Reviewerly/exHarmony huggingface dataset:
Robust Multimodal Image Registration Using Deep Recurrent Reinforcement Learning

The crucial components of a conventional image registration method are the choice of the right feature representations and similarity measures. These two components, although elaborately designed, are somewhat handcrafted using human knowledge. To this end, these two components are tackled in an end-to-end manner via reinforcement learning in this work. Specifically, an artificial agent, which is composed of a combined policy and value network, is trained to adjust the moving image toward the right direction. We train this network using an asynchronous reinforcement learning algorithm, where a customized reward function is also leveraged to encourage robust image registration. This trained network is further incorporated with a lookahead inference to improve the registration capability. The advantage of this algorithm is fully demonstrated by our superior performance on clinical MR and CT image pairs to other state-of-the-art medical image registration methods.
The Pursuit of Patterns in Educational Data Mining as a Threat to Student Privacy

Recent technological advances have led to tremendous capacities for collecting, storing and analyzing data being created at an ever-increasing speed from diverse sources. Academic institutions which offer open and distance learning programs, such as the Hellenic Open University, can benefit from big data relating to its students’ information and communication systems and the use of modern techniques and tools of big data analytics provided that the student’s right to privacy is not compromised. The balance between data mining and maintaining privacy can be reached through anonymisation methods but on the other hand this approach raises technical problems such as the loss of a certain amount of information found in the original data. Considering the learning process as a framework of interacting roles and factors, the discovery of patterns in that system can be really useful and beneficial firstly for the learners and furthermore, the ability to publish and share these results would be very helpful for the whole academic institution.
A hyperintensional logical framework for deontic reasons

Abstract In this paper we argue that normative reasons are hyperintensional and put forward a formal account of this thesis. That reasons are hyperintensional means that a reason for a proposition does not imply that it is also a reason for a logically equivalent proposition. In the first part we consider three arguments for the hyperintensionality of reasons: (i) an argument from the nature of reasons, (ii) an argument from substitutivity and (iii) an argument from explanatory power. In the second part we describe a hyperintensional logic of reasons based on justification logics. Eventually we discuss the philosophical import of this proposal and highlight some limitations and possible developments.
A cloud-agnostic queuing system to support the implementation of deadline-based application execution policies

There are many scientific and commercial applications that require the execution of a large number of independent jobs resulting in significant overall execution time. Therefore, such applications typically require distributed computing infrastructures and science gateways to run efficiently and to be easily accessible for end-users. Optimising the execution of such applications in a cloud computing environment by keeping resource utilisation at minimum but still completing the experiment by a set deadline has paramount importance. As container-based technologies are becoming more widespread, support for job-queuing and auto-scaling in such environments is becoming important. Current container management technologies, such as Docker Swarm or Kubernetes, while provide auto-scaling based on resource consumption, do not support job queuing and deadline-based execution policies directly. This paper presents JQueuer, a cloud-agnostic queuing system that supports the scheduling of a large number of jobs in containerised cloud environments. The paper also demonstrates how JQueuer, when integrated with a cloud application-level orchestrator and auto-scaling framework, called MiCADO, can be used to implement deadline-based execution policies. This novel technical solution provides an important step towards the cost-optimisation of batch processing and job submission applications. In order to test and prove the effectiveness of the solution, the paper presents experimental results when executing an agent-based simulation application using the open source REPAST simulation framework.
Co-Creating "Smart" Sustainable Food Futures with Urban Food Growers

The futuristic visions, infrastructures, and developments of smart cities continue to gather pace, with municipal authorities and businesses in the UK investing increasing amounts of resources into their manifestation. At the same time local communities continue to be hard hit by austerity, with more local services being affected by government cuts, with the North-East of England being particularly affected. In this paper we report on a case study that aimed to explore how the top-down, technocentric, and corporate visions of smart cities stand in contrast to the reality of grassroots communities who are dealing with the consequences of austerity. Our case study focuses on a community of urban food growers. We describe our speculative and participatory approach that we devised for co-designing "smart" urban food-growing futures from the bottom-up with local residents in a deprived neighbourhood of Newcastle upon Tyne, and reflect on how they elicited realities and future visions that stand as a counterpoint to the corporate visions of future cities.
An Improved Fuzzy Min–Max Neural Network for Data Classification

