Reviewerly/exHarmony huggingface dataset:
Daydream: Accurately Estimating the Efficacy of Optimizations for {DNN} Training

Modern deep neural network (DNN) training jobs use complex and heterogeneous software/hardware stacks. The efficacy of software-level optimizations can vary significantly when used in different deployment configurations. It is onerous and error-prone for ML practitioners and system developers to implement each optimization separately, and determine which ones will improve performance in their own configurations. Unfortunately, existing profiling tools do not aim to answer predictive questions such as How will optimization X affect the performance of my model?. We address this critical limitation, and proposes a new profiling tool, Daydream, to help programmers efficiently explore the efficacy of DNN optimizations. Daydream models DNN execution with a fine-grained dependency graph based on low-level traces collected by CUPTI, and predicts runtime by simulating execution based on the dependency graph. Daydream maps the low-level traces using DNN domain-specific knowledge, and introduces a set of graph-transformation primitives that can easily model a wide variety of optimizations. We show that Daydream is able to model most mainstream DNN optimization techniques, and accurately predict the efficacy of optimizations that will result in significant performance improvements.
An Ultrasound Image Despeckling Method Based on Weighted Adaptive Bilateral Filter

Images are widely used in engineering. Unfortunately, ultrasound images are mainly degraded by an intrinsic noise called speckle. Therefore, de-speckling is a critical preprocessing step. Therefore, a robust despeckling method and accurate evaluation of images are suggested. We suggest three phases and a three-step denoising filter. In the first phase, the coefficients of variation are computed from the noisy image. The second phase is a three-step denoising filter. The first step is denoising of extreme levels of homogeneous regions, based on fuzzy homogeneous regions. The second step is a proposed adaptive bilateral filter (ABF). The ABF helps for better denoising based on the three regions which are edge, detail and homogeneous regions. The next step, a weight, is applied to the ABF. This step is for isolated noise denoising. Next, in the third phase, the output image is evaluated by the fuzzy logic approach. The proposed method is compared with other filters in the literature. The experimental outcomes show that the proposed method has better performance than the other filters. That proposed denoising algorithm is able to preserve image details and edges when compared with other denoising methods.
User Context Detection for Relay Attack Resistance in Passive Keyless Entry and Start System

In modern cars, the Passive Keyless Entry and Start system (PKES) has been extensively installed. The PKES enables drivers to unlock and start their cars without user interaction. However, it is vulnerable to relay attacks. In this paper, we propose a secure smartphone-type PKES system model based on user context detection. The proposed system uses the barometer and accelerometer embedded in smartphones to detect user context, including human activity and door closing event. These two types of events detection can be used by the PKES to determine the car owner's position when the car receives an unlocking or a start command. We evaluated the performance of the proposed method using a dataset collected from user activity and 1526 door closing events. The results reveal that the proposed method can accurately and effectively detect user activities and door closing events. Therefore, smartphone-type PKES can prevent relay attacks. Furthermore, we tested the detection of door closing event under multiple environmental settings to demonstrate the robustness of the proposed method.
From Big-Step to Small-Step Semantics and Back with Interpreter Specialisation

We investigate representations of imperative programs as constrained Horn clauses. Starting from operational semantics transition rules, we proceed by writing interpreters as constrained Horn clause programs directly encoding the rules. We then specialise an interpreter with respect to a given source program to achieve a compilation of the source language to Horn clauses (an instance of the first Futamura projection). The process is described in detail for an interpreter for a subset of C, directly encoding the rules of big-step operational semantics for C. A similar translation based on small-step semantics could be carried out, but we show an approach to obtaining a small-step representation using a linear interpreter for big-step Horn clauses. This interpreter is again specialised to achieve the translation from big-step to small-step style. The linear small-step program can be transformed back to a big-step non-linear program using a third interpreter. A regular path expression is computed for the linear program using Tarjan's algorithm, and this regular expression then guides an interpreter to compute a program path. The transformation is realised by specialisation of the path interpreter. In all of the transformation phases, we use an established partial evaluator and exploit standard logic program transformation to remove redundant data structures and arguments in predicates and rename predicates to make clear their link to statements in the original source program.
Towards a realistic GaAs-spin qubit device for a classical error-corrected quantum memory

A realistic multiqubit chip configuration is investigated in detail, showing that building a device with error suppression is within reach with current technology. The study uses full density matrix simulation with accurate noise modeling, bringing to light potential bottlenecks and how to overcome them.
Identity Management Using Blockchain Network for Fail-Safe e-Governance

