Miracle-dz/UniFAR-FTUs huggingface dataset:
Fuzzy Norm-Explicit Product Quantization for Recommender Systems

—As the data resources grow, providing recommendations that best meet the demands has become a vital requirement in business and life to overcome the information overload problem. However, building a system suggesting relevant recommendations has always been a point of debate. One of the most cost-efficient techniques in terms of producing relevant recommendations at a low complexity is Product Quantization (PQ). PQ approaches have continued developing in recent years. This system’s crucial challenge is improving product quantization performance in terms of recall measures without compromising its complexity. This makes the algorithm suitable for problems that require a greater number of potentially relevant items without disregarding others, at high-speed and low-cost to keep up with traffic. This is the case of online shops where the recommendations for the purpose are important, although customers can be susceptible to scoping other products. A recent approach has been exploiting the notion of norm sub-vectors encoded in product quantizers. This research proposes a fuzzy approach to perform norm-based product quantization. Type-2 Fuzzy sets (T2FSs) define the codebook allowing sub-vectors (T2FSs) to be associated with more than one element of the codebook, and next, its norm calculus is resolved by means of integration. Our method finesses the recall measure up, making the algorithm suitable for problems that require querying at most possible potential relevant items without disregarding others. The proposed approach is tested with three public recommender benchmark datasets and compared against seven PQ approaches for Maximum Inner-Product Search (MIPS). The proposed method outperforms all PQ approaches such as NEQ, PQ, and RQ up to +6%, +5%, and +8% by achieving a recall of 94%, 69%, 59% in Netflix, Audio, Cifar60k datasets, respectively. More and over, computing time and complexity nearly equals the most computationally efficient existing PQ method in the state-of-the-art.
The Lima Traffic Light Network Operations and Management System

The research problem arose from the need to implement a system to remotely manage the traffic operations of the network of traffic lights to reduce vehicular congestion. The procedure involved designing the placement of electronic sensors to track the number of vehicles in the area. This allowed for the testing of different operational strategies for modifying the timing of the traffic light signals. A simulation model was used to test these strategies in seven intersections on Av. Petit Thouars in Lima, between Av. Alejandro Tirado and Av. 28 de Julio. Detectors were installed at the access points of secondary access streets to the main road (Av. Petit Thouars) which yielded cumulative positive results. Alternatives 1 and 2 produced a 59% reduction in queues, thus demonstrating the validity of the general hypothesis.
Corporate Social Responsibility and Sustainable Development: The Review of Marketing Implications in Nigerian Tourism and Hospitality

In the past few decades tourism industry has been faced with the challenges of destination images. Much emphasis has been placed on destination positive image due to the important role it places in tourist behaviour towards choosing a destination for holiday. This study aims to investigate the perceptions of tourists about Nigeria as a leisure tourism destination. The outcome of the study shows that Nigeria has a lot of attractions that can support successful leisure tourism, but lack a strong and positive image that can act as a pull factor for its tourism demand.
FEATURE: Advances in Neotropical Ornithology Army-ant following in Neotropical birds: A review and prospectus

Army-ants (particularly swarm-raiding species Eciton burchellii and Labidus praedator ) are keystone predators in Neotropical forests. Hundreds of associated species from diverse taxa depend on them for survival, the most conspicuous of which are the ant-following birds. These birds forage on cryptic arthropods and vertebrates as they attempt to escape raiding army-ants. Despite capturing the attention of tropical biologists for centuries, research on ant-following birds has been largely limited to natural history observations until more recently. Here, we argue that this complex system provides unique and underappreciated opportunities for testing hypotheses in 5 main areas of interest: (1) competitive interactions among attendant birds, (2) cognitive and behavioral adaptations for locating swarms, (3) evolution of ant-following behavior, (4) biogeography of ant-following behavior, and (5) conservation of ant-following birds. For each research area, we review the current state of knowledge and make suggestions for fruitful research avenues that we believe will help address important questions in the fields of ecology
A Scale-Based Ontology of Digital Musical Instrument Design

Subtlety and detail are fundamental to what makes musical instruments special, and worth dedicating a life’s practice to, for designer, maker, player and listener alike. However, research into digital musical instrument (DMI) design tools and processes have so far mainly focused on high-level conceptual concerns and low-level technical abstractions, leaving subtlety and detail underexplored and undervalued. These nuances, and the processes they result from, cannot be fully articulated in words alone, yet they largely define an instrument’s quality, and it is therefore important to understand how they come to be. We introduce a scale-based ontology that divides design details into three levels - macro, meso and micro - and we present a literature review of DMI design from the perspective of this ontology. Finally we extrapolate the ontology to consider its utility in broader contexts, and consider future directions.
exposure among

