Reviewerly/exHarmony huggingface dataset:
Partially Homomorphic Encryption Scheme for Real-Time Image Stream

With over 20 billion Internet of Things (IoT) devices connected today, having the possibility to implement a low-cost security camera is now an option. Camera vendors can provide software to enable remote viewing anywhere around the world. However, the problem arises in the security measures taken. By only providing encrypted services to authenticate a user by login credentials, there still is a vulnerability in the raw stream. Newer types of intrusions like spoofing or man-in-the-middle attacks mean that once they have breached the account, they are able to gain full viewing access to the camera stream. However, by encrypting the stream of these IoT cameras, we have a chance to truly retain privacy and ownership of our data. With IoT devices being limited with on-board memory and CPU speeds, this can be a challenge. To mitigate and offset any CPU-intensive work, we will encrypt the data between our cloudlet device and our edge device by using a partially homomorphic encryption scheme called "Elgamal encryption". This CPU offloading allows the cloudlet to transmit streams at a much higher frame-rate. The proposed system will utilize a Raspberry Pi 4, for our cloudlet with our desktop being the edge.
Aspect Classification for Legal Depositions

Attorneys have interest in having a digital library with suitable services (e.g., summarizing, searching, and browsing) to help them work with large legal deposition corpora. Their needs often involve understanding the semantics of such documents. In the case of tort litigation associated with property and casualty insurance claims, such as relating to an injury, it is important to know not only about liability, but also about events, accidents, physical conditions, and treatments. We hypothesize that a legal deposition consists of various aspects that are discussed as part of the deponent testimony. Accordingly, we developed an ontology of aspects in a legal deposition for accident and injury cases. Using that, we have developed a classifier that can identify portions of text for each of the aspects of interest. Doing so was complicated by the peculiarities of this genre, e.g., that deposition transcripts generally consist of data in the form of question-answer (QA) pairs. Accordingly, our automated system starts with pre-processing, and then transforms the QA pairs into a canonical form made up of declarative sentences. Classifying the declarative sentences that are generated, according to the aspect, can then help with downstream tasks such as summarization, segmentation, question-answering, and information retrieval. Our methods have achieved a classification F1 score of 0.83. Having the aspects classified with good accuracy will help in choosing QA pairs that can be used as candidate summary sentences, and to generate an informative summary for legal professionals or insurance claim agents. Our methodology could be extended to legal depositions of other kinds, and to aid services like searching.
Random Partition Region for Location Privacy Protection on Edge Computing

Edge computing, as a new computing paradigm, appears to solve the record generated by a great deal of edge devices that cannot be processed by traditional cloud computing. The real-time location big data of LBS (Location-Based Services) are transferred from the cloud server to the edge nodes, which further improves the service capacity and promotes the further development of LBS. However, based on the existing partition method, dynamic location data privacy cannot be well protected. In this paper, based on the differential privacy theory, we propose RPS (Random Partition and Selection) algorithm which can protect user's location information. We achieve this by designing a dynamic partition mechanism and noise adding algorithm based on the number of users. We repartition the region in each snapshot and give a new noise probability. Experiment results show that our proposed method has the significant progress in the loss of service quality than the existing partition release, such as UG (Uniform Grid) and AG (Adaptive Grid) mechanism.
Modelling legal procedures

A legal procedure in court proceedings is a sequence of actions in which the last action is (the creation of) a(n individual) norm, where the court settles that it is obligatory in the interest of some agents that other agents bring about a certain state of affairs. This paper models legal procedures by using a variant of Propositional Dynamic Logic (PDL) enriched with a preference operator for prioritising procedural actions. The key reason towards the usage of PDL is that, in procedural law, claims and resolutions resemble programs to be executed. Requests are organised in a preference order and resolutions have their own dynamics of execution (either spontaneously by the one obliged and/or by force of law).
An Algorithm for Calculating the Contribution of Acoustic Features in Speaker Recognition

In order to solve the problem that the model performance will be different if different acoustic features are selected in the speaker recognition field, an algorithm for calculating the contribution degree of acoustic features in speaker recognition is proposed. First of all, the use of increase or decrease in weight method to calculate contribution of each dimension, and then, with a fisher ratio than the acoustic features of each dimension fisher ratio calculation, to undertake the corresponding percentage of fusion after can get the characteristics in the contribution of speaker recognition model. Taking the acoustic feature GFCC and the speaker model i-vector as an example, this paper calculates the contribution rate of each component of GFCC under the speaker recognition by using the proposed new algorithm, analyzes the differences of the three methods, and provides a new method for subsequent feature processing.
Multisensor-Based Predictive Control for Autonomous Parking

