Reviewerly/exHarmony huggingface dataset:
Bilateral personalized attention recommendation algorithm for user data based on neural network

There have been two major obstacles in the process of recommender systems. One is the cold start problem, and the other is the data sparse problem. How to solve these two obstacles is the main direction that needs to be improved and resolved in the field of recommendation systems. The paper designs a network model based on UIEncoder-DAMLPARL and proposes a deep learning recommendation method that combines user reviews and ratings with bilateral personalized attention mechanism. It is proposed that learn the characteristics of user reviews on the basis of convolutional neural networks, so as to enhance the interpretability in recommendations. Then the scoring features and comment features are integrated into a unified neural network model through the DAML model, and learn the representation of the user or item from the comments through the user/item encoder, so as to enhance the personalized recommendation. Finally, the high-order nonlinear interaction between the features is realized through the neural factorization machine, so as to achieve the final score prediction. In addition, to solve the problem of data sparseness, the PARL model, combined with our model, assists in modeling when there are few user reviews and product reviews. It is verified in the experiments that our method can effectively improve the performance of the recommender system on Amazon data sets.
Comparing Corpus-Driven and Corpus-Based Approaches to Diachronic Variation

Focusing on grammatical changes in Late Modern and Present-Day English, the author applies a corpus-driven method to texts from two diachronic corpora, the Representative Corpus of Historical English Registers (ARCHER) and the Corpus of Historical American English (COHA). He compares his findings to those returned by more conventional corpus-based methods, which can be characterized as hypothesis-driven. To this purpose, the study employs automated profiling of large feature sets, such as word- and POS-based mono-, bi- and trigrams, chunks, syntactic dependency labels and measures of constituent order and length. The derived feature profiles are combined in a supervised classification task with a given division of texts into earlier and later corpus subperiods to reveal patterns of over- and underuse. Structures that profiled as over- or under-represented in the diachronic subsections are then browsed for grammatical changes that may have been missed by previous research. According to the author, an advantage of such approaches is that they are theory-neutral and may generate novel hypotheses for investigation. These may then serve as input to further corpus-based approaches.
Study on Model Construction of Multi-agent Negotiation

Abstract In this paper, a multi-agent negotiation model will be constructed which consider the risk preference degree of the participants. And then the model is simulated. The simulation result has verified the effectiveness of the model.
Security evaluation on type‐1 and type‐1‐like 4‐branch generalized Feistel structures and application to reduced‐round Lesamnta‐LW‐BC

Generalized Feistel structures (called GFSs for short) are one of the most popular block cipher structures. They are mainly divided into type-1, type-2 and type-3 GFS. Among them, type-1 and type-1-like ones attracted much attention during the past decades because of the simple design and high implementation efficiency. In this paper, the security of the type-1 and type-1-like 4-branch GFS with substitution permutation round functions against the impossible differential attack are evaluated. For these two structures, 21-round impossible differential distinguishers are constructed when the linear layers P satisfy γ(P) ≥ 2, where γ(P) denotes the primitive index of P. Especially, when γ(P) = 2, the 21-round distinguisher of the type-1 structure is one round longer than before. Furthermore, for a specific block cipher Lesamnta-LW-BC, which takes the type-1-like structure, by exploiting the details of the linear layer, a better 21-round impossible differential distinguisher is constructed, which contains more impossible differentials than before. With this distinguisher, a 27-round impossible differential attack on Lesamnta-LW-BC is performed. The length of this attack is 8 rounds longer than the previous best one. Our results can provide guidance for designing and analysing the type-1 and type-1-like GFS as well as the specific block ciphers which take the structures.
A psychophysical performance-based approach to the quality assessment of image processing algorithms

Image processing algorithms are used to improve digital image representations in either their appearance or storage efficiency. The merit of these algorithms depends, in part, on visual perception by human observers. However, in practice, most are assessed numerically, and the perceptual metrics that do exist are criterion sensitive with several shortcomings. Here we propose an objective performance-based perceptual measure of image quality and demonstrate this by comparing the efficacy of a denoising algorithm for a variety of filters. For baseline, we measured detection thresholds for a white noise signal added to one of a pair of natural images in a two-alternative forced-choice (2AFC) paradigm where each image was selected randomly from a set of n = 308 on each trial. In a series of experimental conditions, the stimulus image pairs were passed through various configurations of a denoising algorithm. The differences in noise detection thresholds with and without denoising are objective perceptual measures of the ability of the algorithm to render noise invisible. This was a factor of two (6dB) in our experiment and consistent across a range of filter bandwidths and types. We also found that thresholds in all conditions converged on a common value of PSNR, offering support for this metric. We discuss how the 2AFC approach might be used for other algorithms including compression, deblurring and edge-detection. Finally, we provide a derivation for our Cartesian-separable log-Gabor filters, with polar parameters. For the biological vision community this has some advantages over the more typical (i) polar-separable variety and (ii) Cartesian-separable variety with Cartesian parameters.
Complex Scene Segmentation Network Based on Multi-scale Encoding-decoding Architecture

