks Using Attention Relation Graph Distillation

Due to the prosperity of Artificial Intelligence (AI) techniques, more and more backdoors are designed by adversaries to attack Deep Neural Networks (DNNs). Although the state-of-the-art method Neural Attention Distillation (NAD) can effectively erase backdoor triggers from DNNs, it still suffers from non-negligible Attack Success Rate (ASR) together with lowered classification ACCuracy (ACC), since NAD focuses on backdoor defense using attention features (i.e., attention maps) of the same order. In this paper, we introduce a novel backdoor defense framework named Attention Relation Graph Distillation (ARGD), which fully explores the correlation among attention features with different orders using our proposed Attention Relation Graphs (ARGs). Based on the alignment of ARGs between teacher and student models during knowledge distillation, ARGD can more effectively eradicate backdoors than NAD. Comprehensive experimental results show that, against six latest backdoor attacks, ARGD outperforms NAD by up to 94.85% reduction in ASR, while ACC can be improved by up to 3.23%.
Preoperative Non-Invasive Prediction of Breast Cancer Molecular Subtypes With a Deep Convolutional Neural Network on Ultrasound Images

This study aimed to develop a deep convolutional neural network (DCNN) model to classify molecular subtypes of breast cancer from ultrasound (US) images together with clinical information.A total of 1,012 breast cancer patients with 2,284 US images (center 1) were collected as the main cohort for training and internal testing. Another cohort of 117 breast cancer cases with 153 US images (center 2) was used as the external testing cohort. Patients were grouped according to thresholds of nodule sizes of 20 mm and age of 50 years. The DCNN models were constructed based on US images and the clinical information to predict the molecular subtypes of breast cancer. A Breast Imaging-Reporting and Data System (BI-RADS) lexicon model was built on the same data based on morphological and clinical description parameters for diagnostic performance comparison. The diagnostic performance was assessed through the accuracy, sensitivity, specificity, Youden's index (YI), and area under the receiver operating characteristic curve (AUC).Our DCNN model achieved better diagnostic performance than the BI-RADS lexicon model in differentiating molecular subtypes of breast cancer in both the main cohort and external testing cohort (all p < 0.001). In the main cohort, when classifying luminal A from non-luminal A subtypes, our model obtained an AUC of 0.776 (95% CI, 0.649-0.885) for patients older than 50 years and 0.818 (95% CI, 0.726-0.902) for those with tumor sizes ≤20 mm. For young patients ≤50 years, the AUC value of our model for detecting triple-negative breast cancer was 0.712 (95% CI, 0.538-0.874). In the external testing cohort, when classifying luminal A from non-luminal A subtypes for patients older than 50 years, our DCNN model achieved an AUC of 0.686 (95% CI, 0.567-0.806).We employed a DCNN model to predict the molecular subtypes of breast cancer based on US images. Our model can be valuable depending on the patient's age and nodule sizes.
Research on the Optimization of Landscape Design Talent Training Mode Under the Background of Rural Revitalization Strategy

Objective: The rural revitalization strategy is an important strategic task for China to build a moderately prosperous society in an all-round way and realize socialist modernization. With the proposal and implementation of the "village revitalization" strategy, there are problems in the talent training model, such as lack of professional theoretical foundation, lack of practical experience, lack of talent support, and shortage of talents. Talents are the key to implementing the strategy of rural revitalization. In the field of landscape design, the existing education model is difficult to effectively train high-quality rural landscape design professionals, and the education model of talent supply is also difficult to meet the needs of rural talents.&#x0D; &#x0D; Method: This paper adopts the methods of literature research, investigation, case analysis and practical research. By consulting related papers and books on the Internet, this paper collects and sorts out the related information of rural landscape design specialty and talent training mode and analyzes the related theoretical background of the research object.&#x0D; &#x0D; Results and conclusion: The students are not very clear about the professional goals of landscape architecture, nor do they have a good understanding of rural revitalization strategies. Teachers have a large proportion of traditional teaching and lack practical experience. However, teaching methods are gradually diversified. Teachers need to improve the depth and broad extension of rural landscape design-related knowledge points and re-examine the curriculum and professional training goals and positioning. At the level of curriculum setting, the combination of theoretical and practical courses has been initially realized.&#x0D; &#x0D; Originality/value: This study elaborates the key points of the rural revitalization strategy, starting from the analysis of the demands of the rural revitalization for the landscape design specialty, discussing the path of training new talent, and providing reference for the higher vocational education to serve the rural revitalization.
Stagnation Detection with Randomized Local Search*

