Reviewerly/exHarmony huggingface dataset:
Abstractive Text Summarization with Multi-Head Attention

In this paper, we present a novel sequence-to-sequence architecture with multi-head attention for automatic summarization of long text. Summaries generated by previous abstractive methods have the problems of duplicate and missing original information commonly. To address these problems, we propose a multi-head attention summarization (MHAS) model, which uses multi-head attention mechanism to learn relevant information in different representation subspaces. The MHAS model can consider the previously predicted words when generating new words to avoid generating a summary of redundant repetition words. And it can learn the internal structure of the article by adding self-attention layer to the traditional encoder and decoder and make the model better preserve the original information. We also integrate the multi-head attention distribution into pointer network creatively to improve the performance of the model. Experiments are conducted on CNN/Daily Mail dataset, which is a long text English corpora. Experimental results show that our proposed model outperforms the previous extractive and abstractive models.
The Assessor's Dilemma: Improving Bug Repair via Empirical Game Theory

Priority inflation occurs when a Quality-Assurance (QA) engineer or a project manager requesting a feature inflates the priority of their task so that developers deliver the fix or the new functionality more quickly. We survey developers and show that priority inflation occurs and misallocates developer time. We are the first to apply empirical game-theoretic analysis (EGTA) to a software engineering problem, specifically priority inflation. First, we extract prioritisation strategies from 42,620 issues from Apache's JIRA, then use <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TaskAssessor</small> , our EGTA-based modelling approach, to confirm conventional wisdom and show that the common process of a QA engineer assigning priority labels is susceptible to priority inflation. We then show that the common mitigation strategy of having a bug triage team assigning priorities does not resolve priority inflation and slows development. We then use mechanism design to devise <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">assessor-throttling</i> , a new, lightweight prioritization process, immune to priority inflation. We show that assessor-throttling resolves 97 percent of high priority tasks, 69 percent better than simply relying on those filing tasks to assign priorities. Finally, we present The Fed, a browser extension for Chrome that supports assessor-throttling.
A single plasmon router based on the V-type three-level quantum dot sandwiched between two plasmonic waveguides

A single photon router with a desired high routing rate is of great importance for quantum networks. Recently, coupling between channeled plasmons and quantum emitters have been widely used to explore the routing feasibility in hybrid systems. As a capable alternative, we suggested a scheme of a single plasmon router using a V-type three-level quantum dot (QD) sandwiched between two plasmonic waveguides. We theoretically investigated the transmission and transfer rates of single plasmons in the complex system via the real-space approach. Our calculations show that the single plasmons from the incident route could be redirected by controlling such parameters as the intensity of the classical field, the detunings and the couplings between the QD and the plasmonic waveguides. Our proposed scheme could be used not only for the design of a single plasmon router but also for the design of quantum switches and quantum couplers in quantum photonic integrated circuits.
Secure Design Flow of FPGA Based RISC-V Implementation

In the process of globalization, heterogeneous SoCs play an important role in an embedded application, security aspects of such a system are crucial. The system is susceptible to many attacks out of which we focus on two main attacks, namely, boot time attacks, where malware are injected to leak information and modify the functionality and run-time software attacks causing memory corruption. In this paper, we propose a hardware/software-based solution to secure the system integrity by providing secure boot which prevents malicious and unauthorized software during startup and Information Flow Tracking (IFT) technique to track the spurious data during run-time and preventing buffer overflow attacks. This proposed solution is implemented on the RISC-V and provides a self-authentication mechanism for FPGAs using TPM.
XCMRC: Evaluating Cross-Lingual Machine Reading Comprehension

We present XCMRC, the first public cross-lingual language understanding (XLU) benchmark which aims to test machines on their cross-lingual reading comprehension ability. To be specific, XCMRC is a Cross-lingual Cloze-style Machine Reading Comprehension task which requires the reader to fill in a missing word (we additionally provide ten noun candidates) in a sentence written in target language (English/Chinese) by reading a given passage written in source language (Chinese/English). Chinese and English are rich-resource language pairs, in order to study low-resource cross-lingual machine reading comprehension (XMRC), besides defining the common XCMRC task which has no restrictions on use of external language resources, we also define the pseudo low-resource XCMRC task by limiting the language resources to be used. In addition, we provide two baselines for common XCMRC task and two for pseudo XCMRC task respectively. We also provide an upper bound baseline for both tasks. We found that for common XCMRC task, translation-based method and multilingual sentence encoder-based method can obtain reasonable performance but still have much room for improvement. As for pseudo low-resource XCMRC task, due to strict restrictions on the use of language resources, our two approaches are far below the upper bound so there are many challenges ahead.
On the Security and Applicability of Fragile Camera Fingerprints

