Reviewerly/exHarmony huggingface dataset:
A SS-CNN on an FPGA for Handwritten Digit Recognition

This paper describes a Super-Skinny Convolutional Neural Network (SS-CNN) and its implementation on a Cyclone IVE field programmable gate array (FPGA), for handwritten digit recognition. This SS-CNN performs state-of-the-art recognition accuracy but with fewer layers and less neurons. Using parameters with 8 bits of precision, the FPGA solutions of this SS-CNN show no recognition accuracy loss when compared to the 32-bit floating point software solution. In addition to high recognition accuracy, both of the proposed FPGA solutions are low power and require little FPGA area. The proposed hardware solutions indicate a 67 to 355 times power savings potential when compared to the software solution. Thus, our SS-CNN provides a high-performance, low computation demands, hardware friendly, and power efficient solution.
Leveraging Affect Transfer Learning for Behavior Prediction in an Intelligent Tutoring System

In this work, we propose a video-based transfer learning approach for predicting problem outcomes of students working with an intelligent tutoring system (ITS). By analyzing a student's face and gestures, our method predicts the outcome of a student answering a problem in an ITS from a video feed. Our work is motivated by the reasoning that the ability to predict such outcomes enables tutoring systems to adjust interventions, such as hints and encouragement, and to ultimately yield improved student learning. We collected a large labeled dataset of student interactions with an intelligent online math tutor consisting of 68 sessions, where 54 individual students solved 2,749 problems. We will release this dataset publicly upon publication of this paper. It will be available at https://www.cs.bu.edu/faculty/betke/research/learning/. Working with this dataset, our transfer-learning challenge was to design a representation in the source domain of pictures obtained “in the wild” for the task of facial expression analysis, and transferring this learned representation to the task of human behavior prediction in the domain of webcam videos of students in a classroom environment. We developed a novel facial affect representation and a user-personalized training scheme that unlocks the potential of this representation. We designed several variants of a recurrent neural network that models the temporal structure of video sequences of students solving math problems. Our final model, named ATL-BP for Affect Transfer Learning for Behavior Prediction, achieves a relative increase in mean F -score of 50 % over the state-of-the-art method on this new dataset.
3D Convolutional Two-Stream Network for Action Recognition in Videos

In recent years, action recognition based on two-stream networks has developed rapidly. However, most existing methods describe incomplete and distorted video content due to cropped and warped frame or clip-level feature extraction. This paper proposed an approach based on deep learning that preserves the complete contextual relation of temporal human actions in videos. The proposed architecture follows the two-stream network with a novel 3D Convolutional Network (ConvNets) and pyramid pooling layer, to design an end-to-end behavioral feature learning method. The 3D ConvNets extract video-level, spatial-temporal features from two input streams, the RGB images and the corresponding optical flow. The multi-scale pyramid pooling layer fixed the generated feature maps into a unified size regardless of input video size. The final predictions are resulted from a fused softmax scores of two streams, and subject to the weighting factor of each stream. Our experimental results suggest spatial stream slightly higher than the temporal stream, and the performance of the trained model is conditionally optimized. The proposed method is experimented on two challenging video action datasets UCF101 and HMDB51, in which our method achieves the most advanced performance above 96.1% on UCF101 dataset.
Relations Between Effort Estimates, Skill Indicators, and Measured Programming Skill

There are large skill differences among software developers, and clients and managers will benefit from being able to identify those with better skill. This study examines the relations between low effort estimates, and other commonly used skill indicators, and measured programming skill. One hundred and four professional software developers were recruited. After skill-related information was collected, they were asked to estimate the effort for four larger and five smaller programming tasks. Finally, they completed a programming skill test. The lowest and most over-optimistic effort estimates for the larger tasks were given by those with the lowest programming skill, which is in accordance with the well-known Dunning-Kruger effect. For the smaller tasks, however, those with the lowest programming skill had the highest and most over-pessimistic estimates. The other programming skill indicators, such as length of experience, company assessed skill and self-assessed skill, were only moderately correlated with measured skill and not particularly useful in guiding developer skill identification. A practical implication is that for larger and more complex tasks, the use of low effort estimates and commonly used skill indicators as selection criteria leads to a substantial risk of selecting among the least skilled developers.
Purely functional GLL parsing

