Reviewerly/exHarmony huggingface dataset:
SPARK-MSNA: Efficient algorithm on Apache Spark for aligning multiple similar DNA/RNA sequences with supervised learning

Abstract Multiple sequence alignment (MSA) is an integral part of molecular biology. But handling massive number of large sequences is still a bottleneck for most of the state-of-the-art software tools. Knowledge driven algorithms utilizing features of input sequences, such as high similarity in case of DNA sequences, can help in improving the efficiency of DNA MSA to assist in phylogenetic tree construction, comparative genomics etc. This article showcases the benefit of utilizing similarity features while performing the alignment. The algorithm uses suffix tree for identifying common substrings and uses a modified Needleman-Wunsch algorithm for pairwise alignments. In order to improve the efficiency of pairwise alignments, a knowledge base is created and a supervised learning with nearest neighbor algorithm is used to guide the alignment. The algorithm provided linear complexity O(m) compared to O ( m 2 ). Comparing with state-of-the-art algorithms (e.g., HAlign II), SPARK-MSNA provided 50% improvement in memory utilization in processing human mitochondrial genome (mt. genomes, 100x, 1.1. GB) with a better alignment accuracy in terms of average SP score and comparable execution time. The algorithm is implemented on big data framework Apache Spark in order to improve the scalability. The source code &amp; test data are available at: https://sourceforge.net/projects/spark-msna/ .
Saliency From Growing Neural Gas: Learning Pre-Attentional Structures for a Flexible Attention System

Artificial visual attention has been an active research area for over two decades. Especially, the concept of saliency has been implemented in many different ways. Early approaches aimed at closely modeling saliency processing with concepts from biological attention to provide (at least in the long run) general-purpose attention for technical systems. More recent approaches have departed from this agenda, turning to more specific attention-guided tasks, most notably the accurate extraction of salient objects, for which large-scale ground truth datasets make it possible to quantify progress. While the first type of models is troubled by weak performance in these specific tasks, the second type, as we show with a new benchmark, has lost the ability to predict saliency in the original sense, which may be an important factor for future general-purpose attention systems. Here, we describe a new approach using growing neural gas to obtain pre-attentional structures for a scene at an early processing stage. On this basis, traditional saliency concepts can be applied while at the same time they can be linked to mechanisms that make models successful in salient object detection. The model shows high performance at predicting traditional saliency and makes substantial progress toward salient object detection, although it cannot reach the top-level performance of some specialized methods. We discuss the important implications of our findings.
A New Algorithm for Autonomous Outdoor Navigation of Robots that Can Fly and Drive

Robots with the ability of dual mode locomotion can drive and fly. Autonomous selection of locomotion mode for minimum resource consumption is a challenge. Previous methods for dual mode navigation requires a 3D map of surroundings, but our proposed method requires a ground route on a 2D map. Locomotion Mode Selection (LMS) algorithm is developed for autonomous navigation of robots with dual mode locomotion. It is a path selection algorithm which selects either drive path or flight path depending upon their travel costs. A hybrid robot follows the selected path during autonomous outdoor dual mode navigation.
Classifying software issue reports through association mining

Software issue reports classification is a significant task in software maintenance and evolution. Despite the research effort being made over the years, the existing issue reports classification techniques are still inadequate. In this paper, we propose a new approach that is inspired by the Classification Associations Rule Mining (CARM) methodology in data mining, and report the testing of our method on 500 software issue reports extracted from an open source issue tracking system. Our experiments show that our method can achieve a high degree of accuracy in classifying software issue reports.
A Multiple-Loss Dual-Output Convolutional Neural Network for Fashion Class Classification

An improved multi-loss multi-output convolutional neural network method was deployed to extract features from a given set of disjointed data (Fashion and Color) with diverse convolutional chunks in a single network. The first convolution block extracts features from the first image dataset (Fashion) and determines the classes to which they belong. The second block is responsible for learning the information encoded in the second set of data (color), classify and append such to the features extracted from the first convolutional block. Each block possesses its loss function which makes the network a multi-loss convolutional neural network. A set of double fully connected output heads are generated at the network terminal; enabling the network to perform predictions on a combination of disjointed labels. To validate the classification ability of our network model, we conducted several experiments with different network parameters and variations of data sizes and obtained remarkable classification results of 98 and 95 on the fashion and color sets respectively.
Performance overhead of container orchestration frameworks for management of multi-tenant database deployments