Camera sensor noise is one of the most reliable device characteristics in digital image forensics, enabling the unique linkage of images to digital cameras. This so-called camera fingerprint gives rise to different applications, such as image forensics and authentication. However, if images are publicly available, an adversary can estimate the fingerprint from her victim and plant it into spurious images. The concept of fragile camera fingerprints addresses this attack by exploiting asymmetries in data access: While the camera owner will always have access to a full fingerprint from uncompressed images, the adversary has typically access to compressed images and thus only to a truncated fingerprint. The security of this defense, however, has not been systematically explored yet. This paper provides the first comprehensive analysis of fragile camera fingerprints under attack. A series of theoretical and practical tests demonstrate that fragile camera fingerprints allow a reliable device identification for common compression levels in an adversarial environment.
Fostering Virtual Guide in Exhibitions

Museums are essential to make culture accessible to the mass audience. Human museum guides are important to explain the presented artifacts to the visitors. Recently, museums started to experiment with enhancing exhibitions through mixed reality. It enables cultural exhibitors to provide each visitor with an individualized virtual guide that adapts to the visitor's interests. The effect of the presence and appearance of a virtual museum guide is, however, unclear. In this paper, we compare a real-world guide with a realistic, an abstract, and an audio-only representation of the virtual guide. Participants followed four multimodal presentations while we investigated the effect on comprehension and perceived co-presence. We found that a realistic representation of a virtual guide increases the perceived co-presence and does not adversely affect the comprehension of learning content in mixed reality exhibitions. Insights from our study inform the design of virtual guides for real-world exhibitions.
Dynamic Erasure Coding Policy Allocation (DECPA) in Hadoop 3.0

Erasure Code (EC) is being tipped to be the next best alternative to Replication for providing redundancy for Cloud Storage. Already major players like Microsoft and Facebook are having initial implementations using Erasure Code. Hadoop 0.20 was the first version that supported Erasure Code (aka HDFS-RAID), but EC was not included in later versions, only to resurface in Hadoop 3.0 (HDFS-EC). Hadoop 3.0.0 supports three default Erasure Code polices which are RS(3,2), RS(6,3) and RS(10,4). To have greater flexibility, in this work we opt for the implementation of new Erasure Code policies [RS(4,3), RS(5,3), RS(7,3), RS(7,4), RS(8,4), RS(9,4)] and the development of a Dynamic Erasure Coding Policy Allocation, based on minimum overhead produced, in order to maximize storage capacity. Three types of dynamic allocation have been proposed and implemented. A performance evaluation of the new polices was conducted in order to find the optional one for a NAS/SAN architecture and the effectiveness of the three implemented Dynamic Allocation of EC policy is provided.
A fast and memory efficient MLCS algorithm by character merging for DNA sequences alignment

Multiple longest common subsequence (MLCS) problem is searching all longest common subsequences of multiple character sequences. It appears in many fields such as data mining, DNA alignment, bioinformatics, text editing and so on. With the increasing in sequence length and number of sequences, the existing dynamic programming algorithms and the dominant point-based algorithms become ineffective and inefficient, especially for large-scale MLCS problems.In this paper, by considering the characteristics of DNA sequences with many consecutively repeated characters, we first design a character merging scheme which merges the consecutively repeated characters in the sequences. As a result, it shortens the length of sequences considered and saves the space of storing all sequences. To further reduce the space and time costs, we construct a weighted directed acyclic graph which is much smaller than widely used directed acyclic graph for MLCS problems. Based on these techniques, we propose a fast and memory efficient algorithm for MLCS problems. Finally, the experiments are conducted and the proposed algorithm is compared with several state-of-the art algorithms. The experimental results show that the proposed algorithm performs better than the compared state-of-the art algorithms in both time and space costs.https://www.ncbi.nlm.nih.gov/nuccore and https://github.com/liusen1006/MLCS.
Predictable Uncertainty-Aware Unsupervised Deep Anomaly Segmentation

Image-based anomaly segmentation is a fundamental topic for image analysis. For medical use, it supports treatments via refined diagnosis and growth rate evaluation of tumors and lesions. Especially, an unsupervised training is expected to generalize to unknown anomalies. Probabilistic models have been used for this purpose, whereby these models are trained to maximize the likelihood of known samples and detect anomalous samples by assigning low likelihoods. Recent studies have proposed a probabilistic model based on deep neural networks (DNNs) called AEs and they achieved significant performance thanks to their flexibility. However, AEs are sensitive to complex structure (e.g., ridges and grooves of a brain) rather than semantic anomalies (e.g., tumors and lesions). We decomposed the approximated log-likelihood into two terms; predictable uncertainty and normalized error. We found that the former represents the complexity of structure. Hence, we propose the normalized error as a novel uncertainty-sensitive score by removing the predictable uncertainty. We evaluated our score by experiments with head magnetic resonance imaging (MRI) datasets and demonstrate the robustness of the proposed normalized error to data complexity.
An Enhanced Image Matching Strategy Using Binary-Stream Feature Descriptors

