Items Of Interest Research Articles

Speech pathology students’ perceived learning outcomes following participation in a fluency booster program

Introduction: Intensive fluency programs are a service delivery option for adults and adolescents who stutter and can act as clinical education opportunities for speech pathology students. Previous research established that student participation in a five day intensive fluency program resulted in increased student confidence, knowledge and interest while decreasing student anxiety. Though effective, this service delivery model is time and resource intensive for students and clients alike. The aim of this study was to investigate students’ levels of confidence, knowledge, interest and anxiety pre- and post-participation in a shorter, two day intensive fluency booster program.Methods: Participants in this study were speech pathology students at an XXXX university. This mixed methods study used pre- and post-program Likert scale surveys to explore students’ perceptions of their learning. In addition, students’ general perceptions of the program were examined through responses to open-ended questions. Quantitative results were analysed using the Wilcoxon Signed Ranks Test and results of the qualitative data were analysed using content analysis.Results: Data was obtained from 68 students. Statistically significant changes (p.001) were observed on all surveyed items of confidence, knowledge, interest and anxiety. In their responses to open-ended questions, students reported that the booster program was well-structured, offered supported learning opportunities and a valued means to interact with clients who stutter. Recommendations for program improvements were noted. Conclusion: Results from this study indicate that a two day fluency booster program may be an appropriate clinical learning opportunity for speech pathology students.

Embarrassment and Shame in People With Parkinson's Disease: A New Tool for Self-Assessment

Shame and embarrassment related to Parkinson's disease (PD) are rarely addressed in clinical practice nor studied in neuroscience research, partly because no specific tool exists to detect them in PD.Objective: To develop a self-applied assessment tool of shame and embarrassment specifically related to PD or its treatment, to promptly identify the presence and severity of these two emotions in PD.Methods: Identification and selection of relevant items were obtained from the collection of PD patients' opinions during support groups and interviews. Several further items were added following a literature review. Subsequently, a two-phase pilot study was performed for identification of ambiguous items and omissions, and to obtain preliminary data on acceptability, reliability, validity and relevance of the new scale (SPARK).Results: A total of 105 PD patients were enrolled in the study. Embarrassment was reported in 85% of patients, while shame was present in 26%. Fifteen percent of patients did not describe any shame or embarrassment. On average, the intensity of these two emotions was low with a marked floor effect in SPARK items and subscales. However, SPARK total score inter-individual variability was important (range 1–84 out of 99). Acceptability and quality of data were satisfactory with no floor or ceiling effects (2.9% each) or missing data. Internal consistency (Cronbach's alpha) was 0.94 for total score and 0.73–0.87 for subscales. The scale correlated ≥0.60 with instruments measuring related constructs. Content validity was satisfactory. SPARK total score strongly correlated with impaired health-related quality of life (rS = 0.81), the propensity to feel embarrassed or ashamed (rS = 0.68 and 0.66, respectively), and anxiety (rS = 0.72) and depression (rS = 0.63) levels. Moderate to high correlations were observed between SPARK total score and apathy (rS = 0.46) and a more pronounced personality trait directed toward harm avoidance (rS = 0.46). No significant differences in SPARK scores were found by sex, education level, PD duration, Hoehn and Yahr stages or PD phenotype.Conclusion: Preliminary analysis of psychometric properties suggests that SPARK could be an acceptable and reliable instrument for assessing shame and embarrassment in PD. SPARK could help healthcare professionals to identify and characterize PD-induced shame and embarrassment.

Selected English-Language Bibliography of Interest for Hungarian Cultural Studies: 2019-2020

As the above title indicates, because of the publication schedule of Hungarian Cultural Studies this bibliography straddles 2019-2020, covering the period since the publication in Fall of 2019 of last year’s bibliography in this journal. Each year’s bibliography may also be supplemented by earlier items, which were retrieved onlyrecently. Although this bibliography series can only concentrate on English-language items, occasional items of particular interest in other languages may be included. For a more extensive bibliography of Hungarian Studies from about 2000 to 2010, for which this is a continuing update, see Louise O. Vasvári, Steven Tötösy de Zepetnek, and Carlo Salzani. “Bibliography for Work in Hungarian Studies as Comparative Central European Studies.” CLCWeb: Comparative Literature and Culture (Library) (2011): http://docs.lib.purdue.edu/clcweblibrary/hungarianstudiesbibliography

