Reviewerly/exHarmony huggingface dataset:
Effective Feature Selection Using Ensemble Techniques and Genetic Algorithm

Individual feature selection algorithms, used for processing high-dimensional multi-source heterogeneous data may lead to weak predictions. The traditional single method process may not ensure the selection of relevant features. The selections of features are susceptible to the changes in input data, and thus fail to perform consistently. These challenges can be overcome by having a robust feature selection algorithm that generates a subset of original features and evaluates the candidate set to check for its relevance. Also, it determines the feasibility of the selected subset of features. The fundamental tasks of selecting feature subset minimize the complexity of the model and help to facilitate the further processing of the model. The limitations of using single feature selection technique can be reduced by combining multiple techniques to generate the effective features. There is a need to design efficient approaches and technique for estimating the feature relevance. This ensemble approach will help to include diversity at input data level, as well as the computational technique. The proposed method—Ensemble Bootstrap Genetic Algorithm (EnBGA)—generates the effective feature subset for the multi-source heterogeneous data. Various univariate and multivariate base selectors are combined together to ensure the robustness and stability of the algorithm. In this pandemic of COVID-19, it’s observed that patients already diagnosed with diseases such as diabetes had an increased mortality rate. The proposed method performs feature analysis for such data, where the Genetic Algorithm searches the feature subset and extracts the most relevant features.
An Efficient Implementation of FPGA-based Object Detection Using Multi-scale Attention

Convolutional neural network (CNN)-based object detection has become an important task in image pre-processing for security video surveillance cameras. Since CNNs require a large amount of computational power, the approach of FPGA-based implementation has emerged as a promising solution owing to its energy efficiency and processing speed. The single-shot multibox detector (SSD) is suitable for this kind of application, while reducing the CNN computational load need to achieve high detection accuracy is still an important challenge for FPGA design. This paper presents an FPGA accelerator for processing SSD object detection. The number of computations can be reduced by the proposed multi-scale spatial attention mechanism. To enhance the efficiency for hardware implementation of the CNN, we propose dynamic quantization on hardware and autonomous memory access control. The developed prototype based on SSD with multi-scale spatial attention mechanism implemented on XCZU7EV exhibits an object detection accuracy of 79.3% mean average precision on the PASCAL VOC dataset. The proposed design achieves high digital signal processor efficiency of 94% and good operation speed of 77.7 msec.
Improved Non-negative Matrix Factorization Algorithm for Sparse Graph Regularization

Aiming at the low recognition accuracy of non-negative matrix factorization (NMF) in practical application, an improved spare graph NMF (New-SGNMF) is proposed in this paper. New-SGNMF makes full use of the inherent geometric structure of image data to optimize the basis matrix in two steps. A threshold value s was first set to judge the threshold value of the decomposed base matrix to filter the redundant information in the data. Using L2 norm, sparse constraints were then implemented on the basis matrix, and integrated into the objective function to obtain the objective function of New-SGNMF. In addition, the derivation process of the algorithm and the convergence analysis of the algorithm were given. The experimental results on COIL20, PIE-pose09 and YaleB database show that compared with K-means, PCA, NMF and other algorithms, the proposed algorithm has higher accuracy and normalized mutual information.
LibGuides: Curriculum Materials: Home

Learn more about educational Curriculum Materials (CurrMats) available from the NSU Libraries.
Research of Occupancy-Based Skyline Pattern Mining

The DOFRA algorithm uses user-specified minimum support threshold and occupancy threshold to find frequent and complete patterns. This process requires constant modification of threshold to adapt to different datasets. From a macro point of view, there is a negative correlation between the pattern support and the pattern length (occupancy), and user-specified minimum threshold mining has a large deviation to the result. In this article, by designing an efficient Pattern-extension-list (PEL) structure to replace the occupancy upper bound calculation in the DOFRA algorithm, a skyline mining algorithm based on the occupancy upper bound calculation (SOPM-occubound) is proposed. Two types of Skyline algorithms based on the PEL structure, SOPM (candidate set) and SOPM-occumax (without candidate itemsets), are used to mine occupancy-based skyline patterns (OBSPs). The advantage of SOPM is that the unreasonable setting of the threshold results in the problem of extremely different data excavated, and it can effectively find the most representative modes in the two dimensions of support and occupancy, and dominate other modes. Finally, the experimental results on several real and simulated datasets show that SOPM algorithm based on PEL structure is superior to SOPM-occubound in terms of running time and memory usage, and SOPM-occumax algorithm does not generate candidate item sets, thus it is the most efficient.
A Method of MOBA Game Lineup Recommendation Based on NSGA-II

