Reviewerly/exHarmony huggingface dataset:
The Need for Being Explicit: Failed Attempts to Construct Implicit Certificates from Lattices

Abstract Global efforts such as the National Institute of Standards and Technology (NIST)’s post-quantum standardization center on cryptographic primitives like public-key encryption and signature schemes that are secure even in the presence of quantum adversaries. In addition, one must also consider efficient certificate management as new technologies like the Internet of Things and 5G wireless networks rely on them. For example, the IEEE Standard for vehicle-to-vehicle communication depends on implicit certificates. However, the only efficient construction available is over elliptic curves, and hence not quantum-secure. This paper investigates approaches to construct implicit certificate schemes from lattices, employing the NIST Round 3 signature schemes Dilithium and Falcon. We consider emulation of the existing implicit certificate scheme and proceed to more innovative techniques like combining the two schemes or pairing them with encryption. Unfortunately, we encounter problems with each design, due to recurring causes like conflicting secret key and signature sizes, unique sampler requirements and the rigidity of the parameter sets. By explaining each of these issues, this paper will hopefully spark ideas for more successful constructions.
What is the intended usage context of this model? - An exploratory study of pre-trained models on various model repositories

There is a trend of researchers and practitioners to directly apply pre-trained models to solve their specific tasks. For example, researchers in software engineering (SE) have successfully exploited the pre-trained language models to automatically generate the source code and comments. However, there are domain gaps in different benchmark datasets. These data-driven (or machine learning based) models trained on one benchmark dataset may not operate smoothly on other benchmarks. Thus, the reuse of pre-trained models introduces large costs and additional problems of checking whether arbitrary pre-trained models are suitable for the task-specific reuse or not. To our knowledge, software engineers can leverage code contracts to maximize the reuse of existing software components or software services. Similar to the software reuse in the SE field, reuse SE could be extended to the area of pre-trained model reuse. Therefore, according to the model card’s and FactSheet’s guidance for suppliers of pre-trained models on what information they should be published, we propose model contracts including the pre- and post-conditions of pre-trained models to enable better model reuse. Furthermore, many non-trivial yet challenging issues have not been fully investigated, although many pre-trained models are readily available on the model repositories. Based on our model contract, we conduct an exploratory study of 1908 pre-trained models on six mainstream model repositories (i.e., the TensorFlow Hub, PyTorch Hub, Model Zoo, Wolfram Neural Net Repository, Nvidia, and Hugging Face) to investigate the gap between necessary pre- and post-condition information and actual specifications. Our results clearly show that (1) the model repositories tend to provide confusing information of the pre-trained models, especially the information about the task’s type, model, training set, and (2) the model repositories cannot provide all of our proposed pre/post-condition information, especially the intended use, limitation, performance, and quantitative analysis. On the basis of our new findings, we suggest that (1) the developers of model repositories shall provide some necessary options (e.g., the training dataset, model algorithm, and performance measures) for each of pre/post-conditions of pre-trained models in each task type, (2) future researchers and practitioners provide more efficient metrics to recommend suitable pre-trained model, and (3) the suppliers of pre-trained models should report their pre-trained models in strict accordance with our proposed pre/post-condition and report their models according to the characteristics of each condition that has been reported in the model repositories.
Systematic solution for optimally energy-efficient turning radius for wheeled skid-steer rovers

A skid-steer rover’s power consumption is highly dependent on the turning radius of its path, with a point turn consuming much more power compared to straight line motion. As energy is the integration of instantaneous power over time, a trade-off between arcs’ turning radii and lengths should be made to minimize energy consumption. Because of the skid-steer rovers’ ability to do point turns the simplest and shortest way to traverse a distance between two points is by doing a point turn-line-point turn (PLP) maneuver. However, we show that wheeled skid-steer rovers there are scenarios where optimal Circle–Line–Circle (CLC) paths consume less energy than PLP paths. Therefore, the goal in this work is to find the best path from among CLC paths; Karush–Kuhn–Tucker (KKT) conditions are used to systematically obtain the optimally energy-efficient answer for the CLC paths. It is assumed that the rovers move forward on hard flat ground. For solving the problem, a new practical constraint constant-vc is suggested. In this paper, comparing the KKT conditions and experimental results reveals that the lowest total energy consumption for CLC paths with or without considering constant-vc constraint is obtained by selecting turning radii equal to R′ (the half of slip-track).
Radio Frequency Fingerprint extraction based on Multiscale Approximate Entropy

