Reviewerly/exHarmony huggingface dataset:
PAT: Preference-Aware Transfer Learning for Recommendation with Heterogeneous Feedback

In this paper, we study an important recommendation problem with heterogeneous feedback of users' grade scores such as 5-star grade scores and like/dislike binary ratings assigned to items. As a response, we address the problem from a transfer learning perspective, i.e., taking the grade scores as the target data and the binary ratings as the auxiliary data, in order to share knowledge between two different types of data more sufficiently. Technically, besides the observed explicit feedback of grade scores and binary ratings, we propose to exploit the implicit preference context beneath the feedback, which is incorporated into the prediction process of users' grade scores to items. Finally, we develop a novel and generic transfer learning solution, i.e., preference-aware transfer (PAT), which embodies several recent algorithms as parts of our solution and special cases. To verify our novel solution, we then conduct extensive empirical studies on two large and public datasets and find that our PAT performs significantly better than the state-of-the-art methods.
FeatureMF – A Novel Collaborative Filtering Recommendation Model

This paper <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> presents a novel matrix factorization (MF) model, called FeatureMF, which takes into account item features and thus addresses the cold-start item and data sparsity problems of collaborative filtering (CF). More specifically, the model extends item latent vectors with item representation learned from metadata. Experiments conducted on a public dataset with two testing views confirm that FeatureMF achieves better recommendation performance than some of the popular state-of-the-art MF models.
Development Money Diary Application Models on Android

Today many people have difficulty controlling their time and finances. This certainly affects self-productivity and work productivity in the office / business. With the establishment of the Money Diary application, it is expected to become a solute task and money management application. The purpose of making this application is to assist users in managing and managing their tasks and finances all at once in an application. The research method used in data collection is literature study and indirect observation. The application development method uses the system development life cycle method with the waterfall model. This model uses a systematic and sequential approach that starts at the level of requirement to the level of maintenance. This application is based on Android developed with the Java programming language. The result is a money diary application that can be used to daily actions, to remind users of the need to pay bills on time, to facilitate financial management for users, to remind users to perform certain actions that were previously planned.
Internal Quasiperiod Queries

Internal pattern matching requires one to answer queries about factors of a given string. Many results are known on answering internal period queries, asking for the periods of a given factor. In this paper we investigate (for the first time) internal queries asking for covers (also known as quasiperiods) of a given factor. We propose a data structure that answers such queries in \(\mathcal {O}(\log n \log \log n)\) time for the shortest cover and in \(\mathcal {O}(\log n (\log \log n)^2)\) time for a representation of all the covers, after \(\mathcal {O}(n \log n)\) time and space preprocessing.
Comparing Phonetic Reduction in Russian as First and Second Language: From Psycholinguistics to Natural Language Processing

In the paper, we argue that it is necessary to collect and analyze casual speech of the speakers from different age groups, both native speakers and those who study a language as a second one, in order to understand the mechanisms of spoken word production and recognition and to improve current automatic systems of natural language processing. We provide a short overview of the corpora of adult, children and adolescent Russian speech we develop and then focus on the methodology and results of a study of phonetic reduction in the speech of 16 Chinese students learning Russian as a foreign language. We found out similar tendencies of phonetic reduction in the speech of the Chinese students and in the speech of native speakers of Russian. At the same time, the speech of Chinese students, unlike the speech of Russian-speaking children aged four to six years, is characterized by a large number of examples with sound changes. The second language learners of Russian usually have different realizations of one and the same word in their speech. The results we obtained can be used for spoken word recognition modeling, as well as for various educational purposes including teaching Russian as a foreign language.
Optimized execution of morphological reconstruction in large medical images on embedded devices

This work presents a hardware/software co-design implementation of the morphological reconstruction targeting a System-on-Chip (SoC) FPGA-based embedded system. Our approach processes large images with fast algorithms. This was achieved by the proposal and use of an execution scheme that partitions the input image into sub-images that are independently processed before a second phase is executed to enable propagation of information among sub-images. The SoC is efficiently used by processing sub-images on hardware (the costly phase), while the software takes care of computations due to discontinuities that are irregular and inefficient for the hardware execution. Several optimizations were proposed, including parallel software and hardware execution and the use of borders to minimize computations in the discontinuities correction. This enables the processing of large images from our use-case brain cancer tissue image analysis application. For an image of $$8192 \times 8192$$ pixels, our co-design solution attains a speedup of 12.7 $$\times$$ vs. the software execution (Dual core ARM A9 Cortex).
Augmenting Cloud Service Discovery Using Ontology

