Reviewerly/exHarmony huggingface dataset:
Improving Pedestrian Detection from a Long-tailed Domain Perspective

Although pedestrian detection has developed a lot recently, there still exists some challenging scenarios, such as small-scale, occlusion and low-light. Current works usually focus on one of these scenarios independently and propose specific methods. However, different challenges may happen at a time simultaneously and change across time, making a specific method infeasible in practice. Therefore we are motivated to design a method which is able to handle various challenges and to obtain reasonable performance across different scenarios. In this paper, we first propose Instance Domain Compactness (IDC) to measure the difference of each instance in the feature space and handle hard cases from a novel long-tailed domain perspective. Specifically, we first propose a Feature Augmentation Module (FAM) to augment the tail instances in the feature space, thereby increasing the number and diversity of tail samples. Besides, a IDC-guided loss weighting module (IDCW) is formulated to adaptively re-weight the loss of each sample so as to balance the optimization procedure. Extensive analysis and experiments illustrate that our method improves the generalization of the model without any extra parameters and achieves comparable results across different challenging scenarios on both CityPersons and Caltech datasets.
DCT and Optimum Parameter Bilateral Filtering-Based Copy-Move Forgery Detection

A digital image can be easily manipulated in today’s rapidly boosting technology. Among different types of image forgery methods, Copy-move forgery (CMF) is one of the most popular forgery methods in which a part of the image is copied and pasted into the same image or a different image. Therefore, the development of forgery detection methods has become a hot topic for both researchers and practitioners. In this study, a new CMF method combined with Discrete Cosine Transform (DCT) and optimum parameter Bilateral Filtering (OBF) is proposed to detect copied areas in a digital image. The visual and quantitative comparisons of the proposed hybrid method (DCT-OBF) have been made with traditional DCT based methods. The proposed method outperformed the former bilateral based method as well as the DCT based methods in terms of visual and quantitative analysis. Moreover, comparisons with conventional block-based methods have been carried out for different post-processing attacks such as image blurring and contrast adjustment. Quantitative comparisons demonstrate that the proposed method is better than the conventional block based methods, especially for post-processed images. For blurring post-processed images, the proposed method have provided higher Precision (18% improvement), F1 score (13% improvement), and Recall (5% improvement) rates than the former block based methods.
Research on CM_HI_pApriori Algorithm Based on Hyperthyroidism

Aiming at the problems of the existing mining algorithm processing data scale, the time complexity of frequent item set storage, and the limitation of the generated association rules, the CM_HI_pApriori (parallel Apriori algorithm of the compressed matrix based on HashMap and Interest) algorithm is proposed. First of all, the parallel data partition method is used to solve the problem of low serialization efficiency. Secondly, HashMap is used to store and query frequent item sets, which makes the time complexity of storing frequent item sets change from linear growth to constant growth. Finally, the interest model is used to solve the problem that association rules do not conform to the actual situation. Experiments show that the CM_HI_pApriori algorithm not only has high scalability and stability in processing large-scale data but also has high accuracy in generating association rules.
Protecting IoT Devices through a Hardware-driven Memory Verification

Internet of things (IoT) devices are appearing in all aspects of our digital life. As such, they have become prime targets for attackers and hackers. An adequate protection against attacks is only possible when the confidentiality and integrity of the data and applications of these devices are secured. State-of-the-art solutions mostly address software and network attacks, but overlook physical/hardware attacks. Such attacks can still exploit software vulnerabilities or even introduce them. In this paper, we present embedded memory security (EMS); it protects against physical tampering of the memory of IoT devices. As a case study, we have equipped a RISC-V based system-on-chip (SoC) with an EMS module. Our experimental results show that EMS successfully can protect the SoC against hardware tampering attacks, while having a low performance overhead.
A Concurrent Training Method of Deep-Learning Autoencoders in a Multi-user Interference Channel

Autoencoder (AE) has been proposed recently as a promising, and potentially disruptive approach to design the physical layer (PHY) of future beyond-5G networks. In this paper, we propose a novel concurrent and interactive training method for autoencoder-based physical layer for the multiuser interference channel scenario, which is highly relevant to beyond-5G dense systems. In our approach, AEs use a concurrent training approach in order to minimise the interference in the presence of other AEs. Deep learning-based AE design for a multi-user interference channel is proposed in a dense urban environment where the AE is adapted to the interference from nearby intelligent base stations (BSs) by considering other BSs randomly distributed over 200 meters. Interference is modelled based on a 3GPP channel model in an urban microcell environment. We simulate and test the system, and compare the performance with a conventional QPSK modulation in an additive white Gaussian noise (AWGN) channel. Results show that at low signal-to-noise ratios, the AE in an interference channel delivers similar performance to that of QPSK in a noisy channel. However, at high signal-to-noise ratios, every trained AE outperforms the theoretical limit for QPSK systems in a noisy channel, in an interference-limited environment. The learned constellations is studied. We show that AEs can augment their own transmitted symbols based on the interference from nearby BSs via a learning process, which can counteract the effects of channel noise.
Experimentally accessible nonseparability criteria for multipartite-entanglement-structure detection

