Reviewerly/exHarmony huggingface dataset:
Decentralized autonomous risk transfer on the blockchain

The rise of blockchain technology and other technological accelerators enables a fully automated decentralized autonomous risk transfer. In this chapter, we explain the functionality and operating principles of this new method of risk transfer. We provide an overview of the corresponding institutional arrangement and its challenges. We identify two main challenges, namely ensuring the quality of the product and gaining a critical network size. A feasible solution for these problems is the usage of system-specific tokens, which are used as a form of digital collateralization. In a short case study, we scrutinize how these theoretical concepts can be translated into practice and show the potential of the idea. While flight delay risk is already handled through decentralized autonomous risk transfer, the biggest benefits can be realized by decentralizing crop and hurricane insurance, including a seamless transfer of the risk to capital markets.
Early Visual Saliency Based on Isolated Optimal Features

Under fast viewing conditions, the visual system extracts salient and simplified representations of complex visual scenes. Saccadic eye movements optimize such visual analysis through the dynamic sampling of the most informative and salient regions in the scene. However, a general definition of saliency, as well as its role for natural active vision, is still a matter for discussion. Following the general idea that visual saliency may be based on the amount of local information, a recent constrained maximum-entropy model of early vision, applied to natural images, extracts a set of local optimal information-carriers, as candidate salient features. These optimal features proved to be more informative than others in fast vision, when embedded in simplified sketches of natural images. In the present study, for the first time, these features were presented in isolation, to investigate whether they can be visually more salient than other non-optimal features, even in the absence of any meaningful global arrangement (contour, line, etc.). In four psychophysics experiments, fast discriminability of a compound of optimal features (target) in comparison with a similar compound of non-optimal features (distractor) was measured as a function of their number and contrast. Results showed that the saliency predictions from the constrained maximum-entropy model are well verified in the data, even when the optimal features are presented in smaller numbers or at lower contrast. In the eye movements experiment, the target and the distractor compounds were presented in the periphery at different angles. Participants were asked to perform a simple choice-saccade task. Results showed that saccades can select informative optimal features spatially interleaved with non-optimal features even at the shortest latencies. Saccades’ choice accuracy and landing position precision improved with SNR. In conclusion, the optimal features predicted by the reference model, turn out to be more salient than others, despite the lack of any clues coming from a global meaningful structure, suggesting that they get preferential treatment during fast image analysis. Also, peripheral fast visual processing of these informative local features is able to guide gaze orientation. We speculate that active vision is efficiently adapted to maximize information in natural visual scenes.
Anomaly Detection for Time Series Data Stream

Time Series is an important data object, which has the characteristics of high dimensionality, large amount of data, and fast data update. In the field of anomaly detection problems, there are problems of data skew and few abnormal data samples, which makes it difficult to train traditional supervised learning models. At the same time, with the rise of the Internet of Things, more and more data exists in the form of streams. In response to the above problems, this paper proposes a anomaly detection method for time series data stream. This method first uses multiple random convolution kernels to perform feature transformation on the time series, and then inputs the obtained feature map into RRCF (Robust random cut forest), and finally scores the samples according to the characteristics of the RRCF, and the ones that exceed the threshold are considered abnormal. This method does not need pre training model for real-time detection of time series data stream, but dynamic maintenance model, so it does not need manual label and has low cost. The experimental results show that the method in this paper has good performance on different data sets. Finally, the algorithm is implemented on the Apache Flink platform, which greatly improves the throughput of the detection system and enables the system to process massive data.
Depth-enhanced gaze following method

Gaze following is the task of detecting the point of attention of where a third person gaze is staring in a single image. Existing studies have made some modifications to architectures or have additionally learned the gaze angle, and have achieved notable performances. However, when a complex scene is given, the methods generally predict incorrect locations because of the lack of depth information in an RGB image. In this paper, we propose a novel three-stage deep neural networks algorithm to tackle such challenging scenes using a depth map. We achieve state-of-the-art performance on the GazeFollow dataset and examine possibilities for the research of depth information in image interpretation. Moreover, a qualitative comparison shows that our method works stably and accurately for complex scenes similar to those found in real-world photographs.
Social Group Optimized Machine-Learning Based Elderly Fall detection Approach Using Interdisciplinary Time-Series Features

