Reviewerly/exHarmony huggingface dataset:
Performance Evaluation of MPTCP Incast Based on Queuing Network

Multipath TCP (MPTCP) is an important protocol, which is used to solve the incremental communication mode in wireless communication networks (WCNs) and data center networks (DCNs) realizing the balance of data transmission and increase the aggregation bandwidth. However, With the wide application of multi host hosts, in the FatTree topology, a performance bottleneck exists that causes throughput collapse in the Incast communication pattern leading to the collapse and seriously affects the network energy. In this paper, based on the queuing network and the Markov property of the MPTCP scheduling process, we propose a multilevel cooperative MPTCP Incast performance evaluation model whose levels are called M/M/N/ <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathrm {m}}^{\mathtt {I}} {\rightarrow }\text{M}$ </tex-math></inline-formula> /M/L/ <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathrm {m}}^{\mathtt {II}} {\rightarrow }\text{M}$ </tex-math></inline-formula> /M/K/ <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${{\mathrm {m}}^{\mathtt{III}}}$ </tex-math></inline-formula> to study the delay performance in MPTCP Incast throughput collapse. The M/M/N/ <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathrm {m}}^{\mathtt {I}} {\rightarrow }\text{M}$ </tex-math></inline-formula> /M/L/ <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathrm {m}}^{\mathtt {II}} {\rightarrow }\text{M}$ </tex-math></inline-formula> /M/K/ <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${{\mathrm {m}}^{\mathtt {III}}}$ </tex-math></inline-formula> levels respectively describe the three levels of data packet arrival, which include the bottleneck links and transmission hotspot Top-of-Rack (ToR) switches in the edge layer, the bottleneck links in the aggregation layer, and the bottleneck links in the transmission layer. This model can calculate the average delay of end-to-end data transmission, which can be used to better analyze the delays involved in the MPTCP Incast throughput collapse. Using NS3 software, we verified the accuracy of the proposed models. The experimental results show that the estimated delays of the proposed model are close to the actual measurement delays and that the edge layer delay proportion is higher. These results help to verify the MPTCP Incast performance bottleneck in the edge layer and provide a theoretical basis for further MPTCP Incast research.
Generating Personas for Products on Social Media: A Mixed Method to Analyze Online Users

The purpose of this research is to develop a methodology that combines the quantitative and the qualitative analysis to generate personas for products on social platforms. The user data on social platforms contain massive information relating to the lifestyle people have and the products people use or are interested. By analyzing the specific content generated by users on social platforms, e.g., content involving the term "tablet," it is possible to reveal how the users consider or use the product, "tablet." By analyzing the users' homepages, the information relating to the users' daily life can be found. We collected 276, 675 pieces of relevant data regarding the product, "tablet," from 12, 965 online users on China's widely used social media platforms. Then automatic user segments and the profiles of each group were generated and structured by natural language processing technology. The results of these quantitative analyses were then qualitatively examined by manual analyses, which provide additional insights and detailed descriptions on the automatically generated persona profiles. In this study, six personas representing distinct user types were created. The mixed method of combining the quantitative and qualitative methods makes the generation of personas faster and more insightful. The generated personas can represent real user behavior and characteristics and can provide insights into the products, which also can provide support on designing new products and optimizing existing products.
Human-Feedback Shield Synthesis for Perceived Safety in Deep Reinforcement Learning

Despite the successes of deep reinforcement learning (RL), it is still challenging to obtain safe policies. Formal verification approaches ensure safety at all times, but usually overly restrict the agent’s behaviors, since they assume adversarial behavior of the environment. Instead of assuming adversarial behavior, we suggest to focus on perceived safety instead, i.e., policies that avoid undesired behaviors while having a desired level of conservativeness. To obtain policies that are perceived as safe, we propose a shield synthesis framework with two distinct loops: (1) an inner loop that trains policies with a set of actions that is constrained by shields whose conservativeness is parameterized, and (2) an outer loop that presents example rollouts of the policy to humans and collects their feedback to update the parameters of the shields in the inner loop. We demonstrate our approach on a RL benchmark of Lunar landing and a scenario in which a mobile robot navigates around humans. For the latter, we conducted two user studies to obtain policies that were perceived as safe. Our results indicate that our framework converges to policies that are perceived as safe, is robust against noisy feedback, and can query feedback for multiple policies at the same time.
Mining in Logarithmic Space

