Reviewerly/exHarmony huggingface dataset:
Unknown area exploration for robots with energy constraints using a modified Butterfly Optimization Algorithm

Butterfly Optimization Algorithm (BOA) is a recent metaheuristic that has been used in several optimization problems. In this paper, we propose a new version of the algorithm (xBOA) based on the crossover operator and compare its results to the original BOA and 3 other variants recently introduced in the literature. We also proposed a framework for solving the unknown area exploration problem with energy constraints using metaheuristics in both single- and multi-robot scenarios. This framework allowed us to benchmark the performances of different metaheuristics for the robotics exploration problem. We conducted several experiments to validate this framework and used it to compare the effectiveness of xBOA with well-known metaheuristics used in the literature through 5 evaluation criteria. Although BOA and xBOA are not optimal in all these criteria, we found that BOA can be a good alternative to many metaheuristics in terms of the exploration time, while xBOA is more robust to local optima; has better fitness convergence; and achieves better exploration rates than the original BOA and its other variants.
Heterogeneous Learning for Scene Graph Generation

Scene Graph Generation (SGG) task aims to construct a graph structure to express objects and their relationships in a scene at a holistic level. Due to the neglect of heterogeneity of feature spaces between objects and relations, coupling of feature representations becomes obvious in current SGG methods, which results in large intra-class variation and inter-class ambiguity. In order to explicitly emphasize the heterogeneity in SGG, we propose a plug-and-play Heterogeneous Learning Branch (HLB), which enhances the independent representation capability of relation features. The HLB actively obscures the interconnection between objects and relation feature spaces via gradient reversal, with the assistance of a link prediction module as information barrier and an Auto Encoder for information preservation. To validate the effectiveness of HLB, we apply HLB to typical SGG methods in which the feature spaces are either homogeneous or semi-heterogeneous, and conduct evaluation on VG-150 dataset. The experimental results demonstrate that HLB significantly improves the performance of all these methods in the common evaluation criteria for SGG task.
Empirical evidence of phishing menace among undergraduate smartphone users in selected universities in Nigeria

In our exploratory quasi-experimental study, 480-student were recruited and exposed to social engineering directives during a university orientation week. The directives phishing attacks were performed for 10 months in 2021. The contents attempted to elicit personal user-data from participants, enticing them to click compromised links. The study aimed to determine cybercrime risks among undergraduates in selected universities in Nigeria, observe responses to socially-engineered attacks, and explore their attitudes to cybercrime risks before/after such attacks. The study generalized that all participants have great deal awareness of cybercrime, and also primed all throughout study to remain vigilant to scams. The study explores various types of scam and its influence on students’ gender and age on perceived safety on susceptibility to phishing scams. Results show that contrary to public beliefs, none of these factors were associated with scam susceptibility and vulnerability rates of the participants.
Interactive Video Corpus Moment Retrieval using Reinforcement Learning

Known-item video search is effective with human-in-the-loop to interactively investigate the search result and refine the initial query. Nevertheless, when the first few pages of results are swamped with visually similar items, or the search target is hidden deep in the ranked list, finding the know-item target usually requires a long duration of browsing and result inspection. This paper tackles the problem by reinforcement learning, aiming to reach a search target within a few rounds of interaction by long-term learning from user feedbacks. Specifically, the system interactively plans for navigation path based on feedback and recommends a potential target that maximizes the long-term reward for user comment. We conduct experiments for the challenging task of video corpus moment retrieval (VCMR) to localize moments from a large video corpus. The experimental results on TVR and DiDeMo datasets verify that our proposed work is effective in retrieving the moments that are hidden deep inside the ranked lists of CONQUER and HERO, which are the state-of-the-art auto-search engines for VCMR.
An Investigation of GCN-based Human Action Recognition Using Skeletal Features

Human action recognition is one of the most challenging and attractive areas in the field of computer vision. Conventional research on human action recognition has mainly focused on data modality of video or optical flow. However, the human skeletal feature has much stronger expressive power of motion dynamics, which is not sensitive to illumination and scene variation. Owing to the advantages of deep learning approaches on skeleton data in recent years, many pilot approaches have been proposed, which are merited by their significant performance enhancements on both baseline and large-scale datasets. This research investigates these models and their breakthroughs, especially focusing on the graph convolution network (GCN) and skeleton-based data techniques. The report work mainly covers the following aspects: comparing RNN, CNN and GCN-based approaches from the perspective of their operational logics; a detailed review of the best referred models in recent years; a development framework of skeletal feature-based human action recognition framework is proposed with preliminary assessments using benchmarking datasets; and finally, the envisaged future directions for skeletal feature-based human action recognition study are discussed.
Multisemantic Path Neural Network for Deepfake Detection

