Reviewerly/exHarmony huggingface dataset:
A New Collaborative Filtering Recommendation Method Based on Transductive SVM and Active Learning

In the collaborative filtering (CF) recommendation applications, the sparsity of user rating data, the effectiveness of cold start, the strategy of item information neglection, and user profiles construction are critical to both the efficiency and effectiveness of the recommendation algorithm. In order to solve the above problems, a personalized recommendation approach combining semisupervised support vector machine and active learning (AL) is proposed in this paper, which combines the benefits of both TSVM (Transductive Support Vector Machine) and AL. Firstly, a “maximum-minimum segmentation” of version space-based AL strategy is developed to choose the most informative unlabeled samples for human annotation; it aims to choose the least data which is enough to train a high-quality model. And then, an AL-based semisupervised TSVM algorithm is proposed to make full use of the distribution characteristics of unlabeled samples by adding a manifold regularization into objective function, which is helpful to make the proposed algorithm to overcome the traditional drawbacks of TSVM. Furthermore, during the procedure of recommendation model construction, not only user behavior information and item information, but also demographic information is utilized. Due to the benefits of the above design, the quality of unlabeled sample annotation can be improved; meanwhile, both the data sparsity and cold start problems are alleviated. Finally, the effectiveness of the proposed algorithm is verified based on UCI datasets, and then it is applied to personalized recommendation. The experimental results show the superiority of the proposed method in both effectiveness and efficiency.
SVM Learning for Default Prediction of Credit Card under Differential Privacy

Currently, financial institutions utilize personal sensitive information extensively in machine learning. It results in significant privacy risks to customers. As an essential standard of privacy, differential privacy is often applied to machine learning in recent years. To establish a prediction model of credit card default under the premise of protecting personal privacy, we consider the problems of customer data contribution difference and data sample distribution imbalance, propose weighted SVM algorithm under differential privacy. Through theoretical analysis, we have ensured the security of differential privacy. The algorithm solves the problem of prediction result deviation caused by sample distribution imbalance and effectively reduces the data sensitivity and noise error. The experimental results show that the algorithm proposed in this paper can accurately predict whether a customer is default while protecting personal privacy.
Data Life Aware Model Updating Strategy for Stream-based Online Deep Learning

Many deep learning applications deployed in dynamic environments change over time, in which the training models are supposed to be continuously updated with streaming data in order to guarantee better descriptions on data trends. However, most of the state-of-the-art learning frameworks support well in offline training methods while omitting online model updating strategies. In this work, we propose and implement iDlaLayer, a thin middleware layer on top of existing training frameworks that streamlines the support and implementation of online deep learning applications. In pursuit of good model quality as well as fast data incorporation, we design a Data Life Aware model updating strategy (DLA), which builds training data samples according to contributions of data from different life stages, and considers the training cost consumed in model updating. We evaluate iDlaLayer's performance through both simulations and experiments based on TensorflowOnSpark with three representative online learning workloads. Our experimental results demonstrate that iDlaLayer reduces the overall elapsed time of MNIST, Criteo and PageRank by 11.3%, 28.2% and 15.2% compared to the periodic update strategy, respectively. It further achieves an average 20% decrease in training cost and brings about 5 % improvement in model quality against the traditional continuous training method.
A Syntactic-Relationship Approach to Construct Well-Informative Knowledge Graphs Representation

Most of the knowledge achieved from research activities are available as computer-like unstructured data written in natural-language papers. Automatically retrieving and representing knowledge from natural-language papers as input for computer processing is complex and challenging. In this paper, we propose a novel syntactic-relationship approach based on natural language processing, efficiently applying clustering algorithms to generate knowledge taxonomies about specific domain texts automatically. The approach considers a cloud computing case study through the collection and analysis of a set of recent publications. To assess our proposal, we conducted a quantitative comparison between different clustering-intrinsic metrics. Results showed higher popularity and coverage of the present proposal than the state-of-the-art, especially when using hierarchical clustering. The differential of our proposal lies in building a well-informative representation of knowledge with only three-quarters of the original textual data, and without any ground truth labeling.
Virtual design of urban planning based on GIS big data and machine learning

In order to build a virtual urban planning model and improve the effect of urban planning, this paper builds a virtual urban planning design model based on GIS big data technology and machine learning algorithms, and proposes a solution that combines multiple features. With the development of polarized SAR in the direction of high resolution, a single feature often cannot fully express the detailed information of ground objects, resulting in poor classification results and low accuracy. The combination of multiple features can express feature information well. In addition, this paper uses the ELM method to plan SAR ground object classification, uses an extreme learning machine classification algorithm with fast learning speed and good classification effect, and uses ELM as a classifier. Finally, this paper designs experiments to explore the performance of the model constructed in this paper from two aspects: detection accuracy and planning score. The research results show that the model constructed in this paper meets the expected goals.
Regulatory Standards and Measures

