Reviewerly/exHarmony huggingface dataset:
A computational model of infant learning and reasoning with probabilities.

Recent experiments reveal that 6- to 12-month-old infants can learn probabilities and reason with them. In this work, we present a novel computational system called Neural Probability Learner and Sampler (NPLS) that learns and reasons with probabilities, providing a computationally sufficient mechanism to explain infant probabilistic learning and inference. In 24 computer simulations, NPLS shows how probability distributions can emerge naturally from neural-network learning of event sequences, providing a novel explanation of infant probabilistic learning and reasoning. Three mathematical proofs show how and why NPLS simulates the infant results so accurately. The results are situated in relation to seven other active research lines. This work provides an effective way to integrate Bayesian and neural-network approaches to cognition. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Credit Card Fraud Detection Using Blockchain and Simulated Annealing k-Means Algorithm

Rani, Poonam Shokeen, Jyoti Agarwal, Amit Bhatghare, Ashish Majithia, Arjun Malhotra, JigyasuA credit card fraud is a criminal activity in which a person’s credit card is used to make transactions he/she does not authorize. Credit card fraud is one of the major factors in causing huge revenue losses to the banking sector which in turn hurt the economy negatively. This paper combines blockchain technology with simulated annealing for credit card fraud detection. Simulated annealing is leveraged into k-means to detect anomalous and fraudulent banking transactions in a private-permissioned blockchain network. Experimental results demonstrate that the proposed approach exhibits high accuracy compared to the simple k-means algorithm.
Three-Dimensional Diffusion Model in Sports Dance Video Human Skeleton Detection and Extraction

The research in this paper mainly includes as follows: for the principle of action recognition based on the 3D diffusion model convolutional neural network, the whole detection process is carried out from fine to coarse using a bottom-up approach; for the human skeleton detection accuracy, a multibranch multistage cascaded CNN structure is proposed, and this network structure enables the model to learn the relationship between the joints of the human body from the original image and effectively predict the occluded parts, allowing simultaneous prediction of skeleton point positions and skeleton point association information on the one hand, and refinement of the detection results in an iterative manner on the other. For the combination problem of discrete skeleton points, it is proposed to take the limb parts formed between skeleton points as information carriers, construct the skeleton point association information model using vector field, and consider it as a feature, to obtain the relationship between different skeleton points by using the detection method. It is pointed out that the reorganization problem of discrete skeleton points in multiperson scenes is an NP-Hard problem, which can be simplified by decomposing it into a set of subproblems of bipartite graph matching, thus proposing a matching algorithm for discrete skeleton points and optimizing it for the skeleton dislocation and algorithm problems of human occlusion. Compared with traditional two-dimensional images, audio, video, and other multimedia data, the 3D diffusion model data describe the 3D geometric morphological information of the target scene and are not affected by lighting changes, rotation, and scale transformation of the target and thus can describe the realistic scene more comprehensively and realistically. With the continuous updating of diffusion model acquisition equipment, the rapid development of 3D reconstruction technology, and the continuous enhancement of computing power, the research on the application of 3D diffusion model in the detection and extraction of a human skeleton in sports dance videos has become a hot direction in the field of computer vision and computer graphics. Among them, the feature detection description and model alignment of 3D nonrigid models are a fundamental problem with very important research value and significance and challenging at the same time, which has received wide attention from the academic community.
Hierarchical Multi-label Text Classification: Self-adaption Semantic Awareness Network Integrating Text Topic and Label Level Information

Hierarchical multi-label text classification is used to assign documents to multiple categories stored in a hierarchical structure. However, the existing methods pay more attention to the local semantic information of the text, and make insufficient use of the label level information. In this paper, we propose a new self-adaption semantic awareness network integrating text topic and label level information. Compared with the existing methods, our method has two contributions: (1) On the basis of the traditional text encoder, we add the global topic information of the text and the hierarchical information of the label, describe the text from different levels and effectively use the correlation between the labels. (2) We design an adaptive fusion strategy that adaptively retains information helpful for classification from three different levels of features and exploits their mutual influence. Experimental results on two benchmark datasets show that our model is better than the state-of-the-art methods on six metrics, especially in the prediction of low-frequency labels with sparse samples.
Four dimensional hyperchaotic communication system based on dynamic feedback synchronization technique for image encryption systems

