Reviewerly/exHarmony huggingface dataset:
A data hiding scheme based on U-Net and wavelet transform

As deep learning was used in the field of data hiding, the capacity of data hiding had been greatly increased. In addition, the wavelet transform in the traditional data hiding technology performed well, and the hidden image was invisible compared with the spatial domain scheme A data hiding scheme based on U-Net and wavelet transform was proposed in this paper, which innovatively combined the advantages of U-Net in image detail feature processing and the ability of wavelet transform to divide image details. Data hiding and extraction were realized through the jointly trained hidden network and extraction network respectively. The wavelet coefficients of the secret data were hidden in the image through the hidden network, and a hidden image with good visual effects could be obtained. The image feature was subdivided into four wavelet coefficients in the extraction network, and finally the secret data image was obtained through inverse wavelet transform. The experimental results shown that the proposed scheme had better hidden data invisibility than the traditional scheme, and the data extraction was more accurate.
High-Speed Interval Type-2 Fuzzy Systems for Dynamic Parameter Adaptation in Harmony Search for Optimal Design of Fuzzy Controllers

Fuzzy systems have become a good solution to the problem of fixed parameters in metaheuristic algorithms, proving their efficiency when performing dynamic parameter adaptations using type-1 and type-2 fuzzy logic. However, the computational cost of type-2 fuzzy systems when using the continuous enhanced Karnik–Mendel (CKM) algorithm for type-reduction, when applied to control and optimization, is too high. Therefore, it is proposed to use an approximation to the CKM algorithm in the type-2 fuzzy system for adjusting the pitch adjustment rate (PArate) parameter in the original harmony search algorithm (HS). The main contribution of this article is to verify that the implementation of the proposed methodology achieves results that are equivalent to the interval type-2 fuzzy system with the CKM algorithm, but in less computing time and also allowing an efficient dynamic parameter adaptation. It is noteworthy that this method is relatively new in the area of metaheuristics algorithms so there is a current interest to work with this methodology. The proposed method was used in optimizing the antecedents and consequents for an interval type-2 fuzzy controller of direct current motor. Experimental results without noise and then with uniform random noise numbers (Gaussian noise) in the controller were obtained to verify that the implementation is efficient when compared to conventional and other existing methods.
Studying the Effectiveness of Conversational Search Refinement Through User Simulation

A key application of conversational search is refining a user’s search intent by asking a series of clarification questions, aiming to improve the relevance of search results. Training and evaluating such conversational systems currently requires human participation, making it unfeasible to examine a wide range of user behaviors. To support robust training/evaluation of such systems, we propose a simulation framework called CoSearcher (Information about code/resources available at https://github.com/alexandres/CoSearcher .) that includes a parameterized user simulator controlling key behavioral factors like cooperativeness and patience. Using a standard conversational query clarification benchmark, we experiment with a range of user behaviors, semantic policies, and dynamic facet generation. Our results quantify the effects of user behaviors, and identify critical conditions required for conversational search refinement to be effective.
A Motion Detection System in Python and Opencv

This paper suggests a motion detection software system that enables us to see the movement around an object or a visual area. Motion detection software is widely preferred as one of the most important security features in recent days. It is used to enhance existing security devices such as motion sensor lighting on indoor and outdoor security cameras. Security cameras are one of the most common devices that use motion detection technology [1]. Now, for better protection, motion detector cameras are preferred. The paper suggested here looks at one of the python motion detection software and detects the movement of objects in front of the camera and determines the time they stay close to the camera.
Quantum Teaching-Learning-Based Optimization algorithm for sizing optimization of skeletal structures with discrete variables

In terms of optimization algorithm improvement, researchers utilize some strategies to establish the right balance between local and global search phases to escape from a local minimum in some NP-hard problems. The approach of quantum-behaved particles, as a novel strategy, uses uncertainty law and a distinct formulation obtained from solving the time-independent Schrodinger differential equation in the delta-potential-well function to update the solution candidates' positions. New formulation defines the local attractors as probable solutions between the best solution and the others to explore all solution space domain. Also, mentioned formulation employs the difference between the average solution and other ones as a new step size. Both local attractors and new step sizes guarantee diversification besides the inherent intensification. In the present paper, the teacher phase of the Teaching-Learning-Based Optimization algorithm (TLBO), which has been introduced recently, is improved using the formulation obtained from solving the time-independent Schrodinger equation to predict the probable positions of optimal solutions. The results show that QTLBO, an acronym for the Quantum Teaching- Learning- Based Optimization, improves the stability and robustness of the TLBO by defining the quantum teacher phase. The two planner rigid frames and two trusses with strength and serviceability constraints are chosen to verify the quality and performance of QTLBO. Comparing the results obtained from the proposed algorithm with those of the standard version of the TLBO algorithm and other literature methods shows that QTLBO increases the chance of finding a better solution besides improving the statistical criteria compared to the current TLBO.
Quiz-Style Question Generation for News Stories

