Reviewerly/exHarmony huggingface dataset:
Tweet to News Conversion: An Investigation into Unsupervised Controllable Text Generation

Text generator systems have become extremely popular with the advent of recent deep learning models such as encoder-decoder. Controlling the information and style of the generated output without supervision is an important and challenging Natural Language Processing (NLP) task. In this paper we define the task of constructing a coherent paragraph from a set of disaster domain tweets, without any parallel data. We tackle the problem by building two systems in pipeline. The first system focuses on unsupervised style transfer to convert the individual tweets into news sentences. The second system stitches together the outputs from the first system to form a coherent news paragraph. We also propose a novel training mechanism, by splitting the sentences into propositions and training the second system to merge the sentences. We create a validation and test set consisting of tweet-sets and their equivalent news paragraphs to perform empirical evaluation. We also perform human evaluations on our model. In a completely unsupervised setting our model was able to achieve a BLEU score of 19.32, while successfully transferring styles and joining tweets to form a meaningful news paragraph.
Construction of Bounding Volume Hierarchies for Triangle Meshes with Mixed Face Sizes

We consider the problem of creating tighter-fitting bounding volumes (more specifically rectangular swept spheres) when constructing bounding volume hierarchies (BVHs) for complex 3D geometries given in the form of unstructured triangle meshes/soups with the aim of speeding up our IPS Path Planner for rigid bodies, where the triangles often have very different sizes. Currently, the underlying collision and distance computation module (IPS CDC) does not take into account the sizes of the triangles when it constructs BVHs using a top-down strategy. To split triangles in a BVH node into two BVH nodes, IPS CDC has to compute both the split axis and the split position. In this work, we use the principal axes of the tensor of inertia as the potential split axes and the center of mass as the split position, where the computations of both the tensor of inertia and the center of mass require knowledge of the areas of the triangles. We show that our method improves performance (up to 20 % faster) of our IPS Path Planner when it is used to plan collision-free disassembly paths for three different test cases taken from manufacturing industries.
Ellipse Recovery From Blurred Binary Images

In this paper, we address the problem of ellipse recovery from blurred shape images. A shape image is a binary-valued (0/1) image in continuous-domain that represents one or multiple shapes. In general, the shapes can also be overlapping. We assume to observe the shape image through finitely many blurred samples, where the 2D blurring kernel is assumed to be known. The samples might also be noisy. Our goal is to detect and locate ellipses within the shape image. Our approach is based on representing an ellipse as the zero-level-set of a bivariate polynomial of degree 2. Indeed, similar to the theory of finite rate of innovation (FRI), we establish a set of linear equations (annihilation filter) between the image moments and the coefficients of the bivariate polynomial. For a single ellipse, we show that the image can be perfectly recovered from only 6 image moments (improving the bound in [Fatemi et al., 2016]). For multiple ellipses, instead of searching for a polynomial of higher degree, we locally search for single ellipses and apply a pooling technique to detect the ellipse. As we always search for a polynomial of degree 2, this approach is more robust against additive noise compared to the strategy of searching for a polynomial of higher degree (detecting multiple ellipses at the same time). Besides, this approach has the advantage of detecting ellipses even when they intersect and some parts of the boundaries are lost. Simulation results using both synthetic and real world images (red blood cells) confirm superiority of the performance of the proposed method against the existing techniques.
Multi-Channel Co-Attention Network for Visual Question Answering

Visual Question Answering (VQA) is to reason out correct answers based on input questions and images. Significant progresses have been made by learning rich embedding features from images and questions by bilinear models. Attention mechanisms are widely used to focus on specific visual and textual information in VQA reasoning process. However, most state-of-the-art methods concentrate on fusing the global multi-modal features, while neglect local features. Besides, the dimension is reduced excessively (from K×2048 to 2048) in general visual attention, which causes a mass of visual information loss. In this paper, we propose a novel multi-channel co-attention network (MC-CAN), which integrates multi-modal features from global level to local level. We design different multi-channel attention mechanisms separately for visual (from K×2048 to M×2048) and textual features at different level of integrations. Additionally, we further improve our proposed approach by combining it with the complementary modules such as the MLB and the Count modules. Experiments on benchmark datasets show that our approach achieves better VQA performance than other state-of-the-art methods.
Analysis of Survey Tools for Recommender Systems in the Selection of Ambient Assisted Living Technologies

This work is a study about a comparison of survey tools and it should help developers in selecting a suited tool for application in an AAL environment. The first step was to identify the basic required functionality of the survey tools used for AAL technologies and to compare these tools by their functionality and assignments. The comparative study was derived from the data obtained, previous literature studies and further technical data. A list of requirements was stated and ordered in terms of relevance to the target application domain. With the help of an integrated assessment method, the calculation of a generalized estimate value was performed and the result is explained. Finally, the planned application of this tool in a running project is explained.
Shape-Adaptive Kernel Network for Dense Object Detection

