The Role of the Judiciary in the Protection of Human Rights and Development: A Middle Eastern Perspective

One of the vital ways to keep human rights safe is by preserving the prevailing role of the judiciary. Standards developed by the judiciary have a significant beneficial effect of making the lives of people better and the accomplishment of the government\u27s goals easier. In addition, these standards may ensure a better understanding of the relationship between the people and their government, on the one hand, and among the members of the international community, on the other. Moreover, major countries, such as the United States, have a great responsibility, by virtue of their international weight and technological advancement, to help promote human rights. The major countries should play an active role, especially in situations where the people of the Middle East are willing to contribute to the development of human rights and to show that they have serious intentions to spread peace and stability

An authorization policy management framework for dynamic medical data sharing

In this paper, we propose a novel feature reduction approach to group words hierarchically into clusters which can then be used as new features for document classification. Initially, each word constitutes a cluster. We calculate the mutual confidence between any two different words. The pair of clusters containing the two words with the highest mutual confidence are combined into a new cluster. This process of merging is iterated until all the mutual confidences between the un-processed pair of words are smaller than a predefined threshold or only one cluster exists. In this way, a hierarchy of word clusters is obtained. The user can decide the clusters, from a certain level, to be used as new features for document classification. Experimental results have shown that our method can perform better than other methods.<br /

Ratliff-Rush Filtrations associated with ideals and modules over a Noetherian ring

Let $R$ be a commutative Noetherian ring, $M$ a finitely generated $R$-module and $I$ a proper ideal of $R$. In this paper we introduce and analyze some properties of $r(I, M)=\bigcup_{k\geqslant 1} (I^{k+1}M: I^kM)$, {\it the Ratliff-Rush ideal associated with $I$ and $M$}. When $M= R$ (or more generally when $M$ is projective) then $r(I, M)= \widetilde{I}$, the usual Ratliff-Rush ideal associated with $I$. If $I$ is a regular ideal and \ann M=0 we show that $\{r(I^n,M) \}_{n\geqslant 0}$ is a stable $I$-filtration. If M_{\p} is free for all {\p}\in \spec R\setminus \mspec R, then under mild condition on $R$ we show that for a regular ideal $I$, $\ell(r(I,M)/{\widetilde I})$ is finite. Further $r(I,M)=\widetilde I$ if A^*(I)\cap \mspec R =\emptyset (here $A^*(I)$ is the stable value of the sequence \Ass (R/{I^n})). Our generalization also helps to better understand the usual Ratliff-Rush filtration. When $I$ is a regular \m-primary ideal our techniques yield an easily computable bound for $k$ such that $\widetilde{I^n} = (I^{n+k} \colon I^k)$ for all $n \geqslant 1$. For any ideal $I$ we show that \widetilde{I^nM}=I^nM+H^0_I(M)\quad\mbox{for all} n\gg 0. This yields that $\widetilde {\mathcal R}(I,M)=\bigoplus_{n\geqslant 0} \widetilde {I^nM}$ is Noetherian if and only if \depth M>0. Surprisingly if $\dim M=1$ then $\widetilde G_I(M)=\bigoplus_{n\geqslant 0} \widetilde{I^nM}/{\widetilde{I^{n+1}M}}$ is always a Noetherian and a Cohen-Macaulay $G_I(R)$-module. Application to Hilbert coefficients is also discussed.Comment: 27 pages. Many minor revisions made, including little changes in title and abstract. Five additional refernces added. To appear in Journal of algebr

A personality aware recommendation system

Les systèmes de recommandation conversationnels (CRSs) sont des systèmes qui fournissent des recommandations personnalisées par le biais d’une session de dialogue en langage naturel avec les utilisateurs. Contrairement aux systèmes de recommandation traditionnels qui ne prennent comme vérité de base que les préférences anciennes des utilisateurs, les CRS impliquent aussi les préférences actuelles des utilisateurs durant la conversation. Des recherches récentes montrent que la compréhension de la signification contextuelle des préférences des utilisateurs et des dialogues peut améliorer de manière significative les performances du système de recommandation. Des chercheurs ont également montré un lien fort entre les traits de personnalité des utilisateurs et les systèmes de recommandation. La personnalité et les préférences sont des variables essentielles en sciences sociales. Elles décrivent les différences entre les personnes, que ce soit au niveau individuel ou collectif. Les approches récentes de recommandation basées sur la personnalité sont des systèmes non conversationnels. Par conséquent, il est extrêmement important de détecter et d’utiliser les traits de personnalité des individus dans les systèmes conversationnels afin d’assurer une performance de recommandation et de dialogue plus personnalisée. Pour ce faire, ce travail propose un système de recommandation conversationnel sensible à la personnalité qui est basé sur des modules qui assurent une session de dialogue et recommandation personnalisée en utilisant les traits de personnalité des utilisateurs. Nous proposons également une nouvelle approche de détection de la personnalité, qui est un modèle de langage spécifique au contexte pour détecter les traits des individus en utilisant leurs données publiées sur les réseaux sociaux. Les résultats montrent que notre système proposé a surpassé les approches existantes dans différentes mesures.A Conversational Recommendation System (CRS) is a system that provides personalized recommendations through a session of natural language dialogue turns with users. Unlike traditional one-shot recommendation systems, which only assume the user’s previous preferences as the ground truth, CRS uses both previous and current user preferences. Recent research shows that understanding the contextual meaning of user preferences and dialogue turns can significantly improve recommendation performance. It also shows a strong link between users’ personality traits and recommendation systems. Personality and preferences are essential variables in computational sociology and social science. They describe the differences between people, both at the individual and collective level. Recent personality-based recommendation approaches are traditional one-shot systems, or “non conversational systems”. Therefore, there is a significant need to detect and employ individuals’ personality traits within the CRS paradigm to ensure a better and more personalized dialogue recommendation performance. Driven by the aforementioned facts, this study proposes a modularized, personality- aware CRS that ensures a personalized dialogue recommendation session using the users’ personality traits. We also propose a novel personality detection approach, which is a context-specific language model for detecting individuals’ personality traits using their social media data. The goal is to create a personality-aware and topic-guided CRS model that performs better than the standard CRS models. Experimental results show that our personality-aware conversation recommendation system has outperformed state-of-the-art approaches in different considered metrics on the topic-guided conversation recommendation dataset

