Reviewerly/exHarmony huggingface dataset:
Personalized Recommendation Algorithm of Tourist Attractions Based on Transfer Learning

With the development of information technology and the popularity of the Internet, the data on the network is growing exponentially. Information overload has become a significant issue for consumers seeking information. A recommendation system was created to detect users’ interests from huge amounts of data and to suit users’ specific information needs. Traditional collaborative filtering recommendation mostly uses scoring data for a recommendation, which has the problem of sparse data, which limits the performance of the recommendation system. On this basis, this paper studies the personalized recommendation algorithm of scenic spots with deep migration. Through the analysis of collaborative filtering recommendation methods, it is found that the traditional collaborative filtering methods only use scoring data for a recommendation, which has the problem of sparse data. Based on the vectorization of user interest, the similarity of user preference is calculated, and the matrix decomposition is carried out in cooperation with user implicit feedback, to integrate the knowledge transfer information into the matrix decomposition model, and make up for the lack of considering the attribute information of scenic spots in the matrix decomposition algorithm, and alleviate the problem of data sparsity. The findings of comparative trials suggest that the personalized scenic location recommendation approach proposed in this study, which is based on the depth migration algorithm, is effective. Compared with the benchmark recommendation method, the recommendation accuracy and recall rate has been improved to a certain extent.
Dremel

LSM-tree-based key-value stores like RocksDB are widely used to support many applications. However, configuring a RocksDB instance is challenging for the following reasons: 1) RocksDB has a massive parameter space to configure; 2) there are inherent trade-offs and dependencies between parameters; 3) optimal configurations are dependent on workload and hardware; and 4) evaluating configurations is time-consuming. Prior works struggle with handling the curse of dimensionality, capturing relationships between parameters, adapting configurations to workload and hardware, and evaluating quickly. We present a system, Dremel, to adaptively and quickly configure RocksDB with strategies based on the Multi-Armed Bandit model. To handle the large parameter space, we propose using fused features, which encode domain-specific knowledge, to work as a compact and powerful representation for configurations. To adapt to the workload and hardware, we build an online bandit model to identify the best configuration. To evaluate quickly, we enable multi-fidelity evaluation and upper-confidence-bound sampling to speed up configuration search. Dremel not only achieves up to ×2.61 higher IOPS and 57% less latency than default configurations but also achieves up to 63% improvement over prior works on 18 different settings with the same or smaller time budget. This paper is an abridged version.
Application of different approaches to generate virtual patient populations for the quantitative systems pharmacology model of erythropoiesis

In a standard situation, a quantitative systems pharmacology model describes a “reference patient,” and the model parameters are fixed values allowing only the mean values to be described. However, the results of clinical trials include a description of variability in patients’ responses to a drug, which is typically expressed in terms of conventional statistical parameters, such as standard deviations (SDs) from mean values. Therefore, in this study, we propose and compare four different approaches: (1) Monte Carlo Markov Chain (MCMC); (2) model fitting to Monte Carlo sample; (3) population of clones; (4) stochastically bounded selection to generate virtual patient populations based on experimentally measured mean data and SDs. We applied these approaches to generate virtual patient populations in the QSP model of erythropoiesis. According to the results of our research, stochastically bounded selection showed slightly better results than the other three methods as it allowed the description of any number of patients from clinical trials and could be applied in the case of complex models with a large number of variable parameters.
Using a Novel Algorithm Based on the Random Vector Functional Link Network and Multi-Verse Optimizer to Forecast Effluent Quality

The treatment of wastewater is a complicated biological reaction process. Reliable effluent prediction is critical in the scientific management of water treatment plants. This research proposes a soft sensor design strategy to address the issues above, Multi-Verse Optimizer (MVO)-based random vector functional link network (MVO-RVFL). The proposed approach is utilized to anticipate real-time effluent data obtained from the Benchmark Simulation Model 1 (BSM1). The results of the experiments demonstrate that the MVO methodology can successfully find the optimum input-hidden weights and hidden biases of the RVFL model while outperforming the original RVFL and other typical machine learning approaches in all types of influent datasets. In the situation of significant water quality variations, the use of the fusion process for model development was also investigated. The experimental results demonstrate that incorporating prior knowledge can effectively improve the model’s ability to cope with unexpected situations.
Context Sensing Attention Network for Video-based Person Re-identification

