Reviewerly/exHarmony huggingface dataset:
Modeling Disease Progression with Deep Neural Networks

Alzheimer's disease (AD), the most common type of dementia, is a severe neurodegenerative disorder. Disease progression modeling (DPM) using longitudinal data is a challenging machine learning task. In this paper, we consider the problem of predicting disease progression measured by the cognitive scores and selecting biomarkers predictive of the progression. Most previous work on predicting AD progression ignore the issue of missing data. Missing data poses a major difficulty for modeling longitudinal data since most statistical models assume feature-complete data. We proposed and applied a minimal recurrent neural network model with skip to data from The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) challenge, comprising longitudinal data of 1677 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI). We have performed extensive experiments to demonstrate the effectiveness of the proposed model using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI).
Server Data Auditing with Encryption Techniques

The management of vast volumes of data with cloud service providers (CSPs) poses data privacy issues. Data confidentiality and privacy could be lost due to cloud administrator, malware, deceptive cloud providers, or other malicious users who may manipulate the data physically transferring the data from one location to another. Hence, corrections to saved data must be reviewed at regular intervals. Cryptography, remote (cloud) data verification, is carried out by third-party auditors (TPAs). Also ideal for public auditing are TPAs, which provide audit services with more efficient analytical and communication capabilities than standard users. Our work focuses on cryptographic algorithms for cloud data auditing and the problems surrounding the validity and privacy of these algorithms. Many approaches to preserving confidentiality and privacy have been suggested in the literature; they are usually categorized according to the different states of the data: static, dynamic, multi-owner, multi-user, etc.
Some results on the Flynn–Poonen–Schaefer conjecture

Abstract For $c \in \mathbb {Q}$ , consider the quadratic polynomial map $\varphi _c(z)=z^2-c$ . Flynn, Poonen, and Schaefer conjectured in 1997 that no rational cycle of $\varphi _c$ under iteration has length more than $3$ . Here, we discuss this conjecture using arithmetic and combinatorial means, leading to three main results. First, we show that if $\varphi _c$ admits a rational cycle of length $n \ge 3$ , then the denominator of c must be divisible by $16$ . We then provide an upper bound on the number of periodic rational points of $\varphi _c$ in terms of the number s of distinct prime factors of the denominator of c . Finally, we show that the Flynn–Poonen–Schaefer conjecture holds for $\varphi _c$ if $s \le 2$ , i.e., if the denominator of c has at most two distinct prime factors.
Fraud Accounts Identification Modelling on Multi-Platform E-Commerce

Nowadays, cybercrime is increasingly prevalent in society. Based on data compiled by the Indonesia National Police, the number of cybercrimes increases by 6.46% annually, with online fraud as the most reported crime with 7.892 cases or 44.40% out of the total cases handled. The modus operandi in online fraud primarily uses manipulated profile account to gain the victims' trust. Therefore, it is necessary to have a common detection model for fraud accounts on multi-platform e-commerce to avoid online fraud. This research uses the Naïve Bayes classification, Decision Tree, and K-NN as the modeling algorithms. The classification test result showed that the optimal performance with the highest accuracy differs among the platform. The green platform reaches the highest accuracy using the K-NN algorithm (90.51%), the red platform went to the optimal performance using the Decision Tree algorithm (96.89%), and the multi-platform reached the optimal performance using the Naïve Bayes algorithm (90.02%).
Kadcast-NG: A Structured Broadcast Protocol for Blockchain Networks

In order to propagate transactions and blocks, today’s blockchain systems rely on unstructured peer-to-peer overlay networks. In such networks, broadcast is known to be an inefficient operation in terms of message complexity and overhead. In addition to the impact on the system performance, inefficient or delayed block propagation may have severe consequences regarding security and fairness of the consensus layer. In contrast, the Kadcast protocol is a structured peer-to-peer protocol for block and transaction propagation in blockchain networks. Kadcast utilizes the well-known overlay topology of Kademlia to realize an efficient broadcast operation with tunable overhead. We study the security and privacy of the Kadcast protocol based on probabilistic models and analyze its resilience to packet losses and node failures. Moreover, we evaluate Kadcast’s block delivery performance, broadcast reliability, efficiency, and security based on advanced network simulations. Lastly, we introduce a QUIC-based prototype implementation of the Kadcast protocol and show its merits through deployment in a global-scale cloud-based testbed.
What Users Want? WARHOL: A Generative Model for Recommendation