Automatic translation of non-repetitive OpenMP to MPI

Cluster platforms with distributed-memory architectures are becoming increasingly available low-cost solutions for high performance computing. Delivering a productive programming environment that hides the complexity of clusters and allows writing efficient programs is urgently needed. Despite multiple efforts to provide shared memory abstraction, message-passing (MPI) is still the state-of-the-art programming model for distributed-memory architectures. ^ Writing efficient MPI programs is challenging. In contrast, OpenMP is a shared-memory programming model that is known for its programming productivity. Researchers introduced automatic source-to-source translation schemes from OpenMP to MPI so that programmers can use OpenMP while targeting clusters. Those schemes limited their focus on OpenMP programs with repetitive communication patterns (where the analysis of communication can be simplified). This dissertation reduces this limitation and presents a novel OpenMP-to-MPI translation scheme that covers OpenMP programs with both repetitive and non-repetitive communication patterns. We target laboratory-size clusters of ten to hundred nodes (commonly found in research laboratories and small enterprises). ^ With our translation scheme, six non-repetitive and four repetitive OpenMP benchmarks have been efficiently scaled to a cluster of 64 cores. By contrast, the state-of-the-art translator scaled only the four repetitive benchmarks. In addition, our translation scheme was shown to outperform or perform as well as the state-of-the-art translator. We also compare the translation scheme with available hand-coded MPI and Unified Parallel C (UPC) programs

D3 Abwab Pavilion

https://openscholarship.wustl.edu/bcs/1386/thumbnail.jp

New Approach for Market Intelligence Using Artificial and Computational Intelligence

