Reviewerly/exHarmony huggingface dataset:
T‐depth reduction method for efficient SHA‐256 quantum circuit construction

To perform a quantum brute force attack on a cryptosystem based on Grover's algorithm, it is necessary to implement a quantum circuit of the cryptographic algorithm. Therefore, an efficient quantum circuit design of a given cryptographic algorithm is essential, especially in terms of quantum security analysis, and it is well known that T-depth should be reduced for time complexity efficiency. In this paper, the authors propose a novel technique to reduce T-depth (and T-count) when some quantum circuits located in between two Toffoli-gates are interchangeable with a controlled phase gate (CP gate), and the authors apply this technique to five types of quantum adders, reducing T-depth by more than 33%. The authors also present new SHA-256 quantum circuits which have a critical path with only three quantum adders while the critical paths of quantum circuits in the previous studies consist of seven or 10 quantum adders, and the authors also apply our technique to the proposed SHA-256 quantum circuits. Four versions of SHA-256 quantum circuit are presented. Among the previous results, T-depth of the circuit with the smallest Width (the number of qubits) 801 was approximately 109,104. On the other hand, T-depth of the proposed SHA-256 quantum circuit with the Width 797 is 16,055, which is remarkably reduced by about 85%. Another proposed quantum circuit only requires 768 qubits, which is the smallest Width compared to the previous results to the best of our knowledge. Furthermore, one other version is the most time-efficient circuit with an overall Toffoli-depth (and T-depth) that is less than 5000.
Learning-Based Online QoE Optimization in Multi-Agent Video Streaming

Video streaming has become a major usage scenario for the Internet. The growing popularity of new applications, such as 4K and 360-degree videos, mandates that network resources must be carefully apportioned among different users in order to achieve the optimal Quality of Experience (QoE) and fairness objectives. This results in a challenging online optimization problem, as networks grow increasingly complex and the relevant QoE objectives are often nonlinear functions. Recently, data-driven approaches, deep Reinforcement Learning (RL) in particular, have been successfully applied to network optimization problems by modeling them as Markov decision processes. However, existing RL algorithms involving multiple agents fail to address nonlinear objective functions on different agents’ rewards. To this end, we leverage MAPG-finite, a policy gradient algorithm designed for multi-agent learning problems with nonlinear objectives. It allows us to optimize bandwidth distributions among multiple agents and to maximize QoE and fairness objectives on video streaming rewards. Implementing the proposed algorithm, we compare the MAPG-finite strategy with a number of baselines, including static, adaptive, and single-agent learning policies. The numerical results show that MAPG-finite significantly outperforms the baseline strategies with respect to different objective functions and in various settings, including both constant and adaptive bitrate videos. Specifically, our MAPG-finite algorithm maximizes QoE by 15.27% and maximizes fairness by 22.47% compared to the standard SARSA algorithm for a 2000 KB/s link.
The impact of geothermal fluid discharge on drainage water and groundwater quality in terms of toxic contaminants in the agricultural Harran Plain, Turkey

The Karaali Geothermal field located in Harran Plain, which has a great agricultural potential for the development of our country, has a great importance because of being a thermal tourism and greenhouse heating source in Sanliurfa province. In this study, the aim was to determine heavy metal contamination, which has direct and indirect serious effects on human health, due to geothermal causes. In this context, a total of 11 sampling points in the Karaali Geothermal fluid (KG), the closest drainage channels where geothermal water is discharged (DW1, DW2, DW3, DW4 and DW5), and the closest groundwater to these points (GW1, GW2, GW3, GW4 and GW5), were sampled during 8 months between May 2020 and March 2021. Heavy metal concentrations were obtained in the order F>Sr>B>Ba>Al>Li>Fe>Cr>V>Mo>Zn>Cu>Zr>Be>Mn>Cd>Co>Pb>Ag>Ni for groundwater and Al>Fe>Sr>F>B>Mn>Ba>Li>Cu>Zn>Cr>Ni>Co>Mo>Be>Pb>Zr>Cd>Ag>V for drainage water. Al, Fe and Mn values were determined to exceed the acceptable values in terms of suitability of the water in the drainage channels for reuse and agricultural irrigation purposes. In groundwater, the limit values in terms of suitability for drinking water were exceeded for Al, B, F, Fe and Mo parameters. In the diagram of pH and metal load, all the samples were classified as “near neutral–high metal.” The study found that six factors in both drainage and groundwater factor analysis separately explained 86.1% and 81.2% of the total variation in the data, respectively. In the cluster, good results were obtained with three different similarity groups for drainage waters and five different similarity groups for groundwater. These results show that the factors responsible for toxic contamination in the drainage channels and especially in the groundwater are primarily related to the use of geothermal fluid in greenhouse cultivation and spa tourism, excessive use of pesticides and fertilizers in agricultural activities, and geogenic processes.
Optimized Deep-Neural Network for Content-based Medical Image Retrieval in a Brownfield IoMT Network