The description of the complex separability structure of quantum states in terms of partially ordered sets has been recently put forward. In this work, we address the question of how to efficiently determine these structures for unknown states. We propose an experimentally accessible and scalable iterative methodology that identifies, on solid statistical grounds, sufficient conditions for nonseparability with respect to certain partitions. In addition, we propose an algorithm to determine the minimal partitions (those that do not admit further splitting) consistent with the experimental observations. We test our methodology experimentally on a 20-qubit IBM quantum computer by inferring the structure of the 4-qubit Smolin and an 8-qubit W state. In the first case, our results reveal that, while the fidelity of the state is low, it nevertheless exhibits the partitioning structure expected from the theory. In the case of the W state, we obtain very disparate results in different runs on the device, which range from nonseparable states to very fragmented minimal partitions with little entanglement in the system. Furthermore, our work demonstrates the applicability of informationally complete positive operator-valued measurements for practical purposes on current noisy intermediate-scale quantum devices.
Research on the Employment Situation of College Mathematics Major Students in China

With the rapid development of higher education and the increasing number of college graduates each year, it is of great significance to research the employment problems of college graduates majoring in mathematics. This research uses the literature analysis method to discuss the research on the employment situation of college graduates majoring in mathematics in the past in China. The results show that: (1) Almost research focused on three aspects: the employment status, its affecting factors, and the cultivation strategies to prompt graduates’ employment. (2) There are obvious deficiencies in some places. Such as research methods. In the past, most researches have adopted the method of theoretical speculation, and few empirical methods have been used, which has greatly reduced the credibility of the research results. (3) There are also some blank spots in previous research. For example, there are rare types of research on the development of mathematical ability and innovation ability of mathematics students based on the employment perspective, and almost no research on how to establish an employment ability evaluation mechanism for mathematics majors. Therefore, future research should start from a broader perspective and adopt empirical methods to make this research more comprehensive, systematic and in-depth.
Dissimilarity space reinforced with manifold learning and latent space modeling for improved pattern classification

Abstract Dissimilarity representation plays a very important role in pattern recognition due to its ability to capture structural and relational information between samples. Dissimilarity space embedding is an approach in which each sample is represented as a vector based on its dissimilarity to some other samples called prototypes. However, lack of neighborhood-preserving, fixed and usually considerable prototype set for all training samples cause low classification accuracy and high computational complexity. To address these challenges, our proposed method creates dissimilarity space considering the neighbors of each data point on the manifold. For this purpose, Locally Linear Embedding (LLE) is used as an unsupervised manifold learning algorithm. The only goal of this step is to learn the global structure and the neighborhood of data on the manifold and mapping or dimension reduction is not performed. In order to create the dissimilarity space, each sample is compared only with its prototype set including its k-nearest neighbors on the manifold using the geodesic distance metric. Geodesic distance metric is used for the structure preserving and is computed using the weighted LLE neighborhood graph. Finally, Latent Space Model (LSM), is applied to reduce the dimensions of the Euclidean latent space so that the second challenge is resolved. To evaluate the resulted representation ad so called dissimilarity space, two common classifiers namely K Nearest Neighbor (KNN) and Support Vector Machine (SVM) are applied. Experiments on different datasets which included both Euclidean and non-Euclidean spaces, demonstrate that using the proposed approach, classifiers outperform the other basic dissimilarity spaces in both accuracy and runtime.
Stating and Deriving Bernoulli’s Theorem as a Special Case of Steady Flow Energy Equation

Steady flow energy equation and Bernoulli's theorem are two main fundamental equations in thermo-fluid area and play a vital role in the energy balance and design of most of the mechanical systems. These two equations have different methods of derivations. SFEE is derived by directly balancing microscopic and macroscopic energies. On the other hand, Bernoulli's equation is derived by integrating Euler's equation, which is derived by considering pressure and self-weight forces on a fluid element of small volume and then balancing it. On application of some special cases like heat and work transfer is not present in the system, throttling is not present; fluid is incompressible along with all assumptions used in Bernoulli's equation; we can derive it from SFEE and it can be used as alternative derivation method for Bernoulli equation. In this paper initially, the actual derivation method of two fundamental equations is presented and then the Bernoulli theorem is derived from SFEE, applying all boundary conditions which are discussed comprehensively.
Electric and fuel car identification based on UAV thermal infrared images using deep convolutional neural networks

