Reviewerly/exHarmony huggingface dataset:
Social media and library marketing

Purpose The basic purpose of this paper is to find out library professionals how these libraries perceive the importance of social media and emerging technologies what would make the difference in the libraries and how they help libraries to create environments flexible and exclusive. Design/methodology/approach This paper reviews various options for libraries to use different forms of social media to market their resources and services. Findings The reviewed literature has identified the importance and use of emerging technologies and social media in libraries. This paper can help library professionals to engage themselves to use social media and emerging technologies to bring innovation into the library sources and services. Originality/value This paper provides a review of social media and emerging technologies used for marketing in the libraries environment.
PrivItem2Vec: A privacy-preserving algorithm for top-N recommendation

To significantly protect the user’s privacy and prevent the user’s preference disclosure from leading to malicious entrapment, we present a combination of the recommendation algorithm and the privacy protection mechanism. In this article, we present a privacy recommendation algorithm, PrivItem2Vec, and the concept of the recommended-internet of things, which is a privacy recommendation algorithm, consisting of user’s information, devices, and items. Recommended-internet of things uses bidirectional long short-term memory, based on item2vec, which improves algorithm time series and the recommended accuracy. In addition, we reconstructed the data set in conjunction with the Paillier algorithm. The data on the server are encrypted and embedded, which reduces the readability of the data and ensures the data’s security to a certain extent. Experiments show that our algorithm is superior to other works in terms of recommended accuracy and efficiency.
Graph Neural Networks With Parallel Neighborhood Aggregations for Graph Classification

We focus on graph classification using a graph neural network (GNN) model that precomputes the node features using a bank of neighborhood aggregation graph operators arranged in parallel. These GNN models have a natural advantage of reduced training and inference time due to the precomputations but are also fundamentally different from popular GNN variants that update node features through a sequential neighborhood aggregation procedure during training. We provide theoretical conditions under which a generic GNN model with parallel neighborhood aggregations (PA-GNNs, in short) are provably as powerful as the well-known Weisfeiler-Lehman (WL) graph isomorphism test in discriminating nonisomorphic graphs. Although PA-GNN models do not have an apparent relationship with the WL test, we show that the graph embeddings obtained from these two methods are injectively related. We then propose a specialized PA-GNN model, called SPIN, which obeys the developed conditions. We demonstrate via numerical experiments that the developed model achieves state-of-the-art performance on many diverse real-world datasets while maintaining the discriminative power of the WL test and the computational advantage of preprocessing graphs before the training process
A polarization diversity CV-QKD detection scheme for channels with strong polarization drift

Continuous-variable quantum key distribution (CV-QKD) provides a theoretical unconditionally secure solution to distribute symmetric keys among users connected through a communication channel. However, polarization drift in standard optical fibres can pose a considerable impairment, as it will degrade the efficiency of the coherent detection scheme if not compensated. In this work, we present a novel receiver configuration for CV-QKD that allows for passive polarization drift compensation. The presented results show that our detection system can effectively compensate random polarization drift even in very adverse scenarios.
Real Time Implementation of Drone Detection using TensorFlow and MobileNetV2-SSD

Drones are Unmanned Aerial Vehicles (UAV) that can be controlled by remote control or software methods. In recent years with the developing technology, drones have started to be used in all areas of our lives. The two most important factors that make drones attractive to use are low price and easy control. Today, UAVs, which provide convenience to people in many fields such as photography, video shooting, advertising, firefighting and search-rescue activities. Also, UAV is used by terrorist organizations in attack actions. Anti-drone systems are used to detect and locate such malicious drones. In this study, a prototype drone detection system called ATA-DRN is designed against drone threats. While creating ATA-DRN, 2 digital servo motors, Raspberry Pi 4 development board, high resolution Web Cam, 7inch HDMI LCD and power supply is used. With this designed system, drone approaching to the target is automatically detected with image processing methods without the need for manpower. In the study, first of all, a data set is obtained from pictures of different types of drones. The data set obtained is divided into two groups as train (80%) and test (20%) in order to train. The model to be used in real-time drone analysis is obtained using TensorFlow and MobileNetV2-SSD. Experiments are carried out by transferring the obtained model to the Raspberry Pi 4 development board. The accuracy of the model created as a result of experimental studies has been proven.
Skew <i>G</i>-codes

