Reviewerly/exHarmony huggingface dataset:
Reversible data hiding in encrypted images using block-based adaptive MSBs prediction

• Propose a reversible data hiding method in encrypted images with a high embedding capacity. • Present a block-level image encryption method based on a chaotic system. • Construct a block-based MSBs predictor. • Propose a data embedding method in the encrypted image based on a block-based adaptive-layer MSB prediction. This paper proposes a reversible data hiding method in encrypted images with a high embedding capacity. In the proposed method, the original image is encrypted using a block-level encryption algorithm based on a chaotic system, which protects the privacy of the original image while maintaining some redundancy of the encrypted image block. For each encrypted block, the data hider first evaluates the embedding capacity based on the accuracy of the most significant bit (MSB) prediction. Then, the data is embedded into every encrypted image block by adaptive-layer MSB substitution. At the receiving side, the receiver first extracts the data from the marked encrypted image and recovers the encrypted image by block-based MSB prediction. Then, the secret message can be obtained with the data hiding key, and the original image can be perfectly reconstructed with the encryption key. Experimental results and analysis show that the embedding capacity is significantly improved.
Did I Miss Anything? A Study on Ranking Fusion and Manual Query Rewriting in Consumer Health Search

In this paper, we describe the methodology and experimental analysis of a twofold strategy for the retrieval of medical relevant information: a ranking fusion and a query reformulation approach. In particular, the query reformulation approach is based on the idea that a query is composed of two parts: the primary term and the secondary term of the query, and that these two parts can be substituted with alternative terms to create a reformulation of the original query. The goal of our work is to evaluate the performances of a search engine over 1) manual query variants; 2) different retrieval functions; 3) w/out pseudo-relevance feedback; 4) reciprocal ranking fusion. We describe the experiments based on the CLEF eHealth 2021 Consumer Health Search Task dataset. The results show that 1) a ranking fusion approach of the baseline models improves MAP significantly; 2) manual query variants open new questions about possible an unintentional bias in the pool of documents that were selected for relevance assessment.
A Concise Overview of LeQua@CLEF 2022: Learning to Quantify

LeQua 2022 is a new lab for the evaluation of methods for "learning to quantify" in textual datasets, i.e., for training predictors of the relative frequencies of the classes of interest $$\mathcal {Y}=\{y_{1}, ..., y_{n}\}$$ in sets of unlabelled textual documents. While these predictions could be easily achieved by first classifying all documents via a text classifier and then counting the numbers of documents assigned to the classes, a growing body of literature has shown this approach to be suboptimal, and has proposed better methods. The goal of this lab is to provide a setting for the comparative evaluation of methods for learning to quantify, both in the binary setting and in the single-label multiclass setting; this is the first time that an evaluation exercise solely dedicated to quantification is organized. For both the binary setting and the single-label multiclass setting, data were provided to participants both in ready-made vector form and in raw document form. In this overview article we describe the structure of the lab, we report the results obtained by the participants in the four proposed tasks and subtasks, and we comment on the lessons that can be learned from these results.
Imbalanced multiclass classification with active learning in strip rolling process

In the strip rolling process, conventional supervised methods cannot effectively address data with an imbalanced number of normal and faulty instances. In this paper, based on a deep belief network, a resampling method is combined with active learning (AL) to address imbalanced multiclass problems. The support vector machine-based synthetic minority oversampling technique was adapted to enrich the training data, whereas the true data distribution and model generalization were changed. A new selection strategy of AL is proposed that forms a function using uncertainty and diversity. AL uses an optimizing set that has a similar distribution with the whole dataset to calculate the informativeness of instances to optimize the model. Based on this step, the model study instances approach decision boundaries to promote performance. The proposed method is validated by five UCI benchmark datasets and strip rolling data, and experiments show that it outperforms conventional methods in tackling imbalanced multiclass problems.
Predicting Abnormalities in Laboratory Values of Patients in the Intensive Care Unit Using Different Deep Learning Models: Comparative Study

