Reviewerly/exHarmony huggingface dataset:
Automatic Classification of Complaint Reports in Waste Management Systems Using TF-IDF, fastText, and BERT

The paper concerns the issue of automatic text classification of complaint letters written in Polish that were sent to the municipal waste management system operating in one of the largest Polish cities. The problem analyzed regards a multi-class classification task with information source separation. The authors compare five approaches, starting from TF-IDF, through word2vec methods, and to transformer-based BERT models. The article includes a detailed analysis of the experiments performed and the data set used. The analysis was performed according to the stratified k-fold cross-validation with 10 folds. The classification results were analyzed using three measures: precision, average F1 score, and weighted F1 score. The results obtained confirm that the BERT-based approach outperforms the other approaches. Indeed, the HerBert large model is recommended for use in similar downstream tasks in Polish.
Measuring the Bytecode Quality of Object-Oriented Software

Software metrics can be delineated as the practice of quantifying various characteristics of the software development process in order to obtain valuable and pertinent information for effectual management throughout the development process. This research work’s aspiration is to investigate quality metrics and develop a method which can be utilized to evaluate the quality object-oriented programs from its bytecode.
Reinforced Event-Driven Evolutionary Algorithm Based on Double Deep Q-network

The real-world optimization task has long been viewed as a noteworthy challenge owing to its enormous search space. To deal with this challenge, evolution algorithm, especially differential evolution algorithm, attracts our attention owing to the excellent robustness. However, for traditional evolution algorithm, how to determine suitable parameters and strategies is a troublesome problem. To deal with the question the reinforced event-driven evolutionary algorithm (REDEA) based on double deep q-network is proposed which embed the double deep q-learning network into differential evolution algorithm with an event-driven controller. To verify the feasibility and superiority of our proposed algorithm, CEC 2013 test suits are utilized and four state-of-arts evolutionary algorithms are involved as the comparisons. The experimental results present that the proposal algorithm obtains comparable capability in most functions.
Multi-Source Multi-Label Learning for User Profiling in Online Games

In online games, user profiling plays a vital role in a variety of personalized services. Current solutions typically treat different dimensions or labels (e.g., willing to pay or not, high, medium, or low appetite for some gameplays) of the full user profiles as independent multi-class/binary classification tasks. However, such one-by-one profiling strategy clearly overlooks the implicitly correlations among profiling tasks, which results in a degraded performance. To cope with this issue, we make the first attempt to formalize this problem as a multi-label learning task. Accordingly, we develop a unified Multi-Source Multi-Label learning framework~(MSML) that well utilizes semantically rich features and labels for boosted user profiling in online games. Specifically, we first introduce a multi-source user representation network that exploits multi-source data in online games to obtain informative user representations. Subsequently, to handle multiple labels, we propose a novel embedding-based multi-label network that consists of two variational autoencoders with disentangled latent spaces. Note that our framework can guarantee the consistency of the training and testing phases by a novel dual-tower design to overcome the limitation of existing approaches that use one coupled decoder for both features and labels. Extensive experiments on six public multi-label datasets and one real-world online game dataset from Justice demonstrate that the proposed framework outperforms the state-of-the-art baseline methods. Moreover, our proposed framework has been successfully deployed in several online games, yielding a significant boost in multi-label user profiling.
Anomaly Detection in Biological Early Warning Systems Using Unsupervised Machine Learning

The use of bivalve mollusks as bioindicators in automated monitoring systems of the aquatic environment allows real-time detection of an emergency situation associated with pollution of the aquatic environment. Data on the behavioral reactions of the bivalve mollusks Unio pictorum (Linnaeus, 1758) were used in the complex of automated monitoring of the aquatic environment developed by the authors. Four unsupervised machine learning methods for detecting an emergency signal in the activity of bivalves were used; elliptic envelope, Isolation forest (iForest), One-class support vector machine (SVM), and local outlier factor (LOF). The assessment was carried out on the basis of experimental materials obtained by an automated system in the channel of the Chernaya River (Crimean Peninsula). The results showed that the use of the elliptic envelope, iForest and LOF methods with a certain hyperparameter tuning makes it possible to detect the anomaly in the mollusk activity data without false alarms (F1 score = 1). Comparison of the anomaly detection time showed the advantage of the iForest method.
An Improved Path Planning Algorithm Based on Artificial Potential Field and Primal-Dual Neural Network for Surgical Robot

