Reviewerly/exHarmony huggingface dataset:
Healthcare Data Collection Using Internet of Things and Blockchain Based Decentralized Data Storage

With the increase in usage of Internet of Things devices (IoT), IoT is used in different sectors such as manufacturing, electric vehicles, home automation and healthcare. The IoT devices collected large volumes of data on different parameters at regular intervals. Storing a massive amount volume of IoT data securely is a complicated task. Presently, the majority of IoT devices use cloud storage to store the data, however, cloud servers require large storage and high computation. Due to third party cloud service provider (CSP) interaction, the management of IoT data security fully depends on the CSP. To manage these problems, a decentralized blockchain based secure storage is proposed in this work. In the proposed scheme, instead of CSP storage location, the patient health information is stored in the blockchain technique and the blockchain miners verify the transactions with the help of Elliptic Curve Cryptography (ECC). The miner verification process dynamically avoids adversary access. Similarly, the certificateless access is used in the proposed system to avoid certificate based issues. The blocks in the blockchain is going to be stored patient details in a decentralized storage location to avoid unauthorized access and ensure the authenticity of data. The use of blockchain eliminates the need for third party public auditing process through immutable storage. This work illustrates secure communication and immutable data storage without the intervention of CSP. The communication overhead reduced by nearly 10 to 40% and authentication improved by 10 to 20% while confidentiality increased by 5% in comparison to existing techniques. Through this technique, data confidentiality, integrity and availability is ensured.
Stochastic co-teaching for training neural networks with unknown levels of label noise

Label noise hampers supervised training of neural networks. However, data without label noise is often infeasible to attain, especially for medical tasks. Attaining high-quality medical labels would require a pool of experts and their consensus reading, which would be extremely costly. Several methods have been proposed to mitigate the adverse effects of label noise during training. State-of-the-art methods use multiple networks that exploit different decision boundaries to identify label noise. Among the best performing methods is co-teaching. However, co-teaching comes with the requirement of knowing label noise a priori. Hence, we propose a co-teaching method that does not require any prior knowledge about the level of label noise. We introduce stochasticity to select or reject training instances. We have extensively evaluated the method on synthetic experiments with extreme label noise levels and applied it to real-world medical problems of ECG classification and cardiac MRI segmentation. Results show that the approach is robust to its hyperparameter choice and applies to various classification tasks with unknown levels of label noise.
RB_BG_MHA: A RoBERTa-Based Model with Bi-GRU and Multi-Head Attention for Chinese Offensive Language Detection in Social Media

Offensive language in social media affects the social experience of individuals and groups and hurts social harmony and moral values. Therefore, in recent years, the problem of offensive language detection has attracted the attention of many researchers. However, the primary research currently focuses on detecting English offensive language, while few studies on the Chinese language exist. In this paper, we propose an innovative approach to detect Chinese offensive language. First, unlike previous approaches, we utilized both RoBERTa’s sentence-level and word-level embedding, combining the sentence embedding and word embedding of RoBERTa’s model, bidirectional GRU, and multi-head self-attention mechanism. This feature fusion allows the model to consider sentence-level and word-level semantic information at the same time so as to capture the semantic information of Chinese text more comprehensively. Second, by concatenating the output results of multi-head attention with RoBERTa’s sentence embedding, we achieved an efficient fusion of local and global information and improved the representation ability of the model. The experiments showed that the proposed model achieved 82.931% accuracy and 82.842% F1-score in Chinese offensive language detection tasks, delivering high performance and broad application potential.
Design and implementation of an efficient container tag dynamic taint analysis

Dynamic taint analysis is a commonly used technique in software security. By tracking the processing of tainted data in the program, dynamic taint analysis can provide users with information on how the variables they are interested in are affected by the program input. The information is stored in tags corresponding to the variables, and the type of the tag determines the level of detail it can store. While integer tags can only indicate whether the taint exists, container tags provide knowledge of which part of the input the taint originated from. This knowledge is crucial for fields such as protocol reverse engineering and fuzzing. Despite their advantages, container tags suffer from low execution efficiency. In some applications, the execution time can increase by thousands of times as compared to the use of integer tags. In this paper, we propose an efficient container tag scheme based on the Reduced Ordered Binary Decision Diagram. The test results indicate that our container tag scheme achieves average speedups of 7.53x and 100.96x compared to the two container tag schemes utilized in libdft64.
HashC: Making deep learning coverage testing finer and faster

