Reviewerly/exHarmony huggingface dataset:
kClusterHub: An AutoML-Driven Tool for Effortless Partition-Based Clustering over Varied Data Types

Partition-based clustering is widely applied over diverse domains. Researchers and practitioners from various scientific disciplines engage with partition-based algorithms relying on specialized software or programming libraries. Addressing the need to bridge the knowledge gap associated with these tools, this paper introduces kClusterHub, an AutoML-driven web tool that simplifies the execution of partition-based clustering over numerical, categorical and mixed data types, while facilitating the identification of the optimal number of clusters, using the elbow method. Through automatic feature analysis, kClusterHub selects the most appropriate algorithm from the trio of k-means, k-modes, and k-prototypes. By empowering users to seamlessly upload datasets and select features, kClusterHub selects the algorithm, provides the elbow graph, recommends the optimal number of clusters, executes clustering, and presents the cluster assignment, through tabular representations and exploratory plots. Therefore, kClusterHub reduces the need for specialized software and programming skills, making clustering more accessible to non-experts. For further enhancing its utility, kClusterHub integrates a REST API to support the programmatic execution of cluster analysis. The paper concludes with an evaluation of kClusterHub’s usability via the System Usability Scale and CPU performance experiments. The results emerge that kClusterHub is a streamlined, efficient and user-friendly AutoML-inspired tool for cluster analysis.
PubHubs identity management

Abstract Finding a combination between privacy and accountability in the online world is a challenge. Too little accountability supports problematic behaviour. Too little privacy undermines individual freedom and has a chilling effect. This paper describes the identity infrastructure of a new open source community platform called PubHubs. It combines local group conversations, via its own adaptation of Matrix, with proportional authentication of users, in local identity spaces. PubHubs thus achieves a combination of privacy and accountability. The technical core of the paper describes the cryptographic protocols for managing digital identities, via both personal attributes and local pseudonyms. Around this core, the roles of digital identities within the PubHubs platform are explained in functional terms, giving participants for instance more certainty about others in a conversation, or giving moderators new tools.
Dynamic three-dimensional shape measurement based on an adaptive phase unwrapping method

We present an adaptive phase unwrapping method based on geometric constraints and the gradient field without additional images for high-speed three-dimensional (3D) shape measurement. Specifically, we reconstruct the 3D geometry of moving objects frame by frame. We first create a reference phase map at the depth provided by the former frame. Then we optimize the depth value by validating the continuity of the computed unwrapped phase based on the modulus of the gradient field and recalculate the correct absolute phase map with the optimal depth value. After reconstructing the 3D geometry of the current frame, 3D data are delivered to the next frame. In particular, a geometric constraint-based method is applied in the first frame. Experiment results indicate that our approach, which requires only three phase-shifted fringe patterns per frame, can measure moving objects with high accuracy and robustness. Additionally, several isolated objects can also be measured by our method if they are continuous.
Image inpainting network based on multi‐level attention mechanism

Abstract Image inpainting networks based on deep learning techniques have been widely used in many important fields. However, most inpainting networks fail to generate desirable repaired images. This may be due to their failure to extract effective features and accurately assign high weights to the undamaged regions. To alleviate these problems, an image inpainting network based on gated convolution and multi‐level attention mechanism (IIN‐GCMAM) is proposed in this paper. This network follows encoder–decoder architecture, consisting of the gated convolution encoder (GC‐encoder) and the multi‐level attention mechanism decoder (MAM‐decoder). The GC‐encoder weighs the extracted features with gated convolutions, which reduces the interference caused by the damaged regions. The multi‐level attention mechanism employed in the MAM‐decoder uses multi‐scale feature maps spatially and channel‐wise to improve the consistency in global structure and the fineness of repaired results. Extensive experiments are conducted on the common datasets, Paris StreetView and CelebA. Experimental results indicate that the proposed IIN‐GCMAM can achieve a good performance on the common evaluation metrics and visual effects. It can achieve 0.0408, 0.720, and 22.27 in MAE, SSIM, and PSNR at the mask ratio of 50%–60%, respectively.
A robust watermarking algorithm against JPEG compression based on multiscale autoencoder

Abstract The network structure of digital watermarking algorithm based on deep learning is usually encoder‐noise layer‐decoder. Most of the existing encoders suffer from the problem of insufficient feature extraction, and the introduction of simulated differentiable joint photographic experts group (JPEG) compression in the noise layer cannot ensure the robustness under real JPEG. In this paper, a watermarking algorithm based on multi‐scale auto‐encoder is proposed, which can effectively extract the image feature information by combining with the channel attention mechanism. At the same time, some parameters of decoder and encoder are shared to reduce redundant feature embedding and improve extraction accuracy. This paper also proposes a robust training scheme against JPEG compression, which can guide the model to store the watermark in the low‐frequency region needed for decoding. Experimental results show that the peak signal‐to‐noise ratio (PSNR) of the proposed algorithm is above 48 and the decoding rate is above 99% under JPEG compression with quality factor Q = 50. Moreover, this scheme can effectively promote the combination of noise layer in training. In addition, the proposed algorithm is also robust to other common network noises.
Video Turing Test: A first step towards human‐level AI