This letter improves the computational efficiency and proposes a methodology to retrieve the missing feature pairs when utilizing binary-based features for image matching. The 64-byte feature descriptors of binary robust invariant scalable keypoints (BRISKs) are rearranged by combining human retina ganglion cells distribution and visual accommodation to speed up the image matching. In addition, an interactive two-sided matching is designed to determine the most probable keypoint pair when a feature point in the reference image is mapped to multiple candidates in the target image. Experimental results indicate that the proposed inverse sorting ring can reduce the processing time by more than 10% compared to the accelerated BRISK while maintaining the same reliability. Also, additional point-to-point feature pairs can be regained from the point-to-multicandidate cases by the proposed method in order to increase the number of matches.
Learning Logic and Proof with an Interactive Theorem Prover

A course developed by Robert Y. Lewis, Floris van Doorn, and the author serves as an undergraduate introduction to mathematical proof, symbolic logicLogic, and interactive theorem provingTheorem proving/proversinteractive theorem proving/provers. The treatment of each topic on its own is routine, and the novelty lies in the way they are combined to form a multifaceted introduction to mathematical reasoning and argumentationArgumentation. Students are required to master three different languagesLanguage: informal mathematicalLanguage language, formal symbolic logicLogic, and a computational proof languageLanguage that lies somewhere in between. Experience teaching the course suggests that students have no trouble keeping the languagesLanguage distinct while at the same appreciating the relationships between them, and that the multiple representationsRepresentations support one another and provide a robust understanding of mathematical proof.
Cyber Security Risks in MENA Region: Threats, Challenges and Countermeasures

Over the last few years, MENA region became an attractive target for cyber-attacks perpetrators. Hackers focus on governmental high valued sectors (i.e. oil and gas) alongside with other critical industries. MENA nations are increasingly investing in Information and Communication Technologies (ICTs) sector, social infrastructure, economic sector, schools and hospitals in the area are now completely based on the Internet. Currently, the position of ICTs became an essential phase of the domestic future and global security structure in the MENA Region, emphasizing the real need for a tremendous development in cybersecurity at a regional level. This environment raises questions about the developments in cybersecurity and offensive cyber tactics; this paper examines and investigates (1) the essential cybersecurity threats in MENA region, (2) the major challenges that faces both governments and organizations (3) the main countermeasures that governments follow to achieve the protection and business continuity in the region. It stresses the need for the importance of cybercrime legislation and higher defenses techniques towards cyberterrorism for MENA nations. It argues for the promotion of a cybersecurity awareness for the individuals as an effective mechanism for facing the current risks of cybersecurity in MENA region.
CryptoAR Wallet

Blockchain technology has recently become popular and its use in business and industry has increased, especially in finance and technology. A blockchain wallet plays a vital role in blockchain industry, but it is difficult to understand and get started, as well as hard to learn how to use blockchain wallet. From the aspect of Interaction Design, we proposed a blockchain cryptocurrency wallet that combine with augmented reality and crypto technology, that is, an Augmented Crypto Wallet (CryptoAR Wallet). We except that browsing and viewing virtual information (On-Chain User Data) through augmented reality, will shorten the distance between user and blockchain wallet services. Though our preliminary design, development and user testing, this in-development application shows its potential on increasing the level of trust and satisfactions, with more comprehensive user experience.
Image Classification Benchmark (ICB)

During any investigative work, it is necessary to confront our solutions to the already existing ones. This requires much work, namely to try to recreate the competing solutions and to use the same experimental conditions in order to obtain an objective comparison. Benchmarking is an activity of comparing performance metrics of systems in order to rank them or the activity of comparing a specific system to state-of-the-art alternatives. It would be convenient to have an image benchmarking ecosystem, not only to evaluate a personal solution but also to compare it with other previously proposed solutions. Upon designing a new or improved image classification pipeline, a convolution neural network or a specific algorithm modifying some detail, a user can add it to the benchmarking ecosystem and get a report on the performance of the solution, which can be compared to other solutions that were previously benchmarked under the same conditions. In this paper we describe the ICB - “Image Classification Benchmark” -, a prototype of a benchmarking ecosystem created to enable this vision. Besides describing how it works and how it was made flexible to incorporate any algorithm, we apply it to a specific comparison, as a proof-of-concept.
LED-based fiber quantum key distribution: toward low-cost applications

