Reviewerly/exHarmony huggingface dataset:
Microwave Techniques for Quantum Computers: State-of-the-Art Control Systems for Quantum Processors

Let's boot up a quantum computer. Far from being a simple push of a button, initializing a prototype quantum computer requires the precise tuning and calibration of many different parameters. Rather than switching transistors on and off, the controller of a quantum processor emits combinations of analog waveforms, each with a characteristic shape, frequency, and duration. These waveforms are used to either manipulate or read out the states of quantum bits (qubits), the basic units of quantum information in the processor. The analog nature of the system inherits many of the complexities of analog computing, including device parameter drift and offsets, system component tolerances and variabilities, and other well-known analog-circuit intricacies. Further, de livering these signals to the target qubits necessitates coordination between multiple low-noise and low-jitter instruments. In spite of these challenges, the quantum computing community has made tremendous progress toward useful quantum machines. We hope to provide an instructive introduction to the control, signal generation, and distribution principles currently used in small quantum systems that operate in the microwave frequency regime.
Hierarchical Discriminative Deep Dictionary Learning

In deep dictionary learning multiple dictionaries are learned based on information at various levels of abstraction. We propose a novel hierarchical discriminative dictionary learning layer embedded within a neural network with an image classification objective. Discrimination is induced in the learned synthesis dictionaries at multiple hierarchical levels in a simple way by a one-hot-code representation of the class labels during the training backward pass. In addition, local sparse representation objectives are approximated during the forward pass, introducing local regularization. We evaluate our proposal on five known datasets and we either outperform state-of-the-art methods or achieve competitive classification results.
Three-Dimensional Measurement for Rigid Moving Objects Based on Multi-Fringe Projection

Multi-fringe projection profilometry, such as the widely used phase-shifting and temporal phase unwrapping algorithms, can achieve high accuracy and robustness in three-dimensional shape reconstruction of static objects.However, the motion of the objects will introduce errors.This paper proposes a new method to reconstruct 3D shape of rigid moving objects on an automated transmission line.First, a new pixel matching method for projected and captured images is proposed.Then, the pixel deviation caused by the motion of the objects in the three-dimensional direction is pre-detected, and the new projection fringes are obtained according to the above pixel matching.Third, the new projection fringes are binarized and the projector is defocused to realize high-speed projection.At last, the captured fringes are offset to reconstruct 3D shape.Experimental results show that the proposed method is effective in 3D shape reconstruction of rigid moving objects.
Approaching surface treatment in prehistoric pottery: Exploring variability in tool traces on pottery surfaces through experimentation

The surface treatment of handmade pottery is often described in ceramological studies of prehistoric collections. However, beyond inferences about its meaning, few works have addressed this issue in depth. For this study, an experimental program has been carried out, where the main variable being explored was the category of tool involved in the fabrication of prehistoric handmade pottery. Therefore, we start from the hypothesis that different tools generate differentiable traces. A catalogue of traces generated by five pottery tools (pebble, flint spatula, pottery spatula, shell spatula and linen rag) was created, with the aim of characterizing and systematizing them. The resulting macroscopic analysis allowed a first qualitative classification of the traces. Microscopic analysis by confocal microscopy then confirmed the classification with quantitative data. The potential of the proposed methodology for traceological and textural analysis of surface treatment in ceramics is highlighted. Hence, the possibility of discriminating different surface treatment techniques opens new perspectives for the study of prehistoric pottery.
From bilingual to multilingual neural‐based machine translation by incremental training

Abstract A common intermediate language representation in neural machine translation can be used to extend bilingual systems by incremental training. We propose a new architecture based on introducing an interlingual loss as an additional training objective. By adding and forcing this interlingual loss, we can train multiple encoders and decoders for each language, sharing among them a common intermediate representation. Translation results on the low‐resource tasks (Turkish‐English and Kazakh‐English tasks) show a BLEU improvement of up to 2.8 points. However, results on a larger dataset (Russian‐English and Kazakh‐English) show BLEU losses of a similar amount. While our system provides improvements only for the low‐resource tasks in terms of translation quality, our system is capable of quickly deploying new language pairs without the need to retrain the rest of the system, which may be a game changer in some situations. Specifically, what is most relevant regarding our architecture is that it is capable of: reducing the number of production systems, with respect to the number of languages, from quadratic to linear; incrementally adding a new language to the system without retraining the languages already there; and allowing for translations from the new language to all the others present in the system.
AN ANALYSIS OF STUDENTS’ DIFFICULTIES IN WRITING DESCRIPTIVE TEXT

