Reviewerly/exHarmony huggingface dataset:
Fire Rescue Model Based on Time Series Analysis

In this paper, we establish relevant models for the problems in fire rescue and solve them.Using the ARIMA model to establish a prediction model for the number of fire rescue calls, and predict the number of fire rescue calls in each month in 2021.According to the characteristics of fire incidents, we divide fire incidents into 7 levels, and use the method of cluster analysis to analyze the spatial correlation of various incident densities in this area, and give the most relevant incident categories in different regions.Grey correlation analysis was used to analyze the relationship between the density of various events and population density in the area.And on this basis, based on the minimum distance between the new fire station and other areas, the Floyd algorithm is used to solve the shortest distance between each area and other areas, and the location where the new fire station should be built is analyzed.
AN APPROACH TO SEMANTIC EDUCATIONAL CONTENT MINING USING NLP

The COVID-19 epidemic had caused one of the most significant disruptions to the global education system.Many educational institutions faced sudden pressure to switch from face-to-face to online delivery of courses.The conventional classes are no longer the primary means of delivery; instead, online education and resources have become the prominent approach.With the increasing demand for supplementary course materials to fulfill the needs of each area of study, students began to use search engines and online resources that contain discussions, practical demonstrations, and tutorial videos to aid students in their studies and course work.This study addresses the underlying challenges of retrieving relevant online educational materials by introducing an intelligent agent for semantic data mining.It works as middleware infrastructure that allow context-aware data processing and mining.YouTube was used to assess the consistency of the proposed model since it returns a large number of results in its search pool.The results showed that using the extraction of topics method, the similarities scores with the proposed model provided favorable results.Furthermore, an improvement in video ranking and sorting was realized.According to the findings, using this method provided users with a more productive and reliable study experience.
Signed Network Node Embedding via Dual Attention Mechanism

In signed networks, GNNs are used to get node embedding by aggregating node neighbor information. Most of the existing methods aggregate neighbor information from the node level, and the different paths between nodes and neighbors will also affect node embedding. The target node and its neighbors have different link positive,negative signs and link directions, which together constitute different paths.These different paths have different contributions to the target node.Based on the structural balance theory and status theory, this paper divides the different paths between nodes and their neighbors into 20 kinds of motifs, which are using to capture the different effects of paths on target nodes. Comprehensive consideration at the node level and path level, SNEDA (Signed Network Embedding via dual attention Mechanism) is proposed based on the graph attention Network. The model has two attention mechanisms: node-level attention captures different influences between nodes at the node level; path-level attention captures the different influences between motifs at the path level. The final vector representation of nodes is obtained by aggregating neighbor information selectively based on important motifs, and the vector representation is applied to link prediction. Experiments on four real social network data sets show that the network representation obtained by the model can improve the accuracy of link prediction. Experimental results demonstrate the effectiveness of the proposed framework through a signed link prediction task on four real-world signed network datasets.
A prototype workflow MIS for supply chain management: architecture, implementation and business evaluation

The continuous evolution of the informatics and networking areas is fuelling the business environment to take advantage of a wide set of new technologies and tools. This paper highlights the business needs on supply chain networks and how information technology (IT) solutions can support them. After providing a state-of-the-art approach to the development of an effective and modern business management system, it introduces a prototype web-based application by providing its analysis and design, as well as its implementation details using various modern software architectural patterns such as the model-view-controller (MVC), the service-oriented architecture (SOA) and the web services (WSs). The abilities and usability of the proposed solution are presented through an operational demonstration. Finally, based on the balanced scorecard methodology, a system evaluation is performed that depicts the high applicability and significance of the proposed system.
Hybrid texture synthesis and interpolated structure image completion

The issue of image completion has been extensively developed for a few dozen years and many computational methods exist to tackle various image completion problems. When only one incomplete image is available, the missing regions are relatively large but their texture can be recovered from surrounding regions, then the texture synthesis methods are usually the most efficient. If an incomplete image contains a large number of missing pixels (e.g. 90%) or small missing regions uniformly distributed across it, low-rank approximation or interpolation-based methods seem to be the best choice. However, when the image of interest contains both types of incompleteness, none of the mentioned methods can be applied. In this study, we propose a hybrid computational method combining the radial basis function interpolation with the texture synthesis to tackle the mentioned problem. The experiments, carried out on various image completion problems, demonstrate that our hybrid strategy considerably outperforms the baseline and selected low-rank completion methods.
Parameter-Efficient Federated Learning for Edge Computing with End Devices Resource Limitation

