Challenges of Multi-Factor Authentication for Securing Advanced IoT (A-IoT) Applications

The unprecedented proliferation of smart devices together with novel communication, computing, and control technologies have paved the way for the Advanced Internet of Things~(A-IoT). This development involves new categories of capable devices, such as high-end wearables, smart vehicles, and consumer drones aiming to enable efficient and collaborative utilization within the Smart City paradigm. While massive deployments of these objects may enrich people's lives, unauthorized access to the said equipment is potentially dangerous. Hence, highly-secure human authentication mechanisms have to be designed. At the same time, human beings desire comfortable interaction with their owned devices on a daily basis, thus demanding the authentication procedures to be seamless and user-friendly, mindful of the contemporary urban dynamics. In response to these unique challenges, this work advocates for the adoption of multi-factor authentication for A-IoT, such that multiple heterogeneous methods - both well-established and emerging - are combined intelligently to grant or deny access reliably. We thus discuss the pros and cons of various solutions as well as introduce tools to combine the authentication factors, with an emphasis on challenging Smart City environments. We finally outline the open questions to shape future research efforts in this emerging field.Comment: 7 pages, 4 figures, 2 tables. The work has been accepted for publication in IEEE Network, 2019. Copyright may be transferred without notice, after which this version may no longer be accessibl

Globalization and corporate growth opportunities in the European ice hockey markets

Examining the growing tendency of North American ice hockey players seeking professional careers in Finland, this study analyses globalization and possible corporate growth opportunities in European ice hockey markets. While assessing the effects on Finnish leagues and the players' professional paths, the study investigates the causes that are driving this migration,such as career sustainability, cultural interaction, and competitive chances. By combining qualitative and quantitative analyses, the study emphasizes how North American imports improve the calibre and exposure of Finnish ice hockey, encourage league operations to be more innovative, and impact fan interaction tactics. Additionally, by establishing player-centric initiatives, strengthening transatlantic alliances, and generating new income sources, the report highlights important chances for companies like PlayHockeyInEurope to capitalize on this trend. The study provides practical insights for influencing the future of hockey in Finland by placing this phenomenon within larger patterns of sports globalization, with possibility of growth for both players and the Finnish League “Liiga”

Enhanced electrical properties of ferroelectric thin films by ultraviolet radiation

Published versio

gSASRec: Reducing Overconfidence in Sequential Recommendation Trained with Negative Sampling

A large catalogue size is one of the central challenges in training recommendation models: a large number of items makes them memory and computationally inefficient to compute scores for all items during training, forcing these models to deploy negative sampling. However, negative sampling increases the proportion of positive interactions in the training data, and therefore models trained with negative sampling tend to overestimate the probabilities of positive interactions a phenomenon we call overconfidence. While the absolute values of the predicted scores or probabilities are not important for the ranking of retrieved recommendations, overconfident models may fail to estimate nuanced differences in the top-ranked items, resulting in degraded performance. In this paper, we show that overconfidence explains why the popular SASRec model underperforms when compared to BERT4Rec. This is contrary to the BERT4Rec authors explanation that the difference in performance is due to the bi-directional attention mechanism. To mitigate overconfidence, we propose a novel Generalised Binary Cross-Entropy Loss function (gBCE) and theoretically prove that it can mitigate overconfidence. We further propose the gSASRec model, an improvement over SASRec that deploys an increased number of negatives and the gBCE loss. We show through detailed experiments on three datasets that gSASRec does not exhibit the overconfidence problem. As a result, gSASRec can outperform BERT4Rec (e.g. +9.47% NDCG on the MovieLens-1M dataset), while requiring less training time (e.g. -73% training time on MovieLens-1M). Moreover, in contrast to BERT4Rec, gSASRec is suitable for large datasets that contain more than 1 million items.Comment: Accepted at ACM RecSys 202

A Systematic Review and Replicability Study of BERT4Rec for Sequential Recommendation