Generalised parsing has become increasingly important in the context of software language design and several compiler generators and language workbenches have adopted generalised parsing algorithms such as GLR and GLL. The original GLL parsing algorithms are described in low-level pseudo-code as the output of a parser generator. This paper explains GLL parsing differently, defining the FUN-GLL algorithm as a collection of pure, mathematical functions and focussing on the logic of the algorithm by omitting implementation details. In particular, the data structures are modelled by abstract sets and relations rather than specialised implementations. The description is further simplified by omitting lookahead and adopting the binary subtree representation of derivations to avoid the clerical overhead of graph construction. Conventional parser combinators inherit the drawbacks from the recursive descent algorithms they implement. Based on FUN-GLL, this paper defines generalised parser combinators that overcome these problems. The algorithm is described in the same notation and style as FUN-GLL and uses the same data structures. Both algorithms are explained as a generalisation of basic recursive descent algorithms. The generalised parser combinators of this paper have several advantages over combinator libraries that generate internal grammars. For example, with the generalised parser combinators it is possible to parse larger permutation phrases and to write parsers for languages that are not context-free. The 'BNF combinator library' is built around the generalised parser combinators. With the library, embedded and executable syntax specifications are written. The specifications contain semantic actions for interpreting programs and constructing syntax trees. The library takes advantage of Haskell's type-system to type-check semantic actions and Haskell's abstraction mechanism enables 'reuse through abstraction'. The practicality of the library is demonstrated by running parsers obtained from the syntax descriptions of several software languages.
Learning Slimming SSD through Pruning and Knowledge Distillation

SSD[2] has a very good performance in the field of object detection. When we use SSD to detect the objects in a image, SSD produces a number of different scale prior boxes on different scale feature maps. Then, it can use the base CNN to extract features for the final Classification and Regression. Though it has simplified the process comparing to Faster RCNN, the FLOPs calculation and the storage size of model are still enormous for ordinary embedded devices. Parameter pruning is a very popular method in model compression for DNN. It can prune the redundant weights in the originally trained model and then obtain a lighter one. However, the accuracy after pruning usually drops. Knowledge distillation[15] is another relatively new training method, which can make up for some decline in the accuracy. The teacher network guides small network training with low accuracy, which enables small models to have better performance. In this paper, we use SSD as the original network in object detection for VOC2007, and then prune the SSD. Finally, we use the method of Knowledge Distillation to improve the accuracy of the model.
Towards a Trusted Marketplace for Wearable Data

Recent advances in wearable technology and the rapid adoption of wearable devices have led to a paradigm shift in the way health related data is generated, stored, and consumed. There are increased opportunities for setting up innovative wearable data markets where real-time, user-generated health data can be easily bought and sold. However, there are several challenges associated with safeguarding the consent, security, and privacy of all market participants, particularly the data owners. In this paper, we discuss some of these challenges and propose a conceptual framework and reference architecture for wearable data marketplaces that builds on top of blockchain technology, and facilitates and incentivises trustworthy data sharing. In particular, we propose an approach whereby user-defined data-sharing policies translate to executable smart contracts to facilitate the data exchange between the data subject and the data consumer. We illustrate how the proposed approach works through an use case scenario.
A Survey on Content Based Crawling for Deep and Surface Web

The World Wide Web contains massive source of content. Fetching of relevant information from the WWW is a very typical task. Web crawler plays an important role to fetch the relevant content from the WWW and for indexing the web pages. To accommodate drastically increasing user requests, an efficient and optimized crawler is required. Content of the surface web pages are available to all users directly for access, but content of the deep web is not exposed to the users. The crawling of the hidden web is even more difficult. Authors have proposed algorithms for different web crawlers for fetching the information from the surface and deep web in an efficient and optimized manner. In this paper, we have reviewed different web crawlers and have classified them based on the information fetched by them. This paper provides a comparative analysis of web crawlers used for fetching the information based on URL, deep and surface web.
BlockHouse: Blockchain-Based Distributed Storehouse System

We propose in this paper BlockHouse, a decentralized/P2P storage system fully based on private blockchains. Each participant can rent his unused storage in order to host data of other members. This system uses a dual Smart Contract and Proof of Retrievability system to automatically check at a fixed frequency if the file is still hosted. In addition to transparency, the blockchain allows a better integration with all payments associated to this type of system ( regular payments, sequestration to ensure good behaviors of users, ...). Except the data transferred between the client and the server, all the actions go through a smart contract in the blockchain in order to log, pay and secure the entire storage process.
Low-Light Image Enhancement Based On Retinex and Saliency Theories

