Miracle-dz/UniFAR-FTUs huggingface dataset:
An Association Prediction Model: GECOL as a Case Study

— Nowadays, there exists a lot of information that can be handled from business transactions and scientific data and information retrieval is simply no longer enough for decision-making. In this paper will supervised machine learning technique is applied to the mine data warehouse for Enterprise Resource Planning (ERP) of the General Electricity Company of Libya (GECOL). This technique has been applied for the first time on the data of production, transportation and distribution departments. These data are in the form of purchase and work orders of operational material strategic equipment spare parts. This technique would extract prediction rules in order to assist the decision-makers of the company to make appropriate future decisions more easily and in less time. A supervised machine learning technique has been adopted and applied for the mining data warehouse. A well-known software package for data mining which is referred to as WEKA tool was adopted throughout this work. The WEKA tool is applied to the collected data from GECOL. The conducted experiments produce prediction models in the form set of rules in order to help responsible employees make the suitable, right and accurate future decision in a simple way and inappropriate time. The collected data were preprocessed to be prepared in a suitable format to be fed to the WEKA system. A set of experiments has been conducted on those data to obtain
Cross‐layer perspective for channel assignment in cognitive radio networks: A survey

With the advent of various emerging wireless products, the usage of limited spectrum has grown exponentially in the recent years. In the next few years, that mobile data traffic globally is expected to grow up to 50 EB/month, which is nearly a five times increase over year 2018. Therefore, it will become extremely difficult to satisfy the ever increasing demand through the current fixed spectrum assignment policy in which spectrum band is exclusively used for the particular applications, and it has also led to underutilization of a significant portion of the spectrum (like TV bands). Cognitive radio networks has emerged as a possible solution for the problem which makes dynamic spectrum access possible for unlicensed user when licensed user is not active. Among various operations of cognitive radio, channel assignment to the unlicensed user is very important. Further, wireless regional area network is one of the most important application of cognitive radio, which provides wireless broadband to the rural area using vacant TV channels. This paper discusses channel assignment techniques considering various functionalities for cognitive radio networks in respect of wireless regional area network in the existing literature. Initially, a comprehensive introduction to both cognitive radio networks and wireless regional area networks is provided, and in the end, the paper summarizes the various issues and research challenges in the channel assignment for wireless regional area networks.
Workflow Analysis, Scheduling, and Chance Constraint Models in Community Pharmacy Operations

Community pharmacy networks provide most of the US population's prescribed medication, but not picking up the medication or using it improperly can lead to problems such as medication non-adherence and medication misuse. This research focuses on improving community pharmacy network services by proposing a change in the role pharmacists play in these networks. A key task pharmacists perform which is a critical step in the medication dispensing process is verifying that the medication filled is the one prescribed and that it does not conflict with other medications the patient is taking. This dissertation proposes that pharmacists provide important counseling services (i.e. PDPC services) to patients inside community pharmacies. We discuss how adding PDPC services changes the workflow of a community pharmacy and discuss strategies to overcome obstacles preventing pharmacists from providing PDPC services. We use a Discrete Event Simulation (DES) model to simulate a local community pharmacy as well as a community pharmacy network to evaluate strategies that can be used to either improve the workflow process internally (internal strategies) or provide an external resource that can be used to provide support to the pharmacy (external strategies). The internal strategies studied are adding a staff member, predicting prescription pick up times, and providing short duration PDPC services in busy hours. The external strategies studied are utilizing a central fill to dispense part of the pharmacy's demand and adding PDPC kiosks to provide PDPC services inside the pharmacy. The effect of each strategy and the extent of its benefits are studied and highlighted in chapters 2 & 3. The central fill location problem was modeled as a chance constraint stochastic P-median capacitated facility location problem. Three extensions to the location model are added and discussed in detail. Several lower bounds were provided to the problem and an efficient solution method was used to solve the problem. Finally the model was applied to a community pharmacy network in PA in a case study. The results showed that ignoring the highest demand scenarios can save the community pharmacy network from having to add an additional central fill.
An Unsupervised Real-Time Framework of Human Pose Tracking From Range Image Sequences