&lt;span&gt;This paper presents the design and simulation of a hyperchaotic communication system based on four dimensions (4D) Lorenz generator. The synchronization technique that used between the master/transmitter and the slave/receiver is based on dynamic feedback modulation technique (DFM). The mismatch error between the master dynamics and slave dynamics are calculated continuously to maintain the sync process. The information signal (binary image) is masked (encrypted) by the hyperchaotic sample x of Lorenz generator. The design and simulation of the overall system are carried out using MATLAB Simulink software. The simulation results prove that the system is suitable for securing the plain-data, in particular the image data with a size of 128×128 pixels within 0.1 second required for encryption, and decryption in the presence of the channel noise. The decryption results for gray and colored images show that the system can accurately decipher the ciphered image, but with low level distortion in the image pixels due to the channel noise. These results make the proposed cryptosystem suitable for real time secure communications.&lt;/span&gt;
Conversational recommendation based on end-to-end learning: How far are we?

Conversational recommender systems (CRS) are software agents that support users in their decision-making process in an interactive way. While such systems were traditionally mostly manually engineered, recent works increasingly rely on machine learning models that are trained on larger corpora of recorded recommendation dialogues between humans. One promise of such end-to-end learning approaches therefore is that they avoid the knowledge-engineering bottlenecks of traditional systems. Recent empirical evaluations of such learning-based systems sometimes demonstrate continuous progress relative to previous systems. Therefore, it may not be entirely clear how useable these systems are on an absolute scale. To address this research question, we evaluated two recent end-to-end learning approaches presented at top-tier scientific conferences with the help of human judges. A first study showed that in both investigated systems about one third of the system responses were not considered meaningful in the given dialogue context, which questions the applicability of these systems in practice. In a second study, we benchmarked the two systems against a trivial rule-based approach, again with human judges. In this second study, the participants considered the quality of the responses of the rule-based approach significantly better on average than those of the learning-based systems. Overall, besides pointing to open challenges of state-of-the-art learning-based approaches, our studies indicate that we must improve our evaluation methodology for CRS to ensure progress in this field.1
Digital Test Instruments Based on Wondershare-Superitem for Supporting Distance Learning Implementation of Assessment Course

This research had the main objective to develop a new form/design in the development of test instruments that valid and reliable.The form of the test instrument that was developed adopted the Superitem concept and was integrated into software called Wondershare.Through the integration, digital format test instruments were realized with the structures of the questions' difficulty level which were tiered from the easiest to the hardest stages.The development of these digital test instruments used a quantitative approach to obtain valid and reliable instruments.The stages of development included: 1) determination of the instrument items, 2) test the validity and reliability of instrument items, 3) determination of final instruments as digital test questions, 4) inputting digital test questions into the Wondershare application, 5) trials of the digital test instruments, 6) revision of digital test instruments, and 7) finalization of digital test instruments.The content validation test of instruments involved two education experts, the reliability test of instruments involved 42 respondents, and the trials of digital test instruments based on Wondershare-Superitem involved 12 respondents.Data collection related to content validation test, instrument reliability, and trials of digital test instruments used the instruments in the form of questionnaires.Analysis of the trial results of digital test instruments using analysis techniques of quantitative descriptive by percentage descriptive formula.The results of this research showed the design of digital format test instruments based on the Wondershare-Superitem that are valid and reliable for supporting distance learning implementation of assessment course.
English-Vietnamese Cross-Lingual Paraphrase Identification Using MT-DNN

Paraphrase identification is a crucial task in natural language understanding, especially in cross-language information retrieval. Nowadays, Multi-Task Deep Neural Network (MT-DNN) has become a state-of-the-art method that brings outstanding results in paraphrase identification [1]. In this paper, our proposed method based on MT-DNN [2] to detect similarities between English and Vietnamese sentences, is proposed. We changed the shared layers of the original MT-DNN from original the BERT [3] to other pre-trained multi-language models such as M-BERT [3] or XLM-R [4] so that our model could work on cross-language (in our case, English and Vietnamese) information retrieval. We also added some tasks as improvements to gain better results. As a result, we gained 2.3% and 2.5% increase in evaluated accuracy and F1. The proposed method was also implemented on other language pairs such as English – German and English – French. With those implementations, we got a 1.0%/0.7% improvement for English – German and a 0.7%/0.5% increase for English – French.
Human Skeleton Detection and Extraction in Dance Video Based on PSO-Enabled LSTM Neural Network