Though Deep Neural Networks (DNNs) have been widely deployed and achieved great success in many domains, they have severe safety and reliability concerns. To provide testing evidence for DNNs’ reliable behaviors, various coverage testing techniques inspired by traditional software testing have been proposed. However, the coverage criteria in these techniques are either not fine enough to capture subtle behaviors of DNNs, or too time-consuming to be applied on large-scale DNNs. In this paper, we propose a coverage testing framework named HashC, which makes mainstream coverage criteria (e.g., NC and KMNC) much finer. Meanwhile, HashC reduces the time complexity of combinatorial coverage testing from polynomial time to linear time. We also develop the corresponding test sample selection method. Our experiments show that, (1) the existing mainstream coverage criteria are becoming finer after being equipped with HashC, (2) HashC greatly accelerates combinatorial coverage testing and can handle the testing of large-scale DNNs.
Dialog generation model based on variational Bayesian knowledge retrieval method

For dialog generation models that introduce external knowledge, the key challenge lies in how to select the relevant knowledge. The existing common method is to directly use a retriever to fetch knowledge according to the prior distribution that is only conditioned on dialog history. In fact, the response also contains crucial information that is helpful for knowledge retrieval. If we can make use of it and conduct posterior retrieval of relevant knowledge according to both dialog history and response, the selected knowledge will be more relevant to the dialog. Therefore, this paper proposes a dialog generation model (DG-VBKR) based on variational Bayesian knowledge retrieval method, which consists of two modules: knowledge retrieval and dialog generation. During the training process, both a prior knowledge retriever and a posterior knowledge retriever are trained simultaneously, and the prior knowledge retriever is guided by the posterior knowledge retriever. Specifically, the KL Divergence is used to measure the difference between the retrieval results of the two retrievers and make the trained prior knowledge retriever achieve a similar effect to the posterior knowledge retriever. In addition, this paper constructs four versions of the proposed model by using GPT-2, BART, TransformerDecoder, and TransformerEnc−Dec in dialog generation module. The experimental results show that the DG-VBKR model achieves certain improvements on several metrics compared to the baseline models. Among them, the GPT-2 version model with the best performance has improved by 7.0% on the PPL metric compared with the model using only prior knowledge retrieval. This indicates that the DG-VBKR model can effectively select knowledge related to the dialog to generate high-quality responses.
Deep graph reconstruction for multi-view clustering

Graph-based multi-view clustering methods have achieved impressive success by exploring a complemental or independent graph embedding with low-dimension among multiple views. The majority of them, however, are shallow models with limited ability to learn the nonlinear information in multi-view data. To this end, we propose a novel deep graph reconstruction (DGR) framework for multi-view clustering, which contains three modules. Specifically, a Multi-graph Fusion Module (MFM) is employed to obtain the consensus graph. Then node representation is learned by the Graph Embedding Network (GEN). To assign clusters directly, the Clustering Assignment Module (CAM) is devised to obtain the final low-dimensional graph embedding, which can serve as the indicator matrix. In addition, a simple and powerful loss function is designed in the proposed DGR. Extensive experiments on seven real-world datasets have been conducted to verify the superior clustering performance and efficiency of DGR compared with the state-of-the-art methods.
The Florence multi-resolution 3D facial expression dataset

In the literature, several 3D face datasets have been collected, aiming at advancing the field of 3D face analysis from different perspectives. Data collection generally follows specific research needs, and the existing 3D face datasets all have different characteristics that are tailored for investigating different tasks, encompassing face recognition, facial expressions and emotions analysis, 3D face reconstruction. However, the majority of these datasets are either collected with high-resolution scanners, or consumer level devices, such as the Kinect, the latter being motivated by the burdensome and costly process of collecting high-quality scans. Differently from 2D imagery, the difference in resolution in 3D data represents a non negligible problem that is under-investigated, and still prevents the successful development of methods that can work in real scenarios. In this paper, we propose a new 3D face dataset, named “Florence Multi-Resolution 3D Facial Expression” (Florence 3DMRE), which aims at bridging the gap between high- and low-resolution 3D face datasets. Its peculiarity consists in (1) including high-resolution (HR) models obtained with a HR scanner, and paired samples collected with a Kinect sensor, (2) LR and HR scans are synchronized and capture extreme and asymmetric facial deformations as used in facial rehabilitation exercises. In total, our dataset consists of 14 subjects, each performing 19 complex and asymmetric expressions. For each of them, we collected a high-resolution scan, and an RGB-D sequence. Finally, to highlight the value of the dataset and the challenges it introduces, we use the collected data to perform baseline experiments for cross-resolution 3D face recognition and reconstruction. The dataset is released for research purposes only, and complies to GDPR for data treatment. The dataset can be found at this link.
Boundary-guided part reasoning network for human parsing

