Reviewerly/exHarmony huggingface dataset:
Language Model as an Annotator: Exploring DialoGPT for Dialogue Summarization

Current dialogue summarization systems usually encode the text with a number of general semantic features (e.g., keywords and topics) to gain more powerful dialogue modeling capabilities.However, these features are obtained via open-domain toolkits that are dialogagnostic or heavily relied on human annotations.In this paper, we show how DialoGPT (Zhang et al., 2020b), a pre-trained model for conversational response generation, can be developed as an unsupervised dialogue annotator, which takes advantage of dialogue background knowledge encoded in DialoGPT.We apply DialoGPT to label three types of features on two dialogue summarization datasets, SAM-Sum and AMI, and employ pre-trained and non pre-trained models as our summarizers.Experimental results show that our proposed method can obtain remarkable improvements on both datasets and achieves new state-of-theart performance on the SAMSum dataset 1 .
Neural Machine Translation with Monolingual Translation Memory

Prior work has proved that Translation memory (TM) can boost the performance of Neural Machine Translation (NMT).In contrast to existing work that uses bilingual corpus as TM and employs source-side similarity search for memory retrieval, we propose a new framework that uses monolingual memory and performs learnable memory retrieval in a crosslingual manner.Our framework has unique advantages.First, the cross-lingual memory retriever allows abundant monolingual data to be TM.Second, the memory retriever and NMT model can be jointly optimized for the ultimate translation goal.Experiments show that the proposed method obtains substantial improvements.Remarkably, it even outperforms strong TM-augmented NMT baselines using bilingual TM.Owning to the ability to leverage monolingual data, our model also demonstrates effectiveness in low-resource and domain adaptation scenarios.
Deep Transfer Tensor Decomposition with Orthogonal Constraint for Recommender Systems

Tensor decomposition is one of the most effective techniques for multi-criteria recommendations. However, it suffers from data sparsity when dealing with three-dimensional (3D) user-item-criterion ratings. To mitigate this issue, we consider effectively incorporating the side information and cross-domain knowledge in tensor decomposition. A deep transfer tensor decomposition (DTTD) method is proposed by integrating deep structure and Tucker decomposition, where an orthogonal constrained stacked denoising autoencoder (OC-SDAE) is proposed for alleviating the scale variation in learning effective latent representation, and the side information is incorporated as a compensation for tensor sparsity. Tucker decomposition generates private users and items' latent factors to connect with OC-SDAEs and creates a common core tensor to bridge different domains. A cross-domain alignment algorithm (CDAA) is proposed to solve the rotation issue between two core tensors in source and target domain. To the best of our knowledge, this is the first work in Tucker decomposition based recommendations to use deep structure to incorporate the side information and cross-domain knowledge. Experiments show that DTTD outperforms state-of-the-art related works.
Applications of Blockchain Technology in Logistics and Supply Chain Management—Insights from a Systematic Literature Review

The most successful applications of Blockchain Technology are still in the area of crypto-currencies, although both scientists and practitioners have discovered the potential of Blockchain Technology in Supply Chain Management. There is a significant theoretical literature on Blockchain Technology, but there exists a lack of published case studies and concrete examples. This paper discusses whether this shortcoming is due to insufficient added value of the technology and identifies other possible reasons. Furthermore, this paper introduces Blockchain Technology, describes the origins of Bitcoin, the structure and core properties of the Blockchain, and examines smart contracts. A comprehensive and structured literature analysis identifies concepts for the use of Blockchain Technology in logistics in terms of economic benefits. Additionally, a cluster analysis regarding the topics of the relevant literature was conducted. One finding of the study is that Blockchain Technology is particularly worthwhile for goods with a high value. Moreover, if the trade volume of the respective goods is low, the advantages of BCT are maximized. At the same time, the demand for transparency and immutability of data must be more important than the protection of sensitive data. In addition to concrete use cases of Blockchains, an exemplary logistics process will be presented within the Luxury Supply Chain, showing the advantages of Blockchain Technology for each individual process step.
AMBERT: A Pre-trained Language Model with Multi-Grained Tokenization

