Reviewerly/exHarmony huggingface dataset:
WeTrace: A privacy-preserving tracing approach

For the protection of people and society against harm and health threats -especially in case of the COVID-19 pandemic -a variety of different disciplines needs to be involved.The data collection of basic and health-related data of individuals in today's highly mobile society does help to plan, protect, and identify next steps health authorities and governments can, shall, or need to plan for or even implement.Thus, every individual, human, and inhabitant of the world is the key player -very different from many past crises'.And since all individuals are involved his/her (a) health and (b) privacy shall be considered in a very carefully crafted balance, not overruling one aspect with another one.Privacy remains key.The solution of the current pandemic's data collection can be based on a fully privacy-preserving application, which can be used by individuals on their mobile devices, such as smartphones, while maintaining at the same time their privacy.Additionally, respective data collected in such a fully distributed setting does help to confine the pandemic and can be achieved in a democratic and very open, but still and especially privacy-protecting manner.Therefore, the WeTrace approach and application designed utilizes the Bluetooth low energy (BLE) communication channel, many modern mobile devices offer, where public-key cryptography is being applied to allow for deciphering of messages for that destination it had been intended for.Since literally every other potential participant only listens to random data, even a brute force attack will not succeed.WeTrace and its Open Source implementation ensure that any receiver of a message knows that this is for him/her, without being able to identify the original sender.
A comparative study on methods and tools for handwritten mathematical expression recognition

Handwritten mathematical expression recognition (HMER) is a challenging task due to factors such as ambiguity, variety of writing styles, and complexity of two-dimensional writing. In this paper, we identify challenges in HMER applications through experiments that simulate real scenarios that go far beyond the usual cases found in literature: variations on luminance; different stroke width, inclination and color; different background pattern; and partially shaded images. The results of state-of-the-art methods (as TAP and Dense-WAP) and a commercial tool (MathPix) are analyzed, using the CROHME 2016 database. We proved that, although the area has had a lot of improvement in recent years, there are still issues to overcome.
Smart Waste Management Using Blockchain

In this article, a new waste management system is proposed based on fog computing and blockchain technology. It has enough leverage to get the features and benefits of both technologies to reach a more practical and suitable solution to cope with some challenges of waste management systems such as lack of participation of citizens in the systems and absence of a trusted environment for the cooperation of companies. The proposed system provides a trustworthy environment for companies that are included in the chain of waste management to cooperate. Also, the system plans to make enough encouragement for citizens and other waste generators, such as businesses, industries, and hospitals, to play a more effective role of separating recyclable waste in source. Finally, municipalities could impose their policies to the system with smart contracts. Then, they can monitor and manage the system more easily and properly.
Deep learning enabled brain shunt valve identification using mobile phones

Accurate information concerning implanted medical devices prior to a Magnetic resonance imaging (MRI) examination is crucial to assure safety of the patient and to address MRI induced unintended changes in device settings. The identification of these devices still remains a very challenging task. In this paper, with the aim of providing a faster device detection, we propose the adoption of deep learning for medical device detection from X-rays. In particular, we propose a pipeline for the identification of implanted programmable cerebrospinal fluid shunt valves using X-ray images of the radiologist workstation screens captured with mobile phone integrated cameras at different angles and illuminations. We compare the proposed convolutional neural network with published methods. Experimental results show that this approach outperforms methods trained on images digitally transferred directly from the scanners and then applied on mobile phones images (mean accuracy 95% vs 77%, Avg. Precision 0.96 vs 0.77, Avg. Recall 0.95 vs 0.77, Avg. F1-score 0.95 vs 0.77) and existing published methods based on transfer learning fine-tuned directly on the mobile phone images (mean accuracy 94% vs 75%, Avg. Precision 0.94 vs 0.75, Avg. Recall 0.94 vs 0.75, Avg. F1-score 0.94 vs 0.75). An automated shunt valve identification system is a promising safety tool for radiologists to efficiently coordinate the care of patients with implanted devices. An image-based safety system able to be deployed on a mobile phone would have significant advantages over methods requiring direct input from X-ray scanners or clinical picture archiving and communication system (PACS) in terms of ease of integration in the hospital or clinical ecosystems.
Category Identification Technique by a Semantic Feature Generation Algorithm

