Reviewerly/exHarmony huggingface dataset:
An Extended Jump Functions Benchmark for the Analysis of Randomized Search Heuristics

Jump functions are the most-studied non-unimodal benchmark in the theory of randomized search heuristics, in particular, evolutionary algorithms (EAs). They have significantly improved our understanding of how EAs escape from local optima. However, their particular structure—to leave the local optimum one can only jump directly to the global optimum—raises the question of how representative such results are. For this reason, we propose an extended class $$\textsc {Jump}_{k,\delta }$$ of jump functions that contain a valley of low fitness of width $$\delta $$ starting at distance k from the global optimum. We prove that several previous results extend to this more general class: for all $$k \le \frac{n^{1/3}}{\ln {n}}$$ and $$\delta < k$$ , the optimal mutation rate for the $$(1+1)$$ EA is $$\frac{\delta }{n}$$ , and the fast $$(1+1)$$ EA runs faster than the classical $$(1+1)$$ EA by a factor super-exponential in $$\delta $$ . However, we also observe that some known results do not generalize: the randomized local search algorithm with stagnation detection, which is faster than the fast $$(1+1)$$ EA by a factor polynomial in k on $$\textsc {Jump}_k$$ , is slower by a factor polynomial in n on some $$\textsc {Jump}_{k,\delta }$$ instances. Computationally, the new class allows experiments with wider fitness valleys, especially when they lie further away from the global optimum.
An experimental analysis of several new swarm intelligence optimization algorithms

With the rapid development of computer technology, algorithm technology is also constantly innovating. The swarm intelligence optimization algorithm has gradually become the most important class of algorithms because of its simple and easy-to-implement characteristics. It has been widely used in optimization problems and provides new ideas for solving complex engineering problems. The latest swarm intelligence algorithms proposed at home and abroad are selected for analysis, including Arithmetic Optimization Algorithm (AOA), Wild Horse Optimization (WHO), Dingo Optimization Algorithm (DOA), and Artificial Hummingbird Algorithm (AHA). The paper uses 12 general benchmark functions and part of CEC2017 test functions for experimental simulation, and further compares the experimental overall performance of these algorithms in phrases of convergence speed, accuracy, and stability. Finally, the future development of the swarm intelligence algorithm is discussed.
Uniform Masking: Enabling MAE Pre-training for Pyramid-based Vision Transformers with Locality

Masked AutoEncoder (MAE) has recently led the trends of visual self-supervision area by an elegant asymmetric encoder-decoder design, which significantly optimizes both the pre-training efficiency and fine-tuning accuracy. Notably, the success of the asymmetric structure relies on the "global" property of Vanilla Vision Transformer (ViT), whose self-attention mechanism reasons over arbitrary subset of discrete image patches. However, it is still unclear how the advanced Pyramid-based ViTs (e.g., PVT, Swin) can be adopted in MAE pre-training as they commonly introduce operators within "local" windows, making it difficult to handle the random sequence of partial vision tokens. In this paper, we propose Uniform Masking (UM), successfully enabling MAE pre-training for Pyramid-based ViTs with locality (termed "UM-MAE" for short). Specifically, UM includes a Uniform Sampling (US) that strictly samples $1$ random patch from each $2 \times 2$ grid, and a Secondary Masking (SM) which randomly masks a portion of (usually $25\%$) the already sampled regions as learnable tokens. US preserves equivalent elements across multiple non-overlapped local windows, resulting in the smooth support for popular Pyramid-based ViTs; whilst SM is designed for better transferable visual representations since US reduces the difficulty of pixel recovery pre-task that hinders the semantic learning. We demonstrate that UM-MAE significantly improves the pre-training efficiency (e.g., it speeds up and reduces the GPU memory by $\sim 2\times$) of Pyramid-based ViTs, but maintains the competitive fine-tuning performance across downstream tasks. For example using HTC++ detector, the pre-trained Swin-Large backbone self-supervised under UM-MAE only in ImageNet-1K can even outperform the one supervised in ImageNet-22K. The codes are available at https://github.com/implus/UM-MAE.
Multi-Phase Information Theory-Based Algorithm for Edge Detection of Aerial Images

