Reviewerly/exHarmony huggingface dataset:
Practical and Secure Outsourcing Algorithms for Solving Quadratic Congruences in Internet of Things

Solving quadratic congruences is a widely applied operation in cryptographic protocols to ensure the data secrecy in the Internet of Things (IoT). Yet it requires unaffordable computation resource for resource-constrained IoT devices when bulk of this type of operations need to be performed. How to efficiently and effectively solve quadratic congruences on IoT devices becomes a challenging issue. To address this problem, in this article, we propose two practical and secure outsourcing algorithms for solving quadratic congruences. Our proposed algorithms enable the IoT devices to outsource the heavy computation of solving quadratic congruences to a single cloud server, and therefore, achieve high efficiency for IoT devices. Meanwhile, we obscure the input and the output so that the outsourcing process does not leak the privacy of the computation, and the IoT devices in our algorithms can detect any misbehavior of the cloud server with a probability of 1. In addition, we take the Rabin encryption algorithm as an example to show how our proposed algorithms can be applied to IoT applications. The theoretical analysis and experimental results support the fact that our proposed algorithms are secure and efficient.
Infrastructural Speculations: Tactics for Designing and Interrogating Lifeworlds

This paper introduces "infrastructural speculations," an orientation toward speculative design that considers the complex and long-lived relationships of technologies with broader systems, beyond moments of immediate invention and design. As modes of speculation are increasingly used to interrogate questions of broad societal concern, it is pertinent to develop an orientation that foregrounds the "lifeworld" of artifacts-the social, perceptual, and political environment in which they exist. While speculative designs often imply a lifeworld, infrastructural speculations place lifeworlds at the center of design concern, calling attention to the cultural, regulatory, environmental, and repair conditions that enable and surround particular future visions. By articulating connections and affinities between speculative design and infrastructure studies research, we contribute a set of design tactics for producing infrastructural speculations. These tactics help design researchers interrogate the complex and ongoing entanglements among technologies, institutions, practices, and systems of power when gauging the stakes of alternate lifeworlds.
Dialogue systems with audio context

Research on building dialogue systems that converse with humans naturally has recently attracted a lot of attention. Most work on this area assumes text-based conversation, where the user message is modeled as a sequence of words in a vocabulary. Real-world human conversation, in contrast, involves other modalities, such as voice, facial expression and body language, which can influence the conversation significantly in certain scenarios. In this work, we explore the impact of incorporating the audio features of the user message into generative dialogue systems. Specifically, we first design an auxiliary response retrieval task for audio representation learning. Then, we use word-level modality fusion to incorporate the audio features as additional context to our main generative model. Experiments show that our audio-augmented model outperforms the audio-free counterpart on perplexity, response diversity and human evaluation.
A comprehensive exploration of semantic relation extraction via pre-trained CNNs

Semantic relation extraction between entity pairs is a crucial task in information extraction from text. In this paper, we propose a new pre-trained network architecture for this task, and it is called the XM-CNN. The XM-CNN utilizes word embedding and position embedding information. It is designed to reinforce the contextual output from the MT-DNNKD pre-trained model. Our model effectively utilized an entity-aware attention mechanisms to detected the features and also adopts and applies more relation-specific pooling attention mechanisms applied to it. The experimental results show that the XM-CNN achieves state-of-the-art results on the SemEval-2010 task 8, and a thorough evaluation of the method is conducted.
Library and learning experiences turned mobile: A comparative study between LIS and non-LIS students

The rapid developments of wireless telecommunication networks and the widespread increase of smartphone ownership around the world have created tremendous impacts on the services provided by institutions of higher education worldwide on several dimensions. Unarguably, mobile technologies have created new and unforeseen opportunities for educators, information services providers and students to experience the new horizon of teaching, learning, as well as knowledge transfer and creation. Taking into consideration that since Library and Information Science (LIS) students are training to become future LIS professionals, they are expected to be ready as well as active in integrating mobile technology into their daily learning practices. This study was set up to examine two groups of students (namely: LIS versus non-LIS students) at Peking University – their attitudes and level of activeness in adopting mobile technology in their daily life, as well as in their learning practices. Quantitative questionnaire survey was used for data collection, and a total number of 319 responses (i.e., LIS, 63 and non-LIS, 256) were collected from this study. Findings of this study reveal that no significant differences were found between the two student groups (LIS versus non-LIS) in many areas. In fact, both LIS and non-LIS student groups were using their smartphones to engage in different learning, research, social networking, pastimes, and recreational activities on similar level. However, it was concerning to note that the LIS student group was slightly less active in accessing the online services and resources provided by their university library. This was a particular finding that went against the researchers' original anticipation. However, such unexpected finding did not mean that these LIS students were necessarily less active or 'open' as mobile learners. For the reason that in comparison to their non-LIS counterparts, they were equally as active as in terms of using their mobile devices for other learning (particularly collaborative learning) and research purposes. Further studies are recommended to determine various factors that are hindering these LIS students from using the university library's online resources and services on a more active level.
Measurement of global and direct normal solar energy radiation in Seri Iskandar and comparison with other cities of Malaysia

