Miracle-dz/UniFAR-FTUs huggingface dataset:
Direct Machine Translation Indonesian-Batak Toba

The translation application was developed using Python and Visual Studio Code as the primary programming environment. Technological progress has led to the younger generation forsaking the Batak language in their daily interactions, perceiving it as inconsequential to their social circle and contributing to the decline of regional languages. There is concern that regional languages, namely the Batak language, may lose significance or face extinction if this trend persists. This translation program is being implemented to aid in preserving regional languages, specifically the Batak language, enabling it to conform to contemporary circumstances. The development of this translation engine utilized the Batak language dictionary. The process of designing, coding, and testing is responsible for creating this direct machine translation. The BLEU score is employed to assess the caliber of machine translation. The study's findings indicate that the % BLEU value for ten sentences was 32.3%. This score falls within the category of sentences whose meaning can be understood, but the sentence structure still needs development. The direct machine translation developed in this Web platform is highly proficient and practical.
Real-Time Object Detection as a Service for UGVs Using Edge Cloud

Autonomous navigation has made significant strides in recent years, finding successful deployment in controlled environments. This achievement has been facilitated by the increased computational power and machine learning techniques. Nevertheless, overcoming numerous challenges is crucial for its widespread adoption across all environments. One notable obstacle involves the provision of dependable, low-latency, and cost-effective data processing solutions for compute-intensive applications. To tackle this challenge, this demo investigates the potential for offloading compute-intensive tasks from a UGV to a nearby edge cloud and characterize the performance in terms of latency and throughput. By doing so, compute-heavy workloads on a UGV are replaced by simple API calls to edge cloud-based services deployed. It also keeps the UGV system design simple, reduces hardware costs, and saves power consumption. This approach offers significant benefits for autonomous vehicles in controlled environments such as campus shuttles, agricultural rovers, and warehouse rovers.
EfficientAD: A Deep Learning Approach for Multi-Stage AD Classification Using Transfer Learning

Alzheimer's disease (AD) is a neurodegenerative disease that has affected millions of elderly people worldwide. It is the most common cause of dementia among the aged population. It affects the cognitive skills of a person making it difficult to perform daily chores. Due to the incurability and progressive nature of AD, it has garnered a huge interest from the researchers. Neurologists treating AD patients analyze various neuroimaging scans using Computer-Aided Diagnosis (CAD) systems. These systems use DL algorithms to detect AD early and accurately. In this paper, various pretrained CNN models namely EfficientNetB7, VGG16, VGG19 and VGG Ensemble models were employed for binary and multistage classification of AD by using transfer learning. This paper achieved an accuracy of 97.88% using the EfficientNetB7, 91.36%, 92.81 % and 95.16% using VGG16, VGG19 and VGG Ensemble models respectively.
Deep Reinforcement Learning for Dynamic Demand in Shared Bicycle Scheduling Problem

In contemporary shared bicycle scheduling systems, a common occurrence is a severe imbalance between regions, with one area suffering from a dearth of bicycles while another area is burdened with an excess. The task of balancing the distribution of vehicles can be abstracted as The Vehicle Routing Problem with Pickup and Delivery and Time Windows (VRPSPDTW). The primary objective is to efficiently schedule vehicles between multiple sites to minimize the overall transportation cost. However, in practical scheduling scenarios, the demand at each site undergoes dynamic fluctuations over time, thereby presenting a formidable challenge for problem-solving. Hence, this study employs a deep reinforcement learning algorithm equipped with a pointer network, constructing a computational environment capable of accommodating the dynamic nature of demand and lifting the constraints on the number of scheduled vehicles. Furthermore, the integration of a node masking mechanism enables the acquisition of high-quality scheduling strategies for addressing the shared bicycle scheduling predicament. Two distinct scheduling strategies are proposed, one prioritizing cost optimization and the other emphasizing user demand fulfillment. Empirical findings substantiate that the proposed model surpasses other conventional algorithms in terms of performance efficacy and computational efficiency.
Judging Knowledge by its Cover: Leveraging Large Language Models in Establishing Criteria for Knowledge Graph Sources Selection

