Session-based Recommendation with Graph Neural Networks

The problem of session-based recommendation aims to predict user actions based on anonymous sessions. Previous methods model a session as a sequence and estimate user representations besides item representations to make recommendations. Though achieved promising results, they are insufficient to obtain accurate user vectors in sessions and neglect complex transitions of items. To obtain accurate item embedding and take complex transitions of items into account, we propose a novel method, i.e. Session-based Recommendation with Graph Neural Networks, SR-GNN for brevity. In the proposed method, session sequences are modeled as graph-structured data. Based on the session graph, GNN can capture complex transitions of items, which are difficult to be revealed by previous conventional sequential methods. Each session is then represented as the composition of the global preference and the current interest of that session using an attention network. Extensive experiments conducted on two real datasets show that SR-GNN evidently outperforms the state-of-the-art session-based recommendation methods consistently.Comment: 9 pages, 4 figures, accepted by AAAI Conference on Artificial Intelligence (AAAI-19

Recurrent Neural Networks with Top-k Gains for Session-based Recommendations

RNNs have been shown to be excellent models for sequential data and in particular for data that is generated by users in an session-based manner. The use of RNNs provides impressive performance benefits over classical methods in session-based recommendations. In this work we introduce novel ranking loss functions tailored to RNNs in the recommendation setting. The improved performance of these losses over alternatives, along with further tricks and refinements described in this work, allow for an overall improvement of up to 35% in terms of MRR and Recall@20 over previous session-based RNN solutions and up to 53% over classical collaborative filtering approaches. Unlike data augmentation-based improvements, our method does not increase training times significantly. We further demonstrate the performance gain of the RNN over baselines in an online A/B test.Comment: CIKM'18, authors' versio

The LEP Trail to Non-Perturbative QCD

This talk summarizes the presentations given in the Hadronic Physics session at the LEPTRE meeting, emphasizing the importance of LEP data in our quest for a successful approach to non-perturbative QCD.Comment: 6 pages, Latex, epsfig. Summary talk of the Hadronic Physics session of the LEPTRE meeting. Rome, April 2001. To be published in the Proceeding

Comparison of thoracic and lumbar erector spinae muscle activation before and after a golf practice session

Lower back pain is commonly associated with golfers. The study aimed: to determine whether thoracic- and lumbar-erector-spinae muscle display signs of muscular fatigue after completing a golf practice session, and to examine the effect of the completed practice session on club head speed, ball speed and absolute carry distance performance variables. Fourteen right-handed male golfers participated in the laboratory-based-study. Surface electromyography (EMG) data was collected from the lead and trail sides of the thoracic- and lumbar-erector-spinae muscle. Normalized root mean squared (RMS) EMG activation levels and performance variables for the golf swings were compared before and after the session. Fatigue was assessed using median frequency (MDF) and RMS during the maximum voluntary contraction (MVC) performed before and after the session. No significant differences were observed in RMS thoracic- and lumbar-erector-spinae muscle activation levels during the five phases of the golf swing and performance variables before and after the session (p > .05). Significant changes were displayed in MDF and RMS in the lead lower lumbar and all trail regions of the erector-spinae muscle when comparing the MVC performed before and after the session (p < .05). Fatigue was evident in the trail side of the erector-spinae muscle after the session

Cross Validation Of Neural Network Applications For Automatic New Topic Identification

There are recent studies in the literature on automatic topic-shift identification in Web search engine user sessions; however most of this work applied their topic-shift identification algorithms on data logs from a single search engine. The purpose of this study is to provide the cross-validation of an artificial neural network application to automatically identify topic changes in a web search engine user session by using data logs of different search engines for training and testing the neural network. Sample data logs from the Norwegian search engine FAST (currently owned by Overture) and Excite are used in this study. Findings of this study suggest that it could be possible to identify topic shifts and continuations successfully on a particular search engine user session using neural networks that are trained on a different search engine data log

TONI BLANK: A CASE STUDY OF THE LANGUAGE OF A SCHIZOPHRENE

Schizophrenia is one of the chronic mental disorders. Patients of schizophrenia cannot communicate with others properly. Also, they cannot produce good utterances syntactically and semantically. This is caused by their language dysfunction. In this research, I am interested in analyzing language dysfunction in schizophrenia. I choose Toni Blank‟s utterances in “Toni Blank Show Session One” as the object of my research. I focus on how schizophrenic‟s language dysfunctions are classified and how these dysfunctions are being analyzed using linguistic framework. To analyze the data, I used Thought, Language, and Communication (TLC) scale and cohesion coherence frameworks. The purpose of this study is to give linguistic analysis about phenomena of language dysfunctions uttered by Toni Blank in “Toni Blank Show Session One”. The data used in this research are utterances which contain language dysfunctions from three episodes in “Toni Blank Show Session One”, entitled “Valentine Day”, “Teroris”, and “Sehat Ala Mas Toni”. I used purposive sampling to collect the data. In analyzing the data, I used Padan and Agih methods by Sudaryanto (1993). To interpret the data, I used cohesion and coherence framework. In 26 utterances which contain schizophrenic‟s language dysfunctions in “Toni Blank Show Session One”, I find that the language dysfunctions which are uttered by Toni are poverty of content, tangentiality, loss of goal, circumstantiality, illogicality, incoherence (word salad), neologism, clanging, echolalia, and self-reference. Poverty of speech, pressure of speech, distractibility, derailment, stilted speech, perseveration, and blocking are not found in the data. Keywords: schizophrenia, language dysfunction, Toni Blan

Tracking Users across the Web via TLS Session Resumption

User tracking on the Internet can come in various forms, e.g., via cookies or by fingerprinting web browsers. A technique that got less attention so far is user tracking based on TLS and specifically based on the TLS session resumption mechanism. To the best of our knowledge, we are the first that investigate the applicability of TLS session resumption for user tracking. For that, we evaluated the configuration of 48 popular browsers and one million of the most popular websites. Moreover, we present a so-called prolongation attack, which allows extending the tracking period beyond the lifetime of the session resumption mechanism. To show that under the observed browser configurations tracking via TLS session resumptions is feasible, we also looked into DNS data to understand the longest consecutive tracking period for a user by a particular website. Our results indicate that with the standard setting of the session resumption lifetime in many current browsers, the average user can be tracked for up to eight days. With a session resumption lifetime of seven days, as recommended upper limit in the draft for TLS version 1.3, 65% of all users in our dataset can be tracked permanently.Comment: 11 page