Safety and accuracy are essential for path planning in a surgical navigation system. In this paper, an improved path planning algorithm is proposed to increase the autonomous level of spine surgery robots for higher safety and accuracy. Firstly, dynamic gravitational constant and piecewise repulsion function are adopted to improve the traditional Artificial Potential Field algorithm to solve common issues of path planning, including local minimum, unable to reach target near obstacles. To better control the pose of the end-effector in operation space, the positions of the two endpoints of the end-effector are further constrained. Secondly, an improved Primal-Dual Neural Network with multiple constraints is proposed to minimize the joint angular velocity norm. The multiple constraints are formulated according to the planned path, the obstacle avoidance of the robot and the joint limits. Moreover, a real-time planned velocity scheme based on the position error of each step is applied to prevent the accumulation of position errors. The simulation results of pedicle screw implantation demonstrate that the robot can Ô¨Ånd the collision-free trajectory and arrive at the target position in various complicated situations. The position error is around [[EQUATION]] m. Finally, two experiments are conducted to verify the proposed algorithm is effective in practice.
A Self-adaptive Hybridized Algorithm with Intelligent Selection Scheme for Global Optimization

Hybrid optimization algorithms were proposed and widely implemented to solve various types of complex global optimization in the past decades. However, the random guess mechanism of the conventional initialization scheme adopted in existing hybridized approaches, tends to stochastically generate the initial population with questionable quality. In this article, a new hybrid optimization optimizer namely self-adaptive hybridized DE with PSO (SADE-PSO) is introduced to regulate the balancing of explorative and exploitative searches to deal with distinct global optimization more efficacious. The self-adaptive scheme is firstly designed into SADE-PSO, in order to propose better initial population. A hybridization framework is employed in SADE-PSO to amalgamate the superiorities of DE and PSO at the same time and facilitate the optimization manifestation with better quality. Extensive simulation results of the proposed SADE-PSO are performed among all five competitors using CEC 2014 test functions. The novel SADE-PSO is observed to have the best search performance due to its capability in tackling majority test functions with 22 best Fmean values.
Adaptive Fuzzy Sliding Mode Controller for Ball and Plate System Optimizing by Advanced Jaya Algorithm

This paper proposes an adaptive fuzzy controller optimized with Jaya optimization algorithm appropriate for a class of robust control applied in control uncertain nonlinear plants including ball and plate system. First, an adaptive fuzzy-based sliding surface is innovatively designed to ensure that the closed-loop system is asymptotically stable based on Lyapunov stability concept. Second, the coefficients of fuzzy structure will be estimated via Jaya optimization technique. The proposed algorithm is applied to regulate the ball position in the ball and plate plant. The comparative results with optimal PID and standard fuzzy control approaches are fully shown as to demonstrate that the proposed controller provides an efficient and robust technique in order to precisely control the nonlinear and uncertain plants.
Intelligent Waveform Optimization for Target Tracking in Radar Sensor Networks

In radar sensor network (RSN), the waveform optimization of radar members is significant to the performance of target tracking. In this paper, we propose an intelligent waveform optimization (IWO) algorithm for RSN, which optimizes the waveforms of all radar members jointly through a global state error covariance matrix. The effectiveness of the proposed IWO algorithm is demonstrated by comparing with the distributed waveform optimization (DWO) algorithm that the radar members optimize waveforms independently. Experiment results illustrate that our algorithm achieves better tracking performance than that of DWO in RSN, in terms of tracking root-mean-square error and entropic-state.
Slicing Storage and Stitching Algorithms for Big Image

In order to solve the problem of storing and reading on big data of map, an image slicing and stitching algorithm is proposed in this paper. This algorithm is divided into five parts: slicing algorithm, calculation of the queue length of the service system, mapping transformation, map stitching, evaluation method. The semantic association of image slicing is proposed, and the slicing algorithm is given. By judging the maximum storage capacity of the remaining space of the server unit, the matching operation is performed with the threshold value. The calculation of the service queue length of map slices is given to realize the fast storage of map slices in the data server. The topological mapping transformation is established, i.e., a projection transformation method is given, so as to complete a mapping transformation from the map sliced by the original map to the map to be viewed. The map is stitched by the location recorded when storing pictures, image slicing features and mapping transformation relations. The evaluation method for the stitched image is presented to minimize the difference between stitched data and original data. Simulation results show that the accuracy rate of the proposed stitching algorithm on image is as high as 92.26%. At the same time, to compare with the existing image stitching algorithms, it not only has higher stitching accuracy, faster stitching process, stronger anti-interference ability, but also has lower information loss. These studies in this paper can solve some current urgent problems, such as, time-consuming and space-consuming resources, network or communication stagnation, not real-time, system congestion, crash and other problems.
Codesign for Generation of Large Random Sequences on Zynq FPGA

