Reviewerly/exHarmony huggingface dataset:
BOLT: a practical binary optimizer for data centers and beyond

Abstract — Performance optimization for large-scale applications has recently become more important as computation continues to move towards data centers. Data-center applications are generally very large and complex, which makes code layout an important optimization to improve their performance. This has motivated recent investigation of practical techniques to improve code layout at both compile time and link time. Although post-link optimizers had some success in the past, no recent work has explored their benefits in the context of modern data-center applications. In this paper, we present BOLT, an open-source post-link optimizer built on top of the LLVM framework. Utilizing sample-based profiling, BOLT boosts the performance of real-world applications even for highly optimized binaries built with both feedback-driven optimizations (FDO) and link-time optimizations (LTO). We demonstrate that post-link performance improvements are complementary to conventional compiler optimizations, even when the latter are done at a whole-program level and in the presence of profile information. We evaluated BOLT on both Facebook data-center workloads and open-source compilers. For data-center applications, BOLT achieves up to 7.0% performance speedups on top of profile-guided function reordering and LTO. For the GCC and Clang compilers, our evaluation shows that BOLT speeds up their binaries by up to 20.4% on top of FDO and LTO, and up to 52.1% if the binaries are built without FDO and LTO.
A balanced power analysis attack resilient adiabatic logic using single charge sharing transistor

The existing Power Analysis Attacks (PAA) resilient adiabatic logic designs exhibit variations in current peaks, have asymmetric structures and suffer from Non-Adiabatic Losses (NAL) during the evaluation phase of the power-clock. However, asymmetric structure and variations in current peaks make the circuit susceptible to PAA. In this paper, we present a novel PAA resilient adiabatic logic which has a symmetric structure, completely removes NAL from the evaluation phase of the power-clock and exhibits minimal variations in current peaks for gates as well as in an 8-bit Montgomery multiplier. The proposed logic has been compared with three existing secure adiabatic logic designs for operating frequencies ranging from 1 MHz to 100 MHz and power-clock scaling ranging from 1.8 V to 0.6 V. Simulation results of the gates show that our proposed logic exhibits the lowest Normalized Energy Deviation (NED) and Normalized Standard Deviation (NSD) at the frequencies mentioned above. In addition, all the 2-input gates using proposed logic dissipate average energy within 0.3% of each other and thus, lowest value of standard deviation at all the simulated frequencies. The simulation results for the 8-bit Montgomery multiplier show that proposed logic exhibits the least value of NED and NSD at all the simulated frequencies and under power-supply scaling.
Evaluation of Google question-answering quality

Purpose The purpose of this paper is to evaluate Google question-answering (QA) quality. Design/methodology/approach Given the large variety and complexity of Google answer boxes in search result pages, existing evaluation criteria for both search engines and QA systems seemed unsuitable. This study developed an evaluation criteria system for the evaluation of Google QA quality by coding and analyzing search results of questions from a representative question set. The study then evaluated Google’s overall QA quality as well as QA quality across four target types and across six question types, using the newly developed criteria system. ANOVA and Tukey tests were used to compare QA quality among different target types and question types. Findings It was found that Google provided significantly higher-quality answers to person-related questions than to thing-related, event-related and organization-related questions. Google also provided significantly higher-quality answers to where- questions than to who-, what- and how-questions. The more specific a question is, the higher the QA quality would be. Research limitations/implications Suggestions for both search engine users and designers are presented to help enhance user experience and QA quality. Originality/value Particularly suitable for search engine QA quality analysis, the newly developed evaluation criteria system expanded and enriched assessment metrics of both search engines and QA systems.
Left-handed superlattice metamaterials for circuit QED

Quantum simulation is a promising field where a controllable system is used to mimic another system of interest, whose properties one wants to investigate. One of the key issues for such simulation is the ability to control the environment the system couples to, be it to isolate the system or to engineer a tailored environment of interest. One strategy recently put forward for environment engineering is the use of metamaterials with negative indices of refraction. Here we build on this concept and propose a circuit-QED simulation of many-body Hamiltonians using superlattice metamaterials. We give a detailed description of a superlattice transmission line coupled to an embedded qubit, and show how this system can be used to simulate the spin-boson model in regimes where analytical and numerical methods usually fail, e.g., the strong coupling regime.
Energy Internet blockchain technology

