Reviewerly/exHarmony huggingface dataset:
A System Approach for Integration of Human-centered Smart Problem-solving Process in Digital Shop Floor Management

In order to remain competitive in a rapidly evolving, digital and connected world, production companies are faced with the task of delivering high quality individual products in shorter production times and fewer costs. In this sense, the major obstacle in front of the companies is the occurring problems in production. Shop floor management (SFM) is a central concept of the lean methodology, which aims to empower employees with the responsibility for the effective and long-term solution of problems and supports this point of view with daily meetings. However, due to the increasing diversity of variants in production, process activities and requirements have become more complex for employees. In this framework, the problem-solving methods have become a cause of extra stress for the employees and, as a consequence, the implementation of failure management systems is not feasible. In this context, it is aimed in this paper to present a digital SFM that can use machine learning algorithms to reduce the workload on workers, to make problem-solving methods efficient, to achieve the production of quality products in a short time while reducing production costs, and to implement a smart failure management system in companies.
Are Two Heads Always Better Than One? Human-AI Complementarity in Multi-criteria Order Planning

Innovative solutions are often crafted through synergizing contributions from a group of diverse entities with complementary strengths and weaknesses. The same is expected to persist with the advent of artificial intelligence (AI). Thus, this work aims to investigate whether the synergistic interaction of human decision makers and optimization (AI) algorithms can significantly improve the solving of challenging, multi-criteria order planning problems. To this end, a Human-AI complementarity framework leveraging on emerging transfer optimization methods is first put forward, enabling the adaptive reuse of experiential priors to inform search. Next, empirical analysis on a carefully designed multi-criteria order planning problem is conducted. Finally, 3 key insights arising from situations where the human supplied prior is perfect, imperfect, or flawed are discussed to adequately address the research question posed.
A Consensus Clustering-based Label Propagation Method for Classification of Science &amp; Technology Resources

With the rapid expansion of the science & technology services industry, abundant science and technology resources were produced which caused great difficulties for the management of science and technology service platform. Due to the high specialization of science & technology resources, the classification task is not easy. This study aims to propose an efficient classification method for science & technology resources classification such as papers, patents, etc. A science & technology text classification framework based on Word2Vec and consensus clustering-based label propagation algorithm was proposed in this paper. To verify the effectiveness of the method, a dataset of science & technology resources from a Chinese science and technology service platform was used for the demonstration. The study provides a new perspective on science & technology resource classification and the method can also serve many different business scenarios.
EDUZONE – A Educational Video Summarizer and Digital Human Assistant for Effective Learning

The availability of technology and the expansive nature of the internet have created a surge in the demand for online learning. Despite so many advantages, there are some existing drawbacks related to online learning. The lengthy recorded video lectures of different subjects and modules in a static manner, are extremely tedious for the learner to understand the contents available. And lack of assistance for academic-related problems of students is also stated as a major issue that comes with online education. EDUZONE provides a reliable solution to mitigate and overcome these challenges. This tool is educational assistance that generates a summarized version of the video lectures which depicts the overall idea of the whole video with the capability of a lecture notes generator along with a digital human which helps to clarify students’ problems and build an efficient conversational flow. The summarized video content can be used by the learners for revisions and as a quick reference before any examinations. In addition to generating short and precise content, EDUZONE also indexes any specific topics to make it easier to find content and generate class notes, highlighting all the important content. Overall EDUZONE can be considered a time-efficient educational assistant which helps students with their studies.
Evaluation of Student Learning Success When Using Augmented Reality Experiences in Engineering Education

Augmented Reality (AR) experiences are more and more used in engineering education. 3D-based content is digitally connected to the real world. Powerful mobile devices and easy-to-use AR software give a good opportunity to implement virtual learning content into face-to-face teaching. This paper describes the application of thirteen AR experiences in the lecture machine elements throughout a complete semester. Instead of using AR just as a singular event this approach analyses the effect of continuous use. A accompanying evaluation determines the effect size for each AR experience. Both the subjective impression of learning success and motivation, as well as an objective monitoring of learning success were made.
Effectuating Communication for the Deaf and Hard-of-Hearing: An Ethnographic Review