Abstract With the progress of artificial intelligence, the study of scene segmentation for complex scene understanding is of great significance. Due to the large number of activities, there are many target categories, large scale changes, many mutual occlusions, difficult target recognition, and large data labeling costs. In order to achieve accurate understanding of the complex scene, this paper proposes to add a scale adaptive feature module on the basis of Encode-Decode, so that the network can make good use of the features and context information of each level to effectively adapt to changes in target size. At the same time, we use the scale size function to weight encode different levels of features, which improves the prediction accuracy of pixels in the intersection area of each class. Experiments conducted on Cityscapes, Put_campus and PASCAL VOC 2012 datasets show that the method in this article is improved by about 1% compared with the three segmentation algorithms such as FCN, PSPNet, and Deeplabv3 +.
Software layer recovery through ego network analysis

The layered architecture style is one way to decompose software systems. Such an architecture consists of hierarchically organized layers that group program elements together. Recovering layering information is essential for the evolution and maintenance of existing software systems. This paper presents a novel approach to recover software layers through ego network analysis. Ego networks have traditionally been used for social network analysis; this paper adapts and tunes it to suit discovering software layers. Specifically, a dependency network is extracted from the source code to create an ego network. The ego network is processed to generate and optimize ego layers in a hierarchy of layers. The paper presents the preliminary evaluation of the approach on a small-scale system. Evaluating the approach on a small scale system provides encouraging results to validate it on large-scale systems.
Automated video editing based on learned styles using LSTM-GAN

Experienced video editors use various editing techniques, including camera movement, types of shots, and shot compositions to create specific video semantics delivering messages to the viewers. In the video production process, the content of the video are essential, but so is the way to compose it. The goal of this work is to train a model learning how to edit the video that meets the videography requirements. This work proposes a deep generative model where both the generator and discriminator are unidirectional LSTM networks to generate the sequences of shot transitions for video editing. The proposed model learns different types of editing transitions from edited video clips. One is the stage performance of Korean music programs, and another is Chinese music programs. By combining different types of shots and camera movements, the proposed AI video editor brings various viewing experiences to the viewers. The quality of the generated shot sequences for video editing are evaluated by three metrics, which are creativity, inheritance, and diversity. The results show that the quality of the synthetic sequences generated by LSTM-GAN are better than those generated by the baseline model (Markov chain or LSTM). In summary, the quality of the sequence generated by LSTM-GAN is better than the quality generated by the Markov chain or LSTM while ensuring creativity, inheritance, and diversity at the same time.
Improving counting sort algorithm via data locality

In this paper, we proposed a cache-friendly hybrid sorting algorithm that combined a non-comparison sorting algorithm (counting sort) with a comparison sort (quick sort) algorithm. The current study leverages the principle of locality to improve the performance of the counting sort algorithm over a large list of integer inputs. We employed a modified version of the original quick sort algorithm to reduce the number of cache misses in counting sort. We also empirically tested the performance of the proposed hybrid algorithm by varying both the range and the quantity of the input values. This hybrid approach not only demonstrated a superior performance over the classic counting sort algorithm but also is capable of facilitating parallelism that was impractical for the classic counting sort algorithm.
Comparative performance of machine learning algorithms for Cardano cryptocurrency forecasting

Cardano is an open-source and decentralized public blockchain platform, with consensus achieved using proof of stake. It can facilitate peer-to-peer transactions with its internal cryptocurrency, ADA, with no third-party involvement. In recent years, machine learning has been proliferating and has made many theoretical breakthroughs that find its application in many fields. The study of the machine learning approach in price prediction in Bitcoin and Ethereum has gained much attention, while relatively little research focuses on ADA forecasting. The experiment objective is to investigate the prediction of ADA's short period future prices dealing with real-world data. A comparative study of the results produced by different machine learning models, data visualizations, and statistical approaches. The experiment indicates that Gradient Boosting is the best-suited algorithm that can be selected to predict future ADA prices for short-term trading strategies.
Research on the fair (t,r,n) threshold secret sharing scheme