This chapter looks at how the revolutionary blockchain technology can be integrated with energy industry, deep dives into potential applications of blockchain technology in different verticals of the industry, and the general challenges ahead of industry that blockchain technology can help to solve. Also this chapter frankly admits that the technology is still in a very nascent stage and has a lot of problems to be solved even in a run before it can be applied in the industry. Besides, three application cases from United States, Australia, and China are analyzed to exemplify the enthusiasm of entrepreneurs all over the world.
To schedule or not to schedule? An investigation of meetings as an inter-team coordination mechanism in largescale agile software development

Coordination of teams is critical when managing large programmes that involve multiple teams. In large-scale software development, work is carried out simultaneously by many developers and development teams. Results are delivered frequently and iteratively, which requires coordination on different levels, e.g., the programme, project, and team levels. Prior studies of knowledge work indicate that such work relies heavily on coordination through "personal" modes such as mutual adjustment between individuals or through scheduled or unscheduled meetings. In agile software development processes, principles and work structures emerge during the project and are not predetermined. We studied how coordination through scheduled and unscheduled meetings changes over time in two large software development programmes relying on agile methods. Our findings include transitions from scheduled to unscheduled meetings and from unscheduled to scheduled meetings. The transitions have been initiated both bottom-up and top-down in the programme organizations. The main implication is that programme management needs to be sensitive to the vital importance of coordination and the coordination needs as they change over time. Further, when starting a program, we recommend to early identify the important scheduled meetings, as having enough scheduled meetings is important to develop a common understanding of domain knowledge.
Robust Visual Tracking Using Multi-Frame Multi-Feature Joint Modeling

It remains a huge challenge to design effective and efficient trackers under complex scenarios, including occlusions, illumination changes and pose variations.To cope with this problem, a promising solution is to integrate the temporal consistency across consecutive frames and multiple feature cues in a unified model.Motivated by this idea, we propose a novel correlation filter-based tracker in this work, in which the temporal relatedness is reconciled under a multi-task learning framework and the multiple feature cues are modeled using a multi-view learning approach.We demonstrate the resulting regression model can be efficiently learned by exploiting the structure of blockwise diagonal matrix.A fast blockwise diagonal matrix inversion algorithm is developed thereafter for efficient online tracking.Meanwhile, we incorporate an adaptive scale estimation mechanism to strengthen the stability of scale variation tracking.We implement our tracker using two types of features and test it on two benchmark datasets.Experimental results demonstrate the superiority of our proposed approach when compared with other state-of-the-art trackers.MATLAB code is available from our project homepage http://bmal.hust.edu.
Extension Removal in Abstract Argumentation – An Axiomatic Approach

This paper continues the rather recent line of research on the dynamics of non-monotonic formalisms. In particular, we consider semantic changes in Dung’s abstract argumentation formalism. One of the most studied problems in this context is the so-called enforcing problem which is concerned with manipulating argumentation frameworks (AFs) such that a certain desired set of arguments becomes an extension. Here we study the inverse problem, namely the extension removal problem: is it possible – and if so how – to modify a given argumentation framework in such a way that certain undesired extensions are no longer generated? Analogously to the well known AGM paradigm we develop an axiomatic approach to the removal problem, i.e. a certain set of axioms will determine suitable manipulations. Although contraction (that is, the elimination of a particular belief) is conceptually quite different from extension removal, there are surprisingly deep connections between the two: it turns out that postulates for removal can be directly obtained as reformulations of the AGM contraction postulates. We prove a series of formal results including conditional and unconditional existence and semantical uniqueness of removal operators as well as various impossibility results – and show possible ways out.
Automating Free Logic in HOL, with an Experimental Application in Category Theory

A shallow semantical embedding of free logic in classical higher-order logic is presented, which enables the off-the-shelf application of higher-order interactive and automated theorem provers for the formalisation and verification of free logic theories. Subsequently, this approach is applied to a selected domain of mathematics: starting from a generalization of the standard axioms for a monoid we present a stepwise development of various, mutually equivalent foundational axiom systems for category theory. As a side-effect of this work some (minor) issues in a prominent category theory textbook have been revealed. The purpose of this article is not to claim any novel results in category theory, but to demonstrate an elegant way to “implement” and utilize interactive and automated reasoning in free logic, and to present illustrative experiments.
Continual Learning with Neural Networks

Continual learning broadly refers to the algorithms which aim to learn continuously over time across varying domains, tasks or data distributions. This is in contrast to algorithms restricted to learning a fixed number of tasks in a given domain, assuming a static data distribution. In this survey we aim to discuss a wide breadth of challenges faced in a continual learning setup and review existing work in the area. We discuss parameter regularization techniques to avoid catastrophic forgetting in neural networks followed by memory based approaches and the role of generative models in assisting continual learning algorithms. We discuss how dynamic neural networks assist continual learning by endowing neural networks with a new capacity to learn further. We conclude by discussing possible future directions.
“Statistics 103” for Multitarget Tracking