Radio Frequency (RF) fingerprinting is the technology of recognizing the transmitter using the non-linear characteristics of an intercepted RF signal. The underlying inevitable impairments of the hardware chain in transmitters are used as unique RF signatures for the distinct identification of various radios. The technique can be applied to distinguish not only between the radio of different make but also between the radios of the same make and type. In this paper, we propose a novel RF fingerprinting method, based on Multi-Scale Approximate Entropy (MSAE) which utilizes the steady-state section of the RF signal, extracted through the Higuchi Fractal Dimension (HFD) method. The MSAE feature extraction method is validated using real-world data-set for Very High Frequency (VHF) radios. The proposed method uses MSAE features which are subsequently fed to Machine Learning (ML) algorithms for classification accuracy comparison. In terms of classification accuracy, the proposed MSAE features outperforms some of the existing steady-state methods, especially at low SNR.
Facial emotions recognition using local monotonic pattern and gray level co-occurrence matrices plant leaf images aided agriculture development

In this article, Local Monotonic Pattern (LMP) paired with Gray Level Co-occurrence Matrix (GLCM) methods are suggested in this study to identify facial emotions with a high identification rate even when the face pictures are rotated. The proposed method extracts image features using the properties of the LMP algorithm and features extracted from the GLCM, which are then fed into the Support Vector Machine (SVM) process that reduces the dimensionality of the features vector and classifies the output into different facial expressions or emotions. The SVM performance rate is then compared to the K-Nearest Neighbor approach (KNN) to see which method produces the best facial emotion identification and categorization. Consequently, the study identified facial emotions in the images using advanced algorithms of GLCM and LMP models to be compared with. As a result, the accuracy of SVM and KNN was utilized to determine the method's usefulness in classification using the application of MATLAB. A result of more than 93% has been achieved using SVM method compared with 89.4% using the KNN for the recognition process. The study also demonstrated that this approach will result in a more classification outcomes if the LMP and GLCM are combined with an edge-based technique which yield in a new method that is more efficient and more effective.
Few-shot object detection via high-and-low resolution representation

This paper proposes a new Few-Shot Object Detection (FSOD) method, which can be trained with only a few samples, allowing the model to recognize new instances. Unlike previous FSOD methods, this work emphasizes the significance of high-and-low resolution features for the detection of object center points in a few samples and proposes a new architecture for few-shot object detection via the combination of high-and-low resolution features. The high-resolution representation module can effectively extract detailed features of the object. Meanwhile, the low-resolution representation module can provide effective semantic information for the object center points, which is more convenient for the network to locate the new object's center and improve the detection accuracy of the network. Several experiments show that our algorithm can reach the most advanced level in the FSOD field, demonstrating its effectiveness.
Predicting and explaining performance and diversity of neural network architecture for semantic segmentation

This paper proposes searching for network architectures which achieve similar performance while promoting diversity, in order to facilitate ensembling. We explain prediction performance and diversity of various network sizes and activation functions applied to semantic segmentation of the CityScapes dataset. We show that both performance and diversity can be predicted from neural network architecture using explainable boosting machines. A grid search of 144 models is performed, and many of the models exhibit no significant difference in mean performance within a 95% confidence interval. Notably, we find the best performing models have varied network architecture parameters. The explanations for performance largely agree with the accepted wisdom of the machine learning community, which shows that the method is extracting information of value. We find that diversity between models can be achieved by varying network size. Moreover, homogeneous network sizes generally show positive correlation in predictions, and larger models tend to converge to similar solutions. These explanations provide a better understanding of the effects of network parameters to deep learning practitioners; they could also be used in place of naïve search methods or a model pool to inform growing an ensemble.
Spatio-temporal regularized shock-diffusion filtering with local entropy for restoration of degraded document images

This paper proposes a novel model for restoration of document images with degraded background caused by show-through/smear or deteriorated documents. The model is formulated as a partial differential equation (PDE) that consists of spatio-temporal regularization of shock filtering and diffusion process, as well as local entropy. Shock filtering is employed to enhance the brightness contrast between texts and background according to image gradients adaptively. Diffusion process is not only responsible for removing selectively noise in the input image, but also suppressing the amplified noise by the shock filtering. Local entropy based source term serves as degradation-corrected term that is responsible for reducing the influence of degradation. A splitting-based algorithm is developed to solve the proposed model numerically, where two typical splitting methods and finite difference are combined. We test the proposed model and numerical scheme on DIBCO 2009–2014 and 2016 datasets. Experimental results indicate that the proposed method works well in restoring document images with degraded background, and achieves comparable performances compared to seven relevant methods for seven DIBCO datasets.
Optimisation of Artificial Neural Network Using Cuckoo Search Algorithm for Damage Detection