Recommendation by Users’ Multimodal Preferences for Smart City Applications

As an essential role in smart city applications, personalized recommender systems help users to find their potentially interested items from their historically generated data. Recently, researchers have started to utilize the massive user-generated multimodal contents to improve recommendation performance. However, previous methods have at least one of the following drawbacks: 1) employing shallow models, which cannot well capture high-level conceptual information; 2) failing to capture personalized user visual preference. In this article, we present a deep users’ multimodal preferences-based recommendation (UMPR) method to capture the textual and visual matching of users and items for recommendation. We extract textual matching from historical reviews. We construct users’ visual preference embeddings to model users’ visual preference and match them with items’ visual embeddings to obtain the visual matching. We apply UMPR on two applications related to smart city: restaurant recommendation and product recommendation. Experiments show that UMPR outperforms competitive baseline methods.

HUPM-MUO: High Utility Pattern Mining under Multiple Utility Objectives

Mining the pattern of interesting items plays a significant role in data analysis and decision-making strategies of real-time applications. Often the term 'interest' in pattern discovery denotes the frequency of the pattern. In recent research, the domain of data mining is considering the utility of the item instead of frequency, which indicates often profit. This manuscript argues that neither utility nor frequency of the itemset alone influence the target objective. Moreover, the profit is not only the utility factor of the itemset, apart from profit, the objectives like storage, saleability and other domain specific requirements can also be the utility factors. In regard to this argument, the manuscript endeavoured to define a novel model that discovers the top-k high utility patterns under multiple utility objectives (HUPM-MUO). The experimental study was carried on various datasets, which portray the performance advantage of the proposed model over the other contemporary models.

Querying Social Media to Track People’s Movements and Discussions Via a Browser Interface

Social networking is micro-blogging site where users post short text-based messages. These messages often relate to a particular topic, event or product and are frequently coupled with the authors’ opinion. This information provides an exciting opportunity for individuals and companies who might benefit from being able to collate and analyze the data to discover opinion on an item of interest. This paper reports on the outcome of a project that aims to produce a web page that queries the social Web (specifically Twitter.com and Foursquare.com.) through the available APIs to track people’s movements and the topic of their discussions. Tweets will be the main data source and queries results will be ranked and sorted out by total frequency in a Web accessible MySQL database. The result shows that the system has been successful in achieving the set aims.

Some Items of Interest to Process R&D Chemists and Engineers

Some Items of Interest to Process R&D Chemists and Engineers

Instance Genetic Selection for Fuzzy Rule-based Systems Optimization to Opinion Classification

Opinion Mining aims to identify people feelings about some item of interest based on content available on the Web, without the user having to find and read all the news about it. As opinions are related to feelings that are often described by imprecise terms, Fuzzy Systems appear as an alternative to treat the information subjectivity. An important task in developing Fuzzy Systems is define the rule base usually based on textual databases in case of opinion mining. However, these databases are extensive and the algorithms used to generate the rule base often result in many rules, making it difficult to achieve accuracy with a low computational cost. To deal with this problem, instance selection can be applied to reduce databases in order to save only relevant data. Thus, the aim of the present study is optimize a Fuzzy System, using instance selection to generate a reduced rule base. As the issue mentioned is a multiobjective problem, which seeks to increase accuracy and reduce the number of rules, we have chosen to apply a Multiobjective Genetic Algorithm, since it has been acknowledged as a promising approach in the literature. The results demonstrate that the Multiobjective Genetic Algorithms can be applied in instance selection for opinion classification problems, presenting a reduction in the number of instances and execution time, without significant changes in accuracy.

Personalised recommendation algorithm based on covariance

Recommender systems are of great significance in predicting the potential interesting items based on the target user's historical selections. However, the recommendation list for a specific user has been found changing vastly when the system changes. So far, a variety of personalised recommendation algorithms have been proposed and most of them are based on similarities, such as collaborative filtering and mass diffusion. Most of the algorithms aimed at recommendation's accuracy. Here, the authors proposed a new Cov (covariance) recommendation method based on correlation coefficients, which can provide precision of recommendation. First, this method can express the positive and negative correlation among random samples without knowing the distribution of items. Then by limiting the popular items in recommendation list, an improved method is proposed, CovH, which can improve the diversity and novelty in recommendation compared with Cov. By applying the two methods on realistic recommendation datasets, such as Movie-Lens, Netflix The authors found that comparing with baseline methods, these two algorithms can not only ensure decent accuracy but also have good performance on diversity and novelty indeed.

