Reviewerly/exHarmony huggingface dataset:
Sequential Movie Genre Prediction Using Average Transition Probability with Clustering

In recent movie recommendations, predicting the user's sequential behavior and suggesting the next movie to watch is one of the most important issues. However, capturing such sequential behavior is not easy because each user's short-term or long-term behavior must be taken into account. For this reason, many research results show that the performance of recommending a specific movie is not very high in a sequential recommendation. In this paper, we propose a cluster-based method for classifying users with similar movie purchase patterns and a movie genre prediction algorithm rather than the movie itself considering their short-term and long-term behaviors. The movie genre prediction does not recommend a specific movie, but it predicts the genre for the next movie to watch in consideration of each user's preference for the movie genre based on the genre included in the movie. Through this, it is possible to provide appropriate guidelines for recommending movies including the genre to users who tend to prefer a specific genre. In particular, in this paper, users with similar genre preferences are organized into clusters to recommend genres, and in clusters that do not have relatively specific tendencies, genre prediction is performed by appropriately trimming genres that are not necessary for recommendation in order to improve performance. We evaluate our method on well-known movie datasets, and qualitatively that it captures personalized dynamics and is able to make meaningful recommendations.
Research on Traffic Sign Detection and Recognition Algorithm Based on Convolutional Neural Network

research-article Research on Traffic Sign Detection and Recognition Algorithm Based on Convolutional Neural Network Share on Authors: GuiHua Yang Guilin University of Technology, China Guilin University of Technology, ChinaView Profile , JiaLe Wei Guilin University of Technology, China Guilin University of Technology, ChinaView Profile Authors Info & Claims ICIIP 2021: 2021 6th International Conference on Intelligent Information ProcessingJuly 2021 Pages 291–298https://doi.org/10.1145/3480571.3480624Online:29 October 2021Publication History 0citation22DownloadsMetricsTotal Citations0Total Downloads22Last 12 Months22Last 6 weeks10 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Provide Direction Despite Uncertainty: Workshops for Creating a Compelling Product Vision

Context: The software-intensive business is characterized by increasing market dynamics, rapid technological changes, and fast-changing customer behaviors. Organizations face the challenge of moving away from traditional roadmap formats to an outcome-oriented approach that focuses on delivering value to the customer and the business. An important starting point and a prerequisite for creating such outcome-oriented roadmaps is the development of a product vision to which internal and external stakeholders can be aligned. However, the process of creating a product vision is little researched and understood. Objective: The goal of this paper is to identify lessons-learned from product vision workshops, which were conducted to develop outcome-oriented product roadmaps. Method: We conducted a multiple-case study consisting of two different product vision workshops in two different corporate contexts. Results: Our results show that conducting product vision workshops helps to create a common understanding among all stakeholders about the future direction of the products. In addition, we identified key organizational aspects that contribute to the success of product vision workshops, including the participation of employees from functionally different departments.
Fine-Grained Categorization Using a Mixture of Transfer Learning Networks

In this paper, we apply a mixture of experts approach to further enhance the accuracy of transfer learning networks on a fine-grained categorization problem, expanding on the work of Firsching and Hashem. Mixture of experts approaches may help to improve accuracy on categorization problems. Likewise, transfer learning is a highly effective technique for solving problems in machine learning of varying complexities. We here illustrate that mixtures of trained transfer learning networks, when applied properly, may further improve categorization accuracy.
A Learn App: Mobile augmented reality vocabulary learning application - Supported through Mada Innovation Program

In cooperation with its strategic partners, Mada Center works to identify the needs of persons with disabilities for assistive technology and digital access solutions in Arabic. The center has supported start-ups and entrepreneurs to localize technological devices and solutions through the Mada Innovation Program, with the aim of improving digital accessibility for PWDs at the local, regional and international levels. This year, Mada and HBKU worked together to develop a mobile app using augmented reality technology and for the first time three dimensional AAC Symbols for learning purpose. The app is named A-Learn developed by Dr. Kamran Khowaja, Dr. Dena Al-Thani, and Dr. Siti Salwah Salim from Hamad Bin Khalifa University.
Analysis on the language features of digital sculptures

Through the thinking and exploration of digital sculpture based on sculpture creation, analyze the element composition and new forms of digital sculpture, explore the new language characteristics of digital sculpture, study the application of digital sculpture, and look forward to the digital sculpture based on the background of new technology. Making some meaningful attempts in the creation, processing, and display process of the new technology is helpful for the entry point and fit of the digital sculpture software in the new technology, and for the objective understanding of digital sculpture.
Improving Model Accuracy with Probability Scoring Machine Learning Models

