Reviewerly/exHarmony huggingface dataset:
Compositional Zoning in Pyrope From Sedimentary Deposits of Bug Area (Ukraine): Implications for Understanding their Provenance

According to earlier concepts detrital pyrope crystals from Paleogene, Neogene and Quaternary sediments of the Upper and Mid dle Bug area are of kimberlitic origin.In this paper we present another hypothesis on their provenance.A purposeful microprobe examination of more than 1000 grains showed that in each sediment sample 1 to 2% of pyrope crystals are chemically inhomoge neous.With the exception of this feature, the discovered zoned garnets are completely identical in chemical composition as well as morphologically to the bulk of the detrital pyropes of the mentioned area, which indicates their single primary sources.In these crystals of 200-300 µm in size the Fe and partly Mn contents gradually increase and Mg decreases from one edge to the other, while contents of other components (Ti, Ca and Cr) do not change, or change very slightly.The (Fe+Mn) -Mg zoning is stron gest in a narrow area of 40-100 µm width near the grain rim.Maximum and minimum values of Mg and Fe content and their ranges in each garnet crystal are individual and differ from each other, but their average gradients of about 0.01 apfu per 10 µm are approximately the same.The total range for Mg in analyzed garnets is 1.38-2.34apfu (normalized to 12 oxygen), which corresponds to 46-75 mol.% of pyrope component; for Fe it is 0.45-1.09apfu and 14 to 37 mol.% of almandine component.The character of chemical patterns (continuous smooth concentration profiles for Mg and Fe, together with an almost uniform dis tri bution of Ti, Cr, Ca) indicates that most likely in the studied garnets there is a primary retrograde diffusioninduced zoning.Ap plication of classical thermobarometry suggests that diffusion started to occur at temperature of about 850 °C and froze at 650 °C, while pressure remained almost unchanged near 2.0 GPa.An estimation of cooling rates required to fit zoning profiles in studied garnets yields values of about 2 to 10 °C/Ma.In summary, in terms of the chemical composition and character of zoning the detrital pyropes of the Upper and Middle Bug area are most comparable to pyropes of orogenic peridotites.Given this, peridotites or similar rocks are suggested as the most probable primary sources of detrital pyropes within this region.However, the question on their location, proximalregional or far distal, has no clear answer.
PANDA - Adapting Pretrained Features for Anomaly Detection

Anomaly detection methods require high-quality features. One way of obtaining strong features is to adapt pre-trained features to anomaly detection on the target distribution. Unfortunately, simple adaptation methods often result in catastrophic collapse (feature deterioration) and reduce performance. DeepSVDD combats collapse by removing biases from architectures, but this limits the adaptation performance gain. In this work, we propose two methods for combating collapse: i) a variant of early stopping that dynamically learns the stopping iteration ii) elastic regularization inspired by continual learning. In addition, we conduct a thorough investigation of Imagenet-pretrained features for one-class anomaly detection. Our method, PANDA, outperforms the state-of-the-art in the one-class and outlier exposure settings (CIFAR10: 96.2% vs. 90.1% and 98.9% vs. 95.6%) .
Advances in Convolutional Neural Networks

Deep Learning, also known as deep representation learning, has dramatically improved the performances on a variety of learning tasks and achieved tremendous successes in the past few years. Specifically, artificial neural networks are mainly studied, which mainly include Multilayer Perceptrons (MLPs), Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). Among these networks, CNNs got the most attention due to the kernel methods with the weight sharing mechanism, and achieved state-of-the-art in many domains, especially computer vision. In this research, we conduct a comprehensive survey related to the recent improvements in CNNs, and we demonstrate these advances from the low level to the high level, including the convolution operations, convolutional layers, architecture design, loss functions, and advanced applications.
TEXTBOOK DISCOURSE READABILITY: GENDER, READING INTEREST, AND SOCIO-ECONOMIC STATUS OF STUDENTS WITH POOR READING ABILITY

Textbook discourse readability is primarily determined by three key factors: the text, the writer, and the reader. Textbook discourse readability from the reader aspect especially among students with poor reading ability is rarely studied. Therefore, this study aimed to investigate textbook discourse readability among junior high school students with poor reading ability based on gender, reading interest, and family socio-economic status. A quantitative survey was conducted on 257 respondents from Indonesia. The data were gathered using a questionnaire and a cloze test. The results showed that textbook discourse readability on all investigated variables was categorized into the frustration level. There was a significant difference in textbook discourse readability among students with poor reading ability based on gender, reading interest, and family socio-economic status. Female students, students with high reading interest, students with well-educated mothers, students with parents earning more than seven million rupiahs (equals to USD480/month) were reported to have a higher textbook discourse readability. These findings suggest the importance of considering students’ individual characteristics and family socio-economic background in writing a textbook and designing reading instructions in junior high schools.
A conversation with Santiago Montesdeoca

