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Flow The Psychology Of Optimal Experience

In this review, we consider the potential functional role of beta-band oscillations, which at present is not yet well understood. We discuss evidence from recent studies on top-down mechanisms involved in cognitive processing, on the motor system and on the pathophysiology of movement disorders that suggest a unifying hypothesis: beta-band activity seems related to the maintenance of the current sensorimotor or cognitive state. We hypothesize that beta oscillations and/or coupling in the beta-band are expressed more strongly if the maintenance of the status quo is intended or predicted, than if a change is expected. Moreover, we suggest that pathological enhancement of beta-band activity is likely to result in an abnormal persistence of the status quo and a deterioration of flexible behavioural and cognitive control.

Beta-band oscillations--signalling the status quo?

Learning Deconvolution Network for Semantic Segmentation

Learning and then recognizing a route, whether travelled during the day or at night, in clear or inclement weather, and in summer or winter is a challenging task for state of the art algorithms in computer vision and robotics. In this paper, we present a new approach to visual navigation under changing conditions dubbed SeqSLAM. Instead of calculating the single location most likely given a current image, our approach calculates the best candidate matching location within every local navigation sequence. Localization is then achieved by recognizing coherent sequences of these “local best matches”. This approach removes the need for global matching performance by the vision front-end - instead it must only pick the best match within any short sequence of images. The approach is applicable over environment changes that render traditional feature-based techniques ineffective. Using two car-mounted camera datasets we demonstrate the effectiveness of the algorithm and compare it to one of the most successful feature-based SLAM algorithms, FAB-MAP. The perceptual change in the datasets is extreme; repeated traverses through environments during the day and then in the middle of the night, at times separated by months or years and in opposite seasons, and in clear weather and extremely heavy rain. While the feature-based method fails, the sequence-based algorithm is able to match trajectory segments at 100% precision with recall rates of up to 60%.

SeqSLAM : visual route-based navigation for sunny summer days and stormy winter nights

The proposed social media crisis mapping platform for natural disasters uses locations from gazetteer, street map, and volunteered geographic information (VGI) sources for areas at risk of disaster and matches them to geoparsed real-time tweet data streams. The authors use statistical analysis to generate real-time crisis maps. Geoparsing results are benchmarked against existing published work and evaluated across multilingual datasets. Two case studies compare five-day tweet crisis maps to official post-event impact assessment from the US National Geospatial Agency (NGA), compiled from verified satellite and aerial imagery sources.

/pdf/real-time-crisis-mapping-of-natural-disasters-using-social-2u5pi6fpjf.pdf

Real-Time Crisis Mapping of Natural Disasters Using Social Media

Entries in microblogging sites are very short. For example, a 'tweet' (a post or status update on the popular microblogging site Twit- ter) can contain at most 140 characters. To comply with this restric- tion, users frequently use abbreviations to express their thoughts, thus producing sentences that are often poorly structured or ungrammatical. As a result, it becomes a challenge to come up with methods for au- tomatically identifying named entities (names of persons, organizations, locations etc.). In this study, we use a four-step approach to automatic named entity recognition from microposts. First, we do some preprocess- ing of the micropost (e.g. replace abbreviations with actual words). Then we use an off-the-shelf part-of-speech tagger to tag the nouns. Next, we use the Google Search API to retrieve sentences containing the tagged nouns. Finally, we run a standard Named Entity Recognizer (NER) on the retrieved sentences. The tagged nouns are returned along with the tags assigned by the NER. This simple approach, using readily available components, yields promising results on standard benchmark data.

/pdf/named-entity-recognition-from-tweets-34rwaq73ch.pdf

Named Entity Recognition from Tweets.

Modern research in Steganography focuses on increasing the capacity without keeping any perceptible signature into the stego-media. In this work, we present a pixel value difference (PVD) based high capacity methodology. Embedding is not restricted only to high contrast pixel pairs. Low contrast pairs are also considered to enhance the capacity. Embedding process varies depending on the local contrast of the pair. Secret message is encrypted before embedding and pixels pairs are chosen in a non-sequential manner, rendering the methodology more secure. Experiment is performed on more than two hundred images. It is observed that embedding by proposed technique is visually imperceptible and can withstand various types of attacks. The benchmark test like StirMark analysis also indicates the strength of the work. Comparison of performance with a number of other methods shows that proposed methodology maintains high PSNR with enhanced capacity.

A PVD based high capacity steganography algorithm with embedding in non-sequential position

Steganography is a very useful technique which aims at preventing loss of privacy during the process of data communication, especially over the internet. It can involve different forms of media like image, video (i.e., image sequence), audio etc. We propose a novel steganographic approach in spatial domain using pixel value differencing (PVD) or sample value differencing (SVD) technique and Galois field (GF (28)) operations in order to provide a two layered security for hiding message bits. Our method not only has a very high embedding capacity, but is also capable of withstanding statistical attacks. The proposed method embeds from 2 to 6 bits of the message per pixel in each image component, whereas it can embed a minimum of 6 bits and a maximum of 13 bits of message per sample in audio component at the expense of no perceivable distortion and loss of the cover media quality.

Sanjoy Kumar Saha

Papers

Named Entity Recognition from Tweets.

A PVD based high capacity steganography algorithm with embedding in non-sequential position

An efficient multi-bit steganography algorithm in spatial domain with two-layer security

Detection of loop closure in SLAM: A DeconvNet based approach

Two-point FFT-based high capacity image steganography using calendar based message encoding