Exploiting information needs and bibliographics for polyrepresentative document clustering

In this paper we explore the potential of combining the principle of polyrepresentation with document clustering. Our idea is discussed and evaluated for polyrepresentation of information needs as wells as for document-based polyrepresentation where bibliographic information is used as representation. The main idea is to present the user with the highly ranked polyrepresentative clusters to support the search process. Our evaluation suggests that our approach is capable of increasing retrieval performance, but performance varies for queries with a high or low number of relevant documents

A probabilistic approach for cluster based polyrepresentative information retrieval

A thesis submitted to the University of Bedfordshire in partial ful lment of the requirements for the degree of Doctor of PhilosophyDocument clustering in information retrieval (IR) is considered an alternative to rank-based retrieval approaches, because of its potential to support user interactions beyond just typing in queries. Similarly, the Principle of Polyrepresentation (multi-evidence: combining multiple cognitively and/or functionally diff erent information need or information object representations for improving an IR system's performance) is an established approach in cognitive IR with plausible applicability in the domain of information seeking and retrieval. The combination of these two approaches can assimilate their respective individual strengths in order to further improve the performance of IR systems. The main goal of this study is to combine cognitive and cluster-based IR approaches for improving the eff ectiveness of (interactive) information retrieval systems. In order to achieve this goal, polyrepresentative information retrieval strategies for cluster browsing and retrieval have been designed, focusing on the evaluation aspect of such strategies. This thesis addresses the challenge of designing and evaluating an Optimum Clustering Framework (OCF) based model, implementing probabilistic document clustering for interactive IR. Thus, polyrepresentative cluster browsing strategies have been devised. With these strategies a simulated user based method has been adopted for evaluating the polyrepresentative cluster browsing and searching strategies. The proposed approaches are evaluated for information need based polyrepresentative clustering as well as document based polyrepresentation and the combination thereof. For document-based polyrepresentation, the notion of citation context is exploited, which has special applications in scientometrics and bibliometrics for science literature modelling. The information need polyrepresentation, on the other hand, utilizes the various aspects of user information need, which is crucial for enhancing the retrieval performance. Besides describing a probabilistic framework for polyrepresentative document clustering, one of the main fi ndings of this work is that the proposed combination of the Principle of Polyrepresentation with document clustering has the potential of enhancing the user interactions with an IR system, provided that the various representations of information need and information objects are utilized. The thesis also explores interactive IR approaches in the context of polyrepresentative interactive information retrieval when it is combined with document clustering methods. Experiments suggest there is a potential in the proposed cluster-based polyrepresentation approach, since statistically signifi cant improvements were found when comparing the approach to a BM25-based baseline in an ideal scenario. Further marginal improvements were observed when cluster-based re-ranking and cluster-ranking based comparisons were made. The performance of the approach depends on the underlying information object and information need representations used, which confi rms fi ndings of previous studies where the Principle of Polyrepresentation was applied in diff erent ways

Polyrepresentative Clustering: A Study of Simulated User Strategies and Representations

Abstract. The principle of polyrepresentation and document clustering are two established methods for Interactive Information Retrieval, which have been used separately so far. In this paper we discuss a cluster based polyrepresentation approach for information need and document based representations. In our work we simulate and evaluate two possible cluster browsing strategies a user could apply to explore the polyrepresentative clusters. In our evaluation we apply information need and bibliographic features on the iSearch collection. Our results suggest that polyrepresentative cluster browsing may be more effective than exploring a ranked list

RETRACTED: Monolayers of pigment-protein complexes on a bare gold electrode:Orientation controlled deposition and comparison of electron transfer rate for two configurations