This article formalizes, under a single common multisensor-based predictive control framework, five different types of parking maneuvers: perpendicular, diagonal for both forward and backward motions, and parallel for backward motions. Since, from a practical point of view, forward parallel parking is usually not advisable, it is not addressed in this work. By moving the effort from motion planning to control, the parking tasks can be completely defined solely from the detected empty parking spots. Additionally, the classical compromise between completeness and computational efficiency when compared to exploration-based path planning techniques is eliminated. The results of a few individual cases are presented and compared against a state-of-the-art path planning approach to illustrate the behavior and performance of the proposed framework as well as results from exhaustive simulations to assess its convergence. As shown in the convergence analyses, the presented approach allows us to park from virtually any sensible initial pose. Finally, real experimentation using a robotized Renault ZOE shows the validity and robustness in the convergence domain of the presented approach.
Classification of Handwritten Chinese Numbers with Convolutional Neural Networks

Deep learning methods have become the key ingredient in the field of computer vision; in particular, convolutional neural networks (CNNs). Appropriating the network architecture and data pre-processing have significant impact on performance. This paper focuses on the classification of handwritten Chinese numbers. Firstly, we applied various methods of pre-processing to our collected image dataset. Secondly, we customised a CNN-based architecture with minimal number of layers and parameters specifically for the task. Experimental results showed that our proposed methods provides superior classification rate of 99.1%. Our results also show that the proposed method has competitive performance compared to smaller neural networks with fewer parameters, e.g. Squeezenet and deeper networks with a larger size and number of parameters, e.g., pre-trained GoogLeNet and MobileNetV2.
Adaptive sampling for UAV sensor network in oil spill management

In this paper we propose a method for adaptive sampling using Unmanned Aerial Vehicles (UAVs) in oil spill management. The goal is to measure and estimate oil spill concentrations at the sea surface, while at the same time identify the leak rates of sources at known positions. First we construct a cost which approximates the benefit of sampling locations at specific times. This cost is based on measures of observability and of persistency of excitation for the oil spill model. A receding horizon Mixed-Integer Linear Programming (MILP) problem is solved in order to find UAV trajectories which are optimal with respect to the cost. For UAV trajectory tracking we use a Lyapunov based controller. The oil spill concentration measurements taken by the UAVs by following these tracks are used in an adaptive observer, which provides state (concentration) and parameter (leak rate) estimates. Under the assumption that the sampling strategy described above lead to uniform complete observability and persistency of excitation, we prove Uniform Global Asymptotic Stability (UGAS) of the state estimation, parameter identification and UAV trajectory tracking errors. Finally, we provide a simulation of the proposed strategy, and compare it with two other strategies.
Identify solar panel defects by using differences between solar panels

Automatic solar panel inspection systems are essential to maintain power generation efficiency and reduce the cost. Thermal images generated by thermographic cameras can be used for solar panel fault diagnosis because defective panels show abnormal temperature. However, it is difficult to identify an anomaly from a single panel image when similar temperature features appear in normal panels and abnormal panels. In this paper, we propose a different feature based method to identify defective solar panels in thermal images. To determine abnormal panel from input panel images, we apply a voting strategy by using the prediction results of subtraction network. In our experiments, we construct two datasets to evaluate our method: the clean panels dataset which is constructed by manually extracted panel images and the noise containing dataset which is consisting of panel images extracted by the automatic panel extraction method. Our method achieves more than 90% classification accuracy on both clean panels dataset and noise containing dataset.
Formation of the mandatory copy system in the Republic of Uzbekistan

The role and importance of the obligatory copy system in the life of the society is quite tangible. This system is the most important source of current acquisition of newly published national literature in libraries and bibliographic institutions. No other source of current acquisition can ensure the replenishment of libraries with such completeness and reliability as the legal deposit system. Wherever a work of literature is published (on the national territory), it will be delivered to libraries using the legal deposit system. The most complete collections of national literature will be available to scholars and specialists only in those libraries that enjoy the right to the legal deposit copy. The system of supplying libraries with the legal deposit copy in the Republic of Uzbekistan actively began to take shape in the 30s. XX century and was carried out through the Republican Library Collector established in Tashkent. The acquisition of book collections in scientific and technical libraries was subordinated to the needs of the branches of science and production developing in the republic and was carried out selectively, but mainly based on the legal deposit copy. This situation persisted in the republics of the Soviet Union, including the Republic of Uzbekistan until 1991. The article briefly presents the characteristics of the system of providing the libraries of the Republic of Uzbekistan with the legal copy of printed materials and presents the modern problem of regulation of the legal deposit system, which guarantees the replenishment of the national information and library stock.
Quantum circuits with classical channels and the principle of deferred measurements

