Reviewerly/exHarmony huggingface dataset:
High dynamic range image compression based on the multi-peak S-shaped tone curve

Tone mapping methods aim to compress the high dynamic range (HDR) images so that they can be displayed on common devices. The tone curve plays a key role in many tone mapping methods, which can directly adjust the range of the HDR image. The S-shaped tone curves can produce impressive performances due to their flexibility. However, the conventional S-shaped tone curve in tone mapping methods is single and had the problem of excessive compressing of the dense grayscale areas, resulting in the loss of details in this area, and insufficient compressing of the sparse grayscale areas, resulting in low contrast of tone mapped image. This paper proposes a multi-peak S-shaped (MPS) tone curve to address these problems. Specifically, the grayscale interval of the HDR image is divided according to the significant peak and valley distribution of the grayscale histogram, and each interval is tone mapped by an S-shaped tone curve. We further propose an adaptive S-shaped tone curve based on the luminance adaptation mechanism of the human visual system, which can effectively reduce the compression in the dense grayscale areas and increase the compression in the sparse grayscale areas, preserving details while improving the contrast of tone mapped images. Experiments show that our MPS tone curve replaces the single S-shaped tone curve in relevant methods for better performance and outperforms the state-of-the-art tone mapping methods.
Sustainable Health-Related Quality of Life in Older Adults as Supported by the vINCI Technology

The vINCI technology represents an innovative instrument developed specifically but not exclusively for older adults by technology researchers together with a medical team specialized in geriatrics and gerontology. It was designed to be independently and effortlessly used by older adults in the comfort and safety of their own environment. It is a modular and flexible platform that can integrate a large array of various sensors and can easily adapt to specific healthcare needs. The pilot study tested sensors and standardized instruments capable of evaluating several care-related parameters and of generating personalized feedback for the user dedicated to optimizing physical activity level, social interaction, and health-related quality of life. Moreover, the system was able to detect and signal events and health-related aspects that would require medical assistance. This paper presents how the innovative vINCI technology improves quality of life in older adults. This is evidenced by the results obtained following the clinical validation of the vINCI technology by older adults admitted to the Ana Aslan National Institute of Gerontology and Geriatrics (NIGG) in Bucharest.
datAFLow: Towards a Data-Flow-Guided Fuzzer

program analyses to model program and input structure, and continuously gather dynamic information about the target.Leveraging dynamic information drives fuzzer efficiency.For example, coverage-guided greybox fuzzers-perhaps the most widely-used class of fuzzer-track code paths executed by the target. 1 This allows the fuzzer to focus its mutations on inputs reaching new code.Intuitively, a fuzzer cannot find bugs in code never executed, so maximizing the amount of code executed should maximize the number of bugs found.Code coverage serves as an approximation of program behavior, and expanding code coverage implies exploring program behaviors.Coverage-guided greybox fuzzers are now pervasive.Their success [2] can be attributed to one fuzzer in particular: American Fuzzy Lop (AFL) [3].AFL is a greybox fuzzer that uses lightweight instrumentation to track edges covered in the target's control-flow graph (CFG).A large body of research has built on AFL [4][5][6][7][8][9][10][11][12].While improvements have been made, most fuzzers still default to edge coverage as an approximation of program behavior.Is this the best we can do?In some targets, control flow offers only a coarse-grained approximation of program behavior.This includes targets whose control structure is decoupled from its semantics (e.g., LR parsers generated by yacc) [13].Such targets require data-flow coverage [13][14][15][16][17]. Whereas control flow focuses on the order of operations in a program (i.e., branch and loop structures), data flow instead focuses on how variables (i.e., data) are defined and used [14]: indeed, there may be no control dependence between variable definition and use sites (see §III for details).In fuzzing, data flow typically takes the form of dynamic taint analysis (DTA).Here, the target's input data is tainted at its definition site and tracked as it is accessed and used at runtime.Unfortunately, accurate DTA is difficult to achieve and expensive to compute (e.g., prior work has found DTA is expensive [18,19] and its accuracy highly variable across implementations [18,20]).Moreover, several real-world programs fail to compile under DTA, increasing deployability concerns.Thus, most widely-deployed greybox fuzzers (e.g., AFL [3], libFuzzer [21], and honggfuzz [22]) eschew DTA in favor of higher fuzzing throughput.While lightweight alternatives to DTA exist (e.g., REDQUEEN [23], GREYONE [19]), the full potential of control-vs.data-flow based fuzzer coverage metrics have not yet been thoroughly explored.To support this exploration, we 1 Miller et al.'s original fuzzer [1] is now known as a blackbox fuzzer, because it has no knowledge of the target's internals.
Design and Proofreading of the English-Chinese Computer-Aided Translation System by the Neural Network

