Reviewerly/exHarmony huggingface dataset:
An In-Network Replica Selection Framework for Latency-Critical Distributed Data Stores

In distributed data stores, performance fluctuations generally occur across servers, especially when the servers are deployed in a cloud environment. Hence, the replica selected for a reading request will directly affect the response latency. However, replica selection is challenging in latency-critical data stores (e.g., key-value stores). Such data stores generally deal with small size data, and clients have to select replicas independently. Even the state-of-the-art algorithm of replica selection (C3) still has considerable room for improving the latency. According to our experiments, compared with C3, using the ideal replica selection (Oracle) reduces the 99th latency by about 34-60 percent. In this article, we propose NetRS to address the fundamental factors that prevent replica selection algorithms from being effective. NetRS is a framework that enables in-network replica selection for distributed data stores. It exploits emerging network devices, including programmable switches and network accelerators, to select replicas for requests. NetRS supports diverse algorithms of replica selection and is suited to the network topology of modern data centers. According to our extensive evaluations, compared with the conventional scheme of clients selecting replicas for requests, NetRS reduces the mean latency by up to 50.3 percent, and the 99th latency by up to 56.3 percent. Moreover, NetRS could effectively cut the response latency even when unexpected events (e.g., workload changes, network device failures), and network congestion occur.
<scp>SnowPac</scp>: a multiscale cubic B-spline wavelet compressor for astronomical images

ABSTRACT As more advanced and complex survey telescopes are developed, the size and scale of data being captured grows at increasing rates. Across various domains, data compression through wavelets has enabled the reduction of data size and increase in computation efficiency. In this paper, we provide qualitative and quantitative tests of a new wavelet-based image compression method compared against the current standard for astronomical images. The analysis is improved by making use of state-of-the-art object detection systems to accurately measure the impact of the compression. We find that a combination of lossy wavelet-based methods, efficient quantization, and lossless dictionary compressors can preserve up to 98 per cent of astronomical objects at a 10:1 compression ratio. This significant reduction in file size also preserves astronomical object properties better than existing methods. These methods help further reduce future workloads for image-heavy processing pipelines.
RnR: Extraction of Visual Attributes from Large-Scale Fashion Dataset

Supervised visual perception models require a large number of annotated inputs so that a model can be trained and validated. However, manual annotation of large-scale datasets can be prohibitive in terms of time and expense. We present an automatic fashion image tagging algorithm that uses detailed item descriptions and carefully selected category information produced by a service provider. Compared to previous work, which used a relatively simple frequency-based heuristic, a set of image attributes extracted by our proposed algorithm could be used to create a model (ResNet-18 architecture) with exhibited 8% higher accuracy than a model created with attributes suggested by DeepFashion. This in-progress work identifies opportunities that carefully chosen visually recognizable attributes can be used to help build a more general and descriptive model.
Study of the energy consumption of graphical interfaces on TFT displays

The problem of energy saving of a mobile device is still relevant, moreover, the need for energy saving increases every year with the advent of the Internet of things. However, despite the significance and effectiveness of previous studies, we still do not know the exact amount of energy consumed by the elements of the mobile device interface. In this regard, our contribution to this study is that we received a specific amount of energy in the established units of measurement consumed by the basic elements of the interface and the main color palette on TFT displays. The data obtained in this study were obtained through many experiments and have some small error associated with the used measurement sensors built into the mobile device. This error was calculated and determined for each element. In addition, the data on the energy consumption of the mobile device were evaluated from a theoretical point of view and a mathematical model of the battery consumption of the mobile device was compiled.
Semi-Supervised Discriminative Transfer Learning in Cross-Language Text Classification

Cross-Language Text Classification (CLTC) has been increasing its attention to multilingual data due to its exponentially growing. CLTC aims to classify text documents in a label-scarce language, by leveraging classification information in a label-rich language. We propose a novel semi-supervised Discriminative Transfer Learning method (DTL) for the CLTC problem of a semi-supervised setting. A small number of paired labeled data in bilingual documents constructs a discriminative transfer model that maximizes the correlations of the documents in both languages, while a large number of unlabeled data are used for accurate data reconstruction. The discriminative transfer model minimizes the discrepancy between bilingual subspaces prioritizing discriminative features to improve the text classification performance without an automatic machine translation that most state-of-the-art methods require. The performance of DTL is empirically and statistically assessed by intensive experiments with the publicly available data, Reuters RCV1/RCV2 collections. The experimental results demonstrate that DTL outperforms several representative state-of-the-art methods in CLTC in terms of accuracy and efficiency.
Distributed Multi-Target Tracking with D-DBSCAN Clustering