In recent years, the volume of medical knowledge and health data has increased rapidly. For example, the increased availability of electronic health records (EHRs) provides accurate, up-to-date, and complete information about patients at the point of care and enables medical staff to have quick access to patient records for more coordinated and efficient care. With this increase in knowledge, the complexity of accurate, evidence-based medicine tends to grow all the time. Health care workers must deal with an increasing amount of data and documentation. Meanwhile, relevant patient data are frequently overshadowed by a layer of less relevant data, causing medical staff to often miss important values or abnormal trends and their importance to the progression of the patient's case.The goal of this work is to analyze the current laboratory results for patients in the intensive care unit (ICU) and classify which of these lab values could be abnormal the next time the test is done. Detecting near-future abnormalities can be useful to support clinicians in their decision-making process in the ICU by drawing their attention to the important values and focus on future lab testing, saving them both time and money. Additionally, it will give doctors more time to spend with patients, rather than skimming through a long list of lab values.We used Structured Query Language to extract 25 lab values for mechanically ventilated patients in the ICU from the MIMIC-III and eICU data sets. Additionally, we applied time-windowed sampling and holding, and a support vector machine to fill in the missing values in the sparse time series, as well as the Tukey range to detect and delete anomalies. Then, we used the data to train 4 deep learning models for time series classification, as well as a gradient boosting-based algorithm and compared their performance on both data sets.The models tested in this work (deep neural networks and gradient boosting), combined with the preprocessing pipeline, achieved an accuracy of at least 80% on the multilabel classification task. Moreover, the model based on the multiple convolutional neural network outperformed the other algorithms on both data sets, with the accuracy exceeding 89%.In this work, we show that using machine learning and deep neural networks to predict near-future abnormalities in lab values can achieve satisfactory results. Our system was trained, validated, and tested on 2 well-known data sets to ensure that our system bridged the reality gap as much as possible. Finally, the model can be used in combination with our preprocessing pipeline on real-life EHRs to improve patients' diagnosis and treatment.
An enhanced user’s speech recognition data analysis and expectation in intelligent personal assistant

Intelligent Personal Assistant (IPA) adoption has ushered in a technological revolution in today's society. There are several well-known IPA agents, like Google Assistant and Cortana, which were both created by Microsoft. The benefits of IPAs' speech comprehension capabilities have resulted in an increase in IPA users year after year. The goal of the study is to look at how an IPA's speech comprehension capability could be improved in the future, making it easier for IPA users to handle their everyday routines. This study will employ a survey and an unstructured interview to learn about users' expectations for an IPA's speech comprehension capabilities and how IPA adaptation affects their daily routine management. To fulfill the research's goal, an IPA with a daily routine management system will be suggested using the methodology and project design that have been determined.
Face recognition system based on the multi-resolution singular value decomposition fusion technique

This study proposes a Fusion, Feature-Level, Face Recognition System (FFLFRS) that is based on the Multi-Resolution, Singular Value Decomposition (MSVD) fusion technique. Face recognition in the FFLFRS is achieved via four processes: face detection, feature extraction, feature fusion, and face classification. In this system, the most significant face features (that is, the eyes, nose, and mouth) are first detected. Then, local and global features are extracted by the Local Binary Pattern (LBP) and Principal Component Analysis (PCA) extraction approaches. Afterwards, the extracted features are fused by the MSVD method and classified by the Artificial Neural Network (ANN). The proposed FFLFRS was verified on 10,000 face images drawn from the face images database of the Olivetti Research Laboratory (ORL). Face recognition performance of this system was contrasted with levels of performance of three state of the art, fusion-level, face recognition systems (FRSs) depending on the Frequency Partition (FP), Laplacian Pyramid (LP), and Covariance Intersection (CI) fusion methods. Ten-thousand images were employed to test the proposed model and assess its performance, which was evaluated in terms of changes in pose, illumination, and expression, besides low resolution and presence of occlusion. The face recognition results of the proposed FFLFRS are encouraging. This system proved to be effective in dealing with images having challenges to face recognition and it could achieve a recognition accuracy as high as 97.78%.
A One-Class Classification method based on Expanded Non-Convex Hulls

This paper presents an intuitive, robust and efficient One-Class Classification algorithm. The method developed is called OCENCH (One-class Classification via Expanded Non-Convex Hulls) and bases its operation on the construction of subdivisible and expandable non-convex hulls to represent the target class. The method begins by reducing the dimensionality of the data to two-dimensional spaces using random projections. After that, an iterative process based on Delaunay triangulations is applied to these spaces to obtain simple polygons that characterizes the non-convex shape of the normal class data. In addition, the method subdivides the non-convex hulls to represent separate regions in space if necessary. The method has been evaluated and compared to several main algorithms of the field using real data sets. In contrast to other methods, OCENCH can deal with non-convex and disjointed shapes. Finally, its execution can be carried out in a parallel way, which is interesting to reduce the execution time.
A Graph Neural Network-based Code Recommendation Method for Smart Contract Development