A Study of Deep Learning-Based Approaches for Session-Based Recommendation Systems

Recommending relevant items of interest for a user is the main purpose of the recommendation system. In the past, those systems achieve the recommended list based on long-term user profiles. However, personal data privacy is becoming a big challenge recently. Thus, the recommendation system needs to reduce the dependence on user profiles while preserving high accuracy on the recommendation. Session-based recommendation is a recently proposed approach for the recommendation system to overcome the issue of user profiles dependency. The relevance of the problem is quite high and has triggered interest among researchers in observing the activities of users. It increased several proposals for session-based recommendation algorithms that aim to predict the next actions. In this paper, we would like to compare the performance of such algorithms by using various datasets and evaluation metrics. A deep learning approach named GRU4REC (Hidasi et al. in Session-based recommendations with recurrent neural networks, 2015) and simpler methods are included in our comparison. Real-world datasets from three different domains are included in our experiment. Our experiments reveal that in some cases of numerous unpopular items dataset, GRU4REC’s performance is lower than expected. However, its performance is significantly increased after applying our proposed sampling method. Therefore, our obtained results suggested that there is still room for improving deep learning session-based recommendation algorithms.

Recurrent convolutional networks for session-based recommendations

Traditional recommender systems help users find items of interest by modeling long-term user profiles, which consist of items the users interacted with in the past. In many real-life applications, however, informative user profiles are not available. Instead, the system recommends by relying on the current user activities within an ongoing session, leading to the emergence of session-based recommendation methods. Among techniques used in such situations, recurrent neural networks (RNNs) present a natural choice thanks to their ability to model the order of session events and capture long-term dependencies. Recently, methods based on convolutional neural networks (CNNs) have also shown their potential in modeling session data, especially in extracting complex local patterns that are predictive of the user target. In this work, we propose a recurrent convolutional architecture that takes the advantages of both complex local features extracted by CNNs and long-term dependencies learned by RNNs from session sequences. Our model has two main layers: the lower layer consists of convolutional filters applied over consecutive session event embeddings, and the upper layer is a gated recurrent unit (GRU) RNN that takes as input the CNN’s output. This hybrid approach provides a flexible and unified network architecture for modeling various important features of session sequences. Experiments conducted on three benchmark datasets demonstrate the superiority of the proposed model over pure RNNs and CNNs models as well as state-of-the-art session-based recommendation methods.

MOTIVASI MASYARAKAT KOTA MAKASSAR MELAKUKAN TRANSAKSI MELALUI E-COMMERCE

This study aims to see the motivation of the Makassar people to transact through e-commerce. The transaction model is relatively new in society, but has become a trend of its own and is so desirable in almost every circle of society. This research is a descriptive qualitative field research conducted in Makassar using a normative theological, phenomenological and Islamic economic approach. The results of this study indicate that transaction motivation through E-commerce consists of rational motivation, that is motivation that occurs due to careful consideration of consumers before deciding to transact and emotional motivation, namely the spontaneous motivation to emerge when seeing unique or interesting items, as well as in conventional transactions. The difference is that in an e-commerce transaction, the community will get value added or added value in the form of or excellent services because of the various facilities available in e-commerce so that the community will feel pampered in the transaction.

Rorty as a Legitimate Member of the Pragmatist Family

The paper will focus on Rorty’s project as it emerges in the compelling introductions to his books from Philosophy and the Mirror of Nature onwards. Even disagreeing with his conclusions, some interesting items suggest that Rorty was a legitimate member of the pragmatist family and that he shared with classic pragmatists much more than a reader can see at first glance. First, Rorty understood better than anyone else that the fight against Kant’s rationalism is crucial to pragmatism considered as a whole. He paralleled Kantism with the history of explaining knowledge as a mirroring representation and he wanted to get rid of it. Second, Rorty shared with classic pragmatists what I call the “synthetic drive” of philosophy, namely, the view of philosophy as the habit of action of looking at anything in a way that makes it “hang together with everything else.” Third, the outcome of the synthetic drive is conceiving philosophy as a transformative activity. In all these cases, Rorty took roads at odds with classic pragmatism but he did not betray the general aim and spirit of the American movement of thought.