Small and medium sized retailers are central to the private sector and a vital contributor to economic growth, but often they face enormous challenges in unleashing their full potential. Financial pitfalls, lack of adequate access to markets, and difficulties in exploiting technology have prevented them from achieving optimal productivity. Market Intelligence (MI) is the knowledge extracted from numerous internal and external data sources, aimed at providing a holistic view of the state of the market and influence marketing related decision-making processes in real-time. A related, burgeoning phenomenon and crucial topic in the field of marketing is Artificial Intelligence (AI) that entails fundamental changes to the skillssets marketers require. A vast amount of knowledge is stored in retailers’ point-of-sales databases. The format of this data often makes the knowledge they store hard to access and identify. As a powerful AI technique, Association Rules Mining helps to identify frequently associated patterns stored in large databases to predict customers’ shopping journeys. Consequently, the method has emerged as the key driver of cross-selling and upselling in the retail industry. At the core of this approach is the Market Basket Analysis that captures knowledge from heterogeneous customer shopping patterns and examines the effects of marketing initiatives. Apriori, that enumerates frequent itemsets purchased together (as market baskets), is the central algorithm in the analysis process. Problems occur, as Apriori lacks computational speed and has weaknesses in providing intelligent decision support. With the growth of simultaneous database scans, the computation cost increases and results in dramatically decreasing performance. Moreover, there are shortages in decision support, especially in the methods of finding rarely occurring events and identifying the brand trending popularity before it peaks. As the objective of this research is to find intelligent ways to assist small and medium sized retailers grow with MI strategy, we demonstrate the effects of AI, with algorithms in data preprocessing, market segmentation, and finding market trends. We show with a sales database of a small, local retailer how our Åbo algorithm increases mining performance and intelligence, as well as how it helps to extract valuable marketing insights to assess demand dynamics and product popularity trends. We also show how this results in commercial advantage and tangible return on investment. Additionally, an enhanced normal distribution method assists data pre-processing and helps to explore different types of potential anomalies.Små och medelstora detaljhandlare är centrala aktörer i den privata sektorn och bidrar starkt till den ekonomiska tillväxten, men de möter ofta enorma utmaningar i att uppnå sin fulla potential. Finansiella svårigheter, brist på marknadstillträde och svårigheter att utnyttja teknologi har ofta hindrat dem från att nå optimal produktivitet. Marknadsintelligens (MI) består av kunskap som samlats in från olika interna externa källor av data och som syftar till att erbjuda en helhetssyn av marknadsläget samt möjliggöra beslutsfattande i realtid. Ett relaterat och växande fenomen, samt ett viktigt tema inom marknadsföring är artificiell intelligens (AI) som ställer nya krav på marknadsförarnas färdigheter. Enorma mängder kunskap finns sparade i databaser av transaktioner samlade från detaljhandlarnas försäljningsplatser. Ändå är formatet på dessa data ofta sådant att det inte är lätt att tillgå och utnyttja kunskapen. Som AI-verktyg erbjuder affinitetsanalys en effektiv teknik för att identifiera upprepade mönster som statistiska associationer i data lagrade i stora försäljningsdatabaser. De hittade mönstren kan sedan utnyttjas som regler som förutser kundernas köpbeteende. I detaljhandel har affinitetsanalys blivit en nyckelfaktor bakom kors- och uppförsäljning. Som den centrala metoden i denna process fungerar marknadskorgsanalys som fångar upp kunskap från de heterogena köpbeteendena i data och hjälper till att utreda hur effektiva marknadsföringsplaner är. Apriori, som räknar upp de vanligt förekommande produktkombinationerna som köps tillsammans (marknadskorgen), är den centrala algoritmen i analysprocessen. Trots detta har Apriori brister som algoritm gällande låg beräkningshastighet och svag intelligens. När antalet parallella databassökningar stiger, ökar också beräkningskostnaden, vilket har negativa effekter på prestanda. Dessutom finns det brister i beslutstödet, speciellt gällande metoder att hitta sällan förekommande produktkombinationer, och i att identifiera ökande popularitet av varumärken från trenddata och utnyttja det innan det når sin höjdpunkt. Eftersom målet för denna forskning är att hjälpa små och medelstora detaljhandlare att växa med hjälp av MI-strategier, demonstreras effekter av AI med hjälp av algoritmer i förberedelsen av data, marknadssegmentering och trendanalys. Med hjälp av försäljningsdata från en liten, lokal detaljhandlare visar vi hur Åbo-algoritmen ökar prestanda och intelligens i datautvinningsprocessen och hjälper till att avslöja värdefulla insikter för marknadsföring, framför allt gällande dynamiken i efterfrågan och trender i populariteten av produkterna. Ytterligare visas hur detta resulterar i kommersiella fördelar och konkret avkastning på investering. Dessutom hjälper den utvidgade normalfördelningsmetoden i förberedelsen av data och med att hitta olika slags anomalier

3D SCINTILLATOR DETECTOR QUENCHING CHARACTERIZATION FOR SCANNING PROTON BEAMS

Proton pencil beam scanning is becoming the standard treatment delivery technique for proton therapy centers. Scanned proton pencil beams provide a highly conformal dose distribution. The complex dose distribution poses challenges for quality assurance measurements leading to sophisticated detector setups and time consuming measurements. Fast 3D measurements are therefore desirable to verify the complex dose distribution and to enable the utilization of the full potential of proton therapy. The overall objective of this project is to improve volumetric scintillators detectors to provide 3D measurements for applications for beam commissioning, quality assurance program, and patient-specific treatment delivery verification. Detectors based on volumetric scintillators are gaining interest for use in proton therapy because they promise fast and high-resolution proton beam measurements. However, the scintillators’ response depends on the ionization density of the incident radiation, termed ionization quenching. For protons and other heavy charged particles, the ionization density, which is quantified as the linear energy transfer (LET), varies as a function of depth. Therefore, quenching introduces a non-linear response to the absorbed dose of proton beams. To fully utilize volumetric scintillator detectors for dose verification, ionization quenching correction factors are needed. Previous studies have shown the feasibility of using multiple cameras to image volumetric scintillators for obtaining real-time measurements, and 3D information. Furthermore, ionization quenching correction models based on the widely used Birks’ equation was shown to have lower dose accuracy at the Bragg peak for low-energy beams. The purpose of this study is to accurately determine the ionization quenching correction factors and to characterize a novel 3D scintillator detector for scanned proton beams. The 3D scintillator detector consisted of a liquid scintillator filled tank imaged by three identical sCMOS cameras. The system exhibited a high spatial (0.20 mm) and temporal resolution (10 ms). It was capable of capturing and verifying the range of all the 94 beam energies delivered by the synchrotron with sub-millimeter accuracy. The use of multiple orthogonally positioned cameras allows for detecting the precise locations of delivered beams in 3D. The beam images captured by the detector were synchronized with synchrotron beam delivery trigger signals. The developed image acquisition technique demonstrates the capability of the detector to capture single spots with a reproducible accuracy of 2%. Ionization quenching correction factors were used to correct the response of scintillators for dose linearity. The EDSE scintillation model was explored which relates the scintillation light emission to the energy deposition by secondary electrons. This project explored key improvements necessary for volumetric scintillator-based detector and demonstrated the capabilities of a novel 3D scintillator detector as a potential comprehensive quality assurance tool and for patient treatment verification detector for spot scanning proton therapy