In this series of interviews with innovation leaders, Ubiquity Associate Editor and software engineer, Dr. Bushra Anjum, sits down with Dr. Santiago Montesdeoca to discuss the evolution of the animation industry as well as the aesthetic constraints creatives face when reconciling their personal artistry with commerce and trends.
Privacy preservation in outsourced mobility traces through compact data structures

Indoor localization is widely used as enabling technology for location-based services, such as advertising, indoor routing, and behavioral analysis. To keep these features available, service providers passively collect a large amount of data that may reveal strictly personal information about an individual. As an example, a timestamped mobility trace acquired in a mall may help the business owner to rearrange the user surroundings relying on a punctual analysis of the user behavior. In this paper we discuss some information processing techniques relying on probabilistic data structures designed to mitigate the user’s privacy leakage. The work is also accompanied by a case study. Our experiments were carried out using well-known networking equipment, Cisco Meraki, which is provided in combination with several primitives designed to passively infer and collect the user position in an indoor environment.
Oort: Informed Participant Selection for Scalable Federated Learning.

Federated Learning (FL) is an emerging direction in distributed machine learning (ML) that enables in-situ model training and testing on edge data. Despite having the same end goals as traditional ML, FL executions differ significantly in scale, spanning thousands to millions of participating devices. As a result, data characteristics and device capabilities vary widely across clients. Yet, existing efforts randomly select FL participants, which leads to poor model and system efficiency. In this paper, we propose Kuiper to improve the performance of federated training and testing with guided participant selection. With an aim to improve time-to-accuracy performance in model training, Kuiper prioritizes the use of those clients who have both data that offers the greatest utility in improving model accuracy and the capability to run training quickly. To enable FL developers to interpret their results in model testing, Kuiper enforces their requirements on the distribution of participant data while improving the duration of federated testing by cherry-picking clients. Our evaluation shows that, compared to existing participant selection mechanisms, Kuiper improves time-to-accuracy performance by 1.2x-14.1x and final model accuracy by 1.3%-9.8%, while efficiently enforcing developer-specified model testing criteria at the scale of millions of clients.
Towards Using Semantic-Web Technologies for Multi-Modal Knowledge Graph Construction

While a multitude of approaches for extracting semantic information from multimedia documents has emerged in recent years, isolating any form of holistic semantic representation from a larger type of document, such as a movie, is not yet feasible. In this paper we present our approaches used in the first instance of the Deep Video Understanding Challenge, using a combination of several multi-modal detectors and an integration scheme informed by methods from the semantic web context in order to determine the capabilities limitations of currently available methods for the extraction of semantic relations between the characters and locations relevant to the narrative of a movie.
Neural Style Transfer Based Voice Mimicking for Personalized Audio Stories

This paper demonstrates a CNN based neural style transfer on audio dataset to make storytelling a personalized experience by asking users to record a few sentences that are used to mimic their voice. User audios are converted to spectrograms, the style of which is transferred to the spectrogram of a base voice narrating the story. This neural style transfer is similar to the style transfer on images. This approach stands out as it needs a small dataset and therefore, also takes less time to train the model. This project is intended specifically for children who prefer digital interaction and are also increasingly leaving behind the storytelling culture and for working parents who are not able to spend enough time with their children. By using a parent's initial recording to narrate a given story, it is designed to serve as a conjunction between storytelling and screen-time to incorporate children's interest through the implicit ethical themes of the stories, connecting children to their loved ones simultaneously ensuring an innocuous and meaningful learning experience.
Computer-generated moiré profilometry based on fringe-superposition

A computer-generated moiré profilometry based on algebraic addition instead of algebraic multiplication is proposed. Firstly, the two AC components of the captured fringe patterns on the reference plane with [Formula: see text] phase difference are retrieved and saved in advance. While measuring, two sinusoidal gratings with [Formula: see text] phase difference are projected onto the measured object alternatively, and the corresponding deformed patterns are captured. Then the AC component of the captured deformed pattern can be separated exactly. When the positive and negative AC component of the captured deformed pattern are added to the two prestored AC components respectively, two moiré fringes only reflect sine and cosine of the object's phase information can be successfully generated via a series of data processing procedures. Finally, the phase distribution of the measured object can be extracted by arctangent of the ratio of these two moiré fringes. Compared with computer-generated moiré profilometry based on algebraic multiplication, this proposed method can reduce the effect of high frequency noise and residual DC component on measurement and improve the measurement accuracy. While compared with [Formula: see text] phase shifting FTP, this method can measure more complex objects with better measurement capability. Experimental results verify the feasibility and validity of the proposed method.
Near Optimality of Finite Memory Feedback Policies in Partially Observed Markov Decision Processes.