Federated learning is an emerging machine learning paradigm for privacy protection for data owners, without private user data leaving the devices. Massive data collection devices are distributed in an edge computing terminal, which provide a scenario for the application of federated learning. In this article, a new federated learning algorithm to edge computing, via using transfer learning technology, is proposed to address the challenges of small data samples and resource-poor devices faced by training of deep neural networks (DNNs) on end devices. Due to edge servers have enough resources to train a DNN model compared with edge devices, the algorithm trains the model on the cloud server by using public data sets and adds batch-normalization (BN) layer which only contains a small set of parameters as patch in the model. Then, edge devices download the pre-training model, the weights of which are fixed except the patch layers. The patch layers parameters are trained by using local data, which aggregate by the edge server.
Global Context-Aware Graph Neural Networks for Session-based Recommendation

Session-based recommendation, which uses the interactive information of anonymous users in a period to predict user preferences, has attracted extensive attention. Existing methods mainly exploit the strengths of graph neural networks (GNNs) in capturing structured data features to model complex item transitions for recommendations. However, there still remain some challenges: 1) many methods neglect to exploit cross-session interactions and fail to integrate the latent intra- and inter-session information effectively. 2) some methods barely explore fine-grained contextual factors underlying in the sessions for session-based recommendation, thus failing to capture more comprehensive user preferences. To solve these issues, we propose a novel method called Global Context-Aware Graph Neural Networks (GCA-GNN) which captures the local-session and cross-session preferences respectively, and captures fine-grained global contextual factors to complement user preferences. Specifically, GCA-GNN models user preferences from two different views: the cross-session view and the local-session view. The former learns collaborative user preferences from a cross-session graph, while the latter is designed to learn users' personal preferences from local-session graphs. Furthermore, a context-aware Capsule Graph Neural Network is employed to extract fine-grained contextual factors, serving as complementary information. And we introduce an auxiliary self-supervised learning task to enhance user preferences. Experiments on benchmark datasets demonstrate the strength of our model over the state-of-the-art methods.
NeuroUnlock: Unlocking the Architecture of Obfuscated Deep Neural Networks

The advancements of deep neural networks (DNNs) have led to their deployment in diverse settings, including safety and security-critical applications. As a result, the characteristics of these models (e.g., the architecture of layers and weight values/distributions) have become sensitive intellectual properties that require protection from malicious users. Extracting the architecture of a DNN through leaky side-channels (e.g., memory access) allows adversaries to (i) clone the model (i.e., build proxy models with similar accuracy profiles), and (ii) craft adversarial attacks. DNN obfuscation thwarts side-channel-based architecture stealing (SCAS) attacks by altering the run-time traces of a given DNN while preserving its functionality. In this work, we expose the vulnerability of state-of-the-art DNN obfuscation methods (based on predictable and reversible modifications employed in a given DNN architecture) to these attacks. We present NeuroUnlock, a novel SCAS attack against obfuscated DNNs. Our NeuroUnlock employs a sequence-to-sequence model that learns the obfuscation procedure and automatically reverts it, thereby recovering the original DNN architecture. We demonstrate the effectiveness of NeuroUnlock by recovering the architecture of 200 randomly generated and obfuscated DNNs running on the Nvidia RTX 2080 TI graphics processing unit (GPU). Moreover, NeuroUnlock recovers the architecture of various other obfuscated (and publicly available) DNNs, such as the VGG-11, VGG-13, ResNet-20, and ResNet-32 networks. After recovering the architecture, NeuroUnlock automatically builds a near-equivalent DNN with only a 1.4% drop in the testing accuracy. We further show that launching a subsequent adversarial attack on the recovered DNNs boosts the success rate of the adversarial attack by 51.7% in average compared to launching it on the obfuscated versions. Additionally, we propose a novel methodology for DNN obfuscation, ReDLock, which eradicates the deterministic nature of the obfuscation and achieves 2.16 x more resilience to the NeuroUnlock attack. We release the NeuroUnlock and the ReDLock as open-source frameworks <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://github.com/Mahya-Ahmadi/NeuroUnlock.
Beyond Stars – Generalized Topologies for Decoupled Search