The finite-set statistics (FISST) foundational approach to multitarget tracking and information fusion was introduced in the mid-1990s and extended in 2001. FISST was devised to be as "engineering-friendly" as possible by avoiding avoidable mathematical abstraction and complexity-and, especially, by avoiding measure theory and measure-theoretic point process (p.p.) theory. Recently, however, an allegedly more general theoretical foundation for multitarget tracking has been proposed. In it, the constituent components of FISST have been systematically replaced by mathematically more complicated concepts-and, especially, by the very measure theory and measure-theoretic p.p.'s that FISST eschews. It is shown that this proposed alternative is actually a mathematical paraphrase of part of FISST that does not correctly address the technical idiosyncrasies of the multitarget tracking application.
Night Time Haze and Glow Removal using Deep Dilated Convolutional Network

In this paper, we address the single image haze removal problem in a nighttime scene. The night haze removal is a severely ill-posed problem especially due to the presence of various visible light sources with varying colors and non-uniform illumination. These light sources are of different shapes and introduce noticeable glow in night scenes. To address these effects we introduce a deep learning based DeGlow-DeHaze iterative architecture which accounts for varying color illumination and glows. First, our convolution neural network (CNN) based DeGlow model is able to remove the glow effect significantly and on top of it a separate DeHaze network is included to remove the haze effect. For our recurrent network training, the hazy images and the corresponding transmission maps are synthesized from the NYU depth datasets and consequently restored a high-quality haze-free image. The experimental results demonstrate that our hybrid CNN model outperforms other state-of-the-art methods in terms of computation speed and image quality. We also show the effectiveness of our model on a number of real images and compare our results with the existing night haze heuristic models.
Efficient Implementation of the SHA-512 Hash Function for 8-Bit AVR Microcontrollers

SHA-512 is a member of the SHA-2 family of cryptographic hash algorithms that is based on a Davies-Mayer compression function operating on eight 64-bit words to produce a 512-bit digest. It provides strong resistance to collision and preimage attacks, and is assumed to remain secure in the dawning era of quantum computers. However, the compression function of SHA-512 is challenging to implement on small 8 and 16-bit microcontrollers because of their limited register space and the fact that 64-bit rotations are generally slow on such devices. In this paper, we present the first highly-optimized Assembler implementation of SHA-512 for the ATmega family of 8-bit AVR microcontrollers. We introduce a special optimization technique for the compression function based on a duplication of the eight working variables so that they can be more efficiently loaded from RAM via the indirect addressing mode with displacement (using the ldd and std instruction). In this way, we were able to achieve high performance without unrolling the main loop of the compression function, thereby keeping the code size small. When executed on an 8-bit AVR ATmega128 microcontroller, the compression function takes slightly less than 60k clock cycles, which corresponds to a compression rate of roughly 467 cycles per byte. The binary code size of the full SHA-512 implementation providing a standard Init-Update-Final (IUF) interface amounts to approximately 3.5 kB.
Diagnostic Validation of a Whole-Slide Imaging Scanner in Cytological Samples: Diagnostic Accuracy and Comparison With Light Microscopy

Digital slides created by whole-slide imaging scanners can be evaluated by pathologists located in remote sites, but the process must be validated before this technology can be applied to routine cytological diagnosis. The aim of this study was to validate a whole-slide imaging scanner for cytological samples. Sixty cytological samples, whose diagnoses were confirmed by gold-standard examinations (histology or flow cytometry), were digitalized using a whole-slide imaging scanner. Digital slides and glass slides were examined by 3 observers with different levels of cytopathological expertise. No significant differences were noted between digital and glass slides in regard to the number of cases correctly diagnosed, or the sensitivity, specificity, or diagnostic accuracy, irrespective of the observers' expertise. The agreements between the digital slides and the gold-standard examinations were moderate to substantial, while the agreements between the glass slides and the gold-standard examinations were substantial for all 3 observers. The intraobserver agreements between digital and glass slides were substantial to almost perfect. The interobserver agreements when evaluating digital slides were moderate between observers 1 and 2 and between observers 1 and 3 while they were substantial between observers 2 and 3. In conclusion, our study demonstrated that the digital slides produced by the whole-slide imaging scanner are adequate to diagnose cytological samples and are similar among clinical pathologists with differing levels of expertise.
Hyperbolic Wasserstein Distance for Shape Indexing