In the theory of Partially Observed Markov Decision Processes (POMDPs), existence of optimal policies have in general been established via converting the original partially observed stochastic control problem to a fully observed one on the belief space, leading to a belief-MDP. However, computing an optimal policy for this fully observed model, and so for the original POMDP, using classical dynamic or linear programming methods is challenging even if the original system has finite state and action spaces, since the state space of the fully observed belief-MDP model is always uncountable. Furthermore, there exist very few rigorous approximation results, as regularity conditions needed often require a tedious study involving the spaces of probability measures leading to properties such as Feller continuity. In this paper, we rigorously establish near optimality of finite window control policies in POMDPs under mild non-linear filter stability conditions and the assumption that the measurement and action sets are finite (and the state space is real vector valued). We also establish a rate of convergence result which relates the finite window memory size and the approximation error bound, where the rate of convergence is exponential under explicit and testable geometric filter stability conditions. While there exist many experimental results and few rigorous asymptotic convergence results, an explicit rate of convergence result is new in the literature, to our knowledge.
Flexible, decentralised access control for smart buildings with smart contracts

Large commercial buildings are complex cyber-physical systems containing expensive and critical equipment that ensure the safety and comfort of their numerous occupants. Yet occupant and visitor access to spaces and equipment within these buildings are still managed through unsystematic, inefficient, and human-intensive processes. As a standard practice, long-term building occupants are given access privileges to rooms and equipment based on their organisational roles, while visitors have to be escorted by their hosts. This approach is conservative and inflexible. In this paper, we describe a methodology that can flexibly and securely manage building access privileges for long-term occupants and short-term visitors alike, taking into account the risk associated with accessing each space within the building. Our methodology relies on blockchain smart contracts to describe, grant, audit, and revoke fine-grained permissions for building occupants and visitors, in a decentralised fashion. The smart contracts are specified through a process that leverages the information compiled from Brick and BOT models of the building. We illustrate the proposed method through a typical application scenario in the context of a real office building and argue that it can greatly reduce the administration overhead, while, at the same time, providing fine-grained, auditable access control.CCS Concepts: Security and privacy; Security services; Computer systems organisation; Embedded and cyber-physical systems; Sensors and actuators
An Optimized GPU-Accelerated Route Planning of Multi-UAV Systems Using Simulated Annealing

Usage of multiple unmanned aerial vehicles (UAV) in a certain mission makes flight route planning more complicated and slower.In order to obtain better performance, in the literature, most of the researchers propose using evolutionary algorithms and artificial intelligence approaches based on heuristics as optimization techniques.In addition to this, parallel programming approaches increase the computation performance.Therefore, this study focuses to discuss and solve the route planning problem for multi-UAV systems by using optimization techniques based on an evolutionary algorithm: simulated annealing.The travel cost and execution time are downsized in this work by optimization on algorithm and code.We implemented CPU based parallel solution to compare results with the GPU-accelerated one.The efficiency and the effectiveness of our parallelized and optimized solution is demonstrated through simulations under different scenarios.The results show that our optimized GPU based parallel solution for route planning problem is up to 1.6 times faster than serial and parallel CPU solutions.Moreover, our optimized GPU solution is better on cost than other solutions.It is shown that our GPU based approach is the fastest one and increases performance thanks to the massive parallelization capabilities of GPUs.
Quality and Machine Translation: An Evaluation of Online Machine Translation of English into Arabic Texts

This study compares the translation outputs of an English into Arabic text using the three machine translators of Google Translate, Microsoft Bing, and Ginger.To carry this evaluation of the machine translation (MT) outputs, an English text and its Arabic counterpart were selected from the UN records.The English source text was segmented into 84 semantic chunks.Depending on the Arabic counterpart model text, each chunk was rated as "correct or incorrect" at the two levels of the translation attributes: fidelity and intelligibility.To perform the quantitative description of the evaluation process, the numbers of fidelity and intelligibility errors and their percentages were calculated.Results of this evaluation process revealed that none of the three translated versions of the source text was perfectly translated.Although the translation of Microsoft Bing was rated the best, Google's translation was found the least accurate due to the high percentage of fidelity and intelligibility errors detected in its translation output.However, the quality of Ginger's translation was found slightly less accurate than that of Microsoft Bing, but remarkably better than Google's translation.The findings of this study imply that these MT applications can be implemented to perform English into Arabic translation to get the broad gist of a source text, but a deep and thorough post-editing process looks essential for a full and accurate understanding of an English into Arabic MT output.The study recommends that more studies are encouraged to continue to assess the quality of MT that will further highlight its weaknesses and the strategies that should be adopted to overcome them.
Semi-supervised Online Multi-Task Metric Learning for Visual Recognition and Retrieval