Smart contracts can be considered as a service in the blockchain system and have been applied in many fields, covering financial products, online games, real estate, transportation and logistics. However, smart contract technology is still in its infancy. Development task is facing many difficulties and challenges, thus providing a set of new or improved development aids for the smart contract ecosystem is an urgent problem that needs to be solved. This paper proposes a smart contract code recommendation method based on graph neural network, which aims to facilitate the development of smart contracts and help developers realize smart contracts faster and more securely. Experimental results show that this method is better than the existing model of smart contract code recommendation in terms of accuracy.
A Fast and Efficient Shape Blending by Stable and Analytically Invertible Finite Descriptors

In a previous work, we have proposed a morphing method based on invertible and stable descriptors that are invariant to Euclidean transformations and to the starting point. The stability guarantees the closeness in the shape sense of the reconstructed intermediate contours. However, this set of descriptors is not defined by a general expression. Here, we propose several sets of stable and invertible finite descriptors expressed by the same formula. Its stability is proven for a subset of this family thanks to the finite-dimension of the invariant space. Such finite dimension results from the Discrete Fourier Transform model that is used instead of Fourier coefficients. Moreover, an analytical general inverse formula is established. The use of the inverse analytical formula and the double utilization of the Fast Fourier Transform ensure an effective blending while being computationally efficient. Finally, we propose a new quantitative criterion for shape morphing. The latter is based on the Euclidean distances between successive curves in a morphing sequence after applying a given registration. Therefore, it allows us to compare the blending results of each set of descriptors. Several experiments are conducted on KIMIA'99 and MPEG-7 datasets. The results highlight the concordance of this criterion with the morphing visual quality and indicate which set of descriptors generates the most appropriate blending.
An Optimization Framework for Federated Edge Learning

The optimal design of federated learning (FL) algorithms for solving general machine learning (ML) problems in practical edge computing systems with quantized message passing remains an open problem. This paper considers an edge computing system where the server and workers have possibly different computing and communication capabilities and employ quantization before transmitting messages. To explore the full potential of FL in such an edge computing system, we first present a general FL algorithm, namely GenQSGD, parameterized by the numbers of global and local iterations, mini-batch size, and step size sequence. Then, we analyze its convergence for an arbitrary step size sequence and specify the convergence results under three commonly adopted step size rules, namely the constant, exponential, and diminishing step size rules. Next, we optimize the algorithm parameters to minimize the energy cost under the time constraint and convergence error constraint, with the focus on the overall implementing process of FL. Specifically, for any given step size sequence under each considered step size rule, we optimize the numbers of global and local iterations and mini-batch size to optimally implement FL for applications with preset step size sequences. We also optimize the step size sequence along with these algorithm parameters to explore the full potential of FL. The resulting optimization problems are challenging non-convex problems with non-differentiable constraint functions. We propose iterative algorithms to obtain KKT points using general inner approximation (GIA) and tricks for solving complementary geometric programming (CGP). Finally, we numerically demonstrate the remarkable gains of GenQSGD with optimized algorithm parameters over existing FL algorithms and reveal the significance of optimally designing general FL algorithms.
Latency-based Vector Scheduling of Many-task Applications for a Hybrid Cloud

A centralized scheduler can become a bottleneck for placing the tasks of a many-task application on heterogeneous cloud resources. We have previously demonstrated that a de-centralized vector scheduling approach based on performance measurements can be used successfully for this task placement scenario. In this paper, we extend this approach to task placement based on latency measurements. Each node collects the performance measurements from its neighbors on an overlay graph, measures the communication latency, and then makes local decisions on where to move tasks. We present a centralized algorithm for configuring the overlay graph based on latency measurements and extend the vector scheduling approach to take latency into considerations. Our experiments in CloudLab demonstrate that this approach results in better performance and resource utilization than without latency information.
Analysis and Compensation of Phase Error Caused by Noise and Nonlinear Response of 3-D Measurement System

For static objects where the fringes are not saturated, the phase accuracy is mainly affected by the unavoidable factors of the introduction of fringe noise and the nonlinear response of the measurement system. The existing noise-induced phase error model is based on a Gaussian noise model, but in fact the noise of the fringe pattern is random. In this work, we establish a new evaluation method that directly converts the noise variance into phase variance, and we compare the anti-noise performance of the three-step and four-step phase-shift methods according to the phase error coefficient. Theoretical and experimental results confirm that the anti-noise performance of the four-step phase-shifting method is 25% higher than that of the three-step phase-shifting method. Subsequently, we also established a phase error model for the nonlinear response of the three-dimensional (3D) measurement system. Since the low measurement efficiency of the existing double <i>N</i>-step phase-shifting (DNPS) method, an improved double <i>N</i>-step phase-shifting (IDNPS) method is proposed. The RMSE of the compensated phase with IDNPS is approximately equal to that with DNPS, but the number of additional fringes with IDNPS is reduced to half that with DNPS. The total time cost of using IDNPS is reduced to 68.5% of using DNPS. Simulations and experiments are conducted to verify the validity of the theoretical analysis results.
Correct-by-Design Interacting Smart Contracts and a Systematic Approach for Verifying ERC20 and ERC721 Contracts With VeriSolid

