Miracle-dz/UniFAR-FTUs huggingface dataset:
EECE 412 Term Project: A Study on SSL Warning Effectiveness

The Secure Sockets Layer (SSL) protocol employes certificates in order to initiate a secure communication channel between the client PC and a web server. Malicious attackers in order to extract personal-sensitive information from other users may set up environments that aim at luring the users into providing their information thinking that they are using a secure channel of communication. In order for malicious attackers to achieve that, forged certificates must be used in order to initiate a SSL session and trick the user into thinking that he is dealing with a legitimate site. In an effort to fight back such attacks browser developers have created SSL warnings that are presented to the user whenever the server certificate seems to be either misconfigured or not issued by a trusted authority. These warning are designed to give the choice to the user to proceed as there are too many legitimate web sites on the web that have misconfigured certificates. It is observed that users tend to disregard those warnings for various reasons rendering them unable to serve their purpose of protecting the user from malicious phishing attacks. Our term project’s goal is to determine the reasons behind users’ disregard to SSL warnings as well as to conclude on actions that should be taken, in future SSL warning development, in order to make them more effective in protecting users. In order to achieve this we conducted a user study in a controlled laboratory environment where we studied the reactions and reasonings of users when presented with a SSL warning while trying to access critical and non critical information
Physical properties of the ionized gas and brightness distribution in NGC 4736

In this work we study the galaxy NGC 4736, using narrow band interference lters imaging centered at the emission lines (Oii)3727+3729 ,H ,( Oiii)5007, H ,( Sii)6716+6730 and (Siii)9070 and nearby continua. We have obtained sizes, positions, emission line absolute fluxes, and continua intensities for 90 Hii regions, mainly distributed in a ring-like structure of 3.2 kpc in diameter. The H luminosities are in the range 37:3 logLH 39: 4e rg s 1 . T he Hii regions size distribution presents a characteristic diameter D0 = 115 pc and veries the relation log(LH) / D 3 . The temperature of the ioniz- ing sources and the metallicity of the Hii regions are re- spectively in the ranges 3: 41 0 4 <T? < 4: 01 0 4 Ka nd 8 : 5 <12 + log (O=H) < 9:3. The masses of the ionizing clusters are in the range 5 10 3 < MT=M < 21 0 5 . T he continua radial surface brightness distribution is better t- ted by the superposition of a de Vaucouleurs', a thin and a thick exponential disk laws. The monochromatic colors show that outside the star forming ring the disk presents a younger stellar population than inside it.
Signaling eco-certification: Implications for service coproduction and resource efficiency

Purpose – The purpose of this paper is to use an eco-friendly service concept framework to demonstrate the effect of credible eco-certification signaling. Design/methodology/approach – The authors examine a cross-sectional data set consisting of 2,481 hotel sites across the US. The authors measure the performance of the operations component of eco-friendly service by operations-driven resource efficiency (ODF), and the performance of the marketing component by customer-driven resource efficiency (CDF). A series of multivariate regressions compare these two resource efficiency measures between credibly eco-certified hotel sites and others. Findings – The results indicate that credible eco-certifications achieve the signaling effect. Eco-certified hotels outperform others in both ODF and CDF measures; and eco-certified hotels still achieve higher CDF after controlling for ODF. Practical implications – The findings suggest that eco-friendly service design requires not only eco-friendly operations but also a .
Moist Orographic Convection: Physical Mechanisms and Links to Surface-Exchange Processes