This research aims to describe the students’ difficulties in writing descriptive text and to find out the causes of students’ difficulties in writing descriptive text. This research uses quantitative descriptive methods. The research subjects are X MIPA 6 students of SMAN 1 Sukodadi who had difficulties when writing descriptive text, the data of this research are the result of students’ worksheet and the students’ answer in interview. This research used documentation and interview to collect the data. Data reduction, data display and drawing conclusion was used to analyze the data. The result of this research shows that the students of X MIPA 6 have difficulty in writing descriptive text, the difficulties are difficulties in generic structure, difficulties in grammar, and difficulties in spelling. While the factors that cause the students’ difficulties in writing descriptive text are lack of proficiency in the text production skills, lack of knowledge relating to the subject contents of the script to be written, and lack of interest in learning English. Keywords: Students’ difficulties, descriptive text
Non-disjoint promise problems from meta-computational view of pseudorandom generator constructions

The standard notion of promise problem is a pair of disjoint sets of instances, each of which is regarded as Yes and No instances, respectively, and the task of solving a promise problem is to distinguish these two sets of instances. In this paper, we introduce a set of new promise problems which are conjectured to be non-disjoint, and prove that hardness of these promise problems gives rise to the existence of hitting set generators (and vice versa). We do this by presenting a general principle which converts any black-box construction of a pseudorandom generator into the existence of a hitting set generator whose security is based on hardness of some promise problem (via a non-black-box security reduction).Applying the principle to cryptographic pseudorandom generators, we introduce The Gap(KSAT vs K) Problem: Given a string x and a parameter s, distinguish whether the polynomial-time-bounded SAT-oracle Kolmogorov complexity of x is at most s, or the polynomial-time-bounded Kolmogorov complexity of x (without SAT oracle) is at least s + O(log |x|). If Gap(KSAT vs K) is NP-hard, then the worst-case and average-case complexity of PH is equivalent. Under the plausible assumption that ENP ≠ E, the promise problem is non-disjoint. These results generalize the non-black-box worst-case to average-case reductions of Hirahara [31] and improve the approximation error from O([MATH HERE]) to O(log n).Applying the principle to complexity-theoretic pseudorandom generators, we introduce a family of Meta-computational Circuit Lower-bound Problems (MCLPs), which are problems of distinguishing the truth tables of explicit functions from hard functions. Our results generalize the hardness versus randomness framework and identify problems whose circuit lower bounds characterize the existence of hitting set generators. For example, we introduce The E vs size(2o(n)) Problem: Given the truth table of a function f, distinguish whether f is computable in exponential time or requires exponential-size circuits to compute. A nearly-linear AC0 ο XOR circuit size lower bound for this promise problem is equivalent to the existence of a logarithmic-seed-length hitting set generator for AC0 ο XOR. Under the plausible assumption that [MATH HERE], the promise problem is non-disjoint (and thus the minimum circuit size is infinity). This is the first result that provides the exact characterization of the existence of a hitting set generator secure against ℭ by the worst-case lower bound against ℭ for a circuit class ℭ = AC0 ο XOR ⊆ TC0. In addition, we prove that a nearly-linear size lower bound against co-nondeterministic read-once branching programs for some promise problem is sufficient for resolving RL = L.We also establish the equivalence between the existence of a derandomization algorithm for uniform algorithms and a uniform lower bound for a problem of approximating Levin's Kt-complexity.
Investigating the Effect of Software Packaging on Modular Structure Stability

In object-oriented development, packages form the basic modular structural components of large-scale software systems. Packaging processes aim to group classes together to provide well-identified functions/services to the re... | Find, read and cite all the research you need on Tech Science Press
Joint measurement of time–frequency entanglement via sum frequency generation

Abstract We propose, analyze, and evaluate a technique for the joint measurement of time–frequency entanglement between two photons. In particular, we show that the frequency sum and time difference of two photons could be simultaneously measured through the sum-frequency generation process, without measuring the time or frequency of each individual photon. We demonstrate the usefulness of this technique by using it to design a time–frequency entanglement based continuous variable superdense coding and a quantum illumination protocol. Performance analysis of these two protocols suggests that the joint measurement of strong time–frequency entanglement of non-classical photon pairs can significantly enhance the performance of joint-measurement based quantum communication and metrology protocols.
MULTI-AGENT PATH PLANNING OF ROBOTIC SWARMS IN AGRICULTURAL FIELDS