At present, complete machine translation (MT) cannot meet the needs of information communication and cultural exchange, and the speed of complete human translation is too slow. Therefore, if MT is used to assist in the process of English-Chinese translation, it can not only prove that machine learning (ML) can translate English to Chinese but also improve the translation efficiency and accuracy of translators through human-machine cooperation. The research on the mutual cooperation between ML and human translation has an important research significance for translation systems. An English-Chinese computer-aided translation (CAT) system is designed and proofread based on a neural network (NN) model. First, it gives a brief overview of CAT. Second, the related theory of the NN model is discussed. An English-Chinese CAT and proofreading system based on the recurrent neural network (RNN) is constructed. Finally, the translation accuracy and proofreading recognition rate of the translation files of 17 different projects under different models are studied and analyzed. The research results reveal that according to the different translation properties of different texts, the average accuracy rate of text translation under the RNN model is 93.96%, and the mean accuracy of text translation under the transformer model is 90.60%. The translation accuracy of the RNN model in the CAT system is 3.36% higher than that of the transformer model. The English-Chinese CAT system based on the RNN model has different proofreading results for sentence processing, sentence alignment, and inconsistency detection of translation files of different projects. Among them, the recognition rate for sentence alignment and the inconsistency detection of English-Chinese translation is high, and the expected effect is achieved. The design of the English-Chinese CAT and proofreading system based on the RNN can make the translation and proofreading be carried out simultaneously, which greatly improves the efficiency of translation work. Meanwhile, the above research methods can improve the problems encountered in the current English-Chinese translation, provide a path for the bilingual translation process, and have certain promotion prospects.
Cyber-Physical System for Industrial Automation Using Quantum Deep Learning

Automation industries are using Industry 4.0 solutions for several reasons. Still, the most important is to make it easier for their employees to get secure information and to work better with people in other departments. The goal of this effort is to improve productivity and efficiency by making it easier to make good decisions at the right time. This will make it possible to make these kinds of improvements. Constraint-based security solutions are becoming increasingly popular as the digital and physical worlds become more connected. Based on the processing speed, the amount of memory that can be used, and the amount of backup power that can be used. The algorithms and security frameworks used in earlier research need many resources. Several researchers have given an overview of the many security problems with the CPS and their studies and recommendations. But constraint-based applications need light security systems that can work with hardware with limited capabilities. We proposed a safety mechanism that can be used with the already-made constraint-based CPS program. To propose Cyber-Physical System for Industrial Automation Using Quantum Deep Learning, We proposed Cyber-Physical System for Industrial Automation that uses Quantum Learning to find attacking patterns.
Evaluation of explainable AI solutions in climate science

Explainable artificial intelligence (XAI) methods serve as a support for researchers to shed light onto the reasons behind the predictions made by deep neural networks (DNNs). XAI methods have already been successfully applied to climate science, revealing underlying physical mechanisms inherent in the studied data. However, the evaluation and validation of XAI performance is challenging as explanation methods often lack ground truth. As the number of XAI methods is growing, a comprehensive evaluation is necessary to enable well-founded XAI application in climate science.In this work we introduce explanation evaluation in the context of climate research. We apply XAI evaluation to compare multiple explanation methods for a multi-layer percepton (MLP) and a convolutional neural network (CNN). Both MLP and CNN assign temperature maps to classes based on their decade. We assess the respective explanation methods using evaluation metrics measuring robustness, faithfulness, randomization, complexity and localization. Based on the results of a random baseline test we establish an explanation evaluation guideline for the climate community. We use this guideline to rank the performance in each property of similar sets of explanation methods for the MLP and CNN. Independent of the network type, we find that Integrated Gradients, Layer-wise relevance propagation and InputGradients exhibit a higher robustness, faithfulness and complexity compared to purely Gradient-based methods, while sacrificing reactivity to network parameters, i.e. low randomisation scores. The contrary holds for Gradient, SmoothGrad, NoiseGrad and FusionGrad. Another key observation is that explanations using input perturbations, such as SmoothGrad and Integrated Gradients, do not improve robustness and faithfulness, in contrast to theoretical claims. Our experiments highlight that XAI evaluation can be applied to different network tasks and offers more detailed information about different properties of explanation method than previous research. We demonstrate that using XAI evaluation helps to tackle the challenge of choosing an explanation method.
Hybrid Statistical and Texture Features with DenseNet 121 for Breast Cancer Classification

