Reviewerly/exHarmony huggingface dataset:
Transparency or Anonymity Leak: Monero Mining Pools Data Publication

Monero is a cryptocurrency that provides anonymity by default for its senders and receivers. The recorded Monero transactions are infeasible to trace without additional information. Monero utilises Proof-of-Work (PoW) as its consensus method. Miners contribute their computing power in exchange for a reward. To stabilise their income, the miners join mining pools. Mining pools coordinate the miners’ mining power in order to generate more reward and distribute the reward based on each miner’s contribution to getting the reward. Some mining pools publish the list of won blocks and payout transactions to the miners to provide transparency of their business. In this work, we investigate anonymity leak in Monero system through analyses on mining pools’ data. We collect published data from ten mining pools’ websites and conduct traceability analyses on the data. We discover that 59.2% inputs of all Monero transaction inputs in our data set are traceable. We also identify that the age of the spent coins is between 2.5 h and 3.3 days. While we propose methods to improve the accountability of the published information, it is also questionable that the mining pools keep publishing such information which reduces the anonymity of their transactions. Our investigation shows that there is no relationship between publishing mining-related information and the success of a mining pool.
A User-based Normalization Multi-Criteria Rating Approach for Hotel Recommendation System

This paper develops a method that implements a proposed user-based normalization multi-criteria rating approach for the hotel recommendation system. The inspirations come from two main reasons. First, data normalization is a common problem in the multi-criteria based recommendation system. It occurs since users have diverse tendencies when giving ratings and that each criterion might have a varied range of ratings. Second, a previous study showed the superiority of the Decoupling technique for solving the normalization problem in a user-based multi-criteria recommendation system. In the meantime, researchers of non-recommendation systems showed that the MinMax technique dominates others – the Decoupling technique was not used in the study. These two facts raise a research question on whether MinMax could also outperform Decoupling when implemented on a user-based multi-criteria recommendation system. Therefore, in this paper, we propose implementing the MinMax normalization technique on a user-based multi-criteria recommendation approach and then comparing the performance to that of the Decoupling. Through series of experiments using the Yelp Hotel Dataset, we confirm that the MinMax technique can significantly improve the quality of a user-based multi-criteria hotel recommendation system better than the Decoupling. That is, the performance of MinMax is more than two times higher than Decoupling.
Online Discriminative Semantic-Preserving Hashing for Large-Scale Cross-Modal Retrieval

Cross-modal hashing has drawn increasing attentions for efficient retrieval across different modalities, and existing methods primarily learn the hash functions in a batch based mode, i.e., offline methods. Nevertheless, the multimedia data often comes in a streaming fashion, which makes the batch based learning methods uncompetitive for large-scale streaming data due to the large memory consumption and calculation. To address this problem, we present an Online Discriminative Semantic-Preserving Hashing (ODSPH) method for large-scale cross-modal retrieval, featuring on fast training speed, low memory consumption and high retrieval accuracy. Within the proposed ODSPH framework, we utilize the newly coming data points to learn the hash codes and update hash functions in a stream manner. When new data comes, the corresponding hash codes are obtained by regressing the class label of the training examples. For hash function, we update it with the accumulated information from each round. Besides, we design a novel momentum updating method to adaptively update the hash function and reduce quantization loss, which can produce discriminative hash codes for high retrieval precision. Extensive experiments on three benchmark datasets show that the proposed ODSPH method improves the retrieval performance over the state-of-the-arts.
Information Design Used In Metro and Public Service System in China

Abstract Information design has become more and more popular these days as we have step into a smart phone new era. This document take a survey on some of the main metro station based in shanghai, and analyzed the pros and cons of the information design in existing metro system. Also explained how better information design can be put to use in order to refine user experience when taking metro.
Multiple Classification with Split Learning

Privacy issues were raised in the process of training deep learning in medical, mobility, and other fields. To solve this problem, we present privacy-preserving distributed deep learning method that allow clients to learn a variety of data without direct exposure. We divided a single deep learning architecture into a common extractor, a cloud model and a local classifier for the distributed learning. First, the common extractor, which is used by local clients, extracts secure features from the input data. The secure features also take the role that the cloud model can employ various task and diverse types of data. The feature contain the most important information that helps to proceed various task. Second, the cloud model including most parts of the whole training model gets the embedded features from the massive local clients, and performs most of deep learning operations which takes severe computing cost. After the operations in cloud model finished, outputs of the cloud model send back to local clients. Finally, the local classifier determined classification results and delivers the results to local clients. When clients train models, our model does not directly expose sensitive information to exterior network. During the test, the average performance improvement was 2.63% over the existing local training model. However, in a distributed environment, there is a possibility of inversion attack due to exposed features. For this reason, we experimented with the common extractor to prevent data restoration. The quality of restoration of the original image was tested by adjusting the depth of the common extractor. As a result, we found that the deeper the common extractor, the restoration score decreased to 89.74.
A Robot Dynamic Target Grasping Method Based on Affine Group Improved Gaussian Resampling Particle Filter

