Reviewerly/exHarmony huggingface dataset:
A state sharding model on the blockchain

Blockchain is an incrementally updated ledger maintained by distributed nodes with excellent features such as decentralization and immutability. However, the development of blockchain is now limited due to storage redundancy. Specifically, nodes cannot afford to store a large amount of data, and the mechanism of setting many ordinary nodes as light nodes sacrifices the most important decentralization feature of the blockchain. This paper proposes the SSChain sharding scheme, which reduces the storage burden of each node on the blockchain without losing the decentralization feature. First, we shard the state in the blockchain through the data structure of Merkle DAG; then, shard all nodes in the blockchain through DHT technology; finally, store the state in the blockchain in different Node and establish a mapping relationship. With this scheme, each node only needs to store the state data allocated to it by the storage system, and can obtain other state data it needs from other nodes. After experimentation, the solution of this paper is feasible. The performance of storage is better than other current solutions, and the scalability of the blockchain is improved.
Password Management: How Secure Is Your Login Process?

Pairs of usernames and passwords are widely used nowadays by mobile and web applications to identify users. The exposure of this data harms both users and vendors. The client-server model is the most common. The provided services implement front-end interfaces that run on the client’s side and back-end interfaces that run on the server side. A proper password management policy administrates the password creation, storage, processing, and transmission in both ends. This article overviews the theory and provides a practical guide for password management and implementation of a safe login process for mobile and web application developers, and IT organizations. An empirical research and several case studies are surveyed for the password habits of three universities, an army school, an IT company, and two accounting offices in the province of Crete in Greece. Moreover, a software benchmark analysis is conducted for the computational demanding primitives of the secure login operations.
Design Patterns for Resource-Constrained Automated Deep-Learning Methods

We present an extensive evaluation of a wide variety of promising design patterns for automated deep-learning (AutoDL) methods, organized according to the problem categories of the 2019 AutoDL challenges, which set the task of optimizing both model accuracy and search efficiency under tight time and computing constraints. We propose structured empirical evaluations as the most promising avenue to obtain design principles for deep-learning systems due to the absence of strong theoretical support. From these evaluations, we distill relevant patterns which give rise to neural network design recommendations. In particular, we establish (a) that very wide fully connected layers learn meaningful features faster; we illustrate (b) how the lack of pretraining in audio processing can be compensated by architecture search; we show (c) that in text processing deep-learning-based methods only pull ahead of traditional methods for short text lengths with less than a thousand characters under tight resource limitations; and lastly we present (d) evidence that in very data- and computing-constrained settings, hyperparameter tuning of more traditional machine-learning methods outperforms deep-learning systems.
Teaching effectively with Zoom: A practical guide to engage your students and help them learn

Professors and teachers are seeking tools to aid their instruction following the emergence of the SARSCoV-2 pandemic and the transition to wide-scale online education. During Fall 2020, many pre-co...
An AI chatbot for the museum based on user Interaction over a knowledge base

Recently, with the advancement of technologies in AI and Knowledge Base, several museums are using chatbots for visitors. One of the problems with these technologies, however is that gradually tends to be of no real interest to visitors owing to the lack of significant interaction, this eventually distracts visitors from experiencing the exhibits. In the demo, we present AIMuBot, an interactive system for searching the information from the museum's knowledge base with natural language. The system has the following characteristics: (1) It supports natural language voice-based interaction with the visitors to ask questions; (2) It provides a voice-based graphical interface to help visitors refine the questions. (3) It retrieves information from the knowledge base for the visitors.
Visual Analytics System for Energy Data in Smart Cities and Buildings

New sensors and devices are being incorporated in modern buildings and cities to facilitate the understanding of its dynamics and improve its efficiency. With this in mind, the combination of information technology and sensors capable of capturing and sharing energy data with other devices can help solve power consumption problems. However, large volumes of these data are collected and stored uninterrupted, becoming a challenge to analyze it to confirm trends, identify hidden patterns, and outliers to aid in decision-making. As an alternative to address the issue, this paper presents an interactive visual analytics system to understand energy data coming from a smart building or city. This system provides ways to analyze the data at different levels of time granularities for the identification of energy trends, patterns, and data outliers. Moreover, it combines different algorithms that allow fulfilling predictive analysis. An analysis with domain experts demonstrates the feasibility and advantages of using the system to monitor energy data.
Quantum computation with generalized dislocation codes