Cancers are aggressive which is coupled with higher mortality rates and remain a major life-threatening factor in humans.Thus, early detection of cancer among patients is important as it showed a great survival chance.Therefore, early detection provides the patients with greater survival chances.The diagnosis performed for the mammography images are quite expensive and also radiations produced during the process were harmful to the patients.Thermography is a cost-effective and invasive method compared to mammography images and thus has reached its popularity.The present research is aimed to create the Machine Learning (ML) models using Convolutional Neural Networks (CNN) approaches which were created based on the machine learning models.The present research work utilized the DMR-IR dataset for the results evaluation and performance evaluation of the model which has been verified with datasets.The feature extraction process utilizes a machine learning algorithm to overcome the problems.The developed models were engaged in sophisticated ways to extract the features to improve the classification of the model.The Hybrid statistical and texture feature extraction technique extracts the features better in turn improved the model training.The results showed that the proposed hybrid feature extraction with the Dense Net 121 model obtained better accuracy of 98.97 %, the precision of 99.45%, Recall of 98.35%, F-score of 96.85%, Sensitivity of 99.4%, and Specificity of 97.98 % when compared to the existing Multi-input CNN that obtained accuracy of 93.8%, precision of 94.1%, Recall of 97.7%, F-score of 91.4%, Sensitivity of 88.9%, and specificity of 96.7%.
Gaussian processes and Fast Marching Square based informative path planning

The exploration of unknown environments is a challenge in robotics. The proposed method approaches this problem by combining the Fast Marching Square path planning technique with the machine learning method called Gaussian processes (GP). The Fast Marching Square method is used to determine the most unexplored areas of the environment and to plan the path of the vehicle from the current position to the selected point. The GP model is used to obtain predictions about the unexplored regions of the environment based on the collected data so far during the exploration. The use of Unmanned Aerial Vehicles (UAVs) for exploration and surveillance has increased exponentially in the recent years, due to their sensor equipment capabilities and their versatility for flying over difficult terrain. By defining the weight each method has on the selection of the next point to explore, we can focus the UAV on the points with more interesting data defined by the user (i.e. bodies of water), the most unexplored regions, or a combination of both. We present an study on the influence of these weights on the mean absolute error (MAE) and predictive variance obtained from the GP model and test the algorithm on a real environment obtained from a satellite image. We show that we are able to generate an accurate depiction of the environment way faster than traditional methods such as the Boustrophedon.
Efficiency-Boosting Federated Learning in Wireless Networks: A Long-Term Perspective

Federated learning (FL) can train a global model from clients' local dataset, which can make full use of the computing resources of clients and performs more extensive and efficient machine learning applications on clients with protecting user information requirements. Many existing works have focused on optimizing FL accuracy within the resource constrained in each individual round. However there are few works comprehensively consider the optimization for latency, accuracy and energy consumption from a long-term perspective in wireless federated learning. Inspired by this, in this paper, we investigate FL in wireless networks where client selection and bandwidth allocation are two crucial factors for improving the FL accuracy as well as reducing latency and energy consumption. We formulate the optimization problem as a mixed-integer problem, which minimizes the cost function over finite rounds while satisfying energy budget constraints of each client from a long-term perspective. To address this optimization problem, we propose the Per-round Energy Drift Plus Cost (PEDPC) algorithm in an online manner, including two parts: client selection and bandwidth allocation, which can be addressed by Increasing Time-Maximum Client Selection (ITMCS) algorithm and Barrier Method, respectively. Finally, the performance of the PEDPC algorithm is verified by extensive simulations in terms of latency, accuracy and energy consumption in IID and NON-IID data distributions.
Human Violence Recognition in Video Surveillance in Real-Time

The automatic detection of human violence in video surveillance is an area of great attention due to its application in security, monitoring, and prevention systems. Detecting violence in real time could prevent criminal acts and even save lives. There are many investigations and proposals for the detection of violence in video surveillance; however, most of them focus on effectiveness and not on efficiency. They focus on overcoming the accuracy results of other proposals and not on their applicability in a real scenario and real-time. In this work, we propose an efficient model for recognizing human violence in real-time, based on deep learning, composed of two modules, a spatial attention module (SA) and a temporal attention module (TA). SA extracts spatial features and regions of interest by frame difference of two consecutive frames and morphological dilation. TA extracts temporal features by averaging all three RGB channels in a single channel to have three frames as input to a 2D CNN backbone. The proposal was evaluated in efficiency, accuracy, and real-time. The results showed that our work has the best efficiency compared to other proposals. Accuracy was very close to the result of the best proposal, and latency was very close to real-time. Therefore our model can be applied in real scenarios and in real-time.
Big Data Assurance: An Approach Based on Service-Level Agreements