This paper proposes a novel clustering-based distributed multi-target tracking algorithm over a sensor network. Each local sensor runs a joint probabilistic data association filter to obtain local state estimation. The estimates are communicated between connected sensors for track-totrack association and fusion. A novel distributed DBSCAN (D-DBSCAN) clustering algorithm is proposed to solve the track-to-track association problem. The proposed algorithm shows advantages in computational efficiency compared with conventional distributed multi-target tracking approaches. Extensive simulations provided substantial evidence for the effectiveness of the proposed algorithm.
Automated Text Classification System Based on Statistical Unified Model

The paper is devoted to the automated text classification system based on a unified model. The main text mining statistical and linguistic approaches are considered. The architecture of the developed system is given. The automated system is analyzed in details. The text data unified model consists of statistical elementary models: substrings, cumulative and the finite difference characteristics. The structure and features of each model are considered. The unified model flexibility is achieved by assigning weights for each elementary model. According to this logic, the system can be modified for different text types. The automated system for literary text classification has been tested. The classification quality was evaluated by precision, recall and f-measure. The model was trained and evaluated on the 6 classes. The total texts number in training set and test set is 600 and 60 respectively. The automated text classification system shows good results, low scores for some texts are explained. The advantages and limitations of the proposed system are shown. In addition, there is research area on the linguistic models inclusion in order to improve the classification quality of the proposed automated system.
A Bayesian approach to benefit–risk assessment in clinical studies with longitudinal data

Chuang-Stein et al. proposed a method for benefit–risk assessment by formulating a five-category multinomial random variable with the first four categories as a combination of benefit and risk, and the fifth category to include subjects who withdraw from study. In this article, we subdivide the single withdrawal category into four sub-categories to consider withdrawal for different reasons. To analyze eight-category data, we propose a two-level multivariate-Dirichlet Model to identify benefit–risk measures at the population level. For individual benefit–risk, we use a log-odds ratio model with Dirichlet process prior. Two methods are applied to a hypothetical clinical trial data for illustration.
Analysis of Grain Condition in Improved Granary Based on Grey Prediction Algorithm

Scientific grain storage plays an important role in ensuring food security and promoting high-efficiency energy-saving operations. The paper provides more accurate reference datas for grain storage work. It can easily monitor the grain situation during the reserve period, and can scientifically predict the future grain development trend more accurately. It takes countermeasure in advance to prevent food disaster and further reduce. The workload of the warehouse clerk and related staff, while ensuring the safe and stable operation of the grain storage. Compared with the traditional Gery Model, the residual correction method is proposed to improve the data prediction accuracy. Combined with the Grey Verhulst model, a new residual-corrected Verhulst model is proposed. The simulation prove that the improved model is more traditional than the traditional one. The model is more conducive to the prediction of volatility data and the prediction accuracy is greatly improved.
Aircraft Segmentation Based On Deep Learning framework : from extreme points to remote sensing image segmentation

Remote sensing image segmentation is a very important technology. Although the segmentation method based on convolutional neural networks (CNNs) has achieved promising results in natural image test set, e.g. VOC or COCO, they provide inferior performance when being transferred to remote sensing images. Due to the limits of labeled remote sensing images, finetuning pre-trained CNNs using remote sensing images do not benefit the image segmentation performance. Inspired by the recent works of interactive segmentation methods which exploit several extreme clicks that are fed into CNNs to improve the accuracy of the segmentation, we propose an effective method to improve the segmentation accuracy, which uses four extreme points (the top, bottom, left, and right) as the guide information. In terms of mIoU, our method achieves 84.4% on remote sensing image dataset, which outperforms the previous work by 23.1%. Compared with the previous interactive segmentation methods, the proposed method achieves superior performance. In addition, an improved method with an extra point is proposed based on the inaccurate part of results obtained by four extreme points. It is very feasible to be applied in an interactive segmentation toolbox.
OSTER: An Orientation Sensitive Scene Text Recognizer with CenterLine Rectification

Scene texts in China are always arbitrarily arranged in two forms: horizontally and vertically. These two forms of texts exhibit distinctive features, making it difficult to recognize them simultaneously. Besides, recognizing irregular scene texts is still a challenging task due to their various shapes and distorted patterns. In this paper, we propose an orientation sensitive network aiming at distinguishing between Chinese horizontal and vertical texts. The learned orientation is then passed into an attention selective network to adjust the attention maps of the sequence recognition model, leading it working for each type of texts respectively. In addition, a lightweight centerline rectification network is adopted, which enables the irregular texts more readable while no redundant labels are needed. A synthetic dataset named SCTD is released to support our training and evaluate the proposed model. Extensive experiments show that the proposed method is capable of recognizing arbitrarily-aligned scene texts accurately and efficiently, achieving state-of-the-art performance over a number of public datasets.
Accurate phase retrieval algorithm based on normalized vector of three interferograms

