Reviewerly/exHarmony huggingface dataset:
Review of New Data Center Network Structure

Nowadays, people ’s lives are increasingly inseparable from big data. The development of big data has a profound impact on human society. At the same time, the generation of massive data has promoted the transformation of data processing methods. In this development trend, cloud computing came into being. Cloud computing is to provide online cloud services to network users by building a large shared platform, so as to achieve on demand allocation of resources. Data centers are the core infrastructure of cloud computing, Therefore, the performance of the data center network determines the quality of service provided by cloud computing. However, traditional data center networks have long been unable to meet the needs of today ’s society for large data centers. This paper summarizes the new data center network structure pro-posed in recent years from the aspects of switch-centric, server-centric, wireless data center, wired and wireless combination, introduces its performance, fault tolerant routing efficiency and other advantages, and looks forward to the future development of data center network structure.
A Deep Learning-Based Real-Time Video Object Contextualizing and Archiving System

In recent years, we can see the presence of CCTV cameras everywhere in developed cities and urban areas. CCTV cameras are widely equipped in public places, buildings, and entertainment areas. Along with that popularity is the issue of effective management and exploration of CCTV cameras to ensure security and safety for people. With a huge data recorded from cameras, we need systems that support monitoring and analysis of camera data as well as instant video data retrieval. In this paper, we introduce a system to contextualize the video content recorded from CCTV cameras in real-time and store data efficiently. The big data generated from the system will make it easier to monitor and operate the camera system to access and search for objects and content in the surveillance cameras.
Intent Classification of Users Conversation using BERT for Conversational Dialogue System

Intent Analysis and detection are currently getting a lot of importance for their significance in both the Industry and Academia. The intent classification relies heavily on the rapidly expanding unstructured data of microblogging networks like Twitter and Facebook. However, the work is extremely difficult because of the social media data’s frequent noise and diversity. Furthermore, because labelled datasets are usually manually annotated, intent analysis frequently suffers from a lack of data. Modern language representation model BERT (Bidirectional Encoder Representation from Transformers), which accurately models’ language, has recently gained interest. In this paper, we developed an Intent Classification Model using BERT for the classification of Questions received from the Users or Humans to specific intents regarding the usage of specific features and components of the car. The dataset for the Classification was developed from the Owner’s Manual of the vehicles. The Classification Model was developed using BERT and produced promising results for the classification of Real Work Text into 199 different categories or Intents. The Model which was trained with 254,412 records plotted an overall categorical accuracy of 98.21%
Linear Codes from Projective Varieties: A Survey

Linear codes can be constructed from classical algebraic varieties or from appropriate subsets of finite geometry by considering projective systems arising from their rational points. This geometric point of view allows to look for linear codes by choosing suitable sets to get immediately length, minimum distance, and spectrum (cf. Lemma 1, Propositions 5, 9, 12, 13, 17). In some cases, it is also possible to build a PD-set or an antiblocking decoding (cf. Propositions 3, 4, 14, Examples of Section 5).
Towards Convergence in Federated Learning via Non-IID Analysis in a Distributed Solar Energy Grid

Federated Learning (FL) is an effective framework for a distributed system that constructs a powerful global deep learning model, which diminishes the local bias and accommodates the successful aggregation of locally trained models with heterogeneous datasets. However, when local datasets have the non-IID attribute, the optimization metric tends to diverge or show unstable convergence in the trajectory space. This paper delves into building a global model for the distributed Smart Grid environment, with regionally cumulated three solar energy datasets from January 2017 to August 2021 in a decentralized power grid in South Korea via FL. This distributed energy network involves local properties and physical distance between the regions, which raises a fundamental question of “Will time-serially curated non-IID local features be effective in constructing a global regression model?”. This paper probes this question by leveraging FL and conducts the theoretically viable non-IID case-by-case convergence analysis, providing the interpretation of the embedded temporal non-IID features and application on real-world data. Moreover, most of the FL studies predetermine the global update period, which lacks applicability when adapting FL in actual practice. As FL is a cumulative-basis structure, the update term is a crucial factor that needs to be carefully selected. This paper articulates this problem and explores the effective update period via multiple experiments on the 4.5 years of solar energy dataset, and to the best of my knowledge, this is the first literature that presents the optimal update period in the FL regression in an energy domain.
SF-YOLOv5: Improved YOLOv5 with swin transformer and fusion-concat method for multi-UAV detection