Artificial Neural Network is a tool of Structural Health Monitoring based on non-destructive methods for finding damage detection. ANN is a parallel distributed network inspired by the biological nervous system and has the potential to learn and identify from experience to improve its performance. Therefore, a trained network can be utilised to classify and examine new data sets that show similar characteristics as that of trained data sets. By virtue of the potential capability of ANN, it can be used to solve both simple linear and complex nonlinear functions. But due to the gradient descent nature of the backpropagation algorithm, the problem of local minima arises and which acts as a great hindrance to the best solution. To overcome this, various evolutionary algorithms based on global minima were used in conjunction with ANN such as Particle Swarm Optimisation, Genetic Algorithm, Cuckoo Search (CS), etc. Cuckoo Search is a powerful meta-heuristic search algorithm derived from the reproduction strategy of the Cuckoo bird. The lesser number of parameters in CS makes it simple and potentially more genetic. This paper presents a method for damage detection in structures by amalgamating a flexible combination of Artificial Neural Network and Cuckoo Search algorithm. CS is employed with ANN to improve the training parameters (weight and bias) by minimising the difference between the real and desired output and these parameters were used for generating the network. For the study, a lattice structure is used to assess the robustness of the created network and this will be compared with efficiency in damage location and identification of the network created using ANN alone. The Study proved that ANN-CS outperforms ANN alone in damage identification, localisation, and quantification.
29110+ST: Integrated Security Practices. Case Study

Data security has become a significant area of interest for everyone involved in developing mobile applications. Therefore, it is crucial to consider that most of the applications security issues are introduced in the development process. According to A. Semeney, Founder of DevTeam.Space, mobile applications are generally developed by small teams or software development VSEs. In this context, this article presents a tool developed with the name of 29110+TS that shows the proposal of security improvements to the ISO/IEC 29110. To validate the proposal and the 29110+TS tool, an expert’s judgment method was implemented with a survey in which the answers gave a positive response to the improvements done to the Base framework and the 29110+TS tool with 90.5%.
Algorithm Assurance: Auditing Applications of Artificial Intelligence

Abstract Algorithm assurance is a specific form of IT assurance that supports risk management and control on applications of risky algorithms in products and in organizations. These algorithms will often be characterized in organizations as applications of Artificial Intelligence (AI), as advanced analytics, or—simply—as predictive models. The aim of this chapter is to introduce the concept of algorithm assurance, to give some background on the relevance and importance of algorithm assurance, and to prepare the auditor for the basic skills needed to organize and execute an algorithm audit. In this chapter we will introduce the algorithm assurance engagement as a specific type of IT audit. After a general discussion of the background of algorithm assurance and the type of IT applications we are concerned with in this type of engagement, we will extensively discuss the scope of an algorithm assurance engagement, how to approach the risk assessment that should take place initially, how to set up and audit plan, and the audit techniques and tools that play a role in an audit plan.
Dynamic analysis of new two‐dimensional fractional‐order discrete chaotic map and its application in cryptosystem

A new fractional difference equation two‐dimensional triangle function combination discrete chaotic map (2D‐TFCDM) based on Caputo derivative is proposed. Using the bifurcation diagram, the maximum Lyapunov exponent, and the phase diagram, the numerical solutions of the fractional difference equations are obtained, and the chaotic behavior is observed numerically. After encrypting the key with elliptic curve cryptosystem, the fractional map is developed as an encryption algorithm and applied to color image encryption. Finally, the proposed encryption system is systematically analyzed from five main aspects, and the results show that the proposed encryption system has a good encryption effect.
Can Undergraduates Get the Experience Required by the Software Industry During Their University?

The use of software standards in the software industry as a requirement to be part of the software development chain has been increasing in recent years. This fact is not an exception for Very Small Entities, as suppliers for medium and large organizations. However, achieving the use of software engineering models and standards is not an easy task for them. One of the principal facts is the human factor. Most engineers in this type of organization are junior engineers with little or no experience working under international software engineering models or standards. This paper provides the results of implementing an international standard in universities to reduce the gap between the requirements of the software engineering industry and the knowledge received at universities. The results show that students can get the required experience by developing real projects following the ISO/IEC 29110 standard.
Liberating TFHE