Dense object detectors that are applied over a regular, dense grid have advanced and drawn their attention in recent days. Their fully convolutional nature greatly advances the computational efficiency of object detectors compared to the two-stage detectors. However, the lack of the ability to adjust shape variation on a regular grid is still limited. In this paper we introduce a new framework, shape-adaptive kernel network, to handle spatial manipulation of input data in convolutional kernel space. At the heart of out approach is to align the original kernel space recovering shape variation of each input feature on regular grid. To this end, we propose a shape-adaptive kernel sampler to adjust dynamic convolutional kernel conditioned on input. To increase the flexibility of geometric transformation, a cascade refinement module is designed, which first estimates the global transformation grid and then estimates local offset in convolutional kernel space. Our experiments demonstrate the effectiveness of the shape-adaptive kernel network for dense object detection on various benchmarks.
Performance evaluation for ranking-based discretisation

Discretisation often constitutes a part of initial data preparation stage. It translates continuous domain of features into granular, by assigning a number of intervals to represent attributes’ values by nominal categories. Typically all real-valued features are subjected to transformations, regardless of their characteristics. The paper presents research on discretisation executed with a discerning approach. To all available attributes, feature selection mechanisms were employed, in the form of rankings that order variables based on their importance. Exploiting this discovered knowledge on attributes, discretisation was then driven by a ranking, and either highest or lowest ranking features were selected for transformation. The influence of selective discretisation on the performance of classification systems was studied for three popular inducers. The procedure was employed in the field of stylometry, and a task of authorship recognition, considered as a binary classification with balanced classes. The experiments show that discretisation based on importance of features can lead to better performance than in the case of transformations applied to all attributes.
Towards Programming in Natural Language: Learning New Functions from Spoken Utterances

Systems with conversational interfaces are rather popular nowadays. However, their full potential is not yet exploited. For the time being, users are restricted to calling predefined functions. Soon, users will expect to customize systems to their needs and create own functions using nothing but spoken instructions. Thus, future systems must understand how laypersons teach new functionality to intelligent systems. The understanding of natural language teaching sequences is a first step toward comprehensive end-user programming in natural language. We propose to analyze the semantics of spoken teaching sequences with a hierarchical classification approach. First, we classify whether an utterance constitutes an effort to teach a new function or not. Afterward, a second classifier locates the distinct semantic parts of teaching efforts: declaration of a new function, specification of intermediate steps, and superfluous information. For both tasks we implement a broad range of machine learning techniques: classical approaches, such as Naïve Bayes, and neural network configurations of various types and architectures, such as bidirectional LSTMs. Additionally, we introduce two heuristic-based adaptations that are tailored to the task of understanding teaching sequences. As data basis we use 3168 descriptions gathered in a user study. For the first task convolutional neural networks obtain the best results (accuracy: 96.6%); bidirectional LSTMs excel in the second (accuracy: 98.8%). The adaptations improve the first-level classification considerably (plus 2.2% points).
Probabilistic Prediction of Solar Generation Based on Stacked Autoencoder and Lower Upper Bound Estimation Method

The lower upper bound estimation method is an important probabilistic prediction method and has been applied to the solar generation forecasting. However, when the input dimension of the lower upper bound estimation method is large, its performance will be seriously affected. To overcome this challenge, a novel probabilistic prediction of solar generation based on stacked autoencoder and lower upper bound estimation method is proposed. In this method, stacked autoencoder is first used to obtain highly compressed features, which are utilized as the input of the lower upper bound estimation method. Besides, to make the target value in the center of the prediction interval as much as possible, inspired by the idea of support vector machine, the mean squared error of prediction interval is introduced to the loss function, which keeps the target value as far as possible from the lower and upper bounds of the prediction interval. To verify the performance of the proposed method, a large number of experiments have been carried out on the freely available dataset. The results show that the proposed method has better forecasting performance.
End-to-End Temporal Feature Aggregation for Siamese Trackers

While siamese networks have demonstrated the significant improvement on object tracking performances, how to utilize the temporal information in siamese trackers has not been widely studied yet. In this paper, we introduce a novel siamese tracking architecture equipped with a temporal aggregation module, which improves the per-frame features by aggregating temporal information from adjacent frames. This temporal fusion strategy enables the siamese trackers to handle poor object appearance like motion blur, occlusion, etc. Furthermore, we incorporate the adversarial dropout module in the siamese network for computing discriminative target features in an end-to-end-fashion. Comprehensive experiments demonstrate that the proposed tracker performs favorably against state-of-the-art trackers.
DHD-Net: A Novel Deep-Learning-based Dehazing Network

