Reviewerly/exHarmony huggingface dataset:
Diversifying recommendations on sequences of sets

Diversifying recommendations on a sequence of sets (or sessions) of items captures a variety of applications. Notable examples include recommending online music playlists, where a session is a channel and multiple channels are listened to in sequence, or recommending tasks in crowdsourcing, where a session is a set of tasks and multiple task sessions are completed in sequence. Item diversity can be defined in more than one way, e.g., as a genre diversity for music, or as a function of reward in crowdsourcing. A user who engages in multiple sessions may intend to experience diversity within and/or across sessions. Intra session diversity is set-based, whereas Inter session diversity is naturally sequence-based. This novel formulation gives rise to four bi-objective problems with the goal of minimizing or maximizing Inter and Intra diversities. We prove hardness and develop efficient algorithms with theoretical guarantees. Our experiments with human subjects on two real datasets show that our diversity formulations do serve different user needs and yield high user satisfaction. Our large-scale experiments on real and synthetic data empirically demonstrate that our solutions satisfy our theoretical bounds and are highly scalable, compared to baselines.
AN OBJECT-ORIENTED UAV 3D PATH PLANNING METHOD APPLIED IN CULTURAL HERITAGE DOCUMENTATION

Abstract. Since image-based 3D model reconstruction can faithfully recover the real texture of cultural heritage with high accuracy, it is widely used in cultural heritage documentation. Given the complexity of manual image acquisition at present, we propose an object-oriented unmanned aerial vehicle (UAV) path planning method to obtain close-up and high-resolution images for the 3D reconstruction of cultural heritage. Four basic geometric classes are defined and can be automatically divided or interactively defined on the surface of an initial coarse model. We propose the concept of aerial strip unit in conventional photogrammetry to generate multiple regular strip units for photography. The optimal flight path connecting each unit is generated considering the obstacle avoidance and the shortest distance. Based on the self-developed 3D engine, we take the Ancient City of Ping Yao and Yellow Crane Tower in China as two cases to design the UAV 3D path planning. Experimental results show that, compared with general planning methods, our method can improve the flight efficiency of UAV and the visual fineness of the reconstruction results.
Geochemical characteristics and growth suitability assessment of Scutellaria baicalensis Georgi in the Earth’s critical zone of North China

Geochemical differentiation of soils has a series of consequences on plant and places pressure on the ecological environment. The quantitative evaluation of element migration in the Earth’s critical zone is a challenging task. In this study, two demonstration study areas of Scutellaria baicalensis Georgi were selected, and multiple chemical weathering indexes, chemical loss fraction, mass migration coefficients and biological enrichment coefficient method were used to assess the ecological and geochemical suitability. The results show that for the element of Fe, Zn, Se, Cu, Co, Ni, Mo and Ge, the degree of weathering and soil maturation, were greater in the rhyolitic tuff area than in the Plagioclase gneiss area. In both research sites, the heavy metal level of samples in Scutellaria baicalensis Georgi did not exceed the standard limits. The plagioclase gneiss region’s surface soil environment was more alkaline, and the content of soil organic matter was lower, resulting in a higher bioenrichment intensity of Ge, Co, Cu, and Se elements in Scutellaria baicalensis Georgi than in the rhyolite-tuff area. The elements of Cd, Nb, Mo, Pb and As are considerably enriched in the soil of the plagioclase gneiss area but lost by leaching in the soil of the rhyolite tuff area, which is connected to the interplay of elemental abundance and human impact in the parent materials. This study provides a good example of how to assess growth suitability of Chinese medicinal materials in the Earth’s critical zone.
Cloud Evidence Tracing System: An integrated forensics investigation system for large-scale public cloud platform

With the rise of cloud computing, many systems are migrating to public cloud platforms. Numerous crimes are committed in the cloud, including the establishment of illegal websites and the storage of illegal data. Using virtualization technology, data can be logically stored in the same virtual host, but also physically distributed across multiple hard drives, clusters, or even countries. In these circumstances, using the traditional forensic method of physical preservation will consume a great deal of resources, which will clog the forensic process. In order to develop an effective cloud investigation solution, two challenges must be overcome. First, the difficulty of collecting data consistently when the VMs (Virtual Machines) involved are deployed across multiple CSPs. Second, the difficulty of keeping track of all the files created during the forensic workflow. We developed CETS (Cloud Evidence Tracing System), which utilizes CSP's existing API to perform a variety of forensic operations including acquisition, preservation, and emulation, as well as data analysis and file management. To evaluate the system, we created three cloud environments in the laboratory, including a forensic target cloud, a preservation cloud, and an emulation cloud, and conducted a series of forensic experiments. CETS was shown to significantly increase the investigator's investigative efficiency and reduce the investigation workflow's resource consumption. Currently, CETS has collected data exceeding 2 PB, rerun more than 2000 virtual hosts, including servers and databases, supported more than 300 investigation cases related to cloud platforms. CETS can be an example system for efficient forensic investigation in large-scale cloud environment.
The Edge of Exploration: An Edge Storage and Computing Framework for Ambient Noise Seismic Interferometry Using Internet of Things Based Sensor Networks