Abstract. Collaborative swarms of robots/UAVs constitute a promising solution for precision agriculture and for automatizing agricultural processes. Since agricultural fields have complex topologies and different constraints, the problem of optimized path routing of these swarms is important to be tackled. Hence, this paper deals with the problem of optimizing path routing for a swarm of ground robots and UAVs in different popular topologies of agricultural fields. Four algorithms (Nearest Neighbour based on K-means clustering, Christofides, Ant Colony Optimisation and Bellman-Held-Karp) are applied on various farm types commonly found around Europe. The results indicate that the problem of path planning and the corresponding algorithm to use, are sensitive to the field topology and to the number of agents in the swarm.
Industry 4.0: The Oil and Gas Sector Security and Personal Data Protection

Industry 4.0 is directly connected to the Internet of things, cyber-physical systems, artificial intelligence, robotics and other advanced technologies.It is these technologies that made fully automated digital production, controlled by intelligent systems in real-time with constant interaction via the Internet, a reality.They turned the production system into a "smart network factory", where all activities are digitally controlled, and the use of financial and material resources becomes more efficient.Along with this, in Industry 4.0, a significant increase in data volumes brought to the forefront data protection issues, including in such a sensitive area as personal data.Illegitimate methods of using personal data to obtain additional preferences have become the goal of some communities of people.Video surveillance data are an integral part of personal data, therefore protection of personal data processed in video surveillance systems has been given increased attention.The video surveillance system includes video cameras, information and communication channels for data transmission, processing devices, analytics and personal data storage.The proposed model is a subsystem of smart video surveillance in the oil and gas sector, that consisting of such subsystems as a smart field, smart grid, smart maintenance, smart transportation, smart security, etc. Conceptual tasks are considered and recommendations for their solution are given.
Small targets recognition in SAR ship image based on improved SSD

Synthetic Aperture Radar(SAR) ship recognition is significant in marine applications and plays an important role in maritime traffic management, fisheries management, and maritime rescue, etc. A major difficulty in SAR ship recognition is that the SAR ships have a small size in images, which results in low recognition accuracy of the SSD. This paper first analyzes the reasons for the low recognition accuracy for small targets in SSD. First, the poor matching of the default boxes leads to a small number of positive samples. Second, the representation <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</sup> bility of low-level feature map for recognizing small targets is weak. Then two strategies are proposed to improve the SSD. Firstly, a default box optimization design method based on Kmeans clustering is proposed, which improves the matching performance of the default boxes. Secondly, a feature fusion method based on deconvolution is proposed, which effectively improves the representation ability of low-level feature maps. The experimental results show that the proposed method can greatly improve the recognition accuracy of SSD for small targets in SAR ship images.
Anti-forensics of diffusion-based image inpainting

Diffusion-based inpainting can be used to repair some damaged parts or remove the undesirable regions in an image. Generally, good visual effects can be achieved after inpainting. However, some traces, such as the differences of local variances and noise pattern, are left in the inpainted image, making it easy for the forensic algorithms to locate the inpainted regions. To eliminate this drawback and achieve an anti-forensics capability, we propose an approach that can remove the traces of the diffusion-based inpainting. Since the pixel values of the inpainted regions are diffused inward by the surrounding pixels, we first analyze the noise pattern of the pixels neighboring the inpainted regions and select the nearby pixels that are directly used for inpainting. After that, a statistical probability model is constructed for each channel in the image, which is used to generate the noise pattern and fill the inpainted regions. Experimental results show that the proposed approach has a good capability for anti-forensics.
Efficient measurement of quality at scale in Facebook video ecosystem

This paper describes FB-MOS metric that measures video quality at scale in Facebook ecosystem. As the quality of uploaded UGC source itself varies widely, FB-MOS consists of both a no-reference component to assess input (upload) quality and a full-reference component, based on SSIM, to assess quality preserved in the transcoding and delivery pipeline. Note that the same video may be watched on a variety of devices (Mobile/laptop/TV) in varying network conditions that cause quality fluctuations; moreover, the viewer can switch between in-line view and full-screen view during the same viewing session. We show how FB-MOS metric accounts for all this variation in viewing condition while minimizing the computation overhead. Validation of this metric on FB-content has shown that SROCC is 0.9147 using internally selected videos. The paper also discusses some of the optimizations to reduce metric computation complexity and scale the complexity in proportion to video popularity.
End-to-End Training for Compound Expression Recognition