One of the major problems in today’s society is the tampering and altering of government records such as police case information, Aadhar card information, driving license and using such false information for obtaining various benefits such as government jobs, passports, visas, and other organizations requiring trust. This project proposes a solution to this problem using blockchain implementation. The idea is to create a block for each individual and branch their various information in these blocks and chain them together. These blocks use hashing and digital signatures as a primary security requirement. Hash values of these blocks are created using the entire contents of the block, and each block contains the hash of previous block, thereby creating a chain. The various hashing algorithms used are SHA-256, and the public-key cryptography is using RSA or ECC algorithm. If someone tries to tamper with the contents of the block, the hash values change and get disconnected from the chain. Every operation on the each of the blocks will be recorded with a time stamp, and it cannot be altered. Hence, this permissioned blockchain network of all the individuals can be used to prevent the tampering and altering of various government records such as police case records, passport records, and PAN and help various organizations such as visa verification and income tax department. This blockchain network provides a solution for all the existing problems in tampering and altering of government records.
Quantum Non-Gaussian Photon Coincidences

Photon coincidences represent an important resource for quantum technologies. They expose nonlinear quantum processes in matter and are essential for sources of entanglement. We derive broadly applicable criteria for quantum non-Gaussian two-photon coincidences that certify a new quality of photon sources. The criteria reject states emerging from Gaussian parametric processes, which often limit applications in quantum technologies. We also analyse the robustness of the quantum non-Gaussian coincidences and compare with the heralded quantum non-Gaussianity of single-photons based on them.
Self-adaptive potential-based stopping criteria for Particle Swarm Optimization with forced moves

Abstract We study the variant of Particle Swarm Optimization that applies random velocities in a dimension instead of the regular velocity update equations as soon as the so-called potential of the swarm falls below a certain small bound in this dimension, arbitrarily set by the user. In this case, the swarm performs a forced move . In this paper, we are interested in how, by counting the forced moves, the swarm can decide for itself to stop its movement because it is improbable to find better candidate solutions than the already-found best solution. We formally prove that when the swarm is close to a (local) optimum, it behaves like a blind-searching cloud and that the frequency of forced moves exceeds a certain, objective function-independent value. Based on this observation, we define stopping criteria and evaluate them experimentally showing that good candidate solutions can be found much faster than setting upper bounds on the iterations and better solutions compared to applying other solutions from the literature.
Presentation of a mixed reality software with a HoloLens headset for a nutrition workshop

Microsoft has recently released a mixed reality headset called HoloLens. This semi-transparent visor headset allows the user who wears it to view the projection of 3D virtual objects placed in its real environment. The user can also grasp these 3D objects, which can also interact with each other. The framework of this new technology taking into account this physical approach (interactions, collisions) is called mixed reality. We had the opportunity to digitally transform a conventional nutrition workshop for patients waiting for bariatric surgery by designing a mixed reality software using the HoloLens headset. This software is called HOLO_NUTRI. In this paper, we present this software and its assessment (acceptance of this technology) by a cohort of thirty patients.
Sound C Code Decompilation for a Subset of x86-64 Binaries

We present FoxDec: an approach to C code decompilation that aims at producing sound and recompilable code. Formal methods are used during three phases of the decompilation process: control flow recovery, symbolic execution, and variable analysis. The use of formal methods minimizes the trusted code base and ensures soundness: the extracted C code behaves the same as the original binary. Soundness and recompilablity enable C code decompilation to be used in the contexts of binary patching, binary porting, binary analysis and binary improvement, with confidence that the recompiled code’s behavior is consistent with the original program. We demonstrate that FoxDec can be used to improve execution speed by recompiling a binary with different compiler options, to patch a memory leak with a code transformation tool, and to port a binary to a different architecture. FoxDec can also be leveraged to port a binary to run as a unikernel, a minimal and secure virtual machine usually requiring source access for porting.
Diagonal catalysts in quantum adiabatic optimization

We propose a protocol for quantum adiabatic optimization whereby an intermediary Hamiltonian that is diagonal in the computational basis is turned on and off during the interpolation. This ``diagonal catalyst'' serves to bias the energy landscape towards a given spin configuration, and we show how this can remove the first-order phase transition present in the standard protocol for the ferromagnetic $p$ spin and the weak-strong cluster problems. The success of the protocol also makes clear how it can fail: biasing the energy landscape towards a state only helps in finding the ground state if the Hamming distance from the ground state and the energy of the biased state are correlated. We present examples where biasing towards low-energy states that are nonetheless very far in Hamming distance from the ground state can severely worsen the efficiency of the algorithm compared to the standard protocol. Our results for the diagonal catalyst protocol are analogous to results exhibited by adiabatic reverse annealing, so our conclusions should apply to that protocol as well.
Feynman-path-type simulation using stabilizer projector decomposition of unitaries

