Reviewerly/exHarmony huggingface dataset:
Accessibility, Natural User Interfaces and Interactions in Museums: The IntARSI Project

In a museum context, people have specific needs in terms of physical, cognitive, and social accessibility that cannot be ignored. Therefore, we need to find a way to make art and culture accessible to them through the aid of Universal Design principles, advanced technologies, and suitable interfaces and contents. Integration of such factors is a priority of the Museums General Direction of the Italian Ministry of Cultural Heritage, within the wider strategy of museum exploitation. In accordance with this issue, the IntARSI project, publicly funded, consists of a pre-evaluation and a report of technical specifications for a new concept of museology applied to the new Museum of Civilization in Rome (MuCIV). It relates to planning of multimedia, virtual, and mixed reality applications based on the concept of “augmented” and multisensory experience, innovative tangible user interfaces, and storytelling techniques. An inclusive approach is applied, taking into account the needs and attitudes of a wide audience with different ages, cultural interests, skills, and expectations, as well as cognitive and physical abilities.
Security Audit of a Blockchain-Based Industrial Application Platform

In recent years, both the Internet of Things (IoT) and blockchain technologies have been highly influential and revolutionary. IoT enables companies to embrace Industry 4.0, the Fourth Industrial Revolution, which benefits from communication and connectivity to reduce cost and to increase productivity through sensor-based autonomy. These automated systems can be further refined with smart contracts that are executed within a blockchain, thereby increasing transparency through continuous and indisputable logging. Ideally, the level of security for these IoT devices shall be very high, as they are specifically designed for this autonomous and networked environment. This paper discusses a use case of a company with legacy devices that wants to benefit from the features and functionality of blockchain technology. In particular, the implications of retrofit solutions are analyzed. The use of the BISS:4.0 platform is proposed as the underlying infrastructure. BISS:4.0 is intended to integrate the blockchain technologies into existing enterprise environments. Furthermore, a security analysis of IoT and blockchain present attacks and countermeasures are presented that are identified and applied to the mentioned use case.
Neural Path Hunter: Reducing Hallucination in Dialogue Systems via Path Grounding

Dialogue systems powered by large pretrained language models exhibit an innate ability to deliver fluent and natural-sounding responses.Despite their impressive performance, these models are fitful and can often generate factually incorrect statements impeding their widespread adoption.In this paper, we focus on the task of improving faithfulness and reducing hallucination of neural dialogue systems to known facts supplied by a Knowledge Graph (KG).We propose NEU-RAL PATH HUNTER which follows a generatethen-refine strategy whereby a generated response is amended using the KG.NEURAL PATH HUNTER leverages a separate tokenlevel fact critic to identify plausible sources of hallucination followed by a refinement stage that retrieves correct entities by crafting a query signal that is propagated over a k-hop subgraph.We empirically validate our proposed approach on the OpenDialKG dataset (Moon et al., 2019) against a suite of metrics and report a relative improvement of faithfulness over dialogue responses by 20.35% based on FeQA (Durmus et al., 2020).
Multiauthority Traceable Ring Signature Scheme for Smart Grid Based on Blockchain

As the next-generation power grid system, the smart grid can realize the balance of supply and demand and help in communication security and privacy protection. However, real-time power consumption data collection might expose the users’ privacy information, such as their living habits and economic conditions. In addition, during the process of data transmission, it may lead to data inconsistency between the user side and the storage side. Blockchain provides tamper-resistant and traceable characteristics for solving these problems, and ring signature schemes provide an anonymous authentication mechanism. Therefore, in this work, we consider the applications of ring signature scheme in smart grid based on blockchain. We introduce the notion of multi-authority traceable ring signature (MA-TRS) scheme for distributed setting. In our scheme, there is an auditing node that can distinguish the identity of the real signer from the ring without any secret information. Last but not least, we prove that the proposed scheme is unforgeable, anonymous, and traceable.
Multi-Agent System Combined With Distributed Data Mining for Mutual Collaboration Classification

Distributed Data Mining (DDM) has been proposed as a means to deal with the analysis of distributed data, where DDM discovers patterns and implements prediction based on multiple distributed data sources. However, DDM faces several problems in terms of autonomy, privacy, performance and implementation. DDM requires homogeneity regarding environment, control, administration and the classification algorithm(s), and such that requirements are too strict and inflexible in many applications. In this paper, we propose the employment of a Multi-Agent System (MAS) to be combined with DDM (MAS-DDM). MAS is a mechanism for creating goal-oriented autonomous agents within shared environments with communication and coordination facilities. We shall show that MAS-DDM is both desirable and beneficial. In MAS-DDM, agents could communicate their beliefs (calculated classification) by covering private and non-sharable data, and other agents decide whether the use of such beliefs in classifying instances and adjusting their prior assumptions about each class of data. In MAS-DDM, we will develop and use a modified Naive Bayesian algorithm because (1) Naive Bayesian has been shown to be the most used algorithm to deal with uncertain data, and (2) to show that even if all agents in MAS-DDM use the same algorithm, MAS-DDM preforms better than DDM approaches with non-communicating processes. Point (2) provide an evidence that the exchange of information between agents helps in increasing the accuracy of the classification task significantly.
Evolution and trends in intelligent tutoring systems research: a multidisciplinary and scientometric view