This paper reviews the current understanding of moist orographic convection and its regulation by surface-exchange processes. Such convection tends to develop when and where moist instability coincides with sufficient terrain-induced ascent to locally overcome convective inhibition. The terrain-induced ascent can be owing to mechanical (airflow over or around an obstacle) and/or thermal (differential heating over sloping terrain) forcing. For the former, the location of convective initiation depends on the dynamical flow regime. In “unblocked” flows that ascend the barrier, the convection tends to initiate over the windward slopes, while in “blocked” flows that detour around the barrier, the convection tends to initiate upstream and/or downstream of the high terrain where impinging flows split and rejoin, respectively. Processes that destabilize the upstream flow for mechanically forced moist convection include large-scale moistening and ascent, positive surface sensible and latent heat fluxes, and differential advection in baroclinic zones. For thermally forced flows, convective initiation is driven by thermally direct circulations with sharp updrafts over or downwind of the mountain crest (daytime) or foot (nighttime). Along with the larger-scale background flow, local evapotranspiration and transport of moisture, as well as thermodynamic heterogeneities over the complex terrain, regulate moist instability in such events. Longstanding limitations in the quantitative understanding of related processes, including both convective preconditioning and initiation, must be overcome to improve the prediction of this convection, and its collective effects, in weather and climate models.
Simulating Human Associations with Linked Data

In recent years, enormous progress has been made in the field of Artificial Intelligence (AI). Especially the introduction of Deep Learning and end-to-end learning, the availability of large datasets and the necessary computational power in form of specialised hardware allowed researchers to build systems with previously unseen performance in areas such as computer vision, machine translation and machine gaming. In parallel, the Semantic Web and its Linked Data movement have published many interlinked RDF datasets, forming the world’s largest, decentralised and publicly available knowledge base. Despite these scientific successes, all current systems are still narrow AI systems. Each of them is specialised to a specific task and cannot easily be adapted to all other human intelligence tasks, as would be necessary for Artificial General Intelligence (AGI). Furthermore, most of the currently developed systems are not able to learn by making use of freely available knowledge such as provided by the Semantic Web. Autonomous incorporation of new knowledge is however one of the pre-conditions for human-like problem solving. This work provides a small step towards teaching machines such human-like reasoning on freely available knowledge from the Semantic Web. We investigate how human associations, one of the building blocks of our thinking, can be simulated with Linked Data. The two main results of these investigations are a ground truth dataset of semantic associations and a machine learning algorithm that is able to identify patterns for them in huge knowledge bases. The ground truth dataset of semantic associations consists of DBpedia entities that are known to be strongly associated by humans. The dataset is published as RDF and can be used for future research. The developed machine learning algorithm is an evolutionary algorithm that can learn SPARQL queries from a given SPARQL endpoint based on a given list of exemplary source-target entity pairs. The algorithm operates in an end-to-end learning fashion, extracting features in form of graph patterns without the need for human intervention. The learned patterns form a feature space adapted to the given list of examples and can be used to predict target candidates from the SPARQL endpoint for new source nodes. On our semantic association ground truth dataset, our evolutionary graph pattern learner reaches a Recall@10 of > 63 % and an MRR (& MAP) > 43 %, outperforming all baselines. With an achieved Recall@1 of > 34% it even reaches average human top response prediction performance. We also demonstrate how the graph pattern learner can be applied to other interesting areas without modification.
Assessment of plantain (Musa sapientum L.) accessions genotypic groups relatedness using simple sequence repeats markers

Plantains are important sources of high-calorie energy in Ghana. They are also of great socio-economic importance in the country, and very important sources of rural income. Although several species exist all over the world, plantains belonging to the AAB group are unique to West Africa and Ghanaian collections have unique features and peculiar taste. Morphological and biochemical characterization are the popular techniques used to characterize plantain genotypes in Ghana. Thus, there is limited report on molecular characterization of plantains genotypes. Characterization based on morphologic characteristics alone may be limited since the expression of quantitative traits is subjective to strong environmental influence. Alternatively, molecular characterization techniques are capable of identifying polymorphism represented by differences in DNA sequences. The objective of this research was therefore to conduct molecular characterization of Ghanaian local accessions of plantain and assess relationship amongst known genotypic groups (populations). This study sampled 40 accessions of plantains representing four popular genotypic groups. Simple Sequence Repeats (SSRs) were used to assess diversity in reference to a set of global Musa collections. The 40 accessions of plantain were clustered into populations as being French plantain, True Horn, False Horn, and Hybrid prior to analysis. PopGene version 32 was used to analyze the data. This revealed that the overall plantain population used have Shannon’s Informative Index (I) value of 0.61±0.28 in the overall plantain population, 100% polymorphism for all loci, 2.7±0.67 and 1.81±0.45 for ne and na respectively. Average heterozygosity was 0.34±0.17, loci mMaCIR231 and mMaCIR07 were the most informative, having I values of 0.85 and 0.81 respectively. The Fis and Fit values were both negative indicating lack of inbreeding and the gene flow value was 0.533. The study also revealed relationship among the various populations (French plantain, True Horn, False Horn, and Hybrid) on basis of molecular characterization. Key words: Characterization, false horn, french plantain, molecular, PopGene, simple sequence repeats, true horn.
Grok-FPGA: Generating real on-chip knowledge for FPGA fine-grain delays using timing extraction