The most preferred approach in the literature on service-level objectives for multi-tenant databases is to group tenants according to their SLA class in separate database processes and find optimal co-placement of tenants across a cluster of nodes. To implement performance isolation between co-located database processes, request scheduling is preferred over hypervisor-based virtualization that introduces a significant performance overhead. A relevant question is whether the more light-weight container technology such as Docker is a viable alternative for running high-end performance database workloads. Moreover, the recent uprise and industry adoption of container orchestration (CO) frameworks for the purpose of automated placement of cloud-based applications raises the question what is the additional performance overhead of CO frameworks in this context. In this paper, we evaluate the performance overhead introduced by Docker engine and two representative CO frameworks, Docker Swarm and Kubernetes, when running and managing a CPU-bound Cassandra workload in OpenStack. Firstly, we have found that Docker engine deployments that run in host mode exhibit negligible performance overhead in comparison to native OpenStack deployments. Secondly, we have found that virtual IP networking introduces a substantial overhead in Docker Swarm and Kubernetes due to virtual network bridges when compared to Docker engine deployments. This demands for service networking approaches that run in true host mode but offer support for network isolation between containers. Thirdly, volume plugins for persistent storage have a large impact on the overall resource model of a database workload; more specifically, we show that a CPU-bound Cassandra workload changes into an I/O-bound workload in both Docker Swarm and Kubernetes because their local volume plugins introduce a disk I/O performance bottleneck that does not appear in Docker engine deployments. These findings imply that solved placement decisions for native or Docker engine deployments cannot be reused for Docker Swarm and Kubernetes.
SRSubBandNet: A New Deep Learning Scheme for Single Image Super Resolution Based on Subband Reconstruction

In this paper, a new scheme for single image super resolution using convolutional neural networks and subband reconstruction theory is proposed. In the design of the network, which is referred to as SRSubBandNet, each subband of the residual signal between the high and low resolution images is reconstructed from all the previous subbands. Skip connections between the first, middle and the last SRBs are utilized to address the gradient vanishing problem in the proposed network. SRSubBandNet provides competitive results in terms of both subjective and objective qualities when applied to various benchmark datasets.
Participatory Design of a Virtual Reality-Based Reentry Training with a Women's Prison

This study examines the participatory design process of a virtual reality (VR) reentry training program with a women's prison. Conceptually drawing on previous work in VR exposure training, this prototype consists of guided, first-person 3D-360° video episodes that depict psychologically stressful situations that women commonly face when returning home. Critical story and production elements, including screenplay, acting, and narration, were created with incarcerated and formerly incarcerated women. The institutional, technological, and cultural restrictions of prison, combined with the tensions of making media with often exploited groups, forced adaptations of participatory design methods. The inclusion of incarcerated female voices resulted in an immersive narrative that reflects this group's specific challenges. The next phase is to evaluate its efficacy against non-immersive comparative trainings for reentry-related anxieties.
A Software Network Model for Software Structure and Faults Distribution Analysis

Since the development of computer science, our lives have become increasingly dependent on software. While we enjoy the benefits and convenience that software programs provide, we cannot ignore issues with software reliability, complexity, and security. Since the introduction of complex networks, people have been using software network to analyze software problems; however, traditional software network models are not currently capable of analyzing software with large scale and complex structures. In this paper, a new software network model is proposed, with which each node in the network can be assigned a set of coordinates that reflect its function-call information and make the disorder of the network graph more orderly. Additionally, characteristics and derivatives of the model are thoroughly examined and analyzed. A case study using the coordinate model combined with bug information is then conducted to analyze five different software programs. The results show that the proposed model can be used to analyze the relationship between nodes or defects distribution and software network parameters, as well as high-risk module excavation through a defect density analysis. Compared to traditional software network models, the model maintains the inner logic relationship of the software systems better, which makes it easier to analyze many aspects of software.
Growkit

The rapid growth of urban populations creates challenges for food production. One solution that is potentially more sustainable than current methods is localized production, in particular food production by individuals at home. Growing food at home is possible, but it is a process that requires motivation, knowledge and skills. Here, we present the design of a sensor platform aimed at helping individuals in urban environments grow food at home by informing them about the needs of their plants and, based on urban farming practices, by connecting them with a network of growers to share knowledge and produce.
Designing the Past

This paper challenges the position that design is a future oriented discipline, and rather turns an eye to the past as potential material for re-design. We claim that what we call 'the past' is far from static, monolithic, immutable, and is rather subjective, fluid, and constantly renegotiated. People constantly engage in re-designing the past by re-elaborating, reckoning, and plainly forgetting. The rewriting of the past, such as in historical revisionism, is often seen as an attempt to wipe-out, and hence re-inscribe and perpetuate, injustice, oppression, and even genocide. With this paper we call for more courage to take ownership of the past as something malleable, to take responsibility for it, and in so doing to open up design opportunities to a plurality of voices.
Accessible Instruments in the Wild

