Reviewerly/exHarmony huggingface dataset:
Nonce-Misuse Security of the SAEF Authenticated Encryption Mode

ForkAE is a NIST lightweight cryptography candidate that uses the forkcipher primitive in two modes of operation – SAEF and PAEF – optimized for authenticated encryption of the shortest messages. SAEF is a sequential and online AEAD that minimizes the memory footprint compared to its alternative parallel mode PAEF, catering to the most constrained devices. SAEF was proven AE secure against nonce-respecting adversaries. Due to their more acute and direct exposure to device misuse and mishandling, in most use cases of lightweight cryptography, nonce reuse presents a very realistic attack vector. Furthermore, many lightweight applications mandate security for their online AEAD schemes against block-wise adversaries. Surprisingly, a very few NIST lightweight AEAD candidates come with provable guarantees against these security threats. In this work we investigate the provable security guarantees of SAEF when nonces are repeated under a refined version of the notion of online authenticated encryption OAE given by Fleischmann et al. in 2012. Using the coefficient H technique we show that, with no modifications, SAEF is OAE secure up to the birthday security bound, i.e., up to $$2^{n/2}$$ processed blocks of data, where n is the block size of the forkcipher. The implications of our work is that SAEF is safe to use in a block-wise fashion, and that if nonces get repeated, this has no impact on ciphertext integrity and confidentiality only degrades by a limited extent up to repetitions of common message prefixes.
Security Accreditation and Software Approval with Smart Contracts

Security accreditation is a vital process used by governmental, military, and international organizations to verify a proper security posture of communication and information systems. Currently, it is a largely manual, time consuming, and highly repetitive process. Smart contracts could offer an opportunity to automate some parts of the security review, increasing speed, transparency, and predictability of the security accreditation, as well as software approval. We analyze the feasibility of supporting security accreditation with smart contracts and present our proof-of-concept implementation based on Hyperledger Fabric.
Evacuation Analysis of Ship Passenger Using Agent Based Modelling Simulation

Abstract Ship accidents that occur in Indonesian have a high frequency. The ship accidents is dominated by fires of Ro-ro or passenger ships which cause material loss, water pollution and loss of human life. In this paper, an analysis of the ship evacuation system will be carried out. The Agent Based Modelling Simulation (ABMS) was using in this study. Initial data required are in the form of ship general arrangement, passenger walking speed, as well as the composition and distribution of passengers. The distribution of ship passengers in Indonesia for the category of women aged less than 30 years is 12%, women aged 30-50 years are 21%, women aged greater than 50 years are 5%, there are no female passengers with disabilities, while for the category of males aged less than 30 years as much as 19%, males with an age range of 30-50 years as much as 31%, males over 50 years old as much as 12%, and no male passengers were found. a man with a disability. The number of ship passengers is conditioned to exceed the passenger capacity, namely 880 passengers should be conditioned to become 1527 passengers. The evacuation simulation of ship passengers using an agent based modelling simulation is run based on the IMO MSC. 1 / Circ. 1533 using 4 cases during the day and night. In the case of a normal night it takes 38.24 minutes to evacuate all passengers, and in the case of a night the evacuation takes 36.45 minutes. In the normal day case it took 29.49 minutes and in the afternoon case the evacuation took 28.12 minutes.
A Semantic Segmentation Network Simulating the Ventral and Dorsal Pathways of the Cerebral Visual Cortex

Aiming at the problem of spatial information loss in the semantic segmentation process, we propose a semantic segmentation network, termed the ventral and dorsal network (VDNet), which simulates the ventral and dorsal pathways of the cerebral visual cortex. The ventral pathway network focuses on extracting semantic information, and the dorsal pathway network focuses on extracting spatial information. We use the semantic enhancement module (SEM) in the ventral pathway network to fuse information of different scales to enhance the extraction of semantic information, and we use the spatial attention module (SAM) in the dorsal pathway network to assign weights to different locations in space to enhance the extraction of spatial information. By fusing the information of the two pathways, the final semantic segmentation result is obtained. Since the dorsal pathway network is used to specifically enhance the extraction of spatial information, the problem of spatial information loss during the segmentation process is effectively improved, and higher segmentation accuracy can be achieved by using only a small backbone network. On the CamVid, Cityscapes and PASCAL VOC 2012 datasets, we achieve the mean intersection over union (mIoU) of 82.1%, 77.8%, and 81.0%, respectively, which verifies the effectiveness of the proposed method.
Multimodal action recognition using variational‐based Beta‐Liouville hidden Markov models

