Reviewerly/exHarmony huggingface dataset:
Topic Modeling of the Pakistani Economy in English Newspapers via Latent Dirichlet Allocation (LDA)

This research paper explores aspects of the Pakistani economy using the Latent Dirichlet Allocation (LDA) technique. The data based on 3,000 articles were collected from two Pakistani English newspapers, Dawn and The News, (2015–2020), through Lexis Nexis database. The headlines of the news articles relevant to Pakistan’s economy, were taken into account. By employing the data-driven approach of the grounded theory, it is found that changes in policies, security preference, textile industry, the shift of energy, inflation, growth and investment, mega projects, sustainable democracy and poverty control need to be focused to overcome the challenges of Pakistan’s economy. It also reveals that mega projects like the China Pakistan Economic Corridor (CPEC) are called to boost Pakistan’s economy. The results show that smooth trading would help reduce poverty in the country.
Facial-expression-aware Emotional Color Transfer Based on Convolutional Neural Network

Emotional color transfer aims to change the evoked emotion of a source image to that of a target image by adjusting color distribution. Most of existing emotional color transfer methods only consider the low-level visual features of an image and ignore the facial expression features when the image contains a human face, which would cause incorrect emotion evaluation for the given image. In addition, previous emotional color transfer methods may easily result in ambiguity between the emotion of resulting image and target image. For example, if the background of the target image is dark while the facial expression is happiness, then previous methods would directly transfer dark color to the source image, neglecting the facial emotion in the image. To solve this problem, we propose a new facial-expression-aware emotional color transfer framework. Given a target image with facial expression features, we first predict the facial emotion label of the image through the emotion classification network. Then, facial emotion labels are matched with pre-trained emotional color transfer models. Finally, we use the matched emotion model to transfer the color of the target image to the source image. Considering none of the existing emotion image databases, which focus on images that contain face and background, we built an emotion database for our new emotional color transfer framework that is called “Face-Emotion database.” Experiments demonstrate that our method can successfully capture and transfer facial emotions, outperforming state-of-the-art methods.
Proof of Mirror Theory for <i>ξ</i> <sub>max</sub> = 2

In ICISC-05, and in the ePrint 2010/287, Patarin claimed a lower bound on the number of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2 q$ </tex-math></inline-formula> tuples of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> -bit strings <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(P_{1}, \ldots, P_{2q}) \in ({\{0,1\}}^{n})^{2q}$ </tex-math></inline-formula> satisfying <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$P_{2i - 1} \oplus P_{2i} = \lambda _{i}$ </tex-math></inline-formula> for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1 \leq i \leq q$ </tex-math></inline-formula> such that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$P_{1}, P_{2}, \ldots $ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$P_{2q}$ </tex-math></inline-formula> are distinct and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\lambda _{i} \in {\{0,1\}} ^{n} \setminus \{0^{n}\}$ </tex-math></inline-formula> . This result is known as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Mirror theory</i> and widely used in cryptography. It stands as a powerful tool to provide a high-security guarantee for many block cipher-(or even ideal permutation-) based designs. In particular, Mirror theory has a direct application in the security of XOR of block ciphers. Unfortunately, the proof of Mirror theory contains some unverifiable gaps and several mistakes. This paper provides a simple and verifiable proof of Mirror theory.
Blockchain technology for IoT edge devices and data security

The Internet of Things (IoT) is expanding rapidly across various trade verticals in real time applications. In IoT-architecture, there is a need to focus offline devices with its extensive-data due to its data processing restrictions and limited storage volume; but, the IoT-edge computing overcomes the above issues and offers a reliable architecture for managing the IoT devices with data. Due to the integration of computing and transaction processing systems, Blockchain in IoT (BIoT) provides high end security, synchronization, intellectual property management, uniqueness, affordability, and data privacy in real-time development. That basic initiative about BIoT-Edge computing technology including IoT layered architecture, IoT security threats and attacks and the operational functions of Edge computing along with architecture models are the foundations that are to be discussed in this proposed chapter. Future research directions of data integrity frameworks with edge computing platform in Blockchain of things are also focused.
Learning With Nested Scene Modeling and Cooperative Architecture Search for Low-Light Vision