The increasing demand for cloud computing has led cloud service providers (CSPs) to offer a variety of services to consumers with different attributes. Currently, cloud consumers are facing problems as they have abundant options for cloud services which may deliver similar functionalities. Moreover, discovering the best and most suitable service which meets the user requirements such as speed, cost, performance, and security is a crucial concern. In order to understand and solve this problem, a novel approach is required which can search and retrieve most suitable services as per the user’s necessities. In this paper an ontology-based cloud service discovery system is presented which can help increase the precision of cloud service discovery actually. The main goal is to categorize the cloud services along with its individualities and store them as objects using ontology. Attributes like cost, security, and SLA (service-level agreements), and so on are defined for these objects. A crawler retrieves and categorizes cloud services and stores them into ontology directory. Once the services are stored in ontology directory, this directory can be utilized for query processing, which processes the query posed by the user and ranks the obtained services based on the concerned attributes. The expected outcome of the posed query will be much accurate and appropriate according to the proposed discovery system.
A Stream Hardware Architecture for Keypoint Matching Based on a Speculative Approach

Detection, description, and matching of keypoints are commonly used as a first step in embedded real-time vision applications such as tracking, 3D reconstruction, visual odometry, or SLAM. In this paper, we present a hardware accelerator for keypoint extraction and matching designed to meet low latency and memory footprint constraints while being scalable and generic. Different pipelines with Harris detector and U-SURF descriptor have been implemented on FPGA targets. Our temporal matching pipeline implementation reaches up to 320 fps at 100 MHz on a Xilinx ZYNQ XC7Z020 for VGA images with less than half an image of latency.
An Efficient Ellipse Detector Based On Region Detection And Arc Pruning

Detecting ellipses accurately and efficiently for real-world images is crucial for various visual-based applications. Most existing methods employ detection strategies throughout images, while most time is spent on the non-ellipse region. Meanwhile, small ellipses are often miss-detected due to the low resolution and fixed parameters of detectors. In this paper, we proposed an effective ellipse detector benefiting from the region detection method, which provides a basic estimation on the region and size of ellipses. Then, a two-level arc pruning strategy is proposed to detect ellipses efficiently while limiting false-positive and false-negative results. Furthermore, for the pre-estimated region without detected ellipses, interpolation method is employed to enlarge the target region, which makes small and blur ellipses to be detected. Experimental results demonstrate that the proposed method achieves competitive accuracy compared with the state-of-the-art methods.
Grappling with Web Technologies: The Problems of Remote Speech Recording

Modern web browsers are becoming operating systems of their own kind, allowing unified access to the underlying hardware. The sound device can thus be used by web-based communication systems, such a Google meet, Zoom and others. This attracts the idea of using such capabilities to record a speech synthesis corpus through the web, with there being cases of use where it is really beneficial – for example, the building of personalised speech synthesis. The present paper shows that although it may appear easy, there are some dark corners to take care of.
The Monte Carlo Type Method of Attack on the RSA Cryptosystem

The RSA cryptosystem is the most widely used cryptosystem, and its security is based on the difficulty of factorization of big integers. We study the possibility of determination of the secret key of an RSA cryptosystem by means of the Monte Carlo method applied to the continued fraction method. We develop and extend similar techniques studied earlier by Wiener, de Weger, and others.
A Software Architecture for Video Analytics

Video analytics is the automatic understanding of complex events occurring in a captured scene, by artificial intelligence. It is a fundamental task for homeland security and crime prevention. Some video analytics tasks are being addressed by at the edge processing approach, which combines algorithmic with hardware development, within the camera system design. The at the edge processing approach looks for real-time performance on specific domains and low network bandwidth requirements. However, it implies that the already installed city-camera-network should be replaced and continuously updated since it introduces constraints to analytics capabilities related to cameras' characteristics and functionalities. Thus, city surveillance by the at the edge processing approach requires a large budget, and continuous investment, to keep the system working. In this paper, we present both faced challenges and obtained achievements during the design process of video-analytics-system software architecture. Interoperability, availability, and security were the prioritized quality attributes by the Quality Attribute Workshop method. A microservices and cloud-based design was the result of applying the Attribute Driven Design method, incorporating software engineering sustainability concerns. As a practical advantage, the designed system can work with the already installed city surveillance camera network and allows incorporating video analytics algorithms as the surveillance system evolves. The evaluation combined the functional and operative prototype as well as the ATAM method, according to which approach is more suited to evaluate a specific driver. The discussion on findings of design an evaluation processes is a takeaway for the reader.
DAGNet: Exploring the Structure of Objects for Saliency Detection