The Knowledge Graph (KG) is a widely used paradigm for knowledge representation that leverages graph structures. It facilitates the discovery of relationships and provides context across various domains. Despite its popularity, there's a notable lack of emphasis on quality control during the KG construction process, particularly concerning the selection of knowledge sources. Our paper addresses this research gap by introducing criteria for selecting high-quality websites during the data acquisition phase of KG construction. We propose a set of criteria—credibility, relevance, content quality, coverage, comprehensiveness, and accessibility—that serves as a benchmark for evaluating potential online knowledge sources for KGs. We determine the weight of each criterion through expert judgment and validation using Large Language Models (LLMs) representing AI technologies. Our panel, consisting of academicians and practitioners, ranks the criteria by importance, with the Rank Ordered Centroid and Rank Reciprocal weighting methods generating weights from these rankings. We employ four LLMs: Google Bard, OpenAI ChatGPT 4, Anthropic Claude-2, and Meta Llama-2. Our findings suggest that LLMs can effectively validate human weighting results, as evidenced by a high correlation (Pearson's $r > 0.9$) between human and LLM criterion weights. Furthermore, hypothesis testing reveals no significant differences ($p > 0.8$), with the models demonstrating high internal consistency. Our study offers valuable insights for academics and industry professionals seeking to improve KG construction processes and establish standards for data quality and verification.
Study of Multiple High-k Dielectric Band Modulated TDGDI GNR FETs

This paper presents a study of graphene nanoribbon (GNR) FETs with different gate dielectrics and/or a combination of high-k and low-k dielectrics in the device. The simulations are done considering the quantum effects within the nonequilibrium function formalism. The use of high-k dielectrics shows improvement in the on-state current (Ion) as well as in the off-state current (Ioff). The drain-induced barrierlowering parameter also improves with high-k dielectrics. The use of high-k dielectrics at the source-channel junction region improves the performance while there is not much significant improvement in performance of the device with high-k dielectrics at the channel-drain junction only. Further analysis of band structure shows the availability of carriers at lower energy levels for high-k dielectric GNR FETs. The band modulation along the channel is possible with the use of more than one different dielectric material in different regions of the gate oxide along the channel direction. The comparative study of different GNR FETs with two or more dielectrics along the channel region i.e., three different gate dielectric insulators (TDGDI) show improvement in performance as compared to a GNR FET that uses a single dielectric.
Semi − Disambiguated Partial Label Learning by Mixing Disambiguation and Non-disambiguation Strategies

Partial Label Learning (PLL) is employed to tackle the issue of ambiguous labeling, where each instance in the training set is associated with a set of labels, but only one label in the associated set is the ground-true label. Two common strategies, disambiguation and non-disambiguation, both achieve some improvements in tackling the PLL problem. However, they each have their own drawbacks. In this work, we develop a novel PLL method, namely, Semi-Disambiguated Partial Label Learning (SDPLL), to mix the two strategies with the goal of enhancing the strengths and making up for the weaknesses of both strategies. In specific, first we adapt label propagation to operate a coarse-level disambiguation, by which we eliminate low-confidence labels from the candidate label set to get the shrinking set of candidate labels. Next, we employ binary decomposition to conduct non-disambiguation treatment on the shrinking version of the dataset. Empirical experiments on both controlled UCI datasets and real-world datasets justify the superiority (at least the competitiveness) of SDPLL over several representative competing models.
A High Gain 100GHz Dipole Antenna Based on Dug-hole AMCs

