Reviewerly/exHarmony huggingface dataset:
Greedy randomized and maximal weighted residual Kaczmarz methods with oblique projection

&lt;abstract&gt;&lt;p&gt;For solving large-scale consistent linear system, a greedy randomized Kaczmarz method with oblique projection and a maximal weighted residual Kaczmarz method with oblique projection are proposed. By using oblique projection, these two methods greatly reduce the number of iteration steps and running time to find the minimum norm solution, especially when the rows of matrix are highly linearly correlated. Theoretical proof and numerical results show that the greedy randomized Kaczmarz method with oblique projection and the maximal weighted residual Kaczmarz method with oblique projection are more effective than the greedy randomized Kaczmarz method and the maximal weighted residual Kaczmarz method respectively.&lt;/p&gt;&lt;/abstract&gt;
Supervised and Unsupervised Categorization of an Imbalanced Italian Crime News Dataset

The automatic categorization of crime news is useful to create statistics on the type of crimes occurring in a certain area. This assignment can be treated as a text categorization problem. Several studies have shown that the use of word embeddings improves outcomes in many Natural Language Processing (NLP), including text categorization. The scope of this paper is to explore the use of word embeddings for Italian crime news text categorization. The approach followed is to compare different document pre-processing, Word2Vec models and methods to obtain word embeddings, including the extraction of bigrams and keyphrases. Then, supervised and unsupervised Machine Learning categorization algorithms have been applied and compared. In addition, the imbalance issue of the input dataset has been addressed by using Synthetic Minority Oversampling Technique (SMOTE) to oversample the elements in the minority classes. Experiments conducted on an Italian dataset of 17,500 crime news articles collected from 2011 till 2021 show very promising results. The supervised categorization has proven to be better than the unsupervised categorization, overcoming 80% both in precision and recall, reaching an accuracy of 0.86. Furthermore, lemmatization, bigrams and keyphrase extraction are not so decisive. In the end, the availability of our model on GitHub together with the code we used to extract word embeddings allows replicating our approach to other corpus either in Italian or other languages.
Multimodal Neural Machine Translation for English–Assamese Pair

Neural machine translation is a state-of-the-art approach for the automatic translation between natural languages. The multimodal concept utilizes textual and image features for improvement in low-resource neural machine translation. There is a lack of a standard multimodal corpus for the English–Assamese low-resource pair. We present a multimodal corpus which is suitable for multimodal translation task of English–Assamese pair. The English–Assamese multimodal corpus is used to implement multimodal neural machine translation models for English-to-Assamese translation and vice-versa. The comparative results of automatic evaluation metrics between text-only and multimodal neural machine translation show multimodal neural machine translation outperforms text-only neural machine translation.
A Hybrid Deep Learning Model for UAVs Detection in Day and Night Dual Visions

Unmanned Aerial Vehicle (UAV) detection for public safety protection is becoming a critical issue in non-fly zones. There are plenty of attempts of the UAV detection using single stream (day or night vision). In this paper, we propose a new hybrid deep learning model to detect the UAV s in day and night visions with a high detection precision and accurate bounding box localization. The proposed hybrid deep learning model is developed with cosine annealing and re-thinking transformation to improve the detection precision and accelerate the training convergence. To validate the hybrid deep learning model, real-world experiments are conducted outdoor in daytime and nighttime, where a surveillance video camera on the ground is set up for capturing the UAV. In addition, the UAV-Catch open database is adopted for offline training of the proposed hybrid model, which enriches training datasets and improves the detection precision. The experimental results show that the proposed hybrid deep learning model achieves 65 % in terms of the mean average detection precision given the input videos in day and night visions.
Challenges of Mining Twitter Data for Analyzing Service Performance: A Case Study of Transportation Service in Malaysia

Literature has shown the prominence of extracting social media data to understand public opinion. However, there are little works on how these opportunities can be realized and the challenges in exploiting the opportunities in the transportation industry. Further, data quality and availability using social media may vary according to different demographics due to population size and languages used. Additionally, most of the related prior studies that show the opportunities of using social media data were conducted in North and South America. With this proposition, we seek to investigate the challenges of using Twitter data with text mining techniques for understanding users' opinions and sentiment through a case study of using the data to assess public transportation service performance specifically in a Malaysian context. Our findings indicate that social media data can only be useful in generating reasonable insights if users could input informative words for forming discussed topics to derive opinion, and incline towards a certain sentiment with adjectives. The findings also identified the need for a more proficient dictionary to classify multilingual tweets. Our research provides original evidence proving the potential of using social media data to assess public transportation services performance which may vary depending on the demographics of the social media users.
Cross-Domain Recognition via Projective Cross-Reconstruction