Blockchain-based smart contracts enable the creation of decentralized applications, which often handle assets of considerable value. While the underlying platforms guarantee the correctness of smart-contract execution, they cannot ensure that the code of a contract is correct. Today, as evidenced by a number of recent security breaches, developers still have a hard time making contracts that work properly.Even though these incidents often exploit contract interaction, prior work on smart-contract verification, vulnerability discovery, and secure development typically considers only individual contracts in isolation. To address this gap, we introduce the <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">VeriSolid</small> framework for the formal verification of contracts that are specified using a abstract state machine based model with rigorous operational semantics. Our model-based approach allows developers to reason about and verify the behavior of a set of interacting contracts at a high level of abstraction. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">VeriSolid</small> allows the generation of Solidity code that is functionally and behaviorally equivalent to verified models, which enables the creation of correct-by-design smart contracts. We additionally introduce a graphical notation (called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">deployment diagrams</i> ) for specifying possible interactions between contract types. Based on this notation, we present a framework for the automated verification, generation, and deployment of contracts that conform to a deployment diagram. To demonstrate the applicability of <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">VeriSolid</small> , we translate existing Ethereum Improvement Proposal (EIP) specifications to temporal properties for two of the most popular contract interfaces: ERC20 and ERC721. We also show you how to write code for the ERC20 and ERC721 interfaces in a way that is safe, and we do this by using <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">VeriSolid</small> . We evaluate our framework on 726 contracts that are currently deployed on the Ethereum blockchain, which include 267 ERC20 and 459 ERC721 contracts. Our experiments indicate that 18% of ERC20 contracts and 4% of ERC721 contracts fail to satisfy the EIP specifications.
Kinect sensors network calibration in controlled environment based on semantic information

Calibrating camera networks is of importance in applications where object of interest must be analyzed either from multiple views or as a single 3D entity. The selected cali-bration procedure mostly depends on the required accuracy and usually involves a dedicated calibration target named calibration marker. This is a pattern printed on 2D sheet or can be a 3D object with dedicated shape and known dimensions. Using such markers however, might not be convenient in practice. Such situations arise when the calibration procedure requires special expertise or time consuming frequent re-calibration. In such cases the dedicated calibration marker can be substituted with objects which are part of the surrounding environment. Usually such objects contain some semantic meaning, e.g. walls, tables etc. In this paper, we propose an approach for calibrating Kinect sensors network using objects with semantic meaning, part of a controlled environment. Our goal is to avoid the necessity for dedicated markers, which complicates the calibration procedure and to deliver simple tools to the end user. The experimental re-sults, show that the proposed approach for semantic calibration delivers high accuracy results, similar to the results of state of the art algorithms for Kinect sensors network calibration.
Sequence Clock: A Dynamic Resource Orchestrator for Serverless Architectures

Function-as-a-service (FaaS) represents the next frontier in the evolution of cloud computing being an emerging paradigm that removes the burden of configuration and management issues from users. This is achieved by replacing the well-established monolithic approach with graphs of standalone, small, stateless, event-driven components called functions. At the same time, from the cloud providers’ perspective, problems such as availability, load balancing and scalability need to be resolved without being aware of the functionality, behavior or resource requirements of their tenants’ code. However, in this context, functions’ containers coexist with others inside a host of finite resources, where a passive resource allocation technique does not guarantee a well-defined quality of service (QoS) in regards to time latency. In this paper, we present Sequence Clock, an expandable latency targeting tool that actively monitors serverless invocations in a cluster and offers execution of a sequential chain of functions, also known as pipelines or sequences, while achieving the targeted time latency. Two regulation methods were utilized, with one of them achieving up to 82% decrease in the severity of time violations and in some cases even eliminating them completely.
Deep Reservoir Computing using Echo State Networks and Liquid State Machine

Reservoir Computing is one of the most recent Machine Learning (ML) algorithm used for time-series prediction. Its main advantage is that it uses a fixed set of weights for the internal network. This paper was focused on Deep Reservoir Computing architectures, starting from simple Echo State Networks (ESN) and Liquid State Machine (LSM) models. The performance of such hybrid architectures was then compared with simple ESN. The final goal was to find the optimal configuration which obtains the best results in terms of training time, prediction time and Root Mean Square Error (RMSE).