Edge detection is the diverse way used to detect boundaries in digital images. Many methods exist to achieve this purpose, yet not all of them can produce results with high detection ratios. Some may have high complexity, and others may require numerous inputs. Therefore, a new multi-phase algorithm that depends on information theory is introduced in this article to detect the edges of aerial images adequately in a fully automatic manner. The proposed algorithm operated by utilizing Shannon and Hill entropies with specific rules along with a non-complex edge detector to record the vital edge information. The proposed algorithm was examined with different aerial images, its performances appraised against six existing approaches, and the outcomes were assessed using three image evaluation methods. From the results, promising performances were recorded as the proposed algorithm performed the best in many aspects and provided satisfactory results. The results of the proposed algorithm had high edge detection ratios as it was able to capture most of the significant edges of the given images. Such findings make the proposed algorithm desirable to be used as a key image detection method with other image-related applications.
LAW ENFORCEMENT IN THE ADMINISTRATION OF JUSTICE: A CASE ANALYSIS

The study is a case analysis on the experiences of the law enforcement officers in the administration of justice. The respondents were 5 selected law enforcement officers from Northern Luzon, who were chosen through convenience sampling. A validated interview guide was utilized in data gathering. Data gathered were then collated, themed and analyzed. The study revealed that the perceived role of the law enforcement in the administration of justice centered on law enforcement, it involves the conduct of arrest, investigation and filing of cases. The law enforcement officer’s experience in the administration of justice is attributable to following protocols, encounter with the accused and trauma from these experiences. It is then concluded that the law enforcement officer’s perception on their role and their experiences in the administration of justice adhere to their mandates as defined by their manual. Such experiences also reflect the human nature of obedience and weakness for the trauma experienced. In the light of the conclusions, it is also recommended that the programs suggested be adapted. KEY WORDS; Law Enforcement, Administration of Justice, Live Experience
Dynamic User Allocation Method and Artificial Intelligence in the Information Industry Financial Management System Application

Based on dynamic user allocation and artificial human intelligence, this paper studies the development and data analysis of financial management in the information industry. In the financial management of enterprises in the information industry, artificial intelligence technology can provide reliable and powerful technical support in data identification and collection, big data processing, and decision-making assistance, thereby greatly improving the quality of accounting information and overall work efficiency of enterprises in the information industry. Help the managers of the information industry make decisions more scientifically and efficiently. The dynamic user allocation method can improve the efficiency of talent selection and the training mode of high-end talents in the information industry, and improve the accuracy of the enterprise financial management system by 6.7%
potential of increased internet penetration to combat corruption in Africa

This paper /study examines the relationship between internet penetration and perceptions of corruption in African countries. In particular, it assesses the potential of the former to address the latter, which has crippled economic growth and development for decades. The way in which corruption has seeped into the continent’s responses to the Covid-19 pandemic underscores that combating corruption is more urgent than ever before. Utilising correlation analysis, this study finds that there is a positive correlation between IP and perceptions of corruption. It concludes with recommendations for combating corruption in Africa and its regressive effects on the continent’s socio-economic development.
Influence of Self-Regulated Strategy Development on the Performance of Virtual Reality-Based Teaching in Online Learning

Virtual reality technology has been fully integrated into the field of education. Such technology has become the focus of online learning that uses efficient and advanced virtual reality technology to improve the efficiency of learners. Online learning also gives full play to the characteristics of free learning time, flexible location, and free learning pace. Although online learning also easily leads to low efficiency due to physical isolation, it can effectively improve the performance of virtual reality-based teaching by using the self-regulated strategy development (SRSD) model. Based on this model, an experiment was conducted on online teaching course for first-year linguistics students of a provincial graduate school in China to test the influence of the model on the performance of virtual reality-based teaching in online learning. Results show that after the SRSD model, a significant difference is observed between pretest scores and post-test scores of graduate students in the experimental group (t=−4.925, p=0.000). A significant difference is observed between pretest self-regulation and post-test self-regulation of graduate students in the experimental group (t=−7.505, p=0.000). Finally, significant differences are observed between the experimental and control groups in the improvement of post-test scores and post-test self-regulation. Conclusions are of important reference value for enriching the application of the SRSD model in virtual reality-based teaching, improving the self-regulation ability of students, and improving the performance of learners by adopting a model more suitable for the characteristics of online teaching.
Developing EFL Teachers' Technological Pedagogical Knowledge Through Practices in Virtual Platform