Blockchains maintain two types of data: Application data and consensus data. Towards long-term blockchain scalability, both of these must be pruned. While a large body of literature has explored the pruning of application data (UTXOs, account balances, and contract state), little has been said about the permanent pruning of consensus data (block headers). We present a protocol which allows pruning the blockchain by garbage collecting old blocks as they become unnecessary. These blocks can simply be discarded and are no longer stored by any miner. We show that all miners can be light miners with no harm to security. Our protocol is based on the notion of superblocks, blocks that have achieved an unusually high difficulty. We leverage them to represent underlying proof-of-work without ever illustrating it, storing it, or transmitting it. After our pruning is applied, the storage and communication requirements for consensus data are reduced exponentially. We develop new probabilistic mathematical methods to analyze our protocol in the random oracle model. We prove our protocol is both secure and succinct under an uninterrupted honest majority assumption for 1/3 adversaries. Our protocol is the first to achieve always secure, always succinct, and online Non-Interactive Proofs of Proof-of-Work, all necessary components for a logarithmic space mining scheme. Our work has applications beyond mining and also constitutes an improvement in state-of-the-art superlight clients and cross-chain bridges.
Engineering Continuous Monitoring of Intrinsic Capacity for Elderly People

Continuous monitoring of the wellbeing state of elderly people is about to become an urgent need in the early future due to population aging. Aiming a unified notion of well-being, we find the Intrinsic Capacity concept in accordance with the SMART BEAR project goals. In this study we mainly focus on the enabling infrastructure, mapping our models to interoperable repositories and to streaming/computing components that can foster monitoring. Our method is also innovative for explicitly combining personalization and risk levels in generating the Intrinsic Capacity score. Leveraging on synthetic data, we represent the outcome trajectories of some sample patients for one-year continuous monitoring and discuss approaches to characterize them based on the exhibited tendency.
The One-Page Setting: A Higher Standard for Evaluating Website Fingerprinting Defenses

To defeat Website Fingerprinting (WF) attacks that threaten privacy on anonymity technologies such as Tor, defenses have been proposed and evaluated under the multi-page setting. The multi-page setting was designed as a difficult setting for the attacker and therefore gives too much of an advantage to the defense, allowing weak defenses to show success. We argue that all WF defenses should instead be evaluated under the one-page setting so that the defender needs to meet a higher standard of success.
ServeNet-LT: A Normalized Multi-head Deep Neural Network for Long-tailed Web Services Classification

Automatic service classification plays an important role in service discovery, selection, and composition. Recently, machine learning has been widely used in service classification. Though promising results are obtained, previous methods are merely evaluated on web services datasets with small-scale data and relatively balanced data, which limit their real-world applications. In this paper, we address the long-tailed web services classification problem with more categories and imbalanced data. Due to the long-tailed distribution of datasets, the existing machine learning and deep learning methods cannot work well. To deal with the long-tailed problem, we propose a normalized multi-head classifier learning strategy, which effectively reduces the classifier bias and benefit the generalization capacity of the extracted features. Extensive experiments are conducted on a large-scale long-tailed web services dataset, and the results show that our model outperforms the 11 compared service classification methods to a large margin.
Fuzzy Broad Learning System Based on Accelerating Amount

For taking out the adjustment process of sparse auto-encoder for broad learning system, Feng <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> proposed fuzzy broad learning system by replacing the feature nodes of broad learning system with Takagi–Sugeno fuzzy systems. In fuzzy broad learning system, artificial parameters selection of ridge regression might result in the decrease in testing accuracy. To overcome this shortcoming of fuzzy broad learning system, this article builds a novel model of fuzzy broad learning system based on accelerating amount by introducing the accelerating amount into fuzzy broad learning system. The theoretical result on the universal approximation property of fuzzy broad learning system based on accelerating amount is presented. Three experiment studies on the regression problems of UCI, fashion MNIST, and medical MNIST datasets are performed to show the improvement in testing accuracy.
Comparative Analysis of various Machine and Deep Learning Models for Face Mask Detection using Digital Images

COVID-19 has majorly influenced life of all human beings worldwide. It is also affecting human's daily life style. During this pandemic situation, face masks have been used worldwide for protection against the disease of Coronavirus. Face masks are the most accepted and operative way to prevent the spread of Covid-19 cases. It is really very difficult to correctly monitor whether the individual have worn face mask or not in order to notify the victim in crowded and hustled places. The face mask detection process generally uses a camera to capture live streaming videos of crowd places and converting them into images. These images acts as input for facial mask recognition or detection systems. The systems for mask detection are developed with a toll-way gate which allows only if the person crossing it has a face mask worn on his/her face or it does not allow the person in. This paper intends to compare various machine and deep learning based models for binary classification of digital images into two classes of images with and without mask. For this, various parameters including Accuracy, Precision, Fl-Score and Recall are obtained for various models. In the experimental results and analysis section, it has been shown that CNN model outperforms the other state-of-the-art models in terms of above parameters.
Evaluation of a Mobile Web Application for Assessment Feedback