Disabled people face many barriers to access in all areas of life, including creative expression. With music making, a lack of accessible instruments can be a major barrier, as well as environmental factors. The Strummi is an accessible instrument based on the guitar, designed as a technology probe to explore the technical and cultural role of guitar-like design and interaction modality. We have been collaborating with Heart n Soul, an arts charity that works with young people and adults with learning disabilities. In this paper, we share findings from the first year of this collaboration, and reflect on the implications for doing HCI research with learning-disabled communities. We took a longitudinal, situated approach with an intentionally simple technology inspired by in the wild and technology probe methodologies, allowing for interest in the Strummi to grow organically.
Robust Image Translation and Completion Based on Dual Auto-Encoder With Bidirectional Latent Space Regression

Automated image translation and completion is a subject of keen interest due to their impact on image representation, interpretation, and enhancement. To date, a conditional or a dual adversarial framework with a convolutional auto-encoder embedded as a generator is known to offer the best accuracy in image translation. However, although the frequency is excellent, the adversarial framework may suffer from a lack of generality, i.e., the accuracy drops when translating incomplete and corrupted data given as untrained noisy input data. This paper proposes an approach to robust image-to-image translation that offers a high level of generality while keeping accuracy high as well. The proposed approach is referred to here as a dual auto-encoder with bidirectional latent space regression or Bi-directionally Associative DualAE, for short. The proposed BA-DualAE is configured with two auto-encoders the individual latent spaces of which are tightly associated by a bidirectional regression network. Once the two auto-encoders are trained independently for their respective domains, and then, the bidirectional regression network is trained to learn the general association between data pairs. With its capability of robust and bidirectional image translation, BA-DualAE performed direct image completion with no iterative search. The experiments with photo-sketch datasets demonstrated that the proposed BA-DualAE is highly robust under incomplete or corrupted data conditions and is far superior to adversarial frameworks in terms of generality and robustness.
Moldy Ghosts and Yeasty Invasions

Hybrid bio-digital games integrate real, biological materials into computer systems. They offer a rich, playful space in which interactions between humans, computers, and non-human organisms can be explored. However, the concept of video game 'glitching' in hybrid bio-digital games, specifically those that result from interactions between the biological and the computer hardware and/or software, have not been explored in great detail. We report two incidences of glitches observed during Mold Rush - a hybrid bio-digital game based on growth patterns of living mold: The creation of an additional game character (Moldy Ghosts), and the gameplay freeze (a Yeasty Invasion). As we interpret our observations, we question the potential for glitches to become valuable tools in framing HCI investigations into designing a productive and meaningful biological-digital interactions. The goal of this paper is to propose three testable routes in which glitches could be implemented. 1) Glitch as a tool for learning 2) Glitch as a precursor for an experience-enhancing game component, and 3) Glitch as an instigator for discourse on ethical implications of bio-digital games.
Ensuring Confidentiality in the Cloud of Things

Leveraging low-cost public clouds for gathering and processing data from large numbers of devices is an appealing approach to unleash the potential of the Internet of Things (IoT). This combination of clouds with the IoT—dubbed Cloud of Things (CoT)—is, however, built on insecure foundations. Public clouds exhibit inherent security vulnerabilities due to multitenancy, and adding resource-constrained IoT devices deployed in the wild further complicates cyber trust. A promising avenue consists in intelligently utilizing partially homomorphic encryption to compute on data in an encrypted form in an untrusted cloud. We summarize experiences gathered while transitioning this vision for confidentiality-preserving CoT to practice for processing continuous queries on streams of sensitive data generated by IoT devices.
Methodology for Efficient Reconfigurable Architecture of Generative Neural Network

Generative neural networks have been developing rapidly in the field of deep learning nowadays. Generative models have obtained much popularity in various applications such as image generation, reading comprehension and style transfer. Convolutional (CONV) and deconvolutional (DeCONV) layers are typical components of generative neural networks. The use of traditional convolution accelerators will cause problems of overlapping and resource under-utilization while doing deconvolutions. There is little research on acceleration of deconvolution implementations. In this paper, we propose efficient reconfigurable architecture of generative neural networks. Firstly, the fast reconfigurable unit (FRU) based on cascaded fast FIR algorithm (CFFA) is proposed to support both convolutions and deconvolutions. The problems of overlapping and resource under-utilization are solved. Secondly, the reconfigurable architecture on the basis of FRUs for CONV and DeCONV layers is proposed accordingly. Thirdly, a novel shift scale quantization method is proposed to uniformly quantize CONV and DeCONV layers. Only integer computations are required with the quantization method. Finally, we choose a typical generative neural network and implement it on Xilinx Zynq ZC706. It is estimated that the performance reaches 62.85 GOPS under 330MHz working frequency on Xilinx ZC706. In brief, the proposed design outperforms existing works significantly, particularly surpasses related reconfigurable design by more than 20 times in terms of performance density.
Transformation to a bird