We report a quantum key distribution (QKD) system that uses light-emitting-diodes (LEDs) at 1310&#x00A0;nm as optical sources. Compared to the normally used laser diodes (LDs), LEDs are easier to manufacture and integrate, and thus have the potential to reduce the costs of practical systems. To demonstrate the feasibility of a low-cost, integratable QKD system that aims at meeting the demand of the last-mile secure communication, we utilize LEDs at 1310&#x00A0;nm as the optical sources, while using only passive optical components and only one single photon detector at the receiver&#x2019;s side. With a repetition rate of 10&#x00A0;MHz, we obtain secure key rates of 10.9&#x00A0;kbps within the experimental time of 1000&#x00A0;s over a fiber length of 1&#x00A0;km.
Reservoir Computing Based on Dynamics of Pseudo-Billiard System in Hypercube

Reservoir computing (RC) is a framework for constructing recurrent neural networks with simple training rule and sparsely and randomly connected nonlinear units. The network (called reservoir) generates complex motion that can be used for many tasks including time series generation and prediction. We construct a reservoir based on the dynamics of the pseudo-billiard system that produce complex motion in a high-dimensional hypercube. In particular, we use the chaotic Boltzmann machine (CBM) whose units exhibit chaotic behavior in the hypercube. The units interact with each other in a time-domain manner through its binary state, and thus an efficient hardware implementation of the system is expected. In order to utilize the CBM as the reservoir, it is necessary to control its chaotic behavior for ensuring the echo state property of RC and establish encoding and decoding for input and output signal. For this purpose, we introduce a reference clock and analyze effects and properties of the reference input. We evaluate the proposed model on the time series generation tasks and show that the model works properly on a broad range of parameter values. Our approach presents a novel mechanism for time-domain information processing and a fundamental technology for a brain like artificial intelligence system.
Investigating Unintended Inputs for One-Handed Touch Interaction Beyond the Touchscreen

Additional input controls such as fingerprint scanners, physical buttons, and Back-of-Device (BoD) touch panels improve the input capabilities on smartphones. While previous work showed the benefits of input beyond the touchscreen, unfavorably designed input controls force detrimental grip changes and increase the likelihood of unintended inputs. Researchers investigated all fingers' comfortable areas to avoid grip changes. However, there is no understanding of unintended BoD inputs which frustrate users and lead to embarrassing mistakes. In this paper, we study the BoD areas in which unintended inputs occur during interaction with the touchscreen. Participants performed common tasks on four smartphones which they held in the prevalent single-handed grip while sitting and walking. We recorded finger movements with a motion capture system and analyzed the unintended inputs. We identified comfortable areas on the back in which no unintended inputs occur and found that the least unintended inputs occurred on 5" devices. We derive three design implications for BoD input to help designers considering reachability and unintended input.
MacDonald codes over the ring $${\mathbb {F}}_{p}+v{\mathbb {F}}_{p}+v^2{\mathbb {F}}_{p}$$

In this paper, we consider MacDonald codes over the finite non-chain ring $${\mathbb {F}}_p+v{\mathbb {F}}_p+v^2{\mathbb {F}}_p$$ and their applications in constructing secret sharing schemes and association schemes, where p is an odd prime and $$v^3=v$$ . We give some structural properties of MacDonald codes first. Then, we study the weight enumerators of torsion codes of these MacDonald codes. As some applications, constructing secret sharing schemes and association schemes is also investigated.
Mixture of Pre-processing Experts Model for Noise Robust Deep Learning on Resource Constrained Platforms

Deep learning on an edge device requires energy efficient operation due to ever diminishing power budget. Intentional low quality data during the data acquisition for longer battery life, and natural noise from the low cost sensor degrade the quality of target output which hinders adoption of deep learning on an edge device. To overcome these problems, we propose simple yet efficient mixture of pre-processing experts (MoPE) model to handle various image distortions including low resolution and noisy images. We also propose to use adversarially trained auto encoder as a pre-processing expert for the noisy images. We evaluate our proposed method for various machine learning tasks including object detection on MS-COCO 2014 dataset, multiple object tracking problem on MOT-Challenge dataset, and human activity classification on UCF 101 dataset. Experimental results show that the proposed method achieves better detection, tracking and activity classification accuracies under noise without sacrificing accuracies for the clean images. The overheads of our proposed MoPE are 0.67% and 0.17% in terms of memory and computation compared to the baseline object detection network.