Anomaly Detection for Soft Security in Cloud Based Auditing of Accounting Systems

Postprin

Revisiting the Tag Relevance Prediction Problem

Traditionally, recommender systems provide a list of suggestions to a user based on past interactions with items of this user. These recommendations are usually based on user preferences for items and generated with a delay. Critiquing recommender systems allow users to provide immediate feedback to recommendations with tags and receive a new set of recommendations in response. However, these systems often require rich item descriptions that contain relevance scores indicating the strength, with which a tag applies to an item. For example, this relevance score could indicate how violent the movie "The Godfather" is on a scale from 0 to 1. Retrieving these data is a very demanding process, as it requires users to explicitly indicate the degree to which a tag applies to an item. This process can be improved with machine learning methods that predict tag relevance. In this paper, we explore the dataset from a different study, where the authors collected relevance scores on movie-tag pairs. In particular, we define the tag relevance prediction problem, explore the inconsistency of relevance scores provided by users as a challenge of this problem and present a method, which outperforms the state-of-the-art method for predicting tag relevance. We found a moderate inconsistency of user relevance scores. We also found that users tend to disagree more on subjective tags, such as "good acting", "bad plot" or "quotable" than on objective tags, such as "animation", "cars" or "wedding", but the disagreement of users regarding objective tags is also moderate.Peer reviewe

Towards quantifiable boundaries for elastic horizontal scaling of microservices

One of the most useful features of a microservices architecture is its versatility to scale horizontally. However, not all services scale in or out uniformly. The performance of an application composed of microservices depends largely on a suitable combination of replica count and resource capacity. In practice, this implies limitations to the efficiency of autoscalers which often overscale based on an isolated consideration of single service metrics. Consequently, application providers pay more than necessary despite zero gain in overall performance. Solving this issue requires an application-specific determination of scaling limits due to the general infeasibility of an application-agnostic solution. In this paper, we study microservices scalability, the auto-scaling of containers as microservice implementations and the relation between the number of replicas and the resulting application task performance. We contribute a replica count determination solution with a mathematical approach. Furthermore, we offer a calibration software tool which places scalability boundaries into declarative composition descriptions of applications ready to be consumed by cloud platforms

A design framework for wireless sensor networks

Wireless sensor networks (sensornets) are wirelessly communicating smart gadgets with the capability of sensing the environment. With the immense applicability of sensornets, there is an increasing need of a general organisational and architectural development framework for sensornet systems. This paper outlines an abstract framework for modelling responsibilities and tasks to sets of nodes according to their vocation. These guidelines are presented with the intension to ease reasoning about a sensornet as a system, and its applications.1st IFIP International Conference on Ad-Hoc NetWorkingRed de Universidades con Carreras en Informática (RedUNCI

The Tag Genome Dataset for Books

Attaching tags to items, such as books or movies, is found in many online systems. While a majority of these systems use binary tags, continuous item-tag relevance scores, such as those in tag genome, offer richer descriptions of item content. For example, tag genome for movies assigns the tag "gangster" to the movie "The Godfather (1972)" with a score of 0.93 on a scale of 0 to 1. Tag genome has received considerable attention in recommender systems research and has been used in a wide variety of studies, from investigating the effects of recommender systems on users to generating ideas for movies that appeal to certain user groups.In this paper, we present tag genome for books, a dataset containing book-tag relevance scores, where a significant number of tags overlap with those from tag genome for movies. To generate our dataset, we designed a survey based on popular books and tags from the Goodreads dataset. In our survey, we asked users to provide ratings for how well tags applied to books. We generated book-tag relevance scores based on user ratings along with features from the Goodreads dataset. In addition to being used to create book recommender systems, tag genome for books can be combined with the tag genome for movies to tackle cross-domain problems, such as recommending books based on movie preferences.Peer reviewe

Rating consistency is consistently underrated : An exploratory analysis of movie-tag rating inconsistency

Publisher Copyright: © 2022 ACM.Content-based and hybrid recommender systems rely on item-tag ratings to make recommendations. An example of an item-tag rating is the degree to which the tag "comedy"applies to the movie "Back to the Future (1985)". Ratings are often generated by human annotators who can be inconsistent with one another. However, many recommender systems take item-tag ratings at face value, assuming them all to be equally valid. In this paper, we investigate the inconsistency of item-tag ratings together with contextual factors that could affect consistency in the movie domain. We conducted semi-structured interviews to identify potential reasons for rating inconsistency. Next, we used these reasons to design a survey, which we ran on Amazon Mechanical Turk. We collected 6,070 ratings from 665 annotators across 142 movies and 80 tags. Our analysis shows that ∼45% of ratings are inconsistent with the mode rating for a given movie-tag pair. We found that the single most important factor for rating inconsistency is the annotator's perceived ease of rating, suggesting that annotators are at least tacitly aware of the quality of their own ratings. We also found that subjective tags (e.g. "funny", "boring") are more inconsistent than objective tags (e.g. "robots", "aliens"), and are associated with lower tag familiarity and lower perceived ease of rating.Peer reviewe