Eliminating haze interference in images is still a challenging problem. In this paper, we consider more systematically the physical hazing mechanisms, combined with deep learning, propose a new end-to-end dehazing network called DHD-Net. For physical hazing mechanisms, we fuse the global atmosphere light, transmission maps, and the atmospheric scattering model for dehazing. For the estimation of global atmosphere light, We propose a deep learning-based haze density estimation algorithm (DL-HDE). We establish a new dataset, of which each data item consists of the hazy image, the transmission map, the haze-free image, and the dense-haze area mask. Our experimental results demonstrate that our proposed DHD-Net has better dehazing performance than state-of-the-art algorithms.
Aesthetic Well-Being and Ethical Design of Technology

Aesthetics is a central quality attribute of a product. Research into the relationship between aesthetics in human–technology interaction and the well-being of older people is still in its infancy. In care homes, aesthetics can play a major role in creating a ‘feeling of home’, which is important when the transition to assisted living may involve multiple changes and losses that affect an older person’s well-being. This chapter discusses the potential of aesthetic design to address older people’s emotional well-being. Aesthetics in technology and technological environments provides a new ethical way of looking at valuable problems in design—meaningfulness in terms of personal and individual symbolic values and the harmonizing potential of artefacts to create a ‘feeling of home’. Promoting the aesthetic well-being of older people in care homes (and in general) deserves further attention.
Shape Mask Generator: Learning to Refine Shape Priors for Segmenting Overlapping Cervical Cytoplasms

Segmenting overlapping cytoplasm of cervical cells plays a crucial role in cervical cancer screening. This task, however, is rather challenging, mainly because intensity (or color) information in the overlapping region is deficient for recognizing occluded boundary parts. Existing methods attempt to compensate intensity deficiency by exploiting shape priors, but shape priors modeled by them have a weak representation ability, and hence their segmentation results are often visually implausible in shape. In this paper, we propose a conceptually simple and effective technique, called shape mask generator, for segmenting overlapping cytoplasms. The key idea is to progressively refine shape priors by learning so that they can accurately represent most cytoplasms’ shape. Specifically, we model shape priors from shape templates and feed them to the shape mask generator that generates a shape mask for the cytoplasm as the segmentation result. Shape priors are refined by minimizing the ‘generating residual’ in the training dataset, which is designed to have a smaller value when the shape mask generator producing shape masks that are more consistent with the image information. The introduced method is assessed on two datasets, and the empirical evidence shows that it is effective, outperforming existing methods.
An Area-Efficient High-Throughput SM4 Accelerator with SCA-Countermeasure for TV Applications

The SM4 algorithm is the first commercial cipher published by China in 2012 which is widely used in WLAN WAPI resource restricted devices. This paper proposed the single-round-iterative architecture which can operate at 500 MHZ clock frequency and reach 2Gbps throughput. In order to resist side channel attack, we changed the S-box structure and add secret sharing during the computation process. According to the CPA result, this hardware is secure under the condition of collecting 1 million power traces. The gate count of this design is about 15.19k, gaining almost 17.7% area reduction to the BMTSM4 which can reach the similar throughput [1].
Authentication of Financial Wallet System and Data Protection using BlockChain

In a BlockChain environment, during validation process or address lookup user personal information may be disclosed or hacked. To prevent this loophole, a Financial Wallet system with authentication and data protection is implemented. In this system, to avoid the disclosure of personal data and to limit the third party intermediaries a BlockChain with Zero knowledge proof is incorporated. To this implementation AES cryptography method is used to secure data. To make it more confidential NFC cards are used to authenticate user instead of passwords.
GeekyNote

Understanding an unfamiliar program is always a daunting task for any programmer, either experienced or inexperienced. Many studies have shown that even an experienced programmer who is already familiar with the code may still need to rediscover the code frequently during software maintenance. The difficulties of program comprehension is much more intense when a system is completely new. One well-known solution to this notorious problem is to create effective technical documentation to make up for the lack of knowledge.
Visual Prediction of Priors for Articulated Object Interaction

