Miracle-dz/UniFAR-FTUs huggingface dataset:
RAT selection for a low battery mobile device for future 5G networks

The growth of wireless communication toward fifth generation will lead to the existence of number of access technologies to provide seamless connectivity and form heterogeneous network environment. Earlier, there was single access technology to run applications, but 5G will have heterogeneous network environment and provide separate network for each application. As compared with 4G, 5G will provide increase in data rate, decrease in delay, increase in quality of service, and so on because of its various enabling technologies. Therefore, for each application, selection of best access network via its enabling technology is an important task. This selection is done either at user terminal side or at network operator side by combining preferences for network attributes and network parameters. In this paper, to enjoy 5G, selection is done in a heterogeneous networks environment for enabling technologies like device‐to‐device communication, spectrum sharing, enhancing quality of experience, energy efficiency, and so on. This selection is done via optimization techniques for a fixed duration video clip that is to be transmitted from a device running low in battery. The selection environment composed of UMTS, WLAN1, and WLAN2 as available networks. The simulation results show that the network selected for each enabling technology supports various features of 5G. Also, error analysis of selection results is done using confidence interval estimate at 90%, 92%, and 95% confidence level. From results obtained, it is seen that different optimization techniques used to access network for different enabling technologies (providing 5G features) prove to be useful for future 5G network.
A Space for Every Student: Assessing the Utility of a Family Friendly Study Room in a University Library

Abstract Students currently in and returning to college are increasingly likely to have children. The demands of academic and family life can conflict, making it difficult for student-parents to navigate both. Brigham Young University recently built a Family Friendly Study Room in the university library to provide a place for student-parents to care for their children while engaging in academic work. This study assessed the impact of the FFSR on patrons. Results show that the space is highly valued and offers both academic and relationship benefits. Moreover, students’ suggestions provide important feedback for future improvements. Recommendations for creating family-friendly spaces in university libraries are discussed.
ENHANCING THE UNDERWATER IMAGE USING STATIONARY WAVELET TRANSFORM

Image enhancement is one of the major issues in the area of image processing. Dredging the ocean’s surface for identifying the living and non-living things is a complicated task in the present era. Identifying the objects in the underwater is done with the help of images. Generally, these images are low contrast gray scale images where the objects cannot be viewed clearly because of the poor quality. As depth of the water increases, the problem of identifying the objects becomes much more complex. The problems vary according to the type of water, depth, distance between the camera and object. This paper deals with enhancing the underwater image using stationary wavelet transform by working on the color correction and white balance. This enhancement process involves few steps. In the first step, histogram equalization is performed on the degraded image for adjusting the contrast of an image. In the second step, white balancing algorithm is performed in order to enhance the dark regions. In the next step, the outputs from step1 and step2 are fused using stationary wavelet transform.
Design an Arm Robot through Prolog Programming Language

This paper aims at furnishing basic definitions for robotics in the span of man-made intelligence. Simple robotics and their faculties such as sensors and actuators are included in the definition. Moreover, intelligent and non-intelligent types of robotics are described in this research work. We generally focus on non-intelligent robotics (program-based robotics) and the setbacks designers can encounter . The methods and designs of an arm robot generally encompass three components: namely, the mechanical assembly of the arm robot, the electronic circuit, and software design employing Prolog software programming language. Our design concentrates generally on programming an arm robot to manipulate its movements. In our case study programming an arm robot we delineate probable resolutions to setbacks we encountered without going intensely into theory, and we demonstrate a stable design for resolving these setbacks via a flowchart. The design of the flowchart is next explained and subsequently coded.
Transferring meditative tasks to virtual reality through bioadaptation

This thesis examines the design process and elements for virtual environments in support of bioadaptive meditation. The systems presented adapt to the user’s real-time bioand neurosignals. These adaptations convey information about the physiological states during the meditation exercise. Two immersive virtual environments were constructed to support and guide the meditation practices. The study gathers insights on bioadaption, virtual reality, how they relate to meditation and explains the development processes of the virtual environments. These are combined to pinpoint best practices, shortcomings and design principles for related virtual environments.
Opportunistic Fog for IoT: Challenges and Opportunities