This work presents two codesign implementations of a true random number generation mechanism. Physical components provided by the programmable logic of FPGA are used for true random seed generation. The seed conditioning and generation of the large sequences were implemented using the block cipher Advanced Encryption Standard (AES) implemented on the Cortex-A9 processor (embedded in the Zynq FPGA) or specific AESNI instructions in modern processors. Our implementations use less than 10% of the available resources on the target FPGAs and pass all the National Institute of Standards and Technology (NIST) tests for random generators.
Geometric Optical Transfer Function and Pupil Sampling Patterns

We have studied several pupil sampling patterns for calculating the optical transfer function corresponding to primary aberrations, and to actual non--diffraction limited optical imaging systems, in the low range of spatial frequencies, i.e., where the geometric approach is valid, by means of the Fourier Transform of the spot diagram. Then, we have compared the diffraction and geometric optical transfer functions to ascertain if there is a sampling pattern that accurately calculates the latter within a 5% of difference, and with the less number of sampling points. The results show that, overall, the non redundant spiral pupil sampling provides the best choice. This could be valuable when optimizing or characterizing optical imaging systems in the non--diffraction limited range, by using only raytracing methods.
Deepwater Oil-Based Drilling Fluid Systembased on Grey Wolf Optimized BP neural network

In this paper, a BP neural network model with 5-9-6 structure is constructed according to various fault gases and different fault types ofWater-based drilling fluid and synthetic-based drilling fluid. While there are few studies relating to oil-based drilling fluid, which is attributed to the potential toxicity of conventional oil-based drilling fluid to the marine environment. However, oil-based drilling fluid is excellent in protecting the borehole stability. To this end, an environmentally friendly deepwater oil-based drilling fluid was developed based on the traditional oil-based drilling fluid. With the advantages such as good borehole stability and good wettability, this drilling fluid will not pollute the marine environment, is resistant to high temperature, and protects hydrocarbon reservoir, which is of great significance for rational exploration and development of deep-sea resources.
Quantum Remote State Preparation Based on Quantum Network Coding

As an innovative theory and technology, quantum network coding has become the research hotspot in quantum network communications. In this paper, a quantum remote state preparation scheme based on quantum network coding is proposed. Comparing with the general quantum remote state preparation schemes, our proposed scheme brings an arbitrary unknown quantum state finally prepared remotely through the quantum network, by designing the appropriate encoding and decoding steps for quantum network coding. What is worth mentioning, from the network model, this scheme is built on the quantum k-pair network which is the expansion of the typical bottleneck network—butterfly network. Accordingly, it can be treated as an efficient quantum network preparation scheme due to the characteristics of network coding, and it also makes the proposed scheme more applicable to the large-scale quantum networks. In addition, the fact of an arbitrary unknown quantum state remotely prepared means that the senders do not need to know the desired quantum state. Thus, the security of the proposed scheme is higher. Moreover, this scheme can always achieve the success probability of 1 and 1-max flow of value k. Thus, the communication efficiency of the proposed scheme is higher. Therefore, the proposed scheme turns out to be practicable, secure and efficient, which helps to effectively enrich the theory of quantum remote state preparation.
Research on heterogeneous acceleration platform based on FPGA

In the context of today’s artificial intelligence, the volume of data is exploding. Although scaling distributed clusters horizontally to cope with the increasing demands on computing power for massive data processing is feasible. But the unlimited addition of nodes will lead to bloated cluster size. Most of the transistors in CPUs are used to build cache memory and control units, which are not efficient for computing operations of massive data processing. Currently, academia uses hardware devices such as GPU (Graphics Processing Unit), ASIC (Application Specific Integrated Circuit), FPGA (Field Programmable Gate Array) to accelerate deep learning, image processing, which require massive computational operations. The paper first discussed the advantages and technical requirements of FPGA acceleration based on the characteristics of the Spark cluster. Then the paper proposed the design of the FPGA-CPU heterogeneous acceleration platform, and introduced the base-two-FFT algorithm. Finally, the paper present and compared the computation time of the base-two-FFT algorithm before and after the acceleration. The results show that the heterogeneous cluster has a speedup ratio of about 1.79 times compared to the CPU cluster.
Convolution Neural-Network-Based Multimodal Disease Risk Prediction Algorithm Using Unstructured and Structured Data