This article proposes a novel data reconstruction method, called projective cross-reconstruction (PCR) for cross-domain recognition. The intrinsic philosophy behind PCR is that the data from different domains but with the same label have a strong correlation and thus they can be reconstructed with each other. To this end, we first rearrange the data of source and target domains to form two new cross-data matrices, and the data with the same label but from different domains can be arranged together. Then, we use two different projection matrices to project the new cross-data into two approximate subspaces and perform the cross-reconstruction without introducing any extra matrix as the reconstruction coefficient matrix. This guarantees that the data from different domains can be interlaced well and the data from different domains but sharing the same label can be aligned together. In doing so, the problem of cross-domain distribution mismatch is solved and a discriminative and transferrable feature representation can be obtained. Moreover, PCR integrates the classifier learning and feature representation learning into a unified framework so that these two tasks can be iteratively improved until the termination condition is met. Extensive experiments on six datasets validate the effectiveness of our proposed PCR, compared with the state-of-the-art methods.
A Case Study of Cloud Computing Service Models for the General Computing Environment in a University

Cloud computing technology has emerged as the basic infrastructure technology in the 4th industrial revolution and has been utilized in various fields. Therefore, the authors analyze the requirements of academic and administrative affairs, education affairs, and affiliated institutions in the university. Proposed cloud computing service models in the universities are implemented using an OpenStack in CentOS 7.3 and Windows. To evaluate the performance, the author compares various performance factors including launching time, booting time, ping time, and software installation time. Lastly, the economic benefits in the university will be evaluated by comparing the adoption cost, maintenance cost, and total cost between the existing client-server system and cloud computing system. Future work aims to implement a cloud computing system that can be applied to the entire university. Finally, based on these studies, the authors will implement a regional base cloud system that can connect all universities in the region with one cloud computing system.
Comparing the expressiveness of the $$\pi $$-calculus and CCS

Abstract This paper shows that the $$\pi $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>π</mml:mi> </mml:math> -calculus with implicit matching is no more expressive than $$\mathrm {CCS}_{\gamma }$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>CCS</mml:mi> <mml:mi>γ</mml:mi> </mml:msub> </mml:math> , a variant of CCS in which the result of a synchronisation of two actions is itself an action subject to relabelling or restriction, rather than the silent action $$\tau $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>τ</mml:mi> </mml:math> . This is done by exhibiting a compositional translation from the $$\pi $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>π</mml:mi> </mml:math> -calculus with implicit matching to $$\mathrm {CCS}_{\gamma }$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>CCS</mml:mi> <mml:mi>γ</mml:mi> </mml:msub> </mml:math> that is valid up to strong barbed bisimilarity. The full $$\pi $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>π</mml:mi> </mml:math> -calculus can be similarly expressed in $$\mathrm {CCS}_{\gamma }$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>CCS</mml:mi> <mml:mi>γ</mml:mi> </mml:msub> </mml:math> enriched with the triggering operation of Meije . I also show that these results cannot be recreated with CCS in the rôle of $$\mathrm {CCS}_{\gamma }$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>CCS</mml:mi> <mml:mi>γ</mml:mi> </mml:msub> </mml:math> , not even up to reduction equivalence, and not even for the asynchronous $$\pi $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>π</mml:mi> </mml:math> -calculus without restriction or replication. Finally I observe that CCS cannot be encoded in the $$\pi $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>π</mml:mi> </mml:math> -calculus.
Multiple Adaptive Strategies-based Rat Swarm Optimizer

Rat Swarm Optimizer (RSO) is a novel Swarm-intelligence based algorithms for solving global optimization problems. Its main idea is simulating the behavior of rats chasing and fighting their prey. There is an improved RSO according to multiple adaptive strategies, named as MARSO, is proposed. The multiple adaptive strategies include adaptive learning exemplars (ALE) and adaptive population size (APS). In this paper, the performance of MARSO was validated on the 29 IEEE CEC2017 functions by comparing with several classic or novel optimization algorithms. The experimental results show these two strategies enable RSO to get more excellent performance.
Detecting Gravitational Waves using Constant-Q Transform and Convolutional Neural Networks