With the continuous development of deep learning techniques, it is now easy for anyone to swap faces in videos. Researchers find that the abuse of these techniques threatens cyberspace security; thus, face forgery detection is a popular research topic. However, current detection methods do not fully use the semantic features of deepfake videos. Most previous work has only divided the semantic features, the importance of which may be unequal, by experimental experience. To solve this problem, we propose a new framework, which is the multisemantic pathway network (MSPNN) for fake face detection. This method comprehensively captures forged information from the dimensions of microscopic, mesoscopic, and macroscopic features. These three kinds of semantic information are given learnable weights. The artifacts of deepfake images are more difficult to observe in a compressed video. Therefore, preprocessing is proposed to detect low-quality deepfake videos, including multiscale detail enhancement and channel information screening based on the compression principle. Center loss and cross-entropy loss are combined to further reduce intraclass spacing. Experimental results show that MSPNN is superior to contrast methods, especially low-quality deepfake video detection.
DRLM: A Robust Drug Representation Learning Method and its Applications

Learning representations from data is a fundamental step for machine learning. High-quality and robust drug representations can broaden the understanding of pharmacology, and improve the modeling of multiple drug-related prediction tasks, which further facilitates drug development. Although there are a number of models developed for drug representation learning from various data sources, few researches extract drug representations from gene expression profiles. Since gene expression profiles of drug-treated cells are widely used in clinical diagnosis and therapy, it is believed that leveraging them to eliminate cell specificity can promote drug representation learning. In this paper, we propose a three-stage deep learning method for drug representation learning, named DRLM, which integrates gene expression profiles of drug-related cells and the therapeutic use information of drugs. Firstly, we construct a stacked autoencoder to learn low-dimensional compact drug representations. Secondly, we utilize an iterative clustering module to reduce the negative effects of cell specificity and noise in gene expression profiles on the low-dimensional drug representations. Thirdly, a therapeutic use discriminator is designed to incorporate therapeutic use information into the drug representations. The visualization analysis of drug representations demonstrates DRLM can reduce cell specificity and integrate therapeutic use information effectively. Extensive experiments on three types of prediction tasks are conducted based on different drug representations, and they show that the drug representations learned by DRLM outperform other representations in terms of most metrics. The ablation analysis also demonstrates DRLM's effectiveness of merging the gene expression profiles with the therapeutic use information.
Feature attention network (FA-Net): a deep-learning based approach for underwater single image enhancement

Underwater image processing and analysis have been a hotspot of study in recent years, as more emphasis has been focused to underwater monitoring and usage of marine resources. Compared with the open environment, underwater image encountered with more complicated conditions such as light abortion, scattering, turbulence, nonuniform illumination and color diffusion. Although considerable advances and enhancement techniques achieved in resolving these issues, they treat low-frequency information equally across the entire channel, which results in limiting the network's representativeness. We propose a deep learning and feature-attention-based end-to-end network (FA-Net) to solve this problem. In particular, we propose a Residual Feature Attention Block (RFAB), containing the channel attention, pixel attention, and residual learning mechanism with long and short skip connections. RFAB allows the network to focus on learning high-frequency information while skipping low-frequency information on multi-hop connections. The channel and pixel attention mechanism considers each channel's different features and the uneven distribution of haze over different pixels in the image. The experimental results shows that the FA-Net propose by us provides higher accuracy, quantitatively and qualitatively and superiority to previous state-of-the-art methods.
CAPER: Coarsen, Align, Project, Refine - A General Multilevel Framework for Network Alignment

Network alignment, or the task of finding corresponding nodes in different networks, is an important problem formulation in many application domains. We propose CAPER, a multilevel alignment framework that Coarsens the input graphs, Aligns the coarsened graphs, Projects the alignment solution to finer levels and Refines the alignment solution. We show that CAPER can improve upon many different existing network alignment algorithms by enforcing alignment consistency across multiple graph resolutions: nodes matched at finer levels should also be matched at coarser levels. CAPER also accelerates the use of slower network alignment methods, at the modest cost of linear-time coarsening and refinement steps, by allowing them to be run on smaller coarsened versions of the input graphs. Experiments show that CAPER can improve upon diverse network alignment methods by an average of 33% in accuracy and/or an order of magnitude faster in runtime.
Observing how deep neural networks understand physics through the energy spectrum of 1D quantum mechanics

