Reviewerly/exHarmony huggingface dataset:
Semantic Embeddings for Food Search Using Siamese Networks

Efficient and effective search is a key driver of business in e-commerce. Functionally, most search systems consist of retrieval and ranking phases. While the use of methods like Learning to Rank (LTR) for (re)ranking has been studied widely, most retrieval systems in the industry are still predominantly based on variants of text matching. Because text matching cannot capture the semantic intent of the query, most out-of-vocabulary (OOV) queries are either not handled at all or poorly handled by matching to similarly-spelled entities. For niche e-commerce like food delivery apps operating on phonetically spelled, non-Western dish names, this problem is even more acute. Pre-trained word embedding models are of limited help because the majority of dish names are words that occur rarely or not at all in most openly available vocabularies. In this work, we present experiments and efficient Siamese network based models to learn dish embeddings from scratch. Compared to current baselines, we demonstrate that these models lead to a 3--5% improvement in Mean Reciprocal Rank (MRR) and [email protected] We also quantify, using a combination of in-house Food Taxonomy and the Davies-Bouldin (DB) index, that the new embeddings capture semantic information with an improvement of up to 20% over baseline.
Private Investigation and Open Source INTelligence (OSINT)

Social Networks has changed the way of developing an investigation as far as people use to explain, graph, show and give details about their life. It is so because people need to communicate and need to share feelings and passionate life. Based on these facts, private investigation uses the information in public databases to make an approach to people, their facts and their details that they publically expose to the rest of the world. So, this chapter explains how to use the OSINT (Open Source INTelligence) methodology for legally become part of the steps on a private investigation. The information is growing up and people expose their images, comment and reference to other ones so it facilities the investigation of getting results. Actually OSINT is the first step that private investigation must consider and this chapter covers and explains why and how to do this.
Improving Dynamic Bounding Box using Skeleton Keypoints for Hand Pose Estimation

Human-Computer Interaction (HCI) studies the design and implementation of computer technology, focused on the interaction between users and machines or computers. One of human-computer interface is using visual-based communication, which is the most widespread area in HCI research. In this paper, we discuss dynamic bounding box that follows the movement of human hand skeleton key points using a single RGB camera. By using wrist hand joint skeleton as setpoint, dynamic bounding box can move according to the hand movement, and the size of the bounding box will scale up automatically according to the hand size and its position from the camera. For hand pose recognition experiment results, the classification accuracy for hand pose estimation using dynamic bounding box performs a good result compare with using static bounding box.
Pruning Deep Neural Networks with L_0-Constrained Optimization

Deep neural networks (DNNs) give state-of-the-art accuracy in many tasks, but they can require large amounts of memory storage, energy consumption, and long inference times. Modern DNNs can have hundreds of million parameters, which make it difficult for DNNs to be deployed in some applications with low-resource environments. Pruning redundant connections without sacrificing accuracy is one of popular approaches to overcome these limitations. We propose two l0-constrained optimization models for pruning deep neural networks layer-by-layer. The first model is devoted to a general activation function, while the second one is specifically for a ReLU. We introduce an efficient cutting plane algorithm to solve the latter to optimality. Our experiments show that the proposed approach achieves competitive compression rates over several state-of-the-art baseline methods.
ClaimViz: Visual Analytics for Identifying and Verifying Factual Claims

Verifying a factual claim made by public figures, aka fact-checking, is a common task of the journalists in the newsrooms. One critical challenge that fact-checkers face is-they have to swift through a large amount of text to find claims that are check-worthy. While there exist some computational methods for automating the fact-checking process, little research has been done on how a system should combine such techniques with visualizations to assist fact-checkers. ClaimViz is a visual analytic system that integrates natural language processing and machine learning methods with interactive visualizations to facilitate the fact-checking process. The design of ClaimViz is based on analyzing the requirements of real fact-checkers and our case studies demonstrate how the system can help users to effectively spot and verify claims.
Research on the Mixed Teaching Mode of College English in the “Internet” Education Environment

China has entered the 21st century, the Internet has entered an era of rapid development, especially in recent years, with the emergence of five G, China’s Internet development has entered a new journey, with the rapid development of the Internet, China’s various industries can not do without the Internet, whether real estate, financial industry or even education industry. In recent years, under the influence of the newly created “Internet” education model, the English teaching methods of major colleges and universities have also undergone great reform. A new teaching mode, which combines the means of past education and teaching with modern information network, makes teaching no longer restricted by time and space. This kind of teaching mode can not only improve students’ interest in learning, meet the needs of modern students, but also improve the quality of English teaching in colleges and universities. This paper further discusses the mixed teaching mode of college English under the environment of Internet education.
Generative Adversarial Networks-Based Pseudo-Random Number Generator for Embedded Processors