Abstract This paper reports an evaluation of a mobile web application, “MyFeedBack”, that can deliver both feedback and marks on assignments to students from their lecturer. It enables them to use any device anywhere, any time to check on, and receive their feedback. It keeps the feedback private to the individual student. It enables and successfully fosters dialogue about the feedback between the students and the educator. Feedback and marks were already being delivered using the institution’s learning environment/management system “Moodle”. The study used a sequential explanatory mixed-method approach. Two hundred thirty-nine (239) participants were reported on their experiences of receiving feedback and divided among several groups: (a) feedback delivered in “Moodle”, (b) formative feedback in “MyFeedBack”, and (c) summative feedback in “MyFeedBack”. Overall, results showed a statistically significant more positive attitude towards “MyFeedBack” than “Moodle”, with the summative assessment subgroup being more positive than the formative subgroup. There was an unprecedented increase in communication and feedback dialogue between the lecturer and the students. Qualitative results enriched and complemented the findings. The paper provides guidelines for an enabling technology for assessment feedback. These offer insight into the extent to which any of the new apps and functionalities that have become available since this study might likely be favourably viewed by learners and help achieve the desired pedagogical outcomes. These include: (1) accessible using any device, making feedback accessible anywhere, anytime; (2) display feedback first (before the grade/mark); (3) enable personalisation of group feedback by the teacher; (4) provide privacy for each student; (5) facilitate dialogue and communication about the feedback; and (6) include a monitoring feature. Three goals already put forward in the literature—(1) making the feedback feel more personal, (2) getting a quicker turnround by making it easier for the teachers to achieve this, and (3) prompting more dialogue between the educators and students—are advanced by this study which shows how they can be supported by software, and that when they are achieved then users strongly approve them.
Applying the Character-Based Chatbots Generation Framework in Education and Healthcare

Users have different expectations while interacting with chatbots. However, current chatbots do not consider the users’ preferences and needs, creating unpleasant biases towards certain user groups. User characteristics play a vital role in predicting preferences and thus eliciting better responses as advocated by Character Computing. A framework was proposed in the literature to build character-based chatbots with adjustable traits. In this paper, we customize the framework modules to build two chatbots for different domains using the same set of chatbot characters. First, we present a character-based chatbot for academic support. It provides students with information regarding the content, dates, timings, locations, and deadlines of their courses. Second, we present a character-based chatbot for COVID-19. It provides users with answers to frequently asked questions, advice and precautions, cases per country, and symptoms checking. Users can select their preferable chatbot character to interact with, and receive personalized responses accordingly. Moreover, we investigated the prediction of a user’s favorite chatbot character based on their personality traits to create adaptable chatbots in the future. Finally, we investigated the effect of a chatbot’s character on its likability and trustworthiness.
Hierarchical gate network for fine-grained visual recognition

The visual classification has achieved unprecedented progress in the last decade, and miscellaneous network architectures have emerged. However, these models yield inferior performance when deployed in fine-grained classification problems, as they are usually devised by enlarging the model capacity or facilitating the optimization, and few concentrate on the problem itself. In this paper, we argue that in most fine-grained classification problems, concepts are intrinsically hierarchically structured rather than evenly distributed, and thus classifying all concepts within a single layer simultaneously deteriorates the discrimination among different categories. Furthermore, the category hierarchy is usually not provided, which fails some existing methods where the human-defined hierarchy is required. In order to tackle these challenges, we propose a new architecture, referred to as Hierarchical Gate Network (HGNet), to exploit the interconnection among hierarchical categories. HGNet adopts an LSTM-like mechanism to transmit dependencies among classes of different levels in the hierarchy. In such a way, the context information in the hierarchical structure is utilized to boost the recognition performance. Experiments conducted on various benchmark datasets, including CUB-200–2011, Stanford Dogs, NABirds, Aircraft, iNaturalist, DeepFashion and DeepFashion2, demonstrate the superiority of the proposed method to the state-of-the-art algorithms.
AGNet: Automatic generation network for skin imaging reports

