Reviewerly/exHarmony huggingface dataset:
Relationship Spatialization for Depth Estimation

Considering the role played by the inter-object relationships in monocular depth estimation (MDE), it is easy to tell that relationships, such as in front of and behind, provide explicit spatial priors. However, it is hard to answer the questions that which relationships contain useful spatial priors for depth estimation, and how much do their spatial priors contribute to the depth estimation? In this paper, we term the task of answering these two questions as ‘Relationship Spatialization’ for Depth Estimation. To this end, we strive to spatialize the relationships by devising a novel learning-based framework. Specifically, given a scene image, its image representations and relationship representations are first extracted. Then, the relationship representations are modified by spatially aligned into the visual space and redundancy elimination. Finally, the modified relationship representations are adaptively weighted to concatenate with the image ones for depth estimation, thus accomplishing the relationship spatialization. Experiments on KITTI, NYU v2, and ICL-NUIM datasets show the effectiveness of the relationship spatialization on MDE. Moreover, adopting our relationship spatialization framework to the current state-of-the-art MDE models leads to marginal improvement on most evaluation metrics.
Safe and Secure Architecture Using Diverse Formal Methods

The distribution of safety functions along the tracks requires the networking of the ECUs (Electronic Control Unit is an embedded system that controls one or more electrical systems or subsystems) that support them, to facilitate their operation and maintenance. The latter enables logs to be sent, commands to be received and sent that will lead to a state change of one of the connected equipment, and the ECU application software to be updated. All these activities are naturally subject to targeted attacks aimed at reducing the availability of the equipment or disrupting its operational safety to the point of creating accidents. This article presents an innovative approach partitioning security and safety on two different computers. One computer connected to the network ensures security and is regularly updated according to known threats. The other computer ensures safety and communicates only through a secure filter. Each computer embeds technological elements that have been specified, implemented and proven with 2 different formal methods.
Communication Skills Requirements of Junior Software Engineers − Analysis of Job Ads

Software engineering (SE) profession requires various technical and non-technical skills. The skills required are influenced by the changes in the field, industry, and global trends. The changes are also reflected in the recruitment. To understand the current situation and expectations from jobseekers, this study investigates how language, intercultural, and communication skills are presented in the job ads applicable to junior software engineers. The study is based on job ads published in the Vacancies job seeking service maintained by Public Employment and Business Service in Finland. Data contained 166 job ads of which 60% were published in Finnish and 40% in English. Data analysis was based on content and thematic analysis. Based on the job ads, language, both Finnish and English, and communication skills were largely presented and required from junior software engineers, but intercultural skills and multiculturalism were almost missing. The job ads described a multifaceted, multidisciplinary communication environment where junior software engineers work. Moreover, Finnish SE labor markets have needs for fluent Finnish and English speakers; Finnish SE working environment is almost bilingual in practice. Intercultural aspects of communication were not generally visible. The peculiarities of intercultural communication are either not understood or not been aware of in designing the job ads. Moreover, the language of the ad indicates the needs and awareness. The job ads written in English highlight English skills, present Finnish skills mainly as an advantage, and emphasize communication and intercultural skills.
Few-shot image classification with improved similarity relationships in self-knowledge distillation

Though deep learning has succeeded in various fields, their performances on tasks without the large-scale dataset are always unsatisfactory. Recently, few-shot learning has been proposed to tackle such an issue. Randomly selecting a few instances from several classes, the few-shot learning learns to learn the generalized transferred features in the feature space. The general belief is that meta-learning leverages a large quantity of similar few-shot tasks to adapt the learner to an unseen task quickly. In this paper, we propose to utilize the well-trained knowledge as the guidance for feature reuse using the same architecture. For concentrating on relationships in knowledge transferring, we further consider the similarity matching loss. Experiments on two commonly used few-shot learning benchmarks show that our proposed approach achieves promising results.
Image Segmentation-Based Bicycle Riding Side Identification Method

This paper attempts to identify which side of the road a bicycle is currently riding on using a common camera for realizing an advanced bicycle navigation system and bicycle riding safety support system. To identify the roadway area, the proposed method performs semantic segmentation on a front camera image captured by a bicycle drive recorder or smartphone. If the roadway area extends from the center of the image to the right, the bicyclist is riding on the left side of the roadway (i.e., the correct riding position in Japan). In contrast, if the roadway area extends to the left, the bicyclist is on the right side of the roadway (i.e., the incorrect riding position in Japan). We evaluated the accuracy of the proposed method on various road widths with different traffic volumes using video captured by riding bicycles in Tsuruoka City, Yamagata Prefecture, and Saitama City, Saitama Prefecture, Japan. High accuracy (>80%) was achieved for any combination of the segmentation model, riding side identification method, and experimental conditions. Given these results, we believe that we have realized an effective image segmentation-based method to identify which side of the roadway a bicycle riding is on.
An Action Recognition Approach with Context and Multiscale Motion Awareness

