Reviewerly/exHarmony huggingface dataset:
Making the Invisible Visible – Techniques for Recovering Deleted SQLite Data Records

Forensic analysis and evidence collection for web browser activity is a recurring problem in digital investigation. It is not unusual for a suspect to cover his traces. Accordingly, the recovery of previously deleted data such as web cookies and browser history are important. Fortunately, many browsers and thousands of apps used the same database system to store their data: SQLite. Reason enough to take a closer look at this product. In this article, we follow the question of how deleted content can be made visible again in an SQLite-database. For this purpose, the technical background of the problem will be examined first. Techniques are presented with which it is possible to carve and recover deleted data records from a database on a binary level. A novel software solution called FQLite is presented that implements the proposed algorithms. The search quality, as well as the performance of the program, is tested using the standard forensic corpus. The results of a performance study are discussed, as well. The article ends with a summary and identifies further research questions.
Efficient Wasserstein Natural Gradients for Reinforcement Learning

A novel optimization approach is proposed for application to policy gradient methods and evolution strategies for reinforcement learning (RL). The procedure uses a computationally efficient \emph{Wasserstein natural gradient} (WNG) descent that takes advantage of the geometry induced by a Wasserstein penalty to speed optimization. This method follows the recent theme in RL of including divergence penalties in the objective to establish trust regions. Experiments on challenging tasks demonstrate improvements in both computational cost and performance over advanced baselines.
Enforcing robust control guarantees within neural network policies

When designing controllers for safety-critical systems, practitioners often face a challenging tradeoff between robustness and performance. While robust control methods provide rigorous guarantees on system stability under certain worst-case disturbances, they often yield simple controllers that perform poorly in the average (non-worst) case. In contrast, nonlinear control methods trained using deep learning have achieved state-of-the-art performance on many control tasks, but often lack robustness guarantees. In this paper, we propose a technique that combines the strengths of these two approaches: constructing a generic nonlinear control policy class, parameterized by neural networks, that nonetheless enforces the same provable robustness criteria as robust control. Specifically, our approach entails integrating custom convex-optimization-based projection layers into a neural network-based policy. We demonstrate the power of this approach on several domains, improving in average-case performance over existing robust control methods and in worst-case stability over (non-robust) deep RL methods.
Adaptive Vision-Based Method for Rotor Dynamic Balance System

This article presents a new adaptive vision-based method (AVBM) of performing automatic detection for rotor balancing, and the online implementation proved that the method achieved rapid real-time optimization of system balancing configuration for a rotor dynamic balance machine. The proposed AVBM integrated 3D sensors and dynamic balancing platform using 3D computer vision technique and dynamic balance algorithm to improve the efficiency of rotor dynamic balancing. AVBM applied 3D ToF sensors on active rotor dynamic balance machines to grab 3D point cloud of rotor shaft and balance sprues. By 3D depth data, the background noise can be removed to detect the positions of shaft center, key phasor and balance sprues of rotor automatically. After combining with unbalance vector from dynamic balancing machine, the AVBM system calculated the optimal balance configuration by the vector analysis algorithm. Compares to conventional methods, conventional rotor dynamic balancing process relies on technicians to mount washers on particular balance sprues based on their experience, therefore uncertainty causes productivity decline. Experiments in industrial examples showed that the proposed AVBM required fewer rounds to achieve acceptance, whereas the conventional industrial rotor balancing method performed by operators required more than three rounds in average. Consider the overall dynamic balancing process for the motor, the processing time required for each motor without AVBM was 348.9 seconds, and the daily rotor balancing count of each dynamic balancing machine was 83. The processing time with AVBM was shortened from 348.9 to 283.9 seconds, the daily rotor balancing count had increased from 83 to 101, and the production improvement had reached 22 %. That is, the proposed AVBM can accurately analyze the dynamic balance and greatly reduce the redundant dynamic balance operations. The main advantage of the proposed AVBM over conventional methods is its efficiency, effectiveness and robustness in online optimization of rotor dynamic balance.
ACTIVITY OF A HIGHER SCHOOL TEACHER ON FORMING SOFT SKILLS FOR STUDENTS OF HUMANITARIAN AND NATURAL SPECIALTIES: FUNCTIONS AND REQUIREMENTS