All known instantiations for fully homomorphic encryption (FHE) produce noisy ciphertexts and rely on a technique called bootstrapping to reduce the noise so as to enable an arbitrary number of homomorphic operations. Bootstrapping is the main performance bottleneck and arguably the biggest obstacle to widespread adoption of FHE. Among the FHE schemes, TFHE and its variations present the appealing property of having a bootstrapping procedure---as well as its extension to programmable bootstrapping---that is relatively light-weight. The essential operations consist of a series of multiplications in (Z/qZ)[X]/(X^N+1). While the NTT is seemingly the natural candidate for evaluating these multiplications in a fast and exact way, it restricts the possible choices for and . To the authors' knowledge, all current implementations of TFHE with a power of two actually employ the FFT over the complex numbers instead. This introduces real numbers to the otherwise purely discrete algorithms, including all the drawbacks of the need to approximate them using finite precision. This work studies the avenues available to apply the NTT in the context of TFHE-like schemes. In particular, it considers various combinations of coefficient rings and quotient polynomials that are compatible with the requirements of the underlying scheme. Importantly, this work provides methods for adapting the (programmable) bootstrapping to quotient polynomials beyond power-of-two cyclotomics. As a side effect, it also demonstrates how this may enhance the programmability of the bootstrapping.
Casing the Vault

Vault applications are a class of mobile apps used to store and hide users' sensitive files (e.g., photos, documents, and even another app) on the phone. In this paper, we perform an empirical analysis of popular vault apps under the scenarios of unjust search and filtration of civilians by authorities (e.g., during civil unrest). By limiting the technical capability of adversaries, we explore the feasibility of inferring the presence of vault apps and uncovering the hidden files without employing sophisticated forensics analysis. Our analysis of 20 popular vault apps shows that most of them do not adequately implement/configure their disguises, which can reveal their existence without technical analysis. In addition, adversaries with rudimentary-level knowledge of the Android system can already uncover the files stored in most of the vault apps. Our results indicate the need for more secure designs for vault apps.
Acceleration of Homomorphic Unrolled Trace-Type Function using AVX512 instructions

More and more data analysis is being outsourced due to the spread of cloud computing. Therefore, protection of the data from privacy violations and information leaks is required. In particular, homomorphic encryption, which allows computation to be performed with encrypted data, is being actively studied as one of the protection method. Ring learning with errors based homomorphic encryption schemes support packing which allows to pack several elements into slots of a plaintext and ciphertext. A trace-type function, which combines shifting slots (rotation) and homomorphic addition to obtain summation of slots, is often used in homomorphic encryption applications and acceleration of the trace-type function is important. In this paper, we further accelerate the trace-type function using Intel AVX512 compared to existing optimized trace-type function with loop unrolling. The results show that our AVX512 version was 1.05-2.30 times speedup compared to the non-AVX512 version.
RADepthNet: Reflectance-aware monocular depth estimation

Monocular depth estimation aims to predict the dense depth map from a single RGB image, which has important applications in 3D reconstruction, automatic driving, and augmented reality. However, existing methods directly feed the original RGB image into the model to extract depth features without avoiding the interference of depth-irrelevant information on depth estimation accuracy, which leads to inferior performance. To remove the influence of depth-irrelevant information and improve depth prediction accuracy, we propose RADepthNet, a novel reflectance-guided network fusing boundary features. Specifically, our method predicts depth maps using three steps: 1) Intrinsic Image Decomposition. We propose a Reflectance extraction module consisting of an encoder-decoder structure to extract depth-related reflectance. We demonstrate that the module can reduce the influence of illumination on depth estimation through an ablation study. 2) Boundary Detection. Boundary extraction module, consisting of an encoder, a refinement block, and an upsample block, is proposed to better predict depth at object boundaries utilizing gradient constraints. 3) Depth Prediction Module. Use a different encoder from 2) to obtain depth features from the reflectance map and fuse boundary features to predict depth. Besides, we proposed FIFADataset, a depth estimation dataset applied in soccer scenarios. Extensive experiments on the public dataset and our proposed FIFADataset show that our method achieves state-of-the-art performance.
Implementation of a real‐time stereo vision algorithm on a cost‐effective heterogeneous multicore platform

Summary Stereo vision is a major computer vision technique commonly used for robotics applications. Existing software implementations of this technique on general‐purpose processors offer low time‐to‐market compared to other platforms. However, such implementations can hardly achieve real‐time and their cost is usually relatively high. These issues can be solved by embedded multicore platforms. In this article, we present a low‐cost, improved software implementation of a stereo matching algorithm in the correlation stage that combines a sparse rank transform with a combination of sum of absolute differences 1‐D and 2‐D box filtering algorithms. A circular buffer scheme is used to optimize memory usage during the rank computation stage. The system runs on a heterogeneous multicore platform (ODROID XU4). Through the extensive use of single instruction multiple data Neon intrinsics, the system can process images with a size of pixels and a disparity range of 20 pixels at a rate of 111 frames per second. The proposed system can be used in mobile robot platforms that require low power consumption while delivering real‐time performance.