Video-based person re-identification (ReID) is challenging due to the presence of various interferences in video frames. Recent approaches handle this problem using temporal aggregation strategies. In this work, we propose a novel Context Sensing Attention Network (CSA-Net), which improves both the frame feature extraction and temporal aggregation steps. First, we introduce the Context Sensing Channel Attention (CSCA) module, which emphasizes responses from informative channels for each frame. These informative channels are identified with reference not only to each individual frame, but also to the content of the entire sequence. Therefore, CSCA explores both the individuality of each frame and the global context of the sequence. Second, we propose the Contrastive Feature Aggregation (CFA) module, which predicts frame weights for temporal aggregation. Here, the weight for each frame is determined in a contrastive manner: i.e., not only by the quality of each individual frame, but also by the average quality of the other frames in a sequence. Therefore, it effectively promotes the contribution of relatively good frames. Extensive experimental results on four datasets show that CSA-Net consistently achieves state-of-the-art performance.
A Model of a Universal Neural Computer with Hysteresis Dynamics for Avionics Problems

The paper proposes a method for implementing a universal neural network processor with hysteresis dynamics. This processor allows a wide range of heterogeneous tasks in real time to be performed without reprogramming and changing their internal structure. Adding hysteresis behavior to the system makes it possible to increase resistance to external influences, the complexity as well as non-linearity of intelligent output. The paper discusses the use of this processor as part of an on-board intelligent avionics system.
Reliable Digital Forensics in the Air

As the drone becomes widespread in numerous crucial applications with many powerful functionalities (e.g., reconnaissance and mechanical trigger), there are increasing cases related to misused drones for unethical even criminal activities. Therefore, it is of paramount importance to identify these malicious drones and track their origins using digital forensics. Traditional drone identification techniques for forensics (e.g., RF communication, ID landmarks using a camera, etc.) require high compliance of drones. However, malicious drones will not cooperate or even spoof these identification techniques. Therefore, we present an exploration for a reliable and passive identification approach based on unique hardware traits in drones directly (e.g., analogous to the fingerprint and iris in humans) for forensics purposes. Specifically, we investigate and model the behavior of the parasitic electronic elements under RF interrogation, a particular passive parasitic response modulated by an electronic system on drones, which is distinctive and unlikely to counterfeit. Based on this theory, we design and implement DroneTrace, an end-to-end reliable and passive identification system toward digital drone forensics. DroneTrace comprises a cost-effective millimeter-wave (mmWave) probe, a software framework to extract and process parasitic responses, and a customized deep neural network (DNN)-based algorithm to analyze and identify drones. We evaluate the performance of DroneTrace with 36 commodity drones. Results show that DroneTrace can identify drones with the accuracy of over 99% and an equal error rate (EER) of 0.009, under a 0.1-second sensing time budget. Moreover, we test the reliability, robustness, and performance variation under a set of real-world circumstances, where DroneTrace maintains accuracy of over 98%. DroneTrace is resilient to various attacks and maintains functionality. At its best, DroneTrace has the capacity to identify individual drones at the scale of 104 with less than 5% error.
COVID-19 Sentiment Analysis applying BERT

The growth of social data on the internet has accelerated during the last two decades. As a result, researchers can access data and information for various academic and commercial purposes. The novel coronavirus disease (COVID-19) is a current pandemic that has sparked widespread concern worldwide. Spreading misleading information on social media platforms like Twitter, on the other hand, is exacerbating the disease’s concern. This research aims to examine tweets and develop a model that can detect public sentiment from social media posts; consequently, necessary precautions can be taken to preserve adequate validity of information for the general public. We believe that various social media platforms have a significant impact on creating public awareness about the disease’s importance and encouraging preventive measures among community members. For this study, we applied the Bidirectional Encoder Representations from Transformers (BERT) model, a new deep-learning technique for text analysis and performance with exceptional multi-class accuracy. We also compared it with six shallow machine learning models.
Resolving Data Sparsity via Aggregating Graph-Based User–App–Location Association for Location Recommendations

Personalized location recommendations aim to recommend places that users want to visit, which can save their decision-making time in daily life. However, the recommending task faces a serious data sparsity problem because users have only visited a small part of total places in a city. This problem directly leads to the difficulty in learning latent representations of users and locations. In order to tackle the data sparsity problem and make better recommendations, users’ app usage records in different locations are introduced to compensated for both users’ interests and locations’ characteristics in this paper. An attributed graph-based representation model is proposed to dig out user–app–location associations with high-order features aggregated. Extensive experiments prove that better representations of users and locations are obtained by our proposed model, thus it greatly improves location recommendation performances compared with the state-of-art methods. For example, our model achieves 13.20%, 10.1%, and 9.44% higher performance than the state-of-art (SOTA) models in Top3Hitrate, Top3Accuracy, and nDCG3, respectively, in the Telecom dataset. In the TalkingData dataset, our model achieves 9.34%, 13.35%, and 8.56% better performance than the SOTA models in Top2Hitrate, Top2Accuracy, and nDCG2, respectively. Furthermore, numerical results demonstrate that our model can effectively alleviate the data sparsity problem in recommendation systems.
Educational Activity of High School Students in the Modern Conditions of the Educational Environment

