Reviewerly/exHarmony huggingface dataset:
Identification of Floods in Palembang Area Using Fuzzy Logic Method of Mamdani and Sugeno

Floods are one of the natural events that often occur, especially in areas that have a large population. Palembang is one of the cities in Sumatra Province which often experiences flooding due to its dense population and lack of infiltration areas. One way to improve flood preparedness is to identify floods by using fuzzy logic. This study aims to identify floods in the Palembang area using fuzzy logic mamdani and Sugeno methods. Flood identification is carried out in 6 stages, namely variable determination, fuzzification, inference, application of fuzzy rules, defuzzification, and evaluation. The variables used in this study are rainfall, temperature, area elevation, and the results of flood identification that have a value of flood or not flood. The results of flood identification using the Mamdani method achieved an accuracy of 82% while the Sugeno method achieved an accuracy of 91%. The results of this study indicate that the mamdani and Sugeno fuzzy logic methods are quite good in identifying floods so that they can improve flood preparedness attitudes.
FET-LM: Flow-Enhanced Variational Autoencoder for Topic-Guided Language Modeling

Variational autoencoder (VAE) is widely used in tasks of unsupervised text generation due to its potential of deriving meaningful latent spaces, which, however, often assumes that the distribution of texts follows a common yet poor-expressed isotropic Gaussian. In real-life scenarios, sentences with different semantics may not follow simple isotropic Gaussian. Instead, they are very likely to follow a more intricate and diverse distribution due to the inconformity of different topics in texts. Considering this, we propose a flow-enhanced VAE for topic-guided language modeling (FET-LM). The proposed FET-LM models topic and sequence latent separately, and it adopts a normalized flow composed of householder transformations for sequence posterior modeling, which can better approximate complex text distributions. FET-LM further leverages a neural latent topic component by considering learned sequence knowledge, which not only eases the burden of learning topic without supervision but also guides the sequence component to coalesce topic information during training. To make the generated texts more correlative to topics, we additionally assign the topic encoder to play the role of a discriminator. Encouraging results on abundant automatic metrics and three generation tasks demonstrate that the FET-LM not only learns interpretable sequence and topic representations but also is fully capable of generating high-quality paragraphs that are semantically consistent.
A complexity scoring model for evaluating complexity of software projects

This research identifies project factors that contribute to project complexity based on the analysis of ten project management subject areas, and of one subject area which is focusing on software development process factors. Subsequently, a project complexity model based on these factors that can be used for assessing the project complexity is presented. This enables organisations to evaluate projects' complexity at early phases, providing important knowledge that may be used for project selection. The followed approach acknowledges the endogenous character of complexity in projects but instead of trying to identify complexity characteristics in project results, it focuses on the complexity of project management processes. The proposed framework can be used for highlighting the most significant complexity areas acting as an important tool for better, more efficient, and more effective project management. The final step of this research is the validation of the proposed model using case studies.
New architectural optical character recognition approach for cursive fonts: the historical Maghrebian font as an example

The historical Maghrebian font is an Arabic font that dominated in several North African lands. Various cultural and scientific papers of major importance were developed using this font. In this paper, the full OCR architecture that is able to treat the specificity of the historical Maghrebian font is revealed. Further, a complete design with the accuracy of each module is provided. The novel OCR architecture includes a binarisation module based on deep neural networks with an accuracy of 98.1%. Moreover, it involves three segmentation tasks based on deep learning approaches for text/non-text separation, columns division and connected components segmentations. The classification task is based on the DenseNet model with an accuracy of 98.95%. The post-processing module is also based on deep learning approaches based on sequential modelling with an accuracy of 81.3%. It also includes a user-feedback stage with an accuracy of 94.7%. The total system accuracy is 89.06%.
APPROACH OF PAKISTANI COURTS REGARDING ADMISSIBILITY OF MODERN DEVICES OR TECHNIQUES IN EVIDENCE

Electronic evidence's notion and meaning are correlated in this article, which covers the whole rank and file. Until this point, the fundamentals of the Evidence Act have been broken down and clarified by many changes in electronic evidence. When discussing whether or not electronic evidence should be admitted into court, several rulings from Pakistan's highest court have been referenced as precedents. In conclusion, we will discuss the precautions and procedures that the Pakistani judicial system has implemented in order to promulgate electronic evidence. In every one of the world's legal systems, post-modern devices and techniques in evidence are recognized and accepted and it is now requirement of modernistic evidence philosophy. The courts attach much weight to such kind of evidence. This paper analyzes the approach of Pakistani courts regarding admissibility of modern devices or techniques in evidence.
LSTS-VPR: Robust Visual Place Recognition in Challenging Environments Using Landmarks Associated with Spatiotemporal and Semantic Information