Images captured from low-light scenes often suffer from severe degradations, including low visibility, color casts, intensive noises, etc. These factors not only degrade image qualities, but also affect the performance of downstream Low-Light Vision (LLV) applications. A variety of deep networks have been proposed to enhance the visual quality of low-light images. However, they mostly rely on significant architecture engineering and often suffer from the high computational burden. More importantly, it still lacks an efficient paradigm to uniformly handle various tasks in the LLV scenarios. To partially address the above issues, we establish Retinex-inspired Unrolling with Architecture Search (RUAS), a general learning framework, that can address low-light enhancement task, and has the flexibility to handle other challenging downstream vision tasks. Specifically, we first establish a nested optimization formulation, together with an unrolling strategy, to explore underlying principles of a series of LLV tasks. Furthermore, we design a differentiable strategy to cooperatively search specific scene and task architectures for RUAS. Last but not least, we demonstrate how to apply RUAS for both low- and high-level LLV applications (e.g., enhancement, detection and segmentation). Extensive experiments verify the flexibility, effectiveness, and efficiency of RUAS.
Improved artificial potential field method for UAV path planning

Aiming at the problem that UAVs are easy to fall into shock and local optimization during path planning of traditional artificial potential field algorithms, which makes UAVs unable to reach the target point, an improved artificial potential field algorithm is proposed. First, the gravitational and repulsive force functions of the traditional artificial potential field method are improved into the gravitational and repulsive force functions with angle factors. Secondly, adaptive weights are added to the gravity and repulsion functions to solve the problem of UAV vibration and local optimization. Finally, the results of path planning are compared and analyzed with multiple algorithms under the same obstacle environment. The simulation results verify the effectiveness and superiority of the improved algorithm.
Trust discovery and information retrieval using artificial intelligence tools from multiple conflicting sources of web cloud computing and e-commerce users

Cloud server adoption turns out to be increasingly well known and profoundly requested because of the unlimited information contribution and retrieval from anyplace and anytime. The existing method has lack of adaptability issues with a trust value calculation based on multidimensional cloud service providers. The truth content discovery algorithm is designed to produce trustworthy information in minimal time along with various types of media information. The technique provides trustworthy information from various news sources with minimal prediction error. It reduces the retrieval time of the query and ranks the news content index based on query matching. Based on an experimental evaluation, the proposed TCD + J48 method provides better results than existing methodologies. It minimizes the query retrieval time (QRT) by 0.9275 s, mean absolute errors (MAE) by 1.22 s and roots mean squared error (RMSE) by 5.14 s. The trustworthy and scalable service providers algorithm (TSSP) is proposed for analyzing the design and relationship among the clients, the broker, and cloud service providers. The trust-based method effectively reduces the cloud client burden and improves the system stability. The proposed technique works based on the data entropy hypothesis to assess the multiattribute-based decision-making. Based on an experimental test, this method reduces the system execution time by 2 msec, the communication cost by 9.33%, and improves the trust scores by 39.33% compared to existing methodologies in the respective parameters and tasks. An efficient feature extraction and classification (EFEC) algorithm is also proposed to extract a feature from opinion words. An EFEC algorithm is used to predict the quantity of positive and negative review opinions. According to an experimental assessment, the proposed technique improves accuracy by 15.05%, precision by 13.7%, recall by 15.59%, and F-measure by 15.07% of the proposed framework compared to existing techniques.
MutualNet: Adaptive ConvNet via Mutual Learning From Different Model Configurations

Most existing deep neural networks are static, which means they can only perform inference at a fixed complexity. But the resource budget can vary substantially across different devices. Even on a single device, the affordable budget can change with different scenarios, and repeatedly training networks for each required budget would be incredibly expensive. Therefore, in this work, we propose a general method called MutualNet to train a single network that can run at a diverse set of resource constraints. Our method trains a cohort of model configurations with various network widths and input resolutions. This mutual learning scheme not only allows the model to run at different width-resolution configurations but also transfers the unique knowledge among these configurations, helping the model to learn stronger representations overall. MutualNet is a general training methodology that can be applied to various network structures (e.g., 2D networks: MobileNets, ResNet, 3D networks: SlowFast, X3D) and various tasks (e.g., image classification, object detection, segmentation, and action recognition), and is demonstrated to achieve consistent improvements on a variety of datasets. Since we only train the model once, it also greatly reduces the training cost compared to independently training several models. Surprisingly, MutualNet can also be used to significantly boost the performance of a single network, if dynamic resource constraints are not a concern. In summary, MutualNet is a unified method for both static and adaptive, 2D and 3D networks. Code and pre-trained models are available at https://github.com/taoyang1122/MutualNet.
Digital Watermarking Technology Based on LDPC Code and Chaotic Sequence