Current recommendation approaches help online merchants predict, for each visiting user, which subset of their existing products is the most relevant. However, besides being interested in matching users with existing products, merchants are also interested in understanding their users’ underlying preferences. This could indeed help them produce or acquire better matching products in the future. We argue that existing recommendation models cannot directly be used to predict the optimal combination of features that will make new products serve better the needs of the target audience. To tackle this, we turn to generative models, which allow us to learn explicitly distributions over product feature combinations both in text and visual space. We develop WARHOL, a product generation and recommendation architecture that takes as input past user shopping activity and generates relevant textual and visual descriptions of novel products. We show that WARHOL can approach the performance of state-of-the-art recommendation models, while being able to generate entirely new products that are relevant to the given user profiles.
Adaptive Radiometric Compensation on Deforming Surfaces

In this research, we propose an adaptive radiometric compensation method, which uses only a projector and a camera, on continuously deforming projection surfaces. Radiometric compensation has been widely studied as a technique for making various objects usable as screens, by canceling out the influence of the color and pattern of the projection target. However, since it is necessary to continuously maintain the inter-pixel correspondence between a projector and a camera, to date, the projection target has been limited to stationary objects. Therefore, we propose a method to estimate the inter-pixel correspondence in real-time, using only a normal projector and camera. The method expands the scope of application of projection mapping greatly, by applying radiometric compensation to deforming clothes, and making them available as screens.
Sampling Training Data for Continual Learning Between Robots and the Cloud

Today’s robotic fleets are increasingly measuring high-volume video and LIDAR sensory streams, which can be mined for valuable training data to continually improve robotic perception models. However, re-training perception models on growing volumes of rich sensory data in central servers (or the “cloud”) incurs significant systems costs for network transfer, cloud storage, human annotation, and cloud computing resources. Hence, we introduce HarvestNet, an intelligent sampling algorithm that resides on-board a robot and reduces system bottlenecks by only storing rare, useful events to continually improve and re-train perception models. HarvestNet significantly improves the accuracy of perception models on our novel dataset of road construction sites, field testing of self-driving cars, and face recognition, while reducing cloud storage, dataset annotation time, and cloud compute time by between 65.7−81.3 $$\%$$ . Further, it is between 1.05−2.58 $$\times $$ more accurate than baseline algorithms and runs on low-power deep learning hardware.
Data-driven Approach for Selection of an Ensemble Model of Profane Words Detection in Social Media

Social media has become a mainstream tool for social engagement, entertainment, and even news sharing. As a result, many people are subscribed to one or multiple social media platforms. Like all social platforms with diverse members, there is need for clean conversation devoid of bullying, profanity and related social ills. Social media platforms require scalable, robust and accurate techniques for the detection of profane words in order to minimize their use and publication. Despite efforts being made to develop techniques for profane word detection, poor accuracy remains a challenge. Therefore, to address the challenge, this paper develops a data-driven approach for algorithm selection, herein called the Data-driven Ensemble Model (DEM). The approach consists of algorithm tuning, extreme feature engineering, crowd-sourcing analytics, and ensemble method selection. Raw data from Twitter social media platform was used to censor profane words and WEKA data mining software was used to conduct evaluation experiments. The experiments covered data preprocessing; crowd-sourcing evaluation; ensemble method selection; algorithm tuning, and model building and performance evaluation. The results of these evaluation experiments showed the Data-driven Ensemble Model (DEM) had a better average accuracy of 94.94% compared to baseline method Support Vector Machines (SVM) of 93.33 % on three profane words datasets.
Integrating Task Information into Few-Shot Classifier by Channel Attention

It has been increasingly recognized that meta-learning-based approaches provide a promising way to handle challenges to few-shot learning. In this paper, we incorporate the channel attention in the main framework of simple-CNAPS proposed by Bateni et al. to develop a model more appropriate for few-shot image classification. In detail, we replace FiLM layers in simple-CNAPS with channel attention blocks which scale the image channels according to the relationship between task information and feature maps rather than only the task information. This replacement makes the feature extractor more expressive. Moreover, it allows us to take the interaction of different image channels into account. In addition, to alleviate the computational bias caused by small sample size, we provide a method to estimate class centers with perturbations. Finally, the effectiveness of the model is verified by experiments on the few-shot image classification benchmark datasets.
A Machine Learning Approach to Peer Connectivity Estimation for Reliable Blockchain Networking