Based on studied materials the author concludes that modern national pedagogical science is taking the first step towards building conceptual foundations for the formation of soft skills by primary school children as part of the concept of “New Ukrainian School”. The issue of soft skills formation in higher education institutions remains highly important. The purpose of the article was to describe functions that the teacher performs in the process of forming soft skills. In the course of study within the framework of each function, the author determined the following requirements to actions of teachers to form soft skills by students: *the educational: the organization of effective communication in various types of extracurricular work; *the didactic: the use of modern pedagogical technologies; *the research: defining principles, approaches and conditions that have a significant impact on the formation of soft skills; *the projective: modeling various types of activities for mastering soft skills; *the organizational and managerial: describing in detail ways of solving the assigned tasks by using specific pedagogical techniques and methods; *the predictive: determining the main areas of activity based on conditions, forms, components, and levels of forecasting; *the methodological: mastering the principles and methodological techniques of organizing and conducting trainings, round tables, workshops, speaking in classes during extracurricular work; *the facilitative: the organization of multilevel interaction in the “teacher-to-student” and “student-to-student” modes in accordance with the “attributive circle” of facilitation process.
Learning perturbation sets for robust machine learning

Although much progress has been made towards robust deep learning, a significant gap in robustness remains between real-world perturbations and more narrowly defined sets typically studied in adversarial defenses. In this paper, we aim to bridge this gap by learning perturbation sets from data, in order to characterize real-world effects for robust training and evaluation. Specifically, we use a conditional generator that defines the perturbation set over a constrained region of the latent space. We formulate desirable properties that measure the quality of a learned perturbation set, and theoretically prove that a conditional variational autoencoder naturally satisfies these criteria. Using this framework, our approach can generate a variety of perturbations at different complexities and scales, ranging from baseline spatial transformations, through common image corruptions, to lighting variations. We measure the quality of our learned perturbation sets both quantitatively and qualitatively, finding that our models are capable of producing a diverse set of meaningful perturbations beyond the limited data seen during training. Finally, we leverage our learned perturbation sets to train models which are empirically and certifiably robust to adversarial image corruptions and adversarial lighting variations, while improving generalization on non-adversarial data. All code and configuration files for reproducing the experiments as well as pretrained model weights can be found in the supplementary material.
Mechanism and design of a novel 8K ultra-high-definition video microscope for microsurgery

The practical application of microscopes using 8K ultra-high definition (UHD) technology is progressing. However, due to insufficiencies in factors such as luminous intensity and stereopsis, it has not been possible to achieve sufficient image quality for close observation of submillimeter order microlymphatic anastomosis using a combination of 8K-UHD cameras with a rigid endoscope. We have improved the quality of microsurgery by the introduction of a new heads up 8K-UHD surgical system. Herein, we show the mechanisms of this next-generation technology that makes optical improvements to the electronic image input data, resolving the initial drawback.We have developed a new 8K-UHD digital microscope system with digital zooming to enable maximum 300X magnification of the surgical field. This system has specific lighting settings for shadows dropped in surgical field to expand the three-dimensional effect while still being a monocular camera. The original mechanism and design enable the increase of the depth of field with optimal angles between the imaging direction and approaching direction towards the surgical field.Assessment during a pre-clinical trial using rats demonstrated that it is possible to perform microlymphatic anastomosis in a heads-up position with a 70-inch 8K-UHD monitor and the 8K-UHD monocular camera system.Performing supermicrosurgery is difficult with conventional surgical microscopes. Our results illustrate the application of this new 8K-UHD microscope system to this new field.
MRRC: multiple role representation crossover interpretation for image captioning with R-CNN feature distribution composition (FDC)

While image captioning through machines requires structured learning and basis for interpretation, improvement requires multiple context understanding and processing in a meaningful way. This research provides a novel concept for context combination and impacts many applications to deal with visual features as an equivalence of descriptions of objects, activities, and events. There are three components of our architecture: Feature Distribution Composition (FDC) Layer Attention, Multiple Role Representation Crossover (MRRC) Attention Layer, and the Language Decoder. FDC Layer Attention helps in generating the weighted attention from RCNN features, MRRC Attention Layer acts as intermediate representation processing and helps in generating the next word attention. A Language Decoder helps in the estimation of the likelihood for the next probable word in the sentence. We demonstrated the effectiveness of FDC, MRRC, regional object feature attention, and reinforcement learning for effective learning to generate better captions from images. The performance of our model enhanced previous performances to 35.3% and created a new standard and theory for representation generation based on logic, better interpretability, and contexts.
Medulloblastoma tumor classification using deep transfer learning with multi-scale EfficientNets