Photosynthetic protein complexes are very efficient in solar energy absorption, excitation transfer, and subsequent electron transfer. These complexes have the potential to be exploited as circuit elements for various bio-hybrid devices, ranging from biosensors to solar cells. In this report, we characterized a bioelectronic composite fabricated by interfacing reaction center-light harvesting 1 (RC-LH1) complex with an un-functionalized gold surface in defined orientation. The orientation of RC-LH1 complex was controlled by using Langmuir-Blodgett (LB) deposition technique: RC-LH1 complexes were attached to the electrode facing either with their primary donor or the acceptor sides by'"forward" or'"reverse" dipping, respectively. Photochronoamperometry was utilized to confirm the integrity of the protein complexes and their orientation. Electrical transport of protein complexes coupled to gold electrode was studied by using conductive atomic force microscopy (C-AFM). Two distinct current-voltage (I-. V) curves were observed for two different deposition schemes, indicating opposite orientations of RC-LH1 complexes on the electrode. I-. V spectroscopy was also carried out under light illumination, the magnitude of current was considerably increased by the light illumination and the asymmetry of the curves was more pronounced. We show that, RC-LH1 complexes attached to the electrode with primary donor side facing the electrode exhibit much faster electron transfer compared to opposite orientatio

Ultra-wideband Sensor Antenna Design for 5G/UWB Based Real Time Location Systems

This paper presents a compact wideband (WB) sensor antenna design for Fifth generation mobile communication network (5G) or UWB based real time locations systems (RTLS). The proposed design is inspired from sleeve dipole structure. This sensor antenna consists of wideband antipodal sleeve (WAS) dipole and sleeve monopole, connect with same feedline. The advantage of antipodal configuration lies in its capability of feeding without any matching balun. So, the proposed antenna is printed on both sides of F4BM350 substrate for ease of feeding. The upper wing of WAS antenna is connected to the inner core of the SMA connector and the back of the antenna is connected to the outer conductor of the SMA connector. This antenna covers a bandwidth ranging from 5.64 - 7.25 GHz with Omni-directional radiation characteristics. A prototype of antenna was fabricated for measurement. The measured results are consistent with the simulated results. Moreover, this antenna can be used as a sensor antenna design for 5G or UWB based real-time location systems, and further can offer applications such as smart parking, localization and positioning

Compact base station antenna based on image theory for UWB/5G RTLS embraced smart parking of driverless cars

The Internet of Thing (IoT) and fifth-generation mobile communication networks (5G) are leading towards a paradigm shift by proving seamless connectivity to a large number of devices. The applications of IoT in smart cities have further attracted local authorities to adopt technologies such as driverless cars, smart parking and smart waste management. This paper presents a compact base station antenna design with enhanced directivity/gain for ultra-wideband (UWB)/5G embraced real-time location systems (RTLS) based smart parking of driverless cars. The proposed base station antenna is based on image theory to achieve enhanced directivity and narrower beam width without using more array elements to keep smaller dimensions. Moreover, the base station antenna consists of an antipodal dipole printed on the opposite side of Rogers 4350 substrate, and a metal plate carefully designed and placed to produce a mirror image in order to achieve a high value of directivity in a specified direction. The advantage behind the antipodal dipole configuration is to avoid the use of extra balun for impedance matching. The half-power beamwidth of 110° is achieved along with 7 dB gain by placing a reflector plane at the distance of 0.25 λo from the proposed antipodal dipole antenna. Also, this antenna provides a bandwidth ranging from 6 to 7.25 GHz, which can be used for UWB or 5G based RTLS systems. Furthermore, the proposed compact antenna design will help to improve the localization accuracy of ultra-wideband RTLS systems for smart parking applications of autonomous cars

TAQWA: Teaching Adolescents Quality Wadhu/Ablution contactlessly using deep learning