Binary classification problems are exceedingly common across corporations, regardless of their industry, with examples including predicting attrition or classifying patients as high-risk vs low-risk. The motivation for this research is to determine techniques that improve prediction accuracy for operationalized models. Collaborating with a national partner, we conducted feature experiments to isolate industry-agnostic factors with the most significant impact on conversion rate. We also use probability scoring to highlight incremental changes in accuracy while we applied several improvement techniques to determine which would significantly increase a model’s predictive power. We compare five algorithms: XGBoost, LGBoost, CatBoost, MLP, and an Ensemble. Our results highlight the superior accuracy of the ensemble, with a final log loss value of 0.5784. We also note that the highest levels of improvement in log loss occurs at the beginning of the process, after downsampling and using engineered custom metrics as inputs to the models.
A Motion Direction Detective Mechanism for Greyscale Images

The visual system of humans and animals is considered to be the most important sensory system, and more than 80% of the necessary information in the interaction with nature comes from vision. And directional information is one of the most basic visual information. The brain needs to obtain various types of simple visual information before it can make a correct evaluation of the external environmental conditions and thus decides how to interact with the external world. Therefore, the correct detection of motion direction is also a particularly important function of the visual system. However, the exact mechanism of motion direction detection in the brain is still unknown. In this paper, we propose a motion direction detection mechanism for grayscale image to detect the direction of object motion occurring in the visual field. The main idea of this mechanism is to use multiple local motion directionally detective neurons that are sensitive to motions of different directions to collect the local motion direction information, and then infer the motion direction of object in the visual field with the global motion directionally detective neurons. A series of experimental results demonstrate the effectiveness and accuracy of the proposed mechanism, which is independent of the shape, size, and position of the object in the visual field. The mechanism is based on grayscale images, so it also has the ability to correctly detect the direction of object motion in binary images.
Opto-Electronic Neural Networks Based on Few-Mode Fiber

In this work, for the first time to our knowledge, the spatial degrees of freedom are introduced to opto-electronic neural networks, and the few-mode fiber based multiplexing is numerically simulated to realize parallel synapses.
The Dynamics of Argumentative Discourse

Arguments have always played a central role within logic and philosophy. But little attention has been paid to arguments as a distinctive kind of discourse, with its own semantics and pragmatics. The goal of this essay is to study the mechanisms by means of which we make arguments in discourse, starting from the semantics of argument connectives such as ‘therefore’. While some proposals have been made in the literature, they fail to account for the distinctive anaphoric behavior of ‘therefore’, as well as for uses of argument connectives in complex arguments, suppositional arguments, arguments with non-declarative conclusions, as well as arguments with parenthetical remarks. We argue that a comprehensive account of arguments requires imposing a distinctive tree-like structure on contexts. We show how to extend our account to accommodate modal subordination and different flavors of argument connectives.
Agent-mediated Recommender System with Detect-Determine-Direct Model for News Website

In competing with various sources of news made available in the digital economy, newspaper companies need to look into proactive way to direct and drive readers in their search for news. Agent-mediated recommender system may sound common to the artificial intelligence world. However, this research paper proposes a deployment of software agents with certain roles and tasks, adopting the Detect-Determine-Direct Model for news website users, based on the user navigation behavior analysis. In this proposed conceptual framework, software agents are assigned with the tasks of identify, drive, decide and method, that are proven computable in earlier research. Taking into account the flexibility of users who may change their behavior once a recommendation is made, this conceptual design of recommender system is expected to be self-sustainable as it improves itself by learning new knowledge retrieved from the change in its environment to support the capacity of its knowledge repository.
Fake Reviews in Online Platforms and the Effort to Fight Them

This paper proposes a model in which oligopolistic firms selling through an online platform can invest in creating positive fake reviews to increase their reputation and negative ones to harm that of their competitors. Therefore, oligopolistic firms’ demand depends on the amount of positive and negative fake reviews. In this context, the online platform optimally chooses the effort to fight fake reviews and the fee it charges to online sellers for each transaction. The novelty of the model lies in incorporating the online platform’s role in fighting fake reviews and its interplay with sellers’ strategic behaviour. The model’s main result is that the platform’s effort has a positive impact not only on consumers’ surplus but also on the oligopolistic firms’ profitability. In its turn, the platform’s optimal effort depends on exogenous parameters, including the demand’s sensitivity to fake reviews. JEL Classifications: D21, D43, L13, L81
$$\ell r$$-Multisemigroups, Modal Quantales and the Origin of Locality