Medulloblastoma (MB) is the most common malignant brain tumor in childhood. The diagnosis is generally based on the microscopic evaluation of histopathological tissue slides. However, visual-only assessment of histopathological patterns is a tedious and time-consuming task and is also affected by observer variability. Hence, automated MB tumor classification could assist pathologists by promoting consistency and robust quantification. Recently, convolutional neural networks (CNNs) have been proposed for this task, while transfer learning has shown promising results. In this work, we propose an end-to-end MB tumor classification and explore transfer learning with various input sizes and matching network dimensions. We focus on differentiating between the histological subtypes classic and desmoplastic/nodular. For this purpose, we systematically evaluate recently proposed EfficientNets, which uniformly scale all dimensions of a CNN. Using a data set with 161 cases, we demonstrate that pre-trained EfficientNets with larger input resolutions lead to significant performance improvements compared to commonly used pre-trained CNN architectures. Also, we highlight the importance of transfer learning, when using such large architectures. Overall, our best performing method achieves an F1-Score of 80.1%.
Gender Recognition of Speech Signal using KNN and SVM

Human speech is formulated by the vibration of vocal cord of human being consisting of different sounds for speaking, singing, expressing emotions and ideas, etc .The vocal cord being an important source of sound with human voice as a part of human sound synthesis [3] [5]. Differences in human speech for male and female are due to physiological difference such as vocal fold thickness or vocal tract length. The vocal tracts of male are mostly longer in comparison to vocal tracts of female. With advancing technology and more and more usage of human computer interaction (HCI) based systems, the speech processing plays an important role in enhancing the HCI system [2] [4]. The reason for development of gender recognition system is its used in gender based virtual assistants, telephonic surveys and voice controlled automatic systems.
YOLOv3_Slim for Face Mask Recognition

Abstract The use of face mask is advised by World Health Organization (WHO) for preventing transmission of Coronavirus disease 2019 (COVID-19). It is of great value to solve the multi-task object detection problem of non-wearing mask, wrong way wearing mask and standard wearing mask. In this paper, a network YOLOv3_Slim based on YOLOv3 is implemented. It’s faster than YOLOv3. Detection speed increased from 15.67 fps to 16.89 fps. In the mean time, we found the effect of the difference of inner class on the classification ability of the model. The large error of inner class will reduce the accuracy of the model and make the attention mechanism ineffective. So after changing the labels of the third data set, We add ECA module to our network. YOLOv3_Slim is more accurate than YOLOv4 in face mask recognition based on our data set. The mAP increased from 89.45% to 92.50%.
Multi-hop teleportation of<i>N</i>-qubit state via Bell states

Multi-hop teleportation is a quantum teleportation scheme for transferring quantum states on a large scale. In this paper, a new multi-hop teleportation protocol is investigated for transferring arbitrary N-qubit states between M-neighbor nodes. In this scheme, intermediate nodes are connected with each other by symmetric entangled Bell states as quantum channels. First, one-hop teleportation of single-qubit, two-qubit and N-qubit states are introduced, then this method is generalized to two-hop and multi-hop teleportation for N-qubit. Also, we calculate the efficiency of this scheme.
Can neural networks acquire a structural bias from raw linguistic data

We evaluate whether BERT, a widely used neural network for sentence processing, acquires an inductive bias towards forming structural generalizations through pretraining on raw data. We conduct four experiments testing its preference for structural vs. linear generalizations in different structure-dependent phenomena. We find that BERT makes a structural generalization in 3 out of 4 empirical domains---subject-auxiliary inversion, reflexive binding, and verb tense detection in embedded clauses---but makes a linear generalization when tested on NPI licensing. We argue that these results are the strongest evidence so far from artificial learners supporting the proposition that a structural bias can be acquired from raw data. If this conclusion is correct, it is tentative evidence that some linguistic universals can be acquired by learners without innate biases. However, the precise implications for human language acquisition are unclear, as humans learn language from significantly less data than BERT.
Application of big data collection-analysis-visualization in the teaching process of colleges and universities under the background of the epidemic

Abstract With the advent of the era of big data, new vision and broad ideas have been provided for college teaching. Using big data-related knowledge to study the teaching system of universities can not only promote the implementation of big data teaching in universities, but also effectively improve the quality of university teaching. This article discusses the technology related to the teaching big data cloud platform. This article introduces the role and application of the association rule algorithm in the process of big data mining, and according to the actual situation, studies the use of this algorithm in the process of big data mining in colleges and universities. In order to further reflect the advantages of its algorithm, it is compared with other algorithms, which proves that the algorithm in this paper has higher execution efficiency and accuracy.
MultiFace: A generic training mechanism for boosting face recognition performance