The article examines some features of the transformation of students’ learning activities as a result of forming the particular educational environment based on digital technologies within the university. The insertion of new digital technologies in training process of the university increases the effectiveness of students’ independent work organisation and the management of their cognitive activity. The author notes the need to evaluate the information and communication technologies applied taking into account the pedagogical principles of teaching. The article presents examples of practical use of popular platforms Moodle, Rosetta Stone. The educational activity organized by the teacher through these computer systems makes it possible to increase the educational results of students, since it is aimed at performing various kinds of tasks that contribute to the development of the student’s competencies within the framework of mastering a specific academic discipline. The educational and cognitive students’ activities while learning a subject by applying digital technologies is considered as an independent process of mastering knowledge aimed at developing continuous learning skills.
Pedestrian Group Detection with K-Means and DBSCAN Clustering Methods

The development of autonomous vehicles has made real-time pedestrian detection and tracking an important research area for protecting human lives and improving society. A key challenge in this area is to improve pedestrian detection accuracy while reducing the processing time for tracking. This challenge can be addressed by detecting and tracking pedestrian groups since pedestrians move in groups. This paper proposes a method of detecting pedestrian groups using unsupervised machine learning methods. After detecting the pedestrians, K-Means and DBSCAN clustering methods were used to detect pedestrian groups based on the coordinates of pedestrians’ bounding boxes. Simulation results of the MOT17 dataset indicate that both clustering techniques could reduce detection and tracking processing times. However, K-Means clustering is more effective than DBSCAN clustering in detecting pedestrian groups as measured by the Silhouette Coefficient score and Adjusted Rand Index (ARI).
Teaching Programming for First-Year Data Science

This paper describes experiences in teaching Python programming as part of a large-enrolment first-year subject, that is a foundation for an undergraduate major in data science and it is also taken by a wide variety of non-majors across the arts, sciences and business fields. The paper focuses on some central design decisions about the content, sequence, approach, and tool support, and we reflect on how they have worked out and what we changed, as we have taught the subject to about 2500 students over four offerings (including two which were entirely on-line due to the pandemic). Particular aspects that are the focus in this paper include: teaching programming through a sequence of common patterns/idioms for data exploration and analysis, rather than in the language-feature focus that is traditional in programming classes; read-then-write-until-correct presentation for each pattern; explicit presentation of a notional machine for execution, in stages as more language complexity is experienced; starting with core Python before then covering use of libraries such as pandas, matplotlib and scikit-learn.
Environmental Quality Optimization of Sustainable Rural Revitalization Strategy Based on Improved Genetic Algorithm

To address the environmental quality of sustainable rural revitalization strategies, an improved genetic algorithm-based approach is proposed, environmental quality of rural revitalization strategies. Firstly, an index system to comprehensively assess the settlement environment of rural people based on the “production-production-production” space is built and then forms the trinity theoretical guidance framework; secondly, value of information entropy used to evaluate the dispersion of an index calculates the weight of each indicator and provides the basis for a comprehensive multi-index assessment; thirdly, by analyzing the spatial differences of rural human settlement environment quality, the quality of human settlement in rural areas has a positive correlation with the level of regional economic development. It shows that based on improved genetic algorithm, the environmental quality of sustainable rural revitalization strategy has been greatly improved, the growth rate of the comprehensive score of rural living environment in most regions reached more than 14%, and the comprehensive score growth rate of some rural living environment was 4.52%.
A Coverage-based Approach to Nondiscrimination-aware Data Transformation

The development of technological solutions satisfying nondiscriminatory requirements is one of the main current challenges for data processing. Back-end operators for preparing, i.e., extracting and transforming, data play a relevant role w.r.t. nondiscrimination, since they can introduce bias with an impact on the entire data life-cycle. In this article, we focus on back-end transformations , defined in terms of Select-Project-Join queries, and on coverage . Coverage aims at guaranteeing that the input, or training, dataset includes enough examples for each (protected) category of interest, thus increasing diversity with the aim of limiting the introduction of bias during the next analytical steps. The article proposes an approach to automatically rewrite a transformation with a result that violates coverage constraints, into the “closest” query satisfying the constraints. The approach is approximate and relies on a sample-based cardinality estimation, thus it introduces a trade-off between accuracy and efficiency. The efficiency and the effectiveness of the approach are experimentally validated on synthetic and real data.
MobiVQA

