Reviewerly/exHarmony huggingface dataset:
Analysis of the Relevance between Title of Product and Search Term

In recent years, the product recommendation algorithm of e-commerce platforms has become more and more important. In this paper, we built a Random Forest Regression model for the problem of predicting the correlation of “search term” and “product title We found this model for the dataset by describing and attributing the products. In the process of numerically calculating features, we applied two types of feature engineering. The first method is to describe and attribute the numbers to the number of words or the length of the sentence. The second method is to use string similarity characteristics to calculate the distance between “search term” and “product title On the results, we got a similar histogram of the correlation scores between the training dataset and the results from the test dataset. The RMSE of the relevance between the training dataset and the predicted value is 0.3179 which indicates that the model is working well.
Real Time In-Game Playstyle Classification Using a Hybrid Probabilistic Supervised Learning Approach

In interactive digital media, such as video games, bringing about an adaptive or personalised experience requires a mechanism for correctly classifying or identifying the player style, before attempting to modify the experience in some way that improves player interest and immersion. This work presents a framework for solving this problem of in-game real time playstyle classification. We propose a hybrid probabilistic supervised learning approach, using Bayesian Inference informed by a K-Nearest Neighbors based likelihood, that is able to classify players in real time at every step within a given game level using only the latest player action or state observation. This improves on current approaches dependent on previous episodic player action trajectories in order to classify the player. Furthermore, we highlight the effect that this representation of the player state-action observation has on the in-game playstyle classification’s accuracy, prediction stability, and generalisability. We apply and test our framework using MiniDungeons, a rogue-like dungeon exploration game, and further evaluate our framework using a natural dataset containing human player action data from the platforming game Super Mario Bros. The experimental results obtained from our approach outperforms existing work in both domains. Furthermore, the evaluation results highlights the ability of our framework to generalise to unseen levels, without the need for additional retraining. Additionally, the Super Mario evaluation results illustrates the scalability of our framework to a more complex game environment with human player data.
A Method of Mapping and Nearest-Neighbor for IBM QX Architecture

In order to solve the problem of mapping a quantum circuit to IBM QX architecture and satisfy connectivity constraints. This paper presents a mapping method, which is divided into two parts: Qubit initial mapping and Nearest-neighbor optimization. To solve the problem of initial mapping of qubits, according to the execution order and interaction of quantum gates in quantum circuit, a qubit mapping order method based on the weight of qubits is proposed. Then, according to the qubit interaction degree in the coupling graph, the initial qubit mapping is completed. In order to solve the nearest-neighbor problem in the mapping process, the quantum cost of non-neighbor two-qubit gate is determined by introducing the lookahead quantum cost method. Our algorithm is evaluated on IBM Q 20. Experimental results show that the proposed algorithm can effectively complete the mapping task even in large-scale benchmark tests. The proposed method is verified by experiments and compared with the existing methods, the optimized results were obtained in 74% of the test circuits.
Learning Spatial Fusion and Matching for Visual Object Tracking

Siamese network based trackers have achieved outstanding performance in visual object tracking, which in essence is the application of the efficient cross-correlation as the matching function. However, it is experimentally found that the cross-correlation based matching function is difficult to generate accurate tracking results in some challenging environments, such as background clutters and fast motion. Thus, a new Siamese-based tracker named SiamFAM is proposed. Specifically, from the perspective of feature fusion, a new matching function named Concatenation is introduced into our tracker, which can reduce the influence of background clutters by fine-grained matching with little computational overhead. Meanwhile, an adaptively spatial feature fusion (ASFF) module is proposed, which can take full use of multi-layer features and reduce poor prediction results during the prediction process. In addition, a refinement module is adopted to reduce the occurrence of tracking drift. Extensive experiments are conducted on six challenging benchmarks, including VOT2016, VOT2019, UAV123, NFS, OTB100, and LaSOT, demonstrating that our tracker is practical and can achieve a leading performance.
Multi-clusters: An Efficient Design Paradigm of NN Accelerator Architecture Based on FPGA