A pseudo-random number generator (PRNG) is a fundamental building block for modern cryptographic solutions. In this paper, we present a novel PRNG based on generative adversarial networks (GAN). A recurrent neural network (RNN) layer is used to overcome the problems of predictability and reproducibility for long random sequences, which is found in the result of the NIST test suite for the previous method. The proposed design generates a random number of 1,099,200-bits with a 64-bit seed. The proposed method is also efficiently implemented on embedded processors by using the Edge TPU. To support the Edge TPU, the proposed GAN based PRNG is converted to a TensorFlow Lite model. During model training, the number of epochs is significantly reduced with the proposed approach. The PRNG generates random numbers in 13.27 ms using the Edge TPU. Also, our PRNG achieved a speed of 1.0 GB/s, which is about 6.25x compared to the speed of other lightweight PRNG. To the best of our knowledge, this is the first GAN based PRNG for embedded processors. Finally, generated random numbers were tested through the NIST random number test suite. Compared with the previous method, the proposed method reduced the percentage of test failures by 2.85x. The result shows that the proposed GAN-based PRNG achieved high randomness even on embedded processors.
Recurrence Plot and Convolutional Neural Networks for Terrain Classification Using Energy Consumption of Multi-legged Robots

In this article, we analyze a Machine Learning model for classifying the energy consumption of a multi-legged robots over different terrains. We introduce a system based in three popular techniques: Recurrence Plot (RP), Convolutional Neural Network (CNN), and Dimensionality Reduction. We use RP for transforming the energy consumption of the robots to grayscale 2D images. Due to computational restrictions, we apply a linear dimensionality reduction technique for transforming the images into a smaller feature space. The CNN is applied for classifying the images and to predict the terrain. We present results using several CNN architectures over real-data obtained in six+ types of terrains.
Integrating Side Channel Security in the FPGA Hardware Design Flow

The design of digital systems has its mainstay in the electronic design automation flows which act as crucial instruments to reduce the effort to realize complex computing platforms. In this work, we investigate the possibility of integrating side channel security analyses within the existing FPGA design flow, to provide a feedback to the hardware designer in a prompt and effective way. To this end, we realize an analysis framework which detects side channel leakage on the power consumption side channel at two well established checkpoints in hardware design, i.e., post synthesis and post implementation. We report the results of the proposed framework when integrated within the commercial Xilinx Vivado design toolchain. As a case study, we employ an open source SoC running a software version of the AES block cipher and provide a taxonomy of the side channel information leakage. The reported results highlight how our approach is able to provide precise insights on the sources of information leakage in the hardware design at hand. In particular, we show that the results of the simulations at post synthesis and post implementation stages provide complementary sets of insights on the information leakage, which, thanks to our methodology, can be traced back to architectural components which are the culprits of the said leakage.
Design and realization of car-like mobile robot based on Automated warehousing

According to the requirements of an automated warehouse, a car-like mobile robot is designed. IPC is master controller of the robot, and it collects data from lidar, IMU and other sensor, then it communicates with MCU-S32K to control steering gear and motor, so as to achieve the purpose of map-building, navigation and positioning. The robot can meet requirements of the simulation and actual running to achieve the target.
Apps Rating Classification on Play Store Using Gradient Boost Algorithm

The increasing number of Android apps available on Google Play Store with the developers' advantages has attracted many Android apps developers' attention. To benefit from developing Android apps is to know the characteristics of high rated applications on the Google Play Store. This research will explore the features of Size, installs, reviews, types (free/paid), rating, Category, content rating, and Price on apps on Google Play Store to determine the characteristics of high rated apps. This research uses a random-forest classifier to identify the most significant features in high rated apps on Google Play Store. This research uses the Gradient Boost Algorithm to identify the most influential attributes in high rating apps on Google Play Store. To classify the high rated apps, writers use the Gradient Boost algorithm that performs better than Random Forest, K-NN, and Decision Tree algorithm with a 99.93% accuracy, 99.91% recall, 99.91% precision, and 0.062 Root Mean Squared Error.
A Ranking Stability Measure for Quantifying the Robustness of Anomaly Detection Methods