The visible spectrum is the most widely used modality for video media. Nonetheless, it is highly dependent on the lighting conditions. Hence, infrared (IR) imaging lower light sensitivity characterisation presents the untapped potential for robust automatic recognition systems. This is applicable to many applications including IR action recognition (AR), which is a relatively young field in IR. As such, in this study, the authors tackle IR and multimodal AR with the proposed utilisation of variational learning of Beta-Liouville (BL) hidden Markov models (HMMs). Furthermore, to the best of the authors' knowledge, this is the first evaluation of the BL HMM in visible AR and in multimodal fusion for AR. They present the results of the proposed model on the infrared action recognition and the IOSB datasets. Experimental results demonstrate promising outcomes. The importance of using IR and multispectral fusion in AR is also highlighted by the results.
Generic Construction of Server-Aided Revocable Hierarchical Identity-Based Encryption

In this paper, we extend the notion of server-aided revocable identity-based encryption (SR-IBE) to the hierarchical IBE (HIBE) setting to obtain the definition of server-aided revocable hierarchical IBE (SR-HIBE), and consider a stronger security called SSR-a/sID-CPA security. Specifically, the security required that the challenge identity $$\mathsf {ID}^*$$ is revoked when both the private key for $$\mathsf {ID}^*$$ or some ancestors of $$\mathsf {ID}^*$$ and the public key for $$\mathsf {ID}^*$$ are revealed, or both the private key and the secret key for $$\mathsf {ID}^*$$ or some (may be different) ancestors of $$\mathsf {ID}^*$$ are revealed. And the adversary can have access to the transformation keys oracle. Then, we propose a generic construction of SR-HIBE schemes from any L-level revocable HIBE scheme and (L + 1)-level HIBE scheme. The security of our generic SR-HIBE scheme inherits those of the underlying building blocks. Furthermore, when the maximum hierarchical depth is one, we obtain a generic construction of SR-IBE schemes from any IBE scheme and two-level HIBE scheme.
Data Structure Visualization on the Web

One challenge remains in teaching and learning data structure is to facilitate students’ understanding of the dynamic presentation from the collection of data elements in memory. Visualizing how each element points to the next, and how a collection of nodes together represent a sequence with graphs or linked-node diagram can help us understand not only the abstract data structure but also the dynamics of the data elements in the program execution. While printing (to a console) is a very common technique for understanding and debugging data structures, we in this paper presents an interactive visualization tool for data structure in Python. Our work focuses on visual and interactive methods to facilitate the communication of data structure and associated data operations by dynamic graphing corresponding to the programming process. By using the inner execution trace, our proposed method can monitor the run-time data structure change and render the corresponding presentation in real-time. The resulting serial of key frames featuring major data structure transformation are available for review and analysis. All these combine to fill the gap between the abstract data representation and a dynamic learning-by-coding process.
Smart Contract Development Model and the Future of Blockchain Technology

Blockchain has become a mainstream technology in our society in recent years. With its nature of secure decentralization, people can create decentralized applications by developing smart contracts on top of a blockchain platform. With blockchain, technology is still in the developing phase, the smart contract development process in blockchain has its unique complexity and uncertainty. The condition will drive the challenge for any developers to work on this issue. In this research, we determine the smart contract development model in creating decentralized applications. Our proposed model aligned with our findings in the systematic mapping process of this study.
A decision support system based on machined learned Bayesian network for predicting successful direct sales marketing

This paper proposes a decision support system based on a machine-learned Bayesian network (BN) to predict the success rate of telemarketing calls for long-term bank deposits. Telemarketing is one of the most common interactive techniques of direct marketing, widely used by financial institutions such as banks to sell long-term deposits. In this study, we develop a BN model that predicts the likelihood that a potential client subscribes to a long-term deposit, which is considered an output variable. The causal relationship among client attributes and outcomes has been identified using the augmented Naïve Bayes approach, a well-known supervised learning algorithm. The impact of each client's attribute on the likelihood of subscribing is predicted. Further, we carry out multiple simulation scenarios using BN’s unique features (forward and backward propagation) to provide more in-depth discussions and analysis on predicting the likelihood of subscription for clients with particular characteristics.
Student’s Perception of Higher Education in Technical Universities and its Connection with Subsequent Professional Development (MSTU, MADI and MIREA - comparative analysis)

The article is devoted to presenting the results of a comparative study of technical University of Bauman, MADI and MIREA. These results were obtained using a sociological questionnaire with closed-type questions. The questionnaire contained nine main thematic blocks: 1. Professional choice; 2. Profession; 3. Study and education; 4. Educating; 5. Professional and personal competence; 6. Teachers; Communication of students; 8. Humanitarian knowledge in the training of an engineer; 9. Intelligentsia. This article presents the results for the first three blocks. They were analyzed and interpreted.
Wrist Rehabilitation in Carpal Tunnel Syndrome by Gaming using EMG Controller