In this paper, a brownfield Internet of Medical Things network is introduced for imaging data that can be easily scaled out depending on the objectives, functional requirements, and the number of facilities and devices connected to it. This is further used to develop a novel Content-based Medical Image Retrieval framework. The developed framework uses DenseNet-201 architecture for generating the image descriptors. Then for classification, the optimized Deep Neural Network model has been configured through a population-based metaheuristic Differential Evolution. Differential Evolution iteratively performs the joint optimization of hyperparameters and architecture of Deep Neural Networks. The competence of the proposed model is validated on three publicly available datasets: Brain Tumor MRI dataset, Covid-19 Radiography database, and Breast Cancer MRI dataset, and by comparing it with selected models over different aspects of performance evaluation. Results show that the convergence rate of the proposed framework is very fast, and it achieves at least 97.28% accuracy across all the models.
Introduction to Imperative Code Execution Machine - a framework for sustainable Salesforce application development

This paper defines a reusable framework called Imperative Code Execution Machine (ICEM) which enables developers to implement application logic using code stored in the form of plain text and evade the necessity of its compilation, even at runtime. ICEM aims to reduce amount of apex code, facilitate rapid development while adopting evolving business requirements, maintenance, testing and eventually shrink Time to Market (TTM) for a given application or functionality over Salesforce platform. The framework has potential to mitigate the challenges that arise with the practice of agile for development of applications with convoluted business logic by shifting the focus from &#x201C;developing&#x201D; a solution to &#x201C;designing&#x201D; a solution.
Autoencoder Based Anomaly Detection and Explained Fault Localization in Industrial Cooling Systems

Anomaly detection in large industrial cooling systems is very challenging due to the high data dimensionality, inconsistent sensor recordings, and lack of labels. The state of the art for automated anomaly detection in these systems typically relies on expert knowledge and thresholds. However, data is viewed isolated and complex, multivariate relationships are neglected. In this work, we present an autoencoder based end-to-end workflow for anomaly detection suitable for multivariate time series data in large industrial cooling systems, including explained fault localization and root cause analysis based on expert knowledge. We identify system failures using a threshold on the total reconstruction error (autoencoder reconstruction error including all sensor signals). For fault localization, we compute the individual reconstruction error (autoencoder reconstruction error for each sensor signal) allowing us to identify the signals that contribute most to the total reconstruction error. Expert knowledge is provided via look-up table enabling root-cause analysis and assignment to the affected subsystem. We demonstrated our findings in a cooling system unit including 34 sensors over a 8-months’ time period using 4-fold cross validation approaches and automatically created labels based on thresholds provided by domain experts. Using 4-fold cross validation, we reached a F1-score of 0.56, whereas the autoencoder results showed a higher consistency score (CS of 0.92) compared to the automatically created labels (CS of 0.62) – indicating that theanomaly is recognized in a very stable manner. The automatically created labels, however, detected anomaly earlier. The main anomaly was found by the autoencoder and automatically created labels, and was also recorded in the log files. Further, the explained fault localization highlighted the most affected component for the main anomaly in a very consistent manner.
Batch Active Learning with Graph Neural Networks via Multi-Agent Deep Reinforcement Learning

Graph neural networks (GNNs) have achieved tremendous success in many graph learning tasks such as node classification, graph classification and link prediction. For the classification task, GNNs' performance often highly depends on the number of labeled nodes and thus could be significantly hampered due to the expensive annotation cost. The sparse literature on active learning for GNNs has primarily focused on selecting only one sample each iteration, which becomes inefficient for large scale datasets. In this paper, we study the batch active learning setting for GNNs where the learning agent can acquire labels of multiple samples at each time. We formulate batch active learning as a cooperative multi-agent reinforcement learning problem and present a novel reinforced batch-mode active learning framework BiGeNe. To avoid the combinatorial explosion of the joint action space, we introduce a value decomposition method that factorizes the total Q-value into the average of individual Q-values. Moreover, we propose a novel multi-agent Q-network consisting of a graph convolutional network (GCN) component and a gated recurrent unit (GRU) component. The GCN component takes both the informativeness and inter-dependences between nodes into account and the GRU component enables the agent to consider interactions between selected nodes in the same batch. Experimental results on multiple public datasets demonstrate the effectiveness and efficiency of our proposed method.
A grounded theory based approach to characterize software attack surfaces