Despite the substantial progress made by computer vision approaches in solving image classification, object detection, and pose estimation, to name a few, activity recognition remains one of the key challenges in computer vision and pattern recognition. This paper proposes a new learning framework based on multiscale spatiotemporal graph convolution layers and a transformer architecture. Even though several approaches present high accuracy in more traditional datasets like NTU, their performance significantly drops when tested in datasets with a high level of ambiguity among activities and an unbalanced number of samples for each class. We evaluated our architecture in the challenging BABEL dataset, where we achieved state of the art in terms of accuracy (65.4%) in action classification when considering both ambiguity and class unbalance. The source code and trained models are publicly available at https://github.com/verlab/AnActionRecognitionApproach_SIBGRAPI_2022.
Refactoring Solidity Smart Contracts to Protect Against Reentrancy Exploits

Solidity is the dominant programming language for specifying smart contracts deployed on the Ethereum blockchain platform. Smart contracts permit the exchange of cryptocurrency, and hence are vulnerable to security attacks, most notably reentrancy exploits. Extensions to the solidity language, therefore, provide safer alternatives via special-purpose syntactic constructs. Based on an analysis of 26,799 contracts actually deployed on the Ethereum platform, it appears that these safer alternatives are indeed adopted. Next, we investigate whether the SMTChecker provided with the Solidity compiler is able to confirm that two safer alternatives (“Check-Effects-Interactions” and “Mutex”) actually prevent against reentrancy exploits.
Online Label Distribution Learning Using Random Vector Functional-Link Network

Compared with multi-label learning (MLL), label distribution learning (LDL) can reflect the significance of relevant labels in samples, so many LDL works have been emerging recently. Nevertheless, existing LDL algorithms cannot cope with the online label distribution data stream that may generate new data distribution regularities, resulting in reduced model accuracy, effectiveness, and stability. This paper proposes a novel LDL framework based on RVFL (random vector functional link) that can effectively and accurately deal with the online data stream, namely OLD_RVFL+, which includes three innovative modules: 1) a novel RVFL+ network for LDL is constructed, namely LD_RVFL+ network. Compared with other SLFNs (single layer feed-forward networks), RVFL+ can address the issues of model over-fitting and model stability and effectively obtain the model weights in the initial training stage with high training efficiency. 2) a novel weight update module based on LD_RVFL+ is developed for the online data stream, which can rapidly update the weight parameters without iterations; 3) a new label thresholding module is proposed to convert the model outputs into a real label distribution for improved accuracy. Experimental comparisons with seven state-of-art label distribution models are carried out on 19 real-world distribution benchmark datasets. Our OLD_RVFL+ outperforms other methods by 5-10% in evaluation performance and outperforms the offline method by 50-100 times in training time under different chunk sizes, demonstrating the effectiveness, efficiency, and stability of the proposed methods.
The Materials Commons Data Repository

Repositories are increasingly used for publishing and sharing scientific data. The Materials Commons is a data repository that follows the FAIR (Findable, Accessible, Inter-operable, Reusable) principles. We demonstrate the challenges with FAIR and how Materials Commons solves them. We also discuss the Nationals Science Data Fabric (NSDF) [1], a project that is democratizing data access, and show how Materials Commons with the NSDF software stack accelerates data access and scientific research.
FedNCF: Federated Neural Collaborative Filtering for Privacy-preserving Recommender System

Recommender systems are collecting user data to provide better personalized services. However, centralized collection and analysis of users' private data will raise privacy concerns and legal risks. The emergence of federated learning enables training a machine learning model from highly decentralized data. This paper extends the Neural Collaborative Filtering (NCF) method using a federated setting and proposes a privacy-preserving federated recommender system named FedNCF, which can train NCF models without needing to know user's private data. We apply differential privacy to the computed gradients to prevent inference attacks. To improve the utility of differential privacy, we propose an adaptive differential privacy approach that meticulously adjusts the noise scale in each iteration controlled by a decay rate. Our proposed approach provides an explicit mathematical expression to estimate the user's privacy loss by truncated Concentrated Differential Privacy (tCDP). Extensive experiments and analysis demonstrate that FedNCF can achieve competitive performance with the centralized NCF meanwhile effectively protect users' privacy.
Relationship Between Speakers' Physiological Structure and Acoustic Speech Signals: Data-Driven Study Based on Frequency-Wise Attentional Neural Network

Quantitatively revealing the relationship between speakers' physiological structure and acoustic speech signals by considering the properties of resonance and antiresonance can help us to extract effective speaker discriminative information (SDI) from speech signals. The conventional quantification method based on F-ratio only considers the power of acoustic speech in each frequency band independently. We propose a novel frequency-wise attentional neural network to learn the nonlinear combined effect of the frequency components on speaker identity. The learned results indicate that antiresonance frequency induced by the nasal cavity is another essential factor for speaker discrimination that the F-ratio method could not reveal. To further evaluate our findings, we designed a non-uniform subband processing strategy based on the learned results for speaker feature extraction and did automatic speaker verification (ASV). The ASV results confirmed that further emphasizing the spectral structure around the antiresonance frequency region can enhance speaker discrimination.
How should we design violin plots?

