Reviewerly/exHarmony huggingface dataset:
Few-Shot Open World Learner

Computer vision based recognition systems in dynamically changing environments require continuously updating datasets with novel detected categories while maintaining equally high performance on previously established classes. These requirements can be addressed by the concept of Open World Learning introduced by (Chen et al., 2018). We propose a novel framework as a solution to the open world learning problem. This solution is based on the few-shot classification strategy in combination with an outlier detection module. The few-shot classifiers utilized in this work provide a similarity-based classification scheme and present an excellent solution to the incremental learning problem natively, requiring no training and only a few labeled examples of new classes before adapting to them. The discovery of new classes is performed by an outlier detection module that utilizes the similarity space created by the few-shot classifier to identify sufficiently different samples from the known classes and remove them from the classification process. This paper highlights the best combination of few-shot and outlier detection algorithms. Extensive experiments with different combinations of these algorithms are conducted in the pursuit of the ideal components of the framework. Results of the experiments show that we are successful in creating a novel implementation of an open world learner with a very limited loss of accuracy compared to the base few-shot algorithm.
The Effectiveness of Outsourcing Cybersecurity Practices: A Study of the Italian Context

The increasing number of cyber-attacks requires an organizational awareness about the disruptive effects of fraud attempts and acts of vandalism on business continuity and, sometimes, on company survival. The context influences the way companies use and adapt these theories in practice, so we consider in this study differences in the effectiveness of cybersecurity best practices between organizations that manage internally or outsource the cybersecurity processes. We conducted a study involving 153 managers’ experts in cybersecurity who responded to a survey on the effectiveness of NIST procedures. Results revealed significant differences in the effectiveness of managing cybersecurity in-house or outsource it. Specifically, major differences can be observed in the variables related to the use of disciplinary processes, the protection of log information, and the use of lessons learned to improve recovery plans. These differences provide further insights for cybersecurity management literature and a practical instrument for organizations willing to adapt their cyber processes to their organizational context.
Unsupervised Machine Learning Approach for Extractive Punjabi Text Summarization

The process of shortening text documents but preserving their overall context and content is text summarization. The key concepts of any text document should be illustrated by a good summary. Text summarization is a key area of Natural Language Processing (NLP) and it uses different NLP tools to extract meaningful information from given text. Abstractive Text Summarization (ATS) and Extractive Text Summarization (ETS) are two main techniques for Text Summarization. In this paper, Punjabi Extractive Text Summarizer is developed by using the unsupervised machine learning approach. The methodology consisting of various modules such as tokenization of the Punjabi text, removal of stop words, generation of the similarity matrix, ranking based on similarity matrix, and summary generation is proposed. The proposed system has been evaluated using the different ROGUE scores (ROUGE-N, ROUGE-L, and ROUGE-S). After evaluation, the three highest scores taken are ROUGE-1 with 0.71 score, ROUGE-L with 0.56 score, and ROUGE-S with 0.56 score.
Facial Recognition Security Alert System

In this paper the design and implementation of a real-time face recognition security system is presented. The security system demonstrates how surveillance can be adapted to offer a greater level of security with facial recognition. The security system was implemented in a four-person household. Results show that the designed facial recognition security system achieved a 100% training and 96% validation accuracy with a 100% accuracy on the test images dataset consisting of unseen images of the four enrolled users.
On reward sharing in blockchain mining pools

This paper proposes a conceptual framework for the analysis of reward sharing schemes in mining pools, such as those associated with Bitcoin. The framework is centered around the reported shares in a pool instead of agents and introduces two new fairness criteria: absolute and relative redistribution. These criteria impose that the addition of a share to a round affects all previous shares of the round in the same way, either in absolute amount or in relative ratio. We characterize two large classes of reward sharing schemes corresponding to each of these fairness criteria in turn. We further show that the intersection of these classes brings about a generalization of the well-known proportional scheme, which in turn leads to a new characterization of the proportional scheme itself.
A multi-label classification approach for detecting test smells over java projects

Software testing plays a critical role in the development and the assurance of the software quality. However, the quality of the code that is responsible for testing may, itself, be affected by poor design choices, known as test smells. In the literature, test smells may be interpreted differently by developers, which in turn can lead to different detection tools and results. In our work, we have selected the mostly commonly used detection tools and have investigated their overall agreement across different projects and different test smells. The found results were evaluated according to the average level of agreement, where we observed a definite disagreement between the tools. To overcome this gap of misinterpretation, we propose in this paper a multi-label classification approach to detect test smells based on a deep representation of the test code. We conducted our experiments using 4 problem-transformation techniques and 4 ensemble techniques. To evaluate our experimental results, we built a benchmark using a tool-based approach for labelling and made it publicly available. Binary Relevance and RAkEL are found to be the best multi-label techniques that achieve high performance results.
Adaptive Control for Cooperative Aerial Transportation Using Catenary Robots

