Profiling Users for Question Answering Communities via Flow-Based Constrained Co-Embedding Model

In this article, we study the task of user profiling in question answering communities (QACs). Previous user profiling algorithms suffer from a number of defects: they regard users and words as atomic units, leading to the mismatch between them; they are designed for other applications but not for QACs; and some semantic profiling algorithms do not co-embed users and words, leading to making the affinity measurement between them difficult. To improve the profiling performance, we propose a neural Flow-based Constrained Co-embedding Model, abbreviated as FCCM. FCCM jointly co-embeds the vector representations of both users and words in QACs such that the affinities between them can be semantically measured. Specifically, FCCM extends the standard variational auto-encoder model to enforce the inferred embeddings of users and words subject to the voting constraint, i.e., given a question and the users who answer this question in the community, representations of the users whose answers receive more votes are closer to the representations of the words associated with these answers, compared with representations of whose receiving fewer votes. In addition, FCCM integrates normalizing flow into the variational auto-encoder framework to avoid the assumption that the distributions of the embeddings are Gaussian, making the inferred embeddings fit the real distributions of the data better. Experimental results on a Chinese Zhihu question answering dataset demonstrate the effectiveness of our proposed FCCM model for the task of user profiling in QACs.

Book Recommendation System Using Machine Learning

Users can use book recommendation systems to search and select books from a number of options available on the web or elsewhere electronic sources. They give the user a little bit selection of products that fit the description, given a large group of objects and a description of the user needs. Our system will simply provide recommendations. Recommendations are based on previous user activity, such as purchase, habits, reviews, and likes. These systems gain lot of interest. In the proposed system, we have a big problem: when the user buys book, we want to recommend some books that the user can enjoy. Buyers also have a great deal of options when it comes to recommending the best and most appropriate books for them. User development privacy while placing small and minor losses of accuracy. Recommendations. The proposed recommendation system will provide user's ability to view and search the publications and using the Support Vector Machine (SVM), will list the most purchased and top rated books based on the subject name given as input. Keywords: Recommender System, Support Vector Machine (SVM), Machine Learning, Classification etc.

A Comparing Collaborative Filtering and Hybrid Recommender System for E-Commerce

Here we are building an collaborative filtering matrix factorization based hybrid recommender system to recommend movies to users based on the sentiment generated from twitter tweets and other vectors generated by the user in their previous activities. To calculate sentiment data has been collected from twitter using developer APIs and scrapping techniques later these are cleaned, stemming, lemetized and generated sentiment values. These values are merged with the movie data taken and create the main data frame.The traditional approaches like collaborative filtering and content-based filtering have limitations like it requires previous user activities for performing recommendations. To reduce this dependency hybrid is used which combines both collaborative and content based filtering techniques with the sentiment generated above. Keywords: machine learning, natural language processing, movie lens data, root mean square equation, matrix factorization, recommenders system, sentiment analysis

Algorithmic Closeness in Mourning: Vernaculars of the Hashtag #grief on TikTok

This study looks at how mourning is expressed using the hashtag #grief on the social media app TikTok using qualitative content analysis. In a dataset of 100 TikTok videos, this article explores how the TikTok ranking algorithms, which orders content based on previous user engagements, may connect people in mourning across the platform and how these platform-enabled interactions may shape grief expressions. The study shows how grief was narrated on TikTok, which sociotechnical templates (such as duets, stitches, and audios) were incorporated into such expressions, and how these expressions of grief challenged societal mourning norms. This article ends with a discussion about how different subcultural norms on TikTok are linked to the way in which ranking algorithms create social connections across the platform. This study proposes that the “algorithmic closeness” of TikTok users in grief allows them to challenge societal mourning norms in imagined safe spaces, shaped by the algorithmic ranking systems on the platform.

Mixed Assessment of Virtual Serious Games Applied in Architectural and Urban Design Education

The creation and usage of serious games on virtual reality (VR) and/or interactive platforms for the teaching of architecture, construction, urban planning, and other derived areas, such as security and risk prevention, require design processes, studies, and research that lead to further consolidation expansion. In that sense, this paper presents two main aims developed: the improvement of a virtual navigation system through the results of previous user studies and mixed research (quantitative and qualitative) improved based on the user perception for educational and professional uses. The VR system used is based on Unreal Engine programming of the HTC Vive sensor. This study is related to the GAME4City 3.0 and a broader project focused on gamified visualization and its educational uses in architectural and urban projects. The results reflect great interest, good usability, and high motivation for further usage for all types of users. However, an apparent resistance to deepen its use continues to be perceived in academia. Based on the research results, weak points of educational gamified systems have been identified, and the main differences and needs in user profiles’ function. With these data, progress regarding implementing this kind of system at the teaching and professional levels must be pursued.