The task of human parsing aims to segment the human body into different semantic regions. Despite advancements in this field, there are still two issues with current works: boundary indistinction and parsing inconsistency. In this paper, we investigate how to utilize structural information and auxiliary information to jointly solve the above two problems. Drawing inspiration from Transformer architecture, a Boundary-guided Part Reasoning Network (BPRNet) is proposed to combine edge information and associated semantics of body parts for human parsing. Specifically, we design a part representation module to represent human body parts as part features. Based on the Transformer decoder, a multi-head self-attention is used to capture the semantic correlation between the human body. Moreover, we propose a boundary-guided module consisting of absolute boundary attention and reinforced boundary attention. They take advantage of edge information and multi-scale image features to jointly constrain cross-attention to extract global features. Experiments and corresponding results on three public datasets show that the proposed method performs favorably against the state-of-the-art methods.
‘Of course you will succeed warrior ': Sensitive closings of WhatsApp conversations by professional foster parents

Good collaboration between professional foster parents (PFPs) and birth parents (BPs) is of great importance for the well-being of out-of-home placed youngsters in family-style group care. Previous studies have shown that WhatsApp has become an important medium in the professional communication between professional foster parents and birth parents as it offers the possibility to send movies and photos in addition to text. This research has identified two ways in which professional foster parents are closing WhatsApp conversations in a sensitive manner. Professional foster parents are setting boundaries by encouraging and making a reference to the future, often accompanied by emoji: (1) Encouragements are apparent in expressions that compliment, comfort and invite to ‘let it go’. (2) Professional foster parents make reference to the future in a wish, proposal or promise. Furthermore, the asymmetrical nature of the relationship becomes clear in the coaching role adopted by professional foster parents, and the observation that solely professional foster parents at times remain silent or do not respond in closing interactions. In short, sensitive boundary setting in closing sequences demonstrates how the institutional character of the relationship is embodied in the WhatsApp communication between professional foster parents and birth parents.
DW: Detected weight for 3D object detection

It is a generic paradigm to treat all samples equally in 3D object detection. Although some works focus on discriminating samples in the training process of object detectors, the issue of whether a sample detects its target GT (Ground Truth) during training process has never been studied. In this work, we first point out that discriminating the samples that detect their target GT and the samples that don’t detect their target GT is beneficial to improve the performance measured in terms of mAP (mean Average Precision). Then we propose a novel approach name as DW (Detected Weight). The proposed approach dynamically calculates and assigns different weights to detected and undetected samples, which suppresses the former and promotes the latter. The approach is simple, low-calculation and can be integrated with available weight approaches. Further, it can be applied to almost 3D detectors, even 2D detectors because it is nothing to do with network structures. We evaluate the proposed approach with six state-of-the-art 3D detectors on two datasets. The experiment results show that the proposed approach improves mAP significantly.
Computationally efficient neural hybrid automaton framework for learning complex dynamics

This paper proposes a computationally efficient and effective data-driven modeling framework for dynamical systems. The proposed modeling framework employs a collection of shallow neural networks known as Extreme Learning Machines (ELMs) to model local system behaviors along with data-driven inferred transitions among local models to establish a neural hybrid automaton model. First, the sampled system inputs are mapped to the corresponding feature spaces to obtain data-driven partitions, which subsequently define the transitions and invariants of the neural hybrid automaton model through a novel data-driven mode clustering process. Then, a collection of ELMs are trained to approximate the local dynamics. The learning processes integrate a segmented data merging procedure for location identification and a local dynamics modeling process. The proposed neural hybrid automaton models can capture behaviors of complex dynamical systems with high modeling precision but significantly lower computational complexities in computationally expensive tasks such as training and verification, which are traditionally considered to be computationally expensive tasks for neural network models. A computationally efficient set-valued reachability analysis method which is commonly used in safety verification is then developed based on interval analysis and a novel Split and Combine process. Finally, applications to modeling the limit cycle and human handwritten motions are presented to show the effectiveness and efficiency of our approach.
Hardware architecture of Dillon’s APN permutation for different primitive polynomials