With the significant increase of social informatization, the emerging information technology represented by machine vision has been applied to more and more scenes. Among them, the detection and extraction of human skeleton in a dance video based on this technology has a huge market demand in education and training. However, the existing detection and extraction technology has the problems of slow recognition speed and low extraction accuracy. Therefore, this paper proposes a neural network based on particle swarm optimization to detect and extract human skeletons in a dance video. Through the research and test on different data sets, it is found that the neural network based on particle swarm optimization algorithm has good detection and extraction ability and has high accuracy for the detection and recognition of human skeleton points. Among them, on all MPII data sets, the average accuracy of PSO-LSTM proposed in this paper is 3.9% higher than that of other optimal algorithms; on the PoseTrack data set, the average accuracy of detection and extraction is improved by 2.3%. The above results show that the neural network based on particle swarm optimization has fast detection speed and good extraction accuracy and can be used for the detection and extraction of human skeleton in a dance video.
Exploiting the Relationship Between Visual and Textual Features in Social Networks for Image Classification with Zero-Shot Deep Learning

One of the main issues related to unsupervised machine learning is the cost of processing and extracting useful information from large datasets. In this work, we propose a classifier ensemble based on the transferable learning capabilities of the CLIP neural network architecture in multimodal environments (image and text) from social media. For this purpose, we used the InstaNY100K dataset and proposed a validation approach based on sampling techniques. Our experiments, based on image classification tasks according to the labels of the Places dataset, are performed by first considering only the visual part, and then adding the associated texts as support. The results obtained demonstrated that trained neural networks such as CLIP can be successfully applied to image classification with little fine-tuning, and considering the associated texts to the images can help to improve the accuracy depending on the goal. The results demonstrated what seems to be a promising research direction.
Design of Chinese-English Wireless Simultaneous Interpretation System Based on Speech Recognition Technology

A Chinese-English wireless simultaneous interpretation system based on speech recognition technology is suggested to solve the problems of low translation accuracy and a high number of ambiguous terms in current Chinese-English simultaneous interpretation systems. The system’s general structure and hardware architecture are summarized. The chairman unit, representative unit, transliteration unit, and auditing unit are the four basic components of the simultaneous interpretation system. The CPU is the nRF24E1 hardware wireless radio frequency transceiver chip, while the chairman machine, representative machine, translator, and auditorium are all created separately. Speech recognition technology is used by the system software to create a speech recognition process that properly produces speech-related semantics. The input text is used as the search criteria, a manual interactive synchronous translation program is created, and the results for the optimum translation impact are trimmed. The experimental findings reveal that this system’s sentence translation accuracy rate is 0.9–1.0, and the number of ambiguous terms is minimal, which is an improvement on previous systems’ low translation accuracy.
Decentralized Data Integrity Scheme for Preserving Privacy in Cloud Computing

Cloud Computing is a dreamed vision of utility computing; users can now remotely store private information. However, this has also granted no physical possession of data outsourced to the cloud. Issues of data integrity and cloud accountability arise due to a lack of control. It is desirable to have a scheme that assures data owners of the correctness of information stored in the cloud without introducing huge computational or communication costs. Most existing schemes in the literature have achieved positive results in this regard. Most of the schemes either use blockchain technology which comes with its high energy consumption or assuming the trustworthiness of a third-party auditor. In this paper, we propose a Provable Data Possession scheme that is resistant to malicious auditors, resistant to key resilient exposure, and able to eliminate the issue of procrastinating auditors. The proposed protocol provides efficient auditing through a decentralized third-party to assure data owners of the integrity of data outsourced to the cloud. The main aim is to assure data owners of the integrity of data outsourced to the cloud without the need for a trusted or semi-honest entity. To show that our approach is completely privacy preserving, we instantiated three steps in the protocol: data initialization, verification, and sampling. The security analysis of the proposed approach proves to be secure, with a reduced computational and communicational cost.
TAM: Temporal Adaptive Module for Video Recognition

Temporal modeling is crucial for capturing spatiotemporal structure in videos for action recognition. Video data is with extremely complex dynamics along its temporal dimension due to various factors such as camera motion, speed variation, and different activities. To effectively capture this diverse motion pattern, this paper presents a new temporal adaptive module ({\bf TAM}) to generate video-specific kernels based on its own feature maps. TAM proposes a unique two-level adaptive modeling scheme by decoupling dynamic kernels into a location sensitive importance map and a location invariant aggregation weight. The importance map is learned in a local temporal window to capture short term information, while the aggregation weight is generated from a global view with a focus on long-term structure. TAM is a principled module and could be integrated into 2D CNNs to yield a powerful video architecture (TANet) with a very small extra computational cost. The extensive experiments on Kinetics-400 and Something-Something datasets, demonstrate that the TAM outperforms other temporal modeling methods consistently owing to its temporal adaptive modeling strategy.
Performance Analysis of Wireless Practical Byzantine Fault Tolerance Networks Using IEEE 802.11

