Chiron: A Robust Recommendation System with Graph Regularizer

Recommendation systems have been widely used by commercial service providers for giving suggestions to users. Collaborative filtering (CF) systems, one of the most popular recommendation systems, utilize the history of behaviors of the aggregate user-base to provide individual recommendations and are effective when almost all users faithfully express their opinions. However, they are vulnerable to malicious users biasing their inputs in order to change the overall ratings of a specific group of items. CF systems largely fall into two categories - neighborhood-based and (matrix) factorization-based - and the presence of adversarial input can influence recommendations in both categories, leading to instabilities in estimation and prediction. Although the robustness of different collaborative filtering algorithms has been extensively studied, designing an efficient system that is immune to manipulation remains a significant challenge. In this work we propose a novel "hybrid" recommendation system with an adaptive graph-based user/item similarity-regularization - "Chiron". Chiron ties the performance benefits of dimensionality reduction (through factorization) with the advantage of neighborhood clustering (through regularization). We demonstrate, using extensive comparative experiments, that Chiron is resistant to manipulation by large and lethal attacks

When Anonymous Controlling Professional Media: A Marginal Voice in Press Freedom Country

The emergence of citizen journalism get a skeptical response from professional journalists based on several reasons such as un-institutional, subjective and nonprofessional (O¨rnebring, 2013; Allan, 2009; Moyo, 2009). This study explores how mainstream media play dominant role in producing fact by excluding citizen journalist apart from their system. The object of the study is ‘Discourse’ about the banned of a controversial article1 written by an anonymous2 citizen journalist named Jilbab Hitam (here in after referred to as the ‘JH’)3 in kompasiana.com4. The issues widespread quickly in cyberspace produce pros cons among internet user including professional journalists, NGO, etc. This research employs Critical Discourse Analysis (CDA) on articles and twitter conversations relevant to the issue. The results of the study show how anonymity becomes dominant Discourse submerging other important issue such us media manipulation and media corruption. Negative representation of anonymity – hoax, liar, provocative – might tend to hamper struggling of internet user freedom of expression

More than a Million Ways to Be Pushed: A High-Fidelity Experimental Dataset of Planar Pushing

Pushing is a motion primitive useful to handle objects that are too large, too heavy, or too cluttered to be grasped. It is at the core of much of robotic manipulation, in particular when physical interaction is involved. It seems reasonable then to wish for robots to understand how pushed objects move. In reality, however, robots often rely on approximations which yield models that are computable, but also restricted and inaccurate. Just how close are those models? How reasonable are the assumptions they are based on? To help answer these questions, and to get a better experimental understanding of pushing, we present a comprehensive and high-fidelity dataset of planar pushing experiments. The dataset contains timestamped poses of a circular pusher and a pushed object, as well as forces at the interaction.We vary the push interaction in 6 dimensions: surface material, shape of the pushed object, contact position, pushing direction, pushing speed, and pushing acceleration. An industrial robot automates the data capturing along precisely controlled position-velocity-acceleration trajectories of the pusher, which give dense samples of positions and forces of uniform quality. We finish the paper by characterizing the variability of friction, and evaluating the most common assumptions and simplifications made by models of frictional pushing in robotics.Comment: 8 pages, 10 figure

Analysis of adversarial attacks against CNN-based image forgery detectors

With the ubiquitous diffusion of social networks, images are becoming a dominant and powerful communication channel. Not surprisingly, they are also increasingly subject to manipulations aimed at distorting information and spreading fake news. In recent years, the scientific community has devoted major efforts to contrast this menace, and many image forgery detectors have been proposed. Currently, due to the success of deep learning in many multimedia processing tasks, there is high interest towards CNN-based detectors, and early results are already very promising. Recent studies in computer vision, however, have shown CNNs to be highly vulnerable to adversarial attacks, small perturbations of the input data which drive the network towards erroneous classification. In this paper we analyze the vulnerability of CNN-based image forensics methods to adversarial attacks, considering several detectors and several types of attack, and testing performance on a wide range of common manipulations, both easily and hardly detectable

Chronic opioid pretreatment potentiates the sensitization of fear learning by trauma.

Despite the large comorbidity between PTSD and opioid use disorders, as well as the common treatment of physical injuries resulting from trauma with opioids, the ability of opioid treatments to subsequently modify PTSD-related behavior has not been well studied. Using the stress-enhanced fear learning (SEFL) model for PTSD, we characterized the impact of chronic opioid regimens on the sensitization of fear learning seen following traumatic stress in mice. We demonstrate for the first time that chronic opioid pretreatment is able to robustly augment associative fear learning. Highlighting aversive learning as the cognitive process mediating this behavioral outcome, these changes were observed after a considerable period of drug cessation, generalized to learning about multiple aversive stimuli, were not due to changes in stimulus sensitivity or basal anxiety, and correlated with a marker of synaptic plasticity within the basolateral amygdala. Additionally, these changes were not observed when opioids were given after the traumatic event. Moreover, we found that neither reducing the frequency of opioid administration nor bidirectional manipulation of acute withdrawal impacted the subsequent enhancement in fear learning seen. Given the fundamental role of associative fear learning in the generation and progression of PTSD, these findings are of direct translational relevance to the comorbidity between opioid dependence and PTSD, and they are also pertinent to the use of opioids for treating pain resulting from traumas involving physical injuries

Improving retention for all students, studying mathematics as part of their chosen qualification, by using a voluntary diagnostic quiz

This case study demonstrates the issues and advantages in encouraging students to take responsibility for their learning and to be better prepared both in terms of knowledge and expectations for their study. The study outlines the improvement in retention achieved when students were encouraged to use a voluntary diagnostic quiz on a first year university mathematics module. Initially the power of the diagnostic quiz, in predicting future success on the module, was identified using predictive analytics. Students were contacted by experienced Education Guidance staff who encouraged them to take the quiz prior to course start with the aim of using their results to steer them to start on the “right” course. The diagnostic quiz total score was made available to the student’s course tutor prior to course start to enable further tailoring of support to individual students. Early indications show an improvement in early module retention. The module in this case study was for distance learning students on an open access mathematics course