Background Secondhand smoke exposure can cause morbidity and premature mortality. However, the global prevalence of, and trends in, secondhand smoke exposure among adolescents are poorly documented. We aimed to assess the prevalence of, and trends in, secondhand smoke exposure among adolescents from 1999 to 2018. Methods We did an analysis of the most recent data from the Global Youth Tobacco Survey (GYTS), a nationally representative, self-administered, school-based cross-sectional survey of tobacco use and related factors among adolescents aged 12–16 years worldwide. Data from 142 countries and territories that had done a GYTS between 2010 and 2018, comprising 711 366 participants, were used to assess the prevalence of secondhand smoke exposure. Data from 131 countries and territories that had
SkyBot - A Novel Autonomous Window Cleaning Drone

Modern skyscrapers with expansive glass facades demand an innovative solution for efficient and safe window cleaning. Conventional methods are labor-intensive, time-consuming, and costly, particularly on skyscrapers like the Burj Khalifa in Dubai. In this paper, SkyBot an autonomous window cleaning drone is introduced. Equipped with eight propellers driven by powerful motors and a lightweight carbon fiber structure, SkyBot ensures stability and strength at great heights. SkyBot has a carrying capacity of up to 11 kg of cleaning supplies, it is designed for portability, efficiency, and environmental friendliness. SkyBot’s cleaning mechanism incorporates a high-pressure washer and a powerful blower for effective cleaning. Real-time monitoring is made possible through live streaming video capabilities. Experimental results affirm that SkyBot has the potential to effectively shorten the cleaning cycle to around 40 days and reduce the operational cost by 47% when compared to conventional methods used to clean BurjKhalifa. SkyBot represents a significant advancement in highrise building maintenance, offering an efficient and safe window cleaning solution for modern skyscrapers.
Environment Safe Biodegradable Green RFID for Green Internet of Things

The Internet of Things (IoT) platform is becoming ubiquitous since its usage is mandatory in a smart environment. IoT solutions have greatly improvised the quality of life of mankind. Internet of Things is connected with high-performance devices that need more power to provide uninterruptable services. So, energy consumption is one of the essential parameters for IoT solutions. Hence, Green IoT emerged as a solution in minimizing energy utilization of IoT devices. Usage of the Internet and applications that run on IoT is inevitable in smart cities. Even though IoT has merit and a positive impact on human life, its usage produces toxic wastes. Such an alarming constraint raised the concern for protecting the environment's nature as well as the smart cities. To mitigate the harmfulness of IoT, finding an alternate solution is mandatory, thus Green IoT has come into the picture. Green IoT is the trend with a lot of future scope for creating eco-friendly smart solutions. To minimize the toxic wastes lot of efforts are needed to reduce carbon footprint. The application of RFID in IoT smart environment is manifold. This paper proposed novel eco-friendly RFID tags. The proposed tags are designed with a water repellent to make it an eco-friendly and bio-degradable one. Moreover, this paper discusses the importance of Green IoT along with an insight into reducing carbon footprint while designing IoT devices.
Network-Friendly Sequential Recommendation with Quality Constraints: A Safe Deep Reinforcement Learning Approach

Network-friendly recommendation have emerged as a promising approach to relieve data traffic congestion without sacrificing user preference. Most of existing works focus on one-stage recommendations, maximizing recommendation quality and reducing network latency for the next request. In this paper, we focus on policy optimization for Network-friendly Sequential Recommendation (NSR), towards maximizing the recommendation quality as well as network performance for the whole session with hard quality constraints. To achieve this goal, we first formulate the NSR problem as a Markov Decision Process (MDP) problem. To characterize the fundamental performance limit, we consider the offline solution by assuming the distributional knowledge of user behavior is known as a prior. In this case, we solve the offline problem through policy iteration. However, user behavior in real-world scenarios is unpredictable, which makes it difficult to know the distributional knowledge of users. To handle this issue, we propose a proximal policy optimization-based algorithm with a safe layer, NSR-PPOSL, to seek NSR online solution. Through extensive simulations, we show that the proposed online method achieves over 80.0% performance of the offline method, under the condition of unknown user behavior. Moreover, our proposed online method outperforms representative benchmark by 13.5% under various network conditions and user behaviors.
Understanding and Identifying Cross-Platform UI Framework Based Potentially Unwanted Apps

Cross-platform UI frameworks may facilitate a new category of Potentially Unwanted Apps, dubbed XPUAs, which uses framework-specific language to implement its UI in the form of cross-platform payload. XPUAs are able to bypass the existing app vetting procedures leveraging their unique technical characteristics and make revenue on addicitive contents that are strictly prohibited by either local laws or app market regulations. In this paper, we first examined the profit chain of XPUAs and then proposed PUAXray, a novel detection system that utilized machine learning to identify XPUAs. PUAXray used a binary classifier that was trained on features extracted from cross-platform payloads, including semantics information and third-party library usage information. We evaluated PUAXray on a dataset that was created for the first time in the community with benign apps from reputable app markets and XPUAs from an industry collaborator. PUAXray achieved 95.4% F1-score in the XPUAs identification task, and proved capable to be extended to other cross-platform UI frameworks.
Contextual Multi-Armed Bandits for Non-Stationary Heterogeneous Mobile Edge Computing