Pre-trained language models such as BERT have exhibited remarkable performances in many tasks in natural language understanding (NLU).The tokens in the models are usually fine-grained in the sense that for languages like English they are words or subwords and for languages like Chinese they are characters.In English, for example, there are multi-word expressions which form natural lexical units and thus the use of coarse-grained tokenization also appears to be reasonable.In fact, both fine-grained and coarse-grained tokenizations have advantages and disadvantages for learning of pre-trained language models.In this paper, we propose a novel pretrained language model, referred to as AM-BERT (A Multi-grained BERT), on the basis of both fine-grained and coarse-grained tokenizations.For English, AMBERT takes both the sequence of words (fine-grained tokens) and the sequence of phrases (coarse-grained tokens) as input after tokenization, employs one encoder for processing the sequence of words and the other encoder for processing the sequence of the phrases, utilizes shared parameters between the two encoders, and finally creates a sequence of contextualized representations of the words and a sequence of contextualized representations of the phrases.Experiments have been conducted on benchmark datasets for Chinese and English, including CLUE, GLUE, SQuAD and RACE.The results show that AMBERT can outperform BERT in all cases, particularly the improvements are significant for Chinese.We also develop a method to improve the efficiency of AMBERT in inference, which still performs better than BERT with the same computational cost as BERT.
MF-Ledger: Blockchain Hyperledger Sawtooth-Enabled Novel and Secure Multimedia Chain of Custody Forensic Investigation Architecture

Due to globalization and worldwide connectivity, multimedia data exchange has increased significantly over the Internet in the last decade.The life cycle of multimedia content is also getting more multifaceted as more people are accessing, sharing, modifying and re-using multimedia information.This poses serious challenges for the multimedia industry to provide integrity, reliability and trustworthiness for multimedia investigations against the growing cybersecurity threats.This paper bridges this gap by enabling a secure and transparent digital forensic investigations process using blockchain technology.MF-Ledger a Blockchain Hyperledger sawtooth-enabled novel, secure and efficient digital forensic investigation architecture is proposed where participating stakeholders create a private network to exchange and agree on different investigation activities before being stored on the blockchain ledger.We have created digital contracts (smart contracts) and implemented them using sequence diagrams to handle the stakeholders' secure interaction in the investigation process.The proposed architectural solution delivers robust information integrity, prevention, and preservation mechanism to permanently and immutably store the evidence (chain of custody) in a private permissioned encrypted blockchain ledger.
Harvesting Crowdsourcing Platforms’ Traffic in Favour of Air Forwarders’ Brand Name and Sustainability

In the modern digitalised era, the total number of businesses and organisations utilising crowdsourcing services has risen, leading to an increase of their website traffic. In this way, there is plenty of space for marketers and strategists to capitalise big data from both their own and the crowdsourcer’s websites. This can lead to a comprehension of factors affecting their brand name, sustainability (gross profit) and consequently visitor influence. The first of the three staged contexts, based on web data, includes the retrieval of web data analytics and metrics from five air forwarding and five crowdsourcing websites in 210 observation days. At stage two, we deployed a diagnostic-exploratory model, through Fuzzy Cognitive Mapping (FCM), and in the last stage, an Agent-Based Model is deployed for data prediction and simulation. We concluded that crowdsourcing referral traffic increases air forwarders’ top 3 keywords volume, and decreases social traffic and total keywords volume, which then boosts their global web rank and gross profit. The exact opposite results occur with crowdsourcing search traffic. To sum up, the contribution of this paper is to offer realistic and well-informed insights to marketers about SEO and SEM strategies for brand name and profit enhancement, based on harvesting crowdsourcing platform traffic.
Catalytic Quantum Teleportation

In this work, we address fundamental limitations of quantum teleportation---the process of transferring quantum information using classical communication and preshared entanglement. We develop a new teleportation protocol based upon the idea of using ancillary entanglement catalytically, i.e., without depleting it. This protocol is then used to show that catalytic entanglement allows for a noiseless quantum channel to be simulated with a quality that could never be achieved using only entanglement from the shared state, even for catalysts with a small dimension. On the one hand, this allows for a more faithful transmission of quantum information using generic states and fixed amount of consumed entanglement. On the other hand, this shows, for the first time, that entanglement catalysis provides a genuine advantage in a generic quantum-information processing task. Finally, we show that similar ideas can be directly applied to study quantum catalysis for more general problems in quantum mechanics. As an application, we show that catalysts can activate so-called passive states, a concept that finds widespread application, e.g., in quantum thermodynamics.
The Surprising Power of Graph Neural Networks with Random Node Initialization

Graph neural networks (GNNs) are effective models for representation learning on relational data. However, standard GNNs are limited in their expressive power, as they cannot distinguish graphs beyond the capability of the Weisfeiler-Leman graph isomorphism heuristic. In order to break this expressiveness barrier, GNNs have been enhanced with random node initialization (RNI), where the idea is to train and run the models with randomized initial node features. In this work, we analyze the expressive power of GNNs with RNI, and prove that these models are universal, a first such result for GNNs not relying on computationally demanding higher-order properties. This universality result holds even with partially randomized initial node features, and preserves the invariance properties of GNNs in expectation. We then empirically analyze the effect of RNI on GNNs, based on carefully constructed datasets. Our empirical findings support the superior performance of GNNs with RNI over standard GNNs.
Dialogue Discourse-Aware Graph Model and Data Augmentation for Meeting Summarization