We propose a classical simulation method for quantum circuits based on decomposing unitary gates into a sum of stabilizer projectors. By only decomposing the non-Clifford gates, we take advantage of the Gottesman-Knill theorem and build a bridge between stabilizer-based simulation and Feynman-path-type simulation. We give two variants of this method: stabilizer-based path-integral recursion (SPIR) and stabilizer projector contraction (SPC). We also analyze further advantages and disadvantages of our method compared to the Bravyi-Gosset algorithm and recursive Feynman path-integral algorithms. We construct a parametrized circuit ensemble and identify the parameter regime in this ensemble where our method offers superior performance. We also estimate the time cost for simulating quantum supremacy experiments with our method and motivate potential improvements of the method.
The KAS corpus of Slovenian academic writing

The paper presents the KAS corpus of Slovenian academic writing, which consists of almost 65,000 B.A./B.Sc., 16,000 M.A./M.Sc. and 1600 Ph.D. theses (5 million pages or 1.7 billion tokens) gathered from the digital libraries of Slovenian higher education institutions via the Slovenian Open Science portal. We discuss the compilation, meta-data, annotation, and distribution of the corpus, which is made freely available via on-line concordancers and is openly available for research through the CLARIN.SI research infrastructure. We also present the tools for mono- and bilingual term extraction and for thesis structure annotation that were developed in the scope of the project, including the manually annotated datasets used to train these tools. This specialised corpus, large by any standards, represents a substantial and highly useful language resource for the study of Slovenian academic writing and for terminology extraction.
A Search History-Driven Offspring Generation Method for the Real-Coded Genetic Algorithm

In evolutionary algorithms, genetic operators iteratively generate new offspring which constitute a potentially valuable set of search history. To boost the performance of offspring generation in the real-coded genetic algorithm (RCGA), in this paper, we propose to exploit the search history cached so far in an online style during the iteration. Specifically, survivor individuals over the past few generations are collected and stored in the archive to form the search history. We introduce a simple yet effective crossover model driven by the search history (abbreviated as SHX). In particular, the search history is clustered, and each cluster is assigned a score for SHX. In essence, the proposed SHX is a data-driven method which exploits the search history to perform offspring selection after the offspring generation. Since no additional fitness evaluations are needed, SHX is favorable for the tasks with limited budget or expensive fitness evaluations. We experimentally verify the effectiveness of SHX over 15 benchmark functions. Quantitative results show that our SHX can significantly enhance the performance of RCGA, in terms of both accuracy and convergence speed. Also, the induced additional runtime is negligible compared to the total processing time.
Exponential upper bounds for the runtime of randomized search heuristics

We argue that proven exponential upper bounds on runtimes, an established area in classic algorithms, are interesting also in heuristic search and we prove several such results. We show that any of the algorithms randomized local search, Metropolis algorithm, simulated annealing, and (1+1) evolutionary algorithm can optimize any pseudo-Boolean weakly monotonic function under a large set of noise assumptions in a runtime that is at most exponential in the problem dimension~$n$. This drastically extends a previous such result, limited to the (1+1) EA, the LeadingOnes function, and one-bit or bit-wise prior noise with noise probability at most $1/2$, and at the same time simplifies its proof. With the same general argument, among others, we also derive a sub-exponential upper bound for the runtime of the $(1,\lambda)$ evolutionary algorithm on the OneMax problem when the offspring population size $\lambda$ is logarithmic, but below the efficiency threshold. To show that our approach can also deal with non-trivial parent population sizes, we prove an exponential upper bound for the runtime of the mutation-based version of the simple genetic algorithm on the OneMax benchmark, matching a known exponential lower bound.
Leveraging Affective Bidirectional Transformers for Offensive Language Detection

Social media are pervasive in our life, making it necessary to ensure safe online experiences by detecting and removing offensive and hate speech. In this work, we report our submission to the Offensive Language and hate-speech Detection shared task organized with the 4th Workshop on Open-Source Arabic Corpora and Processing Tools Arabic (OSACT4). We focus on developing purely deep learning systems, without a need for feature engineering. For that purpose, we develop an effective method for automatic data augmentation and show the utility of training both offensive and hate speech models off (i.e., by fine-tuning) previously trained affective models (i.e., sentiment and emotion). Our best models are significantly better than a vanilla BERT model, with 89.60% acc (82.31% macro F1) for hate speech and 95.20% acc (70.51% macro F1) on official TEST data.
Discriminative Multiple Kernel Concept Factorization for Data Representation