Base station (BS) selection for task offloading in Mobile Edge Computing (MEC) is a challenging problem due to the dynamic nature of MEC systems. The wireless channel as well as the load of BSs are stochastic quantities that can change in a statistically non-stationary fashion. Moreover, the computation capabilities of the BSs are heterogeneous. As the dynamic behaviour of a MEC system is, in practical scenarios, not known in advance, deciding where to offload has to be done under uncertainty about the MEC system and considering its non-stationary and heterogeneous characteristics. This paper investigates latency minimization in MEC with heterogeneous BSs. In order to meet low latency demands, a mobile unit (MU) has to quickly identify the best BS for offloading different computation tasks while facing uncertainty about the non-stationary system dynamics. To solve this problem, we propose a novel piece-wise stationary contextual Multi-Armed Bandit (MAB) algorithm that treats different task types as context and detects non-stationary changes in the BSs' performance. With the use of extensive simulations, we show that our proposed approach outperforms state-of-the-art algorithms, as it quickly adapts to changes in the MEC system and exhibits no penalty during stationary phases.
An Improved Cuckoo Search Based K-Means for Outlier Detection

To enhance the precision and timeliness of outlier detection techniques under data mining background, the characteristic of clustering algorithm is study and analyzed. Addressing the inherent issue of clustering randomly assigned initial points of K-means algorithm, this study introduces an Improved cuckoo search algorithm base on k-means to detect outlier data. Firstly, the cuckoo search algorithm is improved by changing the constant of discovery probability and Lévy flight step size from a constant to a changing value. Then, the improved cuckoo search algorithm and the K-means are combined into a new outlier detection algorithm. Finally, it is applied to the outlier detection of signaling data. Findings from our experiments indicate that this proposed approach outperforms other methods.
A New Image Stitching Method with Entropy and Template Matching

Image stitching has been widely employed in industrial vision, aerial photography, and other fields. Aim to create images with a wide viewing angle, but it easily causes severe artifacts under conditions such as parallax and scene lighting. Therefore, improving the accuracy of image alignment is an important research issue in image stitching. We propose a new image processing technique that combines entropy and normalized correlation coefficient-based template matching for searching a similar region. Then, according to the gray value distribution of the similar area, the Oriented FAST and Rotated BRIEF (ORB) is calculated to improve feature matching accuracy and image alignment.
Performance Evaluation of Vehicle-Centered Traffic Management Using Fog Computing-based Wireless Network

Wireless technology, integrating the Internet of Things (IoT), is playing a vital role in the development of emerging technologies that require a reliable estimation of latency. A tremendous quantity of data is produced by linked IoT devices, and this data is handled via the cloud infrastructure. However, deploying computing techniques as a remedy is inefficient, and the Fog computing permits consumers to interface with the cloud while enabling computation at the edge to solve this issue of concerns. Fog computing is therefore relevant to many applications, including traffic surveillance, and resource management in many practical cases. To alleviate traffic congestion in dense smart cities, an intelligent traffic congestion management system (ITCMS) is presented in this research, where fog computing serves to execute the suggested system. Its effectiveness is measured using performance parameters e.g., execution costs, usage of the network, and round trip time or latency that demonstrate improved functionality, increased accuracy, and convenience in futuristic traffic congestion scenarios.
High Precision Underwater Active Acoustic Positioning Technology Based on Distance Information

The underwater active acoustic positioning technology uses the geometric relationship between the target and the array to calculate the target position. This method needs the capability of accurately measuring the distance between the target and the receiving array, which is an indirect measurement. When the ranging error occurs, it is affected by the error transfer rule, and the positioning precision is low. In order to improve the precision of active acoustic positioning, a high precision active localization algorithm is proposed. In this algorithm, a distance vector model containing environmental parameters was established, and the similarity function was constructed by obtaining the L-p norm of distance vector and copy vector, so as to realize the 3D positioning of the target in the observation area. In order to verify the performance of the algorithm, Monte Carlo simulation is carried out to analyze the distribution of positioning errors. Simulation results show that the proposed method directly matches the distance vector, and the positioning precision is better than the conventional positioning technology, although the positioning precision is also affected by the ranging accuracy.
A Survey on Classic Examples of Constraint Satisfaction Problems with Solutions and Applications