The Deaf and Hard-of-Hearing constitute a significant populace, raising the need for technology, which is becoming ubiquitous in the world, to become more accessible for them. This paper enlists recent efforts and research popular in the accessibility sphere for the DHH and asks research questions based on the review. The DHH Demographic are scattered around the world, and while there exists an International Sign Language, people tend to utilize their regional versions of the Sign Language, impeding solidarity. Systems, representations, and interpretations of the different Sign Languages are divergent. Hence, this paper examines research done solely in the sphere of different renditions of the Sign Language. This paper also discerns certain challenges and suggests how technology can work towards accurate interpretation and communication for the DHH. The use of Computer Vision and Deep Learning Algorithms is especially prevalent in current research, and this paper focuses on identifying some novel frameworks and procedures which have varied and deep applications in the accessibility sphere, not just for the DHH. The purpose of this review paper is to understand current research methods and encourage collaboration of version-specific researchers to further and ameliorate research.
Different Techniques And Algorithms To Combat The Issue Of Money Laundering In Bitcoin

The emergence of Bitcoin has continued to grow both in value and fame, as it was introduced as the first decentralized cryptocurrency. Many studies have shown that criminals are exploiting bitcoin by using it to launder their money, which originates from illegal activities or cybercrime. This paper aims at providing a comparison of detection techniques for preventing money laundering in bitcoin through various methods including graph theory, prevention of mixing services, and machine learning techniques including Random Forest, Shallow Neural Networks, optimizable Decision Trees, Bagging, Boosting algorithms, and Ensemble learning combining Random Forest, Bagging and Extra Tree.
Controller Design and Analysis for Octorotor UAVs

Radar applications that include emergency rescues and natural disasters damage evaluations are critical and difficult to be done by humans. It was proposed to use small Unmanned Aerial Vehicles (UAVs) in such applications because a UAV does not require a human pilot in it. In this work, an Octorotor with an altitude and attitude controller and observer are designed to control the vertical take-off and landing of the Octorotor is proposed. In addition, after reaching a certain altitude, a trajectory controller is designed using linear quadratic regulator (LQR) controller to move the Octorotor in x-, y-directions and to allow a translational movement at a constant velocity. Finally, a simulation was done to experiment the behaviour of the system under wind disturbance. A linearized model was extracted from the nonlinear model of the Octorotor. The results of both controllers clearly outperformed previous work. The model results were obtained via MATLAB/Simulink.
Social Media Trends Analysis using the Bi-LSTM with Multi-Head Attention

In this global world, the usage of social media has produced a vast amount of human-generated data that will be analyzed to determine people’s sentiments. Sentiment analysis refers to the method of automatically grouping web data into different categories. The Proposed work presents bidirectional long-short memory (Bi-LSTM) network based on a multi-head attention mechanism to identify sentiments like business & economics, entertainment, science & technology, or health. We utilized a self-collected dataset from Twitter API. Bi-LSTM is used to capture two-way semantic information and the additional multi-head attention mechanism focuses on outputted information of Bi-LSTM. To assess the performance of the proposed work we utilized Precision, Recall, Accuracy, and f1-score as evaluation metrics. The proposed methodology is also contrasted with well-known sentiment analysis methods including Naive Bayes, Convolution Neural Network, Recurrent Neural Network, and LSTM our model performs best with 98.72% accuracy, 93.65% precision, 94.02% recall, and 93.20% f1-score.
Machine Learning Classification Algorithms for Sentiment Analysis in Arabic: Performance Evaluation and Comparison

Researchers started utilizing and optimizing the state-of-the-art Machine Learning (ML) and Deep Learning (DL) models to benefit Arabic language tools and applications. They employed social media platforms such as Twitter to gather enormous datasets in the Modern Standard Arabic and Arabic dialects, then used the collected datasets to train their models. This noticeable development in the field needs a detailed comparison study to review the work done and highlight the openings for future contributions and improvements. Based on the conducted review, there exists a gap in the time-complexity evaluation of the used ML algorithms in the field of Arabic Sentiment Analysis. Thus, this study presents an experimental approach for determining the time complexity of seven popular ML algorithms in classifying positive and negative Arabic sentences. The results show that the Multi-Layer Perceptron (MLP) and the Support Vector Machine (SVM) have the highest complexity, whereas the Logistic Regression (LR) has the lowest.
A Case Study on the Information Security System of Al Wahda Bank

This case study looks at the information security system in use by the Information Security Unit at Al Wahda Bank in Libya. It is an examination of the policies and tools used for the security of 2 main areas: software & information and data. For software security, the case study examines the security policies and tools for the following 2 aspects: the software development process related to all software developed for the bank, and the protection of all devices from malware. For information and data security, the case study examines the security policies and tools for the following 5 aspects: information and data classification, information and data storage, information and data backup, information and data communication, and information and data disposal. Afterwards, we briefly analyze the information security system. Our analysis leads us to identifying 3 areas for further review: difficulty in monitoring 3rd party software development, inconsistency between policies and actual implementation, and the retention period of ‘unwanted’ information and data.
A Comparison of PV Module Brands and Types in the UAE Work Environment