Fully Convolutional Neural Networks (FCNs) greatly promote the development of saliency detection. However, most of the FCN-based models have suffered from the structure of salient objects challenges. The extracted multi-scale features by previous models could help locate the objects with various scales, but they cannot contribute to effectively locating the objects with complex shapes, especially the salient regions that might intertwine with non-salient regions. Moreover, the style of decoder in previous models cannot adequately filter out the disturbance in low-level features, which is sub-optimal to sharpen the boundary of salient objects. In this paper, we propose DAGNet that explores the structure of salient objects from multi-level features to precisely detect salient objects. Firstly, the new dense multi-scale context extraction modules (DMCEMs) are implemented to transmit the rich structural information flow of salient objects from shallower layers to deeper layers, by which our model can locate the objects with complex shapes. Secondly, attention-based deeply refining modules (ADRMs) are designed in an effective attention-based style to effectively restore the boundary of objects stage-by-stage. In the style, the semantic information of high-level features is utilized to guide the shallow layer to filter out disturbance and refine the high-level features. Considering the salient objects surrounded by a cluttered scene, we propose a global context extraction module (GCEM) that can sufficiently understand the cluttered scene of an image from a global view. Comprehensive experiments indicate that our model is superior to 13 state-of-the-art models on 5 benchmark datasets under different evaluation metrics.
Dimensionality Reduction Via Diffusion Map Improved With Supervised Linear Projection

When performing classification tasks, raw high dimensional features often contain redundant information, and lead to increased computational complexity and overfitting. In this paper, we assume the data samples lie on a single underlying smooth manifold, and define intra-class and inter-class similarities using pairwise local kernel distances. We aim to find a linear projection to maximize the intra-class similarities and minimize the inter-class similarities simultaneously, so that the projected low dimensional data has optimized pairwise distances based on the label information, which is more suitable for a Diffusion Map to do further dimensionality reduction. Numerical experiments on several benchmark datasets show that our proposed approaches are able to extract low dimensional discriminate features that could help us achieve higher classification accuracy.
A Multiplatform Parallel Approach for Lattice Sieving Algorithms

Lattice sieving is currently the leading class of algorithms for solving the shortest vector problem over lattices. The computational difficulty of this problem is the basis for constructing secure post-quantum public-key cryptosystems based on lattices. In this paper, we present a novel massively parallel approach for solving the shortest vector problem using lattice sieving and hardware acceleration. We combine previously reported algorithms with a proper caching strategy and develop hardware architecture. The main advantage of the proposed approach is eliminating the overhead of the data transfer between a CPU and a hardware accelerator. The authors believe that this is the first such architecture reported in the literature to date and predict to achieve up to 8 times higher throughput when compared to a multi-core high-performance CPU. Presented methods can be adapted for other sieving algorithms hard to implement in FPGAs due to the communication and memory bottleneck.
Low-Cost Reservoir Computing using Cellular Automata and Random Forests

High-performance image classification models involve massive computation and an energy cost that are unaffordable for resource-limited platforms. As a solution, reservoir computing based on cellular automata has been proposed, but there is still room for improvement in terms of classification cost. This research builds on the previous work introducing enhancements at both the algorithmic and architectural level. Using a random forest classifier with binary features completely eliminates multiplication operations and 97% of addition operations. Also, memory usage can be decreased by pruning 82% of the least relevant augmented features. An architecture with an increased level of parallelism which processes images in a single pass reduces memory accesses, and reduces 60% of logic by optimizing FPGA mapping. These speed, power, and memory optimizations come at an accuracy tradeoff of a mere 0.6%.
Stimulating Motivations in English Speaking for Students Majoring in Engineering

The standards of professional accreditation require graduate students majoring in engineering are able to make use of foreign language to communicate, compete, and cooperate with foreigners. Yet, because of their special major, there are some problems for these students to speak in the public and their motivation to speak English is deficient. The article first analyzes the reasons why students majoring in engineering lack the motivation of English speaking, and then puts forward four strategies of how to stimulate their motivation, aiming at transferring their extrinsic and instrumental motivation into intrinsic and integrative motivation.
Reversible Embedding in Color Images using Interpolation and Bit Substitution Pattern (BSP)