Hyperbox classifier is an efficient tool for modern pattern classification problems due to its transparency and rigorous use of Euclidian geometry. Fuzzy min-max (FMM) network efficiently implements the hyperbox classifier, and has been modified several times to yield better classification accuracy. However, the obtained accuracy is not up to the mark. Therefore, in this paper, a new improved FMM (IFMM) network is proposed to increase the accuracy rate. In the proposed IFMM network, a modified constraint is employed to check the expandability of a hyperbox. It also uses semiperimeter of the hyperbox along with k-nearest mechanism to select the expandable hyperbox. In the proposed IFMM, the contraction rules of conventional FMM and enhanced FMM (EFMM) are also modified using semiperimeter of a hyperbox in order to balance the size of both overlapped hyperboxes. Experimental results show that the proposed IFMM network outperforms the FMM, k-nearest FMM, and EFMM by yielding more accuracy rate with less number of hyperboxes. The proposed methods are also applied to histopathological images to know the best magnification factor for classification.
Simultaneous observation of higher-order non-classicalities based on experimental photocount moments and probabilities

Abstract Using a sub-Poissonian optical field generated from a weak twin beam by photon-number resolving post-selection we have simultaneously observed higher-order non-classicalities in photocount moments (sub-Poissonian statistics) and probabilities (witnessed by the Klyshko inequalities). Up to the seventh-order non-classicalities in photocount moments simultaneously with up to the eleventh-order non-classicalities in photocount probabilities have been experimentally observed. Non-classicality counting parameters of different orders as experimental counterparts of the theoretical Lee non-classicality depth have been suggested to quantify and also mutually compare the robustness of these non-classicalities against the noise.
Circus, Play and Technology Probes

Physical training can be frustrating and hard, especially for those who experience additional challenges to access and control their proprioceptive senses. In the context of designing for children with Sensory-based Motor Disorder, we designed and deployed a series of Training Technology Probes to be used in circus training. Here we focus on how these were used, tested, and appropriated by children and instructors during a six-week circus training course. Through these explorations, we identified a range of potential benefits from using their functions in training. We present the Physical Training Technology Probes and the benefits they brought to training. We show how the technology functions helped children focus and provided feedback related to posture and balance. Furthermore, their open-ended designs and versatile options for use were crucial in exploring their contributions to training, and in how they helped foster creative engagement with technology and training. Our work contributes towards understanding the specific requirements when designing for the target group, and more generally contributes with design strategies for technology support for skill training.
Optimum information embedding in digital watermarking

In this research, a novel block-based, integer wavelet transform (IWT) domain digital image watermarking scheme for optimum information embedding using a Fuzzy Rule Based System (FRBS) is proposed. There are three famous conflicting parameters in digital image watermarking, namely, robustness, impe rceptibility and payload are linked with each other and a change in one parameter affects others and vice versa. That’s why it is hard to optimize them, jointly because of the inherent non-linearity and in the literature, any pair of parameters is optimized while third is assumed as fixed. In this proposal, this non-linear problem is solved using FRBS by using the logical relationship among three parameters and it consequently suggests the image from the image-bank that may convey the desired payload (capacity) with maximum imperceptibility and robustness. The proposed FRBS is two-fold. Firstly, selection of candidate image blocks from the given image and secondly selection of the candidate coefficients from the already chosen blocks for embedding the desired payload. Images having coefficients greater than a certain threshold are chosen and the payload is embedded. Consequently, the watermarked images are passed through various attacks and the image with maximum robustness is selected. The effectiveness of the proposed scheme is demonstrated through MATLAB simulations and comparison with state-of-the-art techniques.
Secure Digital Service Payments using Zero Knowledge Proof in Distributed Network

Performing a fair exchange without a Trusted Third Party (TTP) was considered to be impossible. With multi party computation and practices like Proof-of-Work (PoW), blockchain accomplishes a fair exchange in a trustless network. Data confidentiality is a key challenge that has to be resolved before adopting blockchain for enterprise applications where tokenized assets will be transferred. Protocols like Zcash are already providing the same for financial transactions but lacks flexibility required to apply in most of the potential use cases of blockchain. Most of the real world application work in a way where a transaction is carried out when a particular action is performed. Also, the zero knowledge proof method used in Zcash, ZKSNARK has certain weaknesses restricting its adoption. One of the major drawbacks of ZKSNARK is that it requires an initial trust setup phase which is difficult to achieve in blockchain ecosystem. ZKSTARK, an interactive zero knowledge proof does not require this phase and also provides security against post quantum attacks. We propose a system that uses two indistinguishable hash functions along with ZKSTARK to improve the flexibility of blockchain platforms. The two indistinguishable hash functions are chosen from SHA3-finalists based on their security, performance and inner designs.
Astral