Electric cars, as an alternative to fuel cars, are growing rapidly in number and help reduce carbon dioxide emissions and build green cities. However, due to the appearance similarities between electric and fuel cars, it is still a challenge to count the proportion of electric cars on the road. Considering that thermal infrared (TIR) imaging technology can obtain the temperature characteristics of cars as well as work at night, this paper proposes a new solution to identify electric and fuel cars in Unmanned aerial vehicle (UAV)-based TIR images. First, a semiautomated labelling method is implemented for training data set generation, which is based on a traditional object tracking algorithm to improve labelling efficiency. Then, two classic deep convolutional neural networks, YOLOv5 and SSD, are used to verify the reliability of the dataset. Experiments prove that the proposed solution can effectively identify electric and fuel cars on the road, with mean of average precision (mAP) up to 0.99. This study is the first attempt to apply UAV-based TIR imaging to electric and fuel car identification and propose the first open data set for relevant research.
Estimation Coefficients for Evaluating the Efficiency of IT Facilitated Online Learning

The paper gives the authors' proposal to apply the estimation coefficients for evaluating the efficiency of education delivered online, i.e. distance learning. In light of the current situation related to the COVID-19 pandemic, distance learning technologies are developing rapidly, making it necessary to ensure their effectiveness against the conventional offline full-time delivery of the educational services. Thus, the urgency of the research is caused by the emerging need to ensure the up-to-the-mark level of online studies which is becoming a global trend nowadays. So, the study pursued the goal of developing the estimation coefficients for measuring the effectiveness of the learning process delivery in the IT facilitated online mode and sharing the results of observations made throughout the pilot study. The appropriate usage of the evaluation models based on these indicators enables a more precise and accurate evaluation of the learning process efficiency in terms of both qualitative and quantitative criteria. To appraise the efficiency of the learning-teaching process, cross comparison of conventional (classical, offline) and distant (remote, online) study modes was performed. Thus, the thorough comparative analysis of the indicators allowed to identify the most important of them, both qualitative and quantitative ones, and synthesize them taking into consideration the relevant impact factors. Based on the further comprehensive analysis rational values of the estimation coefficients were identified and clarified. As a result, it was made possible to formulate and approximate the coefficients which ensure prompt and relatively accurate evaluation of the quality of the learning process delivered online.
Framework for Monitoring the User’s Behavior and Computing the User’s Trust

Traditional access control, simple methods for virus detection, and intrusion detection are unable to manage variety of malicious and network attacks. The number of users might get hacked because of limitation in basic security protection. To implement a secure, reliable, and safe cloud-computing environment, we need to consider the trust issue. A trusted cloud is guaranteed to be safe from user terminals; combined with the concept of a trusted network, it evaluates, forecasts, monitors, and manages the user’s behavior to eliminate malicious datacenter attacks which are performed by unwanted cloud users and hackers; as a result, there is improved cloud security. In this chapter, we propose a Framework for Monitoring the User’s Behavior and Computing the User’s trust (FMUBCT). This model detects abnormal user behavior by creating user-behavior history patterns and compares them with current user behavior. The outcome of the comparison is sent to a trust computation center to calculate a user trust value. FMUBCT is flexible and scalable as it considers more evidence to monitor and evaluate user behavior. Finally, the simulation of FMUBCT shows that the model can effectively evaluate the users.
A Transfer Learning-Based Object Detection and Annotation System: Performance Evaluation for Vehicle Objects from Onboard Camera

It is a challenge to collect the required image data rapidly and effectively for object detection in emergency disaster situations. In this work, we focus on improving the classification accuracy and duration by collecting object images parallelly in the target disaster environment. In this paper, we propose a Transfer Learning (TL)-based object detection and annotation system. Our system has a novel image selection function to reduce the similar images for preparing a dataset that can improve the accuracy and model training duration. Our system train the model considering vehicular objects and their corresponding labels from video data of onboard camera. From the evaluation results, we observed that our proposed image selection function for training can reduce the number of sequential similar images to about 23%. Also, our TL-based object detection system can improve the detection performance compared with conventional learning method.
Bitcoin mining activity and volatility dynamics in the power market

Utilizing a measure of the Bitcoin network's daily electricity load, we document a signifcant volatility effect of Bitcoin mining activity in three prominent electricity markets in the U.S. The volatility effect is found to be increasing over time, particularly with the widespread lockdowns enforced due to the COVID-19 pandemic. The fndings provide novel insight to the non-virtual side of mining and trading of cryptocurrencies and underscore the need for establishing mechanisms to prevent possible destabilizing effects of this growing industry, both from a power consumption and carbon emissions perspective.
Choosing a Logic to Represent the Semantics of Natural Language

