Reviewerly/exHarmony huggingface dataset:
How Robust Is Discourse Processing for Native Readers? The Role of Connectives and the Coherence Relations They Convey

While corpus studies have shown that discourse connectives that convey the same coherence relation can display subtle differences, research on online discourse processing has only focused on a rather limited set of connectives. Yet, different connectives - for example, rare or polyfunctional ones - might elicit different reading patterns. In order to explore this assumption, we test the robustness of discourse processing for French native speakers by measuring the way they process causal and concessive sentences that are conveyed by either an appropriate or inappropriate connective. Throughout three experiments, we change important characteristics of the connectives: we first test frequently used connectives (Experiment 1), secondly less frequent ones (Experiment 2), and finally less frequent connectives that are polyfunctional and for which different functions clearly compete (Experiment 3). Our results show that the processing for incoherent items was affected for all connectives, however readers showed altered reading fluency when infrequent connectives were used. We conclude that discourse processing is quite robust and that readers are able to insert meaning conveyed by rare connectives while still showing the highest reading ease with frequent connectives.
A Dynamic Cyber Security Situational Awareness Framework for Healthcare ICT Infrastructures

The healthcare sectors have experienced a massive technical evolution over the past decade by integration of medical devices with IT at both physical and cyber level for a critical Health Care Information Infrastructure (HCII). HCII provides huge benefits for the health care service delivery but evolving digital interconnectivity among medical and IT devices has also changed the threat landscape. In particular, systems are now more exposed to the cyber-attacks due to sensitivity and criticality of patient health care information and accessibility of medical devices and this pose any potential disruption of healthcare service delivery. There is a need to enhance security and resilience of HCII. In this paper, we present a Cyber Security Situational Awareness Framework that aims to improve the security and resilience of the overall HCII. The framework aims to develop a novel dynamic Situational Awareness approach on the health care ecosystem. We consider bio inspired Swarm Intelligence and its inherent features with the main principles of the Risk and Privacy assessment and management and Incident handling to ensure security and resilience of healthcare service delivery.
Patient Feedback Based Physician Selection in Blockchain Healthcare Using Deep Learning

In the healthcare system, selecting the correct physician is very important. Physicians have specialization and repute also for the treatment of the patient. Blockchain is reliable, anonymous, immutable and distributed ledger technology, best for the healthcare system. In blockchain privacy of the patient is maintained. So for selecting the correct physician, the feedback of previously treated patients by the physician also matters. We proposed a blockchain-based healthcare system that takes feedback from the patients. This proposed system uses a deep learning model based on RNN (Recurrent Neural Network), which optimizes the patient’s feedback and suggests selecting the recommended physician for the next cycle of patient appointment. This proposed system is implemented using PHP-based blockchain and uses a customized hash solving consensus algorithm. This system is implemented in a private blockchain. The deep learning part is implemented using LSTM(Long Short Term Memory) RNN using TensorFlow. Feedback of patients is collected in the blockchain and mark as transactions. The reputed physicians are a miner in the blockchain network. The accuracy of the proposed system is about 70% as the blockchain da does not have many features as the plain dataset.
Toward systematic architectural design of near-term trapped ion quantum computers

Trapped ions (TIs) are a leading candidate for building Noisy Intermediate-Scale Quantum (NISQ) hardware. TI qubits have fundamental advantages over other technologies, featuring high qubit quality, coherence time, and qubit connectivity. However, current TI systems are small in size and typically use a single trap architecture, which has fundamental scalability limitations. To progress toward the next major milestone of 50--100 qubit TI devices, a modular architecture termed the Quantum Charge Coupled Device (QCCD) has been proposed. In a QCCD-based TI device, small traps are connected through ion shuttling. While the basic hardware components for such devices have been demonstrated, building a 50--100 qubit system is challenging because of a wide range of design possibilities for trap sizing, communication topology, and gate implementations and the need to match diverse application resource requirements. Toward realizing QCCD-based TI systems with 50--100 qubits, we perform an extensive application-driven architectural study evaluating the key design choices of trap sizing, communication topology, and operation implementation methods. To enable our study, we built a design toolflow, which takes a QCCD architecture's parameters as input, along with a set of applications and realistic hardware performance models. Our toolflow maps the applications onto the target device and simulates their execution to compute metrics such as application run time, reliability, and device noise rates. Using six applications and several hardware design points, we show that trap sizing and communication topology choices can impact application reliability by up to three orders of magnitude. Microarchitectural gate implementation choices influence reliability by another order of magnitude. From these studies, we provide concrete recommendations to tune these choices to achieve highly reliable and performant application executions. With industry and academic efforts underway to build TI devices with 50-100 qubits, our insights have the potential to influence QC hardware in the near future and accelerate the progress toward practical QC systems.
Fast and Efficient Restoration of Extremely Dark Light Fields