The discovery of gravitational waves from the mergers of binary black holes has opened doors to an unprecedented revolution in the fields of physics and astronomy. However, the signals of gravitational waves with tiny magnitudes are inevitably buried in detector noise, leading to a great demand for the accurate analysis of gravitational wave data. In this paper, based on a set of time-series data containing simulated gravitational waves in a Kaggle competition, we propose a deep learning method by combining constant-Q transform and convolutional neural networks (CNNs), to achieve a promising performance for the detection of gravitational waves. In our method, the gravitational wave signal is firstly transformed into a spectrogram by the constant-Q transform, and is subsequently classified by the CNN network. In particular, EfficientNet-B3 and EfficientNetV2-L are both utilized as the CNN backbones to extract features from spectrograms. After applying an ensemble average of two backbones and the K-Fold cross validation technique, our model reaches an AUC score 0.8786 on the private test set. This result ranks top 5% (63/1219) in the Kaggle leaderboard, and can get a bronze medal in the G2Net Gravitational Wave Detection competition. This work would help increase the sensitivity of interferometers to gravitational wave signals, and potentially accelerate the development of next-generation detectors to explore the Universe.
HRS: Hybrid Recommendation System based on Attention Mechanism and Knowledge Graph Embedding

Traditional recommendation methods often have sparsity and cold start problems in real applications. Researchers found that introducing knowledge graphs into recommender systems as a kind of auxiliary information can alleviate these problems and improve the performance of the recommender systems. This paper put forward a Hybrid Recommendation System based on Attention Mechanism and Knowledge Graph Embedding (HRS). It uses crosscompress unit and preference propagation respectively to enrich the features of items and users. In this process, we use the attention mechanism and knowledge graph embedding to enhance the recommender system. After obtaining the user vector representation and the item vector representation, we can predict the probability of the user clicking on the item. The experiments on three datasets derived from the actual scenes demonstrate the competitiveness of HRS, compared with several state-of-the-art baselines in the recommendation.
Variable Coded Batch Matrix Multiplication

In this paper, we introduce the Variable Coded Distributed Batch Matrix Multiplication (VCDBMM) problem which tasks a distributed system to perform batch matrix multiplication where matrices are not necessarily distinct among batch jobs. Most coded matrix-matrix computation work has broadly focused in two directions: matrix partitioning for computing a single computation task and batch processing of multiple distinct computation tasks. While these works provide codes with good straggler resilience and fast decoding for their problem spaces, these codes would not be able to take advantage of the natural redundancy of re-using matrices across batch jobs. Inspired by Cross-Subspace Alignment codes, we develop Flexible Cross-Subspace Alignments (FCSA) codes that are flexible enough to utilize this redundancy. We provide a full characterization of FCSA codes which allow for a wide variety of system complexities including good straggler resilience and fast decoding. We theoretically demonstrate that, under certain practical conditions, FCSA codes are within a factor of two of the optimal solution when it comes to straggler resilience; our simulations demonstrate that our codes achieve even better optimality gaps in practice.
Report on the 1st workshop on the perspectives on the evaluation of recommender systems (PERSPECTIVES 2021) at RecSys 2021

Evaluation is a central step when it comes to developing, optimizing, and deploying recommender systems. The PERSPECTIVES 2021 workshop at the 15th ACM Conference on Recommender Systems brought together academia and industry to critically reflect on the evaluation of recommender systems. The primary goal of the workshop was to capture the current state of evaluation from different, and maybe even diverging or contradictory perspectives. Date : 25 and 30 September 2021. Website : https://perspectives-ws.github.io/2021/.
Research on Multi-target Association Tracking Algorithm Based on IMM

Radar sensors are important detection and tracking equipment in national defense work. There are multiple targets in actual combat scenarios. It is necessary to quickly and accurately capture suspicious targets in the reconnaissance area and track them in real time. Complex electromagnetic interference conditions and multi-target tracking requirements pose a great challenge to track correlation. This paper establishes a multi-target maneuver scenario and uses a joint probabilistic data interconnection algorithm based on interactive multi-model (IMM) to perform multi-target tracking. Correlation tracking, simulation results show that the algorithm is used to correlate the target maneuver and measurement information with good accuracy. Interactive multi-model filtering can mix the estimation results of each filter to generate a fused state estimation result. The description of the model is more accurate, which improves the tracking accuracy and tracking stability of the algorithm.
The feedback stabilization of finite-state fuzzy cognitive maps

Fuzzy cognitive maps (FCMs) are a kind of cognitive model for knowledge representation and causal inference. Meanwhile, as fuzzy dynamical systems, FCMs have also been widely applied in the control-related fields, such as mobile robots, unmanned aerial vehicles (UAVs), and industrial controls. However, the existing works mainly focused on the practical applications but lacked the necessary theoretical discussions related to the FCM-based control mechanism. As is known, stabilization is one of the fundamental issues in the control fields. Till date, rigorous research on the stabilization of FCMs is still an issue to be studied. In this article, using state feedback control method, the global stabilizations of finite-state FCMs are investigated. First, utilizing the semi-tensor product (STP) of matrices, the algebraic expression of FCM can be derived. Some theorems ensure the sufficient condition for the existence of the state feedback controller of the global stabilization. Second, the constructive design processes of state feedback controllers are discussed in detail. Third, the global stabilization is further extended into partial stabilization, where only specific concepts of FCMs can be stabilized. The corresponding theoretical analysis is implemented. Finally, the effectiveness of the proposed methods is verified by several examples.
A Performance Improvement Model for Cloud Computing Using Simulated Annealing Algorithm