When dealing with complex trajectories, and the interference by the unmanned aerial vehicle (UAV) itself or other flying objects, the traditional detecting methods based on YOLOv5 network mainly focus on one UAV and difficult to detect the multi-UAV effectively. In order to improve the detection method, a novel algorithm combined with swin transformer blocks and a fusion-concat method based on YOLOv5 network, so called SF-YOLOv5, is proposed. Furthermore, by using the distance intersection over union and non-maximum suppression (DIoU-NMS) as post-processing method, the proposed network can remove redundant detection boxes and improve the efficiency of the multi-UAV detection. Experimental results verify the feasibility and effectiveness of the proposed network, and show that the mAP trained on the two datasets used in experiments has been improved by 2.5 and 4.11% respectively. The proposed network can detect multi-UAV while ensuring accuracy and speed, and can be effectively used in the field of UAV monitoring or other types of multi-object detection applications.
A Hybrid CNN-LSTM Random Forest Model for Dysgraphia Classification from Hand-Written Characters with Uniform/Normal Distribution

Parkinson’s disease (PD) Dysgraphia is a disorder that affects most PD patients and is characterized by handwriting anomalies caused mostly by motor dysfunctions. Several effective ways to quantify PD dysgraphia analysis have been used, including online handwriting processing. In this research, an integrated approach, using a combination of a convolutional neural network (CNN) and long short-term memory (LSTM) layers along with a Random Forest (RF) classifier, is proposed for dysgraphia classification. The proposed approach uses uniform and normal distributions to randomly initialize the weights and biases of the CNN and LSTM layers. The CNN-LSTM model predictions are paired with the RF classifier to enhance the model’s accuracy and endurance. The suggested method shows promise in identifying handwriting symbols for those with dysgraphia, with the CNN-LSTM model’s accuracy being improved by the RF classifier. The suggested strategy may assist people with dysgraphia in writing duties and enhance their general writing skills. The experimental results indicate that the suggested approach achieves higher accuracy.
Geochemical assesment of heavy metals in postmining laterite nickel, North Konawe Southeast Sulawesi

Abstract This study aims to determine the concentration and migration of various heavy metals in soil texture. A total of 21 soil samples from Block A (12 samples) and Block B (9 samples), were inductively combined with ICP-OES methodology to determine the concentration levels of Mn,Cr,Ni,Fe,Zn and ICP-MS for Co,Pb,Cd in soil samples as toxic contaminants. The results showed I- geo indicated contamination levels that ranged from practically uncontaminated for Cd, uncontaminated to moderately for Pb, uncontaminated to moderately contaminated soil for Fe, moderately contaminated for Co,Zn,moderately to heavily contaminated For Mn,Ni , heavily contaminated for Cr. I-geo relationship and heavy metal concentration. Although Block A and Block Igeo have the same category, which is of concern are Cd and Pb, the concentrations of these two heavy metals are very small. Generally, Cd and Pb react chemically under acidic conditions, whereas lateritic soil is an alkaline oxide soil. Heavy metals Cr and Mn function freely to control the mobility of Fe, Co, Ni. And Fe only controls Co and Ni. While Pb, Zn Cd are included in the low mobility group or zero contamination. Multidimensional scaling statistics (MDS) for two-dimensional mapping in blocks A Mn, Co, Ni, Cr shows active mobility following the soil depth where the chemical function of Fe controls the mobility of Mn so that the concentration of Mn increases, three-dimensional mapping shows that Cr and Fe function to control the mobility of Co, Mn,Ni so that the concentration increases. Two-dimensional mapping in Block B, Cr and Mn functions freely to control the mobility of Fe, Co, Ni. And Fe only controls Co and Ni. While Pb, Zn Cd are included in the low mobility group or zero contamination, three-dimensional mapping is known to have active concentrations of Cr and Mn and control the mobility of Ni, Co, Fe.
Simulation-Based Considerations on the Rayleigh Criterion in Super-Resolution Techniques Based on Speckle Interferometry

This study sought to explain the physical phenomenon that eludes the constraints of the Rayleigh criterion in the microstructure observation method using speckle interferometry, for which super-resolution has been experimentally proven; the study was conducted using computer simulations. Separating the light from two light sources in close proximity, which exceeded the Rayleigh criterion under incoherent light, was believed to be impossible. The simulation results, however, showed that when coherent light is used, the separation of two close points is not necessarily impossible if the light phases between the two points are different. Furthermore, the resolution of microstructure observation techniques based on speckle interferometry was discussed. A new interpretation of the Rayleigh criterion in super-resolution techniques based on speckle interferometry was reported.
Path planning algorithm in complex environment based on DDPG and MPC