Anomaly detection attempts to learn models from data that can detect anomalous examples in the data. However, naturally occurring variations in the data impact the model that is learned and thus which examples it will predict to be anomalies. Ideally, an anomaly detection method should be robust to such small changes in the data. Hence, this paper introduces a ranking stability measure that quantifies the robustness of any anomaly detector's predictions by looking at how consistently it ranks examples in terms of their anomalousness. Our experiments investigate the performance of this stability measure under different data perturbation schemes. In addition, they show how the stability measure can complement traditional anomaly detection performance measures, such as area under the ROC curve or average precision, to quantify the behaviour of different anomaly detection methods.
Research on the relationship between Covid-19 epidemic and gold price trend based on Linear Regression Model

Under the influence of COVID-19, the stock market had a huge shock, and the US stock market had been blown up many times, and the Dow Jones index once fell by 3000 points. Global investors' concerns about economic and political uncertainties continue to rise. There was a strong risk aversion sentiment in the market, and risk assets such as the stock market and crude oil were sold off by a large margin. The influx of safe-haven funds into the gold market has pushed gold prices higher. At the same time, central banks have launched monetary easing policies to stimulate the economy, which has led to the devaluation of paper money, pushing the gold price to its historical high since 2011. In disaster-prone 2020, studying the trend of gold price can provide strong support for correctly judging the world situation. As the biggest black swan event in 2020, the Covid-19 epidemic has a direct and important impact on the trend of gold prices. Therefore, it is extremely necessary to explore the relationship between the new crown epidemic and the trend of gold price. In this paper, the trend of the gold price, the daily cumulative number of confirmed cases, the cumulative number of deaths, and the cumulative number of cures during the period from 1/22 to 9/23 are selected as data sets. From the existing data and demand, it is not difficult to see that each characteristic data of the new epidemic situation and the trend of the gold prices are available, which is a regression problem to predict the continuous value. Therefore, this paper chooses a linear regression model to fit the relationship between COVID-19 and gold price trends.
Metastatic Cancer Image Classification Based On Deep Learning Method

Using histopathological images to automatically classify cancer is a difficult task for accurately detecting cancer, especially to identify metastatic cancer in small image patches obtained from larger digital pathology scans. Computer diagnosis technology has attracted wide attention from researchers. In this paper, we propose a noval method which combines the deep learning algorithm in image classification, the DenseNet169 framework and Rectified Adam optimization algorithm. The connectivity pattern of DenseNet is direct connections from any layer to all consecutive layers, which can effectively improve the information flow between different layers. With the fact that RAdam is not easy to fall into a local optimal solution, and it can converge quickly in model training. The experimental results shows that our model achieves superior performance over the other classical convolutional neural networks approaches, such as Vgg19, Resnet34, Resnet50. In particular, the Auc-Roc score of our DenseNet169 model is 1.77% higher than Vgg19 model, and the Accuracy score is 1.50% higher. Moreover, we also study the relationship between loss value and batches processed during the training stage and validation stage, and obtain some important and interesting findings.
Visible and Thermal Camera-Based Jaywalking Estimation Using a Hierarchical Deep Learning Framework

Jaywalking is an abnormal pedestrian behavior which significantly increases the risk of road accidents. Owing to this risk, autonomous driving applications should robustly estimate the jaywalking pedestrians. However, the task of robustly estimating jaywalking is not trivial, especially in the case of visible camera-based estimation. In this work, a two-step hierarchical deep learning formulation using visible and thermal camera is proposed to address these challenges. The two steps are comprised of a deep learning-based scene classifier and two scene-specific semantic segmentation frameworks. The scene classifier classifies the visible-thermal image into legal pedestrian crossing and illegal pedestrian crossing scenes. The two scene-specific segmentation frameworks estimate the normal pedestrians and jaywalking pedestrians. The two segmentation frameworks are individually trained on the legal or illegal crossing scenes. The proposed framework is validated on the FLIR public dataset and compared with baseline algorithms. The experimental results show that the proposed hierarchical strategy reports better accuracy than baseline algorithms in real-time.
A Satellite Fault Diagnosis Method Based on Focus Loss Function and Balance Factor

Fault diagnosis is an effective method to protect satellites in orbit. In recent years, researchers have used data-driven methods to provide some solutions, but most of them have not considered the data imbalance in the satellite fault diagnosis. In response to this problem, this paper analyzes the imbalance problem from two aspects: the imbalance of sample distribution and the imbalance of misclassification cost. Additionally, based on the analysis, this paper proposes an online fault diagnosis method based on focus loss function and balance factor, which can focus the loss on the fault samples in training process. Through experiments, the model greatly improves the detection accuracy of fault samples from 49.5% to 93.2%. At the same time, this method can also provide reference for solving the problem of category imbalance in other industrial field.
Categorical Model of Functional Language with Natural Numbers and Boolean Values