Blockchain technology is an emerging technology which has caught the attention of practitioners, governments, business enterprises including the academic researchers in this present century. Its attraction is mainly due to its potential to enhance the human way of doing things including service delivery and consequently leading to happier consumers and stakeholders and providing an edge over competitors, resulting in an improved brand image. Unfortunately, the adoption of a new technology is not all other easy, it takes time and effort. The major issue of the technology is the lack of regulatory measure framework to boost its acceptability among many countries of the world. Both practitioners and scholars have agreed that the technology needs to be validated, regulated, and adopted. Unfortunately, this is yet to be achieved. The chapter examined regulatory standard measures of blockchain technology as a panacea for blockchain technology acceptability. It adopts a literature review approach with a content analysis technique where several but selected views and opinions of countries on the regulatory positions were analysed. Evidence shows that the absence of regulatory measure standard is fear to non-acceptability and accessibility of blockchain technology. It also revealed that a specific regulatory standard is needed to drive the acceptability and accessibility of blockchain technology not only in Saudi Arabia but also in the globe. This paper therefore concludes that a specific regulatory measure and standard is a panacea to the acceptability and accessibility of blockchain technology.
Stakeholder identification for a structured release planning approach in the automotive domain

Context: In regulated domains like automotive, release planning is a complex process. The agreement between traditional product development processes for hardware as well as mechanic systems and agile development approaches for software development is a major challenge. Especially the creation and synchronization of a release plan is challenging. Objective: The aim of this work is to present identified stakeholders of a release plan as an appropriate approach to create transparency in release planning in the automotive domain. Method: Action research to elaborate relevant stakeholders for release planning was conducted at Dr. Ing. h. c. F. Porsche AG. Results: We present a detailed overview of identified stakeholders due to release planning as well as their required content and added value regarding to two pilot projects. The results confirm the fact that almost every stakeholder is involved in a release plan in a certain way. Conclusions: Release planning within a complex project environment and complicated customer constellations is difficult to manage. We discuss how the presented stakeholders could meet with the given conditions in the automotive domain. With this contribution, identified stakeholders of release planning from hardware and software point of view is introduced. It helps to reach transparency and to handle the given complexity.
PMM: A Model for Bangla Parts-of-Speech Tagging Using Sentence Map

The Part-of-speech (POS) tagging is mandatory for almost all kinds of Natural Language Processing (NLP) tasks such as Grammar checking, Machine translation, summary writing, sentiment analysis, information retrievals, and speech processing etc. Having very few successful researches on computational linguistics in Bangla language, it still remains the demand for technology. The existing works on Bangla parts-of-speech tagging require large training data set and not applicable for all language styles. In this research, we proposed Prediction Maximization Model (PMM) for Bangla parts-of-speech tagging. We used statistical data for learning and used rule-based analysis. Hidden Markov Model (HMM) is applied with tag mapping and scoring in PMM to maximize the accuracy by using relatively less statistical training data. PMM achieved 95.6% accuracy that is relatively high compared with two other existing POS tagger which claims the nearest accuracy but with the relatively much higher number of training data sets. In our experiment, we used around 14K unique token as training data for PMM and the other two existing systems and PMM performed best.
Reconstructing the Noise Variance Manifold for Image Denoising

Deep Convolutional Neural Networks (CNNs) have been successfully used in many low-level vision problems like image denoising. Although the conditional image generation techniques have led to large improvements in this task, there has been little effort in providing conditional generative adversarial networks (cGANs) with an explicit way of understanding the image noise for object-independent denoising reliable for real-world applications. The task of leveraging structures in the target space is unstable due to the complexity of patterns in natural scenes, so the presence of unnatural artifacts or over-smoothed image areas cannot be avoided. To fill the gap, in this work we introduce the idea of a cGAN which explicitly leverages structure in the image noise variance space. By learning directly a low dimensional manifold of the image noise variance, the generator promotes the removal from the noisy image only that information which spans this manifold. This idea brings many advantages while it can be appended at the end of any denoiser to significantly improve its performance. Based on our experiments, our model substantially outperforms existing state-of-the-art architectures, resulting in denoised images with less over-smoothing and better detail.
Cross-View Image Synthesis with Deformable Convolution and Attention Mechanism