Recent technological advances have reduced the complexity and cost of developing sensor networks for remote environmental monitoring. However, the challenges of acquiring, transmitting, storing, and processing remote environmental data remain significant. The transmission of large volumes of sensor data to a centralized location (i.e., the cloud) burdens network resources, introduces latency and jitter, and can ultimately impact user experience. Edge computing has emerged as a paradigm in which substantial storage and computing resources are located at the “edge” of the network. In this paper, we present an edge storage and computing framework leveraging commercially available components organized in a tiered architecture and arranged in a hub-and-spoke topology. The framework includes a popular distributed database to support the acquisition, transmission, storage, and processing of Internet-of-Things-based sensor network data in a field setting. We present details regarding the architecture, distributed database, embedded systems, and topology used to implement an edge-based solution. Lastly, a real-world case study (i.e., seismic) is presented that leverages the edge storage and computing framework to acquire, transmit, store, and process millions of samples of data per hour.
The Mining Algorithm of Maximum Frequent Itemsets Based on Frequent Pattern Tree

In the discipline of data mining, association rule mining is an important study topic that focuses on discovering the relationships between database attributes. The maximum frequent itemset comprises the information of all frequent itemsets, which is one of the important difficulties in mining association rules, and certain data mining applications just need to mine the maximum frequent itemsets. As a result, analyzing the maximum frequent itemset mining technique is practical. Considering this, the research introduces FP-MFIA, a new maximum frequent itemset mining approach based on the FP-tree, which is inspired by the data structure of the frequent pattern tree and the idea that the maximum frequent itemset implies all frequent itemsets. First, the FP-MFIA constructs a one-way FP-tree structure, which only has pointers from the root to the leaves, so that only two scans of the FP-tree are required by the FP-MFIA. On the other hand, it redefines a data storage structure MFI-list for maximum frequent itemsets. It can quickly release unnecessary nodes in the FP-tree after scanning it. In this way, not only the information required by the maximum frequent itemsets can be quickly mined but also the space required for storing the maximum frequent itemsets can be reduced, which greatly improves the mining efficiency. Finally, experiments were conducted to compare the mining efficiency of the novel FP-MFIA algorithm to the IDMFIA and DMFIA algorithms. We can see from the findings that the FP-MFIA algorithm is more efficient than the other two techniques.
Super-Pixel Guided Low-Light Images Enhancement with Features Restoration

Dealing with low-light images is a challenging problem in the image processing field. A mature low-light enhancement technology will not only be conductive to human visual perception but also lay a solid foundation for the subsequent high-level tasks, such as target detection and image classification. In order to balance the visual effect of the image and the contribution of the subsequent task, this paper proposes utilizing shallow Convolutional Neural Networks (CNNs) as the priori image processing to restore the necessary image feature information, which is followed by super-pixel image segmentation to obtain image regions with similar colors and brightness and, finally, the Attentive Neural Processes (ANPs) network to find its local enhancement function on each super-pixel to further restore features and details. Through extensive experiments on the synthesized low-light image and the real low-light image, the experimental results of our algorithm reach 23.402, 0.920, and 2.2490 for Peak Signal to Noise Ratio (PSNR), Structural Similarity (SSIM), and Natural Image Quality Evaluator (NIQE), respectively. As demonstrated by the experiments on image Scale-Invariant Feature Transform (SIFT) feature detection and subsequent target detection, the results of our approach achieve excellent results in visual effect and image features.
IMapC: Inner MAPping Combiner to Enhance the Performance of MapReduce in Hadoop