The ability of Light Field (LF) cameras to capture the 3D geometry of a scene in a single photographic exposure has become central to several applications ranging from passive depth estimation to post-capture refocusing and view synthesis. But these LF applications break down in extreme low-light conditions due to excessive noise and poor image photometry. Existing low-light restoration techniques are inappropriate because they either do not leverage LF’s multi-view perspective or have enormous time and memory complexity. We propose a three-stage network that is simultaneously fast and accurate for real world applications. Our accuracy comes from the fact that our three stage architecture utilizes global, local and view-specific information present in low-light LFs and fuse them using an RNN inspired feedforward network. We are fast because we restore multiple views simultaneously and so require less number of forward passes. Besides these advantages, our network is flexible enough to restore a m × m LF during inference even if trained for a smaller n × n (n < m) LF without any finetuning. Extensive experiments on real low-light LF demonstrate that compared to the current state-of-the-art, our model can achieve up to 1 dB higher restoration PSNR, with 9× speedup, 23% smaller model size and about 5× lower floating-point operations.
Faster MDNet for Visual Object Tracking

With the rapid development of deep learning techniques, new breakthroughs have been made in deep learning-based object tracking methods. Although many approaches have achieved state-of-the-art results, existing methods still cannot fully satisfy practical needs. A robust tracker should perform well in three aspects: tracking accuracy, speed, and resource consumption. Considering this notion, we propose a novel model, Faster MDNet, to strike a better balance among these factors. To improve the tracking accuracy, a channel attention module is introduced to our method. We also design domain adaptation components to obtain more generic features. Simultaneously, we implement an adaptive, spatial pyramid pooling layer for reducing model complexity and accelerating the tracking speed. The experiments illustrate the promising performance of our tracker on OTB100, VOT2018, TrackingNet, UAV123, and NfS.
An Optimized Convolutional Neural Network with Combination Blocks for Chinese Sign Language Identification

(Aim) Chinese sign language is an essential tool for hearing-impaired to live, learn and communicate in deaf communities. Moreover, Chinese sign language plays a significant role in speech therapy and rehabilitation. Chinese sign language identification can provide convenience for those hearing impaired people and eliminate the communication barrier between the deaf community and the rest of society. Similar to the research of many biomedical image processing (such as automatic chest radiograph processing, diagnosis of chest radiological images, etc.), with the rapid development of artificial intelligence, especially deep learning technologies and algorithms, sign language image recognition ushered in the spring. This study aims to propose a novel sign language image recognition method based on an optimized convolutional neural network. (Method) Three different combinations of blocks: Conv-BN-ReLU-Pooling, Conv-BN-ReLU, Conv-BN-ReLU-BN were employed, including some advanced technologies such as batch normalization, dropout, and Leaky ReLU. We proposed an optimized convolutional neural network to identify 1320 sign language images, which was called as CNN-CB method. Totally ten runs were implemented with the hold-out randomly set for each run. (Results) The results indicate that our CNN-CB method gained an overall accuracy of 94.88 ± 0.99%. (Conclusion) Our CNN-CB method is superior to thirteen state-ofthe-art methods: eight traditional machine learning approaches and five modern convolutional neural network approaches
Combining UEQ and Eye-Tracking Method as Usability Evaluation for Mobile Apps

Using common and widely usability methods for mobile apps are good but combining two or more usability methods is greater than good. This paper aims to evaluate the user experience of a mobile app by combining the User Experience Questionnaire (UEQ) and eye-tracking methods. The goal is to have further knowledge when combining two methods instead of using only one method. The sample was using 228 participants with UEQ and 20 participants in lab assessment with eye-tracking-specific software. When UEQ is conducting in phase 1 and eye-tracking is conducted in phase 2, new knowledge will be created based on the research and give the developers information to improve the apps. The result shows the attractiveness component has the highest UEQ score, but when it combines with eye-tracking software, not all the components are positive or attractive. Other considerations such as efficiency, perspicuity, dependability, stimulation, and novelty are positive from a UX perspective.
An Extended Intelligent Water Drop Strategy for Process Scheduler in Cloud