With the serial development of neural network models, choosing a superior platform for these complex computing applications is essential. Field-Programmable Gate Array (FPGA) is gradually becoming an accelerating platform that balances power and performance. The design of architecture in neural network accelerator based on FPGA is about two categories, stream and single-engine. Both design paradigms have advantages and disadvantages. The stream is easier to achieve high performance because of model customization but has low kernel compatibility. The single-engine is more flexible but has more scheduling overhead. Therefore, this work proposes a new design paradigm for the neural network accelerator based on FPGA, called the Multi-clusters (MC), which combines the characteristics of the above two design categories. We divide the original network model according to the calculated features. Then, different cores are designed to map these parts separately for efficient execution. The fine-grained pipeline is performed inside the cores. Multiple cores are executed by software scheduling and implement a coarse-grained schedule, thereby improving the overall computing performance. The experimental results show that the accelerator with the MC category achieved 39.7 $$\times $$ times improvement of performance and 7.9 $$\times $$ times improvement of energy efficiency compared with CPU and GPU, and finally obtained nearly 680.3 GOP/s computing performance in the peek.
Regularity Bounded Sensor Clustering

A non-empirical, unsupervised learning, and graph analytic techniques investigated the earthquake sensor station proximity (< 30 km) and neighborhood density (2-5 sensor) constrained geometric properties of New Zealand’s GeoNet seismic network. They are specific to the Propagation of Local Undamped Motion (PLUM) earthquake detection algorithm. The experimental design considered 14 cloud and graph clustering processes, with variable combinations of 10-50 km distance and 2-5 sensor constraints. We prove that a clustering process alone is inadequate and a strict graph r-regularity constrain is essential for forming valid sensor clusters. The goodness of fit measurements revealed HDBSCAN 1 was best for initiating the pipeline. On average 44% of the sensors fit well within the geometric bounds with a 45% cluster quality score. With the regularity relaxed, 60% of the sensors resulted in 42% node degree weighted mean absolute error. The method can be applied to any, proximity and density constrained, sensor network.
Construction and Empirical Study of Teaching Model for Adult Education under the Perspective of Learning Analysis

After years of development, learning analytics is an emerging technology that is about to be widely used. The article introduces the concept and current research status of learning analytics. Then, the article analyzes the current teaching status and problems of adult English teaching as an application scenario, proposes the teaching design based on learning analytics, and introduces how learning analytics empowers adult English teaching from four aspects: student assessment design, teacher preparation design, teaching assistant supervision design and learning report design. Finally, the article introduces the effectiveness of the study in the School of Online Education of Beijing University of Posts and Telecommunications. By analyzing the application data, the article draws two conclusions: first, it can improve the course completion rate and learning performance, and second, it can improve the learning experience of adult students. The article innovatively uses learning analytics to solve the current pain points of adult English teaching, enriching the application of learning analytics in the field of adult education and promoting the development and improvement of adult education at the level of teaching model innovation.
Multiview Regenerative Morphing with Dual Flows

This paper aims to address a new task of image morphing under a multiview setting, which takes two sets of multiview images as the input and generates intermediate renderings that not only exhibit smooth transitions between the two input sets but also ensure visual consistency across different views at any transition state. To achieve this goal, we propose a novel approach called Multiview Regenerative Morphing that formulates the morphing process as an optimization to solve for rigid transformation and optimal-transport interpolation. Given the multiview input images of the source and target scenes, we first learn a volumetric representation that models the geometry and appearance for each scene to enable the rendering of novel views. Then, the morphing between the two scenes is obtained by solving optimal transport between the two volumetric representations in Wasserstein metrics. Our approach does not rely on user-specified correspondences or 2D/3D input meshes, and we do not assume any predefined categories of the source and target scenes. The proposed view-consistent interpolation scheme directly works on multiview images to yield a novel and visually plausible effect of multiview free-form morphing. Code: https://github.com/jimtsai23/MorphFlow
A hybrid heuristic method for multi-color spraying path planning

Path planning is the primary task for multi-color spraying. This paper formulates multi-color spraying path planning as a generalization of the Traveling Salesman Problem (TSP). The spraying patterns with single/dual-nozzle spray gun are given separately. First, multi-color spray path planning of single nozzle spray gun is described as a Clustered Traveling Salesman Problem (CTSP). Greedy randomized adaptive search procedure (GRASP) and variable neighborhood search (VNS) are applied to solution of the problem. Then, multi-color spray path planning of dual-nozzle spray gun is defined as set-Clustered Traveling Salesman Problem (set-CTSP), which is NP-hard similar to CTSP. We propose a new hybrid heuristic algorithm to obtain the minimum path of the spray gun movement. The initial solution of the path is obtained from the color combination probability and GRASP. The neighborhood structure of the solution is continuously changed by four variable neighborhood search operators, and the color combination probability is updated according to the distance cost of the corresponding solution. Finally, extensive experiments with different sizes and patterns have be done verifying the feasibility of multi-color spray path planning based on single nozzle gun and dual-nozzle spray gun, while the dual-nozzle spray gun can reduce the gun travel distance compared to the single-nozzle spray gun.
Mouse-Brain Topology Improved Evolutionary Neural Network for Efficient Reinforcement Learning