Curry–Howard–Lambek isomorphism defines a correspondence between at first sight different theories: Church's computational calculi (which is the foundation of functional programming paradigm), intuitionistic logic, and category theory. In this paper, we focus on the correspondence between the last two theories. We define a simple functional language, and we model it by category theory. We describe a correspondence between language in a motivating example of an evaluation of a simple functional program.
The Impact of the Measurement Process in Intelligent System of Data Gathering Strategies

An intelligent system is a technology that has opened diverse ranges of possibilities due to its availability, value, and accessibility. Such an intelligence resides in the possibility to measure and know the current state of entities or environments. Thus, the measurement process constitutes a key asset for many aspects related to the intelligence of systems (e.g., decision-making) and the relationship with the data gathering strategies. A strategy for formalizing monitoring projects based on entity states and scenarios is introduced. It integrates the visualization pipeline to align the visual communication to measurement requirements. From the methodological point of view, an extension of a measurement and evaluation framework which supports the modeling of entity states and scenarios is considered. The framework allows agreeing on concepts required to formalize a measurement project. Thus, a specialization of the Goal-oriented Context-aware Measurement and Evaluation strategy is introduced using the business process model to describe how scenarios and entity states are articulated jointly with their transition models. Also, the visualization pipeline is integrated into the new strategy to articulate the information need that gives origin to the measurement project jointly with the visual communication strategy. A synthesized case as a proof-of-concept is introduced. In this way, a monitoring strategy aware of scenarios, entity states, and the visualization pipeline into a measurement project is reached. Thus, traceability about each visual perspective is aligned with measurement points of view.
An Approach to Secure Capacity Optimization in Cloud Computing using Cryptographic Hash Function and Data De-duplication

Capacity Optimization in Cloud computing is a common term for innovations used to progress storage use by dwindling stored data. Crucial methodologies used for capacity optimization in cloud computing are data compression and data deduplication. Our paper focuses Capacity Optimization in connection with Deduplication procedures, which stare for redundancy in series of bytes across comparison windows by using cryptographic hash functions as identifiers of unique sequences, then the sequences are matched to the history of other such sequences, and when possible, the first uniquely stored version of a sequence is referenced rather than stored again. Our work shows better results for capacity optimization, which are most frequently found in backup/recovery storage, where storage of iterating versions of backups day to day creates a chance for lessening of space in cloud computing. Hence this study has recognized exploration hole as vast number of capacity of copy records on cloud frameworks from various clients. Consequently, to diminish the amount of work brought about by copied documents, SHA3 calculation has been proposed in this study, which makes the hash esteem for each record and discover the information intelligent compression for capacity streamlining on cloud framework. Our Work also includes Authentication, which is based on Cryptographic hash function and Secure Data Deduplication allows cloud storage and optimization of storage to employ data with concealment.
The Influence Measures of Light Intensity on Machine Learning for Semantic Segmentation

For the human eye, the conversion of light intensity through optic nerve is a non-linear conversion. Therefore, the differences of color caused by light intensity will be reduced by this mechanism. However, the conversion of light for the photosensor in camera is linear conversion, which also causes great influence on the image. Semantic segmentation could be known as a pixel-wise classifier. This technique can be implemented by machine learning or deep learning. In deep learning, the difference in light intensity has a relatively low impact because of relatively strong learning ability. For machine learning algorithms, it will have a significant impact because the classification method is based on RGB values. In this study, the light intensity of the training data would be calibrated and then the random forest model trained from the processed datasets would be compared with the model trained from the unprocessed datasets.
Radium: Improving Dynamic PoW Targeting

Most PoW blockchain protocols operate with a simple mechanism whereby a threshold is set for each block and miners generate block hashes until one of those values falls below the threshold. Although largely effective, this mechanism produces blocks at a highly variable rate and also leaves a blockchain susceptible to chain death, i.e. abandonment in the event that the threshold is set too high to attract any miners. A recent innovation called real-time block rate targeting, or RTT, fixes these problems by reducing the target throughout the mining interval. RTT exhibits much less variable block times and even features the ability to fully adjust the target after each block. However, as we show in this paper, RTT also suffers from a critical vulnerability whereby miners deviate from the protocol to increase their profits. We introduce the Radium protocol, which mitigates this vulnerability in RTT while retaining lower variance block times, responsive target adjustment, and lowering the risk of chain death. We also show that Radium’s susceptibility to the doublespend attack and orphaned blocks remains similar to Bitcoin.