Pose tracking from range image sequences remains a difficult task due to strong noise and serious self-occlusion of human body. Existing work either rely on extremely large and precisely annotated datasets, or rely on accurate human mesh model and GPU acceleration. In this paper, we propose an unsupervised real-time framework of pose tracking from range image sequences. Our framework consists of a visible hybrid model (VHM), a componentwise correspondence optimization (CCO) and a dynamic database lookup (DDL). VHM consists of component sphere sets and component visible spherical point sets which exhibits both simplicity and high accuracy. CCO converts the matching between VHM and input point cloud into several subproblems regarding local rotations of components and a global translation of body abdominal joint, each of which has an efficient closed form solution. DDL is designed to recover correct pose when tracking fails, which effectively mitigates accumulative error during tracking. Experiments on <inline-formula><tex-math notation="LaTeX">$\mathsf{SMMC}$</tex-math></inline-formula>, <inline-formula><tex-math notation="LaTeX">$\mathsf{PDT}$</tex-math></inline-formula>, <inline-formula><tex-math notation="LaTeX">$\mathsf{EVAL}$</tex-math></inline-formula> datasets indicate that our framework not only achieves better or competitive precision compared with state-of-the-art methods, but also produces real-time efficiency in personal computers without GPU acceleration.
DEVELOPMENT OF AUTHENTICATION CODES OF MESSAGES ON THE BASIS OF UMAC WITH CRYPTO-CODE MCELIECE’S SCHEME

The development of decentralized systems and blockchain technology have expanded the range of cryptocurrency-based banking services. The main difference from the hierarchical structures of the organizations of the banking sector (national and commercial banks) is the formation of valid nodes ensuring the confirmation of transactions based on the checking and verification of digital signatures and MAC codes. The Bitcoin protocols use the SHA-256 algorithm to form MAC codes, however, the rapid growth of the system leads to significant time costs not only for mining, but also for validation of the formed blocks. The further development of decentralized systems, increase the number of wall-distributors and full nodes forces us to look for new ways to solve a temporary problem based on using different approaches to providing authentication in decentralized systems. The paper discusses the algorithm for generating UMAC message authentication codes using a McEliece’s crypto-code scheme based on the use of universal hashing functions. A reduced UMAC model (mini-UMAC) and a method for statistical analysis of the collision characteristics of the generated message authentication codes are proposed. Using the reduced UMAC model, collisional characteristics of authentication codes are investigated, it is shown that the use of cryptographic transformation (using the AES algorithm) at the final stage of UMAC leads to a violation of the universal hashing characteristics.
The use of off-the-shelf wearable sensors to analyze daily-living activities and emotional state of a person at the Smart Condo

The Smart Condo TM is a model condo embedded with a wireless sensor network, developed by an interdisciplinary team including researchers from Occupational Therapy, Industrial Design, Pharmacy, and Computing Science. The Smart Condo TM aims to support older adults, including those with physical and cognitive disabilities, to live independently longer [1]. Older adults with complex needs are often limited in their ability to perform necessary daily activities and may require task-specific supports. Continuous health-monitoring systems have the potential to enhance one’s quality of life and help older adults live safely in their home. This thesis describes two studies in the use of off-the-shelf wearable sensors in order to investigate the feasibility of using wearable devices and usefulness in the Smart Condo TM . In the first study, twenty-six participants spent a single two-hour session in the one-bedroom living environment, either alone or in pairs, and performed a scripted protocol of activities of daily living. Twelve of these participants wore the commercial smart eyewear device JINS MEME, which collected electrooculography (EOG), accelerometer and gyroscope data throughout their sessions. This study used an offline classification method to predict the participants’ activities. The approach showed that this method yields equal or better results with a variety of activities compared to approaches that involve more restrictive wearable device setups. The results demonstrate the suitability of JINS MEME for recognition of activities of daily living and identify limitations associated with the current model. In the second study, twenty-one participants viewed a sequence of images from the International Affective Picture System (IAPS) database. Using wearable sensor devices, we collected electroencephalography (EEG), electrooculography (EOG), and kinematic motion data as participants viewed the images; the participants also characterized their own emotional responses to the images. Participants then played the serious game “Whack-a-Mole,” wearing the sensor devices, and played three levels of the game that required varying amounts of cognitive effort. This study describes the method for emotion recognition (Ensemble Classifier and Random Forest) in participants as they played the serious game. This approach showed that emotional state during a task can be determined accurately using data collected from wearable sensor devices, with and without self-reported measures.
A Comparison of Simple Structure Rotation Criteria in Temporal Exploratory Factor Analysis for Event-Related Potential Data

Abstract. It is challenging to apply exploratory factor analysis (EFA) to event-related potential (ERP) data because such data are characterized by substantial temporal overlap (i.e., large cross-loadings) between the factors, and, because researchers are typically interested in the results of subsequent analyses (e.g., experimental condition effects on the level of the factor scores). In this context, relatively small deviations in the estimated factor solution from the unknown ground truth may result in substantially biased estimates of condition effects (rotation bias). Thus, in order to apply EFA to ERP data researchers need rotation methods that are able to both recover perfect simple structure where it exists and to tolerate substantial cross-loadings between the factors where appropriate. We had two aims in the present paper. First, to extend previous research, we wanted to better understand the behavior of the rotation bias for typical ERP data. To this end, we compared the performance of a variety of factor rotation methods under conditions of varying amounts of temporal overlap between the factors. Second, we wanted to investigate whether the recently proposed component loss rotation is better able to decrease the bias than traditional simple structure rotation. The results showed that no single rotation method was generally superior across all conditions. Component loss rotation showed the best all-round performance across the investigated conditions. We conclude that Component loss rotation is a suitable alternative to simple structure rotation. We discuss this result in the light of recently proposed sparse factor analysis approaches.
Investigating Adversarial Attacks against Network Intrusion Detection Systems in SDNs