This paper compares several PV module kinds and brands under the operation conditions of Ras Al-Khaimah, in the UAE. For this purpose, a market survey for the available PV panel in the UAE was conducted and the most common 14 brands were considered in the current study. A PV plant with a total module area equal to 100 m2 was simulated. The simulations were carried out for the 14 brands of PV panels. Annual energy yield and economic performance were utilized as figures of merit for comparison study. According to the calculations, under UAE working conditions, monocrystalline modules offer the highest energy productivity, whereas polycrystalline panels are the most financial outlook. These findings would aid in the selection of the PV module for system installation in the UAE region.
Hardware/Software Co-acceleration of Progressive Learning under Feature Dimension Variation

In this paper, we address the problem of ASIC HW accelerator re-use in the case when the task-based feature set undergoes size changes. The proposed solution is a hybrid Hardware/Software (HW/SW) co-acceleration methodology for incorporating any additional features into the progressive learning model and performing inference without modifying the architecture of the HW accelerator. The co-acceleration methodology has been prototyped on an edge computing platform and compared with a HW-only acceleration in terms of inference throughput, compute resource utilization, and energy efficiency. The hybrid HW-SW co-accelerator is shown to result in a higher inference throughput while consuming less compute resources and energy than the HW-only solution. The results are further supported by using the HW accelerator’s performance counters to profile overall performance under realistic progressive-learning workloads.
An Interpretable Machine Learning Approach For Fault Classification in Bearing Systems

This paper presents an interpretable machine learning approach to classify different types of faults in ball bearing systems with high accuracy. These methods are based on ensemble methods (Random Forest, Gradient Boosting, and AdaBoost) which use data from the fan system collected by vibration monitors mounted on three axes. The proposed technique needs statistical feature inputs to classify different types of faults. We train models and explain their output using SHAP (SHapley Additive exPlanations). In the rigorous numerical study, we point out a suitable explanation for black-box predictive models trained on vibration data. Our model achieves 98 percent classification accuracy when classifying all fault types.
Using Orientation Sensors as a New Secure Lock Screen System for Smartphones

Smartphones are currently used more than ever before. People tend to use them to store private and sensitive information, including online bank accounts, credit card numbers, passwords, private chats, even notes, and personal plans. As individuals rely on their sensitive data in so many critical applications, such as online financial transactions, social media accounts, business, and personal information; smartphones are the most convenient device for such uses. Hence, smartphone security is essential to protect their content from unauthorized users. Lock screen systems are the first layer in securing these sensitive data. It is also the most widely used user interface of a smartphone; it is crucial in ensuring user authentication. This paper presents a novel technique that could replace the traditional lock screen systems, by adopting gesture recognition methods, after using a machine learning (ML) algorithm in analyzing users’ behavior with their smartphones.
Medical Image Segmentation Using Deep Learning and Blending Loss

Semantic segmentation is an important task in medical-supporting. The purpose of semantic segmentation í to identify pre-defined objects, and its pixel-by-pixel location. The most popular method in semantic segmentation was using Convolutional neural network which has considerably improved semantic image segmentation. This work investigates a Blending loss which incorporates into traditional methods. Three popular algorithms are U-Net, PSPNet and FPN are examined carefully to investigate upgrading performance after combining new objective function. Moreover, we did experiments on two medical datasets to avoid bias and verify performance of the new method.
Interactive Drone Control with Gesture and Posture on the Edge Computing Platform

This paper designs a system that combines sensor and multiple neural network models to achieve the function of interactive drones, such as user tracking, gesture and posture control of the drone, and emergency safety protection. To run on the edge computing platform, Jetson Nano, and control the drone, the neural network models such as object detection, face recognition, and gesture recognition were optimized. The drone could be controlled by the user's gestures and postures successfully.
Centroid-Based Clustering Using Sentential Embedding Similarity Measure

Text is treated as a bag of words in traditional text clustering methods, with the similarity between two texts measured by word co-occurrence. While this technique is appropriate for clustering document-level text, it underperforms when clustering short-text fragments like sentences. This is due to compared sentences can be semantically related without any similar words or phrases. This study describes a new variation of short-text clustering method based on the idea of sentential semantic embedding vectors. These embedding vectors represent compared text using semantic and lexical information driven from a lexical knowledge database built to emulate common human knowledge about words in natural human language. On two-sentence datasets with varying degrees of word co-occurrence, we compared the technique’s performance to that of a graph-based clustering algorithm. We argued that our centroid-based method’s higher performance on datasets containing a low degree of word co-occurrence was due to its ability to utilize the available semantic information.
Navigation control of mobile robots using graph neural network and reinforcement learning with fuzzy reward