Abstract Recently a mechanism called stagnation detection was proposed that automatically adjusts the mutation rate of evolutionary algorithms when they encounter local optima. The so-called SD-(1+1) EA introduced by Rajabi and Witt (2022) adds stagnation detection to the classical (1+1) EA with standard bit mutation. This algorithm flips each bit independently with some mutation rate, and stagnation detection raises the rate when the algorithm is likely to have encountered a local optimum. In this article, we investigate stagnation detection in the context of the k-bit flip operator of randomized local search that flips k bits chosen uniformly at random and let stagnation detection adjust the parameter k. We obtain improved runtime results compared with the SD-(1+1) EA amounting to a speedup of at least (1-o(1))2πm, where m is the so-called gap size, that is, the distance to the next improvement. Moreover, we propose additional schemes that prevent infinite optimization times even if the algorithm misses a working choice of k due to unlucky events. Finally, we present an example where standard bit mutation still outperforms the k-bit flip operator with stagnation detection.
Cultivating Clinical Clarity through Computer Vision: A Current Perspective on Whole Slide Imaging and Artificial Intelligence

Diagnostic devices, methodological approaches, and traditional constructs of clinical pathology practice, cultivated throughout centuries, have transformed radically in the wake of explosive technological growth and other, e.g., environmental, catalysts of change. Ushered into the fray of modern laboratory medicine are digital imaging devices and machine-learning (ML) software fashioned to mitigate challenges, e.g., practitioner shortage while preparing clinicians for emerging interconnectivity of environments and diagnostic information in the era of big data. As computer vision shapes new constructs for the modern world and intertwines with clinical medicine, cultivating clarity of our new terrain through examining the trajectory and current scope of computational pathology and its pertinence to clinical practice is vital. Through review of numerous studies, we find developmental efforts for ML migrating from research to standardized clinical frameworks while overcoming obstacles that have formerly curtailed adoption of these tools, e.g., generalizability, data availability, and user-friendly accessibility. Groundbreaking validatory efforts have facilitated the clinical deployment of ML tools demonstrating the capacity to effectively aid in distinguishing tumor subtype and grade, classify early vs. advanced cancer stages, and assist in quality control and primary diagnosis applications. Case studies have demonstrated the benefits of streamlined, digitized workflows for practitioners alleviated by decreased burdens.
Egocentric scene reconstruction from an omnidirectional video

Omnidirectional videos capture environmental scenes effectively, but they have rarely been used for geometry reconstruction. In this work, we propose an egocentric 3D reconstruction method that can acquire scene geometry with high accuracy from a short egocentric omnidirectional video. To this end, we first estimate per-frame depth using a spherical disparity network. We then fuse per-frame depth estimates into a novel spherical binoctree data structure that is specifically designed to tolerate spherical depth estimation errors. By subdividing the spherical space into binary tree and octree nodes that represent spherical frustums adaptively, the spherical binoctree effectively enables egocentric surface geometry reconstruction for environmental scenes while simultaneously assigning high-resolution nodes for closely observed surfaces. This allows to reconstruct an entire scene from a short video captured with a small camera trajectory. Experimental results validate the effectiveness and accuracy of our approach for reconstructing the 3D geometry of environmental scenes from short egocentric omnidirectional video inputs. We further demonstrate various applications using a conventional omnidirectional camera, including novel-view synthesis, object insertion, and relighting of scenes using reconstructed 3D models with texture.
How the 5G Enabled the COVID-19 Pandemic Prevention and Control: Materiality, Affordance, and (De-)Spatialization