Intelligent tutoring systems (ITSs) are a promising integrated educational tool for customizing formal education using intelligent instruction or feedback. In recent decades, ITSs have transformed teaching and learning and associated research. This study examined the evolution and future trends of ITS research with scientometric methods. First, a dataset comprising 1173 relevant publications was compiled from the Web of Science Core Collection databases (including the Science Citation Index Expanded and the Social Science Citation Index). Then, the publication distributions by time, author, institution, country/region, and knowledge sources were analyzed to reveal the multidisciplinary integration paths. Dataset co-occurrence and co-citation analyses were conducted to identify the most popular research issues, the research chronology, and the emerging trends. It was found that: (a) ITS research has been growing in recent years. According to the Price literature exponential growth curve, this field is still in its initial stage while has high potential; (b) computer science, education, psychology, and engineering were the main ITS research knowledge sources, with ITS social science publications since 2007 being higher than ITS natural sciences publications; (c) interactive learning environments, student modeling, teaching/learning strategies, and machine learning have been the most popular research foci; and (d) the Coh-Metrix, problem-centered instruction, and STEM are the current research trends.
Multi-magnification-based machine learning as an ancillary tool for the pathologic assessment of shaved margins for breast carcinoma lumpectomy specimens

The surgical margin status of breast lumpectomy specimens for invasive carcinoma and ductal carcinoma in situ (DCIS) guides clinical decisions, as positive margins are associated with higher rates of local recurrence. The “cavity shave” method of margin assessment has the benefits of allowing the surgeon to orient shaved margins intraoperatively and the pathologist to assess one inked margin per specimen. We studied whether a deep convolutional neural network, a deep multi-magnification network (DMMN), could accurately segment carcinoma from benign tissue in whole slide images (WSIs) of shave margin slides, and therefore serve as a potential screening tool to improve the efficiency of microscopic evaluation of these specimens. Applying the pretrained DMMN model, or the initial model, to a validation set of 408 WSIs (348 benign, 60 with carcinoma) achieved an area under the curve (AUC) of 0.941. After additional manual annotations and fine-tuning of the model, the updated model achieved an AUC of 0.968 with sensitivity set at 100% and corresponding specificity of 78%. We applied the initial model and updated model to a testing set of 427 WSIs (374 benign, 53 with carcinoma) which showed AUC values of 0.900 and 0.927, respectively. Using the pixel classification threshold selected from the validation set, the model achieved a sensitivity of 92% and specificity of 78%. The four false-negative classifications resulted from two small foci of DCIS (1 mm, 0.5 mm) and two foci of well-differentiated invasive carcinoma (3 mm, 1.5 mm). This proof-of-principle study demonstrates that a DMMN machine learning model can segment invasive carcinoma and DCIS in surgical margin specimens with high accuracy and has the potential to be used as a screening tool for pathologic assessment of these specimens.
Block Chain Technology Adoption for Banking Services- Model based on Technology-Organization-Environment theory.

In the millennium era of developing Economy like Indian there is an increasing momentum of Block Chain Technology (BCT) . Researchers examines the drivers and the hurdles with block chain adoption in Banking services in the Technology-Organization-Environment (TOE) framework( (Tornatzky, Fleischer, & Chakrabarti, 1990).Authors highlighted that blockchain technologies can create innovative business solutions as it allows assets traceability, store data in append only database, consensus based on distributed ledger system, cross boarder payments and remittances and digital identity.For blockchain technologies to serve as pragmatic solution for banking and financial services, an understanding of BCT adoption in Indian banks is essential. Based on T-O-E framework, researcher explored the antecedents of BCT adoption, and its outcomes. This study investigated Information Technology officers, bank managers and bank employees in India about their views on BCT adoption through a structured questionnaire. Data collected was analyzed using the PLS-SEM. Findings in this research is framework mapping to the theory .It is concluded that perceived compatibility, perceived cost, relative advantage, perceived security, firm scope, learning culture, top management, competitive pressure, government policies and consumer readiness are the significant factors that affect the adoption of BCT in banking services.This study provides important learning to bank employees and technology vendors. This study will also give insight to the Government in content to the Digital India Program.
Design and development of semi-automatic concept map authoring support tool