Abstract The development of artificial intelligence (AI) agents capable of human‐level understanding of video content and conducting conversations with humans on this basis is a promising application that people expect. However, this is a challenging task that requires the holistic integration of multimodal information with temporal dependencies and reasoning, as well as social and physical commonsense. In addition, the development of appropriate systematic evaluation methods is essential. In this context, we introduce the Video Turing Test (VTT), a blind test used to evaluate human‐likeness in terms of video comprehension ability. Moreover, we propose Vincent as a video understanding AI. We explain the configuration of VTT, the architecture of Vincent to prepare for VTT and the proposed evaluation methods for video comprehension. We also estimate the current intelligence level of AI based on our results and discuss future research directions.
Random logistic machine (RLM): Transforming statistical models into machine learning approach

Data science is booming with big data, and machine learning provides better predictive analyses. However, conventional statistical models can effortlessly interpret the effect estimates, and the prediction models are generally in a closed form. Therefore, this research integrates the logistic regression model’s core with the Random Forest structure to create a blended novel machine learning method, the Random Logistic Machine (RLM). In this way, the new approach preserves the statistical and machine learning advantages. Computer simulation studies examined the predictive ability of RLM, random forest, and Logistic Regression under various scenarios. The results showed that the RLM delivers a comparable performance to Random Forests and Logistic Regression. An application to the Breast Cancer Wisconsin (Diagnostic) Data Set also demonstrates the superior performance of the new approach.
Cross-Version Software Defect Prediction Considering Concept Drift and Chronological Splitting

Concept drift (CD) refers to a phenomenon where the data distribution within datasets changes over time, and this can have adverse effects on the performance of prediction models in software engineering (SE), including those used for tasks like cost estimation and defect prediction. Detecting CD in SE datasets is difficult, but important, because it identifies the need for retraining prediction models and in turn improves their performance. If the concept drift is caused by symmetric changes in the data distribution, the model adaptation process might need to account for this symmetry to maintain accurate predictions. This paper explores the impact of CD within the context of cross-version defect prediction (CVDP), aiming to enhance the reliability of prediction performance and to make the data more symmetric. A concept drift detection (CDD) approach is further proposed to identify data distributions that change over software versions. The proposed CDD framework consists of three stages: (i) data pre-processing for CD detection; (ii) notification of CD by triggering one of the three flags (i.e., CD, warning, and control); and (iii) providing guidance on when to update an existing model. Several experiments on 30 versions of seven software projects reveal the value of the proposed CDD. Some of the key findings of the proposed work include: (i) An exponential increase in the error-rate across different software versions is associated with CD. (ii) A moving-window approach to train defect prediction models on chronologically ordered defect data results in better CD detection than using all historical data with a large effect size (δ≥0.427).
Automated Insights Generation for Datasets Based on Cloud-Based Machine Learning

The article explores the benefits of integrating cloud computing, IIoT, and new machine learning approaches in smart manufacturing's prognostics and health management (PHM). Model-based and data-driven PHM approaches are discussed, highlighting the need for assumptions or extensive training data, respectively. To address computing efficiency challenges, a cloud-based parallel machine learning method using MapReduce is proposed. The study demonstrates improved processing speed and accuracy in tool wear forecast using this approach. Overall, the article presents a promising solution for PHM in smart manufacturing, leveraging cloud-based parallel machine learning to enhance efficiency and effectiveness.
Buyer-Optimal Algorithmic Consumption

We analyze a bilateral trade model in which the buyer's value for the product and the seller's costs are uncertain, the seller chooses the product price, and the product is recommended by an algorithm based on its value and price. We characterize an algorithm that maximizes the buyer's expected payoff and show that the optimal algorithm underrecommends the product at high prices and overrecommends at low prices. Higher algorithm precision increases the maximal equilibrium price and may increase prices across all of the seller's costs, whereas informing the seller about the buyer's value results in a mean-preserving spread of equilibrium prices and a mean-preserving contraction of the buyer’s payoff.
Chapter 11. Word alignment in the Russian-Chinese parallel corpus