Machine-learning based network intrusion detection systems (ML-NIDS) are increasingly popular in the fight against network attacks. In particular, promising detection results have been demonstrated in conjunction with Software-Defined Networks (SDN), in which the logically centralized control plane provides access to data from across the network. However, research into adversarial attacks against machine learning classifiers has highlighted vulnerabilities in a number of fields. These vulnerabilities raise concerns about the implementation of similar classifiers in anomaly-based NIDSs within SDNs. In this work, we investigate the viability of adversarial attacks against classifiers in this field. We implement an anomaly-based NIDS, Neptune, as a target platform that utilises a number of different machine learning classifiers and traffic flow features. We develop an adversarial test tool, Hydra, to evaluate the impact of adversarial evasion classifier attacks against Neptune with the goal of lowering the detection rate of malicious network traffic. The results demonstrate that with the perturbation of a few features, the detection accuracy of a specific SYN flood Distributed Denial of Service (DDoS) attack by Neptune decreases from 100% to 0% across a number of classifiers. Based on these results, recommendations are made as to how to increase the robustness of classifiers against the demonstrated attacks.
Automatic Image Captioning Using Convolution Neural Networks and LSTM

PC vision has turned out to be universal in our general public, with applications in a few fields. Given a lot of pictures, with its inscription, make a prescient model which produces regular, inventive, and intriguing subtitles for the concealed picture. A speedy look at a picture is adequate for a human to call attention to and portray a monstrous measure of insights regarding the visual scene. To rearrange the current issue of producing inscriptions for pictures by making a model which would give exact subtitles to these pictures which can be additionally utilized in other helpful applications and use cases. Be that as it may, this momentous capacity has ended up being a tricky errand for our visual acknowledgment models. Most of the past research in scene acknowledgment has concentrated on naming pictures with a predetermined arrangement of visual classifications and extraordinary advancement has been accom-plished in these undertakings. For a question picture, the past strategies recover pertinent hopeful normal language states by outwardly contrasting the inquiry picture with database pictures. In any case, while shut vocabularies of visual ideas comprise a helpful demonstrating suspicion, they are boundlessly prohibitive when contrasted with the colossal measure of rich depictions that a human can form. These methodologies forced a breaking point on the assortment of inscriptions produced. The model ought to be exempt of suppositions regarding explicit pre decided formats, standards or classes and rather depend on figuring out how to create sentences from the preparation information. The model proposed utilizes Convolution Neural Networks which help to separate highlights of the picture whose subtitle is to be created and afterward by utilizing a probabilistic methodology and Natural Language Processing Techniques reasonable sentences are framed and inscriptions are produced.
An 8.2- $\mu$ W 0.14-mm2 16-Channel CDMA-Like Capacitance-to-Digital Converter

This article presents a multi-channel capacitance-to-digital converter (CDC) circuits using the continuous-time Walsh coding multiplexing. The proposed circuits modulate the input capacitive sensors with orthogonal codes, enable simultaneous read-in of 16 capacitive sensors, and provide the combined 16 digitized outputs in a single conversion. By correlating 16 successive digitized outputs with the corresponding orthogonal codes in post-processing, the 16 capacitive channels can then be demodulated separately. The proposed continuous-time Walsh coding modulation preserves temporal information with minimum circuitry duplication and bandwidth requirement. This chip is fabricated in 130-nm CMOS technology and occupies an area of 0.14 mm<sup>2</sup>. With a reference clock of 4 MHz, the proposed multi-channel CDC achieves 11.4 ENOB with an average conversion time of 482 <inline-formula> <tex-math notation="LaTeX">$\mu \text{s}$ </tex-math></inline-formula>. It consumes 0.51 <inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula>/channel and achieves an FoM of 87 fJ/conversion step.
Data Flow ORB-SLAM for Real-time Performance on Embedded GPU Boards