Some of the designs of violin plots we can find in the literature.Though the original violin plot design has boxplots, the ones used in literature sometimes are enriched with beeswarms, or directly only show the density plot mirrored around an axis.
Button Portraits: Embodying Queer History with Interactive Wearable Artifacts

Button Portraits is a tangible narrative (TN) that represents queer history using artifacts from the Gender and Sexuality Collections at Georgia State University. The experience tells the stories of queer activists who influenced and produced Atlanta’s patchwork of LGBTQ + organizations from the mid 1970s to the present. As a case study, this project offers insights on how wearability, embodiment, and queer archival methods can shape the design and experience of tangible historical narratives and their ability to call for reflection on our relationships to archival materials and history. This paper argues that queer methods can develop and reveal embodied, liminal stories in TNs in the following ways: 1. Using queer methods and queer archival scholarship to understand and design tangible narratives engenders experiences that resist binary narrative categories. 2. Designing queer history tangible narratives requires understanding the sociocultural context and the ways the experience itself can be queered. 3. Embodiment through wearability in a queer TN experience troubles the relation of bodies, spaces, selves, and stories—reinforcing our queer theoretical framing. Overall, this design case study illustrates how tangible storytelling design can be deepened through attention to queer methods, especially when used alongside embodiment and wearability.
Development of Face Mask Detection using SSDLite MobilenetV3 Small on Raspberry Pi 4

This study aimed to develop a mask detection tool with SSDLite MobilenetV3 Small based on Raspberry Pi 4. SSDLite MobilenetV3 Small is a single-stage object detection. The single-stage object detection method is faster than the two-stage detection method. However, it has the disadvantage as the level of accuracy is not as good as the two-stage detection method. In the experiments, we used some methods to compare with SSDLite MobilenetV3, such as: SSDLite MobilenetV3 Large, SSDLite MobilenetV2, SSD MobilenetV2, SSDLite Mobileedets, and SSDMNV2 models. The result is that SSDLite MobilenetV3 is more powerful than other systems for detecting face masks. While the model with the best detection is the SSDLite MobilenetV2 model, the system with the SSDLite MobilenetV3 Small model still detects the use of masks, with a score of 70% accuracy from model accuracy testing in deployment. The limitation is the system with SSDLite MobilenetV3 Small can't detect incorrect masks.
Changes in technology and civilization—A conceptual study of the Metaverse

&lt;p class="15" align="justify"&gt;Advances in media technology continue to drive changes in the shape of human society, and human civilization has stepped into the eve of the Metaverse. Based on this practical background, this paper analyzes the Metaverse concept from three aspects: the proposed background, construction and characteristics of the Metaverse, and analyzes and summarizes the impact of the Metaverse on the current situation of the industry and the future of the industry. This paper argues that the proposal of &lt;span style="font-family: 'Times New Roman';"&gt;M&lt;/span&gt;etaverse is the inevitable development of society and technology, and the endgame of Internet development. The &lt;span style="font-family: 'Times New Roman';"&gt;M&lt;/span&gt;etaverse is a product of the constant innovation of the medium and has evolved from the possible worlds in literature to video games. This paper summarizes the basis of &lt;span style="font-family: 'Times New Roman';"&gt;M&lt;/span&gt;etaverse construction from the perspective of humanities and technologies, and proposes that the &lt;span style="font-family: 'Times New Roman';"&gt;M&lt;/span&gt;etaverse will develop a virtual civilization in terms of humanities, and be supported by both software and hardware technologies in terms of technologies. On the basis of this foundation, it is extracted that the &lt;span style="font-family: 'Times New Roman';"&gt;M&lt;/span&gt;etaverse has two characteristics: civilization and intersectionality. This paper summarizes the influence of &lt;span style="font-family: 'Times New Roman';"&gt;M&lt;/span&gt;etaverse on industry, mainly including the influence of &lt;span style="font-family: 'Times New Roman';"&gt;M&lt;/span&gt;etaverse on the current layout of industry and the influence on the future development of industry. The concept and impact of &lt;span style="font-family: 'Times New Roman';"&gt;M&lt;/span&gt;etaverse and its commercial application in industrial development proposed in this paper lay the corresponding theoretical foundation for the subsequent research on &lt;span style="font-family: 'Times New Roman';"&gt;M&lt;/span&gt;etaverse.&lt;/p&gt;
AI/Human Augmentation: A Study on Chatbot – Human Agent Handovers