A large majority of American adults get at least some of their news from the Internet. Even though many online news products have the goal of informing their users about the news, they lack scalable and reliable tools for measuring how well they are achieving this goal, and therefore have to resort to noisy proxy metrics (e.g., click-through rates or reading time) to track their performance.
Blockchain-Envisioned Trusted Random Oracles for IoT-Enabled Probabilistic Smart Contracts

In modern decentralized Internet-of-Things (IoT)-based sensor communications, pseudonoise-diffusion oracles are heavily investigated as random oracles for data exchange among peer nodes. As these oracles are generated through algorithmic processes, they pass the standard random tests for finite and bounded intervals only. This ensures a false sense of privacy and confidentiality in exchange through open protocol IoT-stacks in public channels, i.e., Internet. Recently, blockchain (BC)-envisioned random sequences as input oracles are proposed about financial applications, and windfall games like roulette, poker, and lottery. These random inputs exhibit fairness, and nondeterminism in SC executions termed as probabilistic smart contracts (PSCs). However, the IoT-enabled PSC process might be controlled and forged through humans, machines, and bot-nodes through physical and computational methods. Moreover, dishonest entities like contract owners, players, and miners can co-ordinate together to form collusion attacks during consensus to propagate false updates, which ensures forged block additions by miners in BC. Motivated by these facts, in this article, we propose a BC-envisioned IoT-enabled PSC scheme, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SaNkhyA</i> , which is executed in three phases. In the first phase, the scheme eliminates colluding dishonest miners through the proposed miner selection algorithm. Then, in the second phase, the elected miners agree through the proposed consensus protocol to generate a stream of random bits. In the third phase, the generated random bit-stream is split through random splitters and fed as input oracles to the proposed PSC among participating entities. In simulation, the scheme ensures a trust probability of 0.38 even at 85% collusion among miners and has an average block processing delay of 1.3 s compared to serial approaches, where the block processing delay is 5.6 s, thereby exhibiting improved scalability. The overall computation and communication cost is 28.48 ms, and 101 bytes, respectively, that indicates the efficacy of the proposed scheme compared to the traditional schemes.
Designing a Virtual Arboretum as an Immersive, Multimodal, Interactive, Data Visualization Virtual Field Trip

This paper describes a virtual field trip application as a new type of immersive, multimodal, interactive, data visualization of a virtual arboretum. Deployed in a game engine, it is a large, open-world simulation, representing 100 hectares and ideal for use when free choice in navigation and high fidelity are required. Although the computer graphics are photorealistic, it is different and unique from other applications that use game art or 2D 360-degree video, because it reflects high information fidelity as a result of the domain expert review, and the integration of geographic information system (GIS) data with drone images. Combined in-game as a data visualization, it is ideal for generating past or future worlds, in addition to representations of the present. Fusing information from many data sources—terrain data, waterbody data, plant inventory, population density data, accurate plant models, bioacoustics, and drone images—its design process and methods could be repeated and used in a wide range of augmented reality (AR) and virtual reality (VR) applications and devices. Results on presence, embodiment, emotions, engagement, and learning are summarized from prior pilot studies for context on use, and are relevant to schools, museums, arboretums, and botanical gardens interested in developing immersive informal learning applications.
Optimal Feature Set Size in Random Forest Regression

One of the most important hyper-parameters in the Random Forest (RF) algorithm is the feature set size used to search for the best partitioning rule at each node of trees. Most existing research on feature set size has been done primarily with a focus on classification problems. We studied the effect of feature set size in the context of regression. Through experimental studies using many datasets, we first investigated whether the RF regression predictions are affected by the feature set size. Then, we found a rule associated with the optimal size based on the characteristics of each data. Lastly, we developed a search algorithm for estimating the best feature set size in RF regression. We showed that the proposed search algorithm can provide improvements over other choices, such as using the default size specified in the randomForest R package and using the common grid search method.
Leveraging Review Properties for Effective Recommendation

