Reviewerly/exHarmony huggingface dataset:
Literature Review: A Comparative Study of Software Defect Prediction Techniques

At the latest, a lot of studies have been conducted to forecast software flaws and determine if modules are defective or not, in order to build high-quality software at a low cost before executing the testing stage. To solve the limitations of prediction, researchers have used a variety of machine learning techniques to build models. Though the time and resources required utilizing these procedures are calculated to be less because study concentrates on only those modules that are projected to be problematic. But a disproportionately big portion of the studies demonstrated low prediction capability, and their accuracy for varied datasets was also not persistent. In this literature survey paper, all the techniques have been studied and analyzed for predicting the bugs in the software. All the approaches being adopted by the researchers for handling the dimension reduction, noise filtering and class imbalancing have been studied in this review. There is still a need for handling feature selection, noise filtering, and class imbalancing, which can further affect the performance and accuracy of the model. To further comprehend these flaws, a full review has been undertaken from 2016 to 2021 period in order to gain a better knowledge of how the various models proposed by a number of authors are still lacking in terms of performing better on the available datasets and accuracy.
One-Hot Conversion: Towards Faster Table-Based A2B Conversion

Arithmetic to Boolean masking (A2B) conversion is a crucial technique in the masking of lattice-based post-quantum cryptography. It is also a crucial part of building a masked comparison which is one of the hardest to mask building blocks for active secure lattice-based encryption. We first present a new method, called one-hot conversion, to efficiently convert from higher-order arithmetic masking to Boolean masking using a variant of the higher-order table-based conversion of Coron et al. Secondly, we specialize our method to perform arithmetic to 1-bit Boolean functions. Our one-hot function can be applied to masking lattice-based encryption building blocks such as masked comparison or to determine the most significant bit of an arithmetically masked variable. In our benchmarks on a Cortex M4 processor, a speedup of 15 times is achieved over state-of-the-art table-based A2B conversions, bringing table-based A2B conversions within the performance range of the Boolean circuit-based A2B conversions.
Recognition of Indian Sign Language Characters Using Convolutional Neural Network

India is home to approximately 63 million people of the Deaf and Hard of Hearing community (DHH). Indian Sign Language (ISL) is used in the deaf community all over India. So there is a need for proper learning aids which require recognition models with high performance. This work aims to recognize Indian Sign Language using a Convolutional Neural Network. A custom dataset that was self-compiled by four different persons was utilized for this study. Image preprocessing is done to get two types of datasets, one with the grayscale format and the other with canny edge detection. Custom CNN model is used. Results indicate that CNN gives 99.87% accuracy for grayscale numerals and 99.76% accuracy for grayscale alphabets. In the case of canny edges, the given architecture gives an accuracy of 99.82% and 99.82% for numerals and alphabets respectively.
Privacy-Preserving Blueprints

If everyone were to use anonymous credentials for all access control needs, it would be impossible to trace wrongdoers, by design. This would make legitimate controls, such as tracing illicit trade and terror suspects, impossible to carry out. Here, we propose a privacy-preserving blueprint capability that allows an auditor to publish an encoding $$\textsf{pk}_{\textsf{A}}$$ of the function $$f(x,\cdot )$$ for a publicly known function f and a secret input x. For example, x may be a secret watchlist, and f(x, y) may return y if $$y\in x$$ . On input her data y and the auditor’s $$\textsf{pk}_{\textsf{A}}$$ , a user can compute an escrow $$Z$$ such that anyone can verify that $$Z$$ was computed correctly from the user’s credential attributes, and moreover, the auditor can recover f(x, y) from $$Z$$ . Our contributions are:
Effective and Efficient Masking with Low Noise Using Small-Mersenne-Prime Ciphers

