New Sampling Lower Bounds via the Separator

Suppose that a target distribution can be approximately sampled by a low-depth decision tree, or more generally by an efficient cell-probe algorithm. It is shown to be possible to restrict the input to the sampler so that its output distribution is still not too far from the target distribution, and at the same time many output coordinates are almost pairwise independent. This new tool is then used to obtain several new sampling lower bounds and separations, including a separation between AC0 and low-depth decision trees, and a hierarchy theorem for sampling. It is also used to obtain a new proof of the Patrascu-Viola data-structure lower bound for Rank, thereby unifying sampling and data-structure lower bounds

Factors of Success in Beekeeping Development Projects and Their Application to South Africa’s Beekeeping Industry

Nearly every country in the world has its own history of beekeeping. From the Swiss leaf hive to the Kenyan top bar hive, the number of ways to keep bees is practically limitless. Such diversity allows for a unique opportunity in the field of development. Many development projects are denigrated for relying on the knowledge and generosity of “white saviors.” Many beekeeping projects are the brainchildren of well-meaning people in developed countries looking for a charitable outlet and attempting to use their “superior” knowledge to enlighten and improve the lives of those less fortunate. While these intentions may well be good, expertise in and understanding of local communities and cultures are invaluable to any development project

Negative Results in Computer Vision: A Perspective

A negative result is when the outcome of an experiment or a model is not what is expected or when a hypothesis does not hold. Despite being often overlooked in the scientific community, negative results are results and they carry value. While this topic has been extensively discussed in other fields such as social sciences and biosciences, less attention has been paid to it in the computer vision community. The unique characteristics of computer vision, particularly its experimental aspect, call for a special treatment of this matter. In this paper, I will address what makes negative results important, how they should be disseminated and incentivized, and what lessons can be learned from cognitive vision research in this regard. Further, I will discuss issues such as computer vision and human vision interaction, experimental design and statistical hypothesis testing, explanatory versus predictive modeling, performance evaluation, model comparison, as well as computer vision research culture

Dagstuhl News January - December 2011

"Dagstuhl News" is a publication edited especially for the members of the Foundation "Informatikzentrum Schloss Dagstuhl" to thank them for their support. The News give a summary of the scientific work being done in Dagstuhl. Each Dagstuhl Seminar is presented by a small abstract describing the contents and scientific highlights of the seminar as well as the perspectives or challenges of the research topic

Algebraic Geometric Secret Sharing Schemes over Large Fields Are Asymptotically Threshold

In Chen-Cramer Crypto 2006 paper \cite{cc} algebraic geometric secret sharing schemes were proposed such that the "Fundamental Theorem in Information-Theoretically Secure Multiparty Computation" by Ben-Or, Goldwasser and Wigderson \cite{BGW88} and Chaum, Cr\'{e}peau and Damg{\aa}rd \cite{CCD88} can be established over constant-size base finite fields. These algebraic geometric secret sharing schemes defined by a curve of genus $g$ over a constant size finite field ${\bf F}_q$ is quasi-threshold in the following sense, any subset of $u \leq T-1$ players (non qualified) has no information of the secret and any subset of $u \geq T+2g$ players (qualified) can reconstruct the secret. It is natural to ask that how far from the threshold these quasi-threshold secret sharing schemes are? How many subsets of $u \in [T, T+2g-1]$ players can recover the secret or have no information of the secret? In this paper it is proved that almost all subsets of $u \in [T,T+g-1]$ players have no information of the secret and almost all subsets of $u \in [T+g,T+2g-1]$ players can reconstruct the secret when the size $q$ goes to the infinity and the genus satisfies $\lim \frac{g}{\sqrt{q}}=0$. Then algebraic geometric secret sharing schemes over large finite fields are asymptotically threshold in this case. We also analyze the case when the size $q$ of the base field is fixed and the genus goes to the infinity

SecondLook: A Prototype Mobile Phone Intervention for Digital Dating Abuse

Digital dating abuse is a form of interpersonal violence carried out using text messages, emails, and social media sites. It has become a significant mental health crisis among the college-going population, nearly half (43%) of college women who are dating report experiencing violent and abusive dating behaviors. Existing technology and non-technology based intervention programs do not provide assistance at the onset of abuse. The overall goal of this dissertation is to create a mobile phone application that consists of a detection tool that classifies abusive digital content exchanged between partners, an educational component that provides information about healthy relationships, and a list of nearby resources for users to locate help. For the user-interface design of this application, we conducted a focus group study and incorporated the themes generated from the study to create our Android prototype. We used this prototype to conduct a usability study to evaluate the overall user-interface design and the effectiveness of the features we incorporated into the app. Due to the lack of a publicly available dataset that could be used to create training and testing sets for the classifiers to detect abusive vs non-abusive text messages in the context of digital dating abuse, we first created and validated a dataset of abusive text messages. This dissertation describes the dataset creation, validation process and the results of an evaluation of different classification and feature extraction techniques. The combination of linear support vector machine, unigram input and tf-idf feature extractor with an accuracy of 91.6% was the most balanced classifier, classifying abusive and non-abusive text messages equally well. Finally, we conducted a user study to investigate different visualization paradigms that will assist users to trust the feedback regarding the possible abusive nature of their online communication. Three different visualization techniques were evaluated using survey questionnaires to understand which one is the most effective in invoking user trust and encourages them to access resources for help