Reviewerly/exHarmony huggingface dataset:
A Survey on VLSI Implementation of AES Algorithm with Dynamic S-Box

In recent research, very large-scale integration (VLSI) design and its implementation plays a major role. It has been considered as compact and contains additional techniques. However, it undergoes security threats like differential power analysis (DPA) attacks, confidential information leakage, suspicious data insertion, brute force attacks, and other severe attacks. Hence, the cryptographic techniques that have been implemented to enhance the security level for VLSI devices is the focus of this survey. The symmetric cryptographic technique of advanced encryption standard (AES) is considered suitable for this security enhancement and it is an advanced encryption process, and so the intelligible data are transmitted in an unintelligible form to confuse the attackers and the data is securely transmitted without any interruption. Different techniques to implement the AES approaches in VLSI devices are analyzed. The dynamic S-box technique is also analyzed for key-dependent processes. The key-dependent S-box of block ciphers is more secure than a general fixed S-box. The strength of the cipher is increased by the usage of an S-box. A dynamic S-box, generated to be key-dependent and with a discrete logarithmic approach, enhanced the S-box nonlinearity. Several measures analyzed regarding security and complexity of nonlinear transformation of S-box improved. Hence, the AES algorithm with dynamic S-box makes the system dynamic, more nonlinear, and with an unbreakable architecture. In this review, it also identified that to implement nonlinear transformation in AES, an S-box component has been used. This survey has been provided as guidelines for VLSI architecture implementation in a more secure way with cryptographic algorithm of AES and a dynamic S-box.
Characterization of Brazilian Software Startups Focusing on Processes, Product Quality and IT Governance

For startups, the quality of software product influences their business and consequently its survival. Many problems can happen at different times. Normally they increase with the amount of customers, the need to evolve the product, to customize it for different customers, etc. Besides this, to meet different demands fastly and with few resources is a constant challenge, because the solutions must be adequate to their conditions. This paper presents preliminary results of a survey, aiming to identify the characteristics of Brazilian startups, with their product comprising software, purposing to characterize the main problems related with the development and the evolution of their products, considering aspects related to product quality, processes and IT management.
Lexicon or grammar? Using memory-based learning to investigate the syntactic relationship between Belgian and Netherlandic Dutch

Abstract This article builds on computational tools to investigate the syntactic relationship between the highly related European national varieties of Dutch, viz. Belgian Dutch (BD) and Netherlandic Dutch (ND). It reports on a series of memory-based learning analyses of the post-verbal distribution of er “there” in adjunct-initial existential constructions like Op het dak staat (er) een schoorsteen “On the roof (there) is a chimney,’, which has been claimed to be among the most notoriously difficult variables in Dutch. On the basis of balanced datasets extracted from Flemish and Dutch newspaper corpora, it is shown that er ’s distribution in both national varieties can be learned to a considerable extent from bare lexical input which is not assigned to higher-level categories. However, whereas this yields good results for ND, BD scores are consistently lower, suggesting that BD cannot do with lexical features alone to attain accuracy scores comparable to ND. This ties in with earlier findings that the more advanced standardization of ND materializes in a higher lexical collocability, whereas Flemish speakers need additional higher-level linguistic information to insert er .
An FPGA-Implemented Parallel System of Face Recognition, for Digital Forensics Applications

With the number of crime cases increasing daily, the Digital Forensics sector is growing rapidly. More and more researchers search for efficient face recognition systems, which will provide fast and accurate results in order to uncover crime suspects. Although many software implemented systems exist, they seem to have speed problems. This is the reason why hardware implemented systems are preferred. In this paper, an FPGA hardware implementation is proposed, which processes four images in parallel in order to reduce the total execution time and the on-line training process is applied. The system operates in a frequency of 66.23 MHz and the total execution time is almost 128 ms due to the on-line training. The Yale face database is used and the accuracy of the implemented system is 80% for 30 consecutive trials. The design is performed for an Artix-7 FPGA.
Research and Application UAV Operation Data Trusted Storage Technology Based on Blockchain

With the wide application of UAV (Unmanned Aircraft Vehicle), UAVs have the characteristics of multiple models and scattered operation. In order to strengthen the management of UAVs and eliminate hidden safety hazards, the Civil Aviation Administration will supervise the operation data of UAVs. Whether UAV operation data can be credibly archived has become an urgent issue to be solved by UAV operators. In this paper, a data trusted storage method is given on the basis of studying the requirements for credible certificate storage of UAV operation data. Blockchain technology is used to propose a reliable storage method for UAV operation data that meets the regulatory requirements of the bureau. And the actual data was used to verify the application of the method.
Optimizing Deep Learning Models for Object Detection

