Mr. DLib: Recommendations-as-a-Service (RaaS) for Academia

Only few digital libraries and reference managers offer recommender systems, although such systems could assist users facing information overload. In this paper, we introduce Mr. DLib's recommendations-as-a-service, which allows third parties to easily integrate a recommender system into their products. We explain the recommender approaches implemented in Mr. DLib (content-based filtering among others), and present details on 57 million recommendations, which Mr. DLib delivered to its partner GESIS Sowiport. Finally, we outline our plans for future development, including integration into JabRef, establishing a living lab, and providing personalized recommendations.Comment: Accepted for publication at the JCDL conference 201

Into the Fire!

I don’t know what the future holds for me. I don’t know where I‘m headed, what I will do, or even what I want out of this life. My life, much like my artwork, is highly influenced by my setting and by those surrounding me. I am learning how to let plans “fail� in order for other opportunities to succeed, and to let go of knowing what is to come in the future. This body of artwork is a practice in relinquishing control over the outcome, and accepting the influence of my environment and my past. Before these objects were pulled from the groundhog kiln at Luck’s Ware in Seagrove, NC, I had very little idea of what they would look like. Using a new clay body, a new studio practice, and an untested slip recipe for surface decoration, I am letting go of the expectation of knowing what is to come

mrsh-mem: Approximate Matching on Raw Memory Dumps

This paper presents the fusion of two subdomains of digital forensics: (1) raw memory analysis and (2) approximate matching. Specifically, this paper describes a prototype implementation named MRSH-MEM that allows to compare hard drive images as well as memory dumps and therefore can answer the question if a particular program (installed on a hard drive) is currently running / loaded in memory. To answer this question, we only require both dumps or access to a public repository which provides the binaries to be tested. For our prototype, we modified an existing approximate matching algorithm named MRSH-NET and combined it with approxis, an approximate disassembler. Recent literature claims that approximate matching techniques are slow and hardly applicable to the field of memory forensics. Especially legitimate changes to executables in memory caused by the loader itself prevent the application of current bytewise approximate matching techniques. Our approach lowers the impact of modified code in memory and shows a good computational performance. During our experiments, we show how an investigator can leverage meaningful insights by combining data gained from a hard disk image and raw memory dumps with a practicability runtime performance. Lastly, our current implementation will be integrable into the volatility memory forensics framework and we introduce new possibilities for providing data driven cross validation functions. Our current proof of concept implementation supports Linux based raw memory dumps

A Fuzzy Hashing Approach Based on Random Sequences and Hamming Distance

Hash functions are well-known methods in computer science to map arbitrary large input to bit strings of a fixed length that serve as unique input identifier/fingerprints. A key property of cryptographic hash functions is that even if only one bit of the input is changed the output behaves pseudo randomly and therefore similar files cannot be identified. However, in the area of computer forensics it is also necessary to find similar files (e.g. different versions of a file), wherefore we need a similarity preserving hash function also called fuzzy hash function. In this paper we present a new approach for fuzzy hashing called bbHash. It is based on the idea to ‘rebuild’ an input as good as possible using a fixed set of randomly chosen byte sequences called building blocks of byte length l (e.g. l= 128 ). The proceeding is as follows: slide through the input byte-by-byte, read out the current input byte sequence of length l , and compute the Hamming distances of all building blocks against the current input byte sequence. Each building block with Hamming distance smaller than a certain threshold contributes the file’s bbHash. We discuss (dis- )advantages of our bbHash to further fuzzy hash approaches. A key property of bbHash is that it is the first fuzzy hashing approach based on a comparison to external data structures. Keywords: Fuzzy hashing, similarity preserving hash function, similarity digests, Hamming distance, computer forensics

Automated Evaluation of Approximate Matching Algorithms on Real Data

Bytewise approximate matching is a relatively new area within digital forensics, but its importance is growing quickly as practitioners are looking for fast methods to screen and analyze the increasing amounts of data in forensic investigations. The essential idea is to complement the use of cryptographic hash functions to detect data objects with bytewise identical representation with the capability to find objects with bytewise similarrepresentations. Unlike cryptographic hash functions, which have been studied and tested for a long time, approximate matching ones are still in their early development stages and evaluation methodology is still evolving. Broadly, prior approaches have used either a human in the loop to manually evaluate the goodness of similarity matches on real world data, or controlled (pseudo-random) data to perform automated evaluation. This work\u27s contribution is to introduce automated approximate matching evaluation on real data by relating approximate matching results to the longest common substring (LCS). Specifically, we introduce a computationally efficient LCS approximation and use it to obtain ground truth on the t5 set. Using the results, we evaluate three existing approximate matching schemes relative to LCS and analyze their performance

Private politics daily: What makes firms the target of internet/media criticism? An empirical investigation of firm, industry, and institutional factors

Private politics refers to situations in which activists or NGOs try to push firms to conform to social standards (regarding, for instance, human rights and environmental protection) without public policy intervention. The existing literature on private politics has focused on large campaigns such as consumer boycotts, and looked at the impact of those boycotts on firms' financial performance and on the likelihood that firms comply with activist demands. Even though these large campaigns are important, focusing on them leads to neglecting the fact that a large portion of the time and resources that activists consecrate to private politics is used to monitor firms and criticize them through Internet posting and media statements, rather than to launch high profile campaigns. Little is known, however, about what drives these activists when they criticize companies, why they target certain companies and not others, and whether this criticism should be considered as a primary step in the production of full-fledged campaigns. In this paper, we fill this gap by exploring a unique international database of CSR-based criticisms against Fortune 500 companies for the 2006-2009 period. This database allows us to look at the impact of a broad range of factors including industry differences, country/institutional differences and firm-specific dimensions, on the likelihood that a certain firm will be targeted by activist critique. Results indicate that criticism is driven by strategic intents. Similar to previous literature, large and visible firms in certain industries are more targeted than others. In addition, these firms also tend to come from countries with strong institutions and high standards of living