Reviewerly/exHarmony huggingface dataset:
Multi-Phase Algorithmic Framework to Prevent SQL Injection Attacks using Improved Machine learning and Deep learning to Enhance Database security in Real-time

Structured Query Language (SQL) Injection constitutes a most challenging type of cyber-attack on the security of databases. SQLI attacks provide opportunities by malicious actors to exploit the data, particularly client personal data. To counter these attacks security measures need to be deployed at all layers, namely application layer, network layer, and database layer; otherwise, the database remains vulnerable to attacks at all levels. Research studies have demonstrated that lack of input validation, incorrect use of dynamic SQL, and inconsistent error handling have continued to expose databased to SQ LI attacks. The security measures commonly deployed presently, being mostly focused on the network layer only, still leave the program code and the database at risk despite well-established approaches such as web server requests filtering, network firewalls and database access control. To overcome this deficiency, a Multi-Phase algorithmic framework is proposed with improved parameterised machine learning and deep learning to enhance database security in real-time at the database layer. The proposed method has been tested within a university and also in one of the branches of a commercial bank. The results show that the proposed method is able to i) prevent SQLi; ii) classify the type of attack during the detection process, and therefore iii) secure the database.
Automatic shadow detection in high-resolution multispectral remote sensing images

Shadows in high-resolution remote sensing satellite images distort both apparent shapes hues leading to lost features and difficult object analysis when processing these images. This negatively affects image color correction, recognition, classification, and urban reconstruction uses for these images. Although many shadow detection methods have been developed, there are still cases of missing detection of small shadows and misclassification of blue, green, and dark areas. We propose a novel logarithmic normalized mixed property-based shadow index (LNMPSI) for shadow detection in high resolution images using analysis of multispectral characteristics to improve on existing methods. Using color images from WorldView-3 and Gaofen-1, our method outperforms existing methods in both quantitative and qualitative terms. With good visual results and average accuracy in excess of 91%, our method is feasible, stable, and effective in detecting shadow regions and further reduces false positives caused by the loss of small details and misclassification of difficult areas.
To what extent a modern teaching style benefits students? Why do teachers act the way they do?

Abstract Background The growth of teaching practices conducted to promote cognitive activation and active participation in the classroom in recent years has become the epicentre of an active debate regarding their effectiveness. Objectives This study is aimed to shed some light on this topic by empirically analysing the causes and consequences of more modern strategies in Spanish secondary schools. Methods The database used in the study was PISA 2018. In order to analyse the causes behind the teaching style followed by teachers, a logit model was used. Likewise, to analyze the consequences that a modern teaching style has on students at the age of 15, we used propensity score analysis techniques. Results and Conclusions Regarding the results obtained through a logit model, it is shown that the likelihood of a modern teaching style decreases if teachers work for a state school or if they usually apply traditional strategies. Similarly, this series of practices are shown to be more common in schools where teachers and students make greater use of digital devices. The second section of results, focused on analysing the effect this type of teaching strategies has on cognitive, non‐cognitive and wellbeing among students, show that, on the one hand, modern teaching style negatively affects students' performance in mathematics, science or language. On the other hand, modern teaching style has been shown to contribute to improving the students' well‐being, motivation and cooperation, only being significant the latter. Moreover, this teaching style contributes to reducing bullying among students.
X-ray ﬂuorescence analysis of sulfate-ion in aqueous solutions by dried drop technique using a portable spectrometer

In this paper authors propose the methodology of X-ray ﬂuorescence determination of sulfate-ion in aqueous solutions with preconcentration by dried drop technique using portable energy dispersive spectrometer NITON FXL 959 GOLDD+. The represented measuring procedure has been tested in a wide range of sulfate-ion concentrations (10–4200 mg/l) during exploration the groundwater samples of the Over Jurassic Aquifer complex of the Moscow region and drainage water of the sulﬁde porphyry-copper mineralization of the Nakhodka ore ﬁeld (Western Chukotka). Veriﬁcation of the measuring procedure is based on results of the water investigation by ion chromatography and titration methods.
Propose-Specific Information Related to Prediction Level at x and Mean Magnitude of Relative Error: A Case Study of Software Effort Estimation

The prediction level at x (PRED(x)) and mean magnitude of relative error (MMRE) are measured based on the magnitude of relative error between real and predicted values. They are the standard metrics that evaluate accurate effort estimates. However, these values might not reveal the magnitude of over-/under-estimation. This study aims to define additional information associated with the PRED(x) and MMRE to help practitioners better interpret those values. We propose the formulas associated with the PRED(x) and MMRE to express the level of scatters of predictive values versus actual values on the left (sigLeft), on the right (sigRight), and on the mean of the scatters (sig). We depict the benefit of the formulas with three use case points datasets. The proposed formulas might contribute to enriching the value of the PRED(x) and MMRE in validating the effort estimation.
Economics of blockchain-based securities settlement