Deep learning models for object detection have gotten larger and larger over the years, spanning from 3.9M trainable parameters for EfficientDet to 209M for the AmoebaNet-based NAS-FPN detector. Different strategies are currently being researched in order to improve the efficiency of deep learning models for object detection, one of which is running the training and inference of the neural network in low precision. Interesting results have been achieved by researchers, starting from the original paradigm of using operators and doing the necessary operations in IEEE single precision (FP32), to achieving similar accuracies of the models using custom minifloat formats (FP8). The results can be pushed even further by using genetic algorithms for hyperparameter tuning, in order to find specific hyperparameter for the FP8 version of the model. In this paper, we will present the results of our experiments utilizing YOLOv3 with hybrid floating-point format (HFP8). One of the experiments in this paper shows how our solution can be used for checking social distancing guidelines which is a very important topic in the current COVID-19 pandemic.
Using community data to improve rural water infrastructure in India

India continues to struggle with the combined effects of water stress alongside poor access to water and sanitation. However, data-driven infrastructure planning is helping to bring a more integrated, sustainable approach to water management and the provision of water, sanitation and hygiene in rural areas. Working with non-governmental organisations in Chhattisgarh and Madhya Pradesh, a project was undertaken to introduce consistent digital data collection, analysis and sharing processes for water resources and water demand data. Key outputs from the project included a toolkit and mobile application, the wider adoption of which could transform rural water access.
Towards Efficient Governance In Distributed Ledger Systems Using High-Performance Computational Nodes

Due to increasing popularity in distributed ledger systems and the increasing demand of a stable model of a blockchain, fitting both for the development of IoT (Internet of Things) and DApps(Decentralized Applications), the current generation of blockchain needs to solve its main problems ranging from scalability to security and to bring improvements, in order to fit the real-world needs of society nowadays. One of many solutions brought by current prospective blockchains is a form of governance through different types of nodes, usually equipped with more computational resources, that have a more important significance in the network. In this paper, we tried to showcase the applicability of democratic governance in the blockchain ecosystem through the use of supernodes, in order to solve some of the current dilemmas. Despite the multitude of the use-cases, we will focus on four that show great potential in improving the blockchain technology, by outlining both their positive and negative points. Besides that, current blockchains that have a form of governance will be analyzed by examining the use-cases of the supernodes as well as the benefits and negative aspects they give to the ecosystem.
ADNet: Temporal Anomaly Detection in Surveillance Videos

Anomaly detection in surveillance videos is an important research problem in computer vision. In this paper, we propose ADNet, an anomaly detection network, which utilizes temporal convolutions to localize anomalies in videos. The model works online by accepting consecutive windows of video clips. Features extracted from video clips in a window are fed to ADNet, which allows to localize anomalies in videos effectively. We propose the AD Loss function to improve abnormal segment detection performance of ADNet. Additionally, we propose to use F1@k metric for temporal anomaly detection. F1@k is a better evaluation metric than AUC in terms of not penalizing minor shifts in temporal segments and punishing short false positive temporal segment predictions. Furthermore, we extend UCF Crime [] dataset by adding two more anomaly classes and providing temporal anomaly annotations for all classes. Finally, we thoroughly evaluate our model on the extended UCF Crime dataset. ADNet produces promising results with respect to F1@k metric. Code and dataset extensions are publicly at https://github.com/hibrahimozturk/temporal_anomaly_detection.
Incremental Training of a Recurrent Neural Network Exploiting a Multi-scale Dynamic Memory

The effectiveness of recurrent neural networks can be largely influenced by their ability to store into their dynamical memory information extracted from input sequences at different frequencies and timescales. Such a feature can be introduced into a neural architecture by an appropriate modularization of the dynamic memory. In this paper we propose a novel incrementally trained recurrent architecture targeting explicitly multi-scale learning. First, we show how to extend the architecture of a simple RNN by separating its hidden state into different modules, each subsampling the network hidden activations at different frequencies. Then, we discuss a training algorithm where new modules are iteratively added to the model to learn progressively longer dependencies. Each new module works at a slower frequency than the previous ones and it is initialized to encode the subsampled sequence of hidden activations. Experimental results on synthetic and real-world datasets on speech recognition and handwritten characters show that the modular architecture and the incremental training algorithm improve the ability of recurrent neural networks to capture long-term dependencies.
Template-based Question Answering analysis on the LC-QuAD2.0 Dataset