Meeting summarization is a challenging task due to its dynamic interaction nature among multiple speakers and lack of sufficient training data. Existing methods view the meeting as a linear sequence of utterances while ignoring the diverse relations between each utterance. Besides, the limited labeled data further hinders the ability of data-hungry neural models. In this paper, we try to mitigate the above challenges by introducing dialogue-discourse relations. First, we present a Dialogue Discourse-Dware Meeting Summarizer (DDAMS) to explicitly model the interaction between utterances in a meeting by modeling different discourse relations. The core module is a relational graph encoder, where the utterances and discourse relations are modeled in a graph interaction manner. Moreover, we devise a Dialogue Discourse-Aware Data Augmentation (DDADA) strategy to construct a pseudo-summarization corpus from existing input meetings, which is 20 times larger than the original dataset and can be used to pretrain DDAMS. Experimental results on AMI and ICSI meeting datasets show that our full system can achieve SOTA performance. Our codes and outputs are available at https://github.com/xcfcode/DDAMS/.
Forecasting of Energy Production for Photovoltaic Systems Based on ARIMA and ANN Advanced Models

Accurate forecasting of solar energy is essential for photovoltaic (PV) plants, to facilitate their participation in the energy market and for efficient resource planning. This article is dedicated to two forecasting models: (1) ARIMA (Autoregressive Integrated Moving Average) statistical approach to time series forecasting, using measured historical data, and (2) ANN (Artificial Neural Network) using machine learning techniques. The main contributions of the authors could be synthetized as follows: (1) analysis and discussion of the experimental and simulated results regarding solar radiation forecast, as well as energy production prediction and forecasting based on ARIMA and ANN models for two case studies: (a) laboratory BIPV system developed at the Polytechnic University of Bucharest and (b) large PV park placed in a specific site of the south of Romania. A variability index of solar radiation was introduced for the model improvement; (2) comparison between the ARIMA and ANN results to highlight the ARIMA model which is more efficient than the ANN one; (3) optimized method defined by the GMDH model (Group Method of Data Handling) proposed to provide a software program for calculation of the PV energy production.
Redirected Walking in Static and Dynamic Scenes Using Visibility Polygons

We present a new approach for redirected walking in static and dynamic scenes that uses techniques from robot motion planning to compute the redirection gains that steer the user on collision-free paths in the physical space. Our first contribution is a mathematical framework for redirected walking using concepts from motion planning and configuration spaces. This framework highlights various geometric and perceptual constraints that tend to make collision-free redirected walking difficult. We use our framework to propose an efficient solution to the redirection problem that uses the notion of visibility polygons to compute the free spaces in the physical environment and the virtual environment. The visibility polygon provides a concise representation of the entire space that is visible, and therefore walkable, to the user from their position within an environment. Using this representation of walkable space, we apply redirected walking to steer the user to regions of the visibility polygon in the physical environment that closely match the region that the user occupies in the visibility polygon in the virtual environment. We show that our algorithm is able to steer the user along paths that result in significantly fewer resets than existing state-of-the-art algorithms in both static and dynamic scenes. Our project website is available at https://gamma.umd.edu/vis_poly/.
Label Verbalization and Entailment for Effective Zero and Few-Shot Relation Extraction

Relation extraction systems require large amounts of labeled examples which are costly to annotate.In this work we reformulate relation extraction as an entailment task, with simple, hand-made, verbalizations of relations produced in less than 15 minutes per relation.The system relies on a pretrained textual entailment engine which is run as-is (no training examples, zero-shot) or further fine-tuned on labeled examples (few-shot or fully trained).In our experiments on TACRED we attain 63% F1 zero-shot, 69% with 16 examples per relation (17% points better than the best supervised system on the same conditions), and only 4 points short of the state-of-the-art (which uses 20 times more training data).We also show that the performance can be improved significantly with larger entailment models, up to 12 points in zero-shot, giving the best results to date on TACRED when fully trained.The analysis shows that our few-shot systems are especially effective when discriminating between relations, and that the performance difference in low data regimes comes mainly from identifying no-relation cases.
A Cost-Sensitive Deep Learning-Based Approach for Network Traffic Classification

Network Traffic Classification (NTC) plays an important role in cyber security and network performance, for example in intrusion detection and facilitating higher quality of service.However, due to the unbalanced nature of traffic datasets, NTC can be extremely challenging and can lead to poor classification performance.While existing NTC methods seek to re-balance data distribution through resampling strategies, such approaches are known to suffer from information loss, overfitting, and increased model complexity.To address these challenges, we propose a new cost-sensitive deep learning approach to increase the robustness of deep learning classifiers against the imbalanced class problem in NTC.First, the dataset is divided into different partitions, and a cost matrix is created for each partition by considering the data distribution.Then, the costs are applied to the cost function layer to penalize classification errors.In our approach, costs are diverse in each type of misclassification because the cost matrix is specifically generated for each partition.To determine its utility, we implement the proposed cost-sensitive learning method in two deep learning classifiers, namely: stacked autoencoder and convolution neural networks.Our experiments on the ISCX VPN-nonVPN dataset show that the proposed approach can obtain higher classification performance on low-frequency classes, in comparison to three other NTC methods.
A Comparative Study of Interaction Time and Usability of Using Controllers and Hand Tracking in Virtual Reality Training