Visual Question Answering (VQA) is a relatively new task where a user can ask a natural question about an image and obtain an answer. VQA is useful for many applications and is widely popular for users with visual impairments. Our goal is to design a VQA application that works efficiently on mobile devices without requiring cloud support. Such a system will allow users to ask visual questions privately, without having to send their questions to the cloud, while also reduce cloud communication costs. However, existing VQA applications use deep learning models that significantly improve accuracy, but is computationally heavy. Unfortunately, existing techniques that optimize deep learning for mobile devices cannot be applied for VQA because the VQA task is multi-modal---it requires both processing vision and text data. Existing mobile optimizations that work for vision-only or text-only neural networks cannot be applied here because of the dependencies between the two modes. Instead, we design MobiVQA, a set of optimizations that leverage the multi-modal nature of VQA. We show using extensive evaluation on two VQA testbeds and two mobile platforms, that MobiVQA significantly improves latency and energy with minimal accuracy loss compared to state-of-the-art VQA models. For instance, MobiVQA can answer a visual question in 163 milliseconds on the phone, compared to over 20-second latency incurred by the most accurate state-of-the-art model, while incurring less than 1 point reduction in accuracy.
Online embedding and clustering of evolving data streams

Abstract Number of connected devices is steadily increasing and this trend is expected to continue in the near future. Connected devices continuously generate data streams and the data streams may often be high dimensional and contain concept drift. Clustering is one of the most suitable methods for real‐time data stream processing, since clustering can be applied with less prior information about the data. Also, data embedding makes the visualization of high dimensional data possible and may simplify clustering process. There exist several data stream clustering algorithms in the literature; however, no data stream embedding method exists. Uniform Manifold Approximation and Projection (UMAP) is a data embedding algorithm that is suitable to be applied on stationary (stable) data streams, though it cannot adapt concept drift. In this study, we describe a novel method EmCStream, to apply UMAP on evolving (nonstationary) data streams, to detect and adapt concept drift and to cluster embedded data instances using a distance or partitioning‐based clustering algorithm. We have evaluated EmCStream against the state‐of‐the‐art stream clustering algorithms using both synthetic and real data streams containing concept drift. EmCStream outperforms DenStream and CluStream, in terms of clustering quality, on both synthetic and real evolving data streams. Datasets and code of this study are available online at https://gitlab.com/alaettinzubaroglu/emcstream .
Improving Real-time Hand Gesture Recognition System for Translation: Sensor Development

A lot of deaf-mute persons rely on sign language as their primary form of communication, which can be difficult for the majority of us to comprehend. The Deaf community’s standard language of communication is currently Sign Language (SL), which is articulated via the use of body motions (hand, face, torso) as their main form of communication and observed through the eyes. The hands convey the majority of the information in sign language.Automated sign language recognition systems that use vision-based technology must extract crucial hand features from real-time image sequences in order to provide comprehensive and accurate categorization. A gesture recognition glove is proposed for real-time translation of English International Sign Language in our proposed method. Firstly, the smart glove will frame-by-frame watch the sign movements. The acquired data will then be analysed and the necessary linguistic data extracted. Secondly, to meet the desired purpose for deaf and mute people words are displayed through an LCD along with voice output.The method includes a gesture extraction phase that uses two types of sensors. The hand gesture image is processed in the approach to distinguish distinct characters, and we construct our own structural circuit design. However, a smart glove prototype graphical user interface application efficiency was tested based on the real-time data pre-processing and output visualization. Finally, both approaches are subjected to the gesture recognition step, the lowest efficiency was 92 % and the highest efficiency was 100%. The primary goal of this study is to introduce a device to society and encourage people with disability to communicate and explore the real world.
Research on the Construction of Simulation Teaching Resource Library for Internet of Things in Transportation

The traffic Internet of Things simulation teaching resource database is an important part of the IoT experimental teaching. This paper constructs a four-tier architecture system of the resource database including hardware level, software level, resource level and application level, analyzes the types, collection technology and storage management methods of simulation teaching resources, and puts forward three resource sharing application modes that are in class and out of class sharing, professional internal and external sharing as well as inside and outside the school sharing, so as to realize the for traffic IoT course teaching mode of combining the theory and practice, both the online and offline available, and no suspension of classes.