Hadoop is a framework for storing and processing huge amounts of data. With HDFS, large data sets can be managed on commodity hardware. MapReduce is a programming model for processing vast amounts of data in parallel. Mapping and reducing can be performed by using the MapReduce programming framework. A very large amount of data is transferred from Mapper to Reducer without any filtering or recursion, resulting in overdrawn bandwidth. In this paper, we introduce an algorithm called Inner MAPping Combiner (IMapC) for the map phase. This algorithm in the Mapper combines the values of recurring keys. In order to test the efficiency of the algorithm, different approaches were tested. According to the test, MapReduce programs that are implemented with the Default Combiner (DC) of IMapC will be 70% more efficient than those that are implemented without one. To make computations significantly faster, this work can be combined with MapReduce.
An image selection framework for automatic report generation

The development of IoT technologies and social network services (SNS) are contributing to the growth of big data. However, the vast amount of data makes it difficult for users to find the information they need, and as a result, the demand for a system that provides the desired information in a well-organized form is increasing. Many studies are being conducted to extract desired information from data, and application studies such as automatic report generation are also being conducted. To generate a report for a given topic, a report generation system is required to extract essential information from big data and re-organize it in a compact form. Image selection system also plays an important role in automatic report generation as insertion of appropriate images can increase the completeness and readability of the report. In this study, we propose an image selection framework for recommending an appropriate image for a part of a report by combining textual information used in text-based image retrieval and visual features used in content-based image retrieval. In addition, the proposed image selection framework adopts an image filtering module that is specially designed for filtering out some images that are not suitable for use in reports. Through experiments on two datasets and comparative experiment with state-of-the-art work, we confirmed that our proposed method recommends images that fit the user’s intention, and its practical applicability.
A Robust Webcam-based Eye Gaze Estimation System for Human-Computer Interaction

Precise eye gaze detection has a multitude of real-life use cases such as the input mechanism for physically disabled persons, driver’s attention detection in vehicles, cheating detection in online exam, augmented reality, medical research and so on. Most of the applications need to support real-time functionality, thus the need for a fast and reliable method for eye gaze detection can be justified. In this research work, we propose a non-wearable and webcam-based eye-gaze detection method that offers multiple benefits in terms of accuracy, robustness, and reliability over existing solutions. We approached gaze detection as a multiclass classification problem, this reduced the complexity of the method and allows the solution to be implemented in a way that allows free head movement of the user. We leveraged the latest innovation and breakthroughs in deep learning to construct a novel eye-gaze detection method that works using the live video feed from any modern webcam with acceptable frame rates for proper real-time applications. We achieved 99% validation accuracy in gaze prediction and 20 FPS on average in real-time applications such as mouse pointer control and scrolling.
ALEDAR: An Attentions-based Encoder-Decoder and Autoregressive model for workload Forecasting of Cloud Data Center

Effective workload forecasting can provide a reference for resource scheduling in cloud data centers. Compared with the normal single data center, the multi-data center has a more complicated architecture design and provides more diverse computing services. The traditional load forecasting models need too much manual intervention to set up the parameters, meanwhile, the emerging neural network methods are not sensitive to the prediction scale and cannot capture the long-term associations between input features. To address these challenges, we propose a hybrid model named Attentions-based LSTM Encoder-Decoder network and Autoregressive model (ALEDAR), which combines neural network and statistical learning methods, to analyze the linear and nonlinear characteristics of the load sequence over time in a multi-cloud data center environment. ALEDAR uses a dual attention-based Encoder-Decoder architecture to extract the relationships between historical workload data, the design can also avoid the deterioration of prediction effect caused by long-range data scale, the output layer is composed of a three-layer perceptron. Moreover, ALEDAR employs an autoregressive module to capture the linear trend of the load sequence and eliminate scale insensitivity of input and output data. The experimental results show that our approach is adaptive and can improve the performance of host workload prediction in both single cloud data center and multi-cloud data center environments. We evaluate the performance of the proposed algorithm on real-world data sets and observe consistent improvement of 9.7% - 34.2% over the state-of-the-art baselines.
Machine Learning-based Online Social Network Privacy Preservation

Online data privacy draws more and more concerns. Online Social Network (OSN) service providers employ anonymization mechanisms to preserve private information and data utility. However, these mechanisms mostly focus on the traditional definitions about privacy and utility. Recently, both benign data scientists and attackers utilize machine learning methods to extract information from OSNs. This paper aims to present a novel angle of balancing privacy and utility under machine learning. The proposed scheme perturbs the data that breaks the attackers' learning results and protect the benign third parties' learning results. To preserve both privacy and utility, we propose two different anonymization approaches to solve the multi-objective optimization problem. The first approach combines the two objectives. It utilizes the deep learning model, Generative Adversarial Network (GAN), to sequentially learns the two objectives and generates graphs. The second approach analyzes the differences between the two objects on structures. It utilizes Integrated Gradient (IG) in learning to break attackers' learning results. It structurally rewires edges to preserve third parties' learning results afterwards. The experiment results show that both approaches work well in privacy preservation.
The Effect of Learning Rate on Fractal Image Coding Using Artificial Neural Networks