Abstract This research, to design and develop a concept map authoring support tool, adopts a semi-automatic concept mapping approach to help teachers create concept maps from English readings. A concept map is widely regarded as a useful teaching and learning tool. It offers many potential advantages apart from representing the students’ knowledge and understanding during learning. Students’ engagement in using and creating concept maps with a computer-enabled concept mapping tool raises concept maps’ potential benefits. It contributes to the learning process and improves the students’ meaningful learning. The Kit-Build concept map framework, which incorporates a technology-enabled concept mapping tool, uses concept map recomposition as its essential learning activity. In learning with Kit-Build, teachers compose concept maps that they want the students to achieve. The teachers’ maps are then decomposed into components from which the students recompose and reflect deeply on their understanding. The difference between teacher’s and students’ concept maps depicts the gap between teachers’ expected understanding and students’ actual understanding. Hence, the teachers’ concept map becomes an essential part of learning with Kit-Build. For some teachers, creating a good concept map for learning is difficult and time-consuming. Hence, support to improve teachers’ productivity in creating concept maps is essential. The findings suggest that the support tool yields better concept mapping efficiency while maintaining concept maps of similar quality. Teachers also found that the support tool was useful. Therefore, semi-automatic concept mapping with the supported Kit-Build concept map authoring tool has been shown to be a better approach.
Deep Graph Matching under Quadratic Constraint

Recently, deep learning based methods have demonstrated promising results on the graph matching problem, by relying on the descriptive capability of deep features extracted on graph nodes. However, one main limitation with existing deep graph matching (DGM) methods lies in their ignorance of explicit constraint of graph structures, which may lead the model to be trapped into local minimum in training. In this paper, we propose to explicitly formulate pairwise graph structures as a quadratic constraint incorporated into the DGM framework. The quadratic constraint minimizes the pairwise structural discrepancy between graphs, which can reduce the ambiguities brought by only using the extracted CNN features. Moreover, we present a differentiable implementation to the quadratic constrained-optimization such that it is compatible with the unconstrained deep learning optimizer. To give more precise and proper supervision, a well-designed false matching loss against class imbalance is proposed, which can better penalize the false negatives and false positives with less overfitting. Exhaustive experiments demonstrate that our method achieves competitive performance on real-world datasets. The code is available at: https://github.com/Zerg-Overmind/QC-DGM.
M6: Multi-Modality-to-Multi-Modality Multitask Mega-transformer for Unified Pretraining

Multimodal pretraining has demonstrated success in the downstream tasks of cross-modal representation learning. However, it is limited to the English data, and there is still a lack of large-scale dataset for multimodal pretraining in Chinese. In this work, we propose the largest dataset for pretraining in Chinese, which consists of over 1.9TB images and 292GB texts. The dataset has large coverage over domains, including encyclopedia, question answering, forum discussion, etc. Besides, we propose a method called M6, referring to Multi-Modality-to-Multi-Modality Multitask Mega-transformer, for unified pretraining on the data of single modality and multiple modalities. The model is pretrained with our proposed tasks, including text-to-text transfer, image-to-text transfer, as well as multi-modality-to-text transfer. The tasks endow the model with strong capability of understanding and generation. We scale the model to 10 billion parameters, and build the largest pretrained model in Chinese. Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities, and the 10B-parameter pretrained model demonstrates strong potential in the setting of zero-shot learning.
Multi-Level View Associative Convolution Network for View-Based 3D Model Retrieval

With the continuous improvement of image processing capabilities, a three-dimensional (3D) model that can contain rich information is becoming the fourth type of multimedia data (in addition to sound, image, and video). Moreover, since there is a wide range of applications of 3D models, how to quickly and effectively obtain the correct target model from the massive data has become a key issue. To date, 3D model retrieval approaches have been proposed, and in these approaches, view-based 3D model retrieval methods can achieve satisfactory performance. In the 3D model retrieval task, the latent relationship mining of all images in a 3D model, the adaptive fusion of different images, and the discriminative feature extraction are the main challenges, but in most existing solutions, these issues are separately performed and they are not explored in an end-to-end network architecture. To solve these issues, in this work, we propose a novel and effective multi-level view associative convolution network ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MLVACN</b> ) to realize view-based 3D model retrieval, where the relationship exploration of multiple-view images, the fusion of different images, and the feature discrimination learning are realized in a unified end-to-end framework. Specifically, we design the group association layer and the block association layer to study the latent relationships among different views from the view-level and the block-level, respectively. Moreover, the weight fusion layer is further designed to adaptively fuse different views in a 3D model. In addition, these three layers are embedded into the <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MLVACN</b> . Finally, the pairwise discrimination loss function is proposed to learn the discriminative features of the 3D model. Extensive experimental results on three 3D model retrieval datasets including ModelNet40, ModelNet10, and ShapeNetCore55 demonstrate that <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MLVACN</b> can outperform state-of-the-art methods in term of mAP. When the ModelNet40 dataset is used, the mAP of <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MLVACN</b> is improved by 13.25%, 7.75%, 3.95%, and 0.61% as compared to those of the MVCNN, GVCNN, PVNet, and MLVCNN methods, respectively.
Temporal Relational Modeling with Self-Supervision for Action Segmentation