Potential solar data are an essential tool for successful solar design and application. However, because of the limited availability of solar radiation stations, spatial resolution is affected whenever an attempt to construct a solar radiation map is made. In this paper, actual solar data were acquired in Universiti Teknologi PETRONAS (UTP), Seri Iskandar, Malaysia (4°24′latitude, 100°58′E longitude, 24 m altitude). The measurements of global solar radiation and direct normal radiation were gathered and analyzed for the whole of 2018. In addition to solar data collection, real-time solar radiation, high accuracy, and related meteorological data were also obtained. With 1-min recorded average values the everyday and monthly solar radiation averages were determined. A record of 1068.10 W/m2 as maximum daily global solar radiation and 915 W/m2 for direct normal radiation was observed on 9 September 2018. Discussions on daily and monthly average clearness index differences are also elaborated in this paper. The acquired data were compared with corresponding data obtained from other selected Malaysian cities and the widely useable data resource, the NASA solar energy model and surface meteorology. Investigation of the data indicated that Seri Iskandar obtains an ample amount of global solar radiation, indicating the strong potential for the use of solar energy.
ROI-Attention Vectorized CNN Model for Static Facial Expression Recognition

When using neural networks to recognize facial expressions, determining which features help identify different expressions is essential, and there is a massive information transmission loss between layers of network with multiple layers. This paper proposes a robust vectorized convolutional neural network (CNN) model that introduces an attention mechanism for extracting features in the region of interests(ROIs) of the face. The ROIs in the facial image are marked before the image is input into the neural network. In particular, the attention concept is adopted in the first layer of the proposed neural network to perform ROIs-related convolution calculation, and ROIs-related convolution calculation results of the specific fields in the ROIs are increased by extracting more robust features. Next, the idea of features' vectors inspired by CapsNet is used in the following layer of the proposed neural network. Multi-level convolutions are used to extract feature vectors of different ROIs for facial expression, and then the feature vectors are reconstructed by a decoder to reconstruct the image. Comprehensive comparative experiments and cross-database experiments are conducted to verify the validity and robustness of our proposed model. The experimental results also demonstrate that our method is very effective in improving the performance of facial expressions recognition.
Learning a cortical parcellation of the brain robust to the MRI segmentation with convolutional neural networks

The parcellation of the human cortex into meaningful anatomical units is a common step of various neuroimaging studies. There have been multiple successful efforts to process magnetic resonance (MR) brain images automatically and identify specific anatomical regions, following atlases defined from cortical landmarks. Those definitions usually rely first on a high-quality brain surface reconstruction. On the other hand, when high accuracy is not a requirement, simpler methods based on warping a probabilistic atlas have been widely adopted. Here, we develop a cortical parcellation method for MR brain images based on Convolutional Neural Networks (ConvNets), a machine-learning method, with the goal of automatically transferring the knowledge obtained from surface analyses onto something directly applicable on simpler volume data. We train a ConvNet on a large (thousand) set of cortical ribbons of multiple MRI cohorts, to reproduce parcellations obtained from a surface method, in this case FreeSurfer. Further, to make the model applicable in a broader context, we force the model to generalize to unseen segmentations. The model is evaluated on unseen data of unseen cohorts. We characterize the behavior of the model during learning, and quantify its reliance on the dataset itself, which tends to give support for the necessity of large training sets, augmentation, and multiple contrasts. Overall, ConvNets can provide an efficient way to parcel MRI images, following the guidance established within more complex methods, quickly and accurately. The trained model is embedded within a open-source parcellation tool available at https://github.com/bthyreau/parcelcortex.
An Approximate Memory Architecture for Energy Saving in Deep Learning Applications