Low-light images enhancement of real scenes is a challenge task, however, existing algorithms always encounter the problems of over-enhancement, noises amplification and low subjective evaluation. One reason of these problems lies in the missing of high-level vision information. Therefore, this paper present a novel low-light image enhancement framework based on retinex and saliency theories. We first adopts a deep neural network model i.e. Saliency Attentive Model to predict the saliency map of low-light image and detect its salient regions. Then, we utilize one retinex model based method to enhance the whole low-light image. Then we fuse the generated saliency map and the enhanced image together to acquire a well-enhanced image. Experiments verify the significance of our algorithm by comparing with general low-light image enhancement method without saliency.
Research on the Blockchain Technology in the Security of Internet of things

At present, with the spread of Internet of things technology, IoT security problems gradually appear, blockchain as a new security project has been paid great attention to by the academic community. Firstly, this paper expounds the technical characteristics of blockchain and clarifies the technical advantages of blockchain. Secondly, the security model of the combination of blockchain and IoT is briefly described, and the workflow of blockchain is analyzed. Thirdly, countermeasures are proposed for the problems existing in the security of consensus mechanism, block capacity and transaction efficiency, the security of user's wallet and the security of intelligent contract in the blockchain.
Toward Human-in-the-Loop Prohibited Item Detection in X-ray Baggage Images

X-ray baggage security screening is a demanding task for aviation and rail transit security; automatic prohibited item detection in X-ray baggage images can help reduce the work of inspectors. However, as many items are placed too close to each other in the baggages, it is difficult to fully trust the detection results of intelligent prohibited item detection algorithms. In this paper, a human-in-the-loop baggage inspection framework is proposed. The proposed framework utilizes the deep-learning-based algorithm for prohibited item detection to find suspicious items in X-ray baggage images, and select manual examination when the detection algorithm cannot determine whether the baggage is dangerous or safe. The advantages of proposed inspection process include: online to capture new sample images for training incrementally prohibited item detection model, and augmented prohibited item detection intelligence with human-computer collaboration. The preliminary experimental results show, human-in-the-loop process by combining cognitive capabilities of human inspector with the intelligent algorithms capabilities, can greatly improve the efficiency of in-baggage security screening.
Multi-Objective Neural Architecture Search for Light-Weight Model

Neural architecture search (NAS) has achieved superior performance in visual tasks by automatically designing an effective neural network architecture. In recent years, deep neural networks are increasingly applied to resource-constrained devices. As a result, in addition to the model performance, model size is another very important factor that requires to consider when designing powerful neural network architectures. Therefore, we propose the multi-objective neural architecture search for light-weight model and name it Light-weight NAS. On one hand, the Light-weight NAS introduces Multiply-ACcumulate (MAC) into the optimize objective to get the architecture with fewer parameters. On the other hand, we simplify the search space and adopt weight sharing to make the search process more efficient. Experimental results indicate that the searched architecture can perform competitive classification accuracy with few parameters on the image classification task, while using less computation cost than the most existing multi-objective NAS approaches.
Some Improvements of Deep Knowledge Tracing

Deep Knowledge Tracing (DKT), along with other machine learning approaches, are biased toward data used during the training step. Thus, for problems where we have few amounts of data for training, the generalization power will be low, the models will tend to give good results on classes containing many examples and poor results on those with few examples. Theses problems are frequent in educational data where for example, there are skills that are very difficult (floor) or very easy to master (ceiling). There will be less data on students that correctly answered questions related to difficult knowledge and that incorrectly answered questions related to knowledge easy to master. In that case, the DKT is unable to correctly predict the student's answers to questions associated with those skills. To improve the DKT, we penalize the model using a 'cost-sensitive' technique. To overcome the problem of the few amounts of data, we propose a hybrid model combining the DKT and expert knowledge. Thus, the DKT is combined with a Bayesian Network (built from domain experts) by using the attention mechanism. The resulting model can accurately track knowledge of students in Logic-Muse Intelligent Tutoring System (ITS), compared to the BKT and the original DKT.
The Divination of Things by Things

As humans our view of the world is predominantly restricted to our own experience and are largely oblivious to the alternate perspective of reality experienced by the objects that cohabit our spaces even though such objects are often integral components of our lives. This paper considers the growing phenomenon whereby non-human objects such as cutlery and appliances are having what might be considered human-like experiences through integration of advanced computational programming. By examining the services provided by Madame Bitsy's Fantastic Future Forecasting and Fortune Telling Emporium for the Internet of Living Things, which is a fully autonomous online fortune telling service for Internet of Things enabled objects and services, we attempt to illuminate what it means to be a digitally connected object.
Towards Computer-Aided Sign Language Recognition Technique: A Directional Review

