Reviewerly/exHarmony huggingface dataset:
Input-based Analysis Approach to Prevent SQL Injection Attacks

SQL injection attack is one of the serious security threat for a database application. It permits attackers to get unauthorized access to the database by inserting malicious SQL code into the database application through user input parameters. In this paper, we propose input-based analysis approach to detect and prevent SQL Injection Attacks (SQLIA), as an alternative to the existing solutions. This technique has two part (i) input categorization and (ii) input verifier. We provide a brief discussion of the proposal w.r.t the literature on security and time cost point of view.
Procedures for solar radiation data gathering and processing and their application to DNI assessment in southern Portugal

This work proposes a step-by-step data gathering and processing methodology of solar irradiance measurements for producing filtered and continuous data series for the assessment of solar energy availability. The method is applied to data from 8 measuring stations in the southern region of Portugal. With the current amount of stations and as the measurement period continuously increases, it allows a better regional characterization of DNI availability and creates conditions for in depth studies. This infrastructure has high relevance for the region, known for its high annual GHI availability and its reduced inter-annual variability, thus a high annual DNI availability is expected. The proposed step-by-step data processing methodology includes a comprehensive set of data filtering, gap filling, and QC procedures based in both physical limits and statistical analysis. An extra QC check, with basis on daily totals, is proposed. The results obtained with the proposed methodology are analyzed and discussed; the results are used for the assessment of mean yearly DNI availability for the region. It is expected that this effort will significantly help to realize the region high potential for the development and implementation of Concentrated Solar Power plants, with annual mean DNI availabilities ranging from 1918 to 2097 kWh/m2/year.
Label-Wise Document Pre-training for Multi-label Text Classification

A major challenge of multi-label text classification (MLTC) is to stimulatingly exploit possible label differences and label correlations. In this paper, we tackle this challenge by developing Label-Wise Pre-Training (LW-PT) method to get a document representation with label-aware information. The basic idea is that, a multi-label document can be represented as a combination of multiple label-wise representations, and that, correlated labels always cooccur in the same or similar documents. LW-PT implements this idea by constructing label-wise document classification tasks and trains label-wise document encoders. Finally, the pre-trained label-wise encoder is fine-tuned with the downstream MLTC task. Extensive experimental results validate that the proposed method has significant advantages over the previous state-of-the-art models and is able to discover reasonable label relationship. The code is released to facilitate other researchers.(https://github.com/laddie132/LW-PT).
Multi-Agent Motion Planning using Deep Learning for Space Applications

State-of-the-art motion planners cannot scale to a large number of systems. Motion planning for multiple agents is an NP (non-deterministic polynomial-time) hard problem, so the computation time increases exponentially with each addition of agents. This computational demand is a major stumbling block to the motion planner's application to future NASA missions involving the swarm of space vehicles. We applied a deep neural network to transform computationally demanding mathematical motion planning problems into deep learning-based numerical problems. We showed optimal motion trajectories can be accurately replicated using deep learning-based numerical models in several 2D and 3D systems with multiple agents. The deep learning-based numerical model demonstrates superior computational efficiency with plans generated 1000 times faster than the mathematical model counterpart.
Robust Property-Preserving Hash Functions for Hamming Distance and More

Robust property-preserving hash (PPH) functions, recently introduced by Boyle, Lavigne, and Vaikuntanathan [ITCS 2019], compress large inputs x and y into short digests h(x) and h(y) in a manner that allows for computing a predicate P on x and y while only having access to the corresponding hash values. In contrast to locality-sensitive hash functions, a robust PPH function guarantees to correctly evaluate a predicate on h(x) and h(y) even if x and y are chosen adversarially after seeing h. Our main result is a robust PPH function for the exact hamming distance predicate $$ \mathsf {HAM}^t(x, y) = {\left\{ \begin{array}{ll} 1 &{}\text {if } d( x, y) \ge t \\ 0 &{} \text {Otherwise}\\ \end{array}\right. } $$ where d(x, y) is the hamming-distance between x and y. Our PPH function compresses n-bit strings into $$\mathcal {O}(t \lambda )$$ -bit digests, where $$\lambda $$ is the security parameter. The construction is based on the q-strong bilinear discrete logarithm assumption. Along the way, we construct a robust PPH function for the set intersection predicate $$ \mathsf {INT}^t(X, Y) = {\left\{ \begin{array}{ll} 1 &{}\text {if } \vert X \cap Y\vert > n - t \\ 0 &{} \text {Otherwise}\\ \end{array}\right. } $$ which compresses sets X and Y of size n with elements from some arbitrary universe U into $$\mathcal {O}(t\lambda )$$ -bit long digests. This PPH function may be of independent interest. We present an almost matching lower bound of $$\varOmega (t \log t)$$ on the digest size of any PPH function for the intersection predicate, which indicates that our compression rate is close to optimal. Finally, we also show how to extend our PPH function for the intersection predicate to more than two inputs.
Advanced Machine Learning for Leukaemia Detection Based on White Blood Cell Segmentation