The brain structures are key indicators to represent the complexity of many cognitive functions, e.g., visual pathways and memory circuits. Inspired by the topology of the mouse brain provided by the Allen Brain Institute, whereby 213 brain regions are linked as a mesoscale connectome, we propose a mouse-brain topology improved evolutionary neural network (MT-ENN). The MT-ENN model incorporates parts of biologically plausible brain structures after hierarchical clustering, and then is tuned by the evolutionary learning algorithm. Two benchmark Open-AI Mujoco tasks were used to test the performance of the proposed algorithm, and the experimental results showed that the proposed MT-ENN was not only sparser (containing only 61% of all connections), but also performed better than other algorithms, including the ENN using a random network, standard long-short-term memory (LSTM), and multi-layer perception (MLP). We think the biologically plausible structures might contribute more to the further development of artificial neural networks.
A Face Structure Attention Network for Face Super-Resolution

Recently, face super-resolution (SR) based on deep-learning has made a remarkable progress. However, it is still a challenging task to super-resolve face images with large upscaling factors. In this paper, we propose a novel face structure attention network (FSANet), which utilizes the facial priors to recover the high-resolution face images with the upscaling factor of 8. Specifically, we firstly utilize a pure SR network to generate an intermediate SR image. Secondly, we construct a prior estimation network to extract structural information. Lastly, given the prior knowledge and the intermediate SR image, an attention SR network is used to reconstruct the final SR image. Furthermore, we propose a facial features attention block (FFAB) and a deep features fusion block (DFFB), which employ spatial attention mechanism and channel-wise attention mechanism respectively, to make full use of the structural prior for better restoration. Extensive experiments on the benchmark datasets demonstrate that our method outperforms the state-of-the-art methods in terms of both quantitative metrics and visual quality.
Towards a CRIS-Integrated Solution for University Press Workflows

Our paper describes the development of research information-based workflows in the Friedrich-Alexander Universität Erlangen-Nürnberg (FAU). We present a data integration platform connecting the university's CRIS and library IT systems. Our plan is to create and improve intra-university workflows for secondary publication of open access papers, and also to avoid duplicate work for the university's authors. We address the challenge of multiple start points within the system, meaning publications can be entered either on the CRIS side or in the library IT system.
Enhanced entangling gates and scalable multizone operations in surface traps with integrated photonics

We present a surface-electrode Paul trap featuring integrated photonics elements, where ions are illuminated with two interfering beams at 729 nm forming a standing wave along the trap axis. In this field configuration, it is possible to excite the motional sidebands of a trapped ion while suppressing the off- resonant coupling to the corresponding carrier transition. This is of particular interest for quantum information processing, as it would allow implementing faster and lower error Mølmer-Sørensen entangling gates.We characterize the optical properties of the chip by scanning with sub-wavelength resolution the position of a single ion through the standing wave and observing Rabi oscillations for carrier and first motional sideband of different quadrupole transitions. We characterize both the strength of resonant couplings and the AC Stark shift induced by off-resonant couplings. We measure noise and drifts in the ion position and observe high stability in the standing wave profile without the need for active phase stabilization.Integrated optics allow to reliably reproduce the same field configuration in different regions of the chip, opening the way for implementing distributed, parallel quantum operations. We show the ability to control ions in different regions of the trap at the same time. We implemented real-time control of the DC voltages controlling the axial configuration of the trapping potential which enables us to load multiple ion crystals, merge, split, and shuttle them across the device.
Tools for Visualizing Logical Schemes among Sentences on Similarity

The authors have been developing tools for identifying logical structure of a sentence in a document to assist readers in comprehending contexts. The sentence including logical relation “implication (→)” is the key to understand contexts of the document, because the sentence containing “implication” are regarded as statements making major claims and inferences in the document. While the key sentence are the most important to understand the contexts, the claims and inferences also require a number of statements describing “premises”, “constraints” and “rationales”, to reinforce their reasoning. These subordinate sentences should be deployed in the same or nearby paragraphs, where the key sentence is involved, to achieve high readability. In this study, the tools for visualizing logical schemes among sentences on their similarities were designed to provide a rough sketch of logical structures of the paragraph to readers. The tools were applied both to visualizing a system specification document and a detective story for evaluation. Through the applications, we confirmed that the tools succeeded in visualizing the logical schemes of the paragraph accurately and providing a perspicuous sketch of the paragraphs to readers.
Spectre attack detection with Neutral Network on RISC-V processor