The problem of mixed static and dynamic obstacle avoidance is essential for path planning in highly dynamic environments. However, the paths formed by grid edges can be longer than the actual shortest paths in the terrain since their headings are artificially constrained. Existing methods can hardly deal with dynamic obstacles. To address this problem, we propose a new algorithm combining Model Predictive Control (MPC) with Deep Deterministic Policy Gradient (DDPG). Firstly, we apply the MPC algorithm to predict the trajectory of dynamic obstacles. Secondly, the DDPG with continuous action space is designed to provide learning and autonomous decision-making capability for robots. Finally, we introduce the idea of the Artificial Potential Field to set the reward function to improve convergence speed and accuracy. In this paper, Matplotlib is used for simulation experiments. The results show that our method has improved considerably in accuracy by 8.11% -43.20% compared with theother methods, and on the length of the path and turning angle by reducing about 50 units and 340 degrees compared with DeepQ Network (DQN), respectively. We also employ Unity 3D to perform simulation experiments in highly uncertain environments, such as squares.
Dual-LightGCN: Dual light graph convolutional network for discriminative recommendation

In recent years, graph neural networks have played a very important role in graph data analysis, and the application of graph convolutional networks (GCN) to recommender systems has been extensively investigated by recent studies. GCNs also recently demonstrated their potential to be analyzed from the point of view of Explainable Artificial Intelligence because of their underlying structure. However, most of the existing GCN-based methods are aggregated of information in one scale space and did not consider aggregation of information in multi-scale space. On this basis, this paper proposes an innovative dual light graph convolutional network model called Dual-LightGCN, which explicitly filters out items disliked by users to ensure more discriminative recommendation. Particularly, our model divides the original user–item interaction graph into two bipartite subgraphs, one subgraph is used to model the preferences between users and items, while the other is used to model the dislike relationships between them. For these two subgraphs, the LightGCN model recommendation is performed on them respectively. In the Movielens-1M dataset, the F1-score in Dual-LightGCN has increased by an average of 26%. We conducted a comprehensive evaluation of the proposed method on two datasets of different sizes and compared it with several state-of-the-art recommendation algorithms, and the results showed that the accuracy and F1-score results were significantly higher than those of other recommendation algorithms. The significantly low computational time required makes the proposed method suitable for successful deployment in various IoT scenarios.
All-in-one aerial image enhancement network for forest scenes

Drone monitoring plays an irreplaceable and significant role in forest firefighting due to its characteristics of wide-range observation and real-time messaging. However, aerial images are often susceptible to different degradation problems before performing high-level visual tasks including but not limited to smoke detection, fire classification, and regional localization. Recently, the majority of image enhancement methods are centered around particular types of degradation, necessitating the memory unit to accommodate different models for distinct scenarios in practical applications. Furthermore, such a paradigm requires wasted computational and storage resources to determine the type of degradation, making it difficult to meet the real-time and lightweight requirements of real-world scenarios. In this paper, we propose an All-in-one Image Enhancement Network (AIENet) that can restore various degraded images in one network. Specifically, we design a new multi-scale receptive field image enhancement block, which can better reconstruct high-resolution details of target regions of different sizes. In particular, this plug-and-play module enables it to be embedded in any learning-based model. And it has better flexibility and generalization in practical applications. This paper takes three challenging image enhancement tasks encountered in drone monitoring as examples, whereby we conduct task-specific and all-in-one image enhancement experiments on a synthetic forest dataset. The results show that the proposed AIENet outperforms the state-of-the-art image enhancement algorithms quantitatively and qualitatively. Furthermore, extra experiments on high-level vision detection also show the promising performance of our method compared with some recent baselines.
Mineralogical and geochemical characterization of the Wonosari formation limestone at Gunungkidul Indonesia as preliminary investigation of Portland cement raw material

Abstract Mineralogical and geochemical investigations were carried out to determine the characteristics of limestone in the Wonosari Formation and to suggest its depositional environment and a preliminary study for the properties of Portland Cement. The study was conducted at Ponjong Area, Gunungkidul Regency, Daerah Istimewa Yogyakarta, Indonesia. The outcrop in this study area is composed of Wonosari layered limestone. Based on the facies interpretation in the research area, there are three depositional units: packstone, wackstone, and grainstone. This area’s depositional environment is the back reef lagoon and sand apron zones that are not related to the open sea. According to XRF data, limestone showed a reasonably high composition of CaO compounds, about 53.78% - 59.09%. Most limestone samples were classified as good quality as Portland Cement Raw Material due to CaO content &gt;49%. The MgO, SiO 2 , Al 2 O 3 , Fe 2 O 3 , SO3, K 2 O, Na 2 O, TiO 2 , and P 2 O 5 compounds also fulfil the Indonesian National Standard (SNI-15-2049-2004). Geochemical content shows limestone has a higher grain percentage than the matrix. The utilization of limestone in research area are expected to be a reference for the development of natural resources in the future that pay attention to the environment for mutual sustainability.
View Synthesis with Scene Recognition for Cross-View Image Localization