For the hearing and speech impaired, Sign Language (SL) is their main communication tool. To enable them communicate smoothly with those who don't understand SL, it is necessary to translate SL into text or speech through recognition. We review the research status and progress in the field of SL recognition, based on the analysis of off-the-shelf SL translation methods and systems. This paper describes a number of recognition methods, and they are divided into data glove-based and vision-based approaches according to the motion capture device; In addition, vision-based approaches are categorized into color-based and depth-based techniques by the used camera. Moreover, this paper critically discusses current SL recognition technology, and points out the problems and challenges, so as to provide guideline for future work.
Automated Coding: The Quest to Develop Programs That Write Programs

Computer scientists have long pondered the possibility of crafting systems capable of creating programs directly from human intent. Recent developments in neural networks are closing the gap between fuzzy user objectives and concrete automated code generation.
Enacting policies in digital health: a case for smart legal contracts and distributed ledgers?

Abstract This paper presents an approach for the enactment of policies in digital health based on our earlier work on the implementation of digital contracts in distributed systems. A formal policy model and an abstract policy language for the expression of healthcare policies are first proposed, leveraging the semantics of the ISO Reference Model for Open Distributed Processing enterprise language standard. Healthcare consent policies included in the HL7 Fast Health Interoperability Resource (FHIR®) standard are used to illustrate the modelling approach. Several distributed ledger and smart legal contract options were considered next as target platforms for implementation. Their benefits are highlighted along with considerations on their use reflecting business concerns of risk, trust and cost.
The World (of CTF) is Not Enough Data: Lessons Learned from a Cyber Deception Experiment

The human side of cyber is fundamentally important to understanding and improving cyber operations. With the exception of Capture the Flag (CTF) exercises, cyber testing and experimentation tends to ignore the human attacker. While traditional CTF events include a deeply rooted human component, they rarely aim to measure human performance, cognition, or psychology. We argue that CTF is not sufficient for measuring these aspects of the human; instead, we examine the value in performing red team behavioral and cognitive testing in a large-scale, controlled human-subject experiment. In this paper we describe the pros and cons of performing this type of experimentation and provide detailed exposition of the data collection and experimental controls used during a recent cyber deception experiment-the Tularosa Study. Finally, we will discuss lessons learned and how our experiences can inform best practices in future cyber operations studies of human behavior and cognition.
Automatic Segmentation of MRI Images for Brain Tumor using unet

More noteworthy test in brain tumor is arranging and huge assessment is assurance of the tumor degree. The non invasive magnetic resonance imaging (MRI) system has risen as a cutting edge analytic device for brain tumors without ionizing radiation. Cerebrum tumor degree division by manually from 3D MRI volumes is a tedious assignment and execution is exceptionally depended on administrator's involvement. In specific circumstance, dependable completely programmed division strategy for the brain tumor division is important for a productive estimation of tumor degree. To investigate this, we propose a completely programmed strategy for brain tumor division that is created utilizing U-Net based deep convolutional network.
«Máquina de Ouver» - From Sound to Type

Typography is considered by many authors the visual representation of language, and through writing, the human being found a way to register and share information. Throughout the history of typography, there were many authors that explored this connection between words and their sounds, trying to narrow the gap between what we say, how we hear it and how it should be read.
User-Specific Visual Attention Estimation Based on Visual Similarity and Spatial Information in Images

This paper presents a method for user-specific visual attention estimation based on visual similarities and spatial information in images. In order to estimate the user- specific visual attention, the proposed method calculates two kinds of saliency maps. One is constructed as a visual similarity-based saliency map, and the other is constructed by considering spatial information of objects in images. The proposed method performs a fusion of these two maps for considering visual similarities and spatial information. This is the biggest contribution of this paper. Therefore, improvement of the estimation performance of the user-specific visual attention is realized.
Remote Fall Detection System for the Elderly

Human fall detection is a major challenge in the healthcare domain. It is estimated that over a third of the population aged 65 years and above, fall each year, making it the most prominent cause of non-fatal injury in their age group. This hinders their sense of independence and increases the need for constant monitoring of their activities. Progress of technology helps facilitate implementation of wearable remote monitoring systems to help detect a fall and implement necessary measures to ensure appropriate medical assistance is provided. This paper outlines the implementation of such a system which helps in the detection of a fall and the notification of the required caretaker via an SMS containing the location of the fallen person, so as to provide timely medical attention.