Currently, advancements in information and communication technology (ICT) and an increased interest in using the Internet for educational purposes have led educators to work in new virtual settings. However, using technology in teaching requires the understanding and information of English educators. Technological Pedagogical Content Knowledge (TPACK) is an educator's knowledge concept regarding integrating technology into education. Also, how educators integrate technology effectively into their classes is a significant issue, as learning environments are influenced by rapid advances in instructional technology. Consistent with this issue, it is critical for teacher development to integrate ICT into educational tasks in the design, application, and assessment of practices in a virtual setting. To this end, the objective of the present review is to consider the role of practice in the virtual platform in developing EFL teachers' TPACK. Ultimately, some implications are presented for the EFL educational stakeholders.
Data Augmented Incremental Learning (DAIL) for Unsupervised Data

Incremental Learning, a machine learning methodology, trains the continuously arriving input data and extends the model's knowledge. When it comes to unlabeled data streams, incremental learning task becomes more challenging. Our newly proposed incremental learning methodology, Data Augmented Incremental Learning (DAIL), learns the ever-increasing real-time streams with reduced memory resources and time. Initially, the unlabeled batches of data streams are clustered using the proposed clustering algorithm, Clustering based on Autoencoder and Gaussian Model (CLAG). Later, DAIL creates an updated incremental model for the labelled clusters using data augmentation. DAIL avoids the retraining of old samples and retains only the most recently updated incremental model holding all old class information. The use of data augmentation in DAIL combines the similar clusters generated with different data batches. A series of experiments verified the significant performance of CLAG and DAIL, producing scalable and efficient incremental model.
Development of formation methodology of international ratings in the context of the world educational space globalization

The article considers the directions of development of methodology for international rankings of higher education institutions. We highlighted the elements of methodology, including principles, systems of indicators, methods of data collection and transformation, and sources of funding. Finally, we concluded about the transformation of rankings as a set of methods for assessing the performance of institutions of higher education in one of the tools to influence the management of universities, including through public funding.
Complex Wavelets and SPIHTL for Edge Feature Enhancement and Optimum Feature Selection with Deep Learning Algorithm for Video Salient Object Detection

Cognitive property of the human visual system to detect salient objects in image and video sequences to correlate with neighbouring objects for information retrieval and understanding is developed for computer vision application. Detecting salient objects with accurate boundaries and features is addressed in this work by using the directional selectivity property of complex wavelets. Computation complexity of processing complex wavelet sub band is addressed by companding the features into eight sub bands and optimum number of features are selected using SPIHTL (Set Partition In Hierarchical Trees List) algorithm. Five layered deep learning algorithm is designed with 2D FFNN ( Feed Forward Neural Network) structure and reordering units to detect salient object features. The 3D DTCWT (Dual Tree Complex Wavelet Transform) model combined with 2D SPIHTL encoder and deep learning algorithm is modelled in MATLAB and is trained to identify 120 objects with PSNR (Peak Signal To Noise Ration) of 40dB. The designed model is demonstrated to detect generic objects. Keywords: Video saliency, DTCWT, Directional Features, SPIHTL, Deep Learning, 2D Neural Network
Foreign libraries in Web 2.0 digital community: User participation types

Various types and formats of user participation in the key library processes like cataloging, user services, OPACs are analyzed based on science periodicals and foreign professional journals published in 2010–2020. Foreign library websites well-established within accessible digital environment has been developing, t o a g reat e xtent, a s t he p latforms f or u ser a pplications. This stimulates user extensive involvement in the key library functions like cataloging (social bookmarks, adding records and reviews) and services (social media, blogs, messengers, wiki-resources, podcsts and wordcasts). Being supported by social media resources that offer diverse channels of communication with their users, the libraries make it possible for the users to create new content and transform their services within “the unified participation framework” which is consistent with the modern digital civilization. The analysis of current practice of user participation in indexing a nd s upply o f n ew r esources a nd n ew m etadata s ets t o t he l ibraries, demonstrates that these processes have not been well developed; reviews and ratings have often to be controlled and filtered. The libraries cannot offer substitute to the Web 2 .0 a pps t hat a re in t he w ake o f g lobal d igital c ivilization. The status of social services as libraries’ additional optional services can be changed through increasing their effectiveness.
Resume Screening using NLP