The combination of chatbots with live chats supported by human agents creates a new type of man-machine coordination problem. Prior research on chatbot interactions has focused mostly on the interaction between end users and chatbots and there is limited research on the interaction between human chat agents and chatbots. This study aims to fill this gap contributing to the body of research on coordinating humans and artificial conversational agents by addressing the Research Question: How can the handover between chatbots and chat employees be handled to ensure good user experience? The study aims to contribute to the emerging discipline of Human-Centered AI providing insights on how to create AI-enabled systems that amplify and augment human abilities while preserving human control by identifying key aspects that need to be considered when integrating chatbots in live chat workflows.
Prediction of Lung Cancer Using Machine Learning Techniques and their Comparative Analysis

Cancer detection is done with the aid of the led expert docs and earlier tiers it may helpful. The opportunity for human error must be there. It produces the probability of error in lung cr detection which necessitates an automatic manner. Afterward, the report aims at early cancer detection through an automatic procedure to decrease human error and make the system more accurate and error-free free this system, use machine learning algorithms such as random forest, logistic regression, support vector machine and, decision tree algorithms to detect lung cancer Th research is conducted on COLAB. With COLAB or “Collaboratory” we can write and run Python in our browser, which requires a zero-configuration., free access to GPU, and is easy to share. We have implemented four algorithms on lung cancer dataset to check the performance based on diagnosis of the four parameters i.e. accuracy, Recall, Harmonic Mean, and Precision, and also presented the comparison of the four algorithms.
A Data Encoding Method for Multipath Anonymous Transmission

Nowadays, with the development of Internet communication, multipath transfer technology has been widely used in heterogeneous network communication. Multipath transfer technology enables efficient data transmission through multiple transmission paths to achieve the effect of aggregated bandwidth. However, traditional anonymous communication networks, such as Tor, I2P, etc., are single streams of data transmission. In the process of data transmission, it is easy to become the target of attacker’s listening, which makes the data easy to be damaged or even intercepted by the attacker, and cannot realize the requirement of secure, efficient and reliable data transmission. Therefore, this paper proposes an anonymous multipath transfer method based on data network coding. First, in the paper we propose a mathematical model for coding based on a circulant matrix. This model is about 80% more efficient in coding flexibility and coding/decoding efficiency compared with Galois Field (GF), and the redundant coding can tolerate 50% of packet loss. Second, a new multipath transfer mechanism is proposed in combination with data network coding, which can effectively avoid anonymity attacks. Finally, through simulation experimental tests, the results show that the method can effectively improve the reliability of data transmission in anonymous communication networks.
Publicly Verifiable Private Set Intersection from Homomorphic Encryption

Private Set Intersection (PSI) enables two mistrusting parties to securely evaluate the intersection of their private inputs, without revealing any additional information. With its wide application in privacy protection, it is required to ensure the correctness of the evaluation, especially in conventional client-server setting (also known as unbalanced PSI). Unfortunately, most existing work cannot verify the integrity of the data and the correctness of the evaluation. In this paper, we propose a new publicly verifiable private set intersection protocol in the malicious setting, based on oblivious pseudo-random function (OPRF), fully homomorphic encryption (FHE), and verifiable computation (VC). The key tool to obtain our new protocol is a new publicly verifiable inner product computation on encrypted data. The protocol supports public verification for computation correctness and integrity under preserving privacy with less round number (only requiring 2 rounds), allows batching technique under Residue Number System (RNS). That is used for enhancing the FHE. Also, we implement our protocol, and the result is close to the most effective unbalanced PSI.
The Adaptive Personalized Federated Meta-Learning for Anomaly Detection of Industrial Equipment

Federated meta-learning solves many challenges for industrial equipment anomaly detection, such as small-samples problems and significant heterogeneity in the equipment environment. However, this method is very sensitive and not easy to manually adjust an appropriate parameter for excellent results. To address this challenge, this paper proposes an adaptive personalized federated meta-learning framework. Specifically, we first design the hyperparameters online learning rate adaptation method to adjust hyperparameters dynamically. Then, we design a training mechanism with an adaptive learning rate based on model-agnostic meta-learning to update local meta-learning models adaptively. We validate our framework in a real anomaly dataset with a variable control approach. The experiments show that the value of the loss function hardly changes with differ learning rates α, and it can guarantee the accuracy to fluctuate within 1%. Even when α is 0, the model still has a good performance. Therefore, our framework has an excellent ability to adjust the hyperparameters. This framework can be used for anomaly detection of industrial equipment in different environments.