In recent years, template-based question answer has picked up steam as a solution for evaluating RDF triples. Once we delve into the domain of template-based question answering, two important questions arise which are, the size of the dataset used as the knowledge base and the process of training used on that knowledge base. Previous studies attempted this problem with the LC-QuAD dataset and recursive neural network for training. This paper studies the same problem with a larger and newer benchmark dataset called LC-QuAD 2.0 and training using different machine learning models. The objective of this paper is to provide a comparative study using the newer LC-QuAD 2.0 dataset that has an updated schema and 30,000 question-answer pairs. Our study will focus on using and comparing two Machine Learning models and 3 different pre-processing techniques to generate results and identify the best model for this problem.
A Smartphone-Based Cursor Position System in Cross-Device Interaction Using Machine Learning Techniques

The use of mobile devices, especially smartphones, has become popular in recent years. There is an increasing need for cross-device interaction techniques that seamlessly integrate mobile devices and large display devices together. This paper develops a novel cross-device cursor position system that maps a mobile device’s movement on a flat surface to a cursor’s movement on a large display. The system allows a user to directly manipulate objects on a large display device through a mobile device and supports seamless cross-device data sharing without physical distance restrictions. To achieve this, we utilize sound localization to initialize the mobile device position as the starting location of a cursor on the large screen. Then, the mobile device’s movement is detected through an accelerometer and is accordingly translated to the cursor’s movement on the large display using machine learning models. In total, 63 features and 10 classifiers were employed to construct the machine learning models for movement detection. The evaluation results have demonstrated that three classifiers, in particular, gradient boosting, linear discriminant analysis (LDA), and naïve Bayes, are suitable for detecting the movement of a mobile device.
Instant Facial Recognition of Criminals Using a Smart Wearable Tech with Gesture Control from a IoT (Internet of Things) Cloud Database

In the current era of the technological advancements, the usage of buttons as human-machine interface has become obsolete and it is advanced to the touch screen. Due to the increasing autonomous technologies, touch and holding a device is also removed and works just based on the gestures of a human users and the interface is directly engaged towards the near eye-vision display. Along with the gesture, the smart eye technology is enabled with an IoT based data system along with the records of the criminals from which the smart device scan and identify the criminals through a camera enabled recognition system.
MK-Means: Detecting evolutionary communities in dynamic networks

K-Means algorithm is probably the most famous and popular clustering algorithm in the world. K-Means algorithm has the advantages of simple structure, easy implementation, high efficiency, fast convergence speed, and good results. It has been widely used in many applications, and many extensions of K-Means have been proposed. Basically, most K-Means variants deal with static data. Recently, the dynamic nature of data has received increasing attention from researchers. Therefore, some studies also use K-Means algorithm to deal with clustering problems in evolutionary data. In this article, we aim to improve past variants of K-Means used in evolutionary clustering. There are two ways to improve this problem. First, past research only considered how the previous clustering results affected the current clustering, but we also considered how the future clustering results affect the current clustering. Secondly, past research applied K-Means from one cycle to another in one pass, but we extended it to multiple passes. These two improvements make the proposed algorithm MK-Means provide more consistent, stable and smooth clustering results than previous models.
The regrouping mayfly optimization algorithm

The newly proposed mayfly optimization (MO) algorithm proved to be capable in optimizing both benchmark functions and engineering problems we met in our real world. Due to the original inspiration of the particle swarm optimization (PSO) algorithm, the MO algorithm would be also stagnated during iterations. In this paper, the regrouping method was introduced to reduce the stagnation for individuals in the MO algorithm. Simulation experiments would be carried out and results confirmed that the regrouping MO algorithm could reduce the stagnation for the MO algorithm in optimizing multimodal benchmark functions.
HUIL-TN &amp; HUI-TN: Mining high utility itemsets based on pattern-growth

