Reviewerly/exHarmony huggingface dataset:
Characterization of 3D Image-Based Biometric Systems in Dynamic Acquisition Conditions

The paper focuses on the analysis of biometric measurements in dynamic acquisition conditions and their impact on the reliability of the recognition judgments. To this aim, a suitable simulator of stereoscopic systems has been designed and realized. It relies on a fully simulated procedure based on the following steps: (i) generation of a set of realistic 3D face models through a proper face simulator software; (ii) definition of an arbitrary trajectory for the face models and stereo images to simulate a set of images acquired in different poses (positions and orientations) of the subject during the movement; (iii) addition of selectable levels of motion blur in a controlled environment, to simulate critical acquisition conditions. This procedure allows ensuring that the recognition results are not due to the natural change of expression of real faces or an imperfect image acquisition device. Moreover, every face model is moved exactly with the same trajectory in front of the stereoscopic system, allowing compare the recognition performances all along the trajectory, also in controlled and under repeatable blur levels. A face biometrics procedure, based on a popular recognition algorithm, is then run on the generated images and the recognition performances are analyzed in detail. The achieved results demonstrated how the motion blur and also the slight differences between the acquired images and the reference ones significantly affect the performance in the recognition of such kinds of systems, thus confirming the usefulness of the proposed simulator.
JELRC: Knowledge Representation Algorithm Combining Logic Rules and Confidence

With the rapid development of the Internet, knowledge bases are no longer static or invariable; dynamic evolution brings new paths, which making knowledge bases expand constantly; information explodes, carrying a lot of uncertainty, yet far from complete. We propose an uncertain reasoning model based on the dynamic knowledge network, which unifies semantic information contained in rules and credibility of facts and realizes the knowledge representation algorithm of joint context relation, rules, and confidence. Aiming at dynamic inflow of knowledge fragments, we propose an algorithm activation strategy to achieve more accurate knowledge reasoning while adapting to the dynamic knowledge network. Experiments show that our proposed algorithm performs better than the existing model on reasoning tasks such as link prediction and triple confidence prediction on the benchmark dataset.
Data compression and decompression method and system for communication system

With the rapid development of mobile communication technology, the demand for signal transmission bandwidth is increasing. Technologies such as carrier aggregation will also increase the amount of data transmitted between the baseband processing unit and the remote radio unit. This puts forward higher requirements for optical fiber transmission capabilities. In the current communication system based on optical fiber transmission, the application of optical modules with a transmission rate of 10Gb/s has become popular. In 5G applications, 40G or 100G optical modules are even required to meet the ever-increasing application requirements. In order to reduce and control the consumption of optical fiber resources, it is necessary to develop a data compression method to reduce data transmission pressure and reduce costs. This paper proposes an IQ data compression and decompression algorithm based on FPGA hardware and according to the characteristics of programmable logic devices. As a result, the antenna system improves the efficiency of radio frequency power amplifiers, and reduces operation and maintenance costs.
Diverse Conversation Generation System with Sentence Function Classification

This paper mainly studies the implementation of diverse conversation generation system based on end-to-end neural networks and sentence classification on sentence function. In existing work, the output of the conversational system is mostly in the form of one type of sentence, for instance, declarative sentence. The type of sentence function is not well distributed. There is still insufficient diversity in the output of conversational system, which could be unattractive to users. A good conversational system could well interact with users by generating diverse output, including asking and responding, driving conversations to go further. Generating different type of output sentence is necessary to conversational systems, which is also challenging. Therefore, this paper introduces the idea of diverse conversation into the generative system, and designs the Diverse Conversation Generation (DCG) model. The model adopts a sentence function classifier trained independently to supervise the model output with modified loss function and back-propagation. The DCG model increases the diversity of output sentence, which could guide user to chat more with the system, extend the quality of conversation, and improve the user experience. The model is experimented on two different sequence-to sequence models, evaluated with perplexity and classify entropy, achieves better performance compared with two base models.
A New Siamese Co-attention Network for Unsupervised Video Object Segmentation

Unsupervised Video Object Segmentation (UVOS) aims to generate accurate pixel-level masks for moving objects without any prior knowledge. A lot of UVOS methods process frames independently by using image segmentation model without considering the temporal information between consecutive frames. Other works rely on RNNs or motion cues to find objects that need to be tracked, these models learn short-term temporal dependencies and thus tend to accumulate errors over time. We propose a new Siamese Co-attention Network to tackle Unsupervised Video Object Segmentation task based on SOLOv2. The Co-attention module in our Siamese Network captures global correspondences between a reference frame and the current one from same video, and it can learn pairwise correlation at any distance to help current frame correctly distinguish primary objects from a global view. Our proposed method is evaluated in TianChi VOS Challenge and DAVIS2017, and the results indicate that it exhibits superior performance.
Justification as a Dynamic Concept

This chapter is focused on the concept of ‘justification’Justification, which I will analyse taking into account various perspectives. Firstly, I will define the notion by itself and review its applications and uses in mainstream EpistemologyMainstream epistemology. Afterwards, I will examine how the concept has been used in Justification LogicsJustification Logic (JL) and some of its contemporary developments. Eventually, I will present a different logical understanding of the concept; and finally, introduce my interpretation of ‘justification’Justification as a process and embed it in the EAK-SchemaEAK-Schema.
Comprehensive Operation of Personalized Astute Wheelchair Having Portable Rooftop and Integrated Piezoelectric Engendered Tires to Assist Societal Advance