Exploration in novel settings can be challenging without prior experience in similar domains. However, humans are able to build on prior experience quickly and efficiently. Children exhibit this behavior when playing with toys. For example, given a toy with a yellow and blue door, a child will explore with no clear objective, but once they have discovered how to open the yellow door, they will most likely be able to open the blue door much faster. Adults also exhibit this behaviour when entering new spaces such as kitchens. We develop a method, Contextual Prior Prediction, which provides a means of transferring knowledge between interactions in similar domains through vision. We develop agents that exhibit exploratory behavior with increasing efficiency, by learning visual features that are shared across environments, and how they correlate to actions. Our problem is formulated as a Contextual Multi-Armed Bandit where the contexts are images, and the robot has access to a parameterized action space. Given a novel object, the objective is to maximize reward with few interactions. A domain which strongly exhibits correlations between visual features and motion is kinemetically constrained mechanisms. We evaluate our method on simulated prismatic and revolute joints <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Protected Distributed Data Storage Based on Residue Number System and Cloud Services

The reliable distributed data storage system based on the Redundant Residue Number System (RRNS) is developed. The structure of the system, data splitting and recovery algorithms based on RRNS are developed. A study of the total time and time spent on converting ASCII-encoded data into a RRNS for files of various sizes is conducted. The research of data recovery time is conducted for the inverse transformation from RRNS to ASCII codes.
Executing native Java code in R: an approach based on a local server

The R language is widely used for data analysis. However, it does not allow for complex object-oriented implementation and it tends to be slower than other languages such as Java, C and C++. Consequently, it can be more computationally efficient to run native Java code in R. To do this, there exist at least two approaches. One is based on the Java Native Interface (JNI) and it has been successfully implemented in the rJava package. An alternative approach consists of running a local server in Java and linking it to an R environment through a socket connection. This alternative approach has been implemented in an R package called J4R. This article shows how this approach makes it possible to simplify the calls to Java methods and to integrate the R vectorization. The downside is a loss of performance. However, if the vectorization is used in conjunction with multithreading, this loss of performance can be compensated for.
Statistical Analysis of Enhanced SDEx Encryption Method Based on SHA-512 Hash Function

The paper presents statistical analysis of the enhanced SDEx method based on SHA-512 hash function. Statistical analysis of the method is performed in a similar way as for the method with the SHA-256, described in [9]. To realize it the ciphertext files were tested to check if they fulfill the conditions of pseudo-randomness. To test the pseudo-randomness of the encrypted files, a test package shared by NIST was used for this purpose. Additionally, compression tests on encrypted files were performed using the WinRAR software. The SDEx method based on SHA-512 hash function is faster and provides similar or higher level of security than AES with 256-bit key. The method can be used to secure end-to-end encryption in data transferring.
Towards Efficiency-Preserving Round Compression in MPC

Reducing the rounds of interaction in secure multiparty computation (MPC) protocols has been the topic of study of many works. One popular approach to reduce rounds is to construct round compression compilers. A round compression compiler is one that takes a highly interactive protocol and transforms it into a protocol with far fewer rounds. The design of round compression compilers has traditionally focused on preserving the security properties of the underlying protocol and in particular, not much attention has been given towards preserving their computational and communication efficiency. Indeed, the recent round compression compilers that yield round-optimal MPC protocols incur large computational and communication overhead. In this work, we initiate the study of efficiency-preserving round compression compilers, i.e. compilers that translate the efficiency benefits of the underlying highly interactive protocols to the fewer round setting. Focusing on the honest majority setting (with near-optimal corruption threshold $$\frac{1}{2} - \varepsilon $$ , for any $$\varepsilon > 0$$ ), we devise a new compiler that yields two round (i.e., round optimal) semi-honest MPC with similar communication efficiency as the underlying (arbitrary round) protocol. By applying our compiler on the most efficient known MPC protocols, we obtain a two-round semi-honest protocol based on one-way functions, with total communication (and per-party computation) cost $$\widetilde{O}(s+n^4)$$ – a significant improvement over prior two-round protocols with cost $$\widetilde{O}(n^\tau s+n^{\tau +1}d)$$ , where $$\tau \ge 2$$ , s is the size of the circuit computing the function and d the corresponding depth. Our result can also be extended to handle malicious adversaries, either using stronger assumptions in the public key infrastructure (PKI) model, or in the plain model using an extra round. An artifact of our approach is that the resultant protocol is "unbalanced" in the amount of computation performed by different parties. We give evidence that this is necessary in our setting. Our impossibility result makes novel use of the "MPC-in-the-head" paradigm which has typically been used to demonstrate feasibility results.
Composing in spacetime with rainbows

There exists a long tradition of incorporating acoustic space as a creative parameter in musical composition and performance. This creative potential has been extended by way of modern sensing and computing technology which allows the position of the listener to act as an input to interactive musical works in immersive, digital environments. Furthermore, the sophistication of sensing technology has reached a point where barriers to implementing these digital interactive musical systems in the physical world are dissolving.