We present a method for cooperative transportation using two catenary robots. Each catenary robot is composed of two quadrotors connected by a hanging cable. Unlike other methods in the literature for aerial transportation using cables, we do not assume that the cables are attached to the object. Instead, the quadrotors wrap cables around the object and pull. Since the cable is not attached to the object, the quadrotors need to avoid slipping by maintaining friction between the cable and the object. In this work, we focus on manipulating objects with cuboid shapes or boxes. We use two catenary robots to pull the box from two opposite edges. Once the robots are in contact with the box, they do not know the contact points between the cable and the object. We propose an adaptive controller to track a reference trajectory without information about the box's contact points, mass, and inertia tensor. We validate our approach through simulations.
Approximate Minimum Selection with Unreliable Comparisons

We consider the approximate minimum selection problem in presence of independent random comparison faults. This problem asks to select one of the smallest k elements in a linearly-ordered collection of n elements by only performing unreliable pairwise comparisons: whenever two elements are compared, there is a small probability that the wrong comparison outcome is observed. We design a randomized algorithm that solves this problem with a success probability of at least 1-q for q∈(0,n-kn) and any k∈[1,n-1] using O(nk⌈log1q⌉) comparisons in expectation (if k≥n or q≥n-kn the problem becomes trivial). Then, we prove that the expected number of comparisons needed by any algorithm that succeeds with probability at least 1-q must be Ω(nklog1q) whenever q is bounded away from n-kn , thus implying that the expected number of comparisons performed by our algorithm is asymptotically optimal in this range. Moreover, we show that the approximate minimum selection problem can be solved using O((nk+loglog1q)log1q) comparisons in the worst case, which is optimal when q is bounded away from n-kn and k=O(nloglog1q) .
Building a predictive model to assist in the diagnosis of cervical cancer

Aim: Cervical cancer is still one of the most common gynecologic cancers in the world. Since cervical cancer is a potentially preventive cancer, earlier detection is the most effective technique for decreasing the worldwide incidence of the illness. Materials and methods: This research presents a novel ensemble technique for predicting cervical cancer risk. Specifically, the authors introduce a voting classifier that aggregates prediction probabilities from multiple machine-learning models: logistic regression, K-nearest neighbor, decision tree, XGBoost and multilayer perceptron. Results: The average accuracy, precision, recall and f1-score of the voting classifier were 96.6, 97.4, 95.9 and 96.6, respectively. Furthermore, the voting algorithm gains average high values for all evaluation metrics (accuracy, precision, recall and f1-score). The f1-score of the algorithm is 96%, which demonstrates the robustness of the model. Conclusion: The findings suggest that the probability of having cervical cancer can be accurately predicted utilizing the voting technique.
Detecting Urban Anomalies Using Factor Analysis and One Class Support Vector Machine

Abstract The detection of anomalies in spatiotemporal traffic data is not only critical for intelligent transportation systems and public safety but also very challenging. Anomalies in traffic data often exhibit complex forms in two aspects, (i) spatiotemporal complexity (i.e. we need to associate individual locations and time intervals formulating a panoramic view of an anomaly) and (ii) multi-source complexity (i.e. we need an algorithm that can model the anomaly degree of the multiple data sources of different densities, distributions and scales). To tackle these challenges, we proposed a three-step method that uses factor analysis to extract features, then uses the goodness-of-fit test to obtain the anomaly score of a single data point and then uses one class support vector machine to synthesize the anomaly score. Finally, we conduct extensive experiments on real-world trip data include taxi and bike data. And these extensive experiments demonstrate the effectiveness of our proposed approach.
Challenges for adopting DevOps in information technology projects

An Information Technology (IT) project deals with IT infrastructure, information systems, or computers for delivering an IT product within a temporary period. Proper application of software development methodologies assists software designers to run IT projects to the success of achieving the satisfaction of project stakeholders. Because of the issues raised by traditional software development methodologies such as the Waterfall model, the IT industry began to employ Agile methodology for IT project management. However, due to the separation of software development and operation teams, Agile methodology also caused problems. DevOps is a new approach adapted to the Agile methodology that collaborates the software development and operation teams in order to provide continuous development of high-quality software in a short period of time. However, there are practical issues reported since DevOps approach is still in its infancy in the IT industry. The purpose of this research is to analyze the use of the DevOps concept in IT Projects by evaluating the challenges and mitigating strategies practiced by software development firms in order to ensure the success of IT projects. This purpose was achieved by performing a literature study and soliciting recommendations from industry professionals using a questionnaire survey. The findings reveal the critical challenges and prioritization of challenges experienced by software firms while adopting DevOps, as well as their practices for overcoming those challenges. The research findings will help IT project development teams and future researchers to develop strategies for making the success of DevOps adoption with Agile methodology in the IT industry.
Selective Nearest Neighbors Clustering