We define syntax and semantics of quantum circuits, allowing measurement gates and classical channels. We define circuit-based quantum algorithms and prove that, semantically, any such algorithm is equivalent to a single measurement that depends only on the underlying quantum circuit. Finally, we use our formalization of quantum circuits to state precisely and prove the principle of deferred measurements.
Sources of Risk and Design Principles of Trustworthy Artificial Intelligence

The importance of artificial intelligence is constantly increasing due to ongoing research successes and the implementation of new applications based on it. It is already described as one of the core technologies of the future. This technology is also increasingly being applied in the field of safety-related applications, which enables the implementation of innovative concepts for novel protection and assistance systems. However, for this to lead to a benefit for human safety and health, a safe or trustworthy artificial intelligence is required. However, the increasing number of accidents related to this technology shows that classical design principles of safe systems still need to be adapted to the new artificial intelligence methods. On the one hand, this requires a basic understanding of the components of trustworthy artificial intelligence, but on the other hand, it also requires an understanding of AI-specific sources of risk. These new sources of risk should be considered in the overall risk assessment of a system based on AI technologies, examined for their criticality, and managed accordingly at an early stage to prevent later failure of the system.
Prediction, Classification and Recommendation in e-Health via Contextual Partitioning

In this paper, we propose a multipurpose contextual partitioning based estimation algorithm. Exploiting the similarities between contexts (side information: such as age, Gender etc.,) related to patient data in healthcare repository or database, multidimensional spheres are generated over Euclidean space. Then, conditional first and second order characteristics are predicted using sample-based mean and covariance. These conditional statistics of particular patient data subset (sphere) serve the following purposes: i) Prediction for missing values (conditional mean), ii) Partitioned principal components for better classification (conditional covariance) and iii) Recommendation for medical Test or physician (conditional covariance). The proposed approach uniformly partitions the context space into spheres, and then, for each sphere estimates the conditional mean and covariance using only the data (excluding the context data) in the selected sphere. Hence, providing three in one solution i.e., Prediction, Classification and Recommendation for healthcare data using conditional probabilistic characteristics. The overall error is decomposed into estimation and approximation errors. In a particular sphere, estimation error is dependent on the number of instances, while approximation error is dependent on the dissimilarity of instances.
Advance Fuzzy Radial Basis Function Neural Network

Shetty, Balaji S. Mahindrakar, Manisha S. Kulkarni, U. V.Data mining discovers meaningful knowledge attributes from provided dataset and further transforms it into meaningful information. Data mining became more popular with the combined use of statistics and machine learning, for various scenarios like improved decision making, revenue and operation improvement, cost reduction, anomalies detection, and many more. It is found that use of inappropriate data mining pattern classification and clustering algorithms may lead to imprecise decisions. Many clustering algorithms perform pattern recognition but provide poor classification accuracy. In order to resolve this problem, the advance fuzzy radial basis function neural network (AFRBFNN) learning algorithm for pattern classification is proposed. The primary objective of this approach is to reduce the misclassification rate. The proposed novel method is generic and can be applied to any kind of labeled dataset for classification. The AFRBFNN algorithm is based on the combination of radial basis function (RBF), fuzzy neural network (FNN), and fuzzy clustering (FC). The approach is evaluated by using six benchmark datasets and the results are further compared with well-known classifiers. The obtained results prove that it is an efficient and accurate method of pattern classification and improves pattern classification accuracy by 5–10% approximately.
On the key composition for post-quantum group messaging and file exchange

Advances in the development of quantum technologies force to create new methods of secure information transfer over insecure networks and channels. In this article, we analyze some important properties of secure group messaging resilient to quantum computer attacks: message authentication and asynchronous key update. We consider options for ensuring message authentication and detect the difference between the requirements for this property in group communications and point-to-point channels. We define the structure of key information and preshared key packages for the new protocol based on the classic X3DH protocol.
Design of Digital Clock Manager Using Tunable BFD–True Random Number Generator