Abstract We investigate how neural networks (NNs) understand physics using 1D quantum mechanics. After training an NN to accurately predict energy eigenvalues from potentials, we used it to confirm the NN’s understanding of physics from four different aspects. The trained NN could predict energy eigenvalues of different kinds of potentials than the ones learned, predict the probability distribution of the existence of particles not used during training, reproduce untrained physical phenomena, and predict the energy eigenvalues of potentials with an unknown matter effect. These results show that NNs can learn physical laws from experimental data, predict the results of experiments under conditions different from those used for training, and predict physical quantities of types not provided during training. Because NNs understand physics in a different way than humans, they will be a powerful tool for advancing physics by complementing the human way of understanding.
Online continual learning via the knowledge invariant and spread-out properties

The goal of continual learning is to provide intelligent agents that are capable of learning continually a sequence of tasks using the knowledge obtained from previous tasks while performing well on prior tasks. However, a key challenge in this continual learning paradigm is catastrophic forgetting, namely adapting a model to new tasks often leads to severe performance degradation on prior tasks. Current memory-based approaches show their success in alleviating the catastrophic forgetting problem by replaying examples from past tasks when new tasks are learned. However, these methods are infeasible to transfer the structural knowledge from previous tasks i.e., similarities or dissimilarities between different instances. Furthermore, the learning bias between the current and prior tasks is also an urgent problem that should be solved. In this work, we propose a new method, named Online Continual Learning via the Knowledge Invariant and Spread-out Properties (OCLKISP), in which we constrain the evolution of the embedding features via Knowledge Invariant and Spread-out Properties (KISP). Thus, we can further transfer the inter-instance structural knowledge of previous tasks while alleviating the forgetting due to the learning bias. We empirically evaluate our proposed method on four popular benchmarks for continual learning: Split CIFAR 100, Split SVHN, Split CUB200 and Split Tiny-Image-Net. The experimental results show the efficacy of our proposed method compared to the state-of-the-art continual learning algorithms.
Econometric Analysis of the Causal Link between Forensic Accounting Techniques and Fraud Prevention in Nigeria

This study examined the effect of forensic accounting techniques on fraud prevention in Nigeria. The study adopted the ordinary least square technique to assess specifically, the effect of computer assisted audit techniques, Ratio Analysis techniques and Trend Analysis technique on fraud detection/prevention in Nigerian banks. The outcome of the study revealed that there is a causal linkage between application of forensic accounting techniques and fraud detection and prevention in banks operating in Nigeria except trend analysis technique which showed no causal linkage with fraud detection/prevention. We therefore, recommend that frequent utilization of forensic audit services will significantly help in the detection, prevention as well as reduction of incidences of fraud in businesses. Furthermore, Nigeria deposit money banks should encourage the use of forensic accounting techniques to curtail fraudulent malpractices in its operations. Other robust techniques for fraud detection/prevention not considered in this study should be examined and given a chance as the three techniques considered in this study explained less than twenty percent (19.94%) of variation in fraud prevention in Nigerian banks, leaving over eighty percent to other techniques not captured in this study.
Human knowledge models: Learning applied knowledge from the data

Artificial intelligence and machine learning have demonstrated remarkable results in science and applied work. However, present AI models, developed to be run on computers but used in human-driven applications, create a visible disconnect between AI forms of processing and human ways of discovering and using knowledge. In this work, we introduce a new concept of “Human Knowledge Models” (HKMs), designed to reproduce human computational abilities. Departing from a vast body of cognitive research, we formalized the definition of HKMs into a new form of machine learning. Then, by training the models with human processing capabilities, we learned human-like knowledge, that humans can not only understand, but also compute, modify, and apply. We used several datasets from different applied fields to demonstrate the advantages of HKMs, including their high predictive power and resistance to noise and overfitting. Our results proved that HKMs can efficiently mine knowledge directly from the data and can compete with complex AI models in explaining the main data patterns. As a result, our study reveals the great potential of HKMs, particularly in the decision-making applications where “black box” models cannot be accepted. Moreover, this improves our understanding of how well human decision-making, modeled by HKMs, can approach the ideal solutions in real-life problems.
Lessons learnt from decentralization of COVID-19 response in a southwest state of Nigeria