Astral is a prototyping tool for authoring mobile and smart object interactive behaviours. It mirrors selected display contents of desktop applications onto mobile devices (smartphones and smartwatches), and streams/remaps mobile sensor data to desktop input events (mouse or keyboard) to manipulate selected desktop contents. This allows designers to use familiar desktop applications (e.g. PowerPoint, AfterEffects) to prototype rich interactive behaviours. Astral combines and integrates display mirroring, sensor streaming and input remapping, where designers can exploit familiar desktop applications to prototype, explore and fine-tune dynamic interactive behaviours. With Astral, designers can visually author rules to test real-time behaviours while interactions take place, as well as after the interaction has occurred. We demonstrate Astral's applicability, workflow and expressiveness within the interaction design process through both new examples and replication of prior approaches that illustrate how various familiar desktop applications are leveraged and repurposed.
Feature Pyramid Hashing

In recent years, deep-networks-based hashing has become a leading approach for large-scale image retrieval. Most deep hashing approaches use the high layer to extract the powerful semantic representations. However, these methods have limited ability for fine-grained image retrieval because the semantic features extracted from the high layer are difficult in capturing the subtle differences. To this end, we propose a novel two-pyramid hashing architecture to learn both the semantic information and the subtle appearance details for fine-grained image search. Inspired by the feature pyramids of convolutional neural network, avertical pyramid is proposed to capture the high-layer features and ahorizontal pyramid combines multiple low-layer features with structural information to capture the subtle differences. To fuse the low-level features, a novel combination strategy, called consensus fusion, is proposed to capture all subtle information from several low-layers for finer retrieval. Extensive evaluation on two fine-grained datasets CUB-200-2011 and Stanford Dogs demonstrate that the proposed method achieves significant performance compared with the state-of-art baselines.
Caladrius: A Performance Modelling Service for Distributed Stream Processing Systems

Real-time stream processing has become increasingly important in recent years and has led to the development of a multitude of stream processing systems. Given the varying job workloads that characterize stream processing, these systems need to be tuned and adjusted to maintain performance targets in the face of variation in incoming traffic. Current auto-scaling systems adopt a series of trials to approach a job's expected performance due to a lack of performance modelling tools. We find that general traffic trends in most jobs lend themselves well to prediction. Based on this premise, we built a system called Caladrius that forecasts the future traffic load of a stream processing job and predicts its processing performance after a proposed change to the parallelism of its operators. Experimental results show that Caladrius is able to estimate a job's throughput performance and CPU load under a given scaling configuration.
Ideas of Things

This paper presents a first glance at the IOT Design Kit, a modular set of design tools that was assembled to help creative teams in the early phases of internet-of-things product design and development. The IoT Design Kit focuses on the practitioner's point of view and can support teams in defining, exploring, crystallising and selecting network-connected product ideas. In comparison to existing toolkits, a main differentiator of the IoT Design Kit is that it offers various starting points, depending on existing knowledge, expertise or project background.
Answering Why-Questions for Subgraph Queries in Multi-attributed Graphs

Subgraph queries have been routinely used to search graphs e.g., social networks and knowledge bases. With little knowledge of underlying data, users often need to rewrite queries multiple times to reach desirable answers. Why-questions are studied to explain missing (as “Why-not” questions) or unexpected answers (as “Why” questions). This paper makes a first step to answer why-questions for subgraph queries in attributed graphs. (1) We approach query rewriting and construct query rewrites, which modify original subgraph queries to identify desired entities that are specified by Why questions. We introduce measures that characterize good query rewrites by incorporating both query editing cost and answer closeness. (2) While computing optimal query rewrite is intractable for Why-questions, we develop feasible algorithms, from exact algorithms to heuristics, and provide query rewrites with (near) optimality guarantees whenever possible, for both Why and Why-not questions. These algorithms dynamically select “picky” operators that ensure to change (estimated) answers closer to desired ones, and incur cost determined by the size of query results and questions only. We also show that these algorithms readily extend to other Why-questions such as Why-empty and Why-so-many. Using real-world graphs, we experimentally verify that our algorithms are effective and feasible for large graphs. Our case study also verifies their application in e.g., knowledge exploration.
Rater-Effect IRT Model Integrating Supervised LDA for Accurate Measurement of Essay Writing Ability