The increase of the digital content on the web is tremendous, not only in volume but also in a variety of platforms by which it is created and added to the digital world, i.e., the internet. This chapter proposed a scheme to identify the category of different image classes depending upon the given query made by the user. Here the chapter proposed a Novel Semantic Feature Generation Algorithm (NSFGA) which purely depends on semantic attributes of the images. This approach is based on a technique to extract semantic features from different classes from images by sliding window, and it is performing a whole scanning of the query image and produces an output which will be the identified category from the different class of image dataset. Here our implementation includes repeated scanning of the image so that the accuracy should be optimum. This project uses the Support Vector Machine (SVM) to identify the required category by training and testing dataset.
Synthesis of web layouts from examples

We present a new technique for synthesizing dynamic, constraint-based visual layouts from examples. Our technique tackles two major challenges of layout synthesis. First, realistic layouts, especially on the web, often contain hundreds of elements, so the synthesizer needs to scale to layouts of this complexity. Second, in common usage scenarios, examples contain noise, so the synthesizer needs to be tolerant to imprecise inputs. To address these challenges we propose a two-phase approach to synthesis, where a local inference phase rapidly generates a set of likely candidate constraints that satisfy the given examples, and then a global inference phase selects a subset of the candidates that generalizes to unseen inputs. This separation of concerns helps our technique tackle the two challenges: the local phase employs Bayesian inference to handle noisy inputs, while the global phase leverages the hierarchical nature of complex layouts to decompose the global inference problem into inference of independent sub-layouts.
Accurate and efficient internal deformation measurement of multiphase/porous materials via segmentation-aided digital volume correlation

In using the regular subvolume-based digital volume correlation (R-DVC) method, calculation points should be defined at the real material phase, and the local deformation within the interrogated subvolumes is assumed to be continuous. However, this basic assumption in R-DVC analysis is often violated when measuring the deformation near the interface when dealing with multiphase materials (including porous materials) or contact problems. This is because discontinuous deformation always presents in the calculation points located at the vicinity of interfaces of various phases. All these factors lead to increased measurement error and/or meaningless calculation burdens when using R-DVC. To address these issues, we propose a segmentation-aided DVC (S-DVC) for accuracy-enhanced internal deformation analysis near the interface. The presented S-DVC first divides the reference volume image into different portions according to the distinct gray scales within different material phases (or background) or objects. Based on the segmented reference volume image, we can ensure that subvolumes only contain the voxels from the same material phase/object and exclude other phases/objects. As such, the error due to undermatched shape function can be minimized and meaningless DVC calculation can be avoided. The accuracy, efficiency, and practicality of S-DVC over R-DVC are validated by a simulated compression test of nodular cast iron (multiphase material) and a real compression experiment of 3D printed polymer (porous material).
Certifying the synthesis of heap-manipulating programs

Automated deductive program synthesis promises to generate executable programs from concise specifications, along with proofs of correctness that can be independently verified using third-party tools. However, an attempt to exercise this promise using existing proof-certification frameworks reveals significant discrepancies in how proof derivations are structured for two different purposes: program synthesis and program verification. These discrepancies make it difficult to use certified verifiers to validate synthesis results, forcing one to write an ad-hoc translation procedure from synthesis proofs to correctness proofs for each verification backend. In this work, we address this challenge in the context of the synthesis and verification of heap-manipulating programs. We present a technique for principled translation of deductive synthesis derivations (a.k.a. source proofs) into deductive target proofs about the synthesised programs in the logics of interactive program verifiers. We showcase our technique by implementing three different certifiers for programs generated via SuSLik, a Separation Logic-based tool for automated synthesis of programs with pointers, in foundational verification frameworks embedded in Coq: Hoare Type Theory (HTT), Iris, and Verified Software Toolchain (VST), producing concise and efficient machine-checkable proofs for characteristic synthesis benchmarks.
Tetrapartite entanglement measures of generalized GHZ state in the noninertial frames

Using a single-mode approximation, we carry out the entanglement measures, e.g., the negativity and von Neumann entropy when a tetrapartite generalized GHZ state is treated in a noninertial frame, but only uniform acceleration is considered for simplicity. In terms of explicit negativity calculated, we notice that the difference between the algebraic average π 4 and geometric average Π 4 is very small with the increasing accelerated observers and they are totally equal when all four qubits are accelerated simultaneously. The entanglement properties are discussed from one accelerated observer to all four accelerated observers. It is shown that the entanglement still exists even if the acceleration parameter r goes to infinity. It is interesting to discover that all 1-1 tangles are equal to zero, but 1-3 and 2-2 tangles always decrease when the acceleration parameter r increases. We also study the von Neumann entropy and find that it increases with the number of the accelerated observers. In addition, we find that the von Neumann entropy S ABCDI , S ABCIDI , S ABICIDI and S AIBICIDI always decrease with the controllable angle θ , while the entropies S 3 – 3 non , S 3 – 2 non , S 3 – 1 non and S 3 – 0 non first increase with the angle θ and then decrease with it.
GraphAttack