As an important branch of modern cryptography, secret sharing can diversify security risks and effectively ensure the security of information. The exchange of secret shares is an important part of secret sharing. How to ensure that the secret shares submitted by each participant does not affect fairness is an important topic to be studied in the field of secret sharing. This scheme proposes a fair (t, r, n) threshold secret sharing scheme, in which honest participants can recover the plaintext, while cheaters cannot recover the plaintext, and can resist collusion attacks by participants to ensure the fairness of the scheme.
Automatic web page data extraction through MD5 trigeminal tree and improved BIRCH

This paper proposes an automatic data extraction algorithm for web pages based on noise reduction and visualization blocks' construction. In this algorithm, we first build an MD5 trigeminal tree of the web page's source files using a Message-Digest Algorithm. And then we arrange some noise reduction. Finally, we construct visualization blocks by an optimized cluster algorithm named BIRCH <sub>A-CF</sub> which could create an area clustering feature forest and complete the clustering by dynamically changing the circle's radius according to the correction factor. We perform experiments on eight different datasets to compare our method with eight baseline methods. The experimental results show that our approach outperforms current methods by providing more accuracy and robustness, it also accelerates noise reduction and reduces the number of nodes effectively.
Topological semantics for default conditional logic

Conditional logics have played an important role in the attempts to investigate the foundations of default reasoning. The conditional connective α ⇒ β is interpreted as a 'normality' statement ('if α, then normally β') or a 'typicality' statement (assertion about prototypical properties of objects). Several conditional logics for defeasible reasoning have been proposed in the literature and the relation of well-known conditional systems to the KLM machinery of logics has been investigated. In this preliminary report, we contribute to the study of topological semantics for Default Conditional Logics. The perspective seems meaningful and promising, once somebody observes two facts: (i) the privileged relation of modal logic S4 to the normality conditionals - the relation has been discovered already from the '90s, and (ii) the prominent relation of S4 to topology, a result that dates back to the work of McKinsey & Tarski from the '40s. We introduce topological semantics for conditional logic, in the style of the Scott-Montague models introduced by B. Chellas in the '70's, further exploited in the filter-based models for conditional of S. Ben-David and R. Ben-Eliyahu-Zohary. We provide some first results on the expressive power of the emerging logics and point directions for future research.
Image Inpainting via Context Discriminator and U-Net

Image inpainting is one of the research hotspots in the field of computer vision and image processing. The image inpainting methods based on deep learning models had made some achievements, but it is difficult to achieve ideal results when dealing with images with the relationship between global and local attributions. In particular, when repairing a large area of image defects, the semantic rationality, structural coherence, and detail accuracy of results need to be improved. In view of the existing shortcomings, this study has proposed an improved image inpainting model based on a fully convolutional neural network and generative countermeasure network. A novel image inpainting algorithm via network has been proposed as a generator to repair the defect image, and structural similarity had introduced as the reconstruction loss of image inpainting to supervise and guide model learning from the perspective of the human visual system to improve the effect of image inpainting. The improved global and local context discriminator networks had used as context discriminators to judge the authenticity of the repair results. At the same time, combined with the adversarial loss, a joint loss has proposed for the training of the supervision model, which makes the content of the real and natural repair area and has attribute consistency with the whole image. To verify the effectiveness of the proposed image inpainting model, the image inpainting effect is compared with the current mainstream image inpainting algorithm on the CelebA-HQ dataset based on subjective and objective indicators. The experimental results show that the proposed method has made progress in semantic rationality, structural coherence, and detail accuracy. The proposed model has a better understanding of the high-level semantics of image, and a more accurate grasp of context and detailed information.
Correctness verification and minimum cost calculating algorithm of comparison network

Comparison Network (CN) is a kind of special structure for parallel sorting. There are two important problems in CN: Verifying the correctness of a given CN and finding the minimum cost CN. For the two problems, this paper establishes a verification model similar to Petri Net for CN based on [0,1] principle, and provides two algorithms for them. The deduplication and pruning strategies are introduced in the algorithms which make better execution efficiency compared with results in the existing literature.
Sentiment analysis of online reviews based on BERT-BiLSTM-CBAM

In this paper, a hybrid model based on BERT-BiLSTM-CBAM is proposed to classify the sentiment of online reviews more accurately. Firstly, the BERT model is used to pre-train the text information to obtain feature vectors; then the feature vectors obtained from Bert pre-training are stitched and reorganized through a bi-directional long and short-term memory network (BiLSTM) and CBAM mechanism to obtain new feature vectors. Finally, these new feature vectors are input to the fully connected layer, and the sentiment category of the text is calculated by the SoftMax function. Experiments on the Amazon reviews and Yelp reviews datasets show that the method is more accurate and reliable on both datasets.
Continuous prediction method of earthquake early warning magnitude for high-speed railway based on support vector machine