Decoupled search decomposes a classical planning task by partitioning its variables such that the dependencies between the resulting factors form a star topology. In this topology, a single center factor can interact arbitrarily with a set of leaf factors. The leaves, however, can interact with each other only indirectly via the center. In this work, we generalize this structural requirement and allow arbitrary topologies. The components must not overlap, i.e., each state variable is assigned to exactly one factor, but the interaction between factors is not restricted. We show how this generalization is connected to star topologies, which implies the correctness of decoupled search with this novel type of decomposition. We introduce factoring methods that automatically identify these topologies on a given planning task. Empirically, the generalized factorings lead to increased applicability of decoupled search on standard IPC benchmarks, as well as to superior performance compared to known factoring methods.
Application of PBL in computer teaching of secondary vocational education

With the continuous development of China&apos;s economy, the demand for high-quality technical and skilled talent is increasing. As a base for cultivating high-skilled talent, secondary vocational education plays an irreplaceable role in China&apos;s economic development. Therefore, secondary vocational schools must adopt more effective education and teaching methods to cultivate students&apos; practical skills and improve their teaching quality. This paper analyzes the teaching effect and explores the teaching reform of secondary vocational schools by carrying out PBL (project-based learning) teaching in the graphic design course of computer specialty in secondary vocational schools.
Pedagogical conditions for the development of diagnostic competence of future special education teacher

The article deals with the problem of improvement of professional training of higher education applicants on the master’s program «Special Education». The current problems of higher educational institutions with regard to teacher training are described. The essence of competent approach of the teacher’s pedagogical work with children with special educational needs is disclosed. The design and implementation model of the educational program which aims at the formation and development of diagnostic competence is described. The aim of the research is to identify the pedagogical conditions for the development of the diagnostic competence of the master graduates.
Using Fiducial Marker for Analyzing Wearable Eye-Tracker Gaze Data Measured While Cooking

The deficits in cognitive functions affect carrying out instrumental activities of daily living. Assistive products oriented to support these activities, e.g., cooking, should predict the user’s behavior. The user’s areas of interest (AOI) calculated from the eye gaze data measured by a wearable eye-tracker can be used for the prediction. We developed a new AOI estimation method suitable for cooking context based on multiple fiducial markers. The evaluation suggested that our method was more feasible than the use of feature extraction and feature matching for AOI estimation on objects in the kitchen, including cooking utensils with their move and overlap, while cooking.
Investigating Echo State Network Performance with Biologically-Inspired Hierarchical Network Structure

The nervous systems of animals exhibit complex recurrent topologies. These networks often show key properties like small-worldness and clustering, forming a complex hierarchical topology that is very different from many standard random graphs. Current machine learning techniques are dominated by feed-forward networks without recurrent connectivity, or specialized architectures for gated recurrent connectivity (such as Long-Short Term Memory Networks). Reservoir computing networks, such as echo state networks, represent an exciting alternative form of recurrent networks, which require limited training data compared to other recurrent networks. Echo state networks, however, typically use randomized network topologies. We introduce a novel reservoir computing network, with a hierarchical network structure inspired by organization of biological networks, utilizing hierarchical stochastic block models. We demonstrate the use of this network for predicting dynamic system evolution, and we compare this network to existing echo state network topologies. We find that the best performing networks utilize our new topology, and, for Mackey-Glass system prediction as well as MNIST classification, the overall distribution of solutions is improved. For the Mackey-Glass system, the median population NRMSE improved from 0.856 to 0.08, and for MNIST the median population accuracy increased from 92.5% to 96.8%. This may be preliminary indication of improving network function through changing network topology. The spectral radii of the produced HSBM topologies do not always obey the echo state property, and we show that both the spectral radius as well as common graph metrics do not predict the best performing networks. This argues for further investigation into the structural properties of high performing networks utilizing reservoir computing.
SYNONYM DETECTION USING SYNTACTIC DEPENDENCY AND NEURAL EMBEDDINGS

Recent advances on the Vector Space Model have significantly improved some NLP applications such as neural machine translation and natural language generation.Although word co-occurrences in context have been widely used in counting-/predicting-based distributional models, the role of syntactic dependencies in deriving distributional semantics has not yet been thoroughly investigated.By comparing various Vector Space Models in detecting synonyms in TOEFL, we systematically study the salience of syntactic dependencies in accounting for distributional similarity.We separate syntactic dependencies into different groups according to their various grammatical roles and then use context-counting to construct their corresponding raw and SVD-compressed matrices.Moreover, using the same training hyperparameters and corpora, we study typical neural embeddings in the evaluation.We further study the effectiveness of injecting human-compiled semantic knowledge into neural embeddings on computing distributional similarity.Our results show that the syntactically conditioned contexts can interpret lexical semantics better than the unconditioned ones, whereas retrofitting neural embeddings with semantic knowledge can significantly improve synonym detection.
ACM UMAP 2022 report