The number of ways to obtain information has increased by 5th Generation Mobile Communication Technology commercial. But easier access to information also means more security issues. Digital watermarking is an effective method to solve the security problem of information. Aiming at the problems of one-dimensional single chaotic image encryption algorithm, such as simple structure, small key space, and the invisibility and robustness of traditional information hiding technology cannot be well reconciled. The improved encryption algorithm based on Low Density Parity Check Code and double Logistic chaotic system is proposed, which can expand the key space, reduce the bit error rate, and enhance the security and anti-interference ability of the system. The feasibility, reliability and practicability of the algorithm are verified by MATLAB simulation.
Knowing What it is: Semantic-Enhanced Dual Attention Transformer

Attention has become an indispensable component of the models of various multimedia tasks like <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Image Captioning</i> (IC) and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Visual Question Answering</i> (VQA). However, most existing attention modules are designed for capturing the spatial dependency, and are still insufficient in semantic understanding, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , the categories of objects and their attributes, which is also critical for image captioning. To compensate for this defect, we propose a novel attention module termed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Channel-wise Attention Block</i> (CAB) to model channel-wise dependency for both visual modality and linguistic modality, thereby improving semantic learning and multi-modal reasoning simultaneously. Specifically, CAB has two novel designs to tackle with the high overhead of channel-wise attention, which are the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">reduction-reconstruction block structure</i> and the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">gating-based attention prediction</i> . Based on CAB, we further propose a novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Semantic-enhanced Dual Attention Transformer</i> (termed SDATR), which combines the merits of spatial and channel-wise attentions. To validate SDATR, we conduct extensive experiments on the MS COCO dataset and yield new state-of-the-art performance of 134.5 CIDEr score on COCO Karpathy test split and 136.0 CIDEr score on the official online testing server. To examine the generalization of SDATR, we also apply it to the task of visual question answering, where superior performance gains are also witnessed. The code and models are publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/xmu-xiaoma666/SDATR</uri> .
Linearity and Uniqueness: An Entente Cordiale

Abstract Substructural type systems are growing in popularity because they allow for a resourceful interpretation of data which can be used to rule out various software bugs. Indeed, substructurality is finally taking hold in modern programming; Haskell now has linear types roughly based on Girard’s linear logic but integrated via graded function arrows, Clean has uniqueness types designed to ensure that values have at most a single reference to them, and Rust has an intricate ownership system for guaranteeing memory safety. But despite this broad range of resourceful type systems, there is comparatively little understanding of their relative strengths and weaknesses or whether their underlying frameworks can be unified. There is often confusion about whether linearity and uniqueness are essentially the same, or are instead ‘dual’ to one another, or somewhere in between. This paper formalises the relationship between these two well-studied but rarely contrasted ideas, building on two distinct bodies of literature, showing that it is possible and advantageous to have both linear and unique types in the same type system. We study the guarantees of the resulting system and provide a practical implementation in the graded modal setting of the Granule language, adding a third kind of modality alongside coeffect and effect modalities. We then demonstrate via a benchmark that our implementation benefits from expected efficiency gains enabled by adding uniqueness to a language that already has a linear basis.
A Fast Circle Detector with Efficient Arc Extraction

Circle detection is a crucial problem in computer vision and pattern recognition. Improving the accuracy and efficiency of circle detectors has important scientific significance and excellent application value. In this paper, we propose a circle detection method with efficient arc extraction. In order to reduce edge redundancy and eliminate crossing points, we present an edge refinement algorithm to refine the edges into single-pixel-wide branchless contour curves. To address the contour curve segmentation difficulty, we improved the CTAR (Chord to Triangular Arms Ratio) corner detection method to enhance corner point detection and segment the contour curves based on corner points. Then, we used the relative position constraint of arcs to improve the circle detection accuracy further. Finally, we verified the feasibility and reliability of the proposed method by comparing our approach with five other methods using three datasets. The experimental results showed that the presented method had the advantages of anti-obscuration, anti-defect, and real-time performance over other methods.
Antipatterns in software classification taxonomies

