Reviewerly/exHarmony huggingface dataset:
The Four-Distance Domination Number in the Ladder and Star Graphs Amalgamation Result and Applications

The study purpose is to determine the four-distance domination number in the amalgamation operation graph, namely the vertex amalgamation result graph of ladder graph Amal(L_m,v,n) with m≥2 and n≥2 and the vertex amalgamation result graph of a star graph with its name Amal(S_m,v,n) with m≥2 and n&gt;2. In addition, the application use the Four-distance domination number on Jember Regency Covid-19 taskforce post-placement. The Importanceof this research, namely the optimal distribution of the Covid-19 task force post. It is not just doing mask surgeries every day on the streets. The optimal referred to can be in the form of integrated handlers in each sub-district or points that are considered to need fast handling so that coordination between posts can respond and immediately identify cases of transmission and potential infections due to interactions with patients who are already positive. The methods used in this research are pattern recognition and axiomatic deductive methods. The results of this study include:γ_4 (Amal(S_m,v,n))=1; for m≥2 and n≥2, γ_4 (Amal(L_m,v,n))={■(1; for 2≤m≤4 @⌊m/8⌋n+1 for m≡0,1,2,3,4 (mod 8)@⌈m/8⌉n; for others m ) ┤ and based on the Indonesia Country, Jember Regency Map, 2 Covid 19 task-force posts are needed to be placed in Balung and Kalisat sub-districts using the Four-distance domination number application.
Mine local homogeneous representation by interaction information clustering with unsupervised learning in histopathology images

Background and objective: The success of data-driven deep learning for histopathology images often depends on high-quality training sets and fine-grained annotations. However, as tumors are heterogeneous and annotations are expensive, unsupervised learning approaches are desirable to obtain full automation. Methods: In this paper, an Interaction Information Clustering (IIC) method is proposed to extract locally homogeneous features in mutually exclusive clusters. Trained in an unsupervised paradigm, the framework learns invariant information from multiple spatially adjacent regions for improved classification. Additionally, an adaptive Conditional Random Field (CRF) model is incorporated to detect spatially adjacent image patches of high morphological homogeneity in an offset-constraint free manner. Results: Empirically, the proposed model achieves an observable improvement of 11.4% on the downstream patch-level classification accuracy, compared with state-of-the-art unsupervised learning approaches. Conclusion: Furthermore, evaluated with our clinically collected histopathology whole-slide images, the proposed model shows high consistency in tissue distribution compared with well-trained supervised learning, which is of important diagnostic significance in clinical practice.
Integration of remote sensing and geophysical data to enhance lithological mapping utilizing the Random Forest classifier: a case study from Komopa, Papua Province, Indonesia

Lithological information is important in mineral resource exploration, geological observations, mine planning or degradation vulnerability assessment. Currently, lithology mapping can be performed in a fast, inexpensive, and easy way using remote sensing data and machine learning. Remote sensing techniques have become a valuable and promising tool for mapping lithological units and searching for minerals. Typically, the integration of remote sensing data with geophysical data provides a better diagnosis to lithological units than single-source mapping methodologies. Accordingly, this study used a combination of remote sensing and airborne geophysical data utilizing the Random Forest algorithm with small training samples to enhance lithology mapping in Komopa, Papua Province, Indonesia. Geophysical data consisting of magnetic, electromagnetic, and radiometric were added one by one gradually to the remote sensing data, which includes Sentinel 2A, ALOS PALSAR, and DEM (digital elevation model) to compare the accuracy of the classification results from each dataset. The results showed that the model that combined remote sensing data and the three types of geophysical data produced the best classification, with an overall accuracy of 0.81, precision of 0.66, recall of 0.47, and F1 score of 0.52. This fused data can increase the accuracy of the classification results by 8% overall accuracy, 6% precision, 11% recall, and 13% F1 score when compared to the model that only used remote sensing data.
E-Commerce Purchase Prediction Based on Graph Neural Networks

This With the booming development of the Internet, many online trading platforms have emerged, and e-commerce shopping is gradually becoming an important way of consumption for people. While e-commerce brings convenience, it also brings the problem of information overload. Ineffectively selecting products at sea wastes time and reduces the efficiency of product distribution. Therefore, product recommendation has become an important way to alleviate the information overload of e-commerce shopping. In this paper, we formulate the problem as a multi-label prediction task and propose a new graph neural network-based framework, the item-relational graph neural network (IRGNN), for the simultaneous discovery of multiple complex relationships. The feature vectors of nodes are encoded and learned based on known network structures, and the learned node features are decoded to explore the statistical patterns of node features and link formation between nodes, and to analyse which features are prone to form links between nodes that possess them. Extensive experiments have been conducted on real-world product data to verify the effectiveness of IRGNN, particularly on large and sparse product graphs.
Tamper-Proof Evidence via Blockchain for Autonomous Vehicle Accident Monitoring