The Russian-Chinese parallel corpus (RuZhCorp) was created in 2016 by sinologists and computational linguists. So far, it has accumulated 1 074 texts and over 4.6 million words that are aligned on a sentence level. To produce word alignment for the entire corpus, we used deep neural networks trained both on the whole RuZhCorp and on a manually aligned at a word level gold dataset. Using the principles presented in previous publications, we compiled the first word-to-word alignment guideline for the Russian-Chinese language pair, which makes the manual alignment process less ambiguous and more consistent. The joint fine-tuning of the LaBSE deep learning model on RuZhCorp and the gold dataset achieved the best AER of 18.9%.
Anticancer Recipe Recommendation Based on Cancer Dietary Knowledge Graph

Many recipes contain ingredients with various anticancer effects, which can help users to prevent cancer, as well as provide treatment for cancer patients, effectively slowing the disease. Existing recipe knowledge graph recommendation systems obtain entity feature representations by mining latent connections between recipes and between users and recipes to enhance the performance of the recommendation system. However, it ignores the influence of time on user taste preferences, fails to capture the dependency between them from the user’s dietary records, and is unable to more accurately predict the user’s future recipes. We use the KGAT to obtain the embedding representation of entities, considering the influence of time on users, and recipe recommendation can be viewed as a long-term sequence prediction, introducing LSTM networks to dynamically adjust users’ personal taste preferences. Based on the user’s dietary records, we infer the user’s preference for the future diet. Combined with the cancer knowledge graph, we provide the user with diet recommendations that are beneficial to disease prevention and rehabilitation. To verify the effectiveness and rationality of PPKG, we compared it with three other recommendation algorithms on the self-created datasets, and the extensive experimental results demonstrate that our algorithm performance performs other algorithms, which confirmed the effectiveness of PPKG in dealing with sequence recommendation.
Fast reconstruction algorithm based on HMC sampling

In Noisy Intermediate-Scale Quantum (NISQ) era, the scarcity of qubit resources has prevented many quantum algorithms from being implemented on quantum devices. Circuit cutting technology has greatly alleviated this problem, which allows us to run larger quantum circuits on real quantum machines with currently limited qubit resources at the cost of additional classical overhead. However, the classical overhead of circuit cutting grows exponentially with the number of cuts and qubits, and the excessive postprocessing overhead makes it difficult to apply circuit cutting to large scale circuits. In this paper, we propose a fast reconstruction algorithm based on Hamiltonian Monte Carlo (HMC) sampling, which samples the high probability solutions by Hamiltonian dynamics from state space with dimension growing exponentially with qubit. Our algorithm avoids excessive computation when reconstructing the original circuit probability distribution, and greatly reduces the circuit cutting post-processing overhead. The improvement is crucial for expanding of circuit cutting to a larger scale on NISQ devices.
Hybrid deep learning model with enhanced sunflower optimization for flood and earthquake detection

Natural catastrophes may strike anywhere at any moment and cause widespread destruction. Most people do not have the necessary catastrophe preparedness knowledge or awareness. The combination of a flood and an earthquake can cause widespread destruction. Natural catastrophes have a domino effect on a country's economy, first by damaging infrastructure and then by taking human lives and other resources. The mortality tolls of both humans and animals have decreased as a result of recent natural disasters. So, we need a mechanism to identify and monitor floods and earthquakes. The suggested system uses a hybrid deep learning analysis to keep an eye on earthquake- and flood-affected areas. In order to boost the efficiency of the presented model, this research presents the improved sunflower optimisation (ESFO). In polynomial time, it determines the best time to schedule events. In view of the need for real-time monitoring of regions vulnerable to flooding and earthquakes, as well as the associated costs and precautions, this study focuses on systems. The suggested technology also sends a notification to the proper authorities whenever a person is detected in the area. In the event of an emergency, it can be used as a backup source of solar power. We then offer the best suitable depth and enable real-time earthquake detection with reduced false alarm rates through practical evaluation. Finally, we demonstrate that the projected model can be successfully deployed in a real-world, dynamic situation after being trained on a range of datasets.
SKT-MOT and DyTracker: A Multiobject Tracking Dataset and a Dynamic Tracker for Speed Skating Video

Speed skating serves as a significant application domain for multiobject tracking (MOT), presenting unique challenges such as frequent occlusion, highly similar appearances, and motion blur. To address these challenges, this paper constructs an MOT dataset called SKT-MOT for speed skating and analyzes the shortcomings of existing datasets and methods. Accordingly, we propose a dynamic MOT method called DyTracker. The method builds upon the DeepSORT baseline and enhances three key modules. At the global level, we design the track dynamic management (TDM) algorithm. In the motion branch, a novel metric is proposed to evaluate occlusion and Kalman filter dynamic update (KFDU) is implemented. In the appearance branch, we account for the difference in human posture and propose the feature dynamic selection and updating (FDSU) strategy. This makes our DyTracker flexible and efficient to achieve a multiobject tracking accuracy (MOTA) of 93.70% and identification F1 (IDF1) score of 92.39% on SKT-MOT, which is a significant advantage over existing SOTA methods. To validate the generalization of our proposed module, two dynamic update modules are inserted into other methods and validated on the public dataset MOT17, and the accuracy is generally improved by 0.2%–0.6%.
Paper Recommendation via Correlation Pattern Mining and Attention Mechanism