Shape space is an active research topic in computer vision and medical imaging fields. The distance defined in a shape space may provide a simple and refined index to represent a unique shape. This work studies the Wasserstein space and proposes a novel framework to compute the Wasserstein distance between general topological surfaces by integrating hyperbolic Ricci flow, hyperbolic harmonic map, and hyperbolic power Voronoi diagram algorithms. The resulting hyperbolic Wasserstein distance can intrinsically measure the similarity between general topological surfaces. Our proposed algorithms are theoretically rigorous and practically efficient. It has the potential to be a powerful tool for 3D shape indexing research. We tested our algorithm with human face classification and Alzheimer's disease (AD) progression tracking studies. Experimental results demonstrated that our work may provide a succinct and effective shape index.
Cryptanalysis and improvement of the novel semi-quantum secret sharing scheme using entangled states

A semi-quantum secret sharing scheme using entangled states was proposed in a recent paper [Mod. Phys. Lett. B 32 (2018) 1850256. https://doi.org/10.1142/S0217984918502561 ]. We find a loophole of this scheme. Based on the intercept-resend attack, we propose a deterministic attack strategy and a probabilistic attack strategy. The malicious participant can escape the detection and obtains the secret. We also suggest two improvements to help increase the security of the original scheme.
An Acoustic-Based Smart Home System for People Suffering from Dementia

Aging-in-place can reduce the progress of dementia syndrome and improve the quality of life of the sufferers and their families. Taking into consideration the fact that numerous neurological research results suggest the use of sound as a stimulus for empowering the memory of the sufferer, an innovative information home support system for people suffering from dementia is proposed. The innovation of the proposed system is found in its application, that is to integrate a home system for assisting with person recognition via a sound-based memory aid service. Furthermore, the system addresses the needs of people suffering from dementia to recognize their familiars and have better interaction and collaboration, without the need for training. The system offers a ubiquitous recognition system, using smart devices like smart-phones or smart-wristbands. When a familiar person is detected in the house, then a sound is reproduced on the smart speakers, in order to stimulate the sufferer’s memory. The system identified all users and reproduced the appropriate sound in 100% of the cases. To the best of the authors’ knowledge, this is the first system of its kind for assisting person recognition via sound ever reported in the literature.
An Effective Gaussian Fitting Approach for Image Contrast Enhancement

Contrast enhancement plays an important role in image processing applications. This paper proposes a low-complexity automatic method for contrast enhancement. The method exploits the high-frequency distribution of an image to estimate an intensity-weighting matrix, which is then used to control the Gaussian fitting curve and shape the distribution of the contrast gain. As such, the curve can be easily designed to enhance the important details hidden in noteworthy regions. Subsequently, the proposed grayscale transformation that is obtained from the Gaussian fitting can rationally express contrast distribution. Unlike previous contrast enhancement methods, our technique is fully automatic and target-oriented in the sense that it can be directly applied to any input image without any parameter adjustment. The experimental results for some of the widely accepted criterions demonstrate the superiority of our proposed method over the conventional enhancement techniques, especially in the aspects of visual pleasure, anti-noise capability, and target-oriented contrast enhancement.
Abstract machines for Open Call-by-Value

The theory of the call-by-value λ-calculus relies on weak evaluation and closed terms, that are natural hypotheses in the study of programming languages. To model proof assistants, however, strong evaluation and open terms are required. Open call-by-value is the intermediate setting of weak evaluation with (possibly) open terms, on top of which Grégoire and Leroy designed one of the abstract machines of Coq. This paper provides a theory of abstract machines for the fireball calculus, the simplest presentation of open call-by-value. The literature contains machines that are either simple but inefficient, as they have an exponential overhead, or efficient but heavy, as they rely on a labeling of environments and a technical optimization. We introduce a machine that is simple and efficient: it does not use labels and it implements the fireball calculus within a bilinear overhead. Moreover, we provide a new fine understanding of how different optimizations impact on the complexity of the overhead, and evidence that the time cost model we work with is minimal.
Networking with Ghosts in the Machine. Speaking to the Internet of Things

(2019). Networking with Ghosts in the Machine. Speaking to the Internet of Things. The Design Journal: Vol. 22, Running with Scissors: 13th International Conference of the European Academy of Design, Dundee, 10-12th April, 2019, edited by Louise Valentine, Joanna Bletcher and Leon Cruickshank, pp. 1187-1199.