Essay-writing tests are widely used in various assessment contexts to measure higher-order abilities of learners. However, a persistent difficulty is that ability measurement accuracy strongly depends on rater characteristics. To resolve this problem, many item response theory (IRT) models have been proposed that can estimate learners’ abilities in consideration of rater effects. One remaining difficulty, however, is that measurement accuracy is reduced when few raters are assigned to each essay, a common situation in practical testing contexts. To address this problem, we propose a new rater-effect IRT model integrating a supervised topic model that can estimate abilities from raters’ scores and the textual content of written essays. By reflecting textual content features in IRT-based ability estimates, the model can improve ability measurement accuracy when there are few raters for each essay. Furthermore, learners’ abilities can be estimated using essay textual content alone, without ratings, when model parameters are known. Finally, scores for unrated essays can be estimated from textual content, so the model can be used for automated essay scoring. We evaluate the effectiveness of the proposed model through experiments using actual data.
Efficiently Repairing Internationalization Presentation Failures by Solving Layout Constraints

Web developers employ internationalization frameworks to automate web page translations and enable their web apps to more easily communicate with a global audience. However, the change of text size in different languages can lead to distortions in the translated web page's layout. These distortions are known as Internationalization Presentation Failures (IPFs). Debugging these IPFs can be a tedious and error-prone process. Previous research efforts to develop an automatic IPF repair technique could compromise the attractiveness and readability of the repaired web page. In this paper, we present a novel approach that can rapidly repair IPFs and maintain the readability and the attractiveness of the web page. Our approach models the correct layout of a web page as a system of constraints. The solution to the system represents the new and correct layout of the web page that resolves its IPFs. In the evaluation, we found that our approach could more quickly produce repairs that were rated as more attractive and more readable than those produced by a prior state-of-the-art technique.
The Effect of System Utilization on Application Performance Variability

Application performance variability caused by network contention is a major issue on dragonfly based systems. This work-in-progress study makes two contributions. First, we analyze real workload logs and conduct application experiments on the production system Theta at Argonne to evaluate application performance variability. We find a strong correlation between system utilization and performance variability where a high system utilization (e.g., above 95%) can cause up to 21% degradation in application performance. Next, driven by this key finding, we investigate a scheduling policy to mitigate workload interference by leveraging the fact that production systems often exhibit diurnal utilization behavior and not all users are in a hurry for job completion. Preliminary results show that this scheduling design is capable of improving system productivity (measured by scheduling makespan) as well as improving user-level scheduling metrics such as user wait time and job slowdown.
The anatomy of efficient FFT and winograd convolutions on modern CPUs

Winograd-based convolution has quickly gained traction as a preferred approach to implement convolutional neural networks (ConvNet) on various hardware platforms because it could require fewer floating point operations than FFT-based or direct convolutions.
Generating steganographic image description by dynamic synonym substitution

With the popularity of photo-sharing websites, it is wise to select these websites on which people usually post images and semantically related descriptive texts as channels of steganography. How to preserve the semantic correlation between the descriptive text and corresponding image for the security of text steganography on these websites is a challenging task. In this paper, we propose a novel steganographic image caption model (SIC) based on a typical image caption model Neural Image Caption (NIC) to automatically generate stego descriptions for given images. In the process of generation, secret information is embedded in the description by an elegant embedding method called dynamic synonym substitution (DSS) which can overcome the main drawback of normal synonym substitution based methods that the statistical characteristics of synonyms frequencies are changed after embedding. Our model takes the advantages of both generation based and synonym substitution based steganography and the good performance is evaluated by subjective and objective experiments. Experimental results show that stego descriptions generated by our model are almost as natural as descriptions generated by NIC and achieve high security against statistical analysis of steganalysis tools.
Mammogram segmentation using multi-atlas deformable registration

Accurate breast region segmentation is an important step in various automated algorithms involving detection of lesions like masses and microcalcifications, and efficient telemammography. While traditional segmentation algorithms underperform due to variations in image quality and shape of the breast region, newer methods from machine learning cannot be readily applied as they need a large training dataset with segmented images. In this paper, we propose to overcome these limitations by combining clustering with deformable image registration. Using clustering, we first identify a set of atlas images that best capture the variation in mammograms. This is done using a clustering algorithm where the number of clusters is determined using model selection on a low-dimensional projection of the images. Then, we use these atlas images to transfer the segmentation to similar images using deformable image registration algorithm. Our technique also overcomes the limitation of very few landmarks for registration in breast images. We evaluated our method on the mini-MIAS and DDSM datasets against three existing state-of-the-art algorithms using two performance metrics, Jaccard Index and Hausdorff Distance. We demonstrate that the proposed approach is indeed capable of identifying different types of mammograms in the dataset and segmenting them accurately.