We introduce \(\ell r\)-multisemigroups as duals of modal quantales and study modal correspondences between equations in these multisemigroups and the domain and codomain axioms of modal quantales. Our results yield new insights on the origin of locality in modal semirings and quantales. They also yield construction principles for modal powerset quantales that cover a wide range of models and applications.
Semantic Perception-Oriented Low-Resource Neural Machine Translation

Pre-training method has been proved to significantly improve the performance of low-resource neural machine translation (NMT), while the common pre-training methods (BERT) uses attention mechanism based on Levenshtein distance (LD) to extract language features, which ignored syntax-related information. In this paper, we proposed a machine translation pre-training method with semantic perception which depend on the traditional position-based modeling, we uses semantic role labels (SRL) to annotate sentences with “predicate-argument” structures at the word level, and merge vectorized SRL with word vectors to deepen the model’s understanding of deep semantics. In addition, to avoid parameter disaster, we proposed a hierarchical knowledge distillation method to fuse the NMT model and pre-training model to adapt to the output probability distribution of the pre-training model. We validated the method in the LDC En-Zh and CCMT2017 Mongolia-Chinese (Mo-Ch), Uyghur-Chinese (Uy-Ch), Tibetan-Chinese (Ti-Ch) tasks. The results show that compared with baseline, our model achieves significant results, which fully illustrates the generalization of the method.
An evaluation of lane changing process based on cloud model and incentive-punishment variable weights

Aiming at the problems of making fuzziness precise and fixed weight in the fuzzy comprehensive evaluation of lane changing process, a lane change evaluation method based on cloud model and incentive-punishment variable weights was proposed. Based on a driving simulator, the highway driving simulation experiment was conducted to obtain the vehicle&#x0027;s parameters in the lane changing process. Five single index evaluation cloud models of safety, smoothness, comfort, efficiency and ecology in the lane changing process were established. The average membership degree of each index belonging to the four levels of excellent, good, fair and poor was obtained. Considering the subjectivity of experts and the objectivity of data samples, the combination weights were obtained. According to the balance and prominence of the indexes, the combination weights were appropriately rewarded and punished to obtain the final weights. Finally, the evaluation method based on the cloud model and incentive-punishment variable weights was used to evaluate 35 lane change processes, and the proposed method was compared with the method based on the constant weights. There are 32 processes for which the results of the two evaluation methods are the same, while there are 3 processes for which the results of the two evaluation methods are different. Through the further analysis of the indexes, it is found that the evaluation results of incentive-punishment variable weights are more reasonable and accurate.
First Comments on Emilio Pasquini’s Papers

The essay is divided into three parts: in the first it offers an overview of the most recent studies dedicated to archives in person, in the disciplinary field of Italianistics and in that of Archivistics; in the second it outlines a biographical profile of the subject producing the archive; in the last one some observations on the archive are made.
Classification Weight Imprinting for Data Efficient Object Detection

Object detection is an important computer vision task which is required in various applications such as autonomous vehicle, robotics, etc. In this paper, a data-efficient object detection methodology with few training samples is proposed to achieve the data-efficient object detection. On this matter, it is commonly known and encountered that the performance of conventional fully supervised approaches typically suffer when data is insufficient. Classification confidence and localization accuracy are expectedly not consistent with unseen categories, which often leads to overfitting of class-specific parameters. In this paper, we thus propose a novel data-efficient classification network using weight imprinting which can be adopted into any anchor-based detector to directly enhance the detection accuracy on data-rare categories, without sacrificing the performance on base categories. The proposed network is data-efficient yet highly effective with the carefully designed weight imprinting head. Experiments show our proposed method can bring significant performance gains to rarely labeled categories on COCO and PASCAL VOC.
Computational self-testing for entangled magic states

In the seminal paper [Metger and Vidick, Quantum '21], they proposed a computational self-testing protocol for Bell states in a single quantum device. Their protocol relies on the fact that the target states are stabilizer states, and hence it is highly non-trivial to reveal whether the other class of quantum states, non-stabilizer states, can be self-tested within their framework. Among non-stabilizer states, magic states are indispensable resources for universal quantum computation. In this letter, we show that a magic state for the CCZ gate can be self-tested while that for the T gate cannot. Our result is applicable to a proof of quantumness, where we can classically verify whether a quantum device generates a quantum state having non-zero magic.
Partitioned Searchable Encryption