Big data management is a key enabling factor for enterprises that want to compete in the global market. Data coming from enterprise production processes, if properly analyzed, can provide a boost in the enterprise management and optimization, guaranteeing faster processes, better customer management, and lower overheads/costs. Guaranteeing a proper big data pipeline is the holy grail of big data, often opposed by the difficulty of evaluating the correctness of the big data pipeline results. This problem is even worse when big data pipelines are provided as a service in the cloud, and must comply with both laws and users' requirements. To this aim, assurance techniques can complete big data pipelines, providing the means to guarantee that they behave correctly, toward the deployment of big data pipelines fully compliant with laws and users' requirements. In this article, we define an assurance solution for big data based on service-level agreements, where a semiautomatic approach supports users from the definition of the requirements to the negotiation of the terms regulating the provisioned services, and the continuous refinement thereof.
Efficient algorithms for quantum information bottleneck

The ability to extract relevant information is critical to learning. An ingenious approach as such is the information bottleneck, an optimisation problem whose solution corresponds to a faithful and memory-efficient representation of relevant information from a large system. The advent of the age of quantum computing calls for efficient methods that work on information regarding quantum systems. Here we address this by proposing a new and general algorithm for the quantum generalisation of information bottleneck. Our algorithm excels in the speed and the definiteness of convergence compared with prior results. It also works for a much broader range of problems, including the quantum extension of deterministic information bottleneck, an important variant of the original information bottleneck problem. Notably, we discover that a quantum system can achieve strictly better performance than a classical system of the same size regarding quantum information bottleneck, providing new vision on justifying the advantage of quantum machine learning.
Experimental Evaluation of Ensemble Learning-Based Models for Twitter Spam Classification

People with malicious intent keep launching attacks in the internet through various means. These attackers are shifting their attacks to social sites such as twitter, facebook Instagram and the likes. One of attack methods is the use of spam in the social media platforms. Social network spam involves using unwanted content that appear on social networking sites such as facebook, twitter, instagram and related ones. Since attackers have shifted attention to using social media platforms for carrying out their nefarious activities there is a need to keep devising security measures to characterise social media based spam attacks. Thisstudy involves experimental evaluation of two ensemble learning models for twitter spam classification. The dataset employed in this study is a publicly available dataset on twitter spam studies. The dataset files are in four different groups, contain different twitter spam evidence. In each of the experimentation, each file in the whole dataset was used. Exploratory analysis of the datasets was carried out, one at a time. Thereafter, label encoding technique was used to handle the categorical feature. Then, two tree-based ensemble learning algorithms namely: Random Forest and Extra Trees algorithms were chosen to build the twitter spam detection models. Each of the set of dataset files was used for the training and testing of machine learning-based twitter spam detection models. The performances of the models built were evaluated and compared. The study revealed that the performances of the twitter spam detection models were promising. In all, the RF-based model recorded better performances in accuracy, precision, recall and f1-score compared to the results in the Extra Trees-based model.
Hierarchical Attention Master–Slave for heterogeneous multi-agent reinforcement learning

Most multi-agent reinforcement learning (MARL) approaches optimize strategy by improving itself, while ignoring the limitations of homogeneous agents that may have single function. However, in reality, the complex tasks tend to coordinate various types of agents and leverage advantages from one another. Therefore, it is a vital research issue how to establish appropriate communication among them and optimize decision. To this end, we propose a Hierarchical Attention Master–Slave (HAMS) MARL, where the Hierarchical Attention balances the weight allocation within and among clusters, and the Master–Slave architecture endows agents independent reasoning and individual guidance. By the offered design, information fusion, especially among clusters, is implemented effectively, and excessive communication is avoided, moreover, selective composed action optimizes decision. We evaluate the HAMS on both small and large scale heterogeneous StarCraft II micromanagement tasks. The proposed algorithm achieves the exceptional performance with more than 80% win rates in all evaluation scenarios, which obtains an impressive win rate of over 90% in the largest map. The experiments demonstrate a maximum improvement in win rate of 47% over the best known algorithm. The results show that our proposal outperforms recent state-of-the-art approaches, which provides a novel idea for heterogeneous multi-agent policy optimization.