The amount by which the artificial neural network weights are updated during the training process is called the learning rate. More precisely, the learning rate is an adjustable parameter used in training neural networks in which small values, often in the interval [0, 1], are handled. The learning rate determines how quickly the model updates its weights to adapt to the problem. Smaller learning rates require more training periods due to small changes to the weights per refresh cycle, while larger learning rates lead to faster changes and require fewer training periods. In this paper, the effect of changing the learning rate value in the artificial neural network designed to solve the inverse problem of fractals was studied. Some results were obtained showing the impact of this change, whether when using large values of the learning rate or small values based on the type of fractal shape required to identify the recursive functions that generate it.
Students’ Perceptions of Gained and Lost Value: A Case Study of a Summer School That Had to Suddenly Move Online

The COVID-19 lockdown induced a sudden migration of traditionally presential learning activities to online domains, as was the case of inter-institutional summer schools. This research corresponds to a case study in which our organization had to reformulate, in less than three months, one of its traditional summer schools while trying to keep the original goals. Through qualitative and quantitative surveys, we aimed at identifying the impact of our reformulation through students’ perception of gained or lost value regarding four topics: (a) online teaching, (b) pre-recorded business cases, (c) online social events, and (d) technical solutions. By analyzing these four topics with emphasis on participants’ knowledge and learning experience, we identified some “tensions” leading to loss of value (i.e., belonging, performing, and organizing). These tensions suggest that future reformulations should be conducted considering students’ backgrounds and motivations.
An Improved L2Net for Repetitive Texture Image Registration with Intensity Difference Heterogeneous SAR Images

Heterogeneous synthetic aperture radar (SAR) images contain more complementary information compared with homologous SAR images; thus, the comprehensive utilization of heterogeneous SAR images could potentially improve performance for the monitoring of sea surface objects, such as sea ice and enteromorpha. Image registration is key to the application of monitoring sea surface objects. Heterogeneous SAR images have intensity differences and resolution differences, and after the uniform resolution, intensity differences are one of the most important factors affecting the image registration accuracy. In addition, sea surface objects have numerous repetitive and confusing features for feature extraction, which also limits the image registration accuracy. In this paper, we propose an improved L2Net network for image registration with intensity differences and repetitive texture features, using sea ice as the research object. The deep learning network can capture feature correlations between image patch pairs, and can obtain the correct matching from a large number of features with repetitive texture. In the SAR image pair, four patches of different sizes centered on the corner points are proposed as inputs. Thus, local features and more global features are fused to obtain excellent structural features, to distinguish between different repetitive textural features, add contextual information, further improve the feature correlation, and improve the accuracy of image registration. An outlier removal strategy is proposed to remove false matches due to repetitive textures. Finally, the effectiveness of our method was verified by comparative experiments.
BIPV Modeling with Artificial Neural Networks: Towards a BIPV Digital Twin

Modeling the photovoltaic (PV) energy output with high accuracy is essential for predicting and analyzing the performance of a PV system. In the particular cases of building-integrated and building-attached photovoltaic systems (BIPV and BAPV, respectively) the time-varying partial shading conditions are a relevant added difficulty for modeling the PV power conversion. The availability of laser imaging detection and ranging (LIDAR) data to create very-high-resolution elevation digital models can be effectively used for computing the shading at high resolution. In this work, an artificial neural network (ANN) has been used to model the power generation of different BIPV arrays on a 5 min basis using the meteorological and solar irradiance on-site conditions, as well as the shading patterns estimated from a digital surface model as inputs. The ANN model has been validated using three years of 5-min-basis monitored data showing very high accuracy (6–16% of relative error depending on the façade). The proposed methodology combines the shading computation from a digital surface model with powerful machine learning algorithms for modeling vertical PV arrays under partial shading conditions. The results presented here prove also the capability of the machine learning techniques towards the creation of a digital twin for the specific case of BIPV systems that complements the conventional monitoring strategies and can be used in the diagnosis of performance anomalies.