Trustworthy context dependency in ubiquitous systems

The modern society is getting increasingly dependent on software applications. These run on processors, use memory and account for controlling functionalities that are often taken for granted. Typically, applications adjust the functionality in response to a certain context that is provided or derived from the informal environment with various qualities. To rigorously model the dependence of an application on a context, the details of the context are abstracted and the environment is assumed stable and fixed. However, in a context-aware ubiquitous computing environment populated by autonomous agents, a context and its quality parameters may change at any time. This raises the need to derive the current context and its qualities at runtime. It also implies that a context is never certain and may be subjective, issues captured by the context’s quality parameter of experience-based trustworthiness. Given this, the research question of this thesis is: In what logical topology and by what means may context provided by autonomous agents be derived and formally modelled to serve the context-awareness requirements of an application? This research question also stipulates that the context derivation needs to incorporate the quality of the context. In this thesis, we focus on the quality of context parameter of trustworthiness based on experiences having a level of certainty and referral experiences, thus making trustworthiness reputation based. Hence, in this thesis we seek a basis on which to reason and analyse the inherently inaccurate context derived by autonomous agents populating a ubiquitous computing environment in order to formally model context-awareness. More specifically, the contribution of this thesis is threefold: (i) we propose a logical topology of context derivation and a method of calculating its trustworthiness, (ii) we provide a general model for storing experiences and (iii) we formalise the dependence between the logical topology of context derivation and its experience-based trustworthiness. These contributions enable abstraction of a context and its quality parameters to a Boolean decision at runtime that may be formally reasoned with. We employ the Action Systems framework for modelling this. The thesis is a compendium of the author’s scientific papers, which are republished in Part II. Part I introduces the field of research by providing the mending elements for the thesis to be a coherent introduction for addressing the research question. In Part I we also review a significant body of related literature in order to better illustrate our contributions to the research field.Dagens samhälle är i allt högre grad beroende av programvara. Exekverbar programvara, kallat applikationer, körs av processorer, använder minne och svarar för kontroll och reglage av funktionalitet som ofta tas för given. Typiskt för en applikation är att den justerar funktionaliteten i respons till en viss situation. En sådan situation präglas av ett antal kontext. Varje kontext i sin tur förses eller härleds från inexakta givare, vilka gestaltar något informellt fenomen med varierande kvaliteter. För att modellera en programvaras beroende av en situation bör dess kontext inexakthet approximeras. Detta förutsätter abstraktion och antaganden av omgivningen vilket följaktligen möjliggör rigorös modellering. Rigorös matematisk modellering förlitar sig dessvärre på atomisitet, dvs. en kontext uppdatering är förutsägbar. Rimligen är detta inte fallet för en funktionalitet med autonomt verksamma aktörer i ubikvitär datateknik, t.ex. på grund av mobilitet. Därför är den gällande kontexten och dess kvaliteter i vilken programvaran exekverar aldrig säker och kan vara subjektiv, vilka utgör ämnen för en kontexts kvalitetsparameter tillförlitlighet. I denna avhandling, undersöks nivån på en kontexts kvalitetsparameter tillförlitlighet samt dess härledning i syfte att ge en klarare presentation av omgivningen åt programvaran. Tillförlitlighetsparametern identifierar en aktörs förväntningar på en kontext samt dess övriga kvalitetsparametrar. Nivån av tillförlitlighet fastställs av den kontext beroende aktören. Därmed fångar tillförlitlighet in eventuella fördomar och förväntningar samt är subjektiv givet ett kontext utfärdat av en aktör. Av detta följer behovet att behandla nivån av tillförlitlighetens (o)säkerhet. Givet detta utformas forskningsfrågan som: I vilken logisk topologi samt hur kan kontext utfärdat av autonoma källor härledas och modelleras formellt för att möta med en kontext medveten applikations krav? Mer specifikt redogör denna avhandling för problemställningar gällande härledning av inexakt data i syftet att användas ändamålsenligt i programvara. I avhandlingen framställs en logisk topologi för kontext härledning, presenteras en generell modell för lagring av erfarenheter samt modelleras beronedeskap formellt inom Aktion System ramverket. Som en följd av detta studerar avhandlingen på vilket sätt det går att ändamålsenligt modellera och beräkna osäker information att presenteras åt en agent som är beroende av den vid körtid. Avhandlingen motiverar tagna beslut genom referenser till relaterad forskning. Tekniskt sett är avhandlingen ett kompendium av vetenskapliga artiklar där skribenten medverkat, vilka är återpublicerade i Del II. Utöver introduktion av forsknings området i Del I, förser denna del nödvändiga element för att avhandlingen kunde förstås som en sammanhängande helhet, inklusive definition av en kontexts härledningstopologi som ett polyträd