ACM UMAP is the premier international conference for researchers and practitioners working on systems that adapt to individual users, to groups of users, and that collect, represent, and model user information. For the first time in the history of the conference, UMAP 2022 ran in a hybrid mode, with the opportunity for speakers and attendees to participate onsite or online. Besides, in this edition, we celebrated the 30th anniversary of UMAP, which started as a workshop on User Modeling in 1986. In this report we comment on the conference organization and attendees' experience.
THE EFFECTS OF HYBRID LEARNING IN ENGLISH READING AT SMA NEGERI 1 BURU

This study aims to investigate the effect of hybrid learning on the development of students reading comprehension in English Social Studies Class X1C at SMA Negeri 1 Buru Namlea, Buru Island, Maluku Province. This research used a quantitative method by applying an Experimental design, , researchers observed two classes them divided them into the experimental group and the control group class. The findings show that the mean score of the student pre-test in the experimental group was 61.1667, and its standard deviation was 8.47749, while the mean score in the control group was 58.1667, and its standard deviation was 8.65869. The differences mean score between both groups was 3. after treatments were given, the mean score of both groups was significantly different. The mean score of the students post-test in the experimental group was 72.1667, with a standard deviation of 8.57858. On the other side, the mean score of the student post-test in the control group was 63.6667, with a standard deviation 7.30297. The difference in score between both groups was 9, and it means that the mean score of the experimental group is higher than the control group, It also indicates that there is no doubt the use of hybrid learning in reading class is highly effective to achieve learning goals.
A Hybrid Translation Model for Pidgin English to English Language Translation

The African continent is made up of people with rich diverse cultures and spoken languages. Despite the diversity, one common point of unification, especially among the West African communities is the spoken pidgin-English language. With the development in web technology and the English language dominancy of web content, this growing population stands disadvantaged in understanding content on the web. To proffer a solution, researchers in machine translation from Pidgin English to the English language have leveraged only unsupervised and supervised Neural Machine Translation-based models. In this paper, we propose a hybrid-strategic model that improves the accuracy of the baseline Neural Machine Translation Model (NMT) in translating pidgin English to the English language. From the JW300 public dataset, we used 22,047 sentence pairs for training our model,1000 for tuning, and 2520 for testing. The Bi-Lingual Evaluation Understudy (BLEU) score was employed as a metric of measurement. From our findings, our hybrid model outperforms the baseline NMT model with a BLEU score of 1.05 on two-level translation. This indicates that the accuracy is dependent on the level and type of hybrid used. Studies that look at in-depth pre-translation strategies for developing translation machine model are green areas for pidgin-English translation.
A new outlier detection algorithm based on observation-point mechanism

Outlier detection is an important branch of data mining research, and has wide applications in the fields of finance, telecommunications, and biology. The traditional nearest neighbor-based outlier detection (NNOD) and local outlier factor-based outlier detection (LOFOD) algorithms generally have high computational complexity and high false-detection rates. This paper proposes an observation-point mechanism-based outlier detection (OPOD) algorithm comprising four core steps: i) generating random observation points in the original data space; ii) estimating the probability density function of distance values between the given observation point and all data points; iii) calculating the probabilities of distance values for the given observation point; and iv) detecting outliers by combining the probabilities corresponding to the different observation points. Extensive experiments are conducted to demonstrate the feasibility, rationality, and effectiveness of the OPOD algorithm. The experimental results show that the OPOD algorithm converges as the number of observation points increases, and can attain better detection performance with lower computation complexity than the NNOD and LOFOD algorithms.
A steepest descent-like method for vector optimization problems with variable domination structure

In many real world problems, the simultaneous minimization of many certain goals is needed.Such problems lead to vector optimization problems with variable domination structure, where the domination structure is given by a set-valued map.In this framework, one of the most important solution concepts is the concept of nondominated points.In this paper, we propose a steepest descent-like method for computing nondominated solutions of smooth unconstrained vector optimization problems with variable domination structure.We obtain that every accumulation point of the generated sequence satisfies a first order necessary condition.We discuss the consequence of this fact in the convex case.