We undertake economic modeling of the behavior of participants in blockchain-based securities settlement implemented through hashed timelock contracts (HTLCs). HTLCs have been noted to have the problem of failure when one of the participants attempted to void the transaction by making a late signature. We propose that the premium should be paid by a structurally favorable participant and show that failure due to late signatures rarely occurs according to the utilities of the participants after the introduction of the premium theoretically. Then we conduct quantitative analysis using KRX bond settlement data and show that the proposed method provides better settlement results than the benchmark model. In particular, our results help regulator or policy makers consider fintechs in their regulatory and policy decisions without significantly departing from their original framework.
DeepBP: A bilinear model integrating multi-order statistics for fine-grained recognition

The bilinear model using second-order statistical features is an important weakly supervised method for fine-grained recognition. Based on this, fusing higher-order statistical features to obtain more discriminant features is an effective approach for improving the performance of the model. However, the existing framework is difficult to fuse higher-order features due to a sharp increase in the number of parameters caused by the increase in fusion order. To address the issue, this paper proposes a DeepBP model composed of a deep network module and a bilinear pooling module. The bilinear module explicitly captures low-order statistical features, and the deep network module implicitly learns high-order features. The two modules are integrated to achieve multi-level information integration. To verify the model's ability, experiments are conducted on the CUB, Cars, and Aircrafts datasets, and the accuracy of 85.6%, 91.6%, and 88.6% is achieved, respectively.
An efficient, secure and verifiable conjunctive keyword search scheme based on rank metric codes over encrypted outsourced cloud data

Public key encryption with keyword search (PEKS) is a cryptographic primitive that allows a secure search for desired keywords of data users by a cloud server using a search token. To the best of our knowledge, we propose the first practical, semantic secure, and post-quantum PEKS scheme based on rank metric codes. The proposed scheme is secure against keyword guessing attack, reaction attack, and key exposure, and enjoys conjunctive keyword search, verifiability of search results using Bloom filter and garbled Bloom filter. We implement the scheme with C++ programming language on a desktop computer. At the security level of 256 bits, the scheme performs all the necessary steps for encryption, keyword search, and verification of search results for one keyword in 22.5 milliseconds. Furthermore, the public key length at this security level is about 5 kilobytes. We show that the scheme is more efficient than the existing relevant post-quantum ones.
Hierarchical fuzzy deep learning system for various classes of images

There has been an increasing interest in the development of deep-learning models for the large data processing such as images, audio, or video. Image processing has made breakthroughs in addressing important problems such as genome-wide biological networks, map interactions of genes and proteins, network, etc. With the increase in sophistication of the system, and other areas such as internet of things, social media, web development, etc., the need for classification of image data has been felt more than ever before. It is more important to develop intelligent approaches that can take care of the sophistication of systems. Several researchers are working on the real-time images to solve the problems related to the classification of images. The algorithms to be developed will have to meet the large image datasets. In this paper, the generalized hierarchical fuzzy deep learning approach is discussed and developed to meet such demands. The objective is to design the algorithm for image classification so that it results in high accuracy. The approach is for real-life intelligent systems and the classification results have been shared for large image datasets such as the YaleB database. The accuracy of the algorithm has been obtained for various classes of images using image thresholding. The development of learning algorithms has been validated on corrupted and noisy data and results of various classes of images are presented.
Resolving Security Issues in the IoT Using Blockchain

The Internet of Things is a system of interconnected smart devices that can communicate with each other and with other devices over the Internet, with or without human-to-human or human-to-computer interaction. Although IoT devices, which have IPs, make living easier, they are also a threat to the security and privacy of people. This research work presents a solution to the problem of security in the network of IoT, based on the idea of implementing the blockchain in IoT. Blockchain is a decentralized technology that adds blocks at the end of the chain. It saves the hash value for every block, and corresponds to the previous block. The decentralized behavior of blockchain is best for IoT as an extensive network, because IoT must not have a single point of failure, and one entity must not decide what to do. All the capable storage devices will save the same data entered from any device, removing the risk of receiving altered data.
Integrating Blockchain for Health Insurance in Indonesia with Hash Authentication