DRAM devices require periodic refresh operations to preserve data integrity. Slowing down the refresh rate can reduce the energy consumption; however, it may cause a loss of data stored in the DRAM cell. This paper proposes a new memory architecture of soft approximation for deep learning applications, which reduces the refresh energy consumption while maintaining accuracy and high performance. Utilizing the error-tolerant property of deep learning applications, the proposed memory architecture avoids the accuracy drop caused by data loss by flexibly controlling the refresh operation for different bits, depending on their criticality. For data storage, the approximate DRAM architecture reorganizes the data so that these data are sorted according to their bit significance. Critical bits are stored in more frequently refreshed devices while non-critical bits are stored in less frequently refreshed devices. In addition, for further reduction of the DRAM energy consumption, this paper combines hard approximation, which reduces the number of accesses to DRAM, with soft approximation. Simulation results show that the refresh energy consumption is reduced by 69.71%, and the total energy consumption is reduced by 26.0 % for the hybrid memory with a negligible drop in both training and testing phases on state-of-the-art deep networks.
Ethical approaches and autonomous systems

In this paper we consider how the three main approaches to ethics – deontology, consequentialism and virtue ethics – relate to the implementation of ethical agents. We provide a description of each approach and how agents might be implemented by designers following the different approaches. Although there are numerous examples of agents implemented within the consequentialist and deontological approaches, this is not so for virtue ethics. We therefore propose a novel means of implementing agents within the virtue ethics approach. It is seen that each approach has its own particular strengths and weaknesses when considered as the basis for implementing ethical agents, and that the different approaches are appropriate to different kinds of system.
RefDiff 2.0: A Multi-Language Refactoring Detection Tool

Identifying refactoring operations in source code changes is valuable to understand software evolution. Therefore, several tools have been proposed to automatically detect refactorings applied in a system by comparing source code between revisions. The availability of such infrastructure has enabled researchers to study refactoring practice in large scale, leading to important advances on refactoring knowledge. However, although a plethora of programming languages are used in practice, the vast majority of existing studies are restricted to the Java language due to limitations of the underlying tools. This fact poses an important threat to external validity. Thus, to overcome such limitation, in this paper we propose RefDiff 2.0, a multi-language refactoring detection tool. Our approach leverages techniques proposed in our previous work and introduces a novel refactoring detection algorithm that relies on the Code Structure Tree (CST), a simple yet powerful representation of the source code that abstracts away the specificities of particular programming languages. Despite its language-agnostic design, our evaluation shows that RefDiff's precision (96 percent) and recall (80 percent) are on par with state-of-the-art refactoring detection approaches specialized in the Java language. Our modular architecture also enables one to seamlessly extend RefDiff to support other languages via a plugin system. As a proof of this, we implemented plugins to support two other popular programming languages: JavaScript and C. Our evaluation in these languages reveals that precision and recall ranges from 88 to 91 percent. With these results, we envision RefDiff as a viable alternative for breaking the single-language barrier in refactoring research and in practical applications of refactoring detection.
Pedestrian Detection at Night in Infrared Images Using an Attention-Guided Encoder-Decoder Convolutional Neural Network

Pedestrian-related accidents are much more likely to occur during nighttime when visible (VI) cameras are much less effective. Unlike VI cameras, infrared (IR) cameras can work in total darkness. However, IR images have several drawbacks, such as low-resolution, noise, and thermal energy characteristics that can differ depending on the weather. To overcome these drawbacks, we propose an IR camera system to identify pedestrians at night that uses a novel attention-guided encoder-decoder convolutional neural network (AED-CNN). In AED-CNN, encoder-decoder modules are introduced to generate multi-scale features, in which new skip connection blocks are incorporated into the decoder to combine the feature maps from the encoder and decoder module. This new architecture increases context information which is helpful for extracting discriminative features from low-resolution and noisy IR images. Furthermore, we propose an attention module to re-weight the multi-scale features generated by the encoder-decoder module. The attention mechanism effectively highlights pedestrians while eliminating background interference, which helps to detect pedestrians under various weather conditions. Empirical experiments on two challenging datasets fully demonstrate that our method shows superior performance. Our approach significantly improves the precision of the state-of-the-art method by 5.1% and 23.78% on the Keimyung University (KMU) and Computer Vision Center (CVC)-09 pedestrian dataset, respectively.
Overview of Blockchain Consensus Mechanism