Deep Convolutional Neural Networks (DCNNs) and their variants have been widely used in large scale face recognition(FR) recently. Existing methods have achieved good performance on many FR benchmarks. However, most of them suffer from two major problems. First, these methods converge quite slowly since they optimize the loss functions in a high-dimensional and sparse Gaussian Sphere. Second, the high dimensionality of features, despite the powerful descriptive ability, brings difficulty to the optimization, which may lead to a sub-optimal local optimum. To address these problems, we propose a simple yet efficient training mechanism called MultiFace, where we approximate the original high-dimensional features by the ensemble of low-dimensional features. The proposed mechanism is also generic and can be easily applied to many advanced FR models. Moreover, it brings the benefits of good interpretability to FR models via the clustering effect. In detail, the ensemble of these low-dimensional features can capture complementary yet discriminative information, which can increase the intra-class compactness and inter-class separability. Experimental results show that the proposed mechanism can accelerate 2–3 times with the softmax loss and 1.2–1.5 times with Arcface or Cosface, while achieving state-of-the-art performances in several benchmark datasets. Especially, the significant improvements on large-scale datasets(e.g., IJB and MageFace) demonstrate the flexibility of our new training mechanism.
Information system for epidemic control: a computational solution addressing successful experiences and main challenges

Purpose The SARS-CoV-2 pandemic has caused a major impact on worldwide public health and economics. The lessons learned from the successful attempts to contain the pandemic escalation revealed that the wise usage of contact tracing and information systems can widely help the containment work of any contagious disease. In this context, this paper investigates other researches on this domain, as well as the main issues related to the practical implementation of such systems and specifies a technical solution. Design/methodology/approach The proposed solution is based on the automatic identification of relevant contacts between infected or suspected people with susceptible people; inference of contamination risk based on symptoms history, user navigation records and contact information; real-time georeferenced information of population density of infected or suspect people; and automatic individual social distancing recommendation calculated through the individual contamination risk and the worsening of clinical condition risk. Findings The solution was specified, prototyped and evaluated by potential users and health authorities. The proposed solution has the potential of becoming a reference on how to coordinate the efforts of health authorities and the population on epidemic control. Originality/value This paper proposed an original information system for epidemic control which was applied for the SARS-CoV-2 pandemic and could be easily extended to other epidemics.
Blockchain-based Roaming and Offload Service Platform for Local 5G Operators

Local 5G Operator (L5GO) concept is one of the most prominent versatile applications of the 5G in the near future. The popularity of L5GOs will trigger a greater number of roaming and offloading events between mobile operators. However, existing static and the operator-assisted roaming and offloading procedures are inefficient for L5GO ecosystem due to poor service quality, data privacy issues, data transferring delays, excessive costs for intermediary parties and existence of roaming fraud. To address these challenges, we propose a blockchain / Distributed Ledger Technology (DLT) based service platform for L5GOs to facilitate efficient roaming and offload services. As the key contribution, blockchain-based smart contract scheme is proposed to establish dynamic and automated agreements between operators. By using smart contracts, we introduce several novel features such as universal wallet for subscribers, service quality based L5GO rating system, user-initiated roaming process and the roaming fraud prevention system to improve the operational quality of a L5GO. A prototype of the proposed platform is emulated with the Ethereum blockchain platform and Rinkeby Testnet to evaluate the performance and justify the feasibility of the proposal. Upon an extensive evaluation on the prototype, it was observed that the proposed platform offered benefits such as cost effective, more secure and reliable experience.
System for dynamic visualization of geomagnetic disturbances according to the data of ground magnetic stations

Due to the established and well-studied effects of the impact of the space environment on technical objects of high-latitude infrastructure, which are most clearly manifested during periods of magnetic storms, the problem of creating an effective open system for visualization and analytical control of geomagnetic field and its variations parameters, especially for the polar regions, is quite acute today.In this paper, using data from magnetic observatories and variation stations included in the SuperMAG project (https://supermag.jhuapl.edu/),based on the Django framework, open web-based GIS platforms and libraries, an approach, the architecture, model and methods of the system of dynamic multiparameter 2D / 3D visualization of the disturbed component of geomagnetic field variations are suggested.In addition to the possibility of dynamic visualization, an important distinctive feature of the proposed system is that the investigated parameters can be represented both as isodynes, which are the isolines of the same intensity of the Earth's magnetic field, and as vectors that form a vector field, which as a result carries additional, and for some problems of geomagnetism -defining information about the dynamics, nature and structure of the distribution of geomagnetic disturbances over the Earth's surface.The beta version of the developed system is available at https://geomagnetic.ru/.