Cancer is a worldwide issue right now. It is generated from blood cells. In fact, the diseases is spread by a change in WBC cells that can cause an unspecified growth in the number of abnormal cells. These immature cells start disrupting the function of normal cells. Processing medical blood images in ML follows a stepwise procedure from pre-processing of blood components to subtype classification. Blood components discrimination, WBC cell identification, noise reduction and cell counting is done by choosing the methodology for segmentation. Its accurate implementation is difficult because of the varying parameters (Size, Eccentricity, Major Arc, Minor Arc and Parameter) of the different WBC types. Usually segmentation is done by extracting nucleus of the WBC cells. Our research is the first that have found some new problems (Division of WBC Nucleus into multiple parts, Distortion in the shape of WBC cell Nucleus, Size Variation and Noise in the form of pixel scratches) associated with the ML methods that remove cell cytoplasm with other blood components (RBC, Platelets and Background) during the nucleus extraction process of WBC cell. Parallelly we have found causes for the mentioned problems, to suggest generic methodology that will produce robust results. Experimental results have shown that methods extracting Nucleus of the WBC cell works only with a certain type of blood images. When we try to generalize the methodology for different types of blood cell images, we are getting the above-mentioned problems. The results had been calculated by the experiments done in MATLAB.
Web-based Exploration of Annotated Multi-Layered Relightable Image Models

We introduce a novel approach for exploring image-based shape and material models registered with structured descriptive information fused in multi-scale overlays. We represent the objects of interest as a series of registered layers of image-based shape and material data. These layers are represented at different scales and can come out of a variety of pipelines. These layers can include both Reflectance Transformation Imaging representations, and spatially varying normal and Bidirectional Reflectance Distribution Function fields, possibly as a result of fusing multi-spectral data. An overlay image pyramid associates visual annotations to the various scales. The overlay pyramid of each layer is created at data preparation time by either one of the three subsequent methods: (1) by importing it from other pipelines, (2) by creating it with the simple annotation drawing toolkit available within the viewer, and (3) with external image editing tools. This makes it easier for the user to seamlessly draw annotations over the region of interest. At runtime, clients can access an annotated multi-layered dataset by a standard web server. Users can explore these datasets on a variety of devices; they range from small mobile devices to large-scale displays used in museum installations. On all these aforementioned platforms, JavaScript/WebGL2 clients running in browsers are fully capable of performing layer selection, interactive relighting, enhanced visualization, and annotation display. We address the problem of clutter by embedding interactive lenses. This focus-and-context-aware (multiple-layer) exploration tool supports exploration of more than one representation in a single view. That allows mixing and matching of presentation modes and annotation display. The capabilities of our approach are demonstrated on a variety of cultural heritage use-cases. That involves different kinds of annotated surface and material models.
Layer-Wise Adaptive Updating for Few-Shot Image Classification

Few-shot image classification (FSIC), which requires a model to recognize new categories via learning from few images of these categories, has attracted lots of attention. Recently, meta-learning based methods have been shown as a promising direction for FSIC. Commonly, they train a meta-learner (meta-learning model) to learn easy fine-tuning weight, and when solving an FSIC task, the meta-learner efficiently fine-tunes itself to a task-specific model by updating itself on few images of the task. In this paper, we propose a novel meta-learning based layer-wise adaptive updating (LWAU) method for FSIC. LWAU is inspired by an interesting finding that compared with common deep models, the meta-learner pays much more attention to update its top layer when learning from few images. According to this finding, we assume that the meta-learner may greatly prefer updating its top layer to updating its bottom layers for better FSIC performance. Therefore, in LWAU, the meta-learner is trained to learn not only the easy fine-tuning model but also its favorite layer-wise adaptive updating rule to improve its learning efficiency. Extensive experiments show that with the layer-wise adaptive updating rule, the proposed LWAU: 1) outperforms existing few-shot classification methods with a clear margin; 2) learns from few images more efficiently by at least 5 times than existing meta-learners when solving FSIC.
Building Decentralized Image Classifiers with Federated Learning