In this paper, we propose a simple data clustering technique that uses the idea of creating a graph on the data points based on nearest neighbors and identifying clusters by finding connected components of the graph thus constructed. The algorithm forms the graph based on a border detection and an outlier detection technique. We also propose a novel outlier detection technique that is suitable for our implementation. We compare our method against state-of-the-art clustering techniques and perform experiments to analyze the effects of various aspects of a dataset on the proposed scheme.
Pixelwise Dynamic Convolution Neural Network for LiDAR Depth Data Interpolation

A light detecting and ranging (LiDAR) system is an important means that takes an omni-directional view to collect precise surrounding 3D information in high sampling frequency. However, due to the architecture of a LiDAR sensor, LiDAR data typically contains much less information in the vertical direction compared to the horizontal direction. In addition, LiDAR data are often defected during measurement. There have been previous researches about LiDAR data interpolation using a high-resolution reference image. However, they suffer from the calibration error due to vibration or shock during operation and do not consider defected data. Here, we propose a reference-free interpolation method with considering the defected LiDAR data using a two-step network architecture. Our proposed method is designed first to exploit the rich information in the horizontal direction of the LiDAR data to restore the defected data and then increase the vertical resolution of LiDAR data. Since the proposed interpolation method is a reference-free approach, it if free from the calibration issue. We confirm that our proposed method shows better performance than existing interpolation methods and is effective as a pre-processing function in other applications. For example, our proposed method can improve the performance of the human segmentation. We also propose a pixelwise dynamic convolution layer as a component of the proposed interpolation method. The proposed dynamic convolution layer adaptively processes each pixel with variable kernels, while existing dynamic layers only use fixed kernels. Experimental results show that the proposed dynamic layer is more effective than existing dynamic layers for LiDAR data interpolation.
CCA Secure Attribute-Hiding Inner Product Encryption from Minimal Assumption

Non-zero inner product encryption (\(\mathsf {NIPE}\)) is a type of inner product encryption where a message can be recovered from a ciphertext if the inner product between the attribute and predicate vectors is non-zero. Most of the existing \(\mathsf {NIPE}\)s focus on hiding messages and the associated attributes are trivially included in the ciphertexts. In this work, we add the attribute-hiding feature to a \(\mathsf {NIPE}\) system providing security in the chosen-ciphertext attack (\(\mathsf {CCA}\)) model. We present a generic transformation of an attribute-hiding \(\mathsf {CCA}\) secure \(\mathsf {NIPE}\) from an inner product functional encryption (\(\mathsf {IPFE}\)) and a quasi-adaptive non-interactive zero-knowledge (\(\mathsf {QANIZK}\)) proof system. This leads us to a set of attribute-hiding \(\mathsf {NIPE}\)s (\(\mathsf {AHNIPE}\)) with security based on several assumptions such as plain Decisional Diffie-Hellman (\(\mathsf {DDH}\)), Learning With Errors (\(\mathsf {LWE}\)) and Decision Composite Reciprocity (\(\mathsf {DCR}\)). Furthermore, to reduce the ciphertext size of our generic \(\mathsf {DDH}\)-based \(\mathsf {AHNIPE}\), we give a more efficient and concrete construction of a \(\mathsf {CCA}\) secure \(\mathsf {AHNIPE}\) based on \(\mathsf {DDH}\) and Kernel Matrix Diffie-Hellman (\(\mathsf {KerMDH}\)) assumption. Considering the fact that \(\mathsf {DDH}\) implies \(\mathsf {KerMDH}\), the latter construction achieves a \(\mathsf {CCA}\) secure \(\mathsf {AHNIPE}\) from minimal assumption to date. Towards the applications of \(\mathsf {AHNIPE}\), we show that \(\mathsf {AHNIPE}\) directly implies an anonymous identity-based revocation (\(\mathsf {IBR}\)) scheme. Consequently, we get the first \(\mathsf {CCA}\) secure \(\mathsf {IBR}\) based on plain \(\mathsf {DDH}\) plus \(\mathsf {KerMDH}\) assumptions, improving the security of any previous anonymous \(\mathsf {CCA}\) secure \(\mathsf {IBR}\) scheme which was known only from pairing-based assumptions in the random oracle model. Moreover, we extend our \(\mathsf {IBR}\) to achieve efficient anonymous identity-based trace and revoked schemes.
An Empirical Investigation Into Deep and Shallow Rule Learning