Due to the robustness of ConvNet landmarks to appearance changes and viewpoint changes, there are some excellent works in ConvNet landmarks based visual place recognition (VPR). However, when dealing with extremely complex environmental changes, the performance of ConvNet landmarks based VPR is also difficult to meet the needs of practical applications because of the lack of information. This paper proposes a robust visual place recognition in challenging environments using landmarks associated with spatiotemporal and semantic Information (LSTS-VPR). LSTS-VPR combines ConvNet landmarks and image sequence to take advantage of the rich information of the objects contained in the sequence at different views and different time periods to filter and obtain more discriminative landmarks such as buildings and trees. LSTS-VPR consists of two modules, namely landmarks associated with spatiotemporal and semantic information-based feature aggregation module (LSTS-FAM) and multi-stage filtering-based feature matching module (MF-FMM). In LSTS-FAM, we use tube linking algorithm to connect the similar landmarks in different frames of one sequence and then calculate the landmark weights to screen the landmarks with high repeatability, which are proportional to the length of the tube. In MF-FMM, we use the semantic similarity, center distance and geometric proportion of landmarks as constraints to obtain the best matching sequence more accurately. Finally, the results of experiments on eight datasets demonstrate that the method proposed in this paper can better cope with a variety of changes including appearance changes, viewpoint changes, and occlusion of dynamic targets than state-of -art VPR approaches.
Standardization Building Block

This chapter, produced by the Standardization Building Blocks (SBB) Roadmap Working Group, describes a wide range of global standards, consortia, and alliance activities enabling and defining future networks use cases, architectures, technical interface specifications, compliance, and test requirements, and regulatory environment over a ten-year time horizon. The primary objective of the SBB Roadmap is to illustrate the “master timeline” for the standardization of wireless communications technologies. With the advent of every new generation of wireless networks, the capabilities of technologies expand, and economic conditions change resulting in an increasingly broader standardization scope. Accordingly, the scope of the SBB includes: •depicting the value chain of the global system integrator Standards Developing Organizations (SDOs), •illustrating the effort of relevant alliances and consortia that drive standardization, and open-source activities. The target audience for this road map are end-users, content producers using networks for content distribution, network service providers, equipment manufacturers, infrastructure vendors, component suppliers, and test and measurement service and equipment providers. This roadmap recommends that the core technology stakeholders take a proactive approach to harmonize standardization with their vision for long-term technology evolution.
Design of Music Teaching Resource Sharing System Based on Mobile Terminal

In the social environment with the rapid development of information technology, the importance of educational technology in the field of education is becoming more and more prominent. Traditional music teaching methods limit the openness and extensibility of music classrooms to a certain extent, and have been unable to meet the current needs of big data. Therefore, it is necessary to design a new music resource sharing system based on the mobile terminal. The hardware part designs the MAC resource controller and the data controller, and the software part first designs the music teaching resource sharing architecture. Secondly, the music teaching resource sharing module is designed based on the mobile terminal, so as to realize the music teaching resource sharing. The system test results show that the designed music teaching resource sharing system has good performance and certain application value, which can be used as a reference for subsequent music teaching.
An Auxiliary Recommendation Method for Online Teaching Resources of Ideological and Political Courses in Colleges Based on Content Association

The rapid growth of network teaching resources brings more learning opportunities for people, but also makes it more and more expensive for users to find the resources they need, and users often get lost in a large number of resources. In view of the problem of low coverage of existing teaching resource recommendation methods, this paper studies a resource recommendation method of online ideological and political courses based on content correlation. In this method, word frequency-inverse document matrix (TF-IDF) is used to mine the keywords of ideological and political teaching resources, and the user interest description matrix is established, and the content correlation degree is calculated by cosine similarity. Top-k online teaching resources of ideological and political courses in universities that are most similar to the target customer’s interest description vector are selected based on the correlation degree and recommended to the user. The results show that the accuracy rate and coverage rate of the proposed method are higher than those of the collaborative filtering and utility-based methods, indicating that the proposed method has better performance.
A Benchmark for Real-Time Anomaly Detection Algorithms Applied in Industry 4.0