Many state-of-the-art recommendation systems leverage explicit item reviews posted by users by considering their usefulness in representing the users’ preferences and describing the items’ attributes. These posted reviews may have various associated properties, such as their length, their age since they were posted, or their rating of the item. However, it remains unclear how these different review properties contribute to the usefulness of their corresponding reviews in addressing the recommendation task. In particular, users show distinct preferences when considering different aspects of the reviews (i.e. properties) for making decisions about the items. Hence, it is important to model the relationship between the reviews’ properties and the usefulness of the reviews while learning the users’ preferences and the items’ attributes. In this paper, we propose to model the reviews with their associated available properties. We introduce a novel review properties-based recommendation model (RPRM) that learns which review properties are more important than others in capturing the usefulness of reviews, thereby enhancing the recommendation results. Furthermore, inspired by the users’ information adoption framework, we integrate two loss functions and a negative sampling strategy into our proposed RPRM model, to ensure that the properties of reviews are correlated with the users’ preferences. We examine the effectiveness of RPRM using the well-known Yelp and Amazon datasets. Our results show that RPRM significantly outperforms a classical and five existing state-of-the-art baselines. Moreover, we experimentally show the advantages of using our proposed loss functions and negative sampling strategy, which further enhance the recommendation performances of RPRM.
Research on Robot Global Path Planning Based on Improved A-star Ant Colony Algorithm

Aiming at the problems of ant colony algorithm in robot global path planning, such as slow convergence speed, large number of iterations, and long search time, an improved path planning algorithm based on A-star algorithm fused with ant colony algorithm is proposed. Firstly, A-star the algorithm optimizes the initial pheromone of the ant colony, and improves the convergence speed of the algorithm. Secondly, by improving the ant colony pheromone enhancement coefficient to avoid the local optimization problem caused by excessive pheromone accumulation in the later period of the ant colony algorithm. Finally through the established grid map, set The environmental penalty coefficient solves the problem that the ant colony algorithm is easy to fall into the local optimum in a complex environment. The experimental results show that the improved A-star ant colony algorithm converges faster and has better global optimization capabilities. Even in a complex environment, it is not easy to fall into the local optimum, and it can effectively solve the problem of robot global path planning.
Stationary and Moving Occupancy Detection Using the SLEEPIR Sensor Module and Machine Learning

Passive infrared (PIR) sensors, currently used for indoor lighting control report frequent false negative detects for stationary occupancy, which takes up almost 50% of the total occupancy rate. To address this, we developed a synchronized low-energy electronically chopped PIR (SLEEPIR) sensor that incorporates a liquid crystal (LC) shutter to chop the long-wave infrared signal received by the on-board PIR sensor. In this paper, we present a SLEEPIR sensor module, integrated with a PIR and machine learning for systematic evaluation of true occupancy detection in daily life. We design complex experimental scenarios containing a series of continuous daily activities and individual actions to simulate realistic environment to the maximum extend. We extract and down select key statistical features using recursive feature elimination with cross validation. In the end, we compared six machine learning models to evaluate the detection performance. Experiments involving continuous daily activities indicate an accuracy of 99.12% by using the support vector machine classifier.
An in situ sulfur isotopic investigation of the origin of Carlin-type gold deposits in Youjiang Basin, southwest China

Debate over the origins of ore-forming fluids and metal accumulation in Carlin-type gold (Au) deposits has hampered the development of genetic models for such deposits. Youjiang Basin in southwest China contains Carlin-type deposits, including the Nibao, Banqi, and Yata deposits in Guizhou, and the Gaolong and Jinya deposits in Guangxi, but there has been little study of the relationships between these deposits, and the origins of the ore-forming fluids and metals remain enigmatic. We conducted in situ laser ablation multi–collector inductively coupled plasma mass spectrometry (LA–MC–ICP–MS) sulfur (S) isotope analyses of different stages of pyrite and arsenopyrite to improve our understanding of the sources and genesis of the deposits. Average δ34S values for ore-stage pyrite in the Nibao, Banqi, Yata, Gaolong, and Jinya deposits are −0.05‰, +4.04‰, +7.02‰, +11.51‰, and −3.80‰, respectively, and those of ore-stage arsenopyrite in the Banqi, Gaolong, and Jinya deposits are +7.33‰, +10.92‰, and −3.84‰, respectively. Average δ34S values of sedimentary–diagenetic (i.e., pre-ore) stage pyrite in the Gaolong and Nibao deposits are −6.41‰ and −15.61‰, respectively. Two source end-members contribute δ34S values of ~0‰, which are deep metamorphic fluids and volcaniclastic rocks in the Nibao deposit, and δ34S values of greater than +10‰ are from mixed basinal fluids or marine sulfates in the Gaolong deposit. The Yata and Banqi deposits are intermediate between the other two deposits. The δ34S values of ore-stage pyrite increase from northwestern platforms to southeastern basins, with this trend also occurring in other Carlin-type deposits in Youjiang Basin. The sources of S and Au were deep Proterozoic metamorphic basement rocks, which were mixed with a sedimentary source. We propose a genetic model for these Carlin-type Au deposits based on a deep metamorphic–hydrothermal fluid system, with the initial ore-forming fluid generated by the Indosinian or Yanshanian orogenies. Ascending fluids mixed with deeply circulating meteoric waters and/or basinal fluids and reacted with wall-rocks in the shallow crust to form the Au deposits. This study confirms that in situ S isotope analysis is effective for investigating ore genesis and assisting in exploration for new ore fields.
Rec-CFSVD++: Implementing Recommendation System Using Collaborative Filtering and Singular Value Decomposition (SVD)++