Inductive rule learning is arguably among the most traditional paradigms in machine learning. Although we have seen considerable progress over the years in learning rule-based theories, all state-of-the-art learners still learn descriptions that directly relate the input features to the target concept. In the simplest case, concept learning, this is a disjunctive normal form (DNF) description of the positive class. While it is clear that this is sufficient from a logical point of view because every logical expression can be reduced to an equivalent DNF expression, it could nevertheless be the case that more structured representations, which form deep theories by forming intermediate concepts, could be easier to learn, in very much the same way as deep neural networks are able to outperform shallow networks, even though the latter are also universal function approximators. However, there are several non-trivial obstacles that need to be overcome before a sufficiently powerful deep rule learning algorithm could be developed and be compared to the state-of-the-art in inductive rule learning. In this paper, we therefore take a different approach: we empirically compare deep and shallow rule sets that have been optimized with a uniform general mini-batch based optimization algorithm. In our experiments on both artificial and real-world benchmark data, deep rule networks outperformed their shallow counterparts, which we take as an indication that it is worth-while to devote more efforts to learning deep rule structures from data.
Explainable information retrieval using deep learning for medical images

Image segmentation is useful to extract valuable information for an efficient analysis on the region of interest. Mostly, the number of images generated from a real life situation such as streaming video, is large and not ideal for traditional segmentation with machine learning algorithms. This is due to the following factors (a) numerous image features (b) complex distribution of shapes, colors and textures (c) imbalance data ratio of underlying classes (d) movements of the camera, objects and (e) variations in luminance for site capture. So, we have proposed an efficient deep learning model for image classification and the proof-of-concept has been the case studied on gastrointestinal images for bleeding detection. The Explainable Artificial Intelligence (XAI) module has been utilised to reverse engineer the test results for the impact of features on a given test dataset. The architecture is generally applicable in other areas of image classification. The proposed method has been compared with state-of-the-art including Logistic Regression, Support Vector Machine, Artificial Neural Network and Random Forest. It has reported F1 score of 0.76 on the real world streaming dataset which is comparatively better than traditional methods.
AFTAB: A RISC-V Implementation with Configurable Gateways for Security

A processor plays an important role in the security of an entire embedded system. There are two reasons for this. One is that a processor is a general-purpose machine, the program of which can be altered for ill-intended purposes. The other factor that adds to the vulnerability of the embedded system is that an embedded processor uses memory addressing for all its instructions and data. This creates a wide-open gateway in and out of a processor by which secure data can be read, unintended data and instructions can be injected in the processor, and the processor can be made to perform unwanted tasks and operations. The remedy we have planned for this is securing the memory gateways and separating them from the rest of the processor architecture. This is to say that we design configurable gateways for instruction and data read and write operations that can be configured to prevent various forms of attacks coming from the processor’s memory. These configurable gateway architectures are applied to a RISC-V architecture that we have implemented at the RT Level.This paper discusses our implementation of RISC-V architecture that we refer to as AFTAB. The paper emphasizes on the memory gateways of this processor and shows its interfaces with the configuration part of the processor and the architecture of the read and write gateways. After a general presentation of security and threads, we show how AFTAB gateways can be designed and configured for certain types of attacks. All works presented in this paper have been described at the RT Level and synthesized. The synthesis results will be presented.
A New Perspective on Enriching Augmented Reality Experiences: Interacting with the Real World

Abstract In the last couple of years, augmented reality (AR) has become one of the most anticipated emerging technologies. The technology is becoming more intimate with people's everyday lives, and user experiences are getting more vivid year by year. However, most of the current AR experiences rely on interacting with virtual content augmented on the real world, becoming less impressive and exciting than they were in the first place. In this article, we explore a new perspective on enriching AR experiences for their emerging mature state. We first investigate interaction schemes in AR environments in detail and highlight potential features in the context of interacting with the real world. We also provide a brief discussion on technical challenges and issues to realize interacting with the real world in online AR scenarios. We believe that this article will initiate new research interests and directions for the next level of AR experiences.
Exploratory Search of GANs with Contextual Bandits

Interactive image retrieval involves users searching a collection of images to satisfy their subjective information needs. However, even large image collections are finite and therefore may not be able to satisfy users. An alternate approach would be to explore a generative adversarial network (GAN) and model users' search intents directly in terms of the latent space used by the GAN to generate images. In this article, we present a simulation study exploring the performance of Gaussian Process bandits in the context of interactive GAN exploration. We used recent advances in interpretable GAN controls to investigate the scalability of different approaches in terms of image space dimensionality. While we present several experiments with promising results, none of the approaches tested scale sufficiently well to explore the entire GAN image space.