Cloud system has emerged as a fast computing technology wherein it delivers its services to users with minimum cost and time. The number of cloud users are also increasing too fast. With this increased number of users, there is a need of efficient algorithms which would be able to maximize the resource utilization, scheduling jobs in optimal manner leading to maximum profit and improved overall cloud performance. Research trends show that meta-heuristic optimization algorithms have been successfully applied to enhance the performance of cloud system. In this research, a simulated annealing based concept has been applied for job scheduling with the aim of minimizing the overall execution time of a job schedule selected from the job pool and balancing the loads in the available virtual machines. The algorithm has been simulated in CloudSim environment and it has been seen that it provides non-dominance optimal solution and is able to achieve reduced execution time of job schedule in comparison to other existing algorithms like FCFS, min-min algorithm and RR and Iterative Improvement.
Detecting and Learning the Unknown in Semantic Segmentation

Abstract Semantic segmentation is a crucial component for perception in automated driving. Deep neural networks (DNNs) are commonly used for this task, and they are usually trained on a closed set of object classes appearing in a closed operational domain. However, this is in contrast to the open world assumption in automated driving that DNNs are deployed to. Therefore, DNNs necessarily face data that they have never encountered previously, also known as anomalies , which are extremely safety-critical to properly cope with. In this chapter, we first give an overview about anomalies from an information-theoretic perspective. Next, we review research in detecting unknown objects in semantic segmentation. We present a method outperforming recent approaches by training for high entropy responses on anomalous objects, which is in line with our theoretical findings. Finally, we propose a method to assess the occurrence frequency of anomalies in order to select anomaly types to include into a model’s set of semantic categories. We demonstrate that those anomalies can then be learned in an unsupervised fashion which is particularly suitable in online applications.
AGFL: A Graph Convolutional Neural Network-Based Method for Fault Localization

Fault localization techniques have been developed for decades. Spectrum Based Fault Localization (SBFL) is a popular strategy in this research topic. However, SBFL is well known for low accuracy, mainly due to simply using a coverage matrix of program executions. In this paper, we propose a method based on graph neural network (AGFL), characterized by the adjacent matrix of the abstract syntax tree and the word vector of each program token. Referring to the Dstar, we calculate the suspiciousness of the statements and rank these statements. The experiment carried on Defects4J, a widely used benchmark, reveals that AGFL can locate 178 of the 262 studied bugs within Top-1, while state-of-the-art techniques at most locate 148 within Top-1. We also investigate the impacts of hyper-parameters (e.g., epoch and learning rate). The results show that AGFL has the best effect when the epoch is 100 and the learning rate is 0.0001. This value of epoch and learning rate increases by 66% compared to the worst on Top-1.
Design and Implementation of College English Teaching System Based on GA Optimized RBF Neural English

Modern information processing technology has been widely used in various fields, and neural network is a highly nonlinear, distributed, time-varying and other characteristics, and can meet the needs of various situations. This paper mainly studies the design and implementation of College English teaching system based on GA optimized RBF neural algorithm. Firstly, a simple model based on genetic algorithm is designed and implemented, and then the corresponding parameter values and expected output results are calculated according to the training sample data. After the optimal solution is obtained, the final results are fed back to the user to achieve the best effect. Finally, the results show that the performance of the system meets the requirements.
Lifelong reinforcement learning with temporal logic formulas and reward machines

Continuously learning new tasks using high-level ideas or knowledge is a key capability of humans. In this paper, we propose lifelong reinforcement learning with sequential linear temporal logic formulas and reward machines (LSRM), which enables an agent to leverage previously learned knowledge to accelerate the learning of logically specified tasks. For a more flexible specification of tasks, we first introduce sequential linear temporal logic (SLTL), which is a supplement to the existing linear temporal logic (LTL) formal language. We then utilize reward machines (RMs) to exploit structural reward functions for tasks encoded with high-level events, and propose an automatic extension of RMs and efficient knowledge transfer over tasks for continuous lifelong learning. Experimental results show that LSRM outperforms methods that learn the target tasks from scratch by taking advantage of the task decomposition using SLTL and the knowledge transfer over RMs during the lifelong learning process.