Tracking and grasping a moving target is currently a challenging topic in the field of robotics. The current visual servo grasping method is still inadequate, as the real-time performance and robustness of target tracking both need to be improved. A target tracking method is proposed based on improved geometric particle filtering (IGPF). Following the geometric particle filtering (GPF) tracking framework, affine groups are proposed as state particles. Resampling is improved by incorporating an improved conventional Gaussian resampling algorithm. It addresses the problem of particle diversity loss and improves tracking performance. Additionally, the OTB2015 dataset and typical evaluation indicators in target tracking are adopted. Comparative experiments are performed using PF, GPF and the proposed IGPF algorithm. A dynamic target tracking and grasping method for the robot is proposed. It combines an improved Gaussian resampling particle filter algorithm based on affine groups and the positional visual servo control of the robot. Finally, the robot conducts simulation and experiments on capturing dynamic targets in the simulation environment and actual environment. It verifies the effectiveness of the method proposed in this paper.
Theorem Proving Using Clausal Resolution: From Past to Present

Modal and temporal logics are extensions to classical logic that have operators that deal with necessity and possibility (modal logics) and such as sometime, always and next (temporal logics). Models are sets of worlds that are connected by an accessibility relation. Restrictions imposed on this relation and the operators allowed give rise to different families of logic. This paper discusses an approach to theorem proving for temporal and modal logics based on clausal resolution. The main ideas are the translation to a normal form and the application of resolution rules that relate to the same world. This research initially focused on propositional linear time temporal logic but has been extended to computation tree logic, monodic first order temporal logic and normal modal logics. We describe the approach, explain the adaptations necessary for the logics mentioned and discuss the results of the provers developed for these logics.
A Formal Model of Algorand Smart Contracts

We develop a formal model of Algorand stateless smart contracts (stateless ASC1). We exploit our model to prove fundamental properties of the Algorand blockchain, and to establish the security of some archetypal smart contracts. While doing this, we highlight various design patterns supported by Algorand. We perform experiments to validate the coherence of our formal model w.r.t. the actual implementation.
Spatio-Temporal Object Detection from UAV On-Board Cameras

We propose a new two stage spatio-temporal object detector framework able to improve detection precision by taking into account temporal information. First, a short-term proposal linking and aggregation method improves box features. Then, we design a long-term attention module that further enhances short-term aggregated features adding long-term spatio-temporal information. This module takes into account object trajectories to effectively exploit long-term relationships between proposals in arbitrary distant frames. Many videos recorded from UAV on-board cameras have a high density of small objects, making the detection problem very challenging. Our method takes advantage of spatio-temporal information to address these issues increasing the detection robustness. We have compared our method with state-of-the-art video object detectors in two different publicly available datasets focused on UAV recorded videos. Our approach outperforms previous methods in both datasets.
Selection of Optimal Bands for Hyperspectral Local Feature Descriptor

The use of hyperspectral images (HSIs) and 3-D data has proven to be an efficient combination for numerous applications. A common method of obtaining corresponding data is 3-D reconstruction from HSI, for which the local feature descriptor is vital to the final accuracy. However, redundant bands may hamper the performance of the descriptor, which is similar to the so-called Hughes phenomenon. Band selection (BS) is effective in overcoming such problems. Existing BS methods fail to select the band in a differentiable manner and, thus, cannot be jointly optimized with downstream tasks. In this letter, we propose a novel end-to-end HSI local feature descriptor network (with joint optimal BS) called HyperDesc. It implements a true band selection (TBS) module by turning BS into a CONCRETE random variable-based differentiable sampling operation. The discrete distribution of each selected band can be learned from input HSI with a nonlocal spectral–spatial attention network. Finally, the selected band is sent to a descriptor network to extract the local feature descriptor. The whole network is trained in an end-to-end manner. Experiments are conducted on multiview close-range HSI. The results show that spectral information provided by selected bands can boost the performance of the descriptor.
Software Development of Intelligent Pension Monitoring System Based on Ant Colony Optimization Algorithm