In recommender systems, Collaborative Filtering (CF) plays an essential role in promoting recommendation services. The conventional CF approach has limitations, namely data sparsity and cold-start. The matrix decomposition approach is demonstrated to be one of the effective approaches used in developing recommendation systems. This paper presents a new approach that uses CF and Singular Value Decomposition (SVD)[Formula: see text] for implementing a recommendation system. Therefore, this work is an attempt to extend the existing recommendation systems by (i) finding similarity between user and item from rating matrices using cosine similarity; (ii) predicting missing ratings using a matrix decomposition approach, and (iii) recommending top-N user-preferred items. The recommender system’s performance is evaluated considering Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Performance evaluation is accomplished by comparing the systems developed using CF in combination with six different algorithms, namely SVD, SVD[Formula: see text], Co-Clustering, KNNBasic, KNNBaseline, and KNNWithMeans. We have experimented using MovieLens 100[Formula: see text]K, MovieLens 1[Formula: see text]M, and BookCrossing datasets. The results prove that the proposed approach gives a lesser error rate when cross-validation ([Formula: see text]) is performed. The experimental results show that the lowest error rate is achieved with MovieLens 100[Formula: see text]K dataset ([Formula: see text], [Formula: see text]). The proposed approach also alleviates the sparsity and cold-start problems and recommends the relevant items.
Learning Robust Dense Retrieval Models from Incomplete Relevance Labels

Recent deployment of efficient billion-scale approximate nearest neighbor (ANN) search algorithms on GPUs has motivated information retrieval researchers to develop neural ranking models that learn low-dimensional dense representations for queries and documents and use ANN search for retrieval. However, optimizing these dense retrieval models poses several challenges including negative sampling for (pair-wise) training. A recent model, called ANCE, successfully uses dynamic negative sampling using ANN search. This paper improves upon ANCE by proposing a robust negative sampling strategy for scenarios where the training data lacks complete relevance annotations. This is of particular importance as obtaining large-scale training data with complete relevance judgment is extremely expensive. Our model uses a small validation set with complete relevance judgments to accurately estimate a negative sampling distribution for dense retrieval models. We also explore leveraging a lexical matching signal during training and pseudo-relevance feedback during evaluation for improved performance. Our experiments on the TREC Deep Learning Track benchmarks demonstrate the effectiveness of our solutions.
Graph Topic Neural Network for Document Representation

Graph Neural Networks (GNNs) such as GCN can effectively learn document representations via the semantic relation graph among documents and words. However, despite a few exceptions, most of the previous work in this line of research does not consider the underlying topical semantics inherited in document contents and the relation graph, making the representations less effective and hard to interpret. In a few recent studies trying to incorporate latent topics into GNNs, the topics have been learned independently from the relation graph modeling. Intuitively, topic extraction can benefit much from the information propagation of the relation graph structure - directly and indirectly connected documents and words have similar topics. In this paper, we propose a novel Graph Topic Neural Network (GTNN) model to mine latent topic semantics for interpretable document representation learning, taking into account the document-document, document-word, and word-word relationships in the graph. We also show that our model can be viewed as semi-amortized inference for relational topic model based on Poisson distribution, with high order correlations. We test our model in several settings: unsupervised, semi-supervised, and supervised representation learning, for both connected and unconnected documents. In all the cases, our model outperforms the state-of-the-art models for these tasks.
Inductive Representation Learning in Temporal Networks via Mining Neighborhood and Community Influences

Network representation learning aims to generate an embedding for each node in a network, which facilitates downstream machine learning tasks such as node classification and link prediction. Current work mainly focuses on transductive network representation learning, i.e. generating fixed node embeddings, which is not suitable for real-world applications. Therefore, we propose a new inductive network representation learning method called MNCI by mining neighborhood and community influences in temporal networks. We propose an aggregator function that integrates neighborhood influence with community influence to generate node embeddings at any time. We conduct extensive experiments on several real-world datasets and compare MNCI with several state-of-the-art baseline methods on various tasks, including node classification and network visualization. The experimental results show that MNCI achieves better performance than baselines.