Graph structures are a natural representation of important and pervasive data. While graph applications have significant parallelism, their characteristic pointer indirect loads to neighbor data hinder scalability to large datasets on multicore systems. A scalable and efficient system must tolerate latency while leveraging data parallelism across millions of vertices. Modern Out-of-Order (OoO) cores inherently tolerate a fraction of long latencies, but become clogged when running severely memory-bound applications. Combined with large power/area footprints, this limits their parallel scaling potential and, consequently, the gains that existing software frameworks can achieve. Conversely, accelerator and memory hierarchy designs provide performant hardware specializations, but cannot support diverse application demands. To address these shortcomings, we present GraphAttack, a hardware-software data supply approach that accelerates graph applications on in-order multicore architectures. GraphAttack proposes compiler passes to (1) identify idiomatic long-latency loads and (2) slice programs along these loads into data Producer/ Consumer threads to map onto pairs of parallel cores. Each pair shares a communication queue; the Producer asynchronously issues long-latency loads, whose results are buffered in the queue and used by the Consumer. This scheme drastically increases memory-level parallelism (MLP) to mitigate latency bottlenecks. In equal-area comparisons, GraphAttack outperforms OoO cores, do-all parallelism, prefetching, and prior decoupling approaches, achieving a 2.87× speedup and 8.61× gain in energy efficiency across a range of graph applications. These improvements scale; GraphAttack achieves a 3× speedup over 64 parallel cores. Lastly, it has pragmatic design principles; it enhances in-order architectures that are gaining increasing open-source support.
Accessible information of a general quantum Gaussian ensemble

Accessible information, which is a basic quantity in quantum information theory, is computed for a general quantum Gaussian ensemble under certain “threshold conditions.” It is shown that the maximizing measurement is Gaussian, constituting a far-reaching generalization of optical heterodyning. This substantially extends the previous result concerning the gauge-invariant case even for a single bosonic mode. A simple sufficient condition is provided that implies the threshold condition for a general Gaussian ensemble. The tightness of the sufficient condition is demonstrated in the single-mode case.
Heterogeneous Side Information-based Iterative Guidance Model for Recommendation

Heterogeneous side information has been widely used in recommender systems to alleviate the data sparsity problem. However, the heterogeneous side information in existing methods provides insufficient guidance for predicting user preferences as its effect is inevitably weakened during utilization. Furthermore, most existing methods cannot effectively utilize the heterogeneous side information to understand users and items. They often neglect the interrelation among various types of heterogeneous side information of a user or an item. As a result, it is difficult for existing methods to comprehensively understand users and items so that the recommender system recommends inappropriate items to users. To overcome the above drawbacks, we propose an interrelation learning-based recommendation method with iterative heterogeneous side information guidance (ILIG). ILIG includes two modules: 1) Iterative Heterogeneous Side Information Guidance Module. It uses heterogeneous side information to iteratively guide the prediction of user preferences, which effectively enhances the effect of the heterogeneous side information. 2) Interrelation Learning-based Portrait Construction Module. It captures the interrelation among various types of heterogeneous side information to comprehensively learn the representations of users and items. To demonstrate the effectiveness of ILIG, we conduct extensive experiments on Movielens-100K, Movielens-1M, and BookCrossing datasets. The experimental results show that ILIG outperforms the state-of-the-art recommender systems.
GPU accelerated parallel reliability-guided digital volume correlation with automatic seed selection based on 3D SIFT

Digital volume correlation (DVC) is a powerful and widely used technique for measuring the internal 3D deformation field of a wide range of materials. One of the most popular DVC algorithms is the reliability-guided DVC (RG-DVC) which is good at dealing with large continuous deformation. However, RG-DVC requires a manually specified seed from which computation starts, and suffers from the efficiency due to a huge amount of computation and data dependency. This paper proposes a GPU accelerated parallel reliability-guided DVC algorithm (CuSIFT-RGDVC) on CUDA, which leverages 3D scale-invariant feature transform (3D SIFT) to assist seed selection to realize fully automation and improves performance utilizing GPU computing. In CuSIFT-RGDVC, reliability-guided displacement tracking (RGDT) is rewritten using sorted array-based batch processing mechanism which is a globally sequential locally parallel model, and multi-granularity parallelism is adopted to maximize GPU utilization. The empirical result shows that the proposed CuSIFT-RGDVC provides up to 29.1x speedup compared with our multi-threaded implementation and achieves the same level of computation speed as the state-of-the-art path-independent DVC without sacrificing accuracy.
Investigating Feature Selection and Explainability for COVID-19 Diagnostics from Cough Sounds