In case of an accident between two autonomous vehicles equipped with emerging technologies, how do we apportion liability among the various players? A special liability regime has not even yet been established for damages that may arise due to the accidents of autonomous vehicles. Would the immutable, time-stamped sensor records of vehicles on distributed ledger help define the intertwined relations of liability subjects right through the accident? What if the synthetic media created through deepfake gets involved in the insurance claims? While integrating AI-powered anomaly or deepfake detection into automated insurance claims processing helps to prevent insurance fraud, it is only a matter of time before deepfake becomes nearly undetectable even to elaborate forensic tools. This paper proposes a blockchain-based insurtech decentralized application to check the authenticity and provenance of the accident footage and also to decentralize the loss-adjusting process through a hybrid of decentralized and centralized databases using smart contracts.
An integrated multi-level feature fusion framework for crowd behaviour prediction and analysis

&lt;p&gt;The uncontrolled outburst in population has led to crowd gatherings in various public places causing panic and disaster in certain unpleasant and extreme conditions. A study on the analysis of crowd accumulation has been carried out for various reasons that include management of crowd, design of a well-planned public space, the possibility for surveillance at every area and transportation systems. A lot of disasters also occurs due to uncontrollable crowd behaviour and poor crowd management. It could result in loss of property, fatalities or casualties. To avoid this, the conduct of a crowd of people has been studied and analyzed to control their movement and traffic. Hence, in this research work, integrated multi-level feature fusion (IMFF) framework is designed to predict the behaviour; further classification based on the local region is carried out to enhance the prediction. In the case of multi-level feature fusion; first level feature fusion utilizes the motion and appearance; second-level feature fusion utilizes the spatial connection and third-level utilizes the temporal connections. Further, the classification approach is integrated based on the local region is used to enhance the crowd behaviour prediction in terms of accuracy and faster. Moreover, performance evaluation is carried out considering the two distinctive datasets.&lt;/p&gt;
Ok Google, Start a Fire. IoT devices as witnesses and actors in fire investigations

Fire incidents are amongst the most destructive events an investigator might encounter, completely transforming a scene with most of the objects left in ashes or highly damaged. Until now, fire investigations relied heavily on burn patterns and electrical artifacts to find possible starting locations, as well as witness statements and more recently witness imagery. As Internet of Things (IoT) devices, often seen as connected smart devices, become more common, the various sensors embedded within them provide a novel source of traces about the environment and events within. They collect and store information in different locations, often not touched by the event, such as remote servers (cloud) or companion smartphones, widening the investigation field for fire incidents. This work presents two controlled fire incidents in apartments that we furnished, equipped with IoT devices, and subsequently burnt. We studied the traces retrievable from the objects themselves after the incident, the companion smartphone apps, and the cloud and assessed the value of the information they conveyed. This research highlighted the pertinence to consider traces from IoT devices in the forensic process of fire investigation.
Absolute and Relative Depth-Induced Network for RGB-D Salient Object Detection

Detecting salient objects in complicated scenarios is a challenging problem. Except for semantic features from the RGB image, spatial information from the depth image also provides sufficient cues about the object. Therefore, it is crucial to rationally integrate RGB and depth features for the RGB-D salient object detection task. Most existing RGB-D saliency detectors modulate RGB semantic features with absolution depth values. However, they ignore the appearance contrast and structure knowledge indicated by relative depth values between pixels. In this work, we propose a depth-induced network (DIN) for RGB-D salient object detection, to take full advantage of both absolute and relative depth information, and further, enforce the in-depth fusion of the RGB-D cross-modalities. Specifically, an absolute depth-induced module (ADIM) is proposed, to hierarchically integrate absolute depth values and RGB features, to allow the interaction between the appearance and structural information in the encoding stage. A relative depth-induced module (RDIM) is designed, to capture detailed saliency cues, by exploring contrastive and structural information from relative depth values in the decoding stage. By combining the ADIM and RDIM, we can accurately locate salient objects with clear boundaries, even from complex scenes. The proposed DIN is a lightweight network, and the model size is much smaller than that of state-of-the-art algorithms. Extensive experiments on six challenging benchmarks, show that our method outperforms most existing RGB-D salient object detection models.
The Rise of GPT-3: Implications for Natural Language Processing and Beyond