Virtual Reality (VR) technology is frequently applied in simulation, particularly in medical training. VR medical training often requires user input either from controllers or free-hand gestures. Nowadays, hand gestures are commonly tracked via built-in cameras from a VR headset. Like controllers, hand tracking can be used in VR applications to control virtual objects. This research developed VR intubation training as a case study and applied controllers and hand tracking for four interactions—namely collision, grabbing, pressing, and release. The quasi-experimental design assigned 30 medical students in clinical training to investigate the differences between using VR controller and hand tracking in medical interactions. The subjects were divided into two groups, one with VR controllers and the other with VR hand tracking, to study the interaction time and user satisfaction in seven procedures. System Usability Scale (SUS) and User Satisfaction Evaluation Questionnaire (USEQ) were used to measure user usability and satisfaction, respectively. The results showed that the interaction time of each procedure was not different. Similarly, according to SUS and USEQ scores, satisfaction and usability were also not different. Therefore, in VR intubation training, using hand tracking has no difference in results to using controllers. As medical training with free-hand gestures is more natural for real-world situations, hand tracking will play an important role as user input for VR medical training. This allows trainees to recognize and correct their postures intuitively, which is more beneficial for self-learning and practicing.
TRC‐YOLO: A real‐time detection method for lightweight targets based on mobile devices

Object detection is one of the main tasks of computer vision. Object detection algorithms usually rely on deep convolutional neural networks, which require the host device to have high computing capabilities, greatly limiting the application of object detection methods for mobile devices with limited computing capabilities, such as embedded devices. Among the current object detection algorithms, the you only look once (YOLO) series takes both speed and accuracy into consideration and is one of the most commonly used methods for object detection. In this article, TRC-YOLO is proposed, which improves the mean average precision (mAP) and real-time detection speed of the model while reducing the size of the model. In TRC-YOLO, the convolution kernel of YOLO v4-tiny is pruned and an expansive convolution layer is introduced into the residual module of the network to produce an hourglass Cross Stage Partial ResNet (CSPResNet) structure. A receptive field block (RFB) that simulates human vision is also added, increasing the receptive field of the model and strengthening the feature extraction ability of the network. In addition, the convolutional block attention module is applied, which combines spatial attention and channel attention, to enhance the effective features of the model and reduce the negative impact of noise on the model. The size of the TRC-YOLO model is 17.8 MB, which is 5.9 MB smaller than YOLO v4-tiny, and the model parameter is 2.983 billion floating point operations per second (BFLOP/s) (3.834 BFLOP/s less than YOLO v4-tiny). In addition, TRC-YOLO achieves a real-time performance of 36.9 frames per second on a Jetson Xavier NX, and its mAP on the PASCAL VOC dataset is 66.4 % (3.83 % higher than YOLO v4-tiny). In addition, the mAP of TRC-YOLO on the MS COCO dataset is 37.7 %, which is 1.9 % higher than that of the baseline model.
Game of Gradients: Mitigating Irrelevant Clients in Federated Learning

The paradigm of Federated learning (FL) deals with multiple clients participating in collaborative training of a machine learning model under the orchestration of a central server. In this setup, each client’s data is private to itself and is not transferable to other clients or the server. Though FL paradigm has received significant interest recently from the research community, the problem of selecting the relevant clients w.r.t. the central server's learning objective is under-explored. We refer to these problems as Federated Relevant Client Selection (FRCS). Because the server doesn't have explicit control over the nature of data possessed by each client, the problem of selecting relevant clients is significantly complex in FL settings. In this paper, we resolve important and related FRCS problems viz., selecting clients with relevant data, detecting clients that possess data relevant to a particular target label, and rectifying corrupted data samples of individual clients. We follow a principled approach to address the above FRCS problems and develop a new federated learning method using the Shapley value concept from cooperative game theory. Towards this end, we propose a cooperative game involving the gradients shared by the clients. Using this game, we compute Shapley values of clients and then present Shapley value based Federated Averaging (S-FedAvg) algorithm that empowers the server to select relevant clients with high probability. S-FedAvg turns out to be critical in designing specific algorithms to address the FRCS problems. We finally conduct a thorough empirical analysis on image classification and speech recognition tasks to show the superior performance of S-FedAvg than the baselines in the context of supervised federated learning settings.