Reviewerly/exHarmony huggingface dataset:
Design and Implementation of a Cross-Camera Suspect Tracking System

Surveillance systems are everywhere deployed for criminal prevention and suspect tracking for specific security events. The recognition of the target person cross multiple cameras remains a challenge because of the large spatio-temporal uncertainty, which means the entry and the exit of persons in cameras are unpredictable. Therefore, this study design and implement a software system that help users to continuous and stable track of the same target person across multiple cameras. Firstly, the system leverages YOLO and Correlation filter to track the suspicious in single camera. The features of the target suspect are also collected in this stage. Once the target person leaves the scope of a camera, the camera network topology is used to predict the candidate entrance cameras based on the geographical information and tracking path in the first stage. In the second stage, the person re-identification algorithm is used to calculate the similarity possibility and help to identify the person automatically between cameras. Experimental results on public datasets show our system can accurately recognize the target suspect across cameras with acceptable system performance.
Community Cellular Networks in the Philippines: Challenges and Opportunities towards Sustainability

Community networks (CNs) are alternative approaches to provide communication access in unserved and underserved areas, where market forces fail to deliver service due to the high risk of investment and minimal returns. This situation resonates in the Philippines with just under 70 % mobile phone penetration. To address this, our team deployed community cellular networks (CCNs) to provide basic 2G voice and SMS services, alongside a novel partnership and operational model. In this paper, we document our experiences and challenges encountered as we test this model in the real world. After almost two years of operations, we reflect on our learnings, which will be applied towards improving our own model and potentially contribute to the development of other approaches in delivering sustainable last-mile communication access.
Blockchain and Its Application in Cybersecurity

Blockchains, in the past decade, have gathered a lot of attraction due to its potential to disrupt the way in which trust in the digital world is generated. Blockchain, the technology powering cryptocurrency worth billions of dollars, is tamper-resistant, immutable ledger that is maintained in a decentralized manner by the miners who do not trust each other. This characteristic of blockchain to induce trust in the untrusted environment, by establishing a commonly agreed upon (among the members of the blockchain network) shared ledger, can be used to solve problems in domains such as finance, supply chain, health care, IoT, etc. In this chapter, we will introduce blockchain and present its applications in cybersecurity.
Automation of Shopping Carts using Technology

The advancements and developments in the field of innovative and creative thinking have played a pivotal role in kick-starting a whole new phase of technology - Internet of Things (IoT). A lot of research is being done in the line of robotics and artificial intelligence, all of which has helped us think of a very effective and economical means of reducing manual labor in our day to day activities. We found our inspiration in a supermarket. Moving shopping carts manually can sometimes become too tedious to control, especially for the elderly and physically disabled. The objective of our work was to solve a major problem of existing shopping carts, which is that they rely on manual labor to move. Our cart uses the principles of image processing to map itself to one particular user using a pattern tag and follows the user based on single object tracking and the distance sensed by ultrasonic sensors interfaced with a Raspberry Pi.
Identification of Cryptographic Algorithms Using Clustering Techniques

Cryptanalysis of cipher text-only attacks is by far the most challenging task in cryptology. This problem has been intimidating researchers as well, and till date no concrete approach has been suggested to address this problem. In this paper, we propose to identify the cryptographic algorithm by analysing the cipher text alone, using the clustering techniques. We have been successful in obtaining results which are able to indicate the cryptographic algorithm used. This methodology of using machine learning algorithms for cryptanalysis is a unique approach, and by looking at our results, it seems to be very promising as well.
A Survey on Deceptive Phishing Attacks in Social Networking Environments

Social phishers continuously adapt the novel trapping techniques of prying usernames and passwords by establishing a friendly relationship through microblog messages using various social networking environments (SNEs) for financial benefits. APWG report of 2018 shows the successive rise in phishing attacks via URLS, fake Web sites, spoofed e-mail links, domain name usage, and social media content (APWG, Anti-Phishing Working Group report [1]). Innumerable defending techniques had been proposed earlier, but are still vulnerable due to exchange of compromised microblogs in SNEs resulting in leaking of confidential information and falling prey to phishers attack. To mitigate the latent fraudulent phishing mechanisms, there is a scope and an immense need to get rid of phishing attacks in SNEs. This paper surveys and analyzes the various social phishing detection and prevention mechanisms that are developed for SNEs.
A New Approach for Moving Object Detection under Varying Illumination Environments