Embedded devices used in security applications are natural targets for physical attacks. Thus, enhancing their side-channel resistance is an important research challenge. A standard solution for this purpose is the use of Boolean masking schemes, as they are well adapted to current block ciphers with efficient bitslice representations. Boolean masking guarantees that the security of an implementation grows exponentially in the number of shares under the assumption that leakages are sufficiently noisy (and independent). Unfortunately, it has been shown that this noise assumption is hardly met on low-end devices. In this paper, we therefore investigate techniques to mask cryptographic algorithms in such a way that their resistance can survive an almost complete lack of noise. Building on seed theoretical results of Dziembowski et al., we put forward that arithmetic encodings in prime fields can reach this goal. We first exhibit the gains that such encodings lead to thanks to a simulated information theoretic analysis of their leakage (with up to six shares). We then provide figures showing that on platforms where optimized arithmetic adders and multipliers are readily available (i.e., most MCUs and FPGAs), performing masked operations in small to medium Mersenne-prime fields as opposed to binary extension fields will not lead to notable implementation overheads. We compile these observations into a new AES-like block cipher, called AES-prime, which is well-suited to illustrate the remarkable advantages of masking in prime fields. We also confirm the practical relevance of our findings by evaluating concrete software (ARM Cortex-M3) and hardware (Xilinx Spartan-6) implementations. Our experimental results show that security gains over Boolean masking (and, more generally, binary encodings) can reach orders of magnitude despite the same amount of information being leaked per share.
STD-NET: Search of Image Steganalytic Deep-Learning Architecture Via Hierarchical Tensor Decomposition

Steganalysis aims to reveal covert communication established via steganography. In the arm race with steganography, steganalysis has evolved from the old-style hand-crafted features set to deep-learning architectures. However, recent studies show that the majority of existing deep steganalysis models have a large amount of redundancy, which leads to a huge waste of storage and computing resources. The existing model compression method cannot flexibly compress the convolutional layer in residual shortcut block so that a satisfactory shrinking rate cannot be obtained. In this paper, we propose STD-NET, an unsupervised deep-learning architecture search approach via hierarchical tensor decomposition for image steganalysis. Our proposed strategy will not be restricted by various residual connections, since this strategy does not change the number of input and output channels of the convolution block. We propose a normalized distortion threshold to evaluate the sensitivity of each involved convolutional layer of the base model to guide STD-NET to compress target network in an efficient and unsupervised approach, and obtain two network structures of different shapes with low computation cost and similar performance compared with the original one. Extensive experiments have confirmed that, on one hand, our model can achieve comparable or even better detection performance in various steganalytic scenarios due to the great adaptivity of the obtained network architecture. On the other hand, the experimental results also demonstrate that our proposed strategy is more efficient and can remove more redundancy compared with previous steganalytic network compression methods.
Optimization of Resource Management for Workload Allocation in Cloud Computing

The resource management necessity of any environment depends on optimization technique. Optimal allocation of resources is an important criterion in load balancing techniques. The workload optimization allocation version is mounted, and the last workload allocation is designed with the aid of thinking about conjunctive and disjunctive constraints. Based on the balanced initialization of workload, the Job shop optimization algorithm changed by the optimal allocation method is used to minimize makespan.
Complete Characterization of Broadcast and Pseudo-signatures from Correlations

Unconditionally secure broadcast is feasible among parties connected by pairwise secure links only if there is a strict two-thirds majority of honest parties when no additional resources are available. This limitation may be circumvented when the parties have recourse to additional resources such as correlated randomness. Fitzi, Wolf, and Wullschleger (CRYPTO 2004) attempted to characterize the conditions on correlated randomness shared among three parties which would enable them to realize broadcast. Due to a gap in their impossibility argument, it turns out that their characterization is incorrect. Using a novel construction we show that broadcast is feasible under a considerably larger class of correlations. In fact, we realize pseudo-signatures, which are information theoretic counterparts of digital signatures using which unconditionally secure broadcast may be obtained. We also obtain a matching impossibility result thereby characterizing the class of correlations on which three-party broadcast (and pseudo-signatures) can be based. Our impossibility proof, which extends the well-know argument of Fischer, Lynch and Merritt (Distr. Comp., 1986) to the case where parties observe correlated randomness, maybe of independent interest.
Investigating TrustZone: A Comprehensive Analysis