In this paper, we study quantum computing with twists. Twists are yet another form of defects in a lattice. They arise from dislocations in the lattice and can be used to encode and process quantum information. Surface codes with twists are also called dislocation codes. Hastings and Geller showed that dislocation codes could provide gains in space-time complexity of quantum computation. In this paper, we undertake a detailed study of generalized dislocation codes. We develop the theory of qubit dislocation codes over arbitrary four-valent and bicolorable lattices. We give a construction to introduce twists in such lattices and also study the structure of logical operators. We then study dislocation codes over odd prime dimensions in square lattices. Using the theory developed, we present protocols for implementing a universal gate set in qubit dislocation codes. We also show how to implement the generalized Clifford group in qudit dislocation codes in odd prime dimensions.
Efficient Data Structures for Range Shortest Unique Substring Queries

Let T[1,n] be a string of length n and T[i,j] be the substring of T starting at position i and ending at position j. A substring T[i,j] of T is a repeat if it occurs more than once in T; otherwise, it is a unique substring of T. Repeats and unique substrings are of great interest in computational biology and information retrieval. Given string T as input, the Shortest Unique Substring problem is to find a shortest substring of T that does not occur elsewhere in T. In this paper, we introduce the range variant of this problem, which we call the Range Shortest Unique Substring problem. The task is to construct a data structure over T answering the following type of online queries efficiently. Given a range [α,β], return a shortest substring T[i,j] of T with exactly one occurrence in [α,β]. We present an O(nlogn)-word data structure with O(logwn) query time, where w=Ω(logn) is the word size. Our construction is based on a non-trivial reduction allowing for us to apply a recently introduced optimal geometric data structure [Chan et al., ICALP 2018]. Additionally, we present an O(n)-word data structure with O(nlogϵn) query time, where ϵ&gt;0 is an arbitrarily small constant. The latter data structure relies heavily on another geometric data structure [Nekrich and Navarro, SWAT 2012].
Identification of linear and non linear curve fitting models using particle swarm optimization algorithm

Identification of approximate model of the physical systems can be achieved by fitting the data. In this paper particle swarm optimization algorithm (PSO) is used for linear and polynomial curve fittings. Data generated from the known models and curve fitting is done by PSO using reverse engineering mechanism at the initial stage. In this process of curve fitting, two types of inertia mechanisms are used in PSO for getting better results. Later, real time financial series forecasting is considered for validating the PSO estimated regression models. Results shows the dynamic inertia weight strategy based PSO yields better fitting and avoids additional decisions on control parameters.
Gated Semantic Difference Based Sentence Semantic Equivalence Identification

This article proposes a novel sentence semantic equivalence identification (SSEI) method by using the semantic difference features between sentences. The lexical differences of a sentence pair are first extracted, and the bidirectional long short term memory (BiLSTM) network is then applied on them to generate the semantic difference representations. Finally, an efficient gate mechanism is proposed to integrate the semantic differences with existing models (called base model) to enhance their encoding capability in the SSEI task. Exhaustive experiments conducted on the standard Quora corpus, and the Large-scale Chinese Question Matching Corpus (LCQMC) show that the proposed gated semantic difference (GSD) method brings significant improvement for different existing state-of-the-art models. When the bidirectional encoder representations from transformers model (BERT) is used as the base model, the accuracy for SSEI on Quora is improved from 90.63% to 91.98%, and the F1 score on the LCQMC is improved from 87.0% to 87.7%, which outperforms the best-published results.
Optimal path planning for intelligent automated wheelchair using DDSRPSO

Purpose This paper aims to find the optimal path using directionally driven self-regulating particle swarm optimization (DDSRPSO) with high accuracy and minimal response time. Design/methodology/approach This paper encompasses optimal path planning for automated wheelchair design using swarm intelligence algorithm DDSRPSO. Swarm intelligence is incorporated in optimization due to the cooperative behavior in it. Findings The proposed work has been evaluated in three different regions and the comparison has been made with particle swarm optimization and self-regulating particle swarm optimization and proved that the optimal path with robustness is from the proposed algorithm. Originality/value The performance metrics used for evaluation includes computational time, success rate and distance traveled.
Development of a visual aid tool for blind people based on faster R-CNN