With the proliferation of Internet of Things (IoT) devices, there is a demand for technologies to support high-velocity, dynamic resource provisioning to provide secure, cost-efficient, and real-time IoT services in resource-constrained environments. Conventional fog computing by itself cannot address such requirements and needs to be complemented with opportunistic fog computing, by providing mobile fog resources on-demand. In this paper, we discuss key issues in this area, and investigate potential solutions from existing work. We conclude this paper with a summary of gaps, and propose an opportunistic architecture for future work.
“A Smile I Could Not Explain”

This article reports a discourse analysis of journals from adult learners during a 1-week residency in Cape Town, South Africa. The theoretical posture is a critical dialogic perspective, making use of a postcolonial understanding of intercultural interactions. The purpose of the study was exploratory. The analysis suggests that demographic variables (e.g., race), prior international travel, and experiences during the residency influence the amount and pace of cognitive change. Results include both questions for future research and suggestions for educators.
Model-based sensorless robot collision detection under model uncertainties with a fast dynamics identification

This article presents a novel model-based sensorless collision detection scheme for human–robot interaction. In order to recognize external impacts exerted on the manipulator with sensitivity and robustness without additional exteroceptive sensors, the method based on torque residual, which is the difference between nominal and actual torque, is adopted using only motor-side information. In contrast to classic dynamics identification procedure which requires complicated symbolic derivation, a sequential dynamics identification was proposed by decomposing robot dynamics into gravity and friction item, which is simple in symbolic expression and easy to identify with least squares method, and the remaining structure-complex torque effect. Subsequently, the remaining torque effect was reformulated to overcome the structural complexity of original expression and experimentally recovered using a machine learning approach named Lasso while keeping the involving candidates number reduced to a certain degree. Moreover, a state-dependent dynamic threshold was developed to handle the abnormal peaks in residual due to model uncertainties. The effectiveness of the proposed method was experimentally validated on a conventional industrial manipulator, which illustrates the feasibility and simplicity of the collision detection method.
Studies on the Impact of Cache Configuration on Multicore Processor

The demand for a powerful memory subsystem is increasing with increase in the number of cores in a multicore processor. The technology adapted to meet the above demands are: increasing the cache size, increasing the number of levels of caches and bymeans of a powerful interconnection network. Caches feeds the processing element at a faster rate. They also provide high bandwidth local memory to work with. In this research, an attempt has beenmade to analyze the impact of cache size on performance of multicore processors by varying L1 and L2 cache size on the multicore processor with internal network (MPIN), also referenced from NIAGRA architecture. As the number of cores increases, traditional on-chip interconnect like bus and crossbar proves to be less efficient as well as suffers from poor scalability. In order to overcome the scalability and efficiency issues in these conventional interconnects, ring based design has been proposed. The effect of interconnect on the performance of multicore processors has been analyzed and a novel scalable on-chip interconnection mechanism (INoC) for multicore processors has been proposed. The benchmark results are presented using a full system simulator. Results shows that, using the proposed INoC,execution time can be significantly reduced, compared with MPIN.Cache size and set-associativity are the features on which the cache performance is dependent. If the cache size is doubled, then the cache performance can increase but at the cost of high hardware, larger area and more power consumption. Moreover, considering the small form-factor of themobile processors, increase in cache size affects the device size and battery running time. Re-organization and reanalysis of cache onfiguration ofmobile processors are required for achieving better cache performance, lower power consumption and chip area. With identical cache size, performance gained can be obtained from a novel cache mechanism. For simulation, we used SPLASH2 benchmark suite.
Exploration of Recent Advances in the Field of Brain Computer Interfaces

A new approach for implementing number of expressions, emotions and, actions to operate objects through the thoughts of brain using a Non-Invasive Brain Computing Interface (BCI) technique has been proposed. In this paper a survey on brain and its operations are presented. The steps involved in the brain signal processing are discussed. The current systems are able to present few expressions and emotions on a single device. The proposed system provides the extended number of expressions on multiple numbers of objects.
Hidden Surface Removal Using BSP Tree with CUDA

Binary Space Partitioning (BSP) Tree can be used for hidden surface removal. In order to hide invisible surfaces, all surfaces are sorted back to front or front to back order. Traversal of BSP Trees for back to front order of faces requires calculation for all BSP Tree nodes, which can be made in parallel manner. NVIDIA’s CUDA (Compute Unified Device Architecture) is a parallel programming model for GPUs, which shows performance over CPUs for massively parallel computations. Using BSP Tree, rendering of faces sorted by CUDA is implemented and rendering times are compared with the ones observed for the case that CUDA is not used. Keywords: BSP Tree, CUDA, hidden surface removal;
Towards intelligent control system for computer numerical control machines