Technological progress brings many significant benefits to our lives, but also new obstacles that we must face. Increasingly widespread sedentary jobs and computer work contribute to various health problems, such as carpal tunnel syndrome. This disease can be relatively successfully treated or prevented through rehabilitation exercises. However, patients often find these exercises boring and they are not determined to perform them precisely. The aim of this paper is to develop a rehabilitation game application providing more entertaining form of exercise and motivate them to regular rehabilitation.
A Prototype Electronic Toy for the Development of Mathematical Logical Reasoning in Children from Five to Seven Years Old Using Python

The technological advance andJurado, Francisco the great importance that it represents when using Information and Communication Technologies (ICT)Donoso, Diego as a tool in the educational process allows us to search for strategies to take advantage of the benefits that can be achieved by involving these advances in initial education,Escobar, Erika in this sense, the development of an electronic device that uses different elements such as readers and labels with Radio Frequency Identification (RF-ID) technology,Mayorga, Tannia a sensor to identify colors, a Raspberry Pi minicomputer, LCD screen,Bilous, Alla among others, are presented, together with the development of strategies and didactics that allow the device made as an electronic toy to an element to help stimulate logical thinking in children between the ages of five and seven, given the already showed importance that early strengthening of abilities such as identifying, relating and operate different types of situations that allow people to function in society in real situations.
Frame-rate up-conversion detection based on convolutional neural network for learning spatiotemporal features

With the advance in user-friendly and powerful video editing tools, anyone can easily manipulate videos without leaving prominent visual traces. Frame-rate up-conversion (FRUC), a representative temporal-domain operation, increases the motion continuity of videos with a lower frame-rate and is used by malicious counterfeiters in video tampering such as generating fake frame-rate video without improving the quality or mixing temporally spliced videos. FRUC is based on frame interpolation schemes and subtle artifacts that remain in interpolated frames are often difficult to distinguish. Hence, detecting such forgery traces is a critical issue in video forensics. This paper proposes a frame-rate conversion detection network (FCDNet) that learns forensic features caused by FRUC in an end-to-end fashion. The proposed network uses a stack of consecutive frames as the input and effectively learns interpolation artifacts using network blocks to learn spatiotemporal features. Moreover, it can cover the following three types of frame interpolation schemes: nearest neighbor interpolation, bilinear interpolation, and motion-compensated interpolation. In contrast to existing methods that exploit all frames to verify integrity, the proposed approach achieves a high detection speed because it observes only six frames to test its authenticity. Extensive experiments were conducted with conventional forensic methods and neural networks for video forensics to validate our research. The proposed work achieved an outstanding performance in terms of detecting the interpolated artifacts of FRUC. The experimental results also demonstrate that our model is robust against an unseen dataset, unlearned frame-rate, and unlearned quality factor. Furthermore, FCDNet can precisely localize the tampered region applied to manipulation along the time-domain through temporal localization.
A Financial Evaluation of a Multiple Inclination, Rooftop-Mounted, Photovoltaic System Where Structured Tariffs Apply: A Case Study of a South African Shopping Centre

Fundamental mathematical principles were applied to calculate the return on investment (ROI) of a multiple inclination, rooftop-mounted, photovoltaic (PV) system that is connected to a structured tariff network. Recent history has seen a disproportionate increase in electricity tariffs within South Africa, enabling an increase in the deployment rates of PV technologies. Given the inherent uncertainty associated with simulating the electricity yield of a PV system, it can become difficult to estimate the ROI in advance. This study contributes to existing knowledge by presenting a process for calculating the ROI of a case study rooftop-mounted PV system using mathematical first principles where structured tariffs apply. The PV modules were mounted to a curved roof structure, ranging from 9° towards a southerly direction to 10° towards a northerly direction. The research results indicate that since the PV system is located within the southern hemisphere, the ROI will increase when PV modules are orientated towards the northerly direction to attain a maximum of R0.0059/Wp/Day at 10°, and a minimum of R0.0025/Wp/Day at 9° towards the southerly direction in the winter season. Summer maximum ROI yielded an average of between R0.0050/Wp/Day and R0.0052/Wp/Day, irrespective of the angle of inclination of the PV modules. It was concluded that PV systems must be designed to favour winter electricity production for increased ROI where structured tariffs apply within a South African context.
Testing for SARS-CoV-2 infection: a key strategy to keeping schools and universities open

Education has been profoundly affected by the COVID-19 pandemic. Lack of access to education has exacerbated the divide between rich and poor and left vulnerable children exposed to domestic violence and hunger. The best way of keeping educational institutions open for in-person learning is to control transmission of SARS-CoV-2 in the wider community1 and to ensure rapid identification of infected staff and students to support timely and tailored public health responses. A comprehensive testing strategy is a core tenet in control, especially as a majority (40–60%) of children and young people have asymptomatic COVID-19,2 and individuals who are pre-symptomatic also present a transmission risk.
Neural Networks Regularization With Graph-Based Local Resampling