Body ownership illusion can lead to give a user a feeling of becoming an avatar as if it was their own body. Furthermore, existing studies have reported that an avatar's appearance can affect a user's behavior, attitude and mental condition. Almost all studies thus far have been focused on a human-like avatar, not that of an animal avatar. Unlike a human, it can be expected that the bird avatar can leads to give psychological effects in a flight experience, because a bird has flight ability. Our previous study reported that the bird avatar enhances the sense of flight significantly (i.e. participants feel as though they are flying in the sky.) than a human avatar in a flight experience. In this present work, we investigate about changes in attitudes with the fear of heights. We conduct experiments where an avatar is raised to a specific altitude after inducing the sense of body ownership. There are no significant differences between conditions. However, the bird avatar is rated higher than control condition in some questionnaire items. In particular, becoming a bird avatar decreases the subjective fear of heights.
MarValous

Emotion analysis in text has drawn recent interests in the software engineering (SE) community. Existing domain-independent techniques for automated emotion/sentiment analysis perform poorly when operated on SE text. Thus, a few SE domain-specific tools are recently developed for detecting sentimental polarities (e.g., positivity, negativity). But, for capturing individual emotional states such as excitation, stress, depression, and relaxation, there is only one recent tool named DEVA, which uses a lexicon-based approach.
Usability of Code Voting Modalities

Internet voting has promising benefits, such as cost reduction, but it also introduces drawbacks: the computer, that is used for voting, learns the voter's choice. Code voting aims to protect the voter's choice by the introduction of voting codes that are listed on paper. To cast a vote, the voters need to provide the voting code belonging to their choice. The additional step influences the usability. We investigate three modalities for entering voting codes: manual, QR-codes and tangibles. The results show that QR-codes offer the best usability while tangibles are perceived as the most novel and fun.
GPU-based parallel optimization for real-time scale-invariant feature transform in binocular visual registration

Scale-invariant feature transform (SIFT) is one of the widely used interest point features. It has been successfully applied in various computer vision algorithms like object detection, object tracking, robotic mapping, and large-scale image retrieval. Although SIFT descriptor is highly robust towards scale and rotation variations, the high computational complexity of the SIFT algorithm inhibits its use in applications demanding real-time response and in algorithms dealing with very large-scale databases. In order to be effective for image matching process in near real-time, the Compute Unified Device Architecture (CUDA) application programming interface of a graphics processing unit (GPU) is incorporated to speed up or improve the SIFT method. Experimental results show that the proposed GPU-based SIFT framework is suitable for image application in real time. It can improve the image matching process both in time and accuracy compared with conventional SIFT method.
Quantum Zeno effect in a circuit-QED system

We study the quantum Zeno effect of the circuit-QED system at finite temperature for both selective measurement and nonselective measurement proposals. For the selective measurement proposal, we explicitly show that the counter-rotating terms (CRTs) become as significant as rotating wave terms (RWTs) with increase of temperature. We also derive an analytic expression for the survival probability which correctly captures the leading contribution for both RWTs and CRTs in the frequent measurement limit. Beyond this limit, we observe a nonexponential decay of the survival probability by further considering the effect of the variation of initial states during repeated measurements. For the nonselective measurement proposal, we find that the survival probability demonstrates the same behavior as in the selective measurement case in the short-time regime while it deviates from an exponential form and approaches a nonvanishing value in the long-time scale due to the repopulation effect.
Improving Two-Thumb Touchpad Typing in Virtual Reality

Two-Thumb Touchpad Typing (4T) using hand-held controllers is one of the common text entry techniques in Virtual Reality (VR). However, its performance is far below that of two-thumb typing on a smartphone. We explored the possibility of improving its performance focusing on the following two factors: the visual feedback of hovering thumbs and the grip stability of the controllers. We examined the effects of these two factors on the performance of 4T in VR in user experiments. Their results show that hover feedback had a significant main effect on the 4T performance, but grip stability did not. We then investigated the achievable performance of the final 4T design in a longitudinal study, and its results show that users could achieve a typing speed over 30 words per minute after two hours of practice.