In this paper, we propose an approach to automatically classify COVID-19 and non-COVID-19 cough samples based on the combination of both feature engineering and deep learning models. In the feature engineering approach, we develop a support vector machine classifier over high dimensional (6373D) space of acoustic features. In the deep learning-based approach, on the other hand, we apply a convolutional neural network trained on the log-mel spectrograms. These two methodologically diverse models are then combined by fusing the probability scores of the models. The proposed system, which ranked 9th on the 2021 Diagnosing COVID-19 using Acoustics (Di- COVA) challenge leaderboard, obtained an area under the receiver operating characteristic curve (AUC) of 0:81 on the blind test data set, which is a 10:9% absolute improvement compared to the baseline. Moreover, we analyze the explainability of the deep learning-based model when detecting COVID-19 from cough signals. Copyright © 2021 ISCA.
Adversarial Attack Based on Perturbation of Contour Region to Evade Steganalysis-Based Detection

Deep neural networks (DNN) are applied in various fields because they afford good performance. However, they seem vulnerable to adversarial examples. Therefore, studies are actively investigating adversarial defenses to improve the robustness of DNNs as well as adversarial attacks to generate more intensive adversarial perturbations. Most existing attack methods excessively modify images by generating overly large distortions or adding perturbations to regions with little impact on the DNN. As a result, the local smoothness of images is not maintained, and changes can be easily detected using a steganalysis-based detector. In this study, we propose a contour attack method to evade steganalysis-based detection. This method extracts the contour region from an image and adds perturbations only to applicable regions, thus maintaining the local smoothness. It can solve the problems mentioned above and effectively evade steganalysis-based detectors. Our experimental results demonstrate that the local smoothness is better than that in the case of original attack methods and that the detection evasion rate for a steganalysis detector is up to 19.9% higher. Further, structural similarity (SSIM) measurements and image comparisons demonstrate the improved imperceptibility of perturbations. In particular, the SSIM values for images subjected to the contour attack are close to 1.0.
Smart Home Privacy Protection Based on the Improved LSB Information Hiding

Smart home is an emerging form of the Internet of Things (IoT), enabling people to enjoy a convenient and intelligent life. The data generated by smart home devices are transmitted through the public channel, which is not secure enough, so the secret data in smart home are easily intercepted by malicious adversaries. In order to solve this problem, this paper proposes a smart home privacy protection method combining DES encryption and the improved Least Significant Bit (LSB) information hiding algorithm, changing the practice of directly exposing smart home secret information to the Internet, first, using Data Encryption Standard (DES) encryption to encrypt the smart home information and second, the improved LSB information hiding algorithm is used to hide the ciphertext, so that the adversary cannot detect the smart home secret information. The goal of the scheme is to provide a double protection for the secure transmission of the smart home secret information. If an attacker wants to carry out an attack, it has to break through at least two defense lines, which seems impossible to do. Experiment results show that the improved LSB algorithm is more robust than the existing algorithms, and it is very safe. Therefore, the scheme proposed in this paper is very practical for protecting the smart home secret information.
Distributed Bayesian Inference Over Sensor Networks

In this article, two novel distributed variational Bayesian (VB) algorithms for a general class of conjugate-exponential models are proposed over synchronous and asynchronous sensor networks. First, we design a penalty-based distributed VB (PB-DVB) algorithm for synchronous networks, where a penalty function based on the Kullback–Leibler (KL) divergence is introduced to penalize the difference of posterior distributions between nodes. Then, a token-passing-based distributed VB (TPB-DVB) algorithm is developed for asynchronous networks by borrowing the token-passing approach and the stochastic variational inference. Finally, applications of the proposed algorithm on the Gaussian mixture model (GMM) are exhibited. Simulation results show that the PB-DVB algorithm has good performance in the aspects of estimation/inference ability, robustness against initialization, and convergence speed, and the TPB-DVB algorithm is superior to existing token-passing-based distributed clustering algorithms.