Learning to generate natural scenes has always been a daunting task in computer vision. This is even more laborious when generating images with very different views. When the views are very different, the view fields have little overlap or objects are occluded, leading the task very challenging. In this paper, we propose to use Generative Adversarial Networks (GANs) based on a deformable convolution and attention mechanism to solve the problem of cross-view image synthesis (see Fig. 1). It is difficult to understand and transform scenes appearance and semantic information from another view, thus we use deformed convolution in the U-net network to improve the network's ability to extract features of objects at different scales. Moreover, to better learn the correspondence between images from different views, we apply an attention mechanism to refine the intermediate feature map thus generating more realistic images. A large number of experiments on different size images on the Dayton dataset [] show that our model can produce better results than state-of-the-art methods.
Design a Proof of Stake Based Directed Acyclic Graph Chain

The concept of blockchain comes from the Bitcoin system where transactions are organized in blocks. However, blocks are not necessary. Researchers have found ways to use a directed acyclic graph (DAG) to build a chain without blocks. Currently, these chains still rely on some kinds of proof of work, which is not environmentally friendly. We here design a DAG chain with a proof of stake for independent nodes in the Internet. We split nodes into two categories. One is client nodes that produce only normal net transactions (NNTs). The other is chain nodes where active chain nodes are selected periodically based on their stakes and aspirations to participate in the chain management. Active chain nodes produce chain transactions (CTs) that may include hashes of NNTs. CTs form the DAG chain, which helps an ordering process of the NNTs. With the ordering ability of transactions from client nodes, the chain naturally supports global state transition. With a brief analysis, we found that the theoretical performance limitation of the chain depends only on the bandwidth and computation power of chain nodes.
How feature fusion can help to improve multi-shot person re-identification performance?

Person re-identification (ReID)-a problem of associating the images of the same person from different camera views, can be divided into two categories of multi-shot and single-shot depending on the number of images used for person representation. Numerous methods have been proposed for person ReID. However, these studies focus either on feature extraction for person representation or metric learning for person matching. Taking into account that each feature has its own representation power for each individual, in this paper, we focus on improving person ReID performance through feature fusion. For this, first we formulate multi-shot person ReID as an information retrieval problem where each person in probe is considered as a query person. Inspired the idea of query-adaptive late fusion proposed for image retrieval in [1], we propose two adaptive late fusion schemes for multi-shot person ReID. To show the robustness of the fusion schemes, we integrate them in a multi-shot person ReID framework where both hand-crafted and deep-learned features are extracted. Experimental results obtained on two benchmark datasets of PRID-2011 and iLIDS-VID show that the matching rates at rank-1 increased up to 5.65% and 14.13% on PRID-2011 and iLIDS-VID, respectively.
Exploration via Progress-Driven Intrinsic Rewards

Traditional exploration methods in reinforcement learning rely on well-designed extrinsic rewards. However, many real-world scenarios involve sparse or delayed rewards. One solution inspired by curious behaviors in animals is to let the agent develop its own intrinsic rewards. In this paper we propose a novel end-to-end curiosity mechanism which uses learning progress as novelty bonus. We compare a policy-based and a visual-based progress bonus to move the agent towards hard-to-learn regions of the state space. We further leverage the agent’s learning to identify the most critical regions, which results in more sample-efficient and global exploration strategies. We evaluate our method on a variety of benchmark environments, including Minigrid, Super Mario Bros., and Atari games. Experimental results show that our method outperforms prior approaches in most tasks in terms of exploration efficiency and average scores, especially for those featuring high-level exploration patterns or with deceptive rewards.
Hierarchical Evolutionary Heuristic A* Search

A* is an informed pathfinding algorithm that uses a heuristic function to determine the best action to take based on the given information. The performance of A* is heavily dependent on the quality of the heuristic function. The heuristic function determines the search speed, accuracy, and memory consumption. Hence, designing good heuristic functions for specific domains becomes the primary research focus on pathfinding algorithms optimization. However, designing new heuristic functions is a difficult task, especially when they are used to solve complex problems. Moreover, a single heuristic function might not be enough to digest all the provided information and return the best guidance during the search. Previous works suggest that multiple heuristics for complex problems can dramatically speed up the search. However, choosing the appropriate combination of heuristic functions is tricky. Current optimization approaches rely on hand-tuning the parameters via trial and error by the engineers over many iterations. There is a need to reduce the difficulty of designing heuristic functions for search performance maximization. In this paper, we develop a novel heuristic search called Hierarchical Evolutionary Heuristic A* (HEHA*) where multiple heuristics are chosen and evolved using Genetic Algorithm. HEHA* combines the techniques of map abstraction, pattern database, and heuristic improvement. The advantage of HEHA* is twofold: 1) it partitions and reduces the search space based on local features to speed-up the search, and 2) it automatically designs and optimizes heuristics for different local regions to maximize the search performance. We test our algorithm on a widely used grid-based map benchmark to compare with A* variants. Our results show that HEHA* outperforms compared with other pathfinding algorithms in terms of execution time and memory consumption.
Evolution and Advancement of Arithmetic Coding over Four Decades

