Reviewerly/exHarmony huggingface dataset:
Semantic Completion and Filtration for Image–Text Retrieval

Image–text retrieval is a vital task in computer vision and has received growing attention, since it connects cross-modality data. It comes with the critical challenges of learning unified representations and eliminating the large gap between visual and textual domains. Over the past few decades, although many works have made significant progress in image–text retrieval, they are still confronted with the challenge of incomplete text descriptions of images, i.e., how to fully learn the correlations between relevant region–word pairs with semantic diversity. In this article, we propose a novel semantic completion and filtration (SCAF) method to alleviate the above issue. Specifically, the text semantic completion module is presented to generate a complete semantic description of an image using multi-view text descriptions, guiding the model to explore the correlations of relevant region–word pairs fully. Meanwhile, the adaptive structural semantic matching module is presented to filter irrelevant region–word pairs by considering the relevance score of each region–word pair, which facilitates the model to focus on learning the relevance of matching pairs. Extensive experiments show that our SCAF outperforms the existing methods on Flickr30K and MSCOCO datasets, which demonstrates the superiority of our proposed method.
Emerging Technologies’ Contribution to the Digital Transformation in Accountancy Firms

Digitalization becomes a key strategy for the success of businesses, which in today’s critical times, are under remarkable pressures and diffused uncertainty. The rapid pace of digitization is forcing deep changes in the modus operandi of organizations. This phenomenon is even more so true for accounting organizations considering that, by implementing blockchain, RPA, cloud, big data, cybersecurity, and AI, accountants might have the most digitized workplace of all. The purpose of this paper is to explore how these emergent technologies are contributing to the digital transformation of accounting firms. Based on a qualitative approach, the methodology consists of a thematic analysis of the academic literature to reveal the synergic effect of the most disruptive emergent technologies for accountancy firms. In addition to the topic of research, the originality of this study is ensured by the fact that it presents both technical and conceptual information, easily digestible for academicians and practitioners skilled in the ICT field, or not. The paper is intended to be a building brick for the literature related to this topic.
State-of-the-Art Survey on Deep Learning-Based Recommender Systems for E-Learning

Recommender systems (RSs) are increasingly recognized as intelligent software for predicting users’ opinions on specific items. Various RSs have been developed in different domains, such as e-commerce, e-government, e-resource services, e-business, e-library, e-tourism, and e-learning, to make excellent user recommendations. In e-learning technology, RSs are designed to support and improve the learning practices of a student or an organization. This survey aims to examine the different works of literature on RSs that corroborate e-learning and classify and provide statistics of the reviewed articles based on their recommendation goals, recommendation techniques used, the target user, and the application platforms. The survey makes a prominent contribution to the e-learning RSs field by providing an overview of current research and traditional and nontraditional recommendation techniques to provide different recommendations for future e-learning. One of the most significant findings to emerge from this survey is that a substantial number of works followed either deep learning or context-aware recommendation techniques, which are considered more efficient than any traditional methods. Finally, we provided comprehensive observations from the quantitative assessment of publications, which can guide and support researchers in understanding the current development for potential future trends and the direction of deep learning-based RSs in e-learning.
Collaborative filtering with sequential implicit feedback via learning users’ preferences over item-sets

Nowadays, the sequential information, i.e., the ordering of the recorded feedback, is one of the most frequently used auxiliary information for developing recommendation algorithms. However, there are still some challenges in collaborative filtering with sequential implicit feedback. For one thing, there may be some co-occurrence and “leap-occurrence” phenomena in users’ behavior sequences. For another, a weak point of utilizing implicit feedback is that the unobserved behaviors do not always indicate “dislike”. In this paper, we focus on these two fundamental challenges in users’ preference learning, i.e., the uncertainty of the ordering of the next items and the uneven quality of the negative samples, and propose a novel solution named COFIS (short for collaborative filtering with sequential implicit feedback via learning users’ preferences over item-sets). Specifically, our COFIS incorporates two learning paradigms (i.e., pairwise and pointwise) and two expression strategies (i.e., “MOO” and “MOS”, representing two different ways to treat the negative item-sets when compared with the positive item-sets). Our COFIS can be applied to existing methods such as many factorization- and deep learning-baseed models. We derive four specific algorithms of our COFIS based on Fossil (denoted as COFIS(-,-) for short) in particular, and further show the results of our COFIS-improved methods (including the factorization-based COFIS, and the deep learning-based SASRec(COFIS) and FMLP-Rec(COFIS)). Notice that we treat the seminal works and their pointwise version as special cases of our COFIS. Empirical studies on four real-world datasets show the superiority of our proposed COFIS compared with the state-of-the-art baselines. Moreover, we also study the impact of some key parameters in our COFIS.
Algebraic fields and rings as a digital signal processing tool