The existing research on MOBA game lineup recommendation mainly takes the heroes with high winning rate in the historical game as the recommendation results, without comprehensively considering the synergy relationship and the counter relationship between heroes. In the recommended results, there will be a situation where a hero has great ability, but the synergy between the heroes is not good, or the recommended hero is counter by the enemy. This paper summarizes the problem as a multi-objective optimization problem, and proposes a MOBA game lineup recommendation method base on NSGA-II. Firstly, based on the performance of the heroes in historical games, the ability of these heroes is quantified. At the same time, according to different heroes as teammates or opponents, the synergy relationship and the counter relationship between heroes are evaluated. Then, NSGA-II is used to solve the multi-objective optimization problem, and the recommendation result is obtained. Finally, the results are evaluated based on prediction models. The method proposed in this paper integrates the hero's ability, the synergy relationship and the counter relationship between heroes, which has strong rationality. Experimental results show that the method has high accuracy, and can provide a new idea for solving this kind of problems.
USET : A network based on Utterance hidden State transfEr for Task-oriented dialogue

Multi-turn dialogue is challenging because semantic information is not only contained in the current utterance, but also in the dialogue context. In fact, understanding multiturn dialogue is a dynamic process. With the increase of dialogue turn, users' understanding is also changing. In this case, we propose a network based on utterance hidden state transfer for task-oriented dialogue (USET). In our model, we first extract the hidden state of previous utterance as the previous comprehension. Then, this comprehension is passed to the next turn. We take the comprehension as a prior knowledge to understand the semantic information in the dialogue context. Finally, we put the previous comprehension and current utterance together to understand current utterance. In order to realize the transfer of comprehension in dialogue, we propose a continuous sample training method: CST, which takes a multi-turn dialogue as a whole to understand. All the sentences in a dialogue are put into a batch for training. Our method makes use of previous comprehension and achieves the information exchange among dialogue. Experimental results on Stanford Multi-Domain dataset demonstrate that our model is superior to existing models. Code is available at https://github.com/season1blue/USET
An improved particle swarm optimization algorithm and its application to solve constrained optimization problems

This paper presents an improved particle swarm optimization algorithm (IPSO) for constrained nonlinear optimization problems. The main improvements of the original PSO are the incorporation of particle local information and the introduction of dynamic inertia weight. A new velocity update equation is proposed, which consider the local information around each particle. During consecutive generations, an dynamic inertia weight is produced to improve the search speed and search precision of IPSO. In addition, a dynamic multi-stage penalty function is used to deal with the constraints and a modified feasibility-based rule is adopted to update the personal best position and the global best position of the IPSO. The effectiveness of the proposed IPSO is validated through a set of benchmark constrained test functions and an engineering optimization problem. The experimental results show that IPSO can obtain some solutions better than or as well as those previously reported in the literature, which reveals that the proposed IPSO is a promising approach to solve this sort of optimizing problems.
Knowledge Graph Representation Learning for Link Prediction with Three-Way Decisions

Relation prediction is one of the important tasks of knowledge graph completion, which aims to predict the missing links between entities. Although many different methods have been proposed, most of them usually follow the closed-world assumption. Specifically, these methods simply treat the unknown triples as errors, which will result in the loss of valuable information contained in the knowledge graphs (KGs). In addition, KGs exist large amounts of long-tail relations, which lack sufficient triples for training, and these relations will seriously affect inference performance. In order to address above-mentioned problems, we propose a novel relation prediction method based on three-way decisions, namely RP-TWD. In this paper, RP-TWD model first obtains the similarity between relations by K-Nearest Neighbors (KNN) to model the semantic associations between them. The semantic association between relations can be considered as supplementary information of long-tail relations, and constrain the learning of KG embeddings. Then, based on the idea of three-way decisions (TWD), the triples of specific relation are further divided into three disjoint regions, namely positive region (POS), boundary region (BND), and negative region (NEG). The introduction of BND intends to represent the uncertainty information contained in unknown triples. The experimental results show that our model has significant advantages in the task of relation prediction compared with baselines.
PTR-MirrorGAN: Parallel Text Redescription module for advancing MirrorGAN