The technology used to help blind people has been developed using various technologies such as an ultrasonic sensor in the form of sonar system in the form of belts and watches. The sonar vision works based on the principle of reflecting a wave with a frequency of 20,000 Hz so that it can be used to determine the distance of an object in a range of up to 3 meters. However, the sonar system cannot identify the object. Computer Vision emerges as one of the most promising solutions. This article focused on developing blind visual aid using a Faster R-CNN identify an object. The Faster R-CNN is instilled on the raspberry pi equipped with a camera. The output is an audio signal about the object and its position refer to the users. The results of this study are able to recognize objects automatically well and accurately.
Knowledge Graph Driven Approach to Represent Video Streams for Spatiotemporal Event Pattern Matching in Complex Event Processing

Complex Event Processing (CEP) is an event processing paradigm to perform real-time analytics over streaming data and match high-level event patterns. Presently, CEP is limited to process structured data stream. Video streams are complicated due to their unstructured data model and limit CEP systems to perform matching over them. This work introduces a graph-based structure for continuous evolving video streams, which enables the CEP system to query complex video event patterns. We propose the Video Event Knowledge Graph (VEKG), a graph-driven representation of video data. VEKG models video objects as nodes and their relationship interaction as edges over time and space. It creates a semantic knowledge representation of video data derived from the detection of high-level semantic concepts from the video using an ensemble of deep learning models. A CEP-based state optimization — VEKG-Time Aggregated Graph (VEKG-TAG) — is proposed over VEKG representation for faster event detection. VEKG-TAG is a spatiotemporal graph aggregation method that provides a summarized view of the VEKG graph over a given time length. We defined a set of nine event pattern rules for two domains (Activity Recognition and Traffic Management), which act as a query and applied over VEKG graphs to discover complex event patterns. To show the efficacy of our approach, we performed extensive experiments over 801 video clips across 10 datasets. The proposed VEKG approach was compared with other state-of-the-art methods and was able to detect complex event patterns over videos with [Formula: see text]-Score ranging from 0.44 to 0.90. In the given experiments, the optimized VEKG-TAG was able to reduce 99% and 93% of VEKG nodes and edges, respectively, with 5.19[Formula: see text] faster search time, achieving sub-second median latency of 4–20[Formula: see text]ms.
Design of Personal Credit Information Sharing Platform Based on Consortium Blockchain

The technical features of blockchain, including decentralization, data transparency, tamper-proofing, traceability, privacy protection and open-sourcing, make it a suitable technology for solving the information asymmetry problem in personal credit reporting transactions. Appling blockchain technology to credit reporting meets the needs of social credit system construction and may become an important technical direction in the future. This paper analyzed the problems faced by China's personal credit reporting market, designed the framework of personal credit information sharing platform based on blockchain 3.0 architecture, and studied the technical details of the platform and the technical advantages. The in-depth integration of blockchain technology and personal credit reporting helps to realize the safe sharing of credit data and reduce the cost of credit data collection, thereby helping the technological and efficiency transformation of the personal credit reporting industry and promoting the overall development of the social credit system.
A Novel Approach on Linear Discriminant Analysis (LDA)

Linear Discriminant Analysis (LDA) is a method used for dimension reduction and classification. By reducing the dimensions of data interpretation it becomes easier. A new LDA-based coordinate transformation (LDA-CT) approach has been developed that does not depend on the statistical nature of data distribution so that it is more robust to the influence of outliers. This approach transforms data from the old coordinates to the new coordinates so that an optimal gradient is obtained which maximizes the separation distance of the two groups in the projection space. Synthetic data are used to test the performance of this new LDA approach compared to existing LDA performance. The experimental results using synthetic data without and with outliers show that compared to the existing LDA, this new approach is able to make generalizations better and more robustly against the influence of outliers. For data that can be separated linearly, the LDA-CT Optimal method is able to separate classes as far as 0.705390519 better than existing LDA which only separates as far as 0.33440611. For data with outliers, LDA-CT Optimal accuracy is better than existing LDA with 91.67% compared to 75%.
A Novel Evaluation Metric for Synthetic Data Generation

Differentially private algorithmic synthetic data generation (SDG) solutions take input datasets $$D_p$$ consisting of sensitive, private data and generate synthetic data $$D_s$$ with similar qualities. The importance of such solutions is increasing both because more and more people realize how much data is collected about them and used in machine learning contexts, as well as a consequence of newly introduced data privacy regulations, e.g. the EU’s General Data Protection Regulation (GDPR). We aim to develop a novel and composite SDG evaluation metric which takes into account macro-statistical dataset similarities and data utility in machine learning tasks against privacy boundaries of the synthetic data. We formalize the mathematical foundations for quantitatively measuring both the statistical similarities and the data utility of synthetic data. We use two well-known datasets containing (potentially) personally identifiable information as inputs ( $$D_p$$ ) and existing SDG algorithms PrivBayes and DPGroupFields to generate synthetic data ( $$D_s$$ ) based on them. We then test our evaluation metric for different values of privacy budget $$\epsilon $$ . Based on our experiments we conclude that the proposed composite evaluation metric is appropriate for quantitatively measuring the quality of synthetic data generated by different SDG solutions and possesses an expected sensitivity to various privacy budget values.
Clothing image classification based on random erasing and residual network