Empirical results in software engineering have long started to show that findings are unlikely to be applicable to all software systems, or any domain: results need to be evaluated in specified contexts, and limited to the type of systems that they were extracted from. This is a known issue, and requires the establishment of a classification of software types. This paper makes two contributions: the first is to evaluate the quality of the current software classifications landscape. The second is to perform a case study showing how to create a classification of software types using a curated set of software systems. Our contributions show that existing, and very likely even new, classification attempts are deemed to fail for one or more issues, that we named as the ‘antipatterns’ of software classification tasks. We collected 7 of these antipatterns that emerge from both our case study, and the existing classifications. These antipatterns represent recurring issues in a classification, so we discuss practical ways to help researchers avoid these pitfalls. It becomes clear that classification attempts must also face the daunting task of formulating a taxonomy of software types, with the objective of establishing a hierarchy of categories in a classification.
Accurate 3D Reconstruction of Dynamic Objects by Spatial-Temporal Multiplexing and Motion-Induced Error Elimination

Three-dimensional (3D) reconstruction of dynamic objects has broad applications, including object recognition and robotic manipulation. However, achieving high-accuracy reconstruction and robustness to motion simultaneously is a challenging task. In this paper, we present a novel method for 3D reconstruction of dynamic objectS, whose main features are as follows. Firstly, a structured-light multiplexing method is developed that only requires 3 patterns to achieve high-accuracy encoding. Fewer projected patterns require shorter image acquisition time, thus, the object motion is reduced in each reconstruction cycle. The three patterns, i.e. spatial-temporally encoded patterns, are generated by embedding a specifically designed spatial-coded texture map into the temporal-encoded three-step phase-shifting fringes. A temporal codeword and three spatial codewords are extracted from the composite patterns using a proposed extraction algorithm. The two types of codewords are utilized separately in stereo matching: the temporal codeword ensures the high accuracy, while the spatial codewords are responsible for removing phase ambiguity. Secondly, we aim to eliminate the reconstruction error induced by motion between frames abbreviated as motion induced error (MiE). Instead of assuming the object to be static when acquiring the 3 images, we derive the motion of projection pixels among frames. Using the extracted spatial codewords, correspondences between different frames are found, i.e. pixels with the same codewords are traceable in the image sequences. Therefore, we can obtain the phase map at each image-acquisition moment without being affected by the object motion. Then the object surfaces corresponding to all the images can be recovered. Experimental results validate the high reconstruction accuracy and precision of the proposed method for dynamic objects with different motion speeds. Comparative experiments show that the presented method demonstrates superior performance with various types of motion, including translation in different directions and deformation.
A Hybrid Cloud Enterprise Strategic Management System

This paper is an attempt to address merging or existing and emerging systems with enterprise strategy in mind that optimizes operational productivity based on different hardware and software. Recent studies have shown that organization's growth and developments have necessitated a further research-driven stance towards technology-based approach by many emerging companies through competition, especially amongst global conglomerates. Hence, the problems of mitigating risk by combining software and hardware systems remain to further espouse the limitless possibilities especially with the global concern of security remain infinite. This presents upfront; the emancipation of humanity in solving earthly and galactic problems using space-based satellites and earth-based transceivers as part of the hardware hegemony. The aim is to create an encompassing integration by checking the effects of browsers, programming languages, plugins, hardware devices, and software; thus acknowledging the diverse complexities amongst technologies when converging and integrating various systems.
Fast multiqubit Rydberg geometric fan-out gates with optimal control technology

Practical quantum computation requires highly efficient implementation of quantum logic gates. The fan-out gate represents such a type of multiqubit controlled gate with one qubit controlling a number of target qubits. Recent efforts for fan-out gates using Rydberg atoms include adiabatic operations along with the dark-state dynamics of Rydberg states [M. Khazali and K. M\o{}lmer, Phys. Rev. X 10, 021054 (2020)] and implementation based on the asymmetric Rydberg-Rydberg interaction [J. T. Young et al., Phys. Rev. Lett. 127, 120501 (2021)]. Inspired by these advances, we propose a fast-operation scheme for geometric multiqubit fan-out gates, in which the target qubits can be executed with arbitrary operations based on the asymmetric Rydberg-Rydberg interaction and time-optimal control technology. One of the main favorable features of our scheme is its performance in a nonadiabatic way and with the time-optimal control technology, by which the shortest smooth geometric path is found. Another feature, that the target qubits could be executed with arbitrary geometric quantum operations, makes our scheme general and useful. Therefore, our scheme provides a promising alternative route toward scalable fault-tolerant quantum information processing based on Rydberg atoms.