Blockchain has achieved great success in cryptocurrency for its peculiarities for security and privacy, which are also important in the wireless network. Therefore, there are growing interests in applying blockchain to the wireless network. Wireless Practical Byzantine Fault Tolerance (PBFT) is considered the most applicable consensus mechanism. However, the existing researches and applications are mostly under wired scenarios. In this paper, we investigated the performance of the wireless PBFT network using IEEE 802.11 under unsaturated situations. The performance is evaluated through three metrics: success probability, delay and throughput. Results suggest that there exists a minimum transmission success probability to achieve the end-to-end performance required for the PBFT consensus protocol.
Surface science motivated by heating of trapped ions from the quantum ground state

For the past two and a half decades, anomalous heating of trapped ions from nearby electrode surfaces has continued to demonstrate unexpected results. Caused by electric-field noise, this heating of the ions' motional modes remains an obstacle for scalable quantum computation with trapped ions. One of the anomalous features of this electric-field noise is the reported nonmonotonic behavior in the heating rate when a trap is incrementally cleaned by ion bombardment. Motivated by this result, the present work reports on a surface analysis of a sample ion-trap electrode treated similarly with incremental doses of Ar+ ion bombardment. Kelvin probe force microscopy and x-ray photoelectron spectroscopy were used to investigate how the work functions on the electrode surface vary depending on the residual contaminant coverage between each treatment. It is shown that the as-fabricated Au electrode is covered with a hydrocarbon film that is modified after the first treatment, resulting in work functions and core-level binding energies that resemble that of atomic-like carbon on Au. Changes in the spatial distribution of work functions with each treatment, combined with a suggested phenomenological coverage and surface-potential roughness dependence to the heating, appear to be related to the nonmonotonic behavior previously reported.
Evaluation of MODIS-derived estimates of the albedo over the Atacama Desert using ground-based spectral measurements

Surface albedo is an important forcing parameter that drives the radiative energy budget as it determines the fraction of the downwelling solar irradiance that the surface reflects. Here we report on ground-based measurements of the spectral albedo (350-2200 nm) carried out at 20 sites across a North-South transect of approximately 1300 km in the Atacama Desert, from latitude 18° S to latitude 30° S. These spectral measurements were used to evaluate remote sensing estimates of the albedo derived from the Moderate Resolution Imaging Spectroradiometer (MODIS). We found that the relative mean bias error (RMBE) of MODIS-derived estimates was within ± 5% of ground-based measurements in most of the Atacama Desert (18-27° S). Although the correlation between MODIS-derived estimates and ground-based measurements remained relatively high (R= 0.94), RMBE values were slightly larger in the southernmost part of the desert (27-30° S). Both MODIS-derived data and ground-based measurements show that the albedo at some bright spots in the Atacama Desert may be high enough (up to 0.25 in visible range) for considerably boosting the performance of bifacial photovoltaic technologies (6-12%).
Source code authorship attribution using file embeddings

The problem of source code authorship attribution is crucial for a few reasons. Security and legal issues are the most popular ones. However, this domain could also help to understand the nature of the personal code style. This type of information could be used, for instance, by IDEs to improve the developer's experience of writing the code. The goal of this study is to construct an interpretable model for source code embeddings generation. Such embeddings should represent the correspondence between the source code and its author.
More Than Two Decades of Research on Verification of UML Class Models: A Systematic Literature Review

Error checking is easy and inexpensive in the initial stages as compared to later stages due to when the development cycle precedes the development cost and efforts also increase. UML class model is a key element of modern software methodologies and creates in the initial stage of software development. Therefore, error detection and rectification of the UML class model may save software development costs and time. This paper presents an overview of UML Class model verification approaches and identifies open issues, current research trends, and other improvement areas. This study uses a systematic literature review as an investigation method with six research questions and assesses 65 papers dated January 1997 to December 2020. From 2124 published research papers, 65 papers are selected and distributed into 7 studies. This work provides an analysis of verification approaches and the automation level of proposed approaches. As a result, it is found that the existing UML class model verification methods provide great efforts to check correctness. However, in some situations (when dealing with large and complex models), they consume a significant amount of time and do not support many important features of the UML class model.