The goal of this study is to investigate the problem of caches side-channel attacks on the open-source RISC-V architecture. Traditionally, these concerns have been addressed by research into hardware enhancements or software defense strategies. Those methods are, unfortunately, extremely hard to accomplish or lead to significant performance degradation. In this article, we investigate at a system for identifying cache side-channel threats such Spectre in real time. We utilize performance counters inside the processor to track the processor’s cache behavior and a neural network to evaluate the acquired data. Since the presence of cache side-channels typically results in a dramatically altered cache utilization behaviors, our neural network would take advantage of it and detect a Spectre attack in our test environment with an accuracy of more than 99%. To summarize, we are able to inform the user when a cache side-channel attack occurs using data from only four counters.
Boosting KG-to-Text Generation via Multi-granularity Graph Representations

KG-to-text generation models aim to generate a natural language text with one or more sentences from input knowledge graphs (KGs). Recent models use either entity-level or word-level graphs as input data to generate texts. The Entity-level graph provides the relations between entities but neglects the relations between words within a single entity and different entities. In contrast, a word-level graph can better consider the rich semantic relations among words but can fail to capture the relations between entities. This paper proposes a novel graph-to-sequence model that encodes both entity-level and word-level KGs to enhance node representations and investigates different decoder architectures to integrate contextualized representations of words and entities effectively. Automatic and human evaluations demonstrate that our method outperforms previous single-granularity methods on two popular graph-to-text benchmarks.
Learning to Identify Sources of Network Diffusion

We propose a deep learning solution to the inverse problem of localizing sources of network diffusion. Invoking graph signal processing (GSP) fundamentals, the problem boils down to blind estimation of a diffusion filter and its sparse input signal encoding the source locations. While the observations are bilinear functions of the unknowns, a mild requirement on invertibility of the graph filter enables a convex reformulation that we solve via the alternating-direction method of multipliers (ADMM). We unroll and truncate the novel ADMM iterations, to arrive at a parameterized neural network architecture for Source Localization on Graphs (SLoG-Net), that we train in an end-to-end fashion using labeled data. This way we leverage inductive biases of a GSP model-based solution in a data-driven trainable parametric architecture, which is interpretable, parameter efficient, and offers controllable complexity during inference. Experiments with simulated data corroborate that SLoG-Net exhibits performance in par with the iterative ADMM baseline, while attaining significant (post-training) speedups.
NFC (Near Field Communication)-Based Canteen Self Service Application at SMA Pomosda Nganjuk

Advances in information technology bring positive impacts and take people to the information age where innovations emerge, ready to assist daily activities effectively and efficiently. Technology penetrates not only the health and government sectors but also the world of formal and non-formal education. All elements in the education sector implement policies related to IT. The research addressed a problem at Pomosda (modern Islamic boarding school) High School Nganjuk in which an outdated student transaction system caused inefficiencies in the management and affected students' spending habits. The proposed system implemented an independent transaction application equipped with NFC technology and data storage with database mysql, with which the students can pay for their food at the school canteen. This system brought efficiency since the hall of residence management can efficiently process transactions and students can be more careful with their spending.
Mining $$\epsilon $$-Closed High Utility Co-location Patterns from Spatial Data

Traditional spatial prevalent co-location pattern mining is discovering groups of spatial features whose instances frequently appear together in nearby areas. However, it is unsuitable for many real-world applications where the significance of these instances must be considered. High utility co-location pattern (HUCP) mining is developed to find highly beneficial patterns by considering the importance of spatial instances. However, the mining result typically contains many HUCPs, making it difficult for users to absorb, comprehend, and apply. This work proposes a compressed representation of HUCPs, $$\epsilon $$ -closed HUCPs, that allow for a user-specified small tolerance of the information between a pattern and its supersets. If the information difference is not larger than the small tolerance it only needs to keep the supersets. Moreover, an efficient algorithm is developed to discover $$\epsilon $$ -closed HUCPs. The proposed algorithm avoids examining many unnecessary candidates; therefore, the performance of mining $$\epsilon $$ -closed HUCPs is significantly improved. A set of different numbers of features, numbers of instances, and distribution of both synthetic and real data sets are employed to evaluate the performance of the proposed method completely. The experimental results show that $$\epsilon $$ -closed balances the compression rate and the UPI error rate and gives a large pattern compression rate within a relatively small range of error rates. Moreover, the proposed algorithm is high-performance on dense and large data sets.