We describe skew G-codes, which are codes that are the ideals in a skew group ring, where the ring is a finite commutative Frobenius ring and G is an arbitrary finite group. These codes generalize many of the well-known classes of codes such as cyclic, quasicyclic, constacyclic codes, skew cyclic, skew quasicyclic and skew constacyclic codes. Additionally, using the skew G-matrices, we can generalize almost all the known constructions in the literature for self-dual codes.
Argos: A toolkit for tracking multiple animals in complex visual environments

Abstract Automatically tracking the positions of multiple animals is often necessary for studying behaviours. This task involves multiple object tracking, a challenging problem in computer vision. Recent advances in machine learning applied to video analysis have been helpful for animal tracking. However, existing tools work well only in homogeneous environments with uniform illumination, features rarely found in natural settings. Moreover, available algorithms cannot effectively process discontinuities in animal motion such as sudden jumps, thus requiring laborious manual review. Here we present Argos, a software toolkit for tracking multiple animals in inhomogeneous environments. Argos includes tools for compressing videos based on animal movement, for generating training sets for a convolutional neural network (CNN) to detect animals, for tracking multiple animals in a video and for facilitating review and correction of the tracks manually, with simple graphical user interfaces. We demonstrate that Argos can help reduce the amount of video data to be stored and analysed, speed up analysis and allow analysing difficult and ambiguous conditions in a scene. Thus, Argos supports multiple approaches to animal tracking suited for varying recording conditions and available computational resources. Together, these tools allow the recording and tracking of movements of multiple markerless animals in inhomogeneous environments over many hours.
HSI-MSER: Hyperspectral Image Registration Algorithm Based on MSER and SIFT

Image alignment is an essential task in many applications of hyperspectral remote sensing images. Before any processing, the images must be registered. The Maximally Stable Extremal Regions (MSER) is a feature detection algorithm that extracts regions by thresholding the image at different grey levels. These extremal regions are invariant to image transformations making them ideal for registration. The Scale-Invariant Feature Transform (SIFT) is a well-known keypoint detector and descriptor based on the construction of a Gaussian scale-space. This article presents a hyperspectral remote sensing image registration method based on MSER for feature detection and SIFT for feature description. It efficiently exploits the information contained in the different spectral bands to improve the image alignment. The experimental results over nine hyperspectral images show that the proposed method achieves a higher number of correct registration cases using less computational resources than other hyperspectral registration methods. Results are evaluated in terms of accuracy of the registration and also in terms of execution time.
Intelligent Path Planning Algorithm for UAV Group Based on Machine Learning

Abstract Aiming at the minimum and local minimum problems existing in the artificial potential field algorithm for obstacle avoidance of UAV, a new path planning method is proposed by using the improved artificial potential field algorithm. Different from the current artificial potential field method, this model starts from the interaction between two aircraft. On the basis of obstacle repulsion, the repulsion force between UAV is increased, and the pre-centroid of the cluster is defined as another source of gravity. The analysis of the algorithm shows that this method can effectively prevent the UAV from falling into the local minimum and enhance the control and obstacle avoidance ability of the UAV cluster. Based on the UAV control model, the path planning design is given and the simulation experiment is carried out. The experimental results show that the UAV cluster control based on this model has better obstacle avoidance performance and target tracking ability.
Design and Implement of Median Filter toward Remote Sensing Images Based on FPGA

In this paper, an improved median filtering algorithm for remote sensing images is proposed and implemented on FPGA. According to the characteristics of FPGA parallel processing, the 5x5 double-sorted median filtering algorithm is optimized, and the 3x3 double-sorted median filtering, 5x5 double-sorted median filtering and bit-level median filtering are implemented by pipelining, which improves the frequency and speed. The final simulation results show that the maximum clock frequency of this design on XC7K325T can reach 419MHz. For remote sensing images of 4096x4096 size, the processing of a single image can spend 40ms, and the throughput rate is 25 images/second, which can meet the requirements of on-orbit real-time processing and can be applied in practice.
The New Experiential Impacts Linked to the Application of Knowledge Management in Businesses