Industry 4.0 describes flexibly combinable production machines enabling efficient fulfillment of individual requirements. Timely and automated anomaly recognition by means of machine self-diagnosis might support efficiency. Various algorithms have been developed in recent years to detect anomalies in data streams. Due to their diverse functionality, the application of different real-time anomaly detection algorithms to the same data stream may lead to different results. Existing algorithms as well as mechanisms for their evaluation and selection are context-independent and not suited to industry 4.0 settings. In this research paper, an industry 4.0 specific benchmark for real-time anomaly detection algorithms is developed on the basis of six design principles in the categories timeliness, threshold setting and qualitative classification. Given context-specific input parameters, the benchmark ranks algorithms according to their suitability for real-time anomaly detection in production datasets. The application of the benchmark is demonstrated and evaluated on the basis of two case studies.
Performance of Machine Learning Methods Using Tweets

Public opinions shared in common platforms like Twitter, Facebook, Instagram, etc. act as the sources of information for experts. Transportation and analysis of such data is very important and difficult due to data regulations and its structure. The pre-processing approaches and word-based dictionaries are used to understand the unprocessed data and make possible the opinions/tweets to be analyzed. Machine learning algorithms learn from past experience and use a variety of statistical, probabilistic and optimization algorithms to detect useful patterns from unstructured data sets. Our study aims to compare the performance of classification algorithms to predict individuals with COVID-19( $$+$$ ) or COVID-19(−) using the emotions among the tweets by text mining procedures. Logistic Regression (LR), Support Vector Machine (SVM), Naive Bayes (NB), Decision Trees (DT), Random Forest (RF), Artificial Neural Networks (ANN), Gradient Boost (GBM) and XGradient algorithms were used to extract the accuracy of model performance of each model for the detection and identification of the disease related to the COVID-19 virus, which has been on the agenda recently.
Explainable Machine Learning for Drug Shortage Prediction in a Pandemic Setting

The COVID-19 pandemic poses new challenges on pharmaceutical supply chain including the delays and shortages of resources which lead to product backorders. Backorder is a common supply chain problem for pharmaceutical companies which affects inventory management and customer demand. A product is on backorder when the received quantity from the suppliers is less than the quantity ordered. Knowing when a product will be on backorder can help companies effectively plan their inventories, propose alternative solutions, schedule deliveries, and build trust with their customers. In this paper, we investigate two problems. One is how to use machine learning classifiers to predict product backorders for a pharmaceutical distributor. The second problem we focused on is what are the particular challenges and solutions for such task under a pandemic setting. This backorder dataset is different from existing benchmark backorder datasets with very limited features. We discuss our challenges for this task including empirical data pre-processing, feature engineering and understanding the special definitions of backorder under the pandemic situation. We discuss experimental design for predicting algorithm and comparison metrics, and demonstrate through experiments that decision tree algorithm outperforms other algorithms for our particular task. We show through explainable machine learning approaches how to interpret the prediction results.
FAPN: Face Alignment Propagation Network for Face Video Super-Resolution

Face video super-resolution (FVSR) aims to use continuous low resolution (LR) video frames to reconstruct face and recover facial details under the premise of ensuring authenticity. The existing video super-resolution (VSR) technology usually uses inter-frame information to achieve better super-resolution (SR) performance. However, due to the complex temporal dependence between frames, as the number of input frames increases, the information cannot be fully utilized, and even wrong information is introduced, resulting in poor performance. In this work, we propose an alignment propagation network for accumulating facial prior information (FAPN). We design a neighborhood information coupling (NIC) module based on optical flow estimation and alignment, where the current frame, the adjacent frames and the SR results of the previous frame are locally fused. The coupled frames are sent to a unidirectional propagation (UP) structure for propagation. Meanwhile, in the UP structure, the facial prior information is filtered and accumulated in the face super-resolution cell (FSRC), and the high-dimensional hidden state is introduced to propagate effective temporal information between frames along the unidirectional structure. Extensive evaluations and comparisons validate the strengths of our approach, FAPN can accumulate more facial details while ensuring the authenticity of the face. And the experimental results demonstrated that the proposed framework achieves better performance on PSNR (up to 0.31 dB), SSIM (up to 0.15 dB) and face recognition accuracy (up to 1.99%) compared with state-of-the-art methods.
Temporal Cross-Attention for Action Recognition