The use of blockchain has received great attention in its adoption as a financial instrument in cryptocurrencies. This phenomenon needs to be considered in the sense not only as a form of financial transactions but also in other fields such as health, which is also a challenge for modern society. In addition, several government policies have also supported the provision of health services as a form of improving people’s living standards in the form of insurance. In this study, we try to design the system by using UML diagram and simulate the use of DApps offered by the Vexanium Ecosystem. For example, three basic activities between patients, doctors, and insurance will be simulated in the form of the transaction ledger. This method allows us to speed up the authentication process that previously needed to be performed for a long time with bureaucracy becoming the rule in smart contracts in a matter of minutes. The evaluation of this method will then be compared with eight existing blockchain projects. The result in healthcare processes is cost savings through increased automation, speed, standardization, and efficiency. All of this can be a preliminary analysis of its application in Indonesia, particularly related to the authentication and recording of medical records.
A Utility-Based Distributed Pattern Mining Algorithm With Reduced Shuffle Overhead

With the arrival of the current digital era and the advancement of information transmission technologies, there has been an unprecedented rise in data. Efficient extraction of useful information from the volumes of data has garnered growing interest from academics and the industry. Data mining research focuses on finding utility patterns in large datasets. But the inherent complications like frequent scans, creation of substantial candidate sets, etc. plague the mining process for large datasets. Distributive architecture-based approaches also prove inefficacious due to high communication overhead over iterations. High communication cost over data exchange both locally and remotely further aggravates the situation. We propose a Communication Cost Effective Utility-based Pattern Mining (CEUPM) algorithm based on the Spark framework to address this issue. Spark accelerates iterative scanning by storing scanned datasets in a memory abstraction called resilient distributed datasets (RDD). RDD operations need a redistribution of data among cluster nodes during processing. To minimize the communication cost incurred during the shuffle process, we adopt a search space division strategy based on data parallelism for a fair and effective task allocation across cluster nodes. Communication overhead is incurred during this redistribution or shuffle process while minimizing costs. Experimental results in four real datasets demonstrate that CEUPM considerably reduces shuffling overhead and outperforms other existing methods in terms of memory usage, communication cost, execution time, and scalability.
The interaction effect of government non-financial support and firm's regulatory compliance on firm innovativeness in Sub-Saharan Africa

Purpose The purpose of this study is to assess the interaction effect of government non-financial support and firms' regulatory compliance on firms' innovativeness. Firms' regulatory compliance with environmental and safety issues has been suggested as one of the reasons why firms innovate. Such compliance provides legitimacy, improves reputation and corporate image, and enhances customer loyalty and competitive advantages, which influence firm innovativeness. However, regulatory compliance is costly and with limited resources, the role of government support is crucial as a moderator, to help firms become more compliant and influence their innovativeness. Design/methodology/approach The study uses data from the World Bank Enterprise Innovation Survey for seven countries in Sub-Saharan Africa. Findings Regulatory compliance has a positive and significant effect on firm innovativeness. Increased use of government non-financial support enhances the level of firm regulatory compliance and the effect of regulatory compliance on firm innovativeness. Originality/value The study contributes to the literature on compliance and firm innovativeness in Africa by showing how the positive effect of regulatory compliance on firm innovativeness is stronger when firms benefit from government non-financial support.
LIGAA: Generative adversarial attack method based on low-frequency information

Recent studies have shown that the performance of deep neural network is extremely susceptible to adversarial examples. Slightly perturbation of the input that are not perceptible to the model may generate incorrect prediction results. Most existing attack methods with gradient or optimization operation cannot generate adversarial examples immediately, the robustness and imperceptibility of the adversarial examples are also not very good. To address this limitation, we propose a novel generative adversarial attack method with low-frequency information called LIGAA, which can achieve end-to-end real-time generation of adversarial examples. It mainly consists of a low-frequency information extractors and two symmetric decoders including a noise decoder and a saliency map decoder. The low-frequency information extractor eliminates the “useless” features in the original input. The noise decoder is used to generate perturbation noise for the entire image region that can be misclassified. The saliency map decoder limits the added noise to the specific areas that have a strong impact on classification, which can effectively enhance the imperceptibility of adversarial examples. Experimental results on CIFAR-10, Imagenette and CIFAR-100 of MobileNetv2 illustrate that the proposed LIGAA has obtained an attack success rate of 86.51%, 88.72% and 60.38%, and the time consumption is 0.001107s, 0.001706s and 0.004125s respectively, which are the best performance in all comparable methods. Particularly, the classification accuracy rate of LIGAA on ResNet-18 doesn’t increase even with the JPEG compress defense, but decreases to 3.11%, which also significantly verifies the attack performance and robustness against the defense method.
Accelerated key distribution method for endogenously secure optical communication by synchronized chaotic system based on fiber channel feature