Nowadays, lot of research had taken place in the field of biomedical and healthcare industry. It is observed from these researches; still healthcare industry lack in analyzing the data more accurately. This motivates me to do research in this specific field, accurate analysis of data (proposed) especially from health care will detect the disease in early stage. It is also observed that accuracy decreased while the quality of healthcare data is faulty. Moreover, different areas exhibit unique features of specific regional ailments, which might weaken the prediction of disease outbreaks. In this paper, we streamline machine-learning algorithms for the successful prediction of a chronic-disease outbreak in disease-frequent communities. Our experimentation based on the modified prediction models over real-life hospital information gathered from Prime India dataset. To overcome the problem of incomplete data, we use a latent factor model to rebuild the lost data. We also considered regional chronic disease of cerebral infarction. We suggest a new convolution neural-network-based multimodal disease risk prediction algorithm using unstructured and structured data from hospital. To the best of our knowledge, none of the presented work concentrated on both information types in field of medical data analytics that is too big. In comparison with several typical prediction calculations, the forecast accuracy of our proposed algorithm reaches 94.8 percent with a convergence speed that is faster than that of the CNN-based unimodal disease risk prediction algorithm.
Graph Explicit Attention Network Based on Predefined Strategy

Graph attention network (GAT) has achieved great success in graph representation learning in recent years. However, the lower Physical properties of GAT in training process severely affects the application of attention mechanism in graph domain. Particularly, the attention coefficients and the multi-head mechanism are calculated or introduced in the process of model training, which obviously increases the storage complexity. In this paper, we present a graph explicit attention network (GEAT), a novel graph attention architecture that leverage predefined strategy to calculate attention coefficients, which combine global structural and node feature information. In this way, GEAT can effectively reduce the storage complexity, improve the training efficiency and alleviate the over-smoothing problem of attention scores. Experiments on benchmark datasets — Core, Citeseer and Pubmed — demonstrate that our model outperforms the state-of-the-art methods with a clear margin in challenging classifcation tasks, while being computationally efficient.
Censorship on social media: The gatekeeping functions of shadowbans in the American Twitterverse

Algorithms play a critical role in steering online attention on social media. Many have alleged that algorithms can perpetuate bias. This study audited shadowbanning, where a user or their content is temporarily hidden on Twitter. We repeatedly tested whether a stratified random sample of American Twitter accounts (n = 25,000) had been subject to various forms of shadowbans. We then identify the type of user and tweet characteristics that predict a shadowban. In general, shadowbans are rare. We find that accounts with bot-like behavior were more likely to face shadowbans, while verified accounts were less likely to be shadowbanned. The replies by Twitter accounts that posted offensive tweets and tweets about politics (from both the left and the right) were more likely to be downtiered. The findings have implications for algorithmic accountability and the design of future audit studies of social media platforms.
Investigation of Employer Attractiveness from an University Students Perspective by Application of Intuitionistic Fuzzy Assessments

The employer brand has a range of dimensions which can be specified as a product of a variety of influencing factors. The combination of those factors are closely related to the level of ‘employer attractiveness’, defined as “the envisioned benefits that a potential employee sees in working for a specific organization”. The paper identifies the significant factors which attract students from national university in south-east Bulgaria “Prof. Dr. Asen Zlatarov” to potential employers, both on a general basis and for specific student segments. Data was gathered from the students through a self-completion electronic questionnaire which includes a total of 35 items in four directions - social value, development value, application values and economic value. A generalized net model to analyze employer attractiveness from perspective of job seekers represented by university students. Intuitionistic fuzzy assessments for evaluation the attractiveness µ, unattractiveness ν of an employer and the degree of uncertainty π were proposed.
High-Precision 14c Measurements of Parenchyma-Rich Hymenolobium Petraeum Tree Species Confirm Bomb-Peak Atmospheric Levels and Reveal Local Fossil-Fuel Co2 Emissions in the Central Amazon

Atmospheric radiocarbon (14C) recorded in tree rings has been widely used for atmospheric 14C calibration purposes and climate studies. But atmospheric 14C records have been highly limited along tropical latitudes. Here we report a sequence from 1937 to 2007 of precisely measured 14C dates in tree rings of the parenchyma-rich Hymenolobium petraeum tree species (Porto Trombetas, 1°S, 56°W) from the Central Brazilian Amazon. H. petraeum has discernible growth-ring boundaries that allow dating techniques to be employed to produce calendrical dates. Bomb-peak tree-ring 14C reconstruction coincides with the broader changes associated with reported values of the Southern Hemisphere atmospheric 14C curve (SH zone 3; values within the ±2σ interval), suggesting that inter-hemispheric air-mass transport of excess-14C injected into the stratosphere during intensive atmospheric nuclear tests is relatively uniform across distinct longitudinal regions. From the early 1970s onwards, H. petraeum had lower 14C values than other pantropical 14C records. Through 14C-based estimation, we found a strong influence of local fossil-fuel CO2 contributions from Porto Trombetas mining operations and shipping traffic on inland waterways. An increase of at least 6.3 ± 0.8 ppm based on fossil-fuel CO2 concentrations has been detected by 14C. Our findings invite further 14C analyses using tree rings of tropical tree species as a potential tracer for a wide range of environmental sources of atmospheric 14C-variability.