Distance metric learning (DML) is critial in many multimedia application tasks. However, it is hard to learn a satisfactory distance metric given only a few labeled samples for each task. In this paper, we proposed a novel semi-supervised online multi-task DML method termed SOMTML, which enables the models describing different tasks to help each other during the metric learning procedure and thus improving their respective performance. Besides, unlabeled data are leveraged to further help alleviate the data deficiency issue in different tasks by designing a novel regularization term, which also allows some prior information to be incorporated. More importantly, a quite efficient algorithm is developed to update the metrics of all tasks adaptively. The proposed SOMTML is experimentally validated in two popular visual analytic-based applications: handwriting digits recognition and face retrieval. We compared the proposed method with competitive single-task and multi-task metric learning approaches. Extensive experimental results demonstrate the effectiveness and efficiency of the proposed SOMTML.
Upholding democracy in a global pandemic: the Israeli elections experience

The COVID-19 pandemic substantially impacted countries' democratic election. On March 2020 Israel held a general parliamentary elections. The participation rate was 71.5%, compared with a 70% rate on the previous elections held in September 2019. We review Israel's actions which lead to a safe elections process and enhanced participation rate.
Neural Ordinary Differential Equations for Intervention Modeling

By interpreting the forward dynamics of the latent representation of neural networks as an ordinary differential equation, Neural Ordinary Differential Equation (Neural ODE) emerged as an effective framework for modeling a system dynamics in the continuous time domain. However, real-world systems often involves external interventions that cause changes in the system dynamics such as a moving ball coming in contact with another ball, or such as a patient being administered with particular drug. Neural ODE and a number of its recent variants, however, are not suitable for modeling such interventions as they do not properly model the observations and the interventions separately. In this paper, we propose a novel neural ODE-based approach (IMODE) that properly model the effect of external interventions by employing two ODE functions to separately handle the observations and the interventions. Using both synthetic and real-world time-series datasets involving interventions, our experimental results consistently demonstrate the superiority of IMODE compared to existing approaches.
LaM-2SRN: A Method Which Can Enhance Local Features and Detect Moving Objects for Action Recognition

Visual attention mechanism has been widely used in computer vision and plays a vital role in the research of human action recognition. In this paper, we explore a novel moving target detection mechanism for human action recognition and propose a new 3D CNN (3D Convolutional Neural Network) model, dubbed LaM-2SRN (Local Features Enhanced and Moving target detected 2Stream-ResNet) for extracting and learning attention-enhanced spatiotemporal features. The contributions of this paper are as follow: First, the traditional CAM (Class Activation Map) based visual attention algorithm is used to obtain the optical flow information of the human region, thus eliminating the influence of irrelevant optical flow information (such as background clutter). Second, the ViBe algorithm is used to identify the moving target in the continuous frame, retain the optical flow information of the moving target, and make it complement with the optical flow information of the human region, to obtain a complete motion descriptor. After the motion information is marked in the video frame, we put the marked video frames and the original video frames into the 3D CNN and 2D CNN models respectively, to acquired mixed descriptor, which serves as the basis for video classification. Different from most of the previous target detection algorithms, this paper not only detects the human target, but also detects the moving foreground targets in the video, so that the pre-trained CNN model can obtain more complete motion information from videos. We use RGB-only video data for evaluation on two benchmarks: UCF101 and HMDB51, the experimental results demonstrate that our LaM-2SRN is comparable to the previous state-of-the-art algorithms.
A New Approach for Making Use of Negative Learning in Ant Colony Optimization

The overwhelming majority of ant colony optimization approaches from the literature is exclusively based on learning from positive examples. Natural examples from biology, however, indicate the potential usefulness of negative learning. Several research works have explored this topic over the last two decades in the context of ant colony optimization, with limited success. In this work we present an alternative proposal for the incorporation of negative learning in ant colony optimization. The results obtained for the capacitated minimum dominating set problem indicate that this approach can be quite useful. More specifically, our extended ant colony algorithm clearly outperforms the standard approach. Moreover, we were able to improve the current state-of-the-art results in 10 out of 36 cases.