Fall can be life-taking for elderly people. Early recognition of fall events can reduce the impact among this people. Fall detection has become an active research area now a days. Many researches have been carried out using a lot of sensors and deep learning algorithms that are costly and computationally expensive. In this work, a fall detection system with a simple accelerometer and interdisciplinary time domain features were introduced. Three publicly available datasets were used and five machine learning classifiers were finally introduced to classify fall events. Dimensionality reduction using principal component analysis was also studied in this work. Finally, machine learning classifiers were optimized using social group optimization to find out best performing hyperparameters. The proposed architecture showed great robustness and efficiency as it achieved almost perfect accuracy, sensitivity and specificity in all three datasets.
A Comprehensive Review on Bot - Discord Bot

The bot’s purpose is to make the Discord server a bit more fun by making it interactive and interesting. This bot brings together various people from all over the world and helps them remain connected to each other. Once people join the server they can use all the features of the bot and join voice channels to communicate or share ideas thoughts or just have a chat. This is a better version of WhatsApp, Microsoft teams and has surpassed both of them in the same field. Discord is a VoIP, instant messaging and digital distribution platform designed for creating communities. Users communicate with voice calls, video calls, text messaging, media and files in private chats or as part of communities called “servers”. Servers are a collection of persistent chat rooms and voice chat channels. Discord runs on Windows, macOS, Android, iOS, Linux, and in web browsers.
Generative Adversarial Network for Detecting Cyber Threats in Industrial Systems

The transition from the information economy to the digital presents new challenges to the community related to the development of breakthrough technologies, a network of cyber-physical systems, artificial intelligence, and big data. When creating digital platforms, a number of difficulties arise: the large dimension of the digital infrastructure and its heterogeneity, poorly established information interaction between the segments, the lack of a common approach to ensuring cybersecurity, and high dependence on personnel qualification and reliability of equipment. The introduction of the digital economy leads to an increase in the risk of cyber threats associated with problems of access control between systems, regulation of information, and control flows. In this paper, for solving cyber threat detection tasks, it is proposed to use generative adversarial neural networks. The paper presents training and testing algorithms of the neural network. The result of the experiments demonstrated high accuracy at cyber threat detection.
Exploring Meta Learning: Parameterizing the Learning-to-learn Process for Image Classification

Meta-learning has emerged as a new paradigm in AI to challenge the limitation of conventional deep learning to acquire only task-specific knowledge. Meta-learning transcends this limitation by extracting the general concepts when learning tasks to apply these concepts later when learning new tasks. One popular meta-learning approach is model-agnostic meta-learning (MAML) which learns tasks by optimizing parameters towards highest generalizability of future tasks. The present paper applied a practical implementation of MAML to conduct an image classification task. Results showed that performance on learning new tasks neared training performance without overfitting. Furthermore, optimal values for inner-loop and outer-loop learning rate were close to default parameter values. Smaller batch sizes with more epochs improved learning in earlier epochs compared to larger batch sizes with fewer epochs. These findings show that MAML is able to transfer the concepts extracted during training effectively on to new tasks which it had not been trained on, similarly to how humans transfer knowledge.
MixTConv: Mixed Temporal Convolutional Kernels for Efficient Action Recognition

To efficiently extract spatiotemporal features of video for action recognition, most state-of-the-art methods integrate 1D temporal convolutional filters into 2D CNN backbones. However, they all exploit 1D temporal convolutional filters of fixed kernel size (i.e., 3) in their network building block, thus have suboptimal temporal modeling capability to handle both long-term and short-term actions. To address this problem, we first investigate the impacts of different kernel sizes for the 1D temporal convolutional filters. Then, we propose a simple yet efficient operation called Mixed Temporal Convolution (MixTConv), which consists of multiple depthwise 1D convolutional filters with different kernel sizes. By plugging MixTConv into the conventional 2D CNN backbone ResNet-50, we further propose an efficient and effective network architecture named MSTNet for action recognition, and achieve state-of-the-art results on multiple large-scale benchmarks.
Distilling knowledge in causal inference for unbiased visual question answering

Current Visual Question Answering (VQA) models mainly explore the statistical correlations between answers and questions, which fail to capture the relationship between the visual information and answers. The performance dramatically decreases when the distribution of handled data is different from the training data. Towards this end, this paper proposes a novel unbiased VQA model by exploring the Casual Inference with Knowledge Distillation (CIKD) to reduce the influence of bias. Specifically, the causal graph is first constructed to explore the counterfactual causality and infer the casual target based on the causal effect, which well reduces the bias from questions and obtain answers without training. Then knowledge distillation is leveraged to transfer the knowledge of the inferred casual target to the conventional VQA model. It makes the proposed method enable to handle both the biased data and standard data. To address the problem of the bad bias from the knowledge distillation, the ensemble learning is introduced based on the hypothetical bias reason. Experiments are conducted to show the performance of the proposed method. The significant improvements over the state-of-the-art methods on the VQA-CP v2 dataset well validate the contributions of this work.
Collaborative VR for Liver Surgery Planning using Wearable Data Gloves: An Interactive Demonstration