The commercial use of neural networks has been greatly curbed by data privacy concerns. As long as the accumulation and use of private data is regarded necessary for integrating neural networks into products, consumers will be reluctant to use or allow access to any deep learning integrated product and producers will be equally deterred from leveraging deep learning for performance improvement. Federated learning was first introduced as a solution to this conundrum in a 2016 paper published by Google titled Communication-Efficient Learning of Deep Networks from Decentralized Data [1]. In this study, we examine how the performance of a decentralized image classifier compares to that of a centralized one. The performance of an image classifier trained across ten devices was compared to a model built with the same architecture but trained centrally on one corpus of training data. The outcome demonstrates that the decentralized model compares quite well to the centrally trained classifier in terms of accuracy, precision and recall.
A Pond Full of Phishing Games - Analysis of Learning Games for Anti-Phishing Education

Game-based learning is a promising approach to anti-phishing education, as it fosters motivation and can help reduce the perceived difficulty of the educational material. Over the years, several prototypes for game-based applications have been proposed, that follow different approaches in content selection, presentation, and game mechanics. In this paper, a literature and product review of existing learning games is presented. Based on research papers and accessible applications, an in-depth analysis was conducted, encompassing target groups, educational contexts, learning goals based on Bloom's Revised Taxonomy, and learning content. As a result of this review, we created the publications on games (POG) data set for the domain of anti-phishing education. While there are games that can convey factual and conceptual knowledge, we find that most games are either unavailable, fail to convey procedural knowledge or lack technical depth. Thus, we identify potential areas of improvement for games suitable for end-users in informal learning contexts.
Aggregating Global and Local Visual Representation for Vehicle Re-IDentification

Vehicle Re-Identification targets at searching for vehicle instances of the same identity with a given query. It has gained an increasing attention recently with a wide application prospects in video surveillance and intelligent transportation. The main challenge lies in how to distinguish the subtle differences between different vehicles, while capturing the slight similarity between the instance of the same vehicle in different viewpoints or illuminations. To this end, most existing methods focus on learning discriminative global representations, which leave the unique local details such as stickers, inspection labels and driver wearing being unexploited. In this paper, we present a novel coarse-to-fine scheme that aggregates global and local visual representations to boost the retrieval accuracy. Specifically, a multi-task learning framework combining an Attribute Learning branch and a Deep Ranking branch (termed ALDR) is first adopted to learn robust global features, which produces an initial ranking list. Then the local similarities between image patches in the initial ranking list and in the query is computed via a Multi-Channel and Multi-Scale Siamese network (termed MCMS-Siam). Finally, the retrieval result is returned after re-ranking the initial list according to such a combination of global and local similarities. Experimental results on the widely-used VehicleID dataset and VECH-WILD dataset demonstrate the merits of the proposed method over the state-of-the-art methods.
Improved whale optimization algorithm based on random hopping update and random control parameter

Whale Optimization Algorithm (WOA) is a relatively novel algorithm in the field of meta-heuristic algorithms. WOA can reveal an efficient performance compared with other well-established optimization algorithms, but there is still a problem of premature convergence and easy to fall into local optimal in complex multimodal functions, so this paper presents an improved WOA, and proposes the random hopping update strategy and random control parameter strategy to improve the exploration and exploitation ability of WOA. In this paper, 24 well-known benchmark functions are used to test the algorithm, including 10 unimodal functions and 14 multimodal functions. The experimental results show that the convergence accuracy of the proposed algorithm is better than that of the original algorithm on 21 functions, and better than that of the other 5 algorithms on 23 functions.
A New Classification Technique: Random Weighted LSTM (RWL)

Due to the unprecedented growth of digital devices and hand held smartphones, users generate several quintillion of data everyday by using social media, blog, youtube, etc. With the advancement of machine learning techniques, we classify these data automatically for critical decision making process. Majority of these classification algorithms are linear in nature and these algorithms show weak performance in predicting class labels when the attributes are complex. For example, human behavior, preference, personality, etc. have numerous non-linear properties and difficult to predict in real life by using these traditional machine learning algorithms. In this paper, we propose a novel non-linear technique based on Long short-term memory (LSTM) architecture. Studies show that Recurrent Neural Network (RNN) and LSTM based models usually predict time and sequential models better than that of other models. We significantly change the operational mechanism of LSTM and achieve outstanding performance in predicting classification problems. We run our algorithm over six different datasets: Iris, Pima Indian, Breast Cancer, Blood Transfusion, StackOverflow, and Banknote Authentication. We compare the performance of our algorithm with other traditional classifiers. Our classifier generally outperforms conventional linear and non-linear classifiers.
An independent three‐factor mutual authentication and key agreement scheme with privacy preserving for multiserver environment and a survey