It is shown that algebraic fields and rings can become a very promising tool for digital signal processing. This is mainly due to the fact that any digital signals change in a finite range of amplitudes and, therefore, there are only a finite set of levels that can correspond to the amplitudes of a signal reduced to a discrete form. This allows you to establish a one-to-one correspondence between the set of levels and such algebraic structures as fields, rings, etc. This means that a function that takes values in any of the algebraic structures containing a finite set of elements can serve as a model of a signal reduced to a discrete form. A special case of such a signal model are functions that take values in Galois fields. It is shown that, along with Galois fields, in certain cases, algebraic rings contain zero divisors can be used to construct signal models. This representation is convenient because in this case it becomes possible to independently operate with the digits of the number that enumerates the signal levels. A simple and intuitive method for constructing rings is proposed, based on an analogy with the method of algebraic extensions.
Intelligent certification for quantum simulators via machine learning

Abstract Quantum simulation is a technology of using controllable quantum systems to study new quantum phases of matter. Certification for quantum simulators is a challenging problem whereas identification and properties estimation are two crucial approaches that can be resorted to. In this work, we propose Ab initio end-to-end machine learning certification protocol briefly named MLCP. The learning protocol is trained with a million-level size of randomized measurement samples without relying on the assistance of quantum tomography. In the light of MLCP, we can identify different types of quantum simulators to observe their distinguishability hardness. We also predict the physical properties of quantum states evolved in quantum simulators such as entanglement entropy and maximum fidelity. The impact of randomized measurement samples on the identification accuracy is analyzed to showcase the potential capability of classical machine learning on quantum simulation results. The entanglement entropy and maximum fidelity with varied subsystem partitions are also estimated with satisfactory precision. This work paves the way for large-scale intelligent certification of quantum simulators and can be extended onto an artificial intelligence center to offer easily accessible services for local quantum simulators in the noisy intermediate-size quantum (NISQ) era.
High Speed and Precision Underwater Biological Detection Based on the Improved YOLOV4-Tiny Algorithm

Realizing high-precision real-time underwater detection has been a pressing issue for intelligent underwater robots in recent years. Poor quality of underwater datasets leads to low accuracy of detection models. To handle this problem, an improved YOLOV4-Tiny algorithm is proposed. The CSPrestblock_body in YOLOV4-Tiny is replaced with Ghostblock_body, which is stacked by Ghost modules in the CSPDarknet53-Tiny backbone network to reduce the computation complexity. The convolutional block attention module (CBAM) is integrated to the algorithm in order to find the attention region in scenarios with dense objects. Then, underwater data is effectively improved by combining the Instance-Balanced Augmentation, underwater image restoration, and Mosaic algorithm. Finally, experiments demonstrate that the YOLOV4-Tinier has a mean Average Precision (mAP) of 80.77% on the improved underwater dataset and a detection speed of 86.96 fps. Additionally, compared to the baseline model YOLOV4-Tiny, YOLOV4-Tinier reduces about model size by about 29%, which is encouraging and competitive.
Institutions, Culture, or Interaction: What Determines the Financial Market Development in Emerging Markets?

In this research, we examine how the quality of institutions promotes financial market development (FMD) in 21 emerging markets (classified by the Financial Times Stock Exchange Group). The moderating role of culture is also empirically tested. For this purpose, a balance panel dataset of 21 emerging markets from 1984 to 2020 is utilized from various secondary data sources. The study applies two-stages least square regression with the instrumental variable, and lag transformation to overcome the endogeneity problem in the nexus of institutions and finance, which is least focused on in prior literature. The empirical findings show that institutional quality and the national culture promote FMD in these economies. The main findings are consistent with law and finance, and financial socialization theories. We argue that academics, policymakers, and researchers should comprehend the critical role of institutional and cultural indicators in forming an effective financial system that may lead to sustainable economic development. This research contributes to the literature on emerging markets in this helpful paradigm. We conclude that quality institutions play a critical role in magnifying the FMD of emerging markets. It is crucial to comprehend the connection between FMD and institutions, as the growth dividend from financial development can be boosted by strengthening institutions and understanding the culture. Our results are robust to alternative measures of institutions and FMD and the correction of potential endogeneity.
Development of Quantum Protocol Modification CSLOE–2022, Increasing the Cryptographic Strength of Classical Quantum Protocol BB84