During generating images from a given text description, two aspects of consistency are usually considered: visual consistency and semantic consistency. Although there has been a great improvement in the use of generative adversarial networks to generate images, it remains a challenging project to ensure that the semantics of the generated images are consistent with the semantics of the input text descriptions. This study proposed a Real-Fake Images Parallel Text Redescription Module (RFIPTRM) for advancing MirrorGAN based on the idea of mirroring and reconstructed the loss of the image generation model by adding the loss between the real text and the text from generated images to the genarators for balancing the textual semantic space. Intuitively, there may be inconsistency between the textual semantic space structure of our real text and the generated image, so we conducted adversarial learning between redescriptive text from the real images and the real text to improve the inconsistency of this text space structure. Comprehensive experiments conducted on the public benchmark datasets CUB and COCO have proved that PTR-MirrorGAN is superior to other methods and achieved a better generation effect.
Knowledge Enhanced Sequential Entity Linking

Entity Linking (EL) is the task of mapping mentions in texts to the corresponding entities in knowledge bases. Existing studies mostly focus on joint disambiguation based on the topical coherence, including graph and sequence models. Sequence models alleviate the complexity caused by graph models, but exist the error propagation that incorrectly disambiguated entities are likely to induce further errors when predicting future mentions. Moreover, it is a huge expense to construct the relationship between entities to explore structured knowledge. To address these problems, we propose a novel method, Knowledge Enhanced Sequential Entity Linking (KESEL), which converts global EL into a sequence decision problem and applies a pre-trained language model to better fuse entity knowledge. Specifically, we firstly utilize multiple features to learn local contextual representations of mentions and candidates respectively. Next, a sequential ERNIE model is introduced to generate knowledgeable representations by dynamically integrating the knowledge of previously referred entities into subsequent mentions disambiguation. Finally, by concatenating the above learned contextual and knowledgeable representations, we make full use of multi-semantic information to improve the performance of EL. Extensive experiments show that our method can achieve competitive or state-of-the-art results.
Random Vector Functional Link Networks for Road Traffic Forecasting: Performance Comparison and Stability Analysis

Nowadays, Machine Learning algorithms enjoy a great momentum in multiple engineering and scientific fields. In the context of road traffic forecasting, the number of contributions resorting to these modeling techniques is increasing steadily over the last decade, in particular those based on deep neural networks. In parallel, randomization based neural networks have progressively garnered the interest of the community due to their learning efficiency and competitive predictive performance. Although these two properties are often sought for practical traffic forecasting solutions, randomization based neural networks have so far been scarcely investigated for this domain. In particular, the instability of these models due to the randomization of part of their parameters is often a deciding factor for discarding them in favor of other modeling choices. This research work sheds light on this matter by elaborating on the suitability of Random Vector Functional Link (RVFL) for road traffic forecasting. On one hand, multiple RVFL variants (single-layer RVFL, deep RVFL and ensemble deep RVFL) are compared to other Machine Learning algorithms over an extensive experimental setup, which comprises traffic data collected at diverse geographical locations that differ in the context and nature of the collected traffic measurements. On the other hand, the stability of RVFL models is analyzed towards providing insights about the compromise between model complexity and performance. The results obtained by the distinct RVFL approaches are found to be similar than those elicited by other data driven methods, yet requiring a much lower number of trainable parameters and thereby, drastically shorter training times and computational effort.
Automatic Checking of Functional Closeness of Web service Composite System

the function closure in Web service composite system is the premise to ensure the correct interaction of the system. This paper realized the automatic detection of function closure in Web service composite system. Firstly, the syntax and semantic interface of Web service is defined and parameter mapping is set up by using of semantic relationship among the parameters. Secondly, the mapping relationship of sending actions and receiving actions is established by merging the parameters of the same action in the same service. Finally, the functional closure of web service composition system can be determined by checking whether all the sending actions have corresponding receiving actions and the receiving parameters in each receiving action have corresponding sending parameters. The algorithms are designed to realize parameter mapping, action mapping and system function closure detection automatically. The effectiveness of the proposed method is verified by an example of network file system.
Description of the Methodology of Using E-learning Technology in the Study of the Subject “Computer Science”

The role of both higher and secondary education at the present stage of development of the Republic of Azerbaijan is mainly measured by the tasks set for it, its transition to a democratic and rule-of-law state, to a market economy, the need to overcome the danger of the country lagging behind the world trends of economic and social development. The main goal of the modernization of education is to create a mechanism for the sustainable development of the education system. To achieve this goal, such important tasks as ensuring State guarantees of accessibility and equal opportunities for obtaining a full-fledged education and achieving a new modern quality of preschool, general and vocational education should be solved as a priority, among others. Within the framework of solving these tasks, the concept of modernization of Russian education at the senior level of general education provides for specialized training and the development of distance education. The model of a general education institution with specialized training at the senior level provides for the possibility of various combinations of academic subjects, which will provide a flexible system of specialized training. This system includes the following types of academic subjects: basic general education, specialized and elective. Elective courses are implemented at the expense of the school component of the curriculum and provide “support” for the study of the main profile subjects, as well as serve for intra-profile specialization of training and the construction of individual educational trajectories. Schools are faced with the task of developing elective courses. On the one hand, solving this problem requires highly qualified specialists, which is extremely difficult to provide, especially in rural areas. On the other hand, today schools, including rural ones, get access to high-speed Internet, therefore, we can say that there is a sufficient technical base for the development of distance learning, which will also solve the problems of specialized training.
Dynamic bandwidth allocation with playback buffer stabilization for QoE-aware adaptive video streaming