In recent times, the emergence of knowledge and its application is fundamental to success and failure, for individuals, societies, business and corporate governance. Therefore, there is the neeed to tap up knowledge, disseminate and use for gaining a competitive advantage, despite the existing difficulties. Hence, having a pre-requisite knowledge on staff recruitment quality, employee’s behavior to change management, inadvertent tacit knowledge, and knowledge update programs and their application will help determine the success of KM implementation in companies. In the process of KM implementation, some of the constraining factors are more general and have consequences on the knowledge management implementation process. However, enhancing and sustaining a competitive advantage and developing further innovative business strategies through the knowledge management implementation process is a means to grow companies, and achieve economic prosperity. This study examines the barriers to knowledge management implementation (KMI) and how they impact the implementation process in ten (10) companies in Ghana. The investigation was conducted using a survey design, which used valid sample adequacy of 295 respondents. Multiple linear regression model and ANOVA tests were applied. Our findings discovered a linked and aggregated impacts on the in dependent variable. The study revealed that staff recruitment quality and knowledge update programs have direct and positive effect on the KM implementation process (see figure 1). However, individual’s inadvertent tacit knowledge and individual’s resistance to change management were not statistically significant on the knowledge implementation process (see figure 1). Hence, companies must see staff recruitment quality, resistance to change management, inadvertent tacit knowledge, and knowledge update programs as variables with either positive or negative impact on KM implementation in businesses. Findings add to existing information in the fields of business and industrial management. We conclude that resistance to change management and tacit knowledge of individual employees are hindrance to knowledge management implementation process which will consequently affect business growth. Therefore, we advance knowledge for business and industrial management for policy consideration.
Quantum coherence induced by a flux qubit coupled by a resonator coherent field through a two-photon interaction

Abstract The intrinsic decoherence effects on a flux qubit coupled to a resonator through a two-photon interaction where the resonator field is initially in coherent and even coherent states are investigated. The qubit-resonator entanglement and coherence loss (mixedness) of the system and its subsystems are examined using entropy and negativity. The ability of the qubit-resonator interaction to generate quantum coherence (qubit-resonator entanglement and mixedness) is shown to be dependent on the initial cavity non-classicality, detuning, and decoherence. For larger values of the qubit-resonator detuning, the initial resonator non-classicality can enhance the generation and stability of quantum coherence. The decoherence degrades the qubit-resonator entanglement and destroys the sudden death-birth entanglement.
A Novel Oversampling Technique to Solve Class Imbalance Problem: A Case Study of Students’ Grades Evaluation

The academic performance of the students is one of the critical aspects in ranking educational institutions, particularly at the secondary level. If the student’s performance is not appropriately defined, then the institution’s reputation is at risk. Therefore, data mining could be used for this purpose, to attain high accuracy. However, the data being incomplete, inaccurate and/or noisy, or with an imbalance class label in the dataset, is highly likely to affect the accuracy of the data mining model. This paper proposes a semi-supervised oversampling method to first prepare a balanced dataset and then to classify the students’ grades into a binary class with overall performance in any given course. The student performance dataset from the UCI machine learning repository is used, which contains student performance related data of two different courses. A detailed validation result shows that the decision tree algorithm performs better with the balanced dataset compared to the imbalanced one.
Research and Analysis of Recommendation Algorithm Based on Convolutional Neural Network

Abstract This paper first introduces the situation that the traditional recommendation algorithms cannot meet the needs of users for accurate and efficient recommendation results because of the continuous growth of data and the increasingly diversified data types. Then it introduces the Deep Learning (DL) algorithm which is getting more and more attention, organizes the research progress of recommendation system based on Convolutional Neural Networks (CNN) in recent years, analyzes its advantages compared with traditional recommendation algorithms. The main research directions and application progress are classified, compared and summarized. Finally, the future development trend of recommendation system based on convolutional neural network is summarized and analyzed.
Application of Neural Network Algorithm Combined with Bee Colony Algorithm in English Course Recommendation

The traditional BP neural network has the disadvantages of easy falling into local minimum and slow convergence speed. Aiming at the shortcomings of BP neural network (BP neural network), an artificial bee colony algorithm (ABC) is proposed to cross-optimize the weight and threshold of BP network parameters. This study is mainly about the application of BP neural network algorithm in English curriculum recommendation technology. It includes the application of BP neural network algorithm in English course recommendation technology, English course teaching design mode, the application of BP neural network algorithm in English course, and the optimal combination of bee colony algorithm and BP neural network. After 4690 iterations, the neural network reaches the target accuracy, and the training is completed. At the same time, the prediction error of the model is less than 10%, which further shows that the performance of the prediction model is good. Therefore, the combination model is recommended in this paper. The results show that the optimization algorithm improves the solution accuracy and speeds up the convergence speed of the network.
A 50.4 GOPs/W FPGA-Based MobileNetV2 Accelerator using the Double-Layer MAC and DSP Efficiency Enhancement