In this paper, we improve the efficiency and effectiveness of the matrix factorization method in the paper recommendation system. We mainly address two problems. First, the vectors based on citation networks are undertrained because newly added papers are rarely cited. Second, current algorithms are mainly based on keyword search or global popularity and lack the organic combination of considering personalized interest and global popularity. To address the above two issues for the paper recommender, we propose a matrix factorization model that combines popularity analysis and attention mechanisms. The model effectively fuses the similarity of the citation network and topic using the multiplicative law, which can alleviate the data sparsity problem. Especially for cold-start papers, we add second-order neighbor nodes to makeup for the problem that newly joined papers don’t get enough training. We propose a keyword attention mechanism that combines user preferences and global popularity to personalize and balance the popularity of papers. Through comprehensive experiments on the CiteULike dataset, we show that our method can significantly improve the paper recommendation effectiveness.
Using Linguistic and Stylometric Features for Satirical News Detection in Bahasa Indonesia

In this paper, we present our work on satirical news detection for Indonesian. Despite the progress of satirical news detection in English, there has been lack of research work for other languages including Indonesian. We start this research by developing INSIDE, a novel dataset for Indonesian Satirical News Detection. The dataset consists of 100 satirical news and 190 its corresponding real news articles that were collected from satirical source and various legitimate online sites. INSIDE is one of the efforts to encourage more research in low-resource language. It also enrich real-world data source for developing cross-lingual model in the area of satirical news detection. Further to dataset development, we conducted experiment on satirical news detection using various linguistic and stylometric features. Results show the effectiveness of our model. In addition to that, certain features such as bag-of-words, writing style and structural features are found to be beneficial for detecting satirical news.
Next Sentence Prediction: The Impact of Preprocessing Techniques in Deep Learning

Text preprocessing in the field of Natural Language Processing (NLP) plays a crucial role in enhancing the model’s ability to understand given tasks. This process helps in avoiding potential errors that could interfere with data processing. This study aims to investigate the effectiveness of four preprocessing techniques (tokenization, case-folding, stopword removal, and stemming) in predicting the next word in a sentence. The case focuses on Indonesian language text documents preprocessing using word2vec embedding and the long short-term memory networks (LSTM) classification model. The evaluation was conducted by incorporating human perception approach and evaluation matrix which focused on the suitability of word sequences and pairs on the prediction results. The result reveals that the combination of tokenizing and casefolding can enrich the meaning of the sentences. Meanwhile the combination of tokenizing, casefolding, stopword removal, and stemming can cause lose and overlap meaning in the sentences. The prediction model performs best on one-word n-grams, with precision 0.1, recall 1.0, and F-score 0.1.
Utilizing YOLO for Efficient Indonesian Sign Language Recognition

As digital cameras become more prevalent in today’s technology, more and more people are reaping their benefits. A tiny group of people, the disabled community, particularly the Deaf and the Speech Impaired, do not benefit as much or even at all. The purpose of this proposed system is to help those who want to converse with the Deaf and Mute through sign language. The system is developed using YOLOv5 algorithm with YOLOv5s as its pre-trained model. The YOLOv5s model will then be used to train new sets of data with brand new configurations. The newly trained model will be used to classify 26 letters of the Indonesian Sign Language alphabets. To test the efficiency of the system, a few scenarios will be performed such as the distance of the camera, background wallpaper, and the area’s luminosity level. The output of this research is the model can detect 26 letters of the Indonesian Sign Language alphabets on real-time without getting affected by the area’s background and luminosity level but the distance between camera and object does hinder the model’s ability to detect the sign languages. The model runs best on training scenario of 70%:20%:10% dataset distribution with 300 epochs, 16 batch size and 0.01 learning rate which results on 99.27% mAP@0.5 value.
Business Process Improvement in the Program Development Division of PT. XYZ Based on Service-Oriented Architecture

Program Development Division at PT. XYZ is a division that oversees many foundations and has direct relations with volunteers and the finance division in terms of business processes. Currently, many business processes are still carried out manually, resulting in a lot of miscommunications and the time required to complete business processes is still quite long. So, in this research a business improvement design analysis was carried out by implementing SOA technology infrastructure. The results of this research resulted in business process improvements in the program development division that were effective in terms of business processes and time, which were measured using simulations in Bizagi Studio.