This article provides an overview of the Generative Pre-trained Transformer 3 (GPT-3) and its significance in natural language processing (NLP).A brief history of NLP and machine learning is presented before delving into the technical details of GPT-3's architecture and training process.The article also compares GPT-3 with previous generations of language models, including GPT-1 and GPT-2.Applications of GPT-3 in NLP are discussed, including text completion and generation, language translation and sentiment analysis, and conversational agents and chatbots.However, the article also acknowledges the limitations and challenges of GPT-3, including bias and ethical concerns, understanding the limitations of GPT-3's training data, and the challenge of evaluating and benchmarking language models.Moreover, the article explores potential applications of GPT-3 beyond NLP, including creative writing and art, scientific research and data analysis, and music and audio production.Finally, the article discusses the future directions for GPT-3 and NLP, including challenges and opportunities for developing even more advanced language models and the implications of GPT-3 for the future of human-machine interaction and the broader field of artificial intelligence research.
The Impact of Effective Leadership on Employee Motivation and Job Satisfaction in Kenya

Purpose: The study was to investigate the impact of effective leadership on employee motivation and job satisfaction in Kenya.&#x0D; Methodology: The study adopted a desktop methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low-cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library.&#x0D; Results: The literature review indicates that effective leadership, specifically transformational leadership, positively impacts employee motivation and job satisfaction in Kenya's public and private sectors. Perceived organizational support, self-efficacy, and effective communication also contribute to employee well-being. However, there are methodological gaps in existing research, emphasizing the need for more rigorous research design and methodology. Further research is needed to investigate the impact of effective leadership on employee motivation and job satisfaction in Kenya and to develop evidence-based policies and interventions aimed at improving employee well-being and organizational performance.&#x0D; Unique Contribution to Theory, Practice and Policy: This study emphasizes the impact of effective leadership, specifically transformational leadership practices, perceived organizational support, self-efficacy, and communication, on employee well-being and organizational performance in Kenya. The study identifies methodological gaps in the literature and suggests the need for more rigorous research designs. These findings can inform evidence-based policies and interventions to improve employee motivation and job satisfaction in Kenya. Organizations should adopt transformational leadership practices and provide a supportive work environment, while policy makers should prioritize the development of effective leadership training programs and policies that promote employee well-being and organizational performance. This study provides a theoretical framework and practical insights for future research and policy development.
Recurrent unit augmented memory network for video summarisation

Abstract Video summarisation can relieve the pressure on video storage, transmission, archiving, and retrieval caused by the explosive growth of online videos in recent years. Most existing supervised video summarisation methods use convolutional neural network (CNN) or recurrent neural network (RNN) to model the temporal dependencies between video frames or video shots. CNN mainly focuses on local information, and RNN loses long‐term information when the input sequence is long, both of which have limited ability to obtain long‐range memory in the video. Therefore, a recurrent unit augmented memory network (RUAMN) for video summarisation is proposed, which effectively utilises the long‐term memory extraction ability of the end‐to‐end memory network (MemN2N) and solves the problem that MemN2N is insensitive to temporal sequence information. At the same time, the proposed RUAMN enhances the process of memory update between multiple computational steps (hops), and finally generates a meaningful video summarisation result. Specifically, the proposed RUAMN is mainly composed of the input module, the global‐and‐local sampling, the memory module and the output module. The input module uses a bidirectional GRU to obtain the forward and backward information of each video frame. Then the global‐and‐local sampling performs global sampling and local sampling on the output sequence of the input module respectively to obtain several shorter sequences, so that the memory modules can capture the fine‐grained relationship features between video frames more effectively. The memory module extracts the long‐term memory information in the feature sequence, and finally the frame‐level importance scores are predicted by the output module. Extensive experiments on benchmark datasets, that is, TVSum and SumMe, demonstrate the superiority of our method over several state‐of‐the‐art supervised video summarisation methods.
Music Generation with Multiple Ant Colonies Interacting on Multilayer Graphs

We propose a methodology for music generation that makes use of Ant Colony Optimization (ACO) algorithms on multilayer graphs. In our methodology we first define a new multilayer graph model of music that contains several voices musical works. Then, we adapt ACO algorithms to allow multiple ant colonies to generate solutions on each layer while interacting with each other. This methodology is illustrated with some example configurations that show how music emerge as a result of the interaction of different simultaneous ant colony optimization instances.
Prior Steps into Knowledge Mapping: Text Mining Application and Comparison