Cryptographically strong functions used as S-boxes in block cyphers are fundamental for the cypher’s security. Their representation as lookup tables is possible for functions of small dimension. For larger dimensions, this is infeasible, especially if resources are limited. An alternative is representing the function as a polynomial over a finite field, and constructing a circuit evaluating this polynomial. We study how the choice of primitive polynomial affects the efficiency of hardware implementations. We take Dillon’s permutation on 6 bits (the only known permutation in even dimension from the cryptographically optimal Almost Perfect Nonlinear functions) as a relevant example, and present hardware architectures, polynomial representations and hardware theoretical complexities for all primitive polynomials of degree six. To compare the efficiency, we report on results obtained from FPGA (Field Programmable Gate Array) implementations. To the best of our knowledge, no similar study has been given in the literature. We observe that using the primitive trinomial y6+y+1 reduces the number of 2-input XOR gates up to 11.7% and the number of XOR gates × Delay metrics up to 13.2% with respect to the worst-case implementation. Therefore, the choice of primitive polynomial can profoundly impact the efficiency of such an implementation, and should be carefully considered.
CFSE: a Chinese short text classification method based on character frequency sub-word enhancement

As a foundation task of natural language processing, text classification is widely used in information retrieval, public opinion analysis, and other related tasks. Facing the problem of sparse features of Chinese short texts, which affects the classification accuracy of Chinese short texts, this paper proposes a Chinese short text classification method based on the Character Frequency Sub-word Enhancement (CFSE), which can effectively improve the classification accuracy of Chinese short texts. First, the initial Chinese-character sequence is mapped to the corresponding Character Frequency Sub-word (CFS) sequence based on the global character1 frequency information. Second, the relationship features among data are extracted based on BiLSTM-Att processing CFS sequence, and the semantic features of the initial Chinese-character sequence are obtained through ERNIE. Finally, these two kinds of features are fused and input into the text classifier to obtain the classification results. Experimental results show that the proposed method can improve the classification accuracy of Chinese short texts.
Considering the Impact of Noise on Machine Learning Accuracy

Modern day smart cyber-physical systems (CPS) and Internet of Things (IoTs), including those deployed in critical devices such as wearables, often use embedded machine learning (ML). Owing to the consistent improvement in the overall performance of artificial neural networks (ANNs), the reliance of these systems on ANNs as an integral component has seen a constant rise. However, ANNs are known to be considerably vulnerable to noise. This, along with the noise being a ubiquitous component of the real-world environment, jeopardizes the accuracy of embedded ML-based systems. This calls for analyzing the impacts of noise on ANNs prior to their deployment in real-world ML-based system, to ensure acceptable ML accuracy. This chapter deals with the issue of analyzing the impacts of noise on trained ANNs. Multiple approaches for studying the impacts and possible noise models are discussed. Various impacts of noise, along with their formalization, on trained ANNs are elaborated. The chapter also provides a suitable framework for analyzing the impacts of noise. To demonstrate the impact of noise on an ANN trained on real-world data quantitatively, the framework is then used for the analysis of a binary classifier trained on genetic attributes of Leukemia patients.
Methodological Challenges, Risks, and Ethical Implications in Game Research

Researcher protection is a growing concern within game research. Researchers who investigate game communities are subject to online harassment, exacerbating the lack of information available in preparation for academic work. In this workshop, we invite researchers to share their experiences and thoughts on methodological challenges and risks that they faced or anticipate facing.
Multimedia Showdown: A Comparative Analysis of Audio, Video, and Avatar-Based Communication

Our new work culture relies heavily on online meetings and computer mediated communication (CMC). However, making an online meeting engaging while keeping communication productive is a major challenge. We collected quantitative data from the user engagement scale (UES) and qualitative data from semi-structured interviews to investigate how user engagement differed. Using the gamified web-conferencing platform Gather, we compared four communication channels: (1) audio-only, (2) audio and video (no avatar), (3) audio and avatar (no video), and (4) audio and video and avatar. We began qualitative data analysis using reflexive thematic analysis. Although the UES results did not reveal significant differences, the preliminary results from the thematic analysis such as people prefer communication platforms designed for specific use cases because video makes them feel more self-conscious, while avatars make them feel more represented. Lastly, we provide a work-in-progress applied definition of user engagement in communication channels with their perspective on individual engagement constructs.
Trouble Maker: Truth or Dare Prompt Generator Based on Markov Chain

As a classic party game, Truth or Dare players always face the challenge of continuously devising clever prompts by themselves, where prompts can often get repetitive or hard to think up quickly. We present Trouble Maker, an open-source physical device that generates whimsical truth-or-dare prompts using Markov chain with a single button press. Our device autonomously generates surprising and unpredictable prompts using a lightweight machine learning algorithm that is printed instantly. ML-prompt generation and thermal printer are integrated altogether as a tangible artifact. Results from the preliminary user study show prompts based on our method amused and surprised players, and the device increased the overall playfulness and engagement of the game.