Advances in deep learning have led to impressive results in recent years. The new technologies such as convolutional neural networks, reinforcement learning and generative adversarial networks have shown a real promise for industrial and real-life applications. In this paper, the results of the experimental research on designing, training and implementation of the intelligent control system for the computer numerical control (CNC) machine were presented. The results indicate that using the generative adversarial technique in conjunction with reinforcement learning is possible to design and train the control systems for the machine tools. Building intelligent models in the absence of large datasets of labelled data is a crucial task. One of the key points of this experimental study is the training of a model of the control system using a set of unmarked data. This is achieved by using a reinforcement learning technique. A designed model can be deployed on the physical machine tools like a computer numerical control machine. At the presented research the laser engraver CNC machine is used. In this paper, the architecture of the computer intelligent control system for the laser engraver and the process of its training are described. The proposed model can be applied to different types of CNC machines.
Automated Boxwood Topiary Trimming with a Robotic Arm and Integrated Stereo Vision*

This paper presents an integrated hardware-software solution to perform fully automated robotic bush trimming to user-specified shapes. In contrast to specialized solutions that can trim only bushes of a certain shape, the approach ensures flexibility via a vision-based shape fitting module that allows fitting an arbitrary mesh into a bush at hand. A trimming planning method considers the available degrees of freedom of the robot arm to achieve effective cutting motions. The performance of the mesh fitting module is assessed in multiple experiments involving both artificial and real plants with a variety of shapes. The trimming accuracy of the overall approach is quantitatively evaluated by inspecting the bush pointcloud before and after robotic trimming, and measuring the change in the deviation from the originally computed target mesh.
An effective back‐off selection technique for reliable beacon reception in VANETs

In safety‐critical scenarios, reliable reception of beacons transmitted by a subject vehicle is critical to avoid vehicle collision. According to the employed contention window sizes in IEEE 802.11p, beacons are transmitted with a small contention window size. As a result, multiple vehicles contend for the shared channel access by selecting the same back‐off slot. This is a perfect recipe for synchronous collisions wherein reliable beacon delivery cannot be guaranteed for any vehicle. We consider the problem of selecting the back‐off slots from the current contention window to provide reliable delivery of beacons transmitted by a subject vehicle to its neighbors. Given a safety scenario, we propose a Pseudo‐Random Number Generator (PRNG)‐inspired back‐off selection (PBS) technique. The proposed technique works on the hypothesis that synchronous collisions of beacons transmitted by a subject vehicle can be reduced if all its neighbors select different back‐off slots (ie, not the back‐off slot selected by the subject vehicle). The discrete‐event simulations demonstrate that PBS can increase the overall message reception from a subject vehicle, in comparison with the uniform random probability back‐off selection in IEEE 802.11p.
Rationally Reappraising ATIS-based Dialogue Systems

The Air Travel Information Service (ATIS) corpus has been the most common benchmark for evaluating Spoken Language Understanding (SLU) tasks for more than three decades since it was released. Recent state-of-the-art neural models have obtained F1-scores near 98% on the task of slot filling. We developed a rule-based grammar for the ATIS domain that achieves a 95.82% F1-score on our evaluation set. In the process, we furthermore discovered numerous shortcomings in the ATIS corpus annotation, which we have fixed. This paper presents a detailed account of these shortcomings, our proposed repairs, our rule-based grammar and the neural slot-filling architectures associated with ATIS. We also rationally reappraise the motivations for choosing a neural architecture in view of this account. Fixing the annotation errors results in a relative error reduction of between 19.4 and 52% across all architectures. We nevertheless argue that neural models must play a different role in ATIS dialogues because of the latter’s lack of variety.
Investigating Political Herd Mentality: A Community Sentiment Based Approach

Analyzing polarities and sentiments inherent in political speeches and debates poses an important problem today. This experiment aims to address this issue by analyzing publicly-available Hansard transcripts of the debates conducted in the UK Parliament. Our proposed approach, which uses community-based graph information to augment hand-crafted features based on topic modeling and emotion detection on debate transcripts, currently surpasses the benchmark results on the same dataset. Such sentiment classification systems could prove to be of great use in today’s politically turbulent times, for public knowledge of politicians’ stands on various relevant issues proves vital for good governance and citizenship. The experiments also demonstrate that continuous feature representations learned from graphs can improve performance on sentiment classification tasks significantly.
Review on flexible job shop scheduling