Peer connectivity plays a significant role in a blockchain network since any poor connectivity may result in the nodes operating on outdated data (e.g., cryptocurrency transactions). Although connectivity information is maintained by individual nodes, such identifier-based information might be unreliable due to the possibility of bogus identifiers. This paper tackles the problem of peer connectivity estimation through data-driven analytics of blockchain traffic for reliable blockchain networking. We define a set of variables to represent traffic characteristics and estimate peer connectivity from the collected data using a machine learning methodology. We also investigate the feasibility of feature prioritization to minimize estimation complexities. Our experimental results show that the presented estimation mechanism makes accurate predictions, with less than 0.1 difference between the measurement and estimation for over 99.7% of predictions. The time complexity measured on a commodity machine shows a microsecond scale for completing a single prediction task, enabling real-time operations.
Realization of College Students’ Creativity through Extra-Curriculum Activity within Additional Educational Programs

The paper aims to reveal the basic aspects of college students’ extra-curriculum activity within additional educational programs. The article emphasizes the role of extra-curriculum activity in a college graduate’s socialization and career development. The author reveals the content of the notion “creative activity”, justifies the necessity of the personally oriented educational approach that takes into account a learner’s natural abilities and interests. Scientific originality of the study includes developing a system of extra-curriculum activities promoting realization of college students’ and teachers’ creative potential. As a result, it is shown that extra-curriculum activities promote development of the additional education system and ensure integrity of pedagogical process that encompasses education, upbringing and personal development.
Inferring Case-Based Reasoners’ Knowledge to Enhance Interactivity

When interacting with a human user, an artificial intelligence needs to have a clear model of the human’s behaviour to make the correct decisions, be it recommending items, helping the user in a task or teaching a language. In this paper, we explore the feasibility of modelling the human as a case-based reasoning agent through the question of how to infer the state of a CBR agent from interaction data. We identify the main parameters to be inferred, and propose a Bayesian belief update as a possible way to infer both the parameters of the agent and the content of their case base. We illustrate our ideas with the simple application of an agent learning grammar rules throughout a sequence of observations.
Improved SSD Algorithm Based on Multi-scale Feature Fusion and Residual Attention Mechanism

Convolutional neural network (CNN) has led to significant progress in object detection. In order to detect the objects in various sizes, the object detectors often exploit the hierarchy of the multi-scale feature maps called feature pyramid, which is readily obtained by the CNN architecture. However, such feature maps do not fully consider the supplementary effect of contextual information on semantics. In this work, we proposed a feature fusion method of residual attention based on the SSD benchmark network call Improved SSD to make full use of context information to improve the characterization ability of feature maps. Besides, we use the residual attention mechanism to reinforce the key features to further improve the detector performance. The experiment result on benchmark dataset PASCAL VOC shows that the map of the proposed method with input image sizes of 300×300 and 512×512 is 78.8% and 80.7%.
Research on the optimal strategy of desert crossing game under known weather conditions

Abstract The “Crossing the Desert” mini-game needs to consider a variety of mutually restrictive factors. Players must coordinate funding and path issues to find the optimal strategy to reach the end within the specified time. Based on the study of crossing the desert when the weather conditions are known, this paper establishes a topology model, and uses priority search and game theory to solve the player’s optimal strategy for passing through the barriers. First, the entire map is digitized and represented by a general topological map; then, given the map and weather distribution, the highest profit of each path is calculated. Specific calculation details require further analysis to arrive at a more practical and efficient route planning plan. The model proposed in this paper only provides a more effective solution for this problem from the perspective of mathematical modeling.
Optimized Memory Encryption for VMs Across Multiple Hosts

Recently, virtual machines (VMs) with a large amount of memory are widely used. It is often not easy to migrate such a large-memory VM because VM migration requires one large destination host. To address this issue, split migration divides the memory of a VM into small pieces and transfers them to multiple destination hosts. The migrated VM exchanges its memory data between the hosts using remote paging. To prevent information leakage from the memory data in an untrusted environment, memory encryption can be used. However, encryption overhead largely affects the performance of the hosts and the VM. This paper proposes SEmigrate for optimizing the memory encryption in split migration and remote paging. SEmigrate avoids decrypting memory data at most of the destination hosts to reduce the overhead and completely prevent information leakage. Also, it selectively encrypts only the memory data containing sensitive information by analyzing the memory of the guest operating system in a VM. SEmigrate could reduce the CPU utilization during encrypted split migration by 6–20% point and improve the performance of the migrated VM with encrypted remote paging to 1.9\(\times \).
Demystifying Tax Evasion Using Variational Graph Autoencoders

Indirect taxation is a significant source of income for any nation. Tax evasion hinders the progress of a nation. It causes a substantial loss to the revenue of a country. We design a model based on variational graph autoencoders and clustering to identify taxpayers who are evading indirect tax by providing false information in their tax returns. We derive six correlation parameters (features) and three ratio parameters from the data submitted by taxpayers in their returns. We derive four latent features from these nine features using variational graph autoencoder and cluster taxpayers using these four latent features. We identify taxpayers located at the boundary of each cluster by using kernel density estimation, which is further investigated to single out tax evaders. We applied our method to the iron and steel taxpayers data set provided by the Commercial Taxes Department, the government of Telangana, India.
Formalizing commitments using the event calculus and RuleML