Intelligent manufacturing deliberates engaging the combined insight of entities, actions and apparatuses to unwaveringly affect the industrial assets. They have developed into a requisite share of entwined modern artefact evolution. The feat points on an autonomous riposte for a wheelchair up shoot as Port-P chair. The personalized astute wheelchair would augment the agility of apiece patient, primarily encompassed of limb affect, indoor as well as outdoor. It is crafted in such a manner that it could be functioned with electrical and mechanical means. During folding, the wheelchair is too dense in proportion and lightweighted that it could be transported effortlessly and maintained with hub motor which outcomes with additional room. It comprises foldable frame embraced with PVC pipes, which in means establishes the cost effectiveness of the product. The chair is encompassed with electrical components along with a joystick attached to its handle, which would assist the user in sovereign movement. Succession of batteries furnished for electrical operation is tailored innate by the frame itself, which itself is a tiny extent employing the feature. For the sake of patients, amid extremity, it is apposite for coarse transportation, which could be deployed with shock absorbers. Self-assurance of the user would aid as complementary to the customary facets among family and society. It’s an astounding feature of engendering charge for its employment through piezoelectricity—one of its kind. Comprehensive operation of personalized astute wheelchair (PAW) having portable rooftop for essential poise and integrated piezoelectric engendered tires, employing the notion of stowage and reuse, assists in societal progression.
Some i-Mark games

Let S be a set of positive integers, and let D be a set of integers larger than 1. The game is an impartial combinatorial game introduced by Sopena (2016), which is played with a single pile of tokens. In each turn, a player can subtract s ∈ S from the pile, or divide the size of the pile by d ∈ D , if the pile size is divisible by d . Sopena partially analyzed the games with S = [ 1 , t − 1 ] and D = { d } for d ≢ 1 ( mod t ) , but left the case d ≡ 1 ( mod t ) open. We solve this problem by calculating the Sprague–Grundy function of for d ≡ 1 ( mod t ) , for all t , d ≥ 2 . We also calculate the Sprague–Grundy function of for all k , and show that it exhibits similar behavior. Finally, following Sopena's suggestion to look at games with | D | > 1 , we derive some partial results for the game , whose Sprague–Grundy function seems to behave erratically and does not show any clear pattern. We prove that each value 0 , 1 , 2 occurs infinitely often in its SG sequence, with a maximum gap length between consecutive appearances.
Research on the Status and Methods of Online Education Enterprise Credit Evaluation in China

In recent years, China's online education industry has developed rapidly, and a series of dishonest phenomena have appeared in the expansion of the industry, which has attracted the attention of regulatory authorities and the market. In 2019, with the Ministry of Education and other six departments jointly issued the Implementation Opinions on Regulating Online Training Outside Schools, and started the filing of education and training APPs. The online education industry has entered a new stage of standardized development. However, the regulatory measures of the black and white list filing system cannot meet the needs of the industry development. In the future, if the online education industry is to achieve long-term and healthy development, it is urgent to promote the credit evaluation of online education enterprises. Firstly, this article discusses the development and regulatory status of the online education industry, and expounds the positive significance of online education enterprise credit ratings to consumers, investors, and government regulators. Then it points out that online education enterprises span the education and the Internet, and have both industry attributes. At the same time, online education enterprises sell and provide teaching services, which belong to the category of e-commerce. On this basis, this article reviews the classic model of corporate credit evaluation, and points out the reference and limitations of the existing models for the credit evaluation of online education enterprises in China. Furthermore, this article proposes that for the various attributes of online education enterprises in China, credit evaluation should be considered from indicators such as the operation of online education enterprises, the teaching quality, e-commerce credit, and Internet user information security.
Adversarial Learning for Cross Layer Security

Spectrum access in the next generation wireless networks will be congested, competitive, and vulnerable to malicious intents of strong adversaries. This compels us to rethink wireless security for a cross-layer solution addressing it as a joint problem for encryption and modulation. We propose a novel neural network generated cross-layer security algorithm where the trusted transmitter encodes a secret message using a shared secret key to generate a secured waveform. This encrypted waveform remains undeciphered by the adversary while the intended receiver can recover the secret. Cooperative learning is introduced to enable our trusted pair to defeat the adversary and learn the encryption and modulation jointly. The model can encode any modulation order and improves both reliability and secrecy capacity compared to prior work. Our results demonstrate that the trusted pair succeeds in achieving secure data transmission while the adversary can not decipher the received cipher data.
Transformation Modeling with Deformable ConvNets for Facial Action Unit Detection

Detecting action units (AUs) is a very challenging problem in computer vision. Current work through the expansion of data samples or use transformation-invariant algorithms to handle the subtle facial appearance change over various regions at different scales. However, these methods lack the inherent mechanism to deal with geometric transformation, which limits the generalization ability of the model. This thesis proposes a novel Deformable ConvNets (DCN) based model named AU-DCN to address this problem. Our approach provides two main contributions: (1) By introducing deformable convolution and deformable RoI pooling two modules to adapt the geometric variations of AUs. (