We attempt to answer the question of which kind of logical language should be chosen to represent the semantics of a broad selection of natural language sentences, and how prevalent different kinds of sentences are that require different levels of logical expressiveness. We examine these requirements for representing the semantics of text in logic by studying a sample of several balanced corpora. Our method is to create lists of words and sentential constructs that can easily be assessed in text, which are then mapped to requirements for logics of different expressiveness. We then run an automated analysis on thousands of sentences from two English corpora and manually validate a sample.
The consistency of arithmetic

Abstract This chapter opens the part of the book that deals with ordinal proof theory. Here the systems of interest are not purely logical ones, but rather formalized versions of mathematical theories, and in particular the first-order version of classical arithmetic built on top of the sequent calculus. Classical arithmetic goes beyond pure logic in that it contains a number of specific axioms for, among other symbols, 0 and the successor function. In particular, it contains the rule of induction, which is the essential rule characterizing the natural numbers. Proving a cut-elimination theorem for this system is hopeless, but something analogous to the cut-elimination theorem can be obtained. Indeed, one can show that every proof of a sequent containing only atomic formulas can be transformed into a proof that only applies the cut rule to atomic formulas. Such proofs, which do not make use of the induction rule and which only concern sequents consisting of atomic formulas, are called simple. It is shown that simple proofs cannot be proofs of the empty sequent, i.e., of a contradiction. The process of transforming the original proof into a simple proof is quite involved and requires the successive elimination, among other things, of “complex” cuts and applications of the rules of induction. The chapter describes in some detail how this transformation works, working through a number of illustrative examples. However, the transformation on its own does not guarantee that the process will eventually terminate in a simple proof.
Data Augmentation for Land Cover Classification Using Generative Adversarial Networks

In machine learning, few shot learning techniques try to generate high prediction accuracy from few training examples. One of those techniques is data augmentation. In this paper, we propose a method for land cover image classification using a conditional generative adversarial network (cGAN) starting from few training samples. First, we train a cGAN with a small dataset to classify images by mapping each pixel of the input image to its class. Second, we augment the images of the dataset by generating new samples using a traditional GAN model, then the labels of the new samples are generated using the previously trained cGAN model. The new augmented labeled data is associated with the original dataset to train the cGAN model again with more samples to improve the classification accuracy. Training experiments were performed on SPARCS publicly available dataset. We tested the proposed method on images of Landsat 8 and Alsat 2 satellites. The classification results show that the performance after data augmentation is better than using the original small dataset.
Face Attributes Extraction with Muti-Task Learning and Knowledge Distillation

The image of a human face contains a lot of key information, and extracting these information can effectively help us understand the facial feature information. In this paper, we propose a multi-task classification network for human faces, which is used to detect the key information of human faces. We use ResNet18 as the basic network and add an attention mechanism to each residual block. Our model includes seven binary classification branches and a regression prediction branch, these branch networks share the same parameters of the backbone network. Finally, we use the method of knowledge distillation to compress the network and reduce the amount of parameters. Since there is no suitable public dataset for our task, we built a dataset containing 76751 images ourselves. we have obtained excellent results in our face dataset. The accuracy of five binary classifications is above 90%, the accuracy of the remaining two binary classifications is above 85%, and the average error of continuous attribute prediction is 3.34.
Development of renewable energy projects using geographic information systems: An applied study to calculate solar irradiance in Samawah Desert, Iraq

Aim to Search to apply Programs Geographic information systems to determine the most appropriate places That you congregate on Solar energy in the Samawah desert And that fall In the western part of Al-Muthanna governorate. And it takes advantage of solar energy In renewable energy projects such as (electric power generation). Stimulate Agriculture and investment in a desert Samawah. Where it is considered Solar radiation The Main Source Renewable energy And theEnvironmentally friendly. The results of the study showed that the amount of solar radiation in the Samawah desertJLocated Its amount In the range (5751.1 to 5953.2) watt-hours / m2, which is an excellent value. And theThis result was obtained through the use of geographic information systems software As that method Calculate the incident solar irradiance Depends On the spatial resolution of the digital model usedinterval Temporale To fall radiation. And theReadings were taken during June and July 2019 Because these months represent the peak solar radiation fall into a desert Samawah. As that Temperatures are high during this period and an interval (two hours) is taken for taking readings. It is found by the search results That there is a high value of solar radiation Who is falling on Samawah desert.