Automatic mobile robot navigation controller design has been researched for decades whereas suffer from its parameters dependence. Reinforcement learning (RL) provides a learning-based solution for solving navigation problem without tuning controller's parameters which dependent plant models. Graph neural network (GNN) has further extended the success of RL by extracting hidden spatial and temporal information from the non-Euclidean data structure and achieved higher training efficiency. However, most of the existing GNN-RL controllers are dependent on abundant structural information rigorously. This requirement is difficult to be satisfied in mobile robot navigation control due to its scarce structural information. We addressed this issue and extended GNN's application to scenarios lacks abundant structural information by proposing a spatial-temporal graph neural network (ST-GNN), whose graph is built by nodes containing the information of different timestamps. By synthesizing the ST-GNN with the reinforcement learning (RL) method, we proposed a ST-GNN based RL controller to achieve mobile robot navigation control. Furthermore, a fuzzy reward system is proposed to speed up the learning. Our experiment results suggest that ST-GNN based controller can obtain a better performance compared with the traditional multi-layer perceptron (MLP) based RL controller and the advantage of fuzzy reward is also verified.
Spam Detection Techniques Recapped

This paper examines the modern defensive measures that deal with the global spam traffic over the Internet. Much of the review consists of cross-referencing recently published material on the subject matter and including the relatively new and promising methods and frameworks alongside them; unimplemented approaches with promising end results in other fields are also referenced and summarized. The highlighted defensive measures primarily cover the individual End-User tier of the Internet and the Domain Name System (DNS).
Fuzzy C-Means and Fuzzy K-Means Algorithms using Fuzzy Functional Dependencies

Fuzzy C-Means (FCM) is c- clustering algorithms for fuzzy dataset of n objects. Fuzzy K-Means (FKM) is k-clustering algorithms of c-clusters. In this paper, we proposed fuzzy c-means clustering algorithm using fuzzy association functional dependencies and Fuzzy K-means MapReduce algorithms using fuzzy association multivalued functional dependencies. We studied fuzzy c-means and fuzzy k-means algorithms in different way. Fuzzy functional dependencies applied on fuzzy c-means clustering and fuzzy k-means clustering. These fuzzy MapReduce algorithms useful quick arrive solutiosn for Clustering. Some examples are given for FCM and FKM.
Semantic Sticker Classification based on Convolutional Neural Network

In instant messaging applications, stickers can convey more emotion than text, and thus, they are frequently and heavily used in instant messaging applications and play a very important role in conversations. However, because of the richness and diversity of stickers, it sometimes takes a lot of time for users to find a suitable one. To facilitate users to find the desired stickers quickly, this study uses a deep learning network, i.e., CNN, for the semantic classification of stickers. We classify the stickers into nine semantic aspects, and each aspect is classified by a deep learning network model with classification accuracy between 85.2% and 98.7%. Based on these nine aspects, we implement a sticker recommendation system for users to find the most suitable stickers according to their application scenarios.
External Layer 2 and Layer 3 Connectivity

So far, you have learned how to get individual ACI fabrics up and running and how to configure physical underlay as well as overlay policies to understand their forwarding behavior and troubleshooting. All this will ensure east-west communication between internal ACI endpoints. But what about north-south traffic, clients accessing datacenter resources and application services from outside of ACI? You need to provide them with Layer 3 routing with the fabric. Additionally, if ACI is not built on greenfield, you need tools to interconnect the fabric with legacy networking equipment, stretch Layer 2 segments, and progressively migrate all the physical devices, usually followed by L3 default gateways to ACI.
Inertial-measurement-based catenary shape estimation of underwater cables for tethered robots

This paper deals with the estimation of the shape of a catenary for a negatively buoyant cable, connecting a pair of underwater robots in a robot chain. The new estimation method proposed here is based on the calculation of local tangents thanks to the data acquired from inertial measurement units (IMUs), which are attached to the cable near its ends. This method is compared with a vision-based estimation method that was developed previously. Experiments are conducted, in the air and in a pool, using a motion capture system for ground truth. The results obtained show that the new method significantly improves the estimation of the catenary height. Furthermore, the identification of the cable shape is not affected by the limits of the camera's field of view and by the image projection, resulting in increased accuracy and range, without singularities.