In terahertz communication systems, artificial magnetic conductor (AMC) can effectively enhance antenna performance. However, the reflection bandwidth of AMC is inherently narrow. By designing a dug-hole AMC structure and optimizing it through a non-periodic arrangement, we achieved a notable antenna gain of 5.9 dBi and a bandwidth of 39.55 GHz at a frequency of 100 GHz. This study underscores the significant research potential of AMC in antenna design.
Implementation of Floating Point CORDIC Algorithm Using 45 nm Technology For COS SINE Generation

This paper focuses on creating a 32-bit floating-point mathematical co-processor core that utilizes the CORDIC algorithm. The primary objective of this design is to enable the calculation of sine and cosine values for 32-bit floating-point numbers through the CORDIC algorithm. This co-processor design adheres to the IEEE-754 standard for single-precision floating-point numbers. These trigonometric functions are implemented using essential arithmetic operations such as addition, subtraction, multiplication, along with various other elementary functions. Verilog code has been developed to model the co-processor core.
Economic Research of Coal-Fired Power Plant Deep Peak Regulation Based on LCOE Model

Under the "double carbon" target, new energy is being connected to the grid on a large scale, and deep peaking of coal power has become a powerful means to promote the consumption of new energy. This paper conducts an in-depth study on the economics of deep peaking of coal power, comprehensively considering the impact of utilization hours, fuel consumption, unit life loss, and environmental costs on peaking power generation expenses. We adopt the Levelized Cost of Electricity model to calculate and compare the comprehensive power generation costs under two different working conditions of capacity units. Furthermore, we analyze the sensitivity of each indicator using a 600MW supercritical unit as an example. The results of this analysis provide valuable suggestions and data to support national policy-making and enterprise operations.
Day-ahead Demand Side Management Optimisation Considering Payback Potential

Demand side management (DSM) is recognised as an emerging form of flexibility to facilitate the integration of renewable energy sources such as photovoltaic (PV) systems. However, DSM can have the unintended effect of introducing new demand peaks, particularly in distribution networks (DNs) as the load that was activated to provide flexibility is paid back (e.g., flexible loads reconnect, storage charges, etc.). To address this challenge, this paper proposes a day-ahead DSM scheduling methodology which optimises load payback effects in DNs. For this purpose, the “available payback potential” is introduced as a metric to explicitly capture the payback associated with DSM. The proposed methodology is demonstrated with applications to a modified IEEE 33 bus test system considering different demand flexibility levels. The results demonstrate how the optimised activation of flexible loads (shifted loads) and subsequent, scheduled, payback, improves the overall performance of the DN (e.g., meeting network limits and reducing losses), and facilitates the integration of available distributed generation.
Tunable Wideband Metamaterial Absorber

A novel tunable wideband metamaterial (MM) absorber is proposed in this paper. The top layer of the structure is composed of a frequency selective surface with PIN diodes, the bottom layer is a metal ground, and the air is between them. The tunable effect of absorbing wave is obtained by adjusting the bias current at both ends of the PIN diode. The absorption band of the structure with a reflection coefficient below -10dB ranges from 2.5 GHz to 10.2 GHz.
Electromagnetic Signature of Hilbert Curve-Based Chipless RFID Tags Using Numerical Analysis

This study describes a chipless Radiofrequency Identification (RFID) tag that is based on the Hilbert Curve Filling technique. The traditional RFID has some security vulnerabilities, so an unauthorized party could obtain information. One alternative is to use fractal (irregular) shaped tags to store information as an electromagnetic signature. It is a chipless passive tag made of irregular unit cells and full-wave Electromagnetic analysis (EM) of the designed tags in Matlab is investigated by exporting them in Computer Simulation Technology (CST) in the frequency range of 2 to 12 GHz. The performance of the tags is analyzed by observing the radar cross-section (RCS) of the tag in linear polarization, which was obtained for four iterations after the simulation.
RGB-Image-Based Forklift Pallet Recognition and Localization