Circuit variation is one of the biggest problems to overcome if Moore's Law is to continue. It is no longer possible to maintain an abstraction of identical devices without huge yield losses, performance penalties, and energy costs. Current techniques such as margining and grade binning are used to deal with this problem. However, they tend to be conservative, offering limited solutions that will not scale as variation increases. Conventional circuits use limited tests and statistical models to determine the margining and binning required to counteract variation. If the limited tests fail, the whole chip is discarded. On the other hand, reconfigurable circuits, such as FPGAs, can use more fine-grained, aggressive techniques that carefully choose which resources to use in order to mitigate variation. Knowing which resources to use and avoid, however, requires measurement of underlying variation. We present Timing Extraction, a methodology that allows measurement of process variation without expensive testers nor highly invasive techniques, rather, relying only on resources already available on conventional FPGAs. It takes advantage of the fact that we can measure the delay of logic paths between any two registers. Measuring enough paths, provides the information necessary to decompose the delay of each path into individual components-essentially, forming a system of linear equations. Determining which paths to measure requires simple graph transformation algorithms applied to a representation of the FPGA circuit. Ultimately, this process decomposes the FPGA into individual components and identifies which paths to measure for computing the delay of individual components. We apply Timing Extraction to 18 commercially available Altera Cyclone III (65 nm) FPGAs. We measure 22×28 logic clusters and the interconnect within and between cluster. Timing Extraction decomposes this region into 1,356,182 components, classified into 10 categories, requiring 2,736,556 path measurements. With an accuracy of ±3.2 ps, our measurements reveal regional variation on the order of 50 ps, systematic variation from 30 ps to 70 ps, and random variation in the clusters with σ=15 ps and in the interconnect with σ=62 ps. Degree Type Dissertation Degree Name Doctor of Philosophy (PhD) Graduate Group Computer and Information Science First Advisor Andre M. DeHon
Analog and mixed-signal boundary-scan : a guide to the IEEE 1149.4 test standard

List of Figures. List of Contributors. Preface. 1. Introduction to the IEEE 1149.4. 2. The Boundary-Scan Standard. 3. IEEE 1149.4 Architecture and Instruction Set. 4. System Test Methodologies Using IEEE 1149.4. 5. Peripheral Cell Design for IEEE 1149.4. 6. Structural Testing. Summary. Index.
Dijital Kibir: Sosyal Medyadaki Narsistik Ritüellere İlişkin Bir İnceleme