A phase retrieval method from three interferograms based on normalized vector is proposed to achieve high accuracy and less time computation. It needn't pre-filtering for the interferograms, which reduces its complexity and shortens the processing time. The difference maps are used to filter out the background intensity after compensating the fluctuation. Then, the normalized vector is used to obtain tested phase distribution with high precision. It is suitable for simple and complex fringes. Moreover, the phase can be correctly extracted when the fluctuation of background intensity and modulation amplitude is existed. The reliability of the algorithm is verified with simulated and experimental data.
Wireless Signal Recognition Based on Deep Learning for LEO Constellation Satellite

In view of the increasing on-board processing capacity, this paper investigates the possibility of the communication reconnaissance on LEO constellation satellite platforms, and proposes a wireless signal recognition algorithm based on deep learning. The proposed algorithm visualizes the wireless signal as a picture based on the basic digital signal processing, as a result, the signal recognition problem is subtly transferred to an object detection problem recurring in the field of Computer Vision (CV). Then, it co-opts deep learning models in CV field in order to realize the end-to-end signal recognition and improve the performance. Validating results on the field-collected signal dataset with 12 types and 4740 samples show that, the algorithm can effectively identify the waveform types and time/frequency coordinates of communication signals with the precision 89%, which is 40% higher than traditional algorithms.
Pollution Control Machine Using Artificial Intelligence And Machine Learning

Research in pollution has been going from past several years and there have been many advancements in conserving and controlling the pollution and here motivated from the same and an important factor of humanity have led us to design a kind of Machine which will not only control the pollution but also convert the residual pollutants into a form that will be used to give the power supply to the machine. The machine is designed like a Traffic light pole with a similar kind of structure and there are many hardware devices put inside it like Electrostatic precipitator for synthesing the pollution and a converter cum inverter. Then the major role is played by some Information and Computing Technologies such as Artificial Intelligence, Computational Logic, Reinforcement Learning, Deep Neural Networks, IoT and Cloud Computing and these all are related to each other facilitating the overall efficient working of PCM.
Towards field-programmable photonic gate arrays

We review some of the basic principles, fundamentals, technologies, architectures and recent advances leading to thefor the implementation of Field Programmable Photonic Field Arrays (FPPGAs).
Second Language Transfer Learning in Humans and Machines Using Image Supervision

In the task of language learning, humans exhibit remarkable ability to learn new words from a foreign language with very few instances of image supervision. The question therefore is whether such transfer learning efficiency can be simulated in machines. In this paper, we propose a deep semantic model for transfer learning words from a foreign language (Japanese) using image supervision. The proposed model is a deep audio-visual correspondence network that uses a proxy based triplet loss. The model is trained with large dataset of multi-modal speech/image input in the native language (English). Then, a subset of the model parameters of the audio network are transfer learned to the foreign language words using proxy vectors from the image modality. Using the proxy based learning approach, we show that the proposed machine model achieves transfer learning performance for an image retrieval task which is comparable to the human performance. We also present an analysis that contrasts the errors made by humans and machines in this task.
Research on the Task Scheduling System for Agricultural Plant Protection UAV

Unmanned Aerial Vehicle (UAV) can greatly reduce manpower in agricultural plant protection such as watering, sowing, pesticide spraying. It helps in creating an autonomous manufacturing sys- tem by executing tasks with less human intervention in time-efficient manner. Consequently, a scheduler is one essential component to be focused on; yet to the best of our knowledge, there are few re- search on practical UAV scheduling. This work proposes an algorithm to schedule the UAV actions in practice such as pesticide spraying, flying and charging. During the execution of the algorithm, the tasks are put into a queue then each task is assigned to the UAV that can complete it in the shortest amount of time, and then the UAVs perform tasks in the order in which they are as- signed. This proposed method is implemented into a scheduler and tested on datasets generated based on a real flight demonstration. Performance evaluation of the scheduler is discussed in details.
Studying Grammatical Evolution’s Mapping Processes for Symbolic Regression Problems