Symmetric searchable encryption (SSE) allows to outsource encrypted data to an untrusted server and retain searching capabilities. This is done without impacting the privacy of both the data and the search/update queries. In this work we put forth a new flavour of symmetric searchable encryption (SSE):Partitioned SSE is meant to capture the cases where the search rights must be partitioned among multiple individuals. We motivate through compelling examples the practical need for such a notion and discuss instantiations based on functional encryption and trapdoor permutations. First we leverage the power of functional encryption (FE). Our construction follows the general technique of encrypting the set of keywords and the presumably larger datafiles separately, a keyword acting as a “pointer” to datafiles it belongs to. To improve on the constraint factors (large ciphertext, slow encryption/decryption procedures) that are inherent in FE schemes, the keyword check is done with the help of a Bloom filter – one per datafile: the crux idea is to split the filter into buckets, and encrypt each bucket separately under an FE scheme. Functional keys are given for binary masks checking if relevant positions are set to 1 inside the underlying bit-vector of the Bloom filter. The second construction we present achieves forward security and stems from the scheme by Bost in CCS’16. We show that a simple tweak of the original construction gives rise to a scheme supporting updates in the partitioned setting. Moreover, the constructions take into account the possibility that some specific users are malicious while declaring their search results.
DBSCAN Parameter Selection Based on K-NN

In this paper, we introduce a parameter selection for the algorithm DBSCAN based on the \(K-neighborhood\). We change the parameters \(\epsilon \) and \(min\_points\) by a \(K-neighborhood\) (named \(\beta \)), scale, and an \(\alpha \) value. We use the scale parameter to balance the dataset. \(\beta \) is used to select \(min\_points-\epsilon \) and \(\alpha \) to reduce the value of \(min\_points\). We use homogeneity, completeness, and v-measure scores over datasets with balanced and unbalanced clusters to evaluate the performance. We compared our results against ACND and DBSCAN with the original parameter selection. Finally, we use our proposal to detect contour over 3D shapes. Our results show better performance in three of the eight datasets, and a better performance into border detection on 3D shapes.
3rd Solution to WAB Challenge

In this article, we propose an effective product retrieval system pipeline robust to large-scale E-commerce platform product data. Our proposed method consists of three parts: detection, retrieval, and post-processing. We first perform the detection task of precisely retrieving the target product and then using the model based on metric learning to retrieve the corresponding item. To improve the search robustness against noise and misleading bounding boxes, we apply post-processing methods such as weighted box fusion and feature connection. Using the proposed method, we won third place in the Watch and Buy: Multimodal Product Identification Challenge
Single-shot, 360-degree, and high-precision three- dimensional shape measurement for human heads based on digital image correlation and plane mirrors

This paper presents a single-shot, 360-degree, and high-precision three-dimensional shape measurement for human heads based on digital image correlation and plane mirrors, which improves the matching speed of DIC by introducing multiple threads.
An overview on sequentially <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si2.svg"><mml:mrow><mml:mi>g</mml:mi></mml:mrow></mml:math>-quotient maps

In this paper, we propose inverse image characterizations of sequentially g-continuous mappings. We introduce the concept of a sequentially g-quotient map and discuss some of the characteristics of sequentially g-quotient mappings. The most common uses of mappings are presented as functional characterizations of g-sequential spaces as domains and ranges of certain mappings. There is also some discussion of examples to illustrate the research methodologies.
Security in a Complex Non-deterministic World

Exponential change is a counterintuitive concept in the classical world. It's hardly surprising since our cognitive and social organizations typically discount many of the observations and patterns [1] that would alert us to such historical periods of upheaval [2]. The current suite of digital technologies has catalyzed a total upending of the previous distribution channels and management systems for the acquisition, distribution and assimilation of data, information and knowledge [3],[4],[5]. Today the "digital generation" learns, interacts and transacts in ways that are foreign and suspicious to some of the current gatekeepers of traditional value storage implementations. [6]
Bengali Continuous Speech Voice-Based Gender Classification

An end-to-end process achieved by the convenient transformation of any purpose. Throughout this knowledge has been implemented by language advancement. In language, machine advancement happens in two ways one is text script processing, another is speech-based processing. A dual-language processing system is much needed for system development. Thus, Language processing over Bangla language is necessary for modernization because of the sub-continent most of the people use as a spoken language. We propose an identification by feature extraction from audio speech voice which is automatic speech recognition. Our model was proposed by supervised learning on continuous speech extraction over audio speech. Due to the outcome of this analysis, many fraud analyses will be possible. The purpose of model learning had used more than two hundred speakers on a voice synthesizer. All features and selected features were both identified with the frequency of male and female. Based on the different classifier performances the highest one is SVM acquired 98.48% also the nearest second is XG-boost.