Abstract By demanding authentication exercises for example communications, electronic cash frameworks, plate encryption, the cryptographic frameworks have become a core aspect in our daily lives. Random numbers are an significant factor in strengthening and anchoring the protection of e-mail messages used in multiple encryption applications, including key ages, encryption, convention coverage, web wagering. Random flight numbers for basic mystery keys are basic to the protection of criphotographical calculations. In a variety of cryptographic systems, actual random number generators (TRNGs), The part has been incorporated, including PIN / secret word age, validation agreements, key age, random cushions and age of nunce. The circuit uses an undetermined random mechanism as a fundamental source, largely as electric commotion. Programmable field door arrays (FPGAs) are the optimal stage for the success of equipment that offers substantial safety conditions. The TRNG suggested is subject to the Xilinx-FPGA Bit Recurrence Recognition Guideline.
Hybrid Input-type Recurrent Neural Network Language Modeling for End-to-end Speech Recognition

The out-of-vocabulary (OOV) words is a problem that impacts recognition accuracy, whether it is the HMM model, DNN model, or end-to-end speech recognition. This paper proposes a hybrid input-type recurrent neural network language model (RNNLM) for end-to-end speech recognition, which uses word and pseudo-morpheme (PM) as a sub-lexical unit during training. The advantage of PM is a new vocabulary, or unseen vocabulary can be reconstructed from sub-lexical units. The results showed that the accuracy of using the proposed method could reduce the error rate by 1.28% compared to the conventional end-to-end technique.
Single Image Defogging Algorithm Based on Sky Region Segmentation

Abstract The visibility, contrast and color fidelity of images acquired under foggy weather will decrease to a certain extent, image defogging is an important part of image processing. Since the dark channel prior is not applicable to the sky area, which results in the color distortion and some noise of the restored fog-free image. Based on the segmentation of the sky region, it is proposed to separately estimate the atmospheric light value and transmission map according to different regions, and finally generates a defogging image using the optimized synthetic global map. In order to divide the sky domain and the non-sky domain more accurately, the two results obtained by the threshold segmentation method for the dark channel map and the improved threshold segmentation method for the original foggy image are considered at the same time. In the step of estimating the transmittance of the sky domain, the dark channel map of sky domain is obtained based on HSI color space, making the sky area more natural after defogging. Experimental results show that the algorithm can achieve good defogging effect, smooth edge transition between regions, and the defogging images are more natural and clear.
Weak-Key Distinguishers for AES

In this paper, we analyze the security of AES in the case in which the whitening key is a weak key. After a systematization of the classes of weak-keys of AES, we perform an extensive analysis of weak-key distinguishers (in the single-key setting) for AES instantiated with the original key-schedule and with the new key-schedule proposed at ToSC/FSE’18. As one of the main results, we show that (almost) all the secret-key distinguishers for round-reduced AES currently present in the literature can be set up for a higher number of rounds of AES if the whitening key is a weak-key. Using these results as starting point, we describe a property for 9-round AES-128 and 12-round AES-256 in the chosen-key setting with complexity $$2^{64}$$ without requiring related keys. These new chosen-key distinguishers – set up by exploiting a variant of the multiple-of-8 property introduced at Eurocrypt’17 – improve all the AES chosen-key distinguishers in the single-key setting. The entire analysis has been performed using a new framework that we introduce here – called “weak-key subspace trails”, which is obtained by combining invariant subspaces (Crypto’11) and subspace trails (FSE’17) into a new, more powerful, attack.
Human-Object Interaction in AR

With the developments of augmented reality, there are more and more electronic games which take the application of augmented reality. However, a lot of these games lack the influence made by realistic scenes, and connection between realistic objects and the process of games. So the project provides a solution regard to design human-object interaction and game content. Besides, the paper introduces some ideas about the combination between human-object interaction with various game factors.
On the Importance of Pooling Layer Tuning for Profiling Side-Channel Analysis

In recent years, the advent of deep neural networks opened new perspectives for security evaluations with side-channel analysis. Profiling attacks now benefit from capabilities offered by convolutional neural networks, such as dimensionality reduction and the inherent ability to reduce the trace desynchronization effects. These neural networks contain at least three types of layers: convolutional, pooling, and dense layers. Although the definition of pooling layers causes a large impact on neural network performance, a study on pooling hyperparameters effect on side-channel analysis is still not provided in the academic community. This paper provides extensive experimental results to demonstrate how pooling layer types and pooling stride and size affect the profiling attack performance with convolutional neural networks. Additionally, we demonstrate that pooling hyperparameters can be larger than usually used in related works and still keep good performance for profiling attacks on specific datasets.