Flexible job shop scheduling problem (FJSP) is an NP-hard combinatorial optimisation problem, which has significant applications in the real world. Due to its complexity and significance, lots of attentions have been paid to tackle this problem. In this study, the existing solution methods for the FJSP in recent literature are classified into exact algorithms, heuristics and meta-heuristics, which are reviewed comprehensively. Moreover, the real-world applications of the FJSP are also introduced. Finally, the development trends of the manufacturing industry are analysed, and the future research opportunities of the FJSP are summarised in detail.
Positioning GSM telephones

Due to the FCC requirement that operators of mobile communications networks be able to accurately locate mobile callers requesting emergency assistance via 911 by the year 2001, there has been a lot of activity among cellular and PCS providers to examine cellular positioning options. This article examines positioning solutions for the GSM group of standards. Worldwide deployment of GSM systems is well underway, and the positioning characteristics of the various systems are similar. The authors examine the ability to derive position information from GSM signals, based on their May 1996 achievement of accurate position measurements using GSM. Features of GSM signals relevant to positioning are analyzed as well as results achieved and the authors' ongoing positioning trials. Finally, other issues related to GSM positioning and mobile phone positioning in general are covered.
Using Constraints on a general Knowledge lexical networK for domain-specific semantic relation extraction and modeling

We introduce a pattern-based approach applied to the semantic relation retrieval and semantic modeling. Our method relies upon the use of a general knowledge lexical semantic network built, shaped, and handled by crowd-sourcing and GWAPs (games with a purpose). Implementing constraints on semantic relations available in the network increases the efficiency of the relation extraction process but also opens a semantic modeling perspective. In terms of (mostly horizontal) relation extraction, we tested our method on radiology reports in French. Our results show the interest of using a general knowledge lexical semantic network for the domain specific textual analysis as well as the interest of implementing series of constraints on semantic relations for the relation retrieval. We recently turned to the analysis of cooking recipes that stand for examples of domain specific instructional texts. Thus, in addition to the semantic relation discovery, we are building a method for the semantic modeling and conceptualization of cooking instructions. Its first results are presented below. Today, our results are available for French but we target extending the lexical network coverage to other languages in the next few years.
Machine Learning Technique and Normalization Cross Correlation Model Applied for Face Recognition

Face recognition systems just like any other biometric systems have continued to stand the test of time as a reliable means of human verification and identification. The high rate of fraud, crime, and terrorism in Nigeria and the world at large makes it increasingly necessary to have recognition systems that will be compatible with security devices currently deployed. However, the accuracy of facial recognition system is dependent on the adequacy of the model applied. This work applies a combination of Support Vector Machine (SVM) and Normalization Cross Correlation (NCC) starting with a preprocessing stage that involves filtering, cropping, normalization as well as histogram equalization of the face images. The facial images were trained and classified using Support Vector Machine then verified by NCC. The experimental study of the model with benchmarked face images showed that the model is very suitable for obtaining a better accuracy level. The False Acceptance Rate (FAR), False Rejection Rate (FRR), Genuine Acceptance Rate (GAR) and Total Error Rate (TER) values established the superiority of the proposed model over some related ones.
The Extract Region of Interest in High-Resolution Palmprint using 2D Image Histogram Entropy Function

The segmentation of high-resolution palmprint is has been challenged and the research in this filed is still limited because of variations in location and distortion of these images. To achieve superior recognition result, accurate segmentation of a region of interest is very crucial. Therefore, in this paper, a novel palmprint extraction method has been presented using a 2D image histogram entropy function and mathematical dilation. The proposed method has two phases. The first phase is the binarization image where the histogram of the image will be determined after applying a median filter to remove noise and then calculating the 2D image histogram entropy function. Finally, the maximum entropy that will be the adaptive threshold value to build a binary palmprint image will be selected. The second phase is to extract the ROI, apply a dilation method on the binary image, then dividing the dilate image into four regions and finding four reference points depending on the white percentage and finally the ROI will be extracted. The publically available high-resolution palmprint THUPALMLAB has been used for testing. The result indicates a high percentage of accuracy up to 93%. The findings strongly indicate that the proposed method was able to extract the palm’s ROI more consistently. These ROIs will be used in the recognition system instead of whole palmprints and hence assists in improving the performance of a traditional palmprint system. High-resolution palmprint images are highly used in the forensic application.