Smart Contracts enable the automated execution of exchanges on the blockchain. From an ontological perspective, smart contracts create and automate the fulfillment of social commitments between actors. Whereas traditional deontic logic is used to make a legal determination in contractual multi-actor interactions, this paper focuses on the consequences of these actions resulting from that determination, thereby shifting the focus from monitoring to execution. The interactions between actors and the consequences in terms of commitments have not yet been formalized for smart contracts. The perspective of smart contracts is interesting, since they are considered to be autonomous agents, able to generate automated actions. We use the Event Calculus as a formal logic to represent and reason about the effects of these automated actions and the resulting commitments. Since the Event Calculus deals with local events and the consideration of time, this approach enables the uniform representation of commitments, including their operations and reasoning rules.
Attention Model Based RNN for Automated Eye-Movement Event Detection

Eye-movement event detection plays an important role in eye tracking and visual perception understanding. Most of the eye-movement event detection algorithms are mainly implemented by manually setting thresholds or designing features to learn thresholds from raw eye-movement data. In this paper, we propose an attention model based recurrent neural network (AM-RNN) for automated eye-movement event detection. The proposed algorithm can automatically classify the raw eye-movement data into three types (fixations, saccades and post-saccadic oscillations) without manual extraction of features. Specifically, the proposed attention module can automatically assign weight to each feature vector in terms of probability distribution of each feature and thus give perception sights to each feature. Experimental results performed on a large-scale hand mark ground truth dataset (Lund 2013 dataset) show that the proposed method outperforms the state-of-the-art eye-movement event detection algorithms.
An Efficient Co-location Pattern Approximation Algorithm Based on Clustering Branches

The spatial co-location pattern represents a set of spatial features, whose instances are frequently associated in the space. However, due to the exponential time complexity of the traditional algorithm, the operation efficiency of the algorithm is not high, especially in the face of massive data mining, it is unable to complete the mining task normally. Therefore, an efficient co-location pattern approximation algorithm is proposed. The new algorithm first clusters according to the feature instances, takes each center as the new instance coordinates, and associates the number of instances of each family. On this basis, the mining area is divided into branches, and the distance threshold is taken for the row spacing, so as to achieve the purpose of fast pruning. On the premise of ensuring high accuracy, the algorithm effectively solves the efficiency problem of traditional algorithms, and effectively solves the spatial colocation pattern mining of massive data. A large number of experiments show that the new algorithm has the advantages of high efficiency, stability and high accuracy.
Fast Positioning of Zero Path Difference of White Light Interference Based on Image Processing

This topic is based on the image processing method to realize the rapid positioning of the zero path difference of white light interference. The more stable and compact Mirau objective lens is used in the interference light path. The objective lens is driven by a stepping motor to change the size of the optical path difference to obtain interference images at different positions. The interference image is transformed into a spectrum signal by Fourier transform, and the high frequency signal is increased and the low frequency signal is reduced through a certain evaluation function, and the interference situation is quantitatively described. The position with the largest evaluation function value is the position of in-focus. Since the interference signals at different positions will be superimposed and affect the focusing judgment, we adopt the method of sub-area focusing to process the upper and lower surfaces of the groove separately to achieve precise focusing. For the step measurement of the 1-word part of a dime coin, the measurement result of the white light interferometer is 17um, and the in-focus position of the upper and lower surfaces measured by this method is 15um, which is basically consistent with the actual value.
Linear Regression Algorithm for Data Science

AI research has focused on improving the decision-making capabilities of computers, i.e., the ability to select high-quality actions in pursuit of a given objective. When the objective is aligned with the values of the human race, this can lead to tremendous benefits. When the objective is misaligned, improving the AI system’s decision-making may lead to worse outcomes for the human race. The objectives of the proposed research are (1) to create a mathematical framework in which fundamental questions of value alignment can be investigated; (2) to develop and experiment with methods for aligning the values of a machine (whether explicitly or implicitly represented) with those of humans; (3) to understand the relationships among the degree of value alignment, the decision-making capability of the machine, and the potential loss to the human; and (4) to understand in particular the implications of the computational limitations of humans and machines for value alignment. The core of our technical approach will be a cooperative, game-theoretic extension of inverse reinforcement learning, allowing for the different action spaces of humans and machines and the varying motivations of humans; the concepts of rational meta reasoning and bounded optimality will inform our investigation of the effects of computational limitations.