5G, the most disruptive innovation, had played a significant role in the COVID-19 pandemic prevention and control. However, as a novel technology and context, we have little knowledge about how 5G enabled pandemic prevention and control. This study collected 212 cases and conducted qualitative research to explore how the 5G worked in prevention and control. Based on the concepts of materiality and affordance, we grounded two affordances of spatialization and de-spatialization from the data. Spatialization provides non-contact ways to complete the tasks which are supposed to be completed in contact, and de-spatialization provides remote operations to complete the tasks which are supposed to be completed on-site. Spatialization and de-spatialization enabled the diagnosis and treatment of the infectors to relieve the unbalance of medical staff, cutting the infectious route to contain the viral spread, and logistic supply to support the prevention and control. Our study offers theoretical contributions to digital pandemic prevention and control, and the literature on 5G also offers practical implications.
Efficient Path Planning in Narrow Passages for Robots With Ellipsoidal Components

Path planning has long been one of the major research areas in robotics, with PRM and RRT being two of the most effective classes of planners. Though generally very efficient, these sampling-based planners can become computationally expensive in the important case of "narrow passages". This paper develops a path planning paradigm specifically formulated for narrow passage problems. The core is based on planning for rigid-body robots encapsulated by unions of ellipsoids. Each environmental feature is represented geometrically using a strictly convex body with a $\mathcal{C}^1$ boundary (e.g., superquadric). The main benefit of doing this is that configuration-space obstacles can be parameterized explicitly in closed form, thereby allowing prior knowledge to be used to avoid sampling infeasible configurations. Then, by characterizing a tight volume bound for multiple ellipsoids, robot transitions involving rotations are guaranteed to be collision-free without needing to perform traditional collision detection. Furthermore, by combining with a stochastic sampling strategy, the proposed planning framework can be extended to solving higher dimensional problems in which the robot has a moving base and articulated appendages. Benchmark results show that the proposed framework often outperforms the sampling-based planners in terms of computational time and success rate in finding a path through narrow corridors for both single-body robots and those with higher dimensional configuration spaces. Physical experiments using the proposed framework are further demonstrated on a humanoid robot that walks in several cluttered environments with narrow passages.
GMN: Generative Multi-modal Network for Practical Document Information Extraction

Document Information Extraction (DIE) has attracted increasing attention due to its various advanced applications in the real world.Although recent literature has already achieved competitive results, these approaches usually fail when dealing with complex documents with noisy OCR results or mutative layouts.This paper proposes Generative Multi-modal Network (GMN) for real-world scenarios to address these problems, which is a robust multimodal generation method without predefined label categories.With the carefully designed spatial encoder and modal-aware mask module, GMN can deal with complex documents that are hard to serialized into sequential order.Moreover, GMN tolerates errors in OCR results and requires no character-level annotation, which is vital because fine-grained annotation of numerous documents is laborious and even requires annotators with specialized domain knowledge.Extensive experiments show that GMN achieves new state-of-the-art performance on several public DIE datasets and surpasses other methods by a large margin, especially in realistic scenes.
textless-lib: a Library for Textless Spoken Language Processing

Textless spoken language processing research aims to extend the applicability of standard NLP toolset onto spoken language and languages with few or no textual resources.In this paper, we introduce textless-lib, a PyTorch-based library aimed to facilitate research in this research area.We describe the building blocks that the library provides and demonstrate its usability by discuss three different use-case examples: (i) speaker probing, (ii) speech resynthesis and compression, and (iii) speech continuation.We believe that textless-lib substantially simplifies research the textless setting and will be handful not only for speech researchers but also for the NLP community at large.The code, documentation, and pre-trained models are available at https://github.com/facebookresearch/textlesslib/.
PAI at SemEval-2022 Task 11: Name Entity Recognition with Contextualized Entity Representations and Robust Loss Functions

This paper describes our system used in the SemEval-2022 Task 11 Multilingual Complex Named Entity Recognition, achieving 3rd for track 1 on the leaderboard.We propose Dictionary-fused BERT, a flexible approach for entity dictionaries integration.The main ideas of our systems are: 1) integrating external knowledge (an entity dictionary) into pretrained models to obtain contextualized word and entity representations 2) designing a robust loss function leveraging a logit matrix 3) adding an auxiliary task, which is an on-top binary classification to decide whether the token is a mention word or not, makes the main task easier to learn.It is worth noting that our system achieves an F1 of 0.914 in the postevaluation stage by updating the entity dictionary to the one of Meng et al. (2021), which is higher than the score of 1st on the leaderboard of the evaluation stage.