Some Items of Interest to Process R&D Chemists and Engineers

Some Items of Interest to Process R&D Chemists and Engineers

Effect of incomplete sampling description in DNA reports on bloodstain pattern analysis and reconstruction of a crime scene.

DNA analysts in forensic laboratories are engaged in analysing and sampling bloodstains from bloodstained items. Detailed and precise descriptions of bloodstains on items of interest are very important for bloodstain pattern analysis (BPA). DNA and BPA reports were examined from forensic laboratories in Serbia (N = 88). About 400 reports were observed from the past three years. First, we analysed descriptions of items (clothing and shoes) in DNA reports, and special attention was paid to descriptions of bloodstains. Subsequently, we estimated the value of descriptions of bloodstained items of interest in linking specific types of bloodstains to the obtained DNA profiles. Observed descriptions of bloodstained items in DNA reports are usually limited to phrases. A major problem exists in cases where several people were injured in the same bloodshed event. Connecting specific types of bloodstains to obtained DNA profiles is essential for the reconstruction of crime events. The complete analysis should therefore include detailed descriptions of all types of observed and sampled bloodstains. In DNA laboratories that are within a larger institute, it would be more appropriate and productive if BPA and DNA experts examined bloodstained items cooperatively. Moderately sized laboratories have a limited number of employees. So, in those DNA laboratories, it would be more appropriate to educate DNA analysts in the basic principles of BPA.

TO THE QUESTION OF THE ESATBLISHENT AND FUNCTIONING OF THE MUSEUM OF THE KYIV SCIENTIFIC RESEARCH INSTITUTE OF FORENSIC EXPERTISE OF THE MINISTRY OF JUSTICE OF UKRAINE

The article deals with the history of the museum from the year 1913 up to 2020. The premises and the exposition of the museum are described. The history of the museum begins with the history of the Kyiv Institute, since the first museum was established under the Cabinet and the Cabinet under the Prosecutor of the Kyiv Judicial Chamber. The museum housed seized items that were material evidence in criminal cases. During the Second World War and after it, the Kyiv Institute did not initially have its premises. For a long time, the institute had problems with the premises, until the question of the dissolution of the institute was raised due to the inability to provide it with the proper premises Up to its one hundred jubilee celebration, the museum has to be restored once again. Particularly, this is the merit of Oleksandr Ruvin, the Director of the Kyiv Institute, the ideological organizer and leader who decided to create the museum and Dytro Hamov,the exemployee of the Kyiv Institute, whose efforts created the exposition.The exhibits for the Museum of the Kyiv Institute have been collected for decades, kept by the staff of the Institute and passed from one expert unit to another. Some of the exhibits were donated by colleagues. Part of the exhibits was selected within the Institute from the other departments (information-reference fund, archive, etc.), part was donated to the museum free of charge by individuals and organizations. The exhibits of the museum are unsuitable for use on a previously scheduled destination, and those left out of the civil circulation, items of scientific interest. In general, it should be noted that the dream of many generations of employees of the Institute came true, the Kyiv Institute operates its own forensic museum. However, the exposition also presents other types of forensics. The exhibits of the museum tell about the glorious past of the Kyiv Institute and set up an optimistic view of its future. The museum exhibits also eloquently confirm that one should not abandon the history of the establishment of the institute until the advent of Soviet rule and the onset of 1923, when the institute was “founded” once again, as this history has taken place and imprinted on the continued existence of both science and expert practice, not only the Kyiv Institute, but all forensic and expert institutions.

Some Items of Interest to Process R&D Chemists and Engineers

Some Items of Interest to Process R&D Chemists and Engineers

Some Items of Interest to Process R&D Chemists and Engineers

Some Items of Interest to Process R&D Chemists and Engineers

A new approach to identify similar users based on customer reviews

Recommender System suggests items of interest to users based on their preferences. These preferences are gauged through various sources such as purchase history, ratings, reviews and browsing behaviour. Collaborative filtering and content based filtering are the two widely used techniques that help in generating recommendations to the target user(s) by identifying similar users to target user or similar items to items of interest. Through this paper a new method to identify similar users based on the similarity of reviews has been proposed.

Interesting Items, Not Necessarily Important, for the Amusement of Our Readers

Interesting Items, Not Necessarily Important, for the Amusement of Our Readers