Most of the work contributed in RDH using image as host has been carried out using grey images. The multiphase nature of color images make them more convenient to high payload applications. This paper presents an interpolation based RDH scheme for color images. The data has been embedded using various Bit Substitution Patterns (BSP) to obtain application specific parameters like PSNR and payload. A particular BSP decides the number of bits embedded and the perceptual performance obtained. The scheme besides being reversible is capable of detecting any tamper caused due to various signal processing and geometric attacks. This has been ensured by embedding a fragile watermark interleaved with the embedding data and scattered throughout various phases of color image.
Data De-Duplication Using SHA (Secure Hash Algorithm)

Now-a-days the number of users using cloud storage has increased so that the data stored has been increased in exponential rates. The data should be secured and the storage should be used efficiently. But a lot of duplicate data is present as two or more users may upload the same data. To make use of the cloud storage efficiently we have to reduce the redundant data hence improving the resources like storage space, disk I/O operations of the cloud vendors. Data De-Duplication is the process to remove redundant data and store only one instance of duplicate data. The objective of the proposed system is to increase the efficient comparison of hash values of a different data blocks and security of data. This paper includes a method for data deduplication using SHA (Secure Hash Algorithm) and AES. SHA is used as it is more secure than other hashing algorithms. The data is encrypted using AES at owner machine itself and using SHA the redundant data will be eliminated.
Personalised Semantic User Interfaces for Games

Players interact with games through user interfaces. Although user interfaces are similar in each game, these are manually developed each time for each game. The Web contains vast sources of content that could be reused to reduce the development time and effort to create user interfaces for games. However, most Web content is unstructured and lacks meaning for machines to be able to process and infer new knowledge. In this paper, we present a vocabulary that defines detailed properties used for describing user interfaces for games, and a vocabulary that defines detailed properties used for describing player’s user interface preferences. Through these vocabularies, semantically-enriched user interface models can be used for automatically generating personalised user interfaces for games.
A Utility-Based Fault Handling Approach for Efficient Job Rescue in Clouds

In recent years, many organizations face challenges when managing large amount of data and data-intensive computing tasks. Cloud computing technology has been widely-used to alleviate these challenges with its on-demand services and distributed architecture. Data replication is one of the most significant strategies to decrease the access latency and improve data availability, data reliability and resource utilization by creating multiple data copies to geographically-distributed data centers. When a fault occurs at a data center, existing jobs that require data access in this data center can be redirected to other data centers, where data replicas are available. This paper proposes a utility-based fault handling (UBFH) approach to rescue the jobs at the faulty data center. Then a fault handling algorithm is developed to determine the direction of job redirection by considering the network performance and job attributes. Our main objective is to achieve better repairability, job rescue utility and job operation profit. The simulation results show that our UBFH approach outperforms HDFS, RR and JSQ approaches in all these aspects.
Hybrid adapted fast correlation FCBF-support vector machine recursive feature elimination for feature selection

This paper investigates feature selection methods based on hybrid architecture using feature selection algorithm called Adapted Fast Correlation Based Feature selection and Support Vector Machine Recursive Feature Elimination (AFCBF-SVMRFE). The AFCBF-SVMRFE has three stages and composed of SVMRFE embedded method with Correlation based Features Selection. The first stage is the relevance analysis, the second one is a redundancy analysis, and the third stage is a performance evaluation and features restoration stage. Experiments show that the proposed method tested on different classifiers: Support Vector Machine SVM and K nearest neighbors KNN provide a best accuracy on various dataset. The SVM classifier outperforms KNN classifier on these data. The AFCBF-SVMRFE outperforms FCBF multivariate filter, SVMRFE, Particle swarm optimization PSO and Artificial bees colony ABC.
Infer The Input To The Generator Of Auxiliary Classifier Generative Adversarial Networks

Generative Adversarial Networks (GANs) are deep-learning-based generative models. This paper presents three methods to infer the input to the generator of auxiliary classifier generative adversarial networks (ACGANs), which are a type of conditional GANs. The first two methods, named i-ACGAN- r and i-ACGAN-d, are “inverting” methods, which obtain an inverse mapping from an image to the class label and the latent sample. By contrast, the third method, referred to as i-ACGAN-e, directly infers both the class label and the latent sample by introducing an encoder into an ACGAN. The three methods were evaluated on two natural scene datasets, using two performance measures: the class recovery accuracy and the image reconstruction error. Experimental results show that i-ACGAN-e outperforms the other two methods in terms of the class recovery accuracy. However, the images generated by the other two methods have smaller image reconstruction errors. The source code is publicly available from https://github.com/XMPeng/Infer-Input-ACGAN.