The notion of Attack Surface refers to the critical points on the boundary of a software system which are accessible from outside or contain valuable content for attackers. The ability to identify attack surface components of software system has a significant role in effectiveness of vulnerability analysis approaches. Most prior works focus on vulnerability techniques that use an approximation of attack surfaces and there have not been many attempts to create a comprehensive list of attack surface components. Although limited number of studies have focused on attack surface analysis, they defined attack surface components based on project specific hypotheses to evaluate security risk of specific types of software applications. In this study, we leverage a qualitative analysis approach to empirically identify an extensive list of attack surface components. To this end, we conduct a Grounded Theory (GT) analysis on 1444 previously published vulnerability reports and weaknesses with a team of three software developers and security experts. We extract vulnerability information from two publicly available repositories: 1) Common Vulnerabilities and Exposures (CVE) and 2) Common Weakness Enumeration (CWE). We ask three key questions: where the attacks come from, what they target, and how they emerge, and to help answer these questions we define three core categories for attack surface components: Entry points, Targets, and Mechanisms. We extract attack surface concepts related to each category from collected vulnerability information using the GT analysis and provide a comprehensive categorization that represents attack surface components of software systems from various perspectives. The paper introduces 254 new attack surface components that did not exist in the literature. The comparison of the proposed attack surface model with prior works indicates that only 6.7% of the identified Code level attack surface components are studied before.
Construction of Intelligent Evaluation Model of English Composition Based on Machine Learning

To automatically grade compositions, automatic composition grading primarily employs statistics, mathematical analysis, machine learning, natural language processing, and other technologies. This paper presents a machine learning-based intelligent evaluation model for English composition. As a result, this paper proposes a word vector grouping-based text content representation method and a vector space model-based text content representation method. The Word2Vec model is first trained with words, then used to generate a test model of word vectors, with the statistical information of the corresponding words in each category serving as content text features. The results show that when features based on word vector clustering are added to the content text, the effect of each model improves significantly, especially the maximum entropy model, which improves by 0.048. The XGBoost model has also seen a significant improvement, going from 0.771 to 0.803. The test corpus is made up of 100 articles chosen at random from the corpus, and the test set is checked for errors. The rate of accuracy is 68 percent. The conclusion demonstrates that the model presented in this paper can help promote English teaching reform and quality-oriented higher education while also easing the burden on teachers and students.
Supervised Contrastive Learning Approach for Contextual Ranking

Contextual ranking models have delivered impressive performance improvements over classical models in the document ranking task. However, these highly over-parameterized models tend to be data-hungry and require large amounts of data even for fine tuning.
Virtual interaction and manipulation control of a hexacopter through hand gesture recognition from a data glove

Abstract The purpose of this study is to realize virtual interaction and manipulation control of a hexacopter based on hand gesture recognition from a designed data glove, to provide an intuitive and visual real-time simulation system for flight control algorithm verification and external control equipment testing. First, the hand gesture recognition from a designed data glove is studied, which can recognize different actions, such as mobile ready, grab, loosen, landing, take-off, and hover. Then, the design of virtual simulation system for hexacopter capture is completed, with the model design of hexacopter and manipulator, and the simulation software design with $CoppeliaSim$ . Finally, virtual simulation experiment of hexacopter grasping and virtual flight control experiment based on data glove are tested, respectively, and quantitatively described. The overall recognition rate is 84.3%, indicating that the data glove produced has the ability to recognize gestures, but its recognition performance is not superior. In gesture recognition, the recognition rate of static gestures is relatively higher than that of dynamic gestures. Among the static gestures, the hover gesture has the highest recognition rate. The average correct rate of static gestures can reach 94%. The lowest recognition rate of dynamic gestures is upward movement, and the average recognition rate of dynamic gestures is 76.1%. The research can be used to remotely operate hexacopter using a data glove in the future and improve the control performance through virtual interaction and manipulation simulation before actual application.
BibleTTS: a large, high-fidelity, multilingual, and uniquely African speech corpus

BibleTTS is a large, high-quality, open speech dataset for ten languages spoken in Sub-Saharan Africa. The corpus contains up to 86 hours of aligned, studio quality 48kHz single speaker recordings per language, enabling the development of high-quality text-to-speech models. The ten languages represented are: Akuapem Twi, Asante Twi, Chichewa, Ewe, Hausa, Kikuyu, Lingala, Luganda, Luo, and Yoruba. This corpus is a derivative work of Bible recordings made and released by the Open.Bible project from Biblica. We have aligned, cleaned, and filtered the original recordings, and additionally hand-checked a subset of the alignments for each language. We present results for text-to-speech models with Coqui TTS. The data is released under a commercial-friendly CC-BY-SA license.
PPIoV: A Privacy Preserving-Based Framework for IoV- Fog Environment Using Federated Learning and Blockchain