Accurate identification and positioning of forklift pallets can effectively improve the automation level of warehousing logistics. Therefore, this paper proposes an algorithm based on SSD network and Canny edge detection operator to address the influence of image background and lighting changes on forklift pallet recognition. The pallet is simplified into an “E” model based on its structural features. The SSD network is utilized to detect and locate the “E” cross-section of the pallet, eliminating the interference of the main image background. The image is transformed into the HSV color space, and background interference is further eliminated by setting color thresholds. Gamma correction is applied to the V channel of the image in the HSV color space to eliminate lighting interference in pallet recognition. The Canny edge detection operator is used to detect the edges of the pallet and achieve segmentation of the “E” cross-section of the pallet. Pallet positioning is accomplished based on the segmentation results. Experimental results demonstrate that the proposed method can achieve pallet recognition and effective positioning in both bright and dark lighting environments.
Hybrid Clarke's Models for Fixed Wireless Deployments in Beyond-5G Systems

Integrated Sensing and Communication (ISAC) has lately been highlighted as a critical enabler for B5G/6G wireless communication. Nevertheless, appropriate channel models that support communication and sensing are one of the main bottlenecks for efficient deployment of ISAC systems. This work addresses the problem of modelling multipath fading channels for ISAC systems for fixed wireless networks with mobile scatterers. We first highlight the limitations of existing models for multipath fading, namely, Jake's and Clarke's models. We then propose a Hybrid Clarke's model where we leverage the fact that all multipaths are not affected by the motion of the mobile scatterers. We then use the proposed channel model to evaluate a Doppler-based intruder detection system. Through various simulation results, we will showcase that the proposed channel model can effectively detect the intruder.
A Convenient User Three Authentication Levels for Internet of Things (IoT) Devices

IoT has shown pervasiveness and prevalence everyday. The IoT nodes continuously generate and transmits data in the network, therefore authentication is needed in order to deliver actual data. A convenient and reliable way of authenticating these nodes is paramount. This work proposes a convenient way for the nodes to authenticate each other without the user being involved, and where the user gets involved, he performs his authentication conveniently. The authentication of the nodes goes through three phases. In the first phase of authentication nodes are authenticated by specially generating a signature which constitutes the specs of the node. The second phase authenticates by calculating the geographical location of the node and third phase prompts the user to enter his password from a bitmap. The password is difficult to crack as it comes in two bitmaps and randomly shuffles its characters each time it prompts. The first phase is sufficient to authenticate the nodes, but if it fails it can go the second phase. The user can get involved only if the two phases failed then he can provide his credential to authenticate the node. The system is resistant to side channel, rank, brute force, MITM, MAC spoofing, DoS, and DoS attacks.
Pneumonia Detection Using Hybrid CNN Model

This medical research introduces a hybrid model using transfer learning from DenseNet201, InceptionResNetV2, and ResNet50 for superior pneumonia detection. Emphasizing the crucial value of early identification, we combine features from these architectures to enhance accuracy. Rigorous experiments on a sizable chest X-ray dataset showcase our model's superiority, achieving 95.67% accuracy, surpassing individual architectures and conventional methods. The model's improved generalization to new cases is attributed to the synergy of transfer learning and diverse architectures. Furthermore, we explore the interpretability of predictions for potential clinical insights. By leveraging these advanced techniques, our project contributes to more effective early-stage pneumonia detection, promising better health care outcomes.
Image Stitching Method Based on Improved ORB-GMS

Aiming at the low efficiency of traditional image stitching and the problems of ghosting and uneven brightness of stitched images, this paper proposes an image stitching method based on improved Oriented FAST and Rotated BRIEF (ORB) and Grid-based Motion Statistics (GMS). The method first extracts the feature points using ORB algorithm, uses Boosted Efficient Binary Local Image Descriptor (BEBLID) to describe the feature points, pre-matches the image using brute force matching algorithm, then constructs geometric constraints and Hamming distance constraints to pre-exclude the matched results, and uses GMS algorithm to reject the wrong matching points, then calculates the homograph matrix using Randomized Sampling Consistency (RANSAC) algorithm, and uses the optimal stitching line combined with multi-band fusion method to realize the fusion of overlapping regions; experimental results show that our method improves the quality of stitched image compared with ORB algorithm and GMS algorithm.
Probe-Based Study of Traffic Variability for the Design of Traffic Light Plans