Concept Factorization (CF) improves Nonnegative matrix factorization (NMF), which can be only performed in the original data space, by conducting factorization within proper kernel space where the structure of data become much clear than the original data space.CF-based methods have been widely applied and yielded impressive results in optimal data representation and clustering tasks.However, CF methods still face with the problem of proper kernel function design or selection in practice.Most existing Multiple Kernel Clustering (MKC) algorithms do not sufficiently consider the intrinsic neighborhood structure of base kernels.In this paper, we propose a novel Discriminative Multiple Kernel Concept Factorization method for data representation and clustering.We first extend the original kernel concept factorization with the integration of multiple kernel clustering framework to alleviate the problem of kernel selection.For each base kernel, we extract the local discriminant structure of data via the local discriminant models with global integration.Moreover, we further linearly combine all these kernel-level local discriminant models to obtain an integrated consensus characterization of the intrinsic structure across base kernels.In this way, it is expected that our method can achieve better results by more compact data reconstruction and more faithful local structure preserving.An iterative algorithm with convergence guarantee is also developed to find the optimal solution.Extensive experiments on benchmark datasets further show that the proposed method outperforms many state-of-the-art algorithms.
Entangling capacities and the geometry of quantum operations

Abstract Quantum operations are the fundamental transformations on quantum states. In this work, we study the relation between entangling capacities of operations, geometry of operations, and positive partial transpose (PPT) states, which are an important class of states in quantum information. We show a method to calculate bounds for entangling capacity, the amount of entanglement that can be produced by a quantum operation, in terms of negativity, a measure of entanglement. The bounds of entangling capacity are found to be associated with how non-PPT (PPT preserving) an operation is. A length that quantifies both entangling capacity/entanglement and PPT-ness of an operation or state can be defined, establishing a geometry characterized by PPT-ness. The distance derived from the length bounds the relative entangling capability, endowing the geometry with more physical significance. We also demonstrate the equivalence of PPT-ness and separability for unitary operations.
Contact tracing management of the COVID-19 pandemic

Objective:The new type of Corona virus (SARS-CoV-2) is the most important item on the agenda, currently affecting our world.The number of cases has exceeded millions and the number of dead has exceeded hundreds of thousands.The precautions to be taken by societies and individuals, especially states, will undoubtedly be the most important task at the end of this epidemic.In our study, analyzes the data and consequences regarding COVID-19, making use of worldwide experiences and national statistics.In the report writing process, evidence-based and scientific tables and charts were used.In these times when most citizens have just begun to encounter the word pandemic, this report has been prepared using the data and scientific facts of the Ministry of Health of the Republic of Turkey.In this study, it was aimed to evaluate "contact tracing management" (detection, referral and follow-up) studies for positive cases with COVID-19 test results and their contacts in Ankara.
Modeling of false information on microblog with block matching and fuzzy neural network

The detection method of microblog false information has been constructed based on block matching and fuzzy neural network to improve the detection accuracy of microblog false information effectively. With this method, we can calculate the rank distance and sample entropy of microblog data according to the evaluation word rank vector of microblog false information, carry out the block matching of false information in fuzzy data set and input the characteristic quantity of microblog false information extracted into the fuzzy neural network classifier for data classification and recognition. So that it has achieved the optimized detection of false information and improved the judgment ability of false information. Finally, the key factors that affect the algorithm are deeply studied through simulation experiments according to the real data of Sina microblog, and the performance state between the proposed algorithm and Fuzzy C-means and Spectral Analysis algorithms is compared and analyzed correspondingly. The results show that the algorithm has good adaptability.
HTTP-level e-commerce data based on server access logs for an online store

Web server logs have been extensively used as a source of data on the characteristics of Web traffic and users' navigational patterns. In particular, Web bot detection and online purchase prediction using methods from artificial intelligence (AI) are currently key areas of research. However, in reality, it is hard to obtain logs from actual online stores and there is no common dataset that can be used across different studies. Moreover, there is a lack of studies exploring Web traffic over a longer period of time, due to the unavailability of long-term data from server logs. The need to develop reliable models of Web traffic, Web user navigation, and e-customer behaviour calls for an up-to-date, large-volume e-commerce dataset on Web traffic. Similarly, AI problems require a sufficient amount of solid, real-life data to train and validate new models and methods. Thus, to meet a demand of a publicly available long-term e-commerce dataset, we collected access log data describing the operation of an online store over a six-month period. Using a program written in the C# language, data were aggregated, transformed, and anonymized. As a result, we release this EClog dataset in CSV format, which covers 183 days of HTTP-level e-commerce traffic. The data will be beneficial for research in many areas, including computer science, data science, management, and sociology.