The quality of experience (QoE) is an index that evaluates the application-level quality of network services. In adaptive video streaming, stabilization of playback buffer level improves the QoE by avoiding video freezing. Although control-theoretic approaches for stabilizing the buffer level are effective to improve the QoE for a single user, they have not considered user preferences of the QoE or bandwidth allocation for multiple users. This study proposes a dynamic bandwidth allocation method that satisfies the QoE requirements of multiple users with different preferences. Simulation results show that the proposed method increases the number of satisfied users and QoE fairness for prioritized users at the expense of the average QoE for all users.
Challenges to Data Collection: Digital Divide Causing Double Marginalization of the Bagri Community in Sindh, Pakistan

In the wake of the Covid-19 pandemic, we conducted qualitative research on the Bagri community in Pakistan’s Sindh province. The Bagri community is considered an indigenous community in Pakistan’s Sindh and Punjab provinces. They also live in some states in India. The community has been referred to as ‘scheduled caste’ in Pakistan’s constitution. They speak Bagri language and practice Hinduism. Unfortunately, some Hindus and Muslims discriminate against the Bagri community and give them a wide berth and the community is seen as untouchable (Shah, 2007). Due to lockdowns caused by Covid 19, it has become difficult to access members of the community in order to collect data. Many of the members of the community have no digital literacy and the few who had mobile phones were contacted through mobile phone, but unfortunately voice quality of the interviews was not good and there was either network or noisy interruptions which made it difficult to understand what the interviewee was saying. Given this difficult situation, we used the strategy of using a friend of a friend to conduct the interviews on our behalf. However, even this solution faced challenges as the community was perceived as untouchable. In this way, the Bagri community was not only socially but also digitally marginalized. Therefore, this qualitative research will explore the digital and social challenges coresearchers faced during data collection, and we discuss how these challenges, were to some extent, surmounted.
Curvilinear Collaborative Metric Learning with Macro-micro Attentions

Although matrix factorization and its variants have proved their effectiveness in learning user preferences, they violate the triangle inequality and may fail to capture the inner grained preference information. Thus, metric learning based models have attracted increasing interests in recommender systems. However, most of these models only measure the distance in Euclidean space, though it is known to limit the prediction performance. To address this problem, this paper proposes curvilinear collaborative metric learning (CCML) as a solution. Rather than learning from Euclidean distance, CCML measures the “path” (i.e. curvilinear distance) between the user and items. Therefore, it is able to better infer information (in geometrical representation) and user-item relations behind the observed data points. Under this setting, the relations between the user and items are not linearly reflected by the relation vector. In order to effectively learn the representation of items, we propose to fuse macro features of categories and micro representations of items to form their latent vectors. Our model can be less prone to be affected by the noise commonly presented in datasets by separately learning macro and micro features. Experimental results on eight public recommendation datasets demonstrate that CCML produces a outstanding performance compared with five competitive baselines. Finally, quantitative analysis is included to verify the efficiency and effectiveness of CCML and the macro-micro module.
YOLOv3-DSN Object Detection Algorithm Based on Depth Wise Separable Convolution

In order to realize the real-time detection of dairy goat objects in the sheep farm, a neural network detection algorithm based on depth wise separable convolution YOLOv3-DSN is proposed. Firstly, the video frames are used to screen out the key frames containing the dairy goats based on the surveillance video of the sheep farm, and construct the dairy goat sample set. Then the K-means clustering method is used to determine the number and dimensions of the object candidate box on the data set, and the GIOU box regression loss function is used to improve the positioning accuracy of the dairy goat regression box. At the same time, the model is optimized through multi-scale training, and the depth wise separable convolutionYOLOv3-DSN network is used to return the object category and position,which realizes end-to-end object detection.Under the circumstance of taking into account accuracy and speed, realize the object detection of sheep farm surveillance video.The experimental results show that compared with SSD and YOLOv3, it can obtain better object detection results in terms of efficiency and accuracy.Provide basic technology for the development of intelligent video surveillance systems for sheep farms and reduce the workload of experimenters.