The integration of the Internet-of-Vehicles (IoV) and fog computing benefits from cooperative computing and analysis of environmental data while avoiding network congestion and latency. However, when private data is shared across fog nodes or the cloud, there exist privacy issues that limit the effectiveness of IoV systems, putting drivers' safety at risk. To address this problem, we propose a framework called PPIoV, which is based on Federated Learning (FL) and Blockchain technologies to preserve the privacy of vehicles in IoV.Typical machine learning methods are not well suited for distributed and highly dynamic systems like IoV since they train on data with local features. Therefore, we use FL to train the global model while preserving privacy. Also, our approach is built on a scheme that evaluates the reliability of vehicles participating in the FL training process. Moreover, PPIoV is built on blockchain to establish trust across multiple communication nodes. For example, when the local learned model updates from the vehicles and fog nodes are communicated with the cloud to update the global learned model, all transactions take place on the blockchain. The outcome of our experimental study shows that the proposed method improves the global model's accuracy as a result of allowing reputed vehicles to update the global model.
Brick Yourself within 3 Minutes

This paper presents an intelligent machine which can automatically convert the captured portrait into a physical gadget made up of LEGO bricks. On the contrary to synthesising a 2D image or a virtual 3D object, generating physical 3D assembly object needs to take physical properties and assembly process into consideration, leading to more challenges. To generate brick models for arbitrary portraits, we formulate the transformation between the attribute space (extracted from 2D images) and the brick model space as a constraint integer programming problem which can be solved with a heuristic search method. Furthermore, as the bricks are physically scattered, we propose an algorithm to generate corresponding assembly instructions for customized figure-featured-bricks to facilitate users' assembly. Meanwhile, we deploy the proposed algorithms on an automatic machine which integrates a camera, a printer, a laptop, and a brick operation unit. Finally, the generated brick models and assembly instructions are evaluated by a large number of users. It is worth noting that the whole system works as an intelligent vending machine, producing a 150-brick-model within 3 minutes.
Emulating Perceptual Evaluation of Voice Using Scattering Transform Based Features

Voice health is traditionally assessed by methods that rely on the perception of a clinician, who integrates auditory and visual cues in order to reach a conclusion about the voice under evaluation. However, these tasks suffer from inter-professional variability due to its subjective nature, which is why more objective, computational-based methods are of interest. Two examples of such subjective tasks are the classification of voices in three types according to their periodicity, also termed voice typing, and the evaluation of six aspects of voice quality by means of the consensus auditory-perceptual evaluation of voice (CAPE-V) protocol. In this paper, two approaches to emulate each of those tasks are introduced, based on simple features extracted from the second-order scattering transform coefficients and support vector machines. Firstly, a system for automatic voice typing was trained and its classification performance was evaluated for intra and inter-dataset trials using two widely known corpora. Accuracies above 80%, comparable to the state-of-the-art, were found for all the experiments conducted. Secondly, a multidimensional, multioutput regression chain model was used to automatically grade the voice quality features of the CAPE-V protocol, obtaining errors and correlation coefficients that are comparable to those found for three human raters.
Addressing Privacy Concerns in Depth Sensors

Image-based assistive solutions raise concerns about the privacy of the individuals being monitored. The issue involves the situation when such technology is used in medical institutions to protect patients’ health and support the personnel. These devices are installed in facilities and process images that include personal and behavioral data during the day. Other types of images than RGB are used to maintain privacy in this type of application, like depth images. Usage of depth cameras in the majority of publications is considered private protective. This paper discusses the issue of privacy in vision-based applications using depth modality. The factors affecting privacy in depth images are presented. The main problem that makes an image non-private is that the subjects’ faces allow identification. This paper compares the Face Recognition (FR) technique between RGB and depth images. In the experimental part, a state-of-the-art model for FR in depth images is developed, which is used to establish boundary conditions when a person is recognized. The performance of FR between these two modalities is compared on two existing datasets containing images in both versions, including the training process. The study aims to determine under which conditions depth cameras preserve privacy and how much privacy they reveal.
Privacy-Preserving Record Linkage with Block-Chains

We present a method for privacy-preserving record linkage of partially de-identified data. The key novelty of our proposed method is the use of a block-chain to store user information, preventing privacy leaks even if all system databases and cryptographic protocols have been compromised by an adversary, including databases containing identifying information. While satisfying this stringent privacy constraint, the system remains able to deterministically link and retrieve de-identified records to unique identities. With logarithmic time complexity to perform linkage and retrieval operations, our method is highly scalable for large-scale use cases. Designed to be HIPAA (Health Insurance Portability and Accountability Act) compliant by default, such systems are easily adaptable to large real-world healthcare systems.