Feature shifts have been shown to be useful for action recognition with CNN-based models since Temporal Shift Module (TSM) was proposed. It is based on frame-wise feature extraction with late fusion, and layer features are shifted along the time direction for the temporal interaction. TokenShift, a recent model based on Vision Transformer (ViT), also uses the temporal feature shift mechanism, which, however, does not fully exploit the structure of Multi-head Self-Attention (MSA) in ViT. In this paper, we propose Multi-head Self/Cross-Attention (MSCA), which fully utilizes the attention structure. TokenShift is based on a frame-wise ViT with features temporally shifted with successive frames (at time $$t+1$$ and $$t-1$$ ). In contrast, the proposed MSCA replaces MSA in the frame-wise ViT, and some MSA heads attend to successive frames instead of the current frame. The computation cost is the same as the frame-wise ViT and TokenShift as it simply changes the target to which the attention is taken. There is a choice about which of key, query, and value are taken from the successive frames, then we experimentally compared these variants with Kinetics400. We also investigate other variants in which the proposed MSCA is used along the patch dimension of ViT, instead of the head dimension. Experimental results show that a variant, MSCA-KV, shows the best performance and is better than TokenShift by 0.1% and then ViT by 1.2%.
A Real-Time Semantic Anomaly Labeler Capturing Local Data Stream Features to Distinguish Anomaly Types in Production

The digitalization entails a significant increase of information that can be used for decision-making in many business areas. In production, the proliferation of smart sensor technology leads to the real-time availability of manifold information from entire production environments. Due to the digitalization, many decision processes are automated. However, humans are supposed to remain at the center of decision-making to steer production. One central area of decision-making in digitalized production is real-time anomaly detection. Current implementations mainly focus on finding anomalies in sensor data streams. This research goes a step further by presenting design, prototypical implementation and evaluation of a real-time semantic anomaly labeler. The core functionality is to provide semantic annotations for anomalies to enable humans to make more informed decisions in real-time. The resulting implementation is flexibly applicable as it uses local data features to distinguish kinds of anomalies that receive different labels. Demonstration and evaluation show that the resulting implementation is capable of reliably labelling anomalies of different kinds from production processes in real-time with high precision.
Lightweight Hyperspectral Image Reconstruction Network with Deep Feature Hallucination

Hyperspectral image reconstruction from a compressive snapshot is an dispensable step in the advanced hyperspectral imaging systems to solve the low spatial and/or temporal resolution issue. Most existing methods extensively exploit various hand-crafted priors to regularize the ill-posed hyperspectral reconstruction problem, and are incapable of handling wide spectral variety, often resulting in poor reconstruction quality. In recent year, deep convolution neural network (CNN) has became the dominated paradigm for hyperspectral image reconstruction, and demonstrated superior performance with complicated and deep network architectures. However, the current impressive CNNs usually yield large model size and high computational cost, which limit the wide applicability in the real imaging systems. This study proposes a novel lightweight hyperspectral reconstruction network via effective deep feature hallucination, and aims to construct a practical model with small size and high efficiency for real imaging systems. Specifically, we exploit a deep feature hallucination module (DFHM) for duplicating more features with cheap operations as the main component, and stack multiple of them to compose the lightweight architecture. In detail, the DFHM consists of spectral hallucination block for synthesizing more channel of features and spatial context aggregation block for exploiting various scales of contexts, and then enhance the spectral and spatial modeling capability with more cheap operation than the vanilla convolution layer. Experimental results on two benchmark hyperspectral datasets demonstrate that our proposed method has great superiority over the state-of-the-art CNN models in reconstruction performance as well as model size.
Towards Scene Understanding for Autonomous Operations on Airport Aprons

Enhancing logistics vehicles on airport aprons with assistant and autonomous capabilities offers the potential to significantly increase safety and efficiency of operations. However, this research area is still underrepresented compared to other automotive domains, especially regarding available image data, which is essential for training and benchmarking AI-based approaches. To mitigate this gap, we introduce a novel dataset specialized on static and dynamic objects commonly encountered while navigating apron areas. We propose an efficient approach for image acquisition as well as annotation of object instances and environmental parameters. Furthermore, we derive multiple dataset variants on which we conduct baseline classification and detection experiments. The resulting models are evaluated with respect to their overall performance and robustness against specific environmental conditions. The results are quite promising for future applications and provide essential insights regarding the selection of aggregation strategies as well as current potentials and limitations of similar approaches in this research domain.