This research presents a unique and innovative approach to teaching young children the proper steps of ablution (wazoo/wudu) by utilizing a non-invasive sensing system integrated with deep learning algorithms. However, most existing ablution detection systems rely on cameras, which raise privacy concerns, face challenges with lighting conditions, and require complex training with long video sequences. We conducted experiments with a group of youngsters to evaluate the system’s effectiveness, demonstrating its potential in fostering a deeper appreciation and comprehension of religious practices among young learners. This innovative privacy-preserving ablution system employs state-of-the-art UWB-radar technology with advanced Deep Learning (DL) techniques to effectively address the challenges mentioned above. The core focus of this system is to categorize the four fundamental ablution steps: Wash Face 3x, Wash Hand 3x, Wash Head 1x, and Wash Feet 3x. By transforming the collected data into spectrograms and harnessing the sophisticated DL models VGG16 and VGG19, the proposed system accurately detects these ablution steps, achieving an impressive maximum accuracy of 97.92% across all categories with the utilization of VGG16

Contactless privacy-preserving head movement recognition using deep learning for driver fatigue detection

Head movement holds significant importance in con-veying body language, expressing specific gestures, and reflecting emotional and character aspects. The detection of head movement in smart or assistive driving applications can play an important role in preventing major accidents and potentially saving lives. Additionally, it aids in identifying driver fatigue, a significant contributor to deadly road accidents worldwide. However, most existing head movement detection systems rely on cameras, which raise privacy concerns, face challenges with lighting conditions, and require complex training with long video sequences. This novel privacy-preserving system utilizes UWB-radar technology and leverages Deep Learning (DL) techniques to address the mentioned issues. The system focuses on classifying the five most common head gestures: Head 45L (HL45), Head 45R (HR45), Head 90L (HL90), Head 90R (HR90), and Head Down (HD). By processing the recorded data as spectrograms and leveraging the advanced DL model VGG16, the proposed system accurately detects these head gestures, achieving a maximum classification accuracy of 84.00% across all classes. This study presents a proof of concept for an effective and privacy-conscious approach to head position classification.</p

Malware detection : a framework for reverse engineered android applications through machine learning algorithms

Today, Android is one of the most used operating systems in smartphone technology. This is the main reason, Android has become the favorite target for hackers and attackers. Malicious codes are being embedded in Android applications in such a sophisticated manner that detecting and identifying an application as a malware has become the toughest job for security providers. In terms of ingenuity and cognition, Android malware has progressed to the point where they’re more impervious to conventional detection techniques. Approaches based on machine learning have emerged as a much more effective way to tackle the intricacy and originality of developing Android threats. They function by first identifying current patterns of malware activity and then using this information to distinguish between identified threats and unidentified threats with unknown behavior. This research paper uses Reverse Engineered Android applications’ features and Machine Learning algorithms to find vulnerabilities present in Smartphone applications. Our contribution is twofold. Firstly, we propose a model that incorporates more innovative static feature sets with the largest current datasets of malware samples than conventional methods. Secondly, we have used ensemble learning with machine learning algorithms such as AdaBoost, SVM, etc. to improve our model’s performance. Our experimental results and findings exhibit 96.24% accuracy to detect extracted malware from Android applications, with a 0.3 False Positive Rate (FPR). The proposed model incorporates ignored detrimental features such as permissions, intents, API calls, and so on, trained by feeding a solitary arbitrary feature, extracted by reverse engineering as an input to the machine

Machine Learning enabled food contamination detection using RFID and Internet of Things system

This paper presents an approach based on radio frequency identification (RFID) and machine learning for contamination sensing of food items and drinks such as soft drinks, alcohol, baby formula milk, etc. We employ sticker-type inkjet printed ultra-high-frequency (UHF) RFID tags for contamination sensing experimentation. The RFID tag antenna was mounted on pure as well as contaminated food products with known contaminant quantity. The received signal strength indicator (RSSI), as well as the phase of the backscattered signal from the RFID tag mounted on the food item, are measured using the Tagformance Pro setup. We used a machine-learning algorithm XGBoost for further training of the model and improving the accuracy of sensing, which is about 90%. Therefore, this research study paves a way for ubiquitous contamination/content sensing using RFID and machine learning technologies that can enlighten their users about the health concerns and safety of their food