Narsisizm fenomeninin ele alindigi bu calismada toplum, birey, kimlik, benlik gibi kavramlarin dijital uzamdaki degisimleri tartisilmaktadir. Gunumuz dunyasinin geist i olarak gorulen narsisizm epidemisi kuresel olcekte artarken sanal ortam narsisizm kulturunun yayginlasmasinda buyuk olcude etkili olmaktadir. Calismanin anlam orgusunun ilk bolumunu narsisizm olgusunun kadim anlam kodlari ve analitik psikolojideki yeri olustururken diger bolumunde sanal dunyanin narsistik ozellikleri uzerinde durulmaktadir. Narsisizm bireysel olmakla birlikte ayni zamanda kulturel bir olgudur. Bununla birlikte calismada narsisizm kulturunun yayginlasmasi cercevesinde bir veba gibi yayilan kibir kavrami ele alinmaktadir. Narsisizmin yayginlik kazanmasinda buyuk oranda etkili oldugu dusunulen medya ve yeni iletisim teknolojilerinin rolu goz onunde bulundurularak kavrama sosyal medyanin etkisi cercevesinden bakilmaktadir. Calismada sosyal medyanin narsisizm epidemisinin yayilmasinda oynadigi rolu ortaya koymak amaclanmaktadir. Bu dogrultuda dunya genelinde en fazla takipcisi olan uc Instagram hesabi secilmistir. Amacsal orneklem cercevesinde secilen fotograflar gostergebilimsel yontemle analiz edilmekte ve sanal dunyanin narsistik rituellerinin izleri surulmektedir. Calismada, narsisizm baglaminda kendilik tapinci, goruntunun ikoniklesmesi, erotizm ve bedenin sublime edilmesi gibi bulgulara ulasilmaktadir.
BIDDING BEHAVIOR IN A NATURAL EXPERIMENT: TV GAME SHOW "I DON T KNOW, MY SPOUSE KNOWS"

Davranisci finans klasik finans ve ekonomi teorilerinin aksine bulgular ve ekonomik aktorlerin bu normal disi davranislarini aciklamayla ilgilidir. Finansal kararlarin verildigi cesitli ortamlar vardir. Finans piyasalari en populer alandir. Ancak muzayede piyasalari davranisci finans arastirmacilari tarafindan ihmal edilmistir. Daha once Almanya’da “Mein Mann Kann” adiyla, daha sonra bir Turk TV kanali tarafindan adapte edilip “Ben Bilmem Esim Bilir” adiyla yayinlanan yarisma programindaki pey surme davranisi incelenmistir. Dogal bir deney olarak asamali, acik katilimli bir muzayedeyi taklit eden bu yarisma icin bilindigi kadariyla yapilan ilk arastirmadir. Riskten kacma ile ilgili cinsiyet, yas, medeni durum acisindan bir fark gozlenmemistir. Ancak kazanan ve kaybedenler arasinda verdikleri teklif acisindan istatistiki olarak onemli fark vardir ve kayiptan kacmanin bir isareti olarak kabul edilmistir. Onceki arastirmalarin cogunun aksine kadin ve erkeklerin esit risk aldiklari gorulmustur. Sonuclarin hem davranisci finans hem de muzayede literaturune katkida bulunmasi ve bundan sonraki arastirmalara isik tutmasi beklenmektedir.
Differentiation Strategy and Performance of Long-distance Bus Companies in Keny

Safe transport is an essential element to the development of an economy of any country. Transport enables movement of people, services and goods, from one location to another one. In Kenya, majority of the population who travel beyond three hundred (300) kilometres commonly use long distance bus service. Travelling for a long distance in a poorly maintained bus can be quite uncomfortable. Some buses are normally over-packed with hard seats, poor ventilation, overly slow or overly speedy and poor sanitation. Consequently, long distances need the most comfortable travelling medium to at least not get over exhausted. In product differentiation strategy, a firm seeks to be unique in its product offing compared to its competitors. This study sought to contribute to knowledge by assessing the influence of differentiation strategy on performance of long-distance bus companies in Kenya. Porter’s generic strategies are the main model anchoring the study. Cross-Sectional survey was used as research design. The population of the study consists of fifty one (51) registered and licensed long distance bus companies in Kenya. Primary and secondary data were collected by the use of a structured questionnaires and review of regulatory bodies’ websites and availed documents. Correlation and regression analyses were used to test hypotheses. Not all organizational performance determinants were included but balanced score card was appropriately used to reflect non-financial and financial indicators. Results show that safety strategy has the most significant influence on performance of long-distance bus companies in Kenya. This was followed by comfort and reliability respectively. The significance of safety measures when it comes to transporting people is quite critical. The central recommendation that the study offers as impetus to strategic management body of knowledge, transport companies and policy makers is the need to consider safety measures as the most essential differentiating features in transport management in order to win the confidence and loyalty of passengers thus enhance performance. The major limitation of this study is that primary data was gathered from three (3) bus company managers only per company. However, additional secondary data was used to validate primary data hence reduce common bias.
Interprofessional Teamwork for Health and Social Care