The constrained satisfaction problems (CSPs) is one of the problem solving approach in artificial intelligence (AI). It assigns values to variables from their respective domains (domain is the set of values for a variable) by satisfying the constraints which connects subset of variables. It uses reasoning, search or combination of these two techniques to solve the problem. This survey identifies cryptarithmetic, Sudoku, N-queens and scheduling as some of the important classic examples which were solved using CSPs. Also, problem formulation using CSPs and various solutions to these classic problems using CSPs techniques are discussed and indexed. Finally, the contemporary applications of each of these examples are identified and discussed. The survey is very much useful to identify the strength of CSPs and its usefulness to solve problems (in various domains) which are based on identified examples.
A 4.48-5.98 GHz Frequency Range LC VCO-Based PLL for Wireless Applications with -138.6 dBc/Hz Phase Noise Suppression at 1 MHz Offset

In this study, advancements in Phase-Locked Loop (PLL) design and the optimization of its performance are thoroughly explored, with specific attention given to harnessing the capabilities of an LC Voltage Controlled Oscillator (LC-VCO) operating within a control voltage range spanning from 0.6 V to 1.6 V. The integration of significantly improved PLL components was meticulously executed using the 0.18 μm CMOS process. Exceptional PLL performance is showcased, with a primary emphasis on the voltage-controlled operation of the LC-VCO within its control voltage range. Expeditious PLL-lock times are achieved with remarkable precision, measuring approximately 1.3 μs at 4.48 GHz and an even faster 1.18 μs at 5.98 GHz. These achievements are complemented by exceptional phase noise suppression, which reaches levels as low as -138.6 dBc/Hz at a 1 MHz offset. The system also demonstrates outstanding stability, with an rms jitter measured at an impressively low 13.57 ps. Furthermore, the LC-VCO stands out with its remarkable VCO gain variation (kVCO) of 1.8 GHz/V, highlighting its exceptional tunability. These comprehensive findings offer invaluable insights into the realm of PLL technology, setting the stage for unparalleled precision and efficiency in wireless communication and signal processing applications.
Smart Gloves for People with Speech Disability

People with speech disabilities face difficulties to interact and communicate effectively. They mostly rely on sign language, which is not understood by the general people. To help the speech disabled community to get better employment opportunities and most importantly to help them lead a normal life, we have proposed a system to convert Bangla sign language to spoken Bangla words. The signs (i.e., gestures) made by a speech disabled person will be captured through different sensors positioned on a glove, and the gestures are then processed to produce the corresponding Bangla words. This paper describes the design methodology of the system and the performance of the implemented prototype. The performance analysis indicates that our prototype can identify Bangla signs with 88.97% accuracy.
An Efficient Feature Optimization Approach with Machine Learning for Detection of Major Depressive Disorder Using EEG Signal

Major Depressive Disorder (MDD) is a mental health condition marked by persistent feelings of sadness, diminished interest in once-enjoyable activities, and an array of physical and emotional symptoms that profoundly disrupt an individual's daily life and functioning. In this circumstance, detecting major depressive disorder in the early phase is required. Identifying Major Depressive Disorder (MDD) involves the extraction of multiple features from unprocessed EEG signals. This study proposed a machine learning approach to identify minimum possible features as 14 significant features by removing highly correlated features and applying a Genetic algorithm with several combinations of maximum features. The proposed method obtained an accuracy of 84.1% in AdaBoost, 80.1% in DecissionTree, 85.5% in RandomForest, 84.9% Gradient Boosting, 84.9% in XG Boosting. To assess performance, Subject-wise five-fold cross-validation is used. This performance is comparable to the performance of all features, which is roughly 1300 features, and as a result, utilizing fewer features maintains nearly the same performance while reducing the time necessary for MDD detection.
Hyperbolic Metric Learning Embedding for User Behavior Sequence

The sequential behavior history is an important data in the field of consumer electronics, A common way is to model all browser history as a graph, thus, each sequence of user behavior can be view as a walk on the certain graph. Further, we could model individual user’s historical behavior as a tree sub-graph. Recently, tree-structure data is widely studied, and various hyperbolic metric learning methods are proposed to find a nice representation into a hyperbolic space for the usage of downstream tasks. In this work, we propose a framework to deal with sequential behavior history defined by tags of website pages, then apply hyperbolic metric learning models to obtain an appropriate representation each user.
Solar Powered IoT based Environment Monitoring System

In an era marked by rapid evolution, the importance of effective environmental monitoring and precise weather prediction cannot be overstated. This paper introduces an independent robotic solution engineered to observe vital environmental metrics such as temperature, humidity, air quality, and levels of harmful gases. The robotic system is constructed around an Arduino, consolidating all sensors for monitoring environmental conditions. It seamlessly incorporates LoRa (Long-Range) technology for seamless real-time data transmission. Furthermore, random forest machine learning algorithms have been deployed to predict current weather conditions like sunny, cloudy, and rainy. Primary idea guiding the incorporation of renewable energy is to address the energy gap in rural areas without access to conventional power sources.