This paper presents the concept of Graph-based Local Resampling of perceptron-like neural networks with random projections (RN-ELM) which aims at regularization of the yielded model. The addition of synthetic noise to the learning set finds some similarity with data augmentation approaches that are currently adopted in many deep learning strategies. With the graph-based approach, however, it is possible to direct resample in the margin region instead of exhaustively cover the whole input space. The goal is to train neural networks with added noise in the margin region, located by structural information extracted from a planar graph. The so-called structural vectors, which are the training set vertices near the class boundary, are obtained from the structural information using Gabriel Graph. Synthetic samples are added to the learning set around the geometric vectors, improving generalization performance. A mathematical formulation that shows that the addition of synthetic samples has the same effect as the Tikhonov regularization is presented. Friedman and pos-hoc Nemenyi tests indicate that outcomes from the proposed method are statistically equivalent to the ones obtained by objective-function regularization, implying that both methods yield smoother solutions, reducing the effects of overfitting.
Blue-Pill Oxpecker: A VMI Platform for Transactional Modification

Although multiple techniques have been proposed with the goal of minimizing the semantic gap in virtual machine introspection, most concentrate on passive observation of the internal state, while there are also a number of proposals with which active modification of the VM's internal state is made possible. However there are issues when modifications are applied, such as keeping a consistent kernel state and avoiding a crash. In this article we propose Oxpecker, a VMI platform for transactional modification. The out-of-VM read access allows an introspector to detect malware in the guest OS (e.g., rootkit) and the transactional write access allows Oxpecker to reliably neutralize the detected threats. To begin a transaction, Oxpecker monitors VM state changes waiting for an idle moment which is free of possible race-conditions in the guest kernel memory. Thereafter, it invokes a VMI client's callback to proceed with reading/writing in its memory. Upon user request or possible exceptions, transaction is rolled back while the transaction ACID properties are maintained at all times. Oxpecker is implemented and evaluated under different real-world workloads. Additionally and as a practical example, a tool is developed, and open sourced, based on Oxpecker with which guest VM processes could be killed.
Hypergraph Attention Isomorphism Network by Learning Line Graph Expansion

Graph neural networks (GNNs) are able to achieve state-of-the-art performance for node representation and classification in a network. But, most of the existing GNNs can be applied to simple graphs, where an edge connects only a pair of nodes. Studies have shown that hypergraphs are effective to model real-world relationships which are of higher order in nature. Recently, graph neural networks are proposed for hypergraphs, but they implicitly use clique or star expansions to convert the hypergraph to a simple graph, or use computationally expensive hypergraph Laplacian.In this work, we propose a novel hypergraph neural network for semi-supervised hypernode classification, which operates directly on the hypergraphs with varying hyperedge sizes. Within each layer, it indirectly works on the line graph of the given hypergraph, without actually forming the line graph explicitly. Moreover, it also employs a self-attention mechanism to learn the weights of those edge relationships. Experimentally, HAIN is able to improve the state-of-the-art hypernode classification performance on all the datasets we use. We make the source code available to ease the reproducibility of the results.
Cloud-Edge Microservice Architecture for DNN-based Distributed Multimedia Event Processing

The rise of Big Data, Internet of Multimedia Things (IoMT), and Deep Neural Network (DNN) enabled the growth of DNN-based Computer Vision solutions to Multimedia Event Processing (MEP) applications. When these are applied to a real-world scenario we notice the importance of having a system with a satisfactory speed that can fit in the limited resources of most IoMT devices. However, most solutions for distributed MEP are dependent on a Cloud architecture, which makes these applications migration to the Edge more challenging. As a response to this, we present a microservice architecture for DNN-based distributed MEP over heterogeneous Cloud-Edge environments. We describe our solution that allows for an easier deployment both on the Edge and on the Cloud. We show that choosing the proper tools for an Edge-Friendly solution can lead to 100 times less resource utilisation. Our preliminary investigation shows promising results, with a reduction in energy consumption by 8% with a minor drawback of 15% in throughput in the Edge and a negligible increase in energy consumption on the Cloud.
Coarse to Fine Ensemble Network for Thyroid Nodule Segmentation

A thyroid nodule is defined as a small lump of tissue (either solid or cystic - filled with fluid), usually more than one-quarter of an inch in diameter, that may protrude from the neck’s surface or may form in the thyroid gland itself. The nodule can be either benign (non-cancerous) or malignant (cancerous). By the age of 45, up to half of the normal people have thyroid nodules that can be seen on an ultrasound. Fortunately, about 95% of thyroid nodules are benign. Recently, artificial intelligence becomes more and more popular in medical image processing. It helps the doctor in some scenarios; one is the thyroid nodule analysis in the ultrasound image. In this competition, we design a robust coarse to fine network that reaches a high-performance nodule segmentation result. Our method could handle some problematic cases, especially with no clear boundary of the thyroid nodule.