List of Boxes, Figures and Tables. The Authors. Series Foreword. Acknowledgements. Glossary. Introduction. 1. Interprofessional teamwork the basics. 2. Current developments affecting interprofessional teamwork. 3. Interprofessional teamwork: key concepts and issues. 4. A conceptual framework for interprofessional teamwork. 5. Using theory to better understand interprofessional teamwork. 6. Interprofessional teamwork interventions. 7. Evaluating interprofessional teamwork. 8. Synthesising studies of interprofessional teamwork. 9. Ways forward. References. Appendices. Index.
ELECTRONICS AND ELECTRICAL ENGINEERING

G. K. Kalognomos, G. E. Boultadakis, A. V. Karakasiliotis, P. V. Frangos Division of Information Transmission Systems and Materials Technology, School of Electrical and Computer Engineering, National Technical University of Athens, 9 Iroon Polytechniou Street, Zografou, GR 15773, Athens, Greece, tel. : +3
A Survey on Last level Cache Partitioning Techniques in Chip Multi-Processors

Chip Multi Processors (CMPs), a new generation of multicore architecture emerged as the base of System on Chip(SoC) paradigm. Multiple processing cores are packed into a single chip here. Each core is capable of executing simple and complex applications in parallel. Memory is being considered as a scarce resource for the application. The multilevel memory hierarchy that involves various levels of caches are being accommodated into the chip in shared/private mode for faster access of data. Of these, the Last Level Cache (LLC) is being shared amongst various processing cores. Multiple applications are accessing the same L2 cache, and thus increases the conflicts in cache entries and this acts as a source of contention. These applications may produce the destructive interference between cores and at the same time occurs cache misses. This will cause a serious effect on the performance and efficiency in terms of energy of the system. In order to mitigate these problems, the shared LLC is being partitioned amongst the applications. Depending on the cache organization, different schemes are used for partitioning that includes an initial placement and replacement policies that helps to develop a good system which will improve the utilization of cache space. This paper presents a study on various cache partitioning schemes used for the LLC in terms of replacement policy, simulating tools and cache design and analyze in term of performance and found that dynamic way based partitioning techniques performs better in terms of energy.
IMPROVED OUTLIER DETECTION USING CLASSIC KNN ALGORITHM

- Outlier detection is used for identification of items, events or observations which do not conform to an expected pattern or other items in dataset. The identification of instances that diverge from the expected behavior is a important task. Existing techniques provides a solution to the problem of anomaly detection in categorical data with a semi supervised setting. The outlier detection approach is based on distance learning for categorical attributes (DILCAs), a distance learning framework was introduced. The key intuition of DILCA is that the distance between the two values of a categorical attribute can be determined by the way, in which they co-occur with the values of other attributes in the data set. Existing techniques work well for ﬁxed-schema data, with low dimensionality. certain applications require privacy preserving publishing of transactional data (or basket data), which involve hundreds or even thousands of dimensions, rendering existing methods unusable. This work proposes novel anonymization methods for sparse high-dimensional data. It is based on approximate Classic K-Nearest Neighbor search in high-dimensional spaces. These representations facilitate the formation of anonymized groups with low information loss, through an efficient linear-time heuristic. Among the proposed techniques, Classic KNN-search yields superior data utility, but incurs higher computational overhead. In addition dimensionality reduction technique is used. In this work healthcare dataset are used