Since the 21st century, the problem of China’s population aging has intensified, and accelerating the development of aging industries to ease social pressure has become an important issue to be solved in China. The development of the intelligent elderly care industry has shared the demand pressure faced by nursing homes to a certain extent. Therefore, in a Chinese society where the economy is developing rapidly, people’s consumption level is rising, and the problem of population aging is increasingly prominent, the intelligent elderly care monitoring system has received increasing attention. The purpose of this paper is to study the software development of intelligent elderly care monitoring system based on ant colony optimization algorithm. This article analyzes the functional requirements of the intelligent elderly care monitoring system based on the ant colony optimization algorithm, introduces the key functional modules of the software, and explains the overall system architecture. This article debugs and tests the functions of the system “blood oxygen saturation test” and “old man fall detection” to complete the system reliability test. In the experiment, the results measured by the blood oxygen saturation detection module of the system are compared with the oxygen saturation measured by the traditional blood oxygen detector. The maximum relative error between the two is 1.13%, and the minimum relative error is 0. It is visible that this blood oxygen saturation test module of the system has high test accuracy and has reached the design goal of the system.
A Chaotic Elephant Herding Optimization Algorithm for Multilayer Perceptron based on Opposition-based Learning

This work proposes a new multilayer perceptron (MLP) training algorithm known as chaotic elephant herding optimization based on opposition-based learning (OCEHO). Two chaotic maps were incorporated into various phases of the standard EHO, as well as an opposition-based learning concept, to improve the global convergence of the population members and also to prevent the algorithm from falling into local optimal solutions. The proposed OCEHO-MLP model was then applied to two separate customer churn data-sets that are used for prediction, which were acquired from GitHub. The proposed model results were compared with those of the standard EHO and PSO used as MLP base trainers, as well as those of conventional MLP based on back-propagation. It is shown that the suggested training method OCEHO is significantly beneficial in enhancing the prediction rate of MLPs, with improved accuracy, convergence, and stability achieved in a fair amount of time.
Learning Co-segmentation by Segment Swapping for Retrieval and Discovery

The goal of this work is to efficiently identify visually similar patterns in images, e.g. identifying an artwork detail copied between an engraving and an oil painting, or recognizing parts a night-time photograph visible in its daytime counterpart. Lack of training data is a key challenge for this co-segmentation task. We present a simple yet surprisingly effective approach to overcome this difficulty: we generate synthetic training pairs by selecting segments in an image and copy-pasting them into another image. We then learn to predict the repeated region masks. We find that it is crucial to predict the correspondences as an auxiliary task and to use Poisson blending and style transfer on the training pairs to generalize on real data.We analyse results with two deep architectures relevant to our joint image analysis task: a transformer-based architecture and Sparse Nc-Net, a recent network designed to predict coarse correspondences using 4D convolutions. We show our approach provides clear improvements for artwork details retrieval on the Brueghel dataset and achieves competitive performance on two place recognition benchmarks, Tokyo247 and Pitts30K. We also demonstrate the potential of our approach for unsupervised image collection analysis by introducing a spectral graph clustering approach to object discovery and demonstrating it on the object discovery dataset of [49] and the Brueghel dataset. Our code and data are available at http://imagine.enpc.fr/~shenx/SegSwap/.
Causal Based Action Selection Policy for Reinforcement Learning

Reinforcement learning (RL) is the de facto learning by interaction paradigm within machine learning. One of the intrinsic challenges of RL is the trade-off between exploration and exploitation. To solve this problem, in this paper, we propose to improve the reinforcement learning exploration process with an agent that can exploit causal relationships of the world. A causal graphical model is used to restrict the search space by reducing the actions that an agent can take through graph queries that check which variables are direct causes of the variables of interest. Our main contributions are a framework to represent causal information and an algorithm to guide the action selection process of a reinforcement learning agent, by querying the causal graph. We test our approach on discrete and continuous domains and show that using the causal structure in the Q-learning action selection step, leads to higher jump-start reward and stability. Furthermore, it is also shown that a better performance is obtained even with partial and spurious relationships in the causal graphical model.
Virtual Reality Application Based on Inertia