Moving object detection for video surveillance is always a challenging task under varying illumination condition in background. In this paper, we proposes a noble approach for detection of moving objects in the presence of varying illumination condition by extracting illumination insensitive feature. The proposed method is three folded. Initially, the illumination insensitive feature (IIF) of frames of video is measured by considering the local near neighbors of logarithmic image. Next, the background is modeled by running average method followed by background subtraction process. Further, morphological operation is carried out to enhance the detected foreground objects. The simulation results and analysis demonstrate that the proposed method improves the performance in terms of quantitative measures such as recall, precision, F-measure and Percentage of correct classification values as compared to other popular existing methods listed in literature.
Applying one-class learning algorithms to predict phage-bacteria interactions

The need to predict phage-bacteria interactions is a nowadays concern to overcome bacterial resistance issue; public genome databases contain highly imbalanced datasets which have hindered this task. Throughout this paper we will investigate, implement and evaluate One-Class Learning algorithms in order to predict phage-bacteria interactions using only positive samples. We will use the programming language Python aided by Scikit-Learn, Tensorflow and keras to develop the machine learning models and test them with real phage-bacteria interactions datasets. We trained the models using cross validation technique generating a gridsearch with all the datasets to find several combinations of hyperparameters available. Furthermore, we optimized those hyperparameters by using Pareto fronts based on seven different performance metrics, improving the efficiency of each algorithm for a given dataset. To refine each algorithm's performance separately we used the ensemble learning technique with an odd number of algorithms by simple voting. Finally, we managed to achieve an overall performance of 80% in predicting phage-bacteria interactions trained only with positive classes, this percentage in practice means that when a patient has an infection resistant to antibiotics, we have 80% of saving the life rather than maybe a 0% while finding the correct phage for the pathogenic host.
Design of pneumonia and pulmonary tuberculosis early detection system based on adaptive neuro fuzzy inference system

Abstract The results of Basic Health Research in 2018 showed the prevalence of pneumonia and pulmonary tuberculosis (TB) in Indonesia 4.0 percent and 0.4 percent, respectively. However, with a minimal number of lung specialists, the handling of lung disease will be too late. There are only 600-700 lung specialists in Indonesia. This amount is very less when compared with existing lung disease cases. The use of ANFIS for early detection of lung disease is growing. However, the system designed is still used for one type of disease. This research will design an expert system based on ANFIS to detect lung disease early, i.e. for pneumonia and pulmonary TB. Subtractive clustering is used for clustering process. The results of the training showed that both models were able to give better performance compared to the model built using conventional clustering methods. The test results show that both models have comparable performance compared to their counterpart.
On the General Construction of Tightly Secure Identity-Based Signature Schemes

Abstract A tightly secure scheme has a reduction, where the reduction loss is a small constant. Identity-based signature (IBS) is an important cryptographic primitive, and tightly secure IBS schemes enjoy the advantage that the security parameter can be optimal to achieve a certain security level. General constructions of IBS schemes (Bellare, M., Namprempre, C., and Neven, G. (2004) Security Proofs for Identity-Based Identification and Signature Schemes. In Proc. EUROCRYPT 2004, May 2–6, pp. 268–286. Springer, Berlin, Interlaken, Switzerland; Galindo, D., Herranz, J., and Kiltz, E. (2006) On the Generic Construction of Identity-Based Signatures With Additional Properties. In Proceedings of ASIACRYPT 2006, December 3–7, pp. 178–193. Springer, Berlin, Shanghai, China) and their security have been extensively studied. However, the security is not tight and how to generally construct a tightly secure IBS scheme remains unknown. In this paper, we concentrate on the general constructions of IBS schemes. We first take an insight into previous constructions and analyze the reason why it cannot achieve tight security. To further study possible tightly secure constructions, we propose another general construction, which could be seen as a different framework of IBS schemes. Our construction requires two traditional signature schemes, whereas the construction by Bellare et al. uses one scheme in a two-round iteration. There are no additional operations in our general construction. Its main advantage is providing the possibility of achieving tight security for IBS schemes in the random oracle model. Combining two known signature schemes, we present an efficient IBS scheme with tight security as an example.
Design Considerations for EM Pulse Fault Injection