Introduction: This report shows the outcomes and lessons learnt from a 3-month intervention focused on decentralization of COVID-19 coordination, testing and contact tracing activities in three hotspot local government areas (LGAs) of a state in the southwest of Nigeria. Methods: A description of COVID-19 outbreak response from the occurrence of the index case was documented. A health facility and community-based intervention implemented in three hotspots LGA as part of response to COVID-19 pandemic from 24th May to 22nd August, 2021 was described. The interventions implemented focused on integrating COVID-19 testing into routine healthcare services in 103 health facilities, engagement of community-based volunteers to conduct contact tracing, and improving coordination of the response through the conduct of incident management meetings at state and LGA levels. The COVID-19 dataset from 22nd February to 22nd August, was obtained from the State Ministry of Health and analyzed. Data were summarized using charts and maps. Results: A higher number of cases (3879) were tested between 24th May to 22nd August, 2021 (during intervention) compared to 1667 cases tested between 23 February to 23 May, 2021 (before intervention) across the three LGAs. Generally, there was a decline in the cumulative number of contacts traced and line-listed during the intervention (778) compared to the period before the intervention (1170) in two of the three LGAs. The number of weekly incident management meetings held improved by 25% at State level, while 83% of weekly LGA meetings were held at the three hot spot LGAs during the intervention compared to the period before the intervention, where no meeting was held at LGA level. Conclusions: The decentralization of the COVID-19 outbreak response from a central approach to the LGA level improved only testing numbers and the number of incident management meetings conducted across the three hot spot LGAs. The number of contacts line-listed, positivity rate and reported cases reduced following the interventions. The need to supplement contact tracing activities using information technology for self-report as done in other climes, as well as engaging community, religious leaders and key community groups as integral members of the contact tracing team was emphasized.
Investigating lecturers’ reasons for adoption of mobile learning in higher education: A case study

&#x0D; &#x0D; &#x0D; The usage of Mobile Learning (M-learning) has grown among Institutes of Higher Learning worldwide as mobile technologies benefit teaching and learning. Despite this interest, there is a lack of M-learning research in formal educational contexts and the reasons for lecturers to adopt M-learning in a Singapore private education institute have been limited. Furthermore, the Teaching and Learning Division (TLD) of the institution which looks after the professional development of lecturers and sponsors innovative technology-mediated learning initiatives like M-learning is keen to find out how lecturers can be supported to encourage a greater usage of M-learning. After interviewing 10 lecturers, the research found that the following five reasons influence lecturers’ adoption of M-learning from most important to the least: Usefulness in Teaching and Learning, Barriers to using M-Learning, Ease of use of mobile technology, Students’ Motivation and Service quality of the M-learning systems. From this, TLD can identify and develop strategies to support the successful integration of M-learning. The five support aspects considered when implementing M-learning are found to be the following in descending order of importance: Professional Development, Social, Organisational, Technical and Advanced features to support teaching and learning. This research has clarified the existing literature in M-learning especially in formal learning contexts where learning is intentional and recognises the role that lecturers play in the future adoption of M-learning.&#x0D; &#x0D; &#x0D;
A Policy-Reuse Algorithm Based on Destination Position Prediction for Aircraft Guidance Using Deep Reinforcement Learning

Artificial intelligence for aircraft guidance is a hot research topic, and deep reinforcement learning is one of the promising methods. However, due to the different movement patterns of destinations in different guidance tasks, it is inefficient to train agents from scratch. In this article, a policy-reuse algorithm based on destination position prediction is proposed to solve this problem. First, the reward function is optimized to improve flight trajectory quality and training efficiency. Then, by predicting the possible termination position of the destinations in different moving patterns, the problem is transformed into a fixed-position destination aircraft guidance problem. Last, taking the agent in the fixed-position destination scenario as the baseline agent, a new guidance agent can be trained efficiently. Simulation results show that this method can significantly improve the training efficiency of agents in new tasks, and its performance is stable in tasks with different similarities. This research broadens the application scope of the policy-reuse approach and also enlightens the research in other fields.