Predicting the user navigation pattern from web logs using weighted support approach

<p class="Abstract"><span id="docs-internal-guid-f3d644ee-7fff-d3c1-15b5-f75fe28d3e2d"><span>A weblog contains the history of previous user navigation pattern. If the customer accesses any portal of organization website, the log is generated in web server, based on sequence of user transaction. The weblog stored in the web server as unstructured format, it contains both positive and negative responses i.e. successful and unsuccessful responses, identifying the positive and negative response is not useful for identifying user behavior of individual user. Initially the successful response is taken, from that conversion of unstructured log format to structured log format through data preprocessing technique. The process of data preprocessor contains three step process data cleaning, user identification and session identification. The pattern is discovered by preprocessing technique from that user navigation pattern is generated. From that navigation pattern classifier technique is applied, the conversion of sequence pattern to sub sequence pattern by clustering technique. This research is to identify the user navigation pattern from weblog. The Improved Spanning classification algorithm classifies the frequent, infrequent and semi frequent pattern. To identify the optimal webpage using classificatopn algorithm from thet user behavior is identified.</span></span></p>

Distributed Architecture to Enhance Systems Protection against Unauthorized Activity via USB Devices

Cyberattacks exploiting Universal Serial Bus (USB) interfaces may have a high impact on individual and corporate systems. The BadUSB is an attack where a USB device’s firmware is spoofed and, once mounted, allows attackers to execute a set of malicious actions in a target system. The countermeasures against this type of attack can be grouped into two strategies: phyiscal blocking of USB ports and software blocking. This paper proposes a distributed architecture that uses software blocking to enhance system protection against BadUSB attacks. This architecture is composed of multiple agents and external databases, and it is designed for personal or corporate computers using Microsoft Windows Operating System. When a USB device is connected, the agent inspects the device, provides filtered information about its functionality and presents a threat assessment to the user, based on all previous user choices stored in external databases. By providing valuable information to the user, and also threat assessments from multiple users, the proposed distributed architecture improves system protection.

Dialogue state tracking accuracy improvement by distinguishing slot-value pairs and dialogue behaviour

<div><p>Dialog state tracking (DST) plays a critical role in cycle life of a task-oriented dialogue system. DST represents the goals of the consumer at each step by dialogue and describes such objectives as a conceptual structure comprising slot-value pairs and dialogue actions that specifically improve the performance and effectiveness of dialogue systems. DST faces several challenges: diversity of linguistics, dynamic social context and the dissemination of the state of dialogue over candidate values both in slot values and in dialogue acts determined in ontology. In many turns during the dialogue, users indirectly refer to the previous utterances, and that produce a challenge to distinguishing and use of related dialogue history, Recent methods used and popular for that are ineffective. In this paper, we propose a dialogue historical context self-Attention framework for DST that recognizes relevant historical context by including previous user utterance beside current user utterances and previous system actions where specific slot-value piers variations and uses that together with weighted system utterance to outperform existing models by recognizing the related context and the relevance of a system utterance. For the evaluation of the proposed model the WoZ dataset was used. The implementation was attempted with the prior user utterance as a dialogue encoder and second by the additional score combined with all the candidate slot-value pairs in the context of previous user utterances and current utterances. The proposed model obtained 0.8 per cent better results than all state-of-the-art methods in the combined precision of the target, but this is not the turnaround challenge for the submission.</p></div>

Surveying the Perception of the Environmental Advantages of an Adaptable Product

The aim of adaptable design is to create products that can easily adapt to different needs. The objective if this study is to analyze the effectivenes in communication to promote an adaptable baby stroller, in order to know the user perception of the advantages derived from its adaptability, as well as the environmental ones, and if there is correlation between them. It is also intended to determine whether age or previous experience with this type of product can influence this perception. To this effect, a study with 54 participants has been conducted. Results show that users percieve the advantages and find the adaptable design interesting. Valuation of the advantages of the product is affected by previous user experience with the need for adaptability. Valuation of the environmental benefits is independent from the degree of experiense, as well as from the age of the participants (between 30 and 45 years old).

Robust Heading Estimation for Indoor Pedestrian Navigation Using Unconstrained Smartphones

Heading estimation using inertial sensors built-in smartphones has been considered as a central problem for indoor pedestrian navigation. For practical daily lives, it is necessary for heading estimation to allow an unconstrained use of smartphones, which means the varying device carrying positions and orientations. As a result, three special human body motion states, namely, random hand movements, carrying position transitions, and user turns, are introduced. However, most existing heading estimation approaches neglect the three motion states, which may render large estimation errors. We propose a robust heading estimation system adapting to the unconstrained use of smartphones. A novel detection and classification method is developed to detect the three motion states timely and discriminate them accurately. For normal working, the user heading is estimated by a PCA-based approach. If a user turn occurs, it is estimated by adding horizontal heading change to previous user heading directly. If one of the other two motion states occurs, it is obtained by averaging estimation results of the adjacent normal walking steps. Finally, an outlier filtering algorithm is developed to smooth the estimation results. Experimental results show that our approach is capable of handling the unconstrained situation of smartphones and outperforms previous approaches in terms of accuracy and applicability.