The use of embedded boards on robots, including unmanned aerial and ground vehicles, is increasing thanks to the availability of GPU equipped low-cost embedded boards in the market. Porting algorithms originally designed for desktop CPUs on those boards is not straightforward due to hardware limitations. In this paper, we present how we modified and customized the open source SLAM algorithm ORB-SLAM2 to run in real-time on the NVIDIA Jetson TX2. We adopted a data flow paradigm to process the images, obtaining an efficient CPU/GPU load distribution that results in a processing speed of about 30 frames per second. Quantitative experimental results on four different sequences of the KITTI datasets demonstrate the effectiveness of the proposed approach. The source code of our data flow ORB-SLAM2 algorithm is publicly available on GitHub.
Resource scheduling and optimization in dynamic and complex transportation settings

Resource optimization has always been a challenge both in traditional fields, such as logistics, and particularly so in most emerging systems in the sharing economy. These systems are by definition founded on the sharing of resources among users, which naturally creates many coordination needs as well as challenges to ensure enough resource supply to cover customer demand. This thesis addresses these challenges in the application of vehicle sharing systems, as well as in the context of multi-operation companies that provide a wide range of services to their users. More specifically, the first part of this thesis focuses on models and algorithms for the optimization of bike sharing systems. Shortage of bikes and docks is a common issue in bike sharing systems, and, to tackle this problem, operators use a fleet of vehicles to redistribute bikes across the network. We study multiple aspects of these operations, and develop models that can capture all user trips that are performed successfully in the system, as well as algorithms that generate complete redistribution plans for the operators to maximize the served demand, in running times that are fast enough to allow real-time information to be taken into account. Furthermore, we propose an approach for the estimation of the actual user demand which takes into account both the lost demand (users that left the system due to lack of bikes or docks) and shifted demand (users that had to walk to nearby stations to find available resources). More accurate demand representations can then be used to inform better decisions for the daily operations, as well as the long-term planning of the system. The second part of this thesis is focused on schedule generation for resources of large companies that must support a complex set of operations. Different operation types come with a variety of constraints and requirements that need to be taken into account. Moreover, specialized employees with a variety of skills and experience levels are required, along with an heterogeneous fleet of vehicles with various properties (e.g., refrigerator vehicles). We introduce the Complex Event Scheduling Problem (CESP), which captures known problems such as pickup-and-delivery and technician scheduling as special cases. We then develop a unified optimization framework for CESP, which relies on a combination of metaheuristics (ALNS) and Linear Program-
Filter for TV and video cameras

An infrared-cut filter for TV and video cameras was calculated and fabricated. The filter contains 28 alternating SiO 2 and TiO 2 layers. The filter was calculated using the principle of unequal-thickness layers. This filter has a transmittance of 95 % in 400 to 650 nm range and 1 % in 700 to1150 nm range.
An Improved AlignedReID Method for Vehicle Re-identification

In this paper, we propose an improved vehicle re-identification method based on the combination between the AlignedReID and the Stochastic Weight Averaging (SWA). AlignedReID extracts a global feature and local features of a vehicle’s image and performs joint learning. Local automatic alignment is achieved by computing the shortest path between the two sets of local features, so that global feature learning can benefit from local feature learning. By running an optimizer with a high constant learning rate, the SWA averages the weight of the model to ensure that a better weight combination can be found. Our improved method surpasses the most advanced methods on the VehicleID dataset and VeRi-776 dataset. In order to better solve the task of vehicle re-identification in residential area, we have made the Oeasy-Parking dataset and experimented with our methods, and achieved good results.
OpenGL programming guide

The OpenGL graphics system is a software interface to graphics hardware. (The GL stands for Graphics Library.) It allows you to create interact ive programs that produce color images of moving three-dimensional objects. With OpenGL, you can con trol computer-graphics technology to produce realistic pictures or ones that depart from reality in imaginative ways. This guide explains how to program with the OpenGL graphics system to deliver th visual effect you want.
Raising insects with an application to enhance students’ self-confidence in interacting with insects

ABSTRACT Insect phobia may last for a lifetime and needs to be overcome. This study aimed to design an application, Insect Garden, for students to learn about and experience how to raise four types of insect: Giant Asian Mantis, Japanese Rhinoceros Beetle, Formosan Stag Beetle, and Seven-Spotted Ladybug. It also investigated how the players’ self-confidence in interacting with insects could be enhanced. Additionally, to understand the affective factors related to gameplay, this study explored the correlates between personality, insect phobia before gameplay, gameplay interest and self-confidence enhancement in interacting with insects. Data were collected from 211 eighth-grade students, from whom 175 useful data were subjected to confirmatory factor analysis and structural equation modeling. The results revealed that Extraversion is positively related to gameplay interest but not to Insect phobia, whereas Neuroticism is positively related to Insect phobia and gameplay interest. Insect phobia is negatively related to self-confidence enhancement in interacting with insects, but gameplay is positively related.