Quantum cryptography protocols make it possible not only to ensure the protection of data transmitted in a communication channel from unauthorized access by intruders, but also to detect the existence of any attempted interception. This scientific direction is currently relevant, since it is related to the problem of security and data protection in current information and communication networks. The article is devoted to quantum cryptography; it describes the development of quantum protocols as quantum key distribution systems. Grounded on the laws of quantum mechanics, the elaboration of modifications of secure data transfer protocols is shown. The authors considered the best-known protocol to be BB84 of quantum key distribution; a more modern modification of this protocol is BB84 Info-Z. Comparative analysis of these has also been carried out. It has been established that the BB84-Info-Z quantum protocol works more efficiently than BB84 since its lower error threshold allows the interceptor to obtain much less information about the secret key. The authors put forward a new idea to improve the BB84 protocol (which has been quite outdated for almost 40 years), due to the increase in modern requirements for quantum cryptography protocols. The modification is called CSLOE-2022. It enables significant intensification of cryptographic strength and the entanglement degree of the interceptor (cryptanalyst), which greatly complicates the very possibility of intercepting information. The ultimate goal of the CSLOE-2022 modification is to complicate the eavesdropping process so much that it can be considered completely useless for an attacker in terms of wasting time and resources. The modification allows exceeding the known speed limit of key generation without repeaters since it uses two sources, the phases of which, in addition to the hundreds of kilometers of fiber between them, are very difficult to stabilize. Comparison of the protocols by working distance showed that for BB84, this distance does not exceed 70 km; for BB84-Info-Z it is similar, at no more than 70 km, and the modification of CSLOE-2022 proposed by the authors theoretically allows increasing the working distance of the quantum protocol to 511 km (7.3 times).
Fine-tuning language models to find agreement among humans with diverse preferences

Recent work in large language modeling (LLMs) has used fine-tuning to align outputs with the preferences of a prototypical user. This work assumes that human preferences are static and homogeneous across individuals, so that aligning to a a single "generic" user will confer more general alignment. Here, we embrace the heterogeneity of human preferences to consider a different challenge: how might a machine help people with diverse views find agreement? We fine-tune a 70 billion parameter LLM to generate statements that maximize the expected approval for a group of people with potentially diverse opinions. Human participants provide written opinions on thousands of questions touching on moral and political issues (e.g., "should we raise taxes on the rich?"), and rate the LLM's generated candidate consensus statements for agreement and quality. A reward model is then trained to predict individual preferences, enabling it to quantify and rank consensus statements in terms of their appeal to the overall group, defined according to different aggregation (social welfare) functions. The model produces consensus statements that are preferred by human users over those from prompted LLMs (>70%) and significantly outperforms a tight fine-tuned baseline that lacks the final ranking step. Further, our best model's consensus statements are preferred over the best human-generated opinions (>65%). We find that when we silently constructed consensus statements from only a subset of group members, those who were excluded were more likely to dissent, revealing the sensitivity of the consensus to individual contributions. These results highlight the potential to use LLMs to help groups of humans align their values with one another.
One-Hot Graph Encoder Embedding

In this article we propose a lightning fast graph embedding method called one-hot graph encoder embedding. It has a linear computational complexity and the capacity to process billions of edges within minutes on standard PC - making it an ideal candidate for huge graph processing. It is applicable to either adjacency matrix or graph Laplacian, and can be viewed as a transformation of the spectral embedding. Under random graph models, the graph encoder embedding is approximately normally distributed per vertex, and asymptotically converges to its mean. We showcase three applications: vertex classification, vertex clustering, and graph bootstrap. In every case, the graph encoder embedding exhibits unrivalled computational advantages.
Comparison of ensemble hybrid sampling with bagging and boosting machine learning approach for imbalanced data

Training an imbalanced dataset can cause classifiers to overfit the majority class and increase the possibility of information loss for the minority class. Moreover, accuracy may not give a clear picture of the classifier’s performance. This paper utilized decision tree (DT), support vector machine (SVM), artificial neural networks (ANN), K-nearest neighbors (KNN) and Naïve Bayes (NB) besides ensemble models like random forest (RF) and gradient boosting (GB), which use bagging and boosting methods, three sampling approaches and seven performance metrics to investigate the effect of class imbalance on water quality data. Based on the results, the best model was gradient boosting without resampling for almost all metrics except balanced accuracy, sensitivity and area under the curve (AUC), followed by random forest model without resampling in term of specificity, precision and AUC. However, in term of balanced accuracy and sensitivity, the highest performance was achieved by random forest with a random under-sampling dataset. Focusing on each performance metric separately, the results showed that for specificity and precision, it is better not to preprocess all the ensemble classifiers. Nevertheless, the results for balanced accuracy and sensitivity showed improvement for both ensemble classifiers when using all the resampled dataset.
Deep Learning-based Image 3D Object Detection for Autonomous Driving: Review