The advent of the Internet and portable devices, including smartphones and watches, has brought unprecedented opportunities for embedded application systems developments. Along with these developments, there is an increasing need for embedded devices to handle important services, such as the ability to pay bills or manage bank accounts remotely via mobile phones. Such applications and developments have also highlighted the issues of cyberattacks and computing network security--these developments have made mobile phones a potential target for malware, trojans, and viruses, so it is critical to design a set of security technologies for embedded devices. In fact, security has become an essential requirement in the process of embedded system design. Thus, ARM has proposed system-level security solutions based on TrustZone technology. TrustZone technology is tightly integrated with Cortex™-A processors and extends the system through the AMBA® AXI bus and specific TrustZone system IP blocks to protect peripherals such as secure memory, encryption blocks, keyboards, and screens from software attacks. It divides the system into TEE (Trusted Execution Environment) and REE (Rich Execution Environment) by hardware and provides intrinsic software security services and interfaces. More precisely, it has built system security by combining hardware and software. It is worth noting that it does not influence performance, power consumption, and area as much as possible. Owing to such characteristics, the technology has gained the wide attention of researchers worldwide. There is lack of systematic documentation of the technology. Therefore, this paper documents the significant progress achieved in the field. In particular, this article mainly analyses the primary mechanism implementation, and how to build the Trusted Execution Environment in different environments. Then, this paper discusses the related research works in the academic field and business applications of the technology. Furthermore, the advantages and weaknesses of the TrustZone technology as well as the proposed possible solutions aiming at the deficiency are outlined. Finally, a comparison of TrustZone technology with another mainstream commercial SGX, and future directions are presented.
Distributed Multi-MMW Radar Fusion for Target Detection and Tracking in Highway Traffic Environment

High-resolution millimeter-wave (MMW) radar is viewed as a low-cost and highly reliable sensor compared to camera, lidar, etc., in moving scenarios and thus has been selected by highway stakeholders as an important roadside detector to detect the movement of traffic vehicles and monitor traffic flow in real time. However, the echo signal of MMW radar in complex highway environment contains not only the signal reflected by target but also spurious signals and other interference signals, which significantly affects the estimation of the target movement state. To solve this problem, an improved vehicle tracking method is designed to simultaneously estimate the polar angle and polar radius in coordinator of MMW radar. Moreover, considering the movement patterns of target vehicles in dynamic uncertain traffic situations, a set of state space models, such as CA, CV, and CT are combined to represent the vehicle movement. In addition, based on the enhanced detection performance of a single radar, the combination of multiple MMW radars’ information was performed to determine the sequential trajectory of the target vehicle on the continuous road sections; then, the historical trajectory of the target vehicle was correlated and fused. Real experiments in highway scenarios show that the method used in this study is effective in deriving the trajectory of the vehicle and improving the positioning accuracy and reliability when the vehicle performs heavy maneuvers.
Diferential method for the formation of relative phase shifts between light beams in two-arm interferometer

The possibility of improving technical means that make it possible to introduce phase shifts into light beams in the interferometer paths is considered. An optical-mechanical phase modulator in the form of a plane-parallel glass plate rotated around an axis lying in its plane was adopted as the initial version of these technical means. A differential method is proposed for the formation of relative phase shifts between beams in a two-arm interferometer. A variant of the modulator has been developed in the form of a pair of rigidly interconnected plates, the planes of which are initially rotated at a given angle. In this case, each of the parallel light beams in the interferometer passes through its own plate. It is shown that in this case a practically linear relationship is provided between the generated relative phase shift and the total angle of rotation of the plates. A variant of using a single plate as a modulator under the established conditions for the incidence of two beams propagating in different arms of the interferometer is considered. The operability of the proposed differential method for controlling the phase shift between light beams is confi rmed by the data of a test experiment. The results obtained will be useful in the development of special tools based on the method of digital speckle pattern interferometry for measurements of the displacement fields of deformable bodies
Multimedia Lu Xun literature online learning based on deep learning

As a great Chinese thinker and writer in the twentieth century, Lu Xun and his literary works are widely known. However, as a successful cultural communication activist, editor and publisher, we still need to conduct in-depth research on Lu Xun in many aspects. Therefore, based on deep learning, this paper constructs an online multimedia learning system of Lu Xun literature. This system takes the relationship between classical Lu Xun literature and modern multimedia technology as the research object, and compare the calculation effect of other different types of algorithms and this dhraa algorithm. Through the availability of data, the dhraa algorithm is significantly better than other algorithms in the recommendation accuracy, thus proving its effectiveness. This system is managed by two servers and one system. The two servers are database server and web server, respectively. After testing, the system has good bearing capacity, can make up for the limited processing capacity of the server, and ensure the system has high performance. Its performance characteristics also show that the system achieved the expected performance. This paper systematically combines Lu Xun’s literature with modern multimedia, which can provide online learning services for Lu Xun’s literature lovers, thus helping scholars to expand Lu Xun’s research field and academic vision. This paper designs an effective online learning system of Lu Xun’s literature by combining deep learning, multimedia technology and Lu Xun’s literature.
Efficient face image super‐resolution with convenient alternating projection network