Security key is necessary for data encryption while transmitting amounts of confidential information. For existing strategies of key distribution, it is the primary challenge that the rate and distance limit, insufficient security, and difficulty of deployment. To solve these problems, we propose an accelerated key distribution method for endogenously secure optical communication by synchronized chaotic system based on BER feature of fiber channel. First, synchronized chaotic equation systems are innovatively realized by identical hardware equipment, chaotic equation, initial value, and micro interference. Then, the synchronization keeping with micro interference of proposed chaotic systems are obtained based on identical BER feature of fiber loop. Innovatively, all synchronous operation controls to proposed chaotic systems are guaranteed by deterministic rule of algorithm operation, and are not limited by amplifier or fiber length. Experimental result demonstrates that proposed chaotic systems are synchronized under long term operation. The key distribution rate (KDR) can reach a rate of 100Gbit/s at 100% key consistency rate. And, the KDR can be augmented by extending hardware resources of key distribution. Simulation is conducted to illustrate the security of chaotic systems by the bifurcation diagram, scatter plot diagram, and initial value sensitive phenomenon. NIST security test suite demonstrates that generated secret key sequences pass the test. Moreover, possible attacks are analysed for the security robustness of proposed scheme. The results illustrate proposed system is a reliable, secure and cost-effective solution for high-speed and long-haul key distribution.
TPPD: Targeted Pseudo Partitioning based Defence for cross-core covert channel attacks

Contemporary computing employs cache hierarchy to fill the speed gap between processors and main memories. In order to optimise system performance, Last Level Caches (LLC) are shared among all the cores. Cache sharing has made them an attractive surface for cross-core timing channel attacks. In these attacks, an attacker running on another core can exploit the access timing of the victim process to infiltrate the secret information. One such attack is called a cross-core Covert Channel Attack (CCA). Timely detection and then prevention of cross-core CCA is critical for maintaining the integrity and security of users, especially in a shared computing environment. In this work, we have proposed an efficient cross-core CCA mitigation technique. We propose a way-wise cache partitioning on targeted sets, only for the processes suspected to be attackers. In this way, the performance impact on the entire LLC is minimised, and benign applications can utilise the LLC to its full capacity. We have used a cycle-accurate simulator (gem5) to analyse the performance of the proposed method and its security effectiveness. It has been successful in abolishing the cross-core covert timing channel attack with no significant performance impact on benign applications. It causes 23% less cache misses in comparison to existing partitioning based solutions while requiring ≈0.26% storage overhead.
Learning visual overlapping image pairs for SfM via CNN fine-tuning with photogrammetric geometry information

Efficient and accurate identification of visual overlapping image pairs is an ongoing challenge for large-scale Structure from Motion (SfM). Recently, CNN-based methods have demonstrated the ability to find visually similar image pairs. BoW (Bag-of-Word) or Visual Vocabulary tree (VoC) with hand-crafted or learning-based local features is yet widely embedded in 3D reconstruction tasks. To explore the corresponding differences, in this work, we fine-tuned several popular CNNs (AlexNet, VGG, ResNet) according to the regularities which are tailored for determining visual overlapping image pairs for SfM. More specifically, a new training dataset (called LOIP) consisting of regular photogrammetric images and crowdsourced images from the Internet is generated by fully considering photogrammetric requirements and 3D mesh models. The local regional overlapping information from paired images was employed in fine-tuning procedure. To aggregate feature maps from various channels, learnable multiple NetVLADs for each regional information are employed to further improve the retrieval performance. Comprehensive experiments have been conducted and the obtained results demonstrate that the image retrieval performance is improved, and the cost time of image matching is significantly reduced by applying the identifications of visual overlapping pairs. Furthermore, the SfM results are basically on par with several state-of-the-art CNN-based and VoC methods.1
Object detection methods on compressed domain videos: An overview, comparative analysis, and new directions

A review of the object detection on compressed domain video is presented in this paper. The literature reviewed spans over the past 32 years (1990–2022), covering the key methods for object detection in video compression. The comprehensive overview of the state-of-art methods is conducted under the MPEG-2, H.264, and HEVC compression standards. We highlight two taxonomies: the way of using motion vector information in the techniques and the video compression standards involved for object detection. In the taxonomy for motion vector information, the object detection methods of using motion vector alone, without using a motion vector, and a mixture of using motion vector and other indexes are deeply discussed. In addition, we provide a unified framework for analyzing the object detection methods in terms of various mainstream video compression standards. Furthermore, the challenges of object detection in compressed domain videos are summarized. Finally, we provide some guidelines for object detection on compressed domain videos and identify a range of future research opportunities.