Purpose Using the strong motion data of K -net in Japan, the continuous magnitude prediction method based on support vector machine (SVM) was studied. Design/methodology/approach In the range of 0.5–10.0 s after the P -wave arrival, the prediction time window was established at an interval of 0.5 s. 12 P -wave characteristic parameters were selected as the model input parameters to construct the earthquake early warning (EEW) magnitude prediction model (SVM-HRM) for high-speed railway based on SVM. Findings The magnitude prediction results of the SVM-HRM model were compared with the traditional magnitude prediction model and the high-speed railway EEW current norm. Results show that at the 3.0 s time window, the magnitude prediction error of the SVM-HRM model is obviously smaller than that of the traditional τ c method and P d method. The overestimation of small earthquakes is obviously improved, and the construction of the model is not affected by epicenter distance, so it has generalization performance. For earthquake events with the magnitude range of 3–5, the single station realization rate of the SVM-HRM model reaches 95% at 0.5 s after the arrival of P -wave, which is better than the first alarm realization rate norm required by “The Test Method of EEW and Monitoring System for High-Speed Railway.” For earthquake events with magnitudes ranging from 3 to 5, 5 to 7 and 7 to 8, the single station realization rate of the SVM-HRM model is at 0.5 s, 1.5 s and 0.5 s after the P -wave arrival, respectively, which is better than the realization rate norm of multiple stations. Originality/value At the latest, 1.5 s after the P -wave arrival, the SVM-HRM model can issue the first earthquake alarm that meets the norm of magnitude prediction realization rate, which meets the accuracy and continuity requirements of high-speed railway EEW magnitude prediction.
Strategic selection of data sources for cyber attack detection in enterprise networks

Cyber attacks leave traces in data sources, such as in log files, memory or data-streams. Detection systems utilize these data sources to detect the application of specific attack techniques. Attack techniques vary considerably in terms of their effectiveness, potential impact and application by threat actors. Data sources, on the other side, may contain traces of one or several attack techniques, and the effort to process their output may differ heavily. Therefore, it is obvious that not all data sources are of equal value for detection and organizations must carefully survey which sources shall be analyzed and what attack techniques need to be found. This paper introduces D3TECT, a process model that describes a procedure for dynamically ranking and selecting data sources suitable for detection. The novelty is that this model accounts for constraints in the selection process. For instance if a certain data source cannot be utilized in a specific setting, e.g., due to data privacy constraints, the discovery of the most important attack techniques are still ensured by the remaining data sources. Eventually, the D3TECT approach solves the challenge of strategically selecting data sources while accounting for their varying usefulness for attack detection. The model is tested with real data, utilizing the MITRE ATT&CK framework and numerous public cyber threat intelligence databases. The paper shows the ranking results and discusses their plausibility to validate D3TECT.
Detecting Tagged People in Camera Images

With the development of technology, cameras are used more widely. It is possible to evaluate the widespread use of cameras in various subjects in daily life. Especially face recognition systems are one of the most important areas of use of cameras. Facial recognition systems can be used in many areas such as cyber security, entertainment, security applications of daily used devices, and faster and easier transactions in financial areas. Although a lot of progress has been made in this regard, face recognition systems are still used widely enough because it is thought that they have weaknesses in terms of security. Many scientists are working on facial recognition. In this study, it is aimed to detect the faces of people determined from videos or live camera images in the best and safest way. Yolov4 object detection algorithm, a ready-made algorithm, was used for the detection of human faces on images. The faces of the people in the images were detected by training the data set we created in the Yolov4 algorithm. An accuracy of 99.1 has been achieved for detecting people's faces on images. The data set we created with pictures of certain people is trained in the CNN algorithm. The faces of the people detected on the images were classified on the model trained with the CNN algorithm for the identification of the people, and the accuracy value was examined for the detection of the identified people on the video recordings or live images from the cameras.
RANC

In this work we present Reward-All Nakamoto-Consensus (RANC), a Proof-of-Work cryptocurrency that resiliently rewards each miner with a number of coins that is directly proportional to its individual mining power, rather than to its relative share of the entire network's mining power as done in Bitcoin. Under this approach, the security of mining in RANC achieves near-perfect incentive compatibility, and near-zero censorship susceptibility, for adversarial mining shares up to 45%, but at the cost of regression in subversion-gain resilience. Moreover, mining rewards in RANC exhibit significantly lower variance for non-majority miners compared to NC, enabling dependable reward stability. Consequently, depending on the network transaction-fees, RANC improves miner's waiting time for rewards, and incentivizes forming mining pools smaller than required in Bitcoin for equal reward stability. A detailed specification of RANC is presented, along with an evaluation of the practicality and efficiency achieved by our prototype RANC implementation.