Abstract The existing deep learning‐based face super‐resolution techniques can achieve satisfactory performance. However, these methods often incur large computational costs, and deeper networks generate redundant features. Some lightweight reconstruction networks also present limited representation ability because they ignore the entire contour and fine texture of the face for the sake of efficiency. Here, the authors propose a convenient alternating projection network (CAPN) for efficient face super‐resolution. First, the authors design a novel alternating projection block cascaded convolutional neural network to alternately achieve content consistency and learn detailed facial feature differences between super‐resolution and ground‐truth face images. Second, the self‐correction mechanism enabled the convolutional layer to capture faithful features that facilitate adaptive reconstruction. Moreover, a convenient connection operation can reduce the generation of redundant facial features while maintaining accurate reconstruction information. Extensive experiments demonstrated that the proposed CAPN can effectively reduce the computational cost while achieving competitive qualitative and quantitative results compared to state‐of‐the‐art super‐resolution methods.
DEVELOPMENT OF A LOW-COST SOLAR PYRANOMETER

Effective solar projects require effective solar data collection, however with the high cost of pyranometers in the market, there arose the need to construct a low-cost device for the purpose of solar radiation measurements. A device was thus developed in the University of Port Harcourt, River’s state, Nigeria by utilizing a photodiode, a thermistor, a lcd among other readily available components. Using a reference pyranometer for calibration, the device was able to deliver a mean global solar radiation value of 16.00MJ/m2/day, while the maximum value obtained during the test period was 20.70MJ/m2/day. The performance of the locally developed device compares and competes favourably with what is available in the market.
Reputation based proof of cooperation: an efficient and scalable consensus algorithm for supply chain applications

Abstract The growing interest in blockchain technology has gained a lot of attention in Supply Chain Management (SCM) and sparked the quest for decentralized, scalable, efficient and trustworthy consensus schemes. Traditional blockchains rely on computationally expensive consensus mechanisms with low throughput and high latency. This paper conducts a performance evaluation of several existing consensus protocols to illustrate blockchain’s shortcomings in terms of consensus and propose a new consensus algorithm: Reputation based proof of cooperation (RPoC). The RPoC algorithm uses a layered architecture to segment the nodes that participate in the consensus phase in order to improve scalability and efficiency while maintaining trust among peers. The layered design addresses the issues of flexibility and scalability and breaks down the extensive mining process into segments. Rather than choosing a few nodes for mining, the proposed consensus process involves all network nodes, making it more efficient, decentralized and scalable. Through extensive theoretical analysis and experimentation, the suitability of the proposed algorithm is established in terms of scalability and efficiency.
Jaccard matrix for nonlinear filter statistics

We propose the Jaccard matrix (JM) and the Jaccard cell (JC), define them as the extended concepts of the Jaccard index, and theoretically and numerically analyze them. The data on the Euclidean plane can derive the JM as a sparse matrix. We show the JC inherits the feature of similarity of the Jaccard index as the exponential function of mutual information. We theoretically and numerically confirm that the local correlation coefficient of the data on the Euclidean plane relates the JC to the mutual information. Although one could potentially select an arbitrary cell size of the grid to make the JM, the knowledge we can obtain from the matrix decreases if the cell size is too big or too small to distinguish the data clusters appropriately. Therefore, the JM needs a computational procedure to determine the cell size within the appropriate scale. Maximizing the variance of the JCs supports determining the unique cell size, which value locates in the middle range of the parabolic function of the cell-size parameter. The JM could derive an index extracting nonlinear correlation of the data. The maximized standard deviation of the JCs as such an index is a decreasing function of the noise scale of the data under the constraint conditions. The ability to determine the homogeneous rectangular grid pattern of the JM might be a significant feature for finding nonlinear correlation. We would summarize this study as that of a nonlinear filter working as an efficient component of explainable AI and statistics.
A novel energy efficient two‐dimensional pilot muting mechanism for non‐stationary channel fading environment

Abstract To support communication channels with non‐stationary properties, the pilots are usually configured for the channel worst case in order to ensure the accuracy of tracking the channel, which inevitably causes a waste of resources. To solve this issue, first, a flexible two‐dimensional pilot muting mechanism is proposed by exploiting the time and frequency diversity of the pilot sequence of the resource grid for non‐stationary channel fading environment. Then a super‐resolution based non‐stationary channel recovery scheme for data channel recovery is further proposed for two‐dimensional pilot muting, and the energy efficiency of a resource block is also maximized. Numerical results show that the proposed mechanism can bring about as much as 8 dB performance gain in system energy efficiency.