Electromagnetic-fault injection (EM-FI) setups are appealing since they can be made at a low cost, achieve relatively high spatial resolutions, and avoid the need of tampering with the PCB or packaging of the target. In this paper we first sketch the importance of understanding the pulse characteristics of a pulse injection setup in order to successfully mount an attack. We then look into the different components that make up an EM-pulse setup and demonstrate their impact on the pulse shape. The different components are then assembled to form an EM-pulse injection setup. The effectiveness of the setup and how different design decisions impact the outcome of a fault injection campaign are demonstrated on a 32-bit ARM microcontroller.
Knowledge representation for designing an Intelligent Tutoring System in learning of courses about Algorithms

Nowadays building the intelligent systems for education plays an important role to construct the smart city. Knowledge engineering provides technology to build the Intelligent Tutoring System (ITS) supporting for learning. In this paper, a model for representing the knowledge of courses about algorithms is presented. This knowledge model is the integrating between ontology and frames, called Integ-model. Based on the structure of Integ-model, problems about querying on the knowledge and visualizing of algorithms have been proposed and solved. The Integ-model is applied to organize the knowledge base of the intelligent system for learning Data Structures and Algorithms. This system can illustrate algorithms's process and the working of data structures in the course. It also helps learners to query the content of lessons in the course.
Fast high-precision displacement measurement system based on fringe image analysis techniques

Measurement systems with high precision, fast speeds, and large ranges are required in current technological and industrial fields. In this work, a system was constructed based on the Michelson interferometer and a “soft tracking” measurement scheme. First, an algorithm based on the Radon transform and grayscale projection was proposed to detect the inclination angle of the interference fringe. Next, a raw interference image was rotated to be vertical and projected to a one-dimensional form so that the fringe pitch could be calculated using the algorithm based on Fourier spectrum analysis. Finally, a spatial similarity matching method was developed to obtain the amount of fringe shifting. Each component of the algorithm is discussed, and experimental results are presented. The results showed the effectiveness of the measurement system. The system could measure a displacement of 40 μm within 100 ms, which was 35 times improvement in the system efficiency compared to our previous work. Finally, discussions of the measurement errors and the analysis of the influence factors are presented to comprehensively introduce the properties of the measurement system.
Similarity Metric for Millions of Unlabeled Face Images

Lately, image classification with unknown labels has been the focus of many research related to social media and cyber-security. In order to cluster images into labels, a similarity metric is used to compare images and based on the output a decision is made. In this article, we propose a Convolution Neural Network (CNN) based metric that achieves state of the art results. The proposed approach learns the priority features that differentiate two given input faces. The metric is trained on MSCeleb (10M faces of 100k individuals) and VGGFace2 (3.3M faces of 9k individuals) then tested using LFW (13 K faces of 5,749 individuals), MSCeleb, and VGGFace2 datasets. The network is evaluated through <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$F_{\beta}$</tex> measure and accuracy. The metric proposed in this paper achieves state of the art <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$F_{\beta}$</tex> measure of 0.94 for LFW benchmark.
Fusion of Image-text attention for Transformer-based Multimodal Machine Translation

In recent years, multimodal machine translation has become one of the hot research topics. In this paper, a machine translation model based on self-attention mechanism is extended for multimodal machine translation. In the model, an Image-text attention layer is added in the end of encoder layer to capture the relevant semantic information between image and text words. With this layer of attention, the model can capture the different weights between the words that is relevant to the image or appear in the image, and get a better text representation that fuses these weights, so that it can be better used for decoding of the model. Experiments are carried out on the original English-German sentence pairs of the multimodal machine translation dataset, Multi30k, and the Indonesian-Chinese sentence pairs which is manually annotated by human. The results show that our model performs better than the text-only transformer-based machine translation model and is comparable to most of the existing work, proves the effectiveness of our model.
Comparing Grouping Results Between Cluster Analysis and Q-Methodology

The primary purpose of this Student Research Poster Paper is to discuss two grouping methodologies: cluste analysis and the Q-Methodology. Each of these statistical methodologies quantitatively group similar individuals but do so in two separate ways. In cluster analysis, individuals are grouped by optimizing proximity measures. For example, the single link clustering algorithm groups individuals together that have the smallest Euclidean distance from each other. In Q-Methodology, individuals are grouped by evaluating person-to-person correlations. For example, if two individuals have a correlation of 0.78, they are likely to be grouped together whereas individuals with a correlation of 0.09 are likely to be in different groups. In this paper, we outline multiple clustering approaches, the grouping mechanism in Q-Methodology, and discuss the differences between these two approaches when grouping participants. During this discussion, we will use an example from our own engineering education research to compare grouping results from the same data set. This paper contributes to the research field by describing and utilizing a relatively unknown methodology in engineering education. It will also add to our knowledge of cluster analysis techniques and compare those algorithms to another robust grouping method.
An Efficient Approach to Information Hiding Through Image Steganography using Edge Detection