Bibliometrics is increasingly being used by the knowledge community and librarians to easily analyze patterns in knowledge. In the field, the use of data from databases that provide bibliometric information is not always completely clean, so pre-processing is required. Several previous studies have shown that bibliometric analysis begins with a simple pre-processing step. The goal of this research is to use text mining to perform pre-processing to find the basic terms of the keywords that appear – to essentially construct a controlled vocabulary for a bibliographic dataset. The method used in this study is cleaning keywords with the stemming method using RapidMiner software. Bibliometrix was used to compare the results. A total of 85 keywords were combined into basic words. Using the built process, this study discovers differences in the network built between raw data and data that has been pre-processed, resulting in differences in the analysis that will be produced. The built process can also be reused in a variety of real-world situations.
Pengelompokan Kemiskinan di Indonesia Menggunakan Time Series Based Clustering

Indonesia has a strong commitment in achieving the 2030 SDGs, which one of the targets is reducing poverty. Poverty itself is defined as the inability of an individual or group to meet the basic needs of both food and non-food. During the pandemic, Indonesia experienced an increase in poverty percentage, which peaked in September 2020 with 10.19%. The latest data in March 2022, showed that the number has decreased by 0.75% or equivalent of 1.79 million people. However, the decrease does not encompass all provinces, there are several provinces that still suffer from increasing poverty. Therefore, it is necessary to group the provinces in Indonesia based on its percentage of poverty, in order to provide more appropriate treatment. The analysis method used in this research is the time series-based clustering with dtw distance. The clustering algorithm used is hierarchical cluster complete linkage. Based on the analysis result, the use of dtw distance can increase the silhouette coefficient value to 0.75 compared to using the Euclidean distance. The silhouette coefficient is one of the parameters used to determine the goodness of the clustering results, where the value of 0.75 can already be said to be very good clustering results. The optimum result of the clustering is a total of 3 groups with low, medium, and high poverty categories, where NTT, Papua, and West Papua provinces have the highest poverty rates but have significant progress in reducing poverty.
Early signs of cancer present in the fine detail of mammograms

The gist of abnormality can be rapidly extracted by medical experts from global information in medical images, such as mammograms, to identify abnormal mammograms with above-chance accuracy—even before any abnormalities are localizable. The current study evaluated the effect of different high-pass filters on expert radiologists’ performance in detecting the gist of abnormality in mammograms, especially those acquired prior to any visibly actionable lesions. Thirty-four expert radiologists viewed unaltered and high-pass filtered versions of normal and abnormal mammograms. Abnormal mammograms consisted of obvious abnormalities, subtle abnormalities, and currently normal mammograms from women who would go to develop cancer in 2–3 years. Four levels of high-pass filtering were tested (0.5, 1, 1.5, and 2 cycles per degree (cpd) after brightening and contrast normalizing to the unfiltered mammograms. Overall performance for 0.5 and 1.5 did not change compared to unfiltered but was reduced for 1 and 2 cpd. Critically, filtering that eliminated frequencies below 0.5 and 1.5 cpd significantly boosted performance on mammograms acquired years prior appearance of localizable abnormalities. Filtering at 0.5 did not change the radiologist’s decision criteria compared to unfiltered mammograms whereas other filters resulted in more conservative ratings. The findings bring us closer to identifying the characteristics of the gist of the abnormal that affords radiologists detection of the earliest signs of cancer. A 0.5 cpd high-pass filter significantly boosts subtle, global signals of future cancerous abnormalities, potentially providing an image enhancement strategy for rapid assessment of impending cancer risk.
AARN: Anchor‐guided attention refinement network for inshore ship detection

Abstract Inshore ship detection is an important task in several fields, for example, maritime transportation, maritime supervision, and port management. However, due to the diversified categories and locations of different ships and interference of complex surroundings, capturing discriminative characteristics of multi‐scale inshore ships for accurate detection is still challenging. Here, an anchor‐guided attention refinement network (AARN) is proposed to alleviate the problems by prominently designing an attention feature filter module (AFFM) and an anchor‐guided alignment detection module (AADM). In AFFM, of which the attention supervision generated from high‐level semantic features, is used to highlight informative target cues and suppress background interference when establishing a four‐layer feature pyramid. In AADM, the anchor‐aligned features are adopted to eventually identify potential inshore ships, which both alleviates misalignment between refined anchors and pyramidal features and improves the performance further. Extensive experiments conducted on the public Seaships7000 dataset verify the contributions of the proposed modules and the effectiveness of our method for detecting multi‐scale inshore ships in comparison to both domain‐specific and general CNN‐based methods.