In recent years, high utility itemsets (HUIs) mining has been an active research topic in data mining. In this study, we propose two efficient pattern-growth based HUI mining algorithms, called High Utility Itemset based on Length and Tail-Node tree (HUIL-TN) and High Utility Itemset based on Tail-Node tree (HUI-TN). These two algorithms avoid the time-consuming candidate generation stage and the need of scanning the original dataset multiple times for exact utility values. A novel tree structure, named tail-node tree (TN-tree) is proposed as a key element of our algorithms to maintain complete utililty-information of existing itemsets of a dataset. The performance of HUIL-TN and HUI-TN was evaluated against state-of-the-art reference methods on various datasets. Experimental results showed that our algorithms exceed or close to the best performance on all datasets in terms of running time, while other algorithms can only excel in certain types of dataset. Scalability tests were also performed and our algorithms obtained the flattest curves among all competitors.
Graphical password based mouse behavior technique

Abstract This paper proposes a new password generation technique on the basis of mouse motion and a special case location recognized by the number of clicks to protect sensitive data for different companies. Two, three special locations click points for the users has been proposed to increase password complexity. Unlike other currently available random password generators, the path and number of clicks will be added by admin, and authorized users have to be training on it. This method aims to increase combinations for the graphical password generation using mouse motion for a limited number of users. A mathematical model is developed to calculate the performance of the password. The proposed technique in this paper allows the user to keep the ease-of-use in the mouse motion, while minimizing the risk of password guessing. A comparative evaluation has been conducted against a traditional password. The results show that the proposed approach improves the complexity 200% for fix position technique and two variants technique but more than 200% for three variants technique.
An efficient system for grammatical error correction on mobile devices

As per the analysis by lemongrad, among 1,121 million English speakers globally, there are 743 million nonnative and 378 million native English speakers (7:3 Ratio). With the increasing number of non-native English speakers, there has been a lot of ongoing research on automated Grammatical Error Correction (GEC). Despite the advances in this field, a GEC system for mobile devices with a low model size, quick inference time and high accuracy is a pressing need. Existing solutions for mobile devices are server-based, which poses a potential risk of data privacy to the user. To this end, we propose an on-device hybrid system which consists of various components, such as preprocessor with spell-checker, Deep Neural Network (DNN) model, N-gram language model, rule-corrector for GEC. User's input text is passed through the proposed system sequentially to get the grammatically correct, contextually enhanced and profanity checked output sentence. Our novel system is at par with other available models for mobile devices with inference time of 95ms, module size of 11.6 MB and F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.5</sub> score of 52.3 on CoNLL-2014 [1] (test).
Smart Grid Implementation with Consortium Blockchain: A Proposed Model for Bangladesh

The 21 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> century has seen a significant increase in worldwide electricity consumption with the invention of new technologies. Meeting the increasing power demand efficiently with the classic grid arrangement has become more challenging. To meet this energy demand, following the worldwide trend of grid modernization, Bangladesh is also looking forward to upgrading their existing grid infrastructure into the smart grid. To ameliorate that process, this paper proposes a model of a consortium blockchain from the perspective of Bangladesh. Unlike the existing grids, the proposed smart grid will be able to ensure user security with the help of state-of-the-art technologies. The proposed system will provide features like easier grid integration of different renewable sources, bidirectional power flow to the grid, and smart energy monitoring. To solve the data vulnerability issues and to defend against denial of services and malware attacks on the smart grid, the proposed grid will store its data in a consortium blockchain, which is one of the most secure technologies and only the grid authority will have full access over their user's data. Furthermore, a secure transaction method has been proposed in this paper, that will ensure the integrity of the transaction between the grid authority and the consumers.
Threat Modeling of Cyber-Physical Systems in Practice

Traditional Cyber-physical Systems (CPSs) were not built with cybersecurity in mind. They operated on separate Operational Technology (OT) networks. As these systems now become more integrated with Information Technology (IT) networks based on IP, they expose vulnerabilities that can be exploited by the attackers through these IT networks. The attackers can control such systems and cause behavior that jeopardizes the performance and safety measures that were originally designed into the system. In this paper, we explore the approaches to identify threats to CPSs and ensure the quality of the created threat models. The study involves interviews with eleven security experts working in several different domains. We found through these interviews that the practitioners use a combination of various threat modeling methods, approaches, and standards together when they perform threat modeling of given CPSs. Key challenges practitioners face are: they cannot transfer the threat modeling knowledge that they acquire in a cyber-physical domain to other domains, threat models of modified systems are often not updated, and the reliance on mostly peer-evaluation and quality checklists to ensure the quality of threat models. The study warns about the difficulty to develop secure CPSs and calls for research on developing practical threat modeling methods for CPSs, techniques for continuous threat modeling, and techniques to ensure the quality of threat models.