Least Significant Bit (LSB) substitution is the most popular information hiding scheme in spatial steganography. Its popularity lies in the fact that it has very little effect on the cover image and secret message can remain largely unidentified by the human eye, hence, making information exchange more secure. LSB substitution can be further optimised by combining it with edge detection techniques. This is because different regions in cover image can store different number of bits. Complex regions or regions having an edge are less sensitive to change and can hide more data than smooth regions. In this research, a steganographic approach is proposed that combines edge detection based steganographic techniques with Optimal Pixel Adjustment Process (OPAP). Through experiments it is demonstrated that proposed method leads to better image quality at high payloads than even some of the state-of-the-art high capacity steganographic methods. It is also shown that proposed method can have embedding rates up to 3.407 bpp at PSNR value of 35dB, which can't be discerned by the Human Visual System (HVS). This is greater than any other method in literature.
Scalable Recommendations Using Clustering Based Collaborative Filtering

The development and growth in recommender systems address the issue of information overload faced by the online users while searching for products or services. However, recommender systems typically face challenges like data sparsity and scalability as they often handle large datasets. The most widely used recommendation technique is Collaborative Filtering (CF) that pins down the recommendations on the opinions of the most similar users. The core of a CF algorithm is the similarity computations among the users or items, which becomes extremely expensive when new users and items join the system at a very rapid rate. The proposed work deals with this scalability problem by implementing a clustering based CF approach. Typically in a recommendation problem there exists a set of users, a set of items and a rating matrix, that records the ratings assigned by the users to the items. In this work, we first partition the set of users using a CURE (Clustering using representatives) based method and then leverage the resultant clusters to formulate recommendations for the target user. In the proposed method, the CF algorithm is not applied to the entire user-item database, rather the algorithm is applied separately to each of the clusters resulting in reduced recommendation time. Moreover, Clustering also helps to improve the sparsity problem by reducing the dimension of the rating matrix and filtering out noisy data. The results of the experiments conducted on MovieLens-10M and MovieLens-20M datasets indicate that our method significantly reduces the runtime and at the same time preserves good recommendation quality.
Teaching Web Programming Using the MEAN Stack

Web programming is a mandatory course in the syllabus of any BSc degree in Informatics Engineering. But, nowadays, designing and teaching an up-to-date web programming course is a very demanding challenge due to the fast technological advances that imposes a continuous update of the concepts, techniques, and tools. Nowadays, from a professional perspective, web development is starting to converge on merging front-end and back-end development. This requires new skills for web developers, traditionally separated into two groups (front-end and back-end), that are still not accompanied by academia web programming courses. This future trend motivates us for designing a higher education course that combines Web design and server-side programming skills in a unified full-stack of Web technologies to develop modern web applications. The paper intends to present and discuss a new higher education course for a curricular unit in Web programming of a degree in Informatics in the European higher education system. The course is aligned with the new trends, providing students with fundamentals and basic skills in the development of Web applications, using the full-stack approach to Web development supported by the MEAN stack architecture.
T-BASIR: Finding Shutdown Bugs for Cloud-Based Applications in Cloud Spot Markets

One of the major advantages of cloud spot instances in cloud computing is to allow stakeholders to economically deploy their applications at much lower costs than that of other types of cloud instances. In exchange, spot instances are often exposed to revocations (i.e., terminations) by cloud providers. With spot instances becoming pervasive, terminations have become a part of the normal behavior of cloud-based applications; thus, these applications may be left in an incorrect state leading to certain bugs. Unfortunately, these applications are not designed or tested to deal with this behavior in the cloud environment, and as a result, the advantages of cloud spot instances could be significantly minimized or even entirely negated. We propose a novel solution to automatically find these bugs and locate their causes in the source code. We evaluate our solution using 10 popular open-source applications. The results show that our solution not only finds more instances and different types of these bugs compared to the random approach, but it also locates the causes of these bugs to help developers improve the design of the shutdown process and is more efficient in finding instances of these bugs since it interposes at the system call layer.