For a long time, expressions have been something that human beings are proud of. That is an essential difference between us and machines. With the development of computers, we are more eager to develop communication between humans and machines, especially communication with emotions. The emotional growth of computers is similar to the growth process of each of us, starting with a natural, intimate, and vivid interaction by observing and discerning emotions. Since the basic emotions, angry, disgusted, fearful, happy, neutral, sad and surprised are put forward, there are many researches based on basic emotions at present, but few on compound emotions. However, in real life, people’s emotions are complex. Single expressions cannot fully and accurately show people’s inner emotional changes, thus, exploration of compound expression recognition is very essential to daily life. In this paper, we recommend a scheme of combining spatial and frequency domain transform to implement end-to-end joint training based on model ensembling between models for appearance and geometric representations learning for the recognition of compound expressions in the wild. We are mainly devoted to digging the appearance and geometric information based on deep learning models. For appearance feature acquisition, we adopt the idea of transfer learning, introducing the ResNet50 model pretrained on VGGFace2 for face recognition to implement the fine-tuning process. Here, we try and compare two minds, one is that we utilize two static expression databases FER2013 and RAF Basic for basic emotion recognition to fine tune, the other is that we fine tune the model on the input three channels composed of images generated by DWT2 and WAVEDEC2 wavelet transforms based on rbio3.1 and sym1 wavelet bases respectively. For geometric feature acquisition, we firstly introduce a densesift operator to extract facial key points and their histogram descriptions. After that, we introduce deep SAE with a softmax function, stacked LSTM and Sequence-to-Sequence with stacked LSTM and define their structures by ourselves. Then, we feed the salient key points and their descriptions into three models to train respectively and compare their performances. When the model training for appearance and geometric features learning is completed, we combine the two models with category labels to achieve further end-to-end joint training, considering that ensembling models, which describe different information, can further improve recognition results. Finally, we validate the performance of our proposed framework on an RAF Compound database and achieve a recognition rate of 66.97%. Experiments show that integrating different models, which express different information, and achieving end-to-end training can quickly and effectively improve the performance of the recognition.
3D Virtual Pancreatography

We present 3D virtual pancreatography (VP), a novel visualization procedure and application for non-invasive diagnosis and classification of pancreatic lesions, the precursors of pancreatic cancer. Currently, non-invasive screening of patients is performed through visual inspection of 2D axis-aligned CT images, though the relevant features are often not clearly visible nor automatically detected. VP is an end-to-end visual diagnosis system that includes: A machine learning based automatic segmentation of the pancreatic gland and the lesions, a semi-automatic approach to extract the primary pancreatic duct, a machine learning based automatic classification of lesions into four prominent types, and specialized 3D and 2D exploratory visualizations of the pancreas, lesions and surrounding anatomy. We combine volume rendering with pancreas- and lesion-centric visualizations and measurements for effective diagnosis. We designed VP through close collaboration and feedback from expert radiologists, and evaluated it on multiple real-world CT datasets with various pancreatic lesions and case studies examined by the expert radiologists.
Running Refraction Networking for Real

Abstract Refraction networking is a next-generation censorship circumvention approach that locates proxy functionality in the network itself, at participating ISPs or other network operators. Following years of research and development and a brief pilot, we established the world’s first production deployment of a Refraction Networking system. Our deployment uses a highperformance implementation of the TapDance protocol and is enabled as a transport in the popular circumvention app Psiphon. It uses TapDance stations at four physical uplink locations of a mid-sized ISP, Merit Network, with an aggregate bandwidth of 140 Gbps. By the end of 2019, our system was enabled as a transport option in 559,000 installations of Psiphon, and it served upwards of 33,000 unique users per month. This paper reports on our experience building the deployment and operating it for the first year. We describe how we overcame engineering challenges, present detailed performance metrics, and analyze how our system has responded to dynamic censor behavior. Finally, we review lessons learned from operating this unique artifact and discuss prospects for further scaling Refraction Networking to meet the needs of censored users.
Technological Fundamentals Of Creating Interactive Electronic Courses

This article describes the importance and technological basics of interactive e-learning in today's educational process.Furthermore, the opportunity to use interactive e-learning courses for the learning process in remote education is highlighted.
Meaningful Secret Image Sharing Scheme with High Visual Quality Based on Natural Steganography

The (k,n)-threshold Secret Image Sharing scheme (SISS) is a solution to image protection. However, the shadow images generated by traditional SISS are noise-like, easily arousing deep suspicions, so that it is significant to generate meaningful shadow images. One solution is to embed the shadow images into meaningful natural images and visual quality should be considered first. Limited by embedding rate, the existing schemes have made concessions in size and visual quality of shadow images, and few of them take the ability of anti-steganalysis into consideration. In this paper, a meaningful SISS that is based on Natural Steganography (MSISS-NS) is proposed. The secret image is firstly divided into n small-sized shadow images with Chinese Reminder Theorem, which are then embedded into RAW images to simulate the images with higher ISO parameters with NS. In MSISS-NS, the visual quality of shadow images is improved significantly. Additionally, as the payload of cover images with NS is larger than the size of small-sized shadow images, the scheme performs well not only in visual camouflage, but also in other aspects, like lossless recovery, no pixel expansion, and resisting steganalysis.