Image-based localization has been widely used for autonomous vehicles, robotics, augmented reality, etc., and this is carried out by matching a query image taken from a cell phone or vehicle dashcam to a large scale of geo-tagged reference images, such as satellite/aerial images or Google Street Views. However, the problem remains challenging due to the inconsistency between the query images and the large-scale reference datasets regarding various light and weather conditions. To tackle this issue, this work proposes a novel view synthesis framework equipped with deep generative models, which can merge the unique features from the outdated reference dataset with features from the images containing seasonal changes. Our design features a unique scheme to ensure that the synthesized images contain the important features from both reference and patch images, covering seasonable features and minimizing the gap for the image-based localization tasks. The performance evaluation shows that the proposed framework can synthesize the views in various weather and lighting conditions.
Dataset construction method of cross-lingual summarization based on filtering and text augmentation

Existing cross-lingual summarization (CLS) datasets consist of inconsistent sample quality and low scale. To address these problems, we propose a method that jointly supervises quality and scale to build CLS datasets. In terms of quality supervision, the method adopts a multi-strategy filtering algorithm to remove low-quality samples of monolingual summarization (MS) from the perspectives of character and semantics, thereby improving the quality of the MS dataset. In terms of scale supervision, the method adopts a text augmentation algorithm based on the pretrained model to increase the size of CLS datasets with quality assurance. This method was used to build an English-Chinese CLS dataset and evaluate it with a reasonable data quality evaluation framework. The evaluation results show that the dataset is of good quality and large size. These outcomes show that the proposed method may comprehensively improve quality and scale, thereby resulting in a high-quality and large-scale CLS dataset at a lower cost.
Some properties of substitutions in the framework of similarity relations

A substitution is a mapping from variables to terms. There is a consensus in the Logic Programming community that the work with substitutions is a source of errors due to their poor algebraic properties. This problem increases when we manipulate substitutions in the framework of similarity relations, where some classical concepts have more complex definitions and the intuition of the result of the operations involving them is easily lost. In this work we analyze some algebraic properties of substitutions in the framework of similarity relations. Specifically, we study the lattice structure of the set of idempotent substitutions with the definition of the weak parallel composition and weak parallel factorization operations, acting as the join and meet of the lattice. Also we relate these operations with the classical operation of composition of substitutions. The aim is to provide fuzzy logic programming researchers with reasoning tools with richer properties that facilitate formal proofs and, even, able to define compositional and parallel semantics.
Sharing Augmented Reality between a Patient and a Clinician for Assessment and Rehabilitation in Daily Living Activities

In rehabilitation settings that exploit Mixed Reality, a clinician risks losing empathy with the patient by being immersed in different worlds, either real and/or virtual. While the patient perceives the rehabilitation stimuli in a mixed real–virtual world, the physician is only immersed in the real part. While in rehabilitation, this may cause the impossibility for the clinician to intervene, in skill assessment, this may cause difficulty in evaluation. To overcome the above limitation, we propose an innovative Augmented Reality (AR) framework for rehabilitation and skill assessment in clinical settings. Data acquired by a distributed sensor network are used to feed a “shared AR” environment so that both therapists and end-users can effectively operate/perceive it, taking into account the specific interface requirements for each user category: (1) for patients, simplicity, immersiveness, engagement and focus on the task; (2) for clinicians/therapists, contextualization and natural interaction with the whole set of data that is linked with the users’ performances in real-time. This framework has a strong potential in Occupational Therapy (OT) but also in physical, psychological, and neurological rehabilitation. Hybrid real and virtual environments may be quickly developed and personalized to match end users’ abilities and emotional and physiological states and evaluate nearly all relevant performances, thus augmenting the clinical eye of the therapist and the clinician-patient empathy. In this paper, we describe a practical exploitation of the proposed framework in OT: setting-up the table for eating. Both a therapist and a user wear Microsoft HoloLens 2. First, the therapist sets up the table with virtual furniture. Next, the user places the corresponding real objects (also in shape) to match them as closely as possible to the corresponding virtual ones. The therapist’s view is augmented during the test with motion, balance, and physiological estimated cues. Once the training is completed, he automatically perceives deviations in the position and attitude of each object and the elapsed time. We used a camera-based localization algorithm achieving a level of accuracy of 5 mm with a confidence level of 95% for position and 1° for rotation. The framework was designed and tested in collaboration with clinical experts of Villa Rosa rehabilitation hospital in Pergine (Italy), involving both a set of patients and healthy users to demonstrate the effectiveness of the designed architecture and the significance of the analyzed parameters between healthy users and patients.