In terms of the education system, developing countries of the world are still characterized with unequal school systems due to perpetuating inequality, and poverty. It is said that children who are admitted into the top schools are bound to achieve good grades in mathematics and sciences, unlike children in the next 6600 schools combined. This is a result of the majority of learners not having access to proper educational facilities, such as scientific labs and other technological means that would be in favor of the learner, unlike the rest of their counterparts in private or privileged schools. This paper presents a virtual reality science application, specifically focusing on the concept of inertia, which was achieved using various techniques such as 3D visualization and computer graphics to provide a platform for training and education of the scientific concept. Studies show that with the use of this technology, users were able to improve their subject scores, and it assisted with memory retention as well. With just the aid of a cell phone and virtual reality glasses, the ordinary learner will now be able to navigate his way around certain scientific concepts and experiments. Furthermore, the learner will be able to comprehend the scientific concept that is presented in front of their eyes, and this will ensure that every learner has equitable access to the learning content.
Motion Attitude Recognition and Behavior Prediction Algorithm Based on Wireless Sensor Network

The wireless sensor network is an integral part of the physical information system. Disperse sensors through a set of special spaces track and record the natural state of the environment and manage the information collected in a central location. The sensors use wireless connections to create their own networks. Wireless sensor network technology has the advantages of flexible deployment and convenient use and has played an important role in the field of user behavior recognition. By deploying wireless sensor network technology, users can collect daily information, capture users’ behavior habits, and analyze users’ health status. In the deployment and application of this type of technology, it is very important to build an effective model of the logical sequence relationship of the monitored person’s behavior. The sensor data can be sent to the target user through wireless transmission. Action recognition is often based on a single feature for learning and judgment, so there are many difficulties in practical applications. This article aims to study motion shake awareness and action prediction algorithms based on wireless sensor networks. Aiming at the research of human pose recognition algorithm, to optimize the overall performance of the model, this article suggests the use of multimodal input, uses a 2D and 3D network structure, and finally, proposes two network weighted fusion strategies. Aiming at the research of pedestrian motion discrimination, this article offers a behavior prediction algorithm based on multifeature joint learning. The algorithm adds the feature vectors output by gesture recognition and mask prediction and uses a cross-entropy cost function to jointly learn and predict classification. The results of the survey show that the pedestrian gesture recognition and motion recognition algorithm based on the wireless sensor network proposed in this paper has good performance and can be widely used in real scenes such as video surveillance. The accuracy of the gesture recognition algorithm in the UCF101 dataset and the HMDB51 dataset was 96% and 72%, respectively.
Neural Topic Models for Short Text Using Pretrained Word Embeddings and Its Application To Real Data

Latent Dirichlet Allocation (LDA) is a typical example of a topic model that estimates the latent topics of sentences. It is widely used in topic discovery, information retrieval, and document modeling. In recent years, with the advancement of research about neural networks, topic models using neural networks, such as NVLDA and ProdLDA, have been presented. Since it is easy to use accelerators such as GPUs for training these, topic modeling for large corpora can be done efficiently. Topic models are also used for short texts such as social networking sites and product reviews, where the number of words in a document is often short. This means that the co-occurrence information of words is also extremely less, and it is difficult to infer latent topics for easy understanding by humans when training with only the target corpus. In this paper, we investigated whether trained word embedding vectors on other large corpora such as Wikipedia compensate for the lack of word information in short texts. While previous studies have been conducted on long texts such as newspaper articles, we specifically checked the effect on short texts. As a result, we confirmed that under many conditions, Topic Coherence evaluation using Wikipedia was improved by using word embedding. However, we were not able to achieve stable high performance in terms of topic diversity. We also used this approach for topic modeling of tweets related to COVID-19.
Market Basket Analysis Approach to Machine Learning

Market Basket Analysis is an important aspect of a retail organization's analytical framework for deciding where products should be placed and developing sales promotions for various segments of consumers to increase customer loyalty and, as a result, benefit. The market is the well-known activity of ARM ultimately used for business intelligent decisions. Market Basket Analysis is a data mining technique that can be used in various fields, such as marketing, bioinformatics, education field, nuclear science, etc. Our objective here is that traders can further improve their business. At the same time, they can make more profits and invest more and more. This will be of great benefit. If the business of traders increases, many people will get employment, as well as customers, will be able to make their way of life easier by getting familiar with different things. Here is a discussion on their methods so that traders in Bangladesh can make proper investments in the right place. The frequent itemsets are mined from the database using the Apriori algorithm and then the association rules are generated. The project will assist supermarket managers in determining the relationship between the items that their customers purchase.