Cloud computing is a most emerging paradigm with high resource power for working in most heterogeneous computing environment. Cloud computing provides the collection of computing resources present in data centres. These data centres are responsible for the managing user's request and optimal utilization of resources. There is massive amount of task submitted at a time by the user so it is a challenge for public cloud provider to manage the task in such a way so that the performance of the service may not decrease. An extended intelligent water drop (EIWD) algorithm is an efficient modified nature inspired efficient task scheduling strategy which focuses on the multiple objectives to schedule the workflow in the cloud environment. The workflow is gaining more attention in the cloud environment because it is emerged as a paradigm in the advanced complex computing environment. The strategy is simulated with diverse cost models on the simulation platform of cloud (open-source) and results are compared and discussed with different nature inspired strategies to find the optimal result.
Automated Platform Governance Through Visibility and Scale: On the Transformational Power of AutoModerator

When platforms use algorithms to moderate content, how should researchers understand the impact on moderators and users? Much of the existing literature on this question views moderation as a series of decision-making tasks and evaluates moderation algorithms based on their accuracy. Drawing on literature from the field of platform governance, I argue that content moderation is more than a series of discrete decisions but rather a complex system of rules, mechanism, and procedures. Research must therefore articulate how automated moderation alters the broader regime of governance on a platform. To demonstrate this, I report on the findings of a qualitative study on the Reddit bot AutoModerator, using interviews and trace ethnography. I find that the scale of the bot allows moderators to carefully manage the visibility of content and content moderation on Reddit, fundamentally transforming the basic rules of governance on the platform.
Pipelined High-Throughput NTT Architecture for Lattice-Based Cryptography

Lattice-based cryptography is a powerful cryptographic primitive that can achieve post-quantum security. The most computational-intensive operations in the lattice-based cryptographic schemes are the polynomial multiplications over the ring, which can be accelerated by adopting the number theoretic transform (NTT) in practical applications. This paper proposes a novel hardware accelerator for the NTT algorithm for lattice-based cryptography applications, which can achieve full utilization for all the hardware components. The key ideas involve exploiting well-designed folding sets and applying the folding transformations to adapt the fast Fourier transform (FFT) multi-path delay commutator architectures and a lightweight modular multiplier.
ChordAR: An Educational AR Game Design for Children’s Music Theory Learning

Augmented reality (AR) technology, with its unique immersive interactive experience of virtual reality integration, can show children abstract concepts and theories in an intuitive way. In this paper, we improve the high-precision position and low delay interaction of AR recognition area through edge calculation, so as to enhance the immersion of children’s chord knowledge learning. Meanwhile, multisource fusion perception of audio and touch is used, and the AI algorithm model is used to monitor physical events (cloud AI training and edge execution) and effectively realize intelligent services such as chord combination, melody perception, and control. Chord knowledge in music theory is presented to children through multichannels (kinesthetic, visual, auditory, etc.) situationally, to improve children’s learning efficiency and experience. We invited 12 children to participate in user experiments to test usability of ChordAR. The test results showed that children can master the method of playing the game through simple learning, ultimately correctly imitating C, G, and E chords, and experience full immersion in the game. Finally, we discuss the positive effects of ChordAR on children’s learning and creativity and make suggestions for future AR games.
The Seismic Early Warning System of Mexico (SASMEX): A Retrospective View and Future Challenges

The earthquake early warning system of Mexico, SASMEX, has 30 years of uninterrupted and successful operation. During this time, the system recorded ∼9,800 earthquakes and broadcast 111 alerts. Alerting was simplified recently, avoiding the emission of two types of alerts. Only earthquakes above a magnitude threshold, dependent on distance to the target city are alerted. SASMEX disseminates early warnings using dedicated receivers, public loudspeakers, multi-hazard radios, and participating TV and radio stations. It is estimated that ∼25 million people receive alert messages from SASMEX. Cell-broadcasting messaging, necessary for the timely delivery of alerts, is not implemented by the local cellular phone operators. The addition of cell phone communication would increase the number of users benefitting from the system. SASMEX does not publish ground motion predictions at the time of issuing the alert . Instead, it distributes a map of peak ground acceleration in Mexico City ∼1 minute after the arrival of strong motion, via electronic messaging. The accepted practice for the population in general is to evacuate at the sound of the alert. This is useful in schools and low-rise buildings, where people are generally drilled to evacuate rapidly. It is not effective in high-rise buildings and where large numbers of people concentrate. Finding protection and not trying to evacuate may be a better option, as it is recommended by other seismic early warning systems. The damaging 19 September 2017 earthquake underlined the difficulties of alerting earthquakes at close distances. Using a different sound of the alert or a countdown may be advisable, so people understand they have less time than normally assumed. There are few social studies on the use of the alert. It is suggested to conduct these studies to explore better ways to use and communicate the seismic alert, including automatic processes to shut down hazardous facilities.
Why does the president tweet this? Discovering reasons and contexts for politicians’ tweets from news articles