Temporal relational modeling in video is essential for human action understanding, such as action recognition and action segmentation. Although Graph Convolution Networks (GCNs) have shown promising advantages in relation reasoning on many tasks, it is still a challenge to apply graph convolution networks on long video sequences effectively. The main reason is that large number of nodes (i.e., video frames) makes GCNs hard to capture and model temporal relations in videos. To tackle this problem, in this paper, we introduce an effective GCN module, Dilated Temporal Graph Reasoning Module (DTGRM), designed to model temporal relations and dependencies between video frames at various time spans. In particular, we capture and model temporal relations via constructing multi-level dilated temporal graphs where the nodes represent frames from different moments in video. Moreover, to enhance temporal reasoning ability of the proposed model, an auxiliary self-supervised task is proposed to encourage the dilated temporal graph reasoning module to find and correct wrong temporal relations in videos. Our DTGRM model outperforms state-of-the-art action segmentation models on three challenging datasets: 50Salads, Georgia Tech Egocentric Activities (GTEA), and the Breakfast dataset. The code is available at https://github.com/redwang/DTGRM.
Goten: GPU-Outsourcing Trusted Execution of Neural Network Training

Deep learning unlocks applications with societal impacts, e.g., detecting child exploitation imagery and genomic analysis of rare diseases. Deployment, however, needs compliance with stringent privacy regulations. Training algorithms that preserve the privacy of training data are in pressing need. Purely cryptographic approaches can protect privacy, but they are still costly, even when they rely on two or more non-colluding servers. Seemingly-"trivial" operations in plaintext quickly become prohibitively inefficient when a series of them are "crypto-processed," e.g., (dynamic) quantization for ensuring the intermediate values would not overflow. Slalom, recently proposed by Tramer and Boneh, is the first solution that leverages both GPU (for efficient batch computation) and a trusted execution environment (TEE) (for minimizing the use of cryptography). Roughly, it works by a lot of pre-computation over known and fixed weights, and hence it only supports private inference. Five related problems for private training are left unaddressed. Goten, our privacy-preserving training and prediction framework, tackles all five problems simultaneously via our careful design over the "mismatched" cryptographic and GPU data types (due to the tension between precision and efficiency) and our round-optimal GPU-outsourcing protocol (hence minimizing the communication cost between servers). It 1) stochastically trains a low-bitwidth yet accurate model, 2) supports dynamic quantization (a challenge left by Slalom), 3) minimizes the memory-swapping overhead of the memory-limited TEE and its communication with GPU, 4) crypto-protects the (dynamic) model weight from untrusted GPU, and 5) outperforms a pure-TEE system, even without pre-computation (needed by Slalom). As a baseline, we build CaffeScone that secures Caffe using TEE but not GPU; Goten shows a 6.84x speed-up of the whole VGG-11. Goten also outperforms Falcon proposed by Wagh et al., the latest secure multi-server cryptographic solution, by 132.64x using VGG-11. Lastly, we demonstrate Goten's efficacy in training models for breast cancer diagnosis over sensitive images.
EarlyBERT: Efficient BERT Training via Early-bird Lottery Tickets

Heavily overparameterized language models such as BERT, XLNet and T5 have achieved impressive success in many NLP tasks.However, their high model complexity requires enormous computation resources and extremely long training time for both pretraining and fine-tuning.Many works have studied model compression on large NLP models, but only focusing on reducing inference time while still requiring an expensive training process.Other works use extremely large batch sizes to shorten the pre-training time, at the expense of higher computational resource demands.In this paper, inspired by the Early-Bird Lottery Tickets recently studied for computer vision tasks, we propose EarlyBERT, a general computationally-efficient training algorithm applicable to both pre-training and fine-tuning of large-scale language models.By slimming the self-attention and fullyconnected sub-layers inside a transformer, we are the first to identify structured winning tickets in the early stage of BERT training.We apply those tickets towards efficient BERT training, and conduct comprehensive pre-training and fine-tuning experiments on GLUE and SQuAD downstream tasks.Our results show that EarlyBERT achieves comparable performance to standard BERT, with 35∼45% less training time.