Computing efficient traffic signal plans is often based on the amount of traffic in an intersection, its distribution over the various intersection movements and hours as well as on performance metrics such as traffic delay. In their simple and typical form, plans are fixed in the same hour over weekdays. This allows low operation costs without the necessity for traffic detection and monitoring tools. A critical factor in the potential efficiency of such plans is the similarity of traffic patterns over the days along each of the intersection movements. In this paper, we study traffic variability and propose simple metrics to measure it based on traffic volume and traffic delay. We propose an automatic probe data-based method, for city-wide estimation of traffic variability. We discuss how such measures can be used for signal planning such as an indication of which intersections can benefit from dynamic but expensive traffic detection tools or in selecting plan resolution. Likewise, we discuss various methods to mitigate the impact of such variability. We demonstrate the framework based on real traffic statistics to study the traffic variability in the city of Haifa along its 162 intersections.
Enhanced Multi-Modal Knowledge Graphs Representation Learning via Self-Attention Fusion

The complexity and diversity of multi-modal knowledge graphs arise from their ability to fuse information from multiple sources. Traditional knowledge graph representation learning faces challenges in effectively capturing and efficiently processing the complexity of multi-modal knowledge graphs. Moreover, conventional methods primarily rely on embeddings, overlooking the richness of multi-modal information, further diminishing the effectiveness of representation learning. To address these issues, this study proposes an Enhanced Multi-modal Knowledge Graph Representation Learning model (MATUCKER) based on the core of TuckER, which adaptively fuses multi-modal information using multi-head self-attention. In this approach, we employ the ResNet model to encode image features of multi-modal entities. These encoded features are then concatenated with the semantic embeddings of multi-modal entities and fed into a multi-head self-attention mechanism for fusion. The resulting multi-modal entity feature encodings are subsequently input into the TuckER model for representation learning. The model's performance is evaluated on the publicly available OpenBG-IMG dataset for link prediction tasks. The experimental results clearly demonstrate that our proposed model significantly outperforms both traditional methods and the multi-modal representation learning model, TransAE, in terms of multi-modal representation learning performance. We achieved significant improvements in key performance metrics compared to the TuckER backbone: the Mean Reciprocal Rank (MRR) increased by 3%, the HITS@10 metric improved by 5%, and the Mean Rank (MR) decreased by approximately 1,000.
Sentiment Analysis of Financial News Data using TF-IDF and Machine Learning Algorithms

Blogs, online forums, comment sections, and social networking sites like Facebook, Twitter (now known as X), and Instagram can all be called social media. The growing use of social media has made some unstructured data available, which can benefit us if we clean, structure, and analyze the data. Twitter is a popular microblogging social media platform where people share and express their opinions about any topic. The act of analyzing these opinions of people is called sentimental analysis. Sentimental analysis can be helpful to individuals, businesses, government agencies, etc. In this study, tweets related to financial news were extracted, labeled, and analyzed to capture the opinions of people around the world. This paper proposes a novel machine learning-based approach to analyze social media data for sentiment analysis. The presented approach is divided into three steps. The first stage is preprocessing, where the tweets are refined and filtered. In the second stage, feature extraction was performed using Term Frequency and Inverse Document Frequency (TF-IDF). The third stage involves using the extracted features to make predictions using machine learning algorithms. Three machine learning models were used, namely, random forest classifier (RF), Naïve Bayes (NB), and k-nearest neighbor (KNN). The evaluation results show that both NB and RF perform better than KNN in accuracy, precision, Recall, and F1-score metrics. These results also show an overwhelmingly positive opinion regarding financial news.