Summary In the past decades, the demand for remote mutual authentication and key agreement (MAKA) scheme with privacy preserving grows rapidly with the rise of the right to privacy and the development of wireless networks and Internet of Things (IoT). Numerous remote MAKA schemes are proposed for various purposes, and they have different properties. In this paper, we survey 49 three‐factor–based remote MAKA schemes with privacy preserving from 2013 to 2019. None of them can simultaneously achieve security, suitability for multiserver environments, user anonymity, user untraceability, table free, public key management free, and independent authentication. Therefore, we propose an efficient three‐factor MAKA scheme, which achieves all the properties. We propose a security model of a three‐factor–based MAKA scheme with user anonymity for multiserver environments and formally prove that our scheme is secure under the elliptic curve computational Diffie‐Hellman problem assumption, decisional bilinear Diffie‐Hellman problem assumption, and hash function assumption. We compare the proposed scheme to relevant schemes to show our contribution and also show that our scheme is sufficiently efficient for low‐power portable mobile devices.
Online Safety Assurance for Learning-Augmented Systems

Recently, deep learning has been successfully applied to a variety of networking problems. A fundamental challenge is that when the operational environment for a learning-augmented system differs from its training environment, such systems often make badly informed decisions, leading to bad performance. We argue that safely deploying learning-driven systems requires being able to determine, in real-time, whether system behavior is coherent, for the purpose of defaulting to a reasonable heuristic when this is not so. We term this the online safety assurance problem (OSAP). We present three approaches to quantifying decision uncertainty that differ in terms of the signal used to infer uncertainty. We illustrate the usefulness of online safety assurance in the context of the proposed deep reinforcement learning (RL) approach to video streaming. While deep RL for video streaming bests other approaches when the operational and training environments match, it is dominated by simple heuristics when the two differ. Our preliminary findings suggest that transitioning to a default policy when decision uncertainty is detected is key to enjoying the performance benefits afforded by leveraging ML without compromising on safety.
Real-time Safety Helmet Detection System based on Improved SSD

The detection of the safety helmet is difficulties due to the ariable ighting, weather changes and complex background. We proposed a deep learning detection method to detect safety helmet to solve the problems of low accuracy and poor robustness of traditional detection methods. This method is based on the SSD (Single Shot MultiBox Detector) object detection and improved the network. First, we used the fusion of multi-layer to consideration of shallow low sematic information and deep semantic information, which improves the sensitivity of the network to small target detection. Second, we proposed lightweight network structure of compresses the network, reducing the amount of parameters and calculations of the model. Third, we made safety helmet datasets to train and test the improved network model, and the model is compared with the original SSD. The results show that the detection accuracy of the model is 86.75%, which is similar to SSD, but the detection speed has been improved significantly, which is 295% higher than SSD, up to 83 frame/s. Experiments show that the improved network model can significantly improve the detection speed while ensuring the detection accuracy and meet the real-time detection requirements.
Mobile Cloud computing architecture for massively parallelizable geometric computation

Cloud Computing is one of the most disruptive technologies of this century. This technology has been widely adopted in many areas of the society. In the field of manufacturing industry, it can be used to provide advantages in the execution of the complex geometric computation algorithms involved on CAD/CAM processes. The idea proposed in this research consists in outsourcing part of the load to be computed in the client machines to the cloud through the Mobile Cloud Computing paradigm. This practice gives substantial benefits to both the clients and the software-provider in terms of costs, flexibility, ubiquity and performance. In this document, an outsourcing architecture is proposed based on this paradigm. Extensive experiments have been done using highly parallelizable computational geometry operations to show the strengths and weaknesses of the proposal in combination of specialized computing platforms in the cloud. The results suggest that there are some issues that affect the overall performance and the stability of the QoS: the network communication delay, and the number of simultaneous clients and multiple requests. Some solutions have been proposed to face these challenges.
Tentris – A Tensor-Based Triple Store

The number and size of RDF knowledge graphs grows continuously. Efficient storage solutions for these graphs are indispensable for their use in real applications. We present such a storage solution dubbed Tentris. Our solution represents RDF knowledge graphs as sparse order-3 tensors using a novel data structure, which we dub hypertrie. It then uses tensor algebra to carry out SPARQL queries by mapping SPARQL operations to Einstein summation. By being able to compute Einstein summations efficiently, Tentris outperforms the commercial and open-source RDF storage solutions evaluated in our experiments by at least 1.8 times with respect to the average number of queries it can serve per second on three datasets of up to 1 billion triples. Our code, evaluation setup, results, supplementary material and the datasets are provided at https://tentris.dice-research.org/iswc2020 .