Politicians’ tweets can have important political and economic implications. However, limited context makes it hard for readers to instantly and precisely understand them, especially from a causal perspective. The triggers for these tweets may have been reported in news prior to the tweets, but simply finding similar news articles would not serve the purpose, given the following reasons. First, readers may only be interested in finding the reasons and contexts (we call causal backgrounds) for a certain part of a tweet. Intuitively, such content would be politically relevant and accord with public’s recent attention, which is not usually reflected within the context. Besides, the content should be human-readable, while the noisy and informal nature of tweets hinders regular Open Information Extraction systems. Second, similarity does not capture causality and the causality between tweet contents and news contents is beyond the scopes of causality extraction tools. Meanwhile, it will be non-trivial to construct a high-quality tweet-to-intent dataset. We propose the first end-to-end framework for discovering causal backgrounds of politicians’ tweets by: 1. Designing an Open IE system considering rule-free representations for tweets; 2. Introducing sources like Wikipedia linkage and edit history to identify focal contents; 3. Finding implicit causalities between different contexts using explicit causalities learned elsewhere. We curate a comprehensive dataset of interpretations from political journalists for 533 tweets from 5 US politicians. On average, we obtain the correct answers within top-2 recommendations. We make our dataset and framework code publicly available. • A first end-to-end framework discovering causal backgrounds for politicians’ tweets. • A clause-based Open IE system with considerations on rule-free representations. • We find implicit causalities between tweets and news articles. • Experiment results correspond well with political journalists’ analysis. • We publish a first well-curated tweet-intent-interpretation benchmark dataset.
Automated Detection of Spectre and Meltdown Attacks Using Explainable Machine Learning

Spectre and Meltdown attacks exploit security vulnerabilities of advanced architectural features to access inherently concealed memory data without authorization. Existing defense mechanisms have three major drawbacks: (i) they can be fooled by obfuscation techniques, (ii) the lack of transparency severely limits their applicability, and (iii) it can introduce unacceptable performance degradation. In this paper, we propose a novel detection scheme based on explainable machine learning to address these fundamental challenges. Specifically, this paper makes three important contributions. (1) Our work is the first attempt in applying explainable machine learning for Spectre and Meltdown attack detection. (2) Our proposed method utilizes the temporal differences of hardware events in sequential timestamps instead of overall statistics, which contributes to the robustness of ML models against evasive attacks. (3) Extensive experimental evaluation demonstrates that our approach can significantly improve detection efficiency (38.4% on average) compared to state-of-the-art techniques.
A Stacked Bidirectional LSTM Model for Classifying Source Codes Built in MPLs

Over the years, programmers have improved their programming skills and can now write code in many different languages to solve problems. A lot of new code is being generated all over the world regularly. Since a programming problem can be solved in many different languages, it is quite difficult to identify the problem from the written source code. Therefore, a classification model is needed to help programmers identify the problems built (written/developed) in Multi-Programming Languages (MPLs). This classification model can help programmers learn better programming. However, source code classification models based on deep learning are still lacking in the field of programming education and software engineering. To address this gap, we propose a stacked Bidirectional Long Short-Term Memory (Bi-LSTM) neural network-based model for classifying source codes developed in MPLs. To accomplish this research, we collect a large number of real-world source codes from the Aizu Online Judge (AOJ) system. The proposed model is trained, validated, and tested on the AOJ dataset. Various hyperparameters are fine-tuned to improve the performance of the model. Based on the experimental results, the proposed model achieves an accuracy of about 93% and an F1-score of 89.24%. Moreover, the proposed model outperforms the state-of-the-art models in terms of other evaluation matrices such as precision (90.12%) and recall (89.48%).
Effective Reskilling of Foreign-Born People at Universities - The Software Development Academy

Contribution: An intensive three-month educational program can be used for rapid integration of foreign-born people into the IT industry. A novel method for integrating industrial needs with the practical parts of a bachelor’s Computer Science program. Background: The program was motivated by (1) the societal need to increase the meaningful integration of immigrants into the workforce, and (2) the demand for IT specialists in the IT labor market. Intended outcomes: An effective intensive software developer program with a high level of industrial integration and a working matching model for employment. Application design: The program consists of three different phases; recruitment of participants, training and job matching. The training is divided into six modules using five different teaching methods. An evaluation model, based on passive and active data, is implemented with fast learning loops for teachers and participants. Findings: The program has been run seven times with 263 unemployed participants of different nationalities. On average 82.6 percent of the participants found employment in the IT industry within 5 months of the course ending. Female participants are in the majority and are more successful in securing employment. The findings suggest that it was possible to rapidly prototype and deliver an advanced reskilling program within a university setting and use it as a positive method to support newcomers find meaningful work that has a direct benefit for the local IT industry, as well as for the wider society.