Storage capabilities of a 4-junction single electron trap with an on-chip resistor

We report on the operation of a single electron trap comprising a chain of four Al/AlOx/Al tunnel junctions attached, at one side, to a memory island and, at the other side, to a miniature on-chip Cr resistor R=50 kOhm which served to suppress cotunneling. At appropriate voltage bias the bi-stable states of the trap, with the charges differing by the elementary charge e, were realized. At low temperature, spontaneous switching between these states was found to be infrequent. For instance, at T=70 mK the system was capable of holding an electron for more than 2 hours, this time being limited by the time of the measurement.Comment: 3 pages of text and 2 figure

Superconducting Electrometer Based on the Resistively Shunted Bloch Transistor

We have fabricated the Bloch transistor shunted on-chip by a small-sized Cr resistor with Rs about 1 kOhm. The Bloch transistor normally consists of two small Josephson junctions connected in series, which in our case have been replaced by two superconducting interferometer loops, each with two junctions in parallel. A capacitively coupled gate is supplied to control the induced charge of the small intermediate electrode (island) of the transistor. The measured I-V curves show no hysteresis and correspond to the operation of a effective Josephson junction at the high-damping and strong-noise limits. The critical current of the system was found to be close to its nominal value, that is in accordance with the electromagnetic environment theory. The I-V curves were modulated by the gate with a period of e and a maximum swing of about 2 /mu_V. Such rather moderate modulation results from the Josephson-to- charging energies ratio, Ej/Ec about 9, in our sample being far from its optimum value of 0.3 up to 1.Comment: To be published in IEEE Transactions on Applied Superconductivity, June 199

A Peer-Based Approach on Analyzing Hacked Twitter Accounts

Social media has become an important part of the lives of their hundreds of millions of users. Hackers make use of the large target audience by sending malicious content, often by hijacking existing accounts. This phenomenon has caused widespread research on how to detect hacked accounts, where different approaches exist. This work sets out to analyze the possibilities of including the reactions of hacked Twitter accounts’ peers into a detection system. Based on a dataset of six million tweets crawled from Twitter over the course of two years, we select a subset of tweets in which users react to alleged hacks of other accounts. We then gather and analyze the responses to those messages to reconstruct the conversations made. A quantitative analysis of these conversations shows that 30% of the users that are allegedly being hacked reply to the accusations, suggesting that these users acknowledge that their account was hacked

Evaluating Recommender Systems: Survey and Framework

The comprehensive evaluation of the performance of a recommender system is a complex endeavor: many facets need to be considered in configuring an adequate and effective evaluation setting. Such facets include, for instance, defining the specific goals of the evaluation, choosing an evaluation method, underlying data, and suitable evaluation metrics. In this paper, we consolidate and systematically organize this dispersed knowledge on recommender systems evaluation. We introduce the “Framework for EValuating Recommender systems” (FEVR) that we derive from the discourse on recommender systems evaluation. In FEVR, we categorize the evaluation space of recommender systems evaluation. We postulate that the comprehensive evaluation of a recommender system frequently requires considering multiple facets and perspectives in the evaluation. The FEVR framework provides a structured foundation to adopt adequate evaluation configurations that encompass this required multi-facettedness and provides the basis to advance in the field. We outline and discuss the challenges of a comprehensive evaluation of recommender systems, and provide an outlook on what we need to embrace and do to move forward as a research community

Support the Underground: Characteristics of Beyond-Mainstream Music Listeners

Music recommender systems have become an integral part of music streaming services such as Spotify and Last.fm to assist users navigating the extensive music collections offered by them. However, while music listeners interested in mainstream music are traditionally served well by music recommender systems, users interested in music beyond the mainstream (i.e., non-popular music) rarely receive relevant recommendations. In this paper, we study the characteristics of beyond-mainstream music and music listeners and analyze to what extent these characteristics impact the quality of music recommendations provided. Therefore, we create a novel dataset consisting of Last.fm listening histories of several thousand beyond-mainstream music listeners, which we enrich with additional metadata describing music tracks and music listeners. Our analysis of this dataset shows four subgroups within the group of beyond-mainstream music listeners that differ not only with respect to their preferred music but also with their demographic characteristics. Furthermore, we evaluate the quality of music recommendations that these subgroups are provided with four different recommendation algorithms where we find significant differences between the groups. Specifically, our results show a positive correlation between a subgroup's openness towards music listened to by members of other subgroups and recommendation accuracy. We believe that our findings provide valuable insights for developing improved user models and recommendation approaches to better serve beyond-mainstream music listeners.Comment: Accepted for publication in EPJ Data Science - link to published version will be adde