Grammatical Evolution (GE) is a variant of Genetic Programming (GP) that uses a BNF-grammar to create syntactically correct solutions. GE is composed of the following components: the Problem Instance, the BNF-grammar (BNF), the Search Engine (SE) and the Mapping Process (MP). GE allows creating a distinction between the solution and search spaces using an MP and the BNF to translate from genotype to phenotype, that avoids invalid solutions that can be obtained with GP. One genotype can generate different phenotypes using a different MP. There exist at least three MPs widely used in the art-state: Depth-first (DF), Breadth-first (BF) and $$ \pi $$Grammatical Evolution (piGE). In the present work DF, BF, and piGE have been studied in the Symbolic Regression Problem. The results were compared using a statistical test to determine which MP gives the best results.
Natura multimodale e creatività del linguaggio poetico

The exceptional nature of poetic language – testified by its patent untranslatability – represents a problem for philosophy, and particularly for analytic philosophy, which aims to provide an overarching explanation of ordinary language. It is difficult to explain the peculiar creativity of poetic language, starting from a finite basic vocabulary and a finite set of rules to be used by an interpreter who has finite powers. Poetry seems indeed not to respect the semantic innocence, spreading new meanings not only according to the context, but even to the graphic and phonological form of the words. Mechanisms, such as quotation and conversational implicatures, have been used to respect semantic innocence and to show at the same time that poetic language is not deviant from the ordinary use of language. This article suggests an alternative route, "imagistic", to account for the specific creativity of poetic language, without violating the principle of semantic innocence and without hypothesizing a discontinuity with ordinary language. Particularly referring to metaphor, it aims to show that poetic language does not convey as much further meanings, implicit in the use of literal terms, but rather images, evoked by and dependent on these terms, which could explain both poetry emergent properties and untranslatability. "Imageability" is not a feature of the "poetic word", but rather of any word and, particularly, of concrete words. The creativity of poetic language lies in deepening the multimodal nature of concrete language, which is different from the abstract and conceptual nature of philosophical language.
An Implementation of House Rental Platform with Blockchain Technology

Blockchain is a technology that does not rely on third parties to store, verify, transfer and communicate network data through its own decentralized nodes. Important features of the blockchain include decentralization, independence, security, and anonymity. This study mainly uses Ethereum blockchain technology to establish a combination of money flow and transparency for implementing a rental trading platform, such that the platform can trade, control and autonomously rent a house without third-party management. In addition, all the transaction information can be recorded in the blockchain for everyone to search. For the landlords, the platform can save their cost of promotion. For the tenants, they can check the past transaction records of the house, confirm the past rental process of the house, and find a more suitable house.
Results From Expert Survey on System Analysis Process Activities

Abstract System analysis is a crucial and complex step in software engineering process, which affects the overall success of the project and quality of the project outcome. Even though Agile methods have become widely popular, these methods have no structure when it comes to requirements elicitation and specification, which can have impact on whether a project has favourable outcome. Nevertheless, regardless of the chosen approach by industry practitioners, it is important to identify, which activities are currently performed, and analyse the causes and possible issues, which are encountered. The paper presents results from expert survey on the importance of activities related to requirements elicitation, analysis and specification process and the use of tools to support this process. Delphi method, which is used to evaluate the responses, is described. Lists of activities are ranked according to importance and additional information on expert responses is given in the paper. The information can give an insight into the activities and tools that are used in the industry.
Infer Latent Privacy for Attribute Network in Knowledge Graph

The information of the real world is stored as triplets (head entity, relation, tail entity) in knowledge graphs. They are extremely useful resources for many intelligent applications but suffer from incompleteness. This paper proposes a knowledge graph representation model to infer latent privacy based on the existing data in attribute network. In our model, considering the nodes are heterogeneous, we classify the nodes into attribute nodes and entity nodes. In order to protect the privacy of entities, we don't follow the previous methods to learn and store the feature embedding of each entity in knowledge graph. Our model focuses in capturing the restriction patterns of attribute nodes, which is safe when merging data from various sources. Given a triplet (entity node, relation, attribute node), firstly, we get the embedding of the entity node by using a sophisticated way to utilize all the information of the node, not only the node connections but also the external text information. Then, we infer the attribute node for the entity node in a certain relation. Finally, we calculate the probability that the triplet is exist. In experiments, we evaluate our model on the tasks of triplet classification and link prediction. Evaluation results show that our approach outperforms the state-of-the-art methods with an accuracy rate of 90.0% in the task of triplet classification on the person attribute knowledge graph FB13. Besides, our model reaches promising performance by MeanRank =5.10, Hits@l = 35.14% and Hits@5=64.94% in the task of conference prediction on the academic network DBLP.