Medical imaging has been increasingly adopted in the process of medical diagnosis, especially for skin diseases, where diagnoses based on skin pathology are extremely accurate. The diagnostic reports of skin pathology images has the distinguishing features of extreme repetitiveness and rigid formatting. However, reports written by inexperienced radiologists and pathologists can have a high error rate, and even experienced clinicians can find the reporting task both tedious and time-consuming. To address this challenge, this paper studies the automatic generation of diagnostic reports based on images of skin pathologies. A novel deep learning-based image caption framework named the automatic generation network (AGNet), which is an effective network for the automatic generation of skin imaging reports, is proposed. The proposed AGNet consists of four parts: (1) the image model that extracts features and classifies images; (2) the language model that codes data and generates words using comprehensible language; (3) the attention module that connects the "tail" of the image model and the "head" of the language model, and computes the relationship between images and captions; (4) the embedding and labeling module that processes the input caption data. In case study, The AGNet is verified on a skin pathological image dataset and compared with several state-of-the-art models. The results show that the AGNet achieves the highest scores of the evaluation metrics of image caption among all comparison models, demonstrating the promising performance of the proposed method.
Talking Detection in Collaborative Learning Environments

We study the problem of detecting talking activities in collaborative learning videos. Our approach uses head detection and projections of the log-magnitude of optical flow vectors to reduce the problem to a simple classification of small projection images without the need for training complex, 3-D activity classification systems. The small projection images are then easily classified using a simple majority vote of standard classifiers. For talking detection, our proposed approach is shown to significantly outperform single activity systems. We have an overall accuracy of 59% compared to 42% for Temporal Segment Network (TSN) and 45% for Convolutional 3D (C3D). In addition, our method is able to detect multiple talking instances from multiple speakers, while also detecting the speakers themselves.
A multi-exposure fusion framework for contrast enhancement of hazy images employing dynamic stochastic resonance

Current imaging devices coupled with advanced hardware and software are smart enough to enhance low light images taken in clear weather. But in hazy or foggy environments, the captured images are of degraded quality. To address this issue, image processing algorithms are employed to enhance the degraded images to make useful for extracting meaningful features. In this study, we propose a haze removal algorithm to improve the color and contrast of images captured in hazy environments. The first step involves generation of images with various exposures using the theory of dynamic stochastic resonance. The images are then fused in a multi-scale fusion framework crafting weight maps viz. haze density, chromaticity, and luminance gradient. The fusion process focuses on uniformly enhancing the dark and bright regions of the image. However, it may overemphasize haze affected regions. Therefore, in the second step, the atmospheric scattering equation is referred and its modified version is applied that accomplishes the haze removal task. Quantitative and qualitative analyses demonstrate the effectiveness of the proposed method.
A Quantum Natural Language Processing Approach to Musical Intelligence

There has been tremendous progress in Artificial Intelligence (AI) for music, in particular for musical composition and access to large databases for commercialisation through the Internet. We are interested in further advancing this field, focusing on composition. In contrast to current ‘black-box’ AI methods, we are championing an interpretable compositional outlook on generative music systems. In particular, we are importing methods from the Distributional Compositional Categorical (DisCoCat) modelling framework for Natural Language Processing (NLP), motivated by musical grammars. Quantum computing is a nascent technology, which is very likely to impact the music industry in time to come. Thus, we are championing a Quantum Natural Language Processing (QNLP) approach to develop a new generation of intelligent musical systems. This work follows previous experimental implementations of DisCoCat linguistic models on quantum hardware. In this chapter, we present Quanthoven, the first proof-of-concept ever built, which (a) demonstrates that it is possible to program a quantum computer to learn to classify music that conveys different meanings and (b) illustrates how such a capability might be leveraged to develop a system to compose meaningful pieces of music. After a discussion about our current understanding of music as a communication medium and its relationship to natural language, the chapter focuses on the techniques developed to (a) encode musical compositions as quantum circuits, and (b) design a quantum classifier. The chapter ends with demonstrations of compositions created with the system.
Using Question Answering Rewards to Improve Abstractive Summarization

Neural abstractive summarization models have drastically improved in the recent years.However, the summaries generated by these models generally suffer from issues such as: not capturing the critical facts in source documents, and containing facts that are inconsistent with the source documents.In this work, we present a general framework to train abstractive summarization models to alleviate such issues.We first train a sequence-to-sequence model to summarize documents, and then further train this model in a Reinforcement Learning setting with question-answering based rewards.We evaluate the summaries generated by the this framework using multiple automatic measures and human judgements.The experimental results show that the question-answering rewards can be used as a general framework to improve neural abstractive summarization.Particularly, the results from human evaluations show that the summaries generated by our approach are preferred over 30% of the time over the summaries generated by general abstractive summarization models.