Convolutional neural network (CNN) models, e.g. MobileNetV2 [1] and Xception, are based on depthwise separable convolution. They exhibit over $40 \times(64 \times)$ reduction of the number of parameters (operations) when compared to the VGG16 for the ImageNet inference, while maintaining the TOP-1 accuracy at 72 %. With an 8-bit quantization, the required memory for storing the model can be further compressed by $4 \times$. This multitude of model sizes compression facilitates real-time complex machine learning tasks implemented on a low-power FPGA apt for Internet-of-Things edge computation. Previous effect [2] has improved its computational energy efficiency by exploiting the model sparsity, but the effectiveness drops in already-compressed modern CNN models. As a result, further advancing the CNN accelerator’s energy efficiency with new techniques is desirable. [3] is a scalable adder tree for energy-efficient depthwise separable convolution computation, and [4] is a frame-rate enhancement technique; both failed to handle the extensive memory access during separable convolution that dominates the power consumption of the CNN accelerators. Herein we propose a double-layer multiply-accumulate (MAC) scheme to evaluate two layers within the bottleneck layer in a pipelining manner. It results significant reduction of the memory access to the feature maps. On top of that we also innovate a double-operation digital signal processor (DSP) to enhance the throughput of the accelerator by benefiting the use of a high-precision DSP for computing two fixed-point operations in one clock cycle.
Dynamic order Markov model for categorical sequence clustering

Markov models are extensively used for categorical sequence clustering and classification due to their inherent ability to capture complex chronological dependencies hidden in sequential data. Existing Markov models are based on an implicit assumption that the probability of the next state depends on the preceding context/pattern which is consist of consecutive states. This restriction hampers the models since some patterns, disrupted by noise, may be not frequent enough in a consecutive form, but frequent in a sparse form, which can not make use of the information hidden in the sequential data. A sparse pattern corresponds to a pattern in which one or some of the state(s) between the first and last one in the pattern is/are replaced by wildcard(s) that can be matched by a subset of values in the state set. In this paper, we propose a new model that generalizes the conventional Markov approach making it capable of dealing with the sparse pattern and handling the length of the sparse patterns adaptively, i.e. allowing variable length pattern with variable wildcards. The model, named Dynamic order Markov model (DOMM), allows deriving a new similarity measure between a sequence and a set of sequences/cluster. DOMM builds a sparse pattern from sub-frequent patterns that contain significant statistical information veiled by the noise. To implement DOMM, we propose a sparse pattern detector (SPD) based on the probability suffix tree (PST) capable of discovering both sparse and consecutive patterns, and then we develop a divisive clustering algorithm, named DMSC, for Dynamic order Markov model for categorical sequence clustering. Experimental results on real-world datasets demonstrate the promising performance of the proposed model.
Online Hard Patch Mining Using Shape Models and Bandit Algorithm for Multi-Organ Segmentation

Hard sample selection can effectively improve model convergence by extracting the most representative samples from a training set. However, due to the large capacity of medical images, existing sampling strategies suffer from insufficient exploitation for hard samples or high time cost for sample selection when adopted by 3D patch-based models in the field of multi-organ segmentation. In this paper, we present a novel and effective online hard patch mining (OHPM) algorithm. In our method, an average shape model that can be mapped with all training images is constructed to guide the exploration of hard patches and aggregate feedback from predicted patches. The process of hard mining is formalized as a multi-armed bandit problem and solved with bandit algorithms. With the shape model, OHPM requires negligible time consumption and can intuitively locate difficult anatomical areas during training. The employment of bandit algorithms ensures online and sufficient hard mining. We integrate OHPM with advanced segmentation networks and evaluate them on two datasets containing different anatomical structures. Comparative experiments with other sampling strategies demonstrate the superiority of OHPM in boosting segmentation performance and improving model convergence. The results in each dataset with each network suggest that OHPM significantly outperforms other sampling strategies by nearly 2% average Dice score.
Three-dimensional path planning of UVAs based on simulated annealing and particle swarm optimization hybrid algorithm

Particle swarm optimization algorithm is widely used in UAVs path planning, but it is easy to fall into local optimal solution in the later stage of optimization. In order to solve this problem, a simulated annealing and particle swarm optimization hybrid algorithm is proposed in this paper. The simulation of the algorithm is carried out in a complex environment of 4 peaks and 2 no-fly zones. Simulation results show that the proposed algorithm can find the optimal path after 20 particle iterations, and the optimization has high stability and efficiency. The proposed algorithm have great significance for the safe operation of UAVs in the future.