Preoperative planning for liver surgery is a critical procedure to assess a potential resection and it supports surgeons to define the affected vessels and resection volume. Traditional surgical planning systems are widely used to support the planning with the usage of desktop-based 3D and 2D visualizations. However, desktop-based systems offer limited interactions and visualizations compared to virtual reality (VR) [3]. A suitable technique to support collaboration among surgeons is required. Our previous works [1], [2] illustrate that using collaborative VR is essential to enhance communication, teamwork, and over-distance collaboration. In this work, we present a collaborative VR prototype to support liver surgery planning with intuitive interactions using wearable data gloves and VR controllers. The users can explore the patient data in both 2D and 3D representations. Thereafter, a virtual resection is specified by drawing lines on the 3D model representation. The virtual re-section is further refined to keep safety margins from the tumors. Moreover, real-time risk maps are visualized to support the surgeons during the modification. Finally, the results of the virtual resection are visualized as resection volumes with their indicated amount and colors. Future work aims to conduct an extensive clinical study to compare the suitability of these input devices.
Deep Realistic Novel View Generation for City-Scale Aerial Images

In this paper we introduce a novel end-to-end framework for generation of large, aerial, city-scale, realistic synthetic image sequences with associated accurate and precise camera metadata. The two main purposes for this data are (i) to enable objective, quantitative evaluation of computer vision algorithms and methods such as feature detection, description, and matching or full computer vision pipelines such as 3D reconstruction; and (ii) to supply large amounts of high quality training data for deep learning guided computer vision methods. The proposed framework consists of three main modules, a 3D voxel renderer for data generation, a deep neural network for artifact removal, and a quantitative evaluation module for Multi-View Stereo (MVS) as an example. The 3D voxel renderer enables generation of seen or unseen views of a scene from arbitrary camera poses with accurate camera metadata parameters. The artifact removal module proposes a novel edge-augmented deep learning network with an explicit edgemap processing stream to remove image artifacts while preserving and recovering scene structures for more realistic results. Our experiments on two urban, city-scale, aerial datasets for Albuquerque (ABQ), NM and Los Angeles (LA), CA show promising results in terms of structural similarity to real data and accuracy of reconstructed 3D point clouds.
Yes we care!-Certification for machine learning methods through the care label framework

Machine learning applications have become ubiquitous. Their applications range from embedded control in production machines over process optimization in diverse areas (e.g., traffic, finance, sciences) to direct user interactions like advertising and recommendations. This has led to an increased effort of making machine learning trustworthy. Explainable and fair AI have already matured. They address the knowledgeable user and the application engineer. However, there are users that want to deploy a learned model in a similar way as their washing machine. These stakeholders do not want to spend time in understanding the model, but want to rely on guaranteed properties. What are the relevant properties? How can they be expressed to the stake- holder without presupposing machine learning knowledge? How can they be guaranteed for a certain implementation of a machine learning model? These questions move far beyond the current state of the art and we want to address them here. We propose a unified framework that certifies learning methods via care labels. They are easy to understand and draw inspiration from well-known certificates like textile labels or property cards of electronic devices. Our framework considers both, the machine learning theory and a given implementation. We test the implementation's compliance with theoretical properties and bounds.
Word Complexity is in the Eye of the Beholder

Lexical complexity is a highly subjective notion, yet this factor is often neglected in lexical simplification and readability systems which use a "one-size-fits-all" approach.In this paper, we investigate which aspects contribute to the notion of lexical complexity in various groups of readers, focusing on native and nonnative speakers of English, and how the notion of complexity changes depending on the proficiency level of a non-native reader.To facilitate reproducibility of our approach and foster further research into these aspects, we release a dataset of complex words annotated by readers with different backgrounds.
Automatic speaker verification from affective speech using Gaussian mixture model based estimation of neutral speech characteristics