Abstract: Presently, we have seen the technology reaching new heights than ever ahead. But each association has a different way of working but for this they need people who have a specific skill set. Selection of the candidate is done on the base of seeing the skill set mentioned in the person's capsule who is applying at the organization. Generally, resumes are sorted manually but, going through the resumes of these people manually is extremely time- consuming and lower effective as there are chances of human intervention error. Hence, we have proposed a design which will sort all the resumes according to the demand of the company and reduce the working time for further recruitment process. In this design we are going to use the technology of Artificial Intelligence and Natural Language Processing. We are going to train the model for recognizing the words which actually define the skill set of the person and demanded by the association by rendering in Python language. The algorithm will work in such a way that when a resume is to be scanned it will search only for the words according to the demand by the company and sort it accordingly. The required resume is shortlisted and the rest are auto rejected by the model. This will give high effectiveness compared to the manual sorting and give produce good results. This will lots of time and work of recruiters. Keywords: Information Retrieval, Natural Language Processing, Resume Parser, Resume Analyzer, Resume Summarisation, cv sorting, e-recruitment, Content-based Recommendation
Forecasting stock market with nanophotonic reservoir computing system based on silicon optomechanical oscillators

The essence of stock market forecasting is to reveal the intrinsic operation rules of stock market, however it is a terribly arduous challenge for investors. The application of nanophotonic technology in the intelligence field provides a new approach for stock market forecasting with its unique advantages. In this work, a novel nanophotonic reservoir computing (RC) system based on silicon optomechanical oscillators (OMO) with photonic crystal (PhC) cavities for stock market forecasting is implemented. The long-term closing prices of four representative stock indexes are accurately forecast with small prediction errors, and the forecasting results with distinct characteristics are exhibited in the mature stock market and emerging stock market separately. Our work offers solutions and suggestions for surmounting the concept drift problem in stock market environment. The comprehensive influence of RC parameters on forecasting performance are displayed via the mapping diagrams, while some intriguing results indicate that the mature stock markets are more sensitive to the variation of RC parameters than the emerging stock markets. Furthermore, the direction trend forecasting results illustrate that our system has certain direction forecasting ability. Additionally, the stock forecasting problem with short listing time and few data in the stock market is solved through transfer learning (TL) in stock sector. The generalization ability (GA) of our nanophotonic reservoir computing system is also verified via four stocks in the same region and industry. Therefore, our work contributes to a novel RC model for stock market forecasting in the nanophotonic field, and provides a new prototype system for more applications in the intelligent information processing field.
Bridging the gap between expressivity and efficiency in stream reasoning: a structural caching approach for IoT streams

In today's data landscape, data streams are well represented. This is mainly due to the rise of data-intensive domains such as the Internet of Things (IoT), Smart Industries, Pervasive Health, and Social Media. To extract meaningful insights from these streams, they should be processed in real time, while solving an integration problem as these streams need to be combined with more static data and their domain knowledge. Ontologies are ideal for modeling this domain knowledge and facilitate the integration of heterogeneous data within data-intensive domains such as the IoT. Expressive reasoning techniques, such as OWL2 DL reasoning, are needed to completely interpret the domain knowledge and for the extraction of meaningful decisions. Expressive reasoning techniques have mainly focused on static data environments, as it tends to become slow with growing datasets. There is thus a mismatch between expressive reasoning and the real-time requirements of data-intensive domains. In this paper, we take a first step towards bridging the gap between expressivity and efficiency while reasoning over high-velocity IoT data streams for the task of event enrichment. We present a structural caching technique that eliminates reoccurring reasoning steps by exploiting the characteristics of most IoT streams, i.e., streams typically produce events that are similar in structure and size. Our caching technique speeds up reasoning time up to thousands of times for fully fledged OWL2 DL reasoners and even tenths and hundreds of times for less expressive OWL2 RL and OWL2 EL reasoners.
The Benefits of Depth Information for Head-Mounted Gaze Estimation

In this work, we investigate the hypothesis that adding 3D information of the periocular region to an end-to-end gaze-estimation network can improve gaze-estimation accuracy in the presence of slippage, which occurs quite commonly for head-mounted AR/VR devices. To this end, using UnityEyes we generate a simulated dataset with RGB and depth-maps of the eye with varying camera placement to simulate slippage artifacts. We generate different noise profiles for the depth-maps to simulate depth sensor noise artifacts. Using this data, we investigate the effects of different fusion techniques for combining image and depth information for gaze estimation. Our experiments show that under an attention-based fusion scheme, 3D information can significantly improve gaze-estimation and compensates well for slippage induced variability. Our finding supports augmenting 2D cameras with depth-sensors for the development of robust end-to-end appearance based gaze-estimation systems.