BERT4Rec is an effective model for sequential recommendation based on the Transformer architecture. In the original publication, BERT4Rec claimed superiority over other available sequential recommendation approaches (e.g. SASRec), and it is now frequently being used as a state-of-the art baseline for sequential recommendations. However, not all subsequent publications confirmed this result and proposed other models that were shown to outperform BERT4Rec in effectiveness. In this paper we systematically review all publications that compare BERT4Rec with another popular Transformer-based model, namely SASRec, and show that BERT4Rec results are not consistent within these publications. To understand the reasons behind this inconsistency, we analyse the available implementations of BERT4Rec and show that we fail to reproduce results of the original BERT4Rec publication when using their default configuration parameters. However, we are able to replicate the reported results with the original code if training for a much longer amount of time (up to 30x) compared to the default configuration. We also propose our own implementation of BERT4Rec based on the Hugging Face Transformers library, which we demonstrate replicates the originally reported results on 3 out 4 datasets, while requiring up to 95% less training time to converge. Overall, from our systematic review and detailed experiments, we conclude that BERT4Rec does indeed exhibit state-of-the-art effectiveness for sequential recommendation, but only when trained for a sufficient amount of time. Additionally, we show that our implementation can further benefit from adapting other Transformer architectures that are available in the Hugging Face Transformers library (e.g. using disentangled attention, as provided by DeBERTa, or larger hidden layer size cf. ALBERT).Comment: This paper is accepted at the Reproducibility track of the ACM RecSys '22 conferenc

Effective and Efficient Training for Sequential Recommendation using Recency Sampling

Many modern sequential recommender systems use deep neural networks, which can effectively estimate the relevance of items but require a lot of time to train. Slow training increases expenses, hinders product development timescales and prevents the model from being regularly updated to adapt to changing user preferences. Training such sequential models involves appropriately sampling past user interactions to create a realistic training objective. The existing training objectives have limitations. For instance, next item prediction never uses the beginning of the sequence as a learning target, thereby potentially discarding valuable data. On the other hand, the item masking used by BERT4Rec is only weakly related to the goal of the sequential recommendation; therefore, it requires much more time to obtain an effective model. Hence, we propose a novel Recency-based Sampling of Sequences training objective that addresses both limitations. We apply our method to various recent and state-of-the-art model architectures - such as GRU4Rec, Caser, and SASRec. We show that the models enhanced with our method can achieve performances exceeding or very close to stateof-the-art BERT4Rec, but with much less training time.Comment: This full research paper is accepted at 16th ACM Conference on Recommender Systems (ACM RecSys

Les Chinois à Kiakhta (1728-1917)

Cet article porte sur les activités des Chinois à Kiakhta, ville qui fut pendant longtemps, avec Maimaicheng de l’autre côté de la frontière, la seule porte ouverte au commerce entre la Russie et la Chine. La période étudiée se situe entre 1727 et 1917. L’article insiste sur le fait que la vie des Chinois ainsi que leurs contacts avec les Russes de Kiakhta étaient régulés de façon stricte par des instructions officielles et secrètes émanant du gouvernement chinois. Néanmoins, malgré ces règles et grâce à l’hospitalité des Russes, les Chinois se sentaient chez eux à Troitskosavsk et à Kiakhta. Non seulement ils y commerçaient mais ils y rendaient visite à leurs amis russes à l’occasion de fêtes ou juste pour passer un moment avec eux. Il est symptomatique que dans les moments difficiles, notamment en cas de catastrophes naturelles, l’entraide entre Russes et Chinois ait fonctionné à Kiakhta. L’article porte plus particulièrement sur la période postérieure à la signature des traités chinois des années 1858-1860, quand Kiakhta perdit son importance et que le commerce russo-chinois se fit tout le long de la frontière séparant les deux empires. Il montre que ce fut là un tournant inattendu pour les marchands russes, qu’ils fussent de Kiakhta ou non, puisqu’ils se virent contraints d’entrer en compétition avec les Chinois, cette fois à l’intérieur même de leur pays. Pour conclure, on dira que Kiakhta a joué un rôle extrêmement positif non seulement dans le commerce russo-chinois mais aussi dans les relations culturelles entre les peuples russes et chinois.This paper considers the vital activities of Chinese in Kiakhta, which, for a long time, together with Maimacheng, served as the only trade gate between Russia and China. It suggests that between 1727-1917, the life of Chinese in Maimacheng, as well as their contacts with Russians in Kiakhta, was strictly regulated by the Chinese government through laws that were unknown to the Russian merchants. Nevertheless these restrictions were not disruptive, and, thanks to Russian hospitality, the Chinese felt at home in Kiakhta and Troitskosavsk. They not only stayed there for a long time for trade, but also went there on visits to Russian friends and to celebrate different festivals. It is significant that at difficult times, for example in struggles with natural disasters, the Chinese and Russians helped each other. The article stresses in particular the period after the signing of the Russian-Chinese treaties in 1858-1860, when Kiakhta lost its exceptional importance, and Russian-Chinese trade was carried out along all perimeters of the border between the two empires. This new trading opportunity was an unexpected turn for Kiakhtian merchants as well as for ordinary traders, who were forced to compete with Chinese “at home.” The article concludes that Kiakhta has played an influential role not only in Russian-Chinese trade, but also in cultural interactions between the Russian and Chinese peoples