The advent of the Bitcoin system has brought another boom in the Internet era. In a very short time, many Blockchain systems come into being successively, whose decentration, consensus mechanisms, intelligent contract, and other characteristics make them applicable to various fields such as finance, education, medical, technology, etc. The consensus mechanism is the core of Blockchain technology. And a good consensus mechanism plays a very important role in the stable operation of the Blockchain system. The continuous improvement of consensus mechanisms such as PoW (proof-of-work), PoS (proof-of-stake), DPoS (delegated-proof-of-stake), and PBFT (Practical Byzantine Fault Tolerance) has led to the evolution of Blockchain technology to Blockchain 3.0. Starting from the issue of Byzantine generals, this article analyzes common consensus mechanisms based on existing Blockchain applications and then evaluates their consistency. This paper has a unique perspective, selection and comment on the latest progress and important literature in the field of block chain. At last, it also has a high degree of summary and prospect to the existing problems and the future development trend.
Evaluating the Use of Augmented Reality Technology to Improve Construction Management Student’s Spatial Skills

There is a continual challenge within the construction industry to achieve schedule, budget, and quality expectations at a time when projects are more complex, and their design and assembly involve some measure of abstract spatial skills. The outcomes of teaching these spatial skills in today's construction management classroom often fall short of meeting the needs of industry. This research advocates the use of augmented reality – which allows one to view the real world with the addition of external information intended to provide a new understanding of what is being seen. This study found that students perceived ease of effort and satisfaction by using augmented reality to help them with their spatial skills learning. In fact, it was discovered that the use of augmented reality benefited the cognitive process. Augmented reality is fast proving advantageous in many other industries, and with the availability of smaller, more powerful consumer mobile devices, augmented reality can become a practical tool for the construction industry as well. The addition of these technologies, as a complement to drafting and plan reading courses, can enhance postsecondary education, in addition to preparing the construction industry's future workforce.
Suitability of Eye Tracking in Assessing the Visual Perception of Architecture—A Case Study Concerning Selected Projects Located in Cologne

This article discusses the visual perception of selected buildings located in the historic centre of Cologne, Germany, that have been designed by outstanding architects. It presents eye-tracking research, both from a theoretical perspective and that of its application potential in, among other fields, psychology, management, architecture and urban planning. It also presents an experiment which was performed to evaluate the suitability of eye tracking in the assessment of the visual perception of architecture and its surroundings, utilising the case study method and members of Generation Z as the subject population. Analysis of the experiment’s results enabled the authors to formulate commentary on findings concerning typically observed attractors and distractors in the perception of architecture and its surroundings depending on context-specific conditions. The study provided evidence of the suitability of eye tracking in the assessment of the visual perception of works of architecture and indicated the possibility of continuing research concerning the assessment and shaping of the state of awareness and knowledge of architecture and urban planning, which can significantly affect public participation in urban governance.
Multimodal Document Image Classification

State-of-the-art methods for document image classification rely on visual features extracted by deep convolutional neural networks (CNNs). These methods do not utilize rich semantic information present in the text of the document, which can be extracted using Optical Character Recognition (OCR). We first study the performance of state-of-the-art text classification approaches when applied to noisy text obtained from OCR. We then show that fusing this textual information with visual CNN methods produces state-of-the-art results on the RVL-CDIP classification dataset.
Blind Leading the Sighted

Voice-activated personal assistants (VAPAs) are becoming smaller, cheaper, and more accurate, such that they are now prevalent in homes (e.g., Amazon Echo, Sonos One) and on mobile devices (e.g., Google Assistant, Apple Siri) around the world. VAPAs offer considerable potential to individuals who are blind, offering efficiencies over gesture-based input on touchscreen devices. However, research is just beginning to reveal the ways in which these technologies are used by people who are blind. In the first of two studies, we interviewed 14 blind adults with experience of home and/or mobile-based VAPAs, surfacing myriad accessibility, usability, and privacy issues for this community. A second study analyzing podcast content from 28 episodes relating to blind interactions with VAPAs was then undertaken to validate and extend findings from the first study. In addition to verifying prior findings, we learned that blind users wanted to leverage VAPAs for more productivity-oriented tasks and increased efficiency over other interaction modalities. We conclude that (1) VAPAs need to support a greater variety of AI personas, each specializing in a specific type of task; (2) VAPAs need to maintain continuity of voice interaction for both usability and accessibility; and (3) blind VAPA users, and especially blind technology podcasters, are expert voice interface users who should be incorporated into design processes from the beginning. We argue that when the blind lead the sighted through voice interface design, both blind and sighted users can benefit.