Intra-speaker variability, caused by emotional speech, is a real threat to the performance of speaker recognition systems. In fact, as human beings, we are constantly changing our emotional state. While many efforts have been made to increase automatic speaker verification (ASV) robustness towards channel effects or spoofing attacks, only a handful of studies have addressed the detrimental consequences of affective speech. In this paper, we propose a new method to minimize the mismatch between neutral and affective speech. To this end, a Gaussian mixture model is used to learn a prior probability distribution of the neutral speech for a given speaker (i.e., characterizing his/her source space). This knowledge is then used to minimize the differences between target (affective) and source (neutral) spaces. The proposed method is validated across four multi-lingual emotional datasets. Experimental results show a consistent improvement in performance across eight emotional states, with significant reductions of equal error rate relative to the baseline. • New method to mitigate detrimental effects of affective speech on speaker verification. • The method is emotion independent as no prior information from emotional speech is required. • The ASV performance of neutral speech is not compromised by the proposed method, an issue commonly observed in compensation techniques.
Taking a fresh look at FAIR for research software

(Patterns 2, 100222-1–100222-4; March 12, 2021) In the preparation of this opinion, a draft of Figure 1 was submitted instead of the final version. Figure 1 has now been corrected online and appears below. This change does not affect the conclusions of the paper. The authors sincerely apologize to the readers for any confusion that may have resulted from this error.Figure 1. Summarizing software as increasingly FAIR research objects (original)View Large Image Figure ViewerDownload (PPT) Taking a fresh look at FAIR for research softwareKatz et al.PatternsMarch 12, 2021In BriefSoftware is increasingly essential in most research, and much of this software is developed specifically for and during research. To make this research software findable, accessible, interoperable, and reusable (FAIR), we need to define exactly what FAIR means for research software and acknowledge that software is a living and complex object for which it is impossible to propose one solution that fits all software. Full-Text PDF Open Access
A distributional semantics-based information retrieval framework for online social networks

Online social networks are considered to be one of the most disruptive platforms where people communicate with each other on any topic ranging from funny cat videos to cancer support. The widespread diffusion of mobile platforms such as smart-phones causes the number of messages shared in such platforms to grow heavily, thus more intelligent and scalable algorithms are needed for efficient extraction of useful information. This paper proposes a method for retrieving relevant information from social network messages using a distributional semantics-based framework powered by topic modeling. The proposed framework combines the Latent Dirichlet Allocation and distributional representation of phrases (Phrase2Vec) for effective information retrieval from online social networks. Extensive and systematic experiments on messages collected from Twitter (tweets) show this approach outperforms some state-of-the-art approaches in terms of precision and accuracy and better information retrieval is possible using the proposed method.
Camoufler: Accessing The Censored Web By Utilizing Instant Messaging Channels

Free and open communication over the Internet is considered a fundamental human right, essential to prevent repressions from silencing voices of dissent. This has led to the development of various anti-censorship systems. Recent systems have relied on a common blocking resistance strategy i.e., incurring collateral damage to the censoring regimes, if they attempt to restrict such systems. However, despite being promising, systems built on such strategies pose additional challenges, viz., deployment limitations, poor QoS etc. These challenges prevent their wide scale adoption.
The Urgency of Cyber Security in Secure Networks

In today's world, there is an unprecedented access to different online shopping sites, online banking sites, social media, etc. Even doctors can diagnose diseases when a patient watching TV at the comfort of their home. Everything can be done with just one tap. Paying utility bills, online shopping (Amazon, Flipkart, eBay, etc.), buying grocery and even salon services are done online, but every individual is concerned about security while using the INTERNET. Cyber Security then came into existence and our personal data is encrypted and can only be accessed by the Encryption key (passwords). Many Cyber-Crimes took place in recent years like credit card frauds, malware, online bunco, and so on. 50% and more users are the victims of cyber-crime. These crimes make people a bit conscious while they are online. Many frauds have taken place and the guilty for the same was found in no time because of Cyber Law. Today's world there is an unbound access to different online shopping sites, online banking sites, social media etc. Everything can be done by just one tap. Paying utility bills, online shopping (Amazon, Flip Kart, eBay etc.), buying grocery and even salon services are done online, but every individual is concerned about security while using INTERNET. Internet itself is a very complex platform, so developers felt the need of security in their applications/websites. Cyber Security then came into existence and our personal data is encrypted and can only be accessed by the Encryption key (passwords). Many Cyber-Crimes took place in recent years like credit card frauds, malware, online bunco, and so on. 50% and more users are the victims of cyber-crime. Many frauds have taken place and the guilty for the same was found in no time because of Cyber Law.