&lt;p&gt;An accurate and robust perception system is key to understanding the driving environment of autonomous driving and robots. Autonomous driving needs 3D information about objects, including the object’s location and pose, to understand the driving environment clearly. A camera sensor is widely used in autonomous driving because of its richness in color, texture, and low price. The major problem with the camera is the lack of 3D information, which is necessary to understand the 3D driving environment. Additionally, the object’s scale change and cclusion make 3D object detection more challenging. Many deep learning-based methods, such as depth estimation, have been developed to solve the lack of 3D information. This survey presents the image 3D object detection 3D bounding box encoding techniques, feature extraction techniques, and evaluation metrics of 3D object detection. The image-based methods are categorized based on the technique used to estimate an image’s depth information, and insights are added to each method. Then, state-of-the-art (SOTA) monocular and stereo camera-based methods are summarized. We also compare the performance of the selected 3D object detection models and present challenges and future directions in 3D object detection.&lt;/p&gt;
Smooth Complete Coverage Trajectory Planning Algorithm for a Nonholonomic Robot

The complete coverage path planning is a process of finding a path which ensures that a mobile robot completely covers the entire environment while following the planned path. In this paper, we propose a complete coverage path planning algorithm that generates smooth complete coverage paths based on clothoids that allow a nonholonomic mobile robot to move in optimal time while following the path. This algorithm greatly reduces coverage time, the path length, and overlap area, and increases the coverage rate compared to the state-of-the-art complete coverage algorithms, which is verified by simulation. Furthermore, the proposed algorithm is suitable for real-time operation due to its computational simplicity and allows path replanning in case the robot encounters unknown obstacles. The efficiency of the proposed algorithm is validated by experimental results on the Pioneer 3DX mobile robot.
Self-Adapting Spherical Search Algorithm with Differential Evolution for Global Optimization

The spherical search algorithm is an effective optimizer to solve bound-constrained non-linear global optimization problems. Nevertheless, it may fall into the local optima when handling combination optimization problems. This paper proposes an enhanced self-adapting spherical search algorithm with differential evolution (SSDE), which is characterized by an opposition-based learning strategy, a staged search mechanism, a non-linear self-adapting parameter, and a mutation-crossover approach. To demonstrate the outstanding performance of the SSDE, eight optimizers on the CEC2017 benchmark problems are compared. In addition, two practical constrained engineering problems (the welded beam design problem and the pressure vessel design problem) are solved by the SSDE. Experimental results show that the proposed algorithm is highly competitive compared with state-of-the-art algorithms.
An Optimization-Based Technology Applied for Face Skin Symptom Detection

Face recognition segmentation is very important for symptom detection, especially in the case of complex image backgrounds or noise. The complexity of the photo background, the clarity of the facial expressions, or the interference of other people’s faces can increase the difficulty of detection. Therefore, in this paper, we have proposed a method to combine mask region-based convolutional neural networks (Mask R-CNN) with you only look once version 4 (YOLOv4) to identify facial symptoms by this new method. We use the face image dataset from the public image databases DermNet and Freepic as the training source for the model. Face segmentation was first applied with Mask R-CNN. Then the images were imported into ResNet-101, and the facial features were fused with region of interest (RoI) in the feature pyramid networks (FPN) structures. After removing the non-face features and noise, the face region has been accurately obtained. Next, the recognized face area and RoI data were used to identify facial symptoms (acne, freckle, and wrinkles) with YOLOv4. Finally, we use Mask R-CNN, and you only look once version 3 (YOLOv3) and YOLOv4 are matched to perform the performance analysis. Although, the facial images with symptoms are relatively few. We still use a limited amount of data to train the model. The experimental results show that our proposed method still achieves 57.73%, 60.38%, and 59.75% of mean average precision (mAP) for different amounts of data. Compared with other methods, the mAP was more than about 3%. Consequently, using the method proposed in this paper, facial symptoms can be effectively and accurately identified.
Breast Cancer Detection using Decision Tree and K-Nearest Neighbour Classifiers

Data mining has the most important role in healthcare for discovering hidden relationships in big datasets, especially in breast cancer diagnostics, which is the most popular cause of death in the world. In this paper two algorithms are applied that are decision tree and K-Nearest Neighbour for diagnosing Breast Cancer Grad in order to reduce its risk on patients. In decision tree with feature selection, the Gini index gives an accuracy of %87.83, while with entropy, the feature selection gives an accuracy of %86.77. In both cases, Age appeared as the most effective parameter, particularly when Age&lt;49.5. Whereas Ki67 appeared as a second effective parameter. Furthermore, K- Nearest Neighbor is based on the minimum error rate, and the test maximum accuracy for K_value selection with an accuracy of 86.24%. Where the distance metric has been assigned using the Euclidean approach. From previous models, it seems that Breast Cancer Grade2 is the most prevalent type. For the future perspective, a comparative study could be performed to compare the supervised and unsupervised data mining algorithms.