Abstract The traditional clothing classification method mainly consists of manually extracting obvious features such as color, texture and edge of the image. These artificial feature extraction methods are cumbersome and feature recognition rate is not high. In recent years, Deep Residual Network (ResNet) has been widely used in various fields by increasing network depth to obtain higher recognition accuracy. In this paper, the ResNet model is applied to the classification of clothing images, and on this basis, its data pooling layer is improved so that it can learn more rich features of image data. Clothing images are easy to be deformed and occluded. In this paper, a random erasing data enhancement algorithm is used to integrate and improve the model to improve the generalization ability of the ResNet model to such data. The final experimental results show that the classification accuracy of the improved residual model on clothing data in this paper is improved by 2.43%. At the same time, after integrating the random erasure data enhancement algorithm, the generalization ability of the model has been further improved.
Outfit2Vec: Incorporating Clothing Hierarchical MetaData into Outfits’ Recommendation

Fashion Personalisation is emerging as a major service that online retailers and brands are competing to provide. They aim to deliver more tailored recommendations to increase revenues and satisfy customers by providing them options of similar items according to their purchase history. However, many online retailers still struggle with turning customers' data into actionable and intelligent recommendations that reflect their personalised and preferred taste of style. On the other hand due to the ever increasing use of social media, fashion brands invest in influencers' marketing to advertise their brands to reach a larger segment of customers who strongly trust their influencers' choices. In this context the textual and visual analysis of social media can be used to extract semantic knowledge about customers' preferences that can be further applied in generating tailored online shopping recommendations. As style lies in the details of outfits, recommendation models should leverage the fashion metadata ranging from clothing categories and subcategories to attributes such as materials and patterns to overall style description in order to generate fine-grained recommendations. Recently, several recommendation algorithms suggested to model the latent representations of items and users with neural word embeddings approaches which showed improved results. Inspired by Paragraph Vector neural embeddings model, we present Outfit2vec and PartialOutfit2vec in which we leverage the complex relationship between user's fashion metadata while generating outfits' embeddings. In this paper, we also describe a methodology to generate representative vectors of hierarchically-composed fashion outfits. We evaluate our models using different strategies in comparison to the paragraph embedding models on an extensively-annotated Instagram dataset on recommendation and multi-class style classification tasks. Our models achieve better results specially in whole outfits' ranking evaluations with an average of 22% increase.
Design of File Multi-Cloud Secure Storage System Based on Web and Erasure Code

With the continuous development of Internet technology, cloud storage has been widely applied and developed. Users can store data through the network anytime, anywhere. However, the frequent occurrence of a series of cloud storage data security events show that cloud storage not only provides data storage services for users, but also poses huge threats and challenges to users' data security. Aiming at the problem of user data security in cloud storage environment, the system introduces multi-cloud storage system on the basis of the traditional single cloud storage system. The system uses Erasure Code to block the original data, and encrypts the data blocks using AES (Advanced Encryption Standard) encryption technology, then stores the encrypted data blocks in different cloud storages terminals. Thus, the hidden danger of data loss or leakage due to relying on a cloud service provider is eliminated, the existing security problems of cloud storage system are improved, and the security and reliability of the multi-cloud secure storage system are improved.
Dual Phone AR: Exploring the use of Phones as Controllers for Mobile Augmented Reality

The possible interactions with Mobile Augmented Reality applications today are largely limited to on-screen gestures and spatial movement. There is an opportunity to design new interaction methods that address common issues and go beyond the screen. Through this project, we explore the idea of using a second phone as a controller for mobile AR experiences. We develop prototypes that demonstrate the use of a second phone controller for tasks such as pointing, selecting, and drawing in 3D space. We use these prototypes and insights from initial remote evaluations to discuss the benefits and drawbacks of such an interaction method. We conclude by outlining opportunities for future research on Dual Phone AR for multiple usage configurations, and in collaborative settings.