Arithmetic Coding (AC) is a form of entropy encoding used in lossless data compression. It is a well-known, state of the art technique, in which the frequently seen symbols are encoded with fewer bits than rarely seen symbols. It has been widely used since last four decades. Many researchers worked on it to improve its performance and they successfully experimented with it. This technique has also been in use in combination with other techniques to gain surprising results. In this survey paper, an effort is made to recap a number of accomplishments from 1976 to 2017 regarding Arithmetic Coding. This study provides an insight for new researchers to know how this technique evolved with time and how major achievements were made using this technique. This paper gives a comparison of AC with another well-known technique named Huffman Coding. Comparison with its contemporary counterparts shows that it is better in performance almost in every situation.
Trajectory Generation with Fast Lidar-based 3D Collision Avoidance for Agile MAVs

Micro aerial vehicles (MAVs), are frequently used for exploration, examination, and surveillance during search and rescue missions. Manually piloting these robots under stressful conditions provokes pilot errors and can result in crashes with disastrous consequences. Also, during fully autonomous flight, planned high-level trajectories can be erroneous and steer the robot into obstacles. In this work, we propose an approach to efficiently compute smooth, time-optimal trajectories MAVs that avoid obstacles. Our method first computes a trajectory from the start to an arbitrary target state, including position, velocity, and acceleration. It respects input- and state-constraints and is thus dynamically feasible. Afterward, we efficiently check the trajectory for collisions in the 3D-point cloud, recorded with the onboard lidar. We exploit the piecewise polynomial formulation of our trajectories to analytically compute axis-aligned bounding boxes (AABB) to speed up the collision checking process. If collisions occur, we generate a set of alternative trajectories in real-time. Alternative trajectories bring the MAV in a safe state, while still pursuing the original goal. Subsequently, we choose and execute the best collision-free alternative trajectory based on a distance metric. The evaluation in simulation and during a real firefighting exercise shows the capability of our method.
Developing Blockchain Supported Collective Intelligence in Decentralized Autonomous Organizations

The arrival of Blockchain and cryptographic assets-driven decentralized technologies has brought to the world an entirely new spectrum of possibilities regarding social, financial, and technological innovation. While the first applications of Blockchain technology were for digital currency transactions, that same technology is now being applied to the development of the Decentralised Web (DWeb), including recording the movement of data, registering unique user names and even data storage. It has become possible to achieve superior levels of transparency, security and fairness to the public, especially when compared to the rather limited business models that dominates WEB 2.0 commercial products. By providing a reliable, open and programmable accounting system, Blockchain has led to the invention of the Decentralized Autonomous Organization (DAO), which are open, self-organized networks coordinated by crypto-economic incentives and self-executing code, and cooperating around shared goals. DAOs have attracted the brightest minds to work in the Blockchain space, however, such experts still lack critical elements for an adequate decentralized governance and efficient collective decision-making. Joint research conducted by the bitfwd community and University of New South Wales (UNSW) in Sydney, Australia, and Vilnius Gediminas Technical University (VGTU) in Vilnius, Lithuania, sought to deepen our understanding of the development of blockchain supported collective intelligence in DAOs. This qualitative study identifies the main problem areas and highlights the importance of social value creating technological solutions by developing collective intelligence. Behind our research is the evaluation of Alchemy (https://alchemy.daostack.io/) DAO platform, an open source software toolset that allows for decentralized governance, allocation of resources and collective decision-making co-developed in the DAOstack (https://daostack.io/) ecosystem.
An Unsupervised Learning Short Text Clustering Method

Abstract Due to the continuous development of Natural Language Processing (NLP), the task of short text categorization has been paid more and more attention. In short text clustering, the high-dimensional sparseness of text representation matrix becomes a challenging problem. This paper proposes a deep embedded method for feature extraction and clustering allocation using auto encoder of sentence distributed embedding. This method maps from data space to low-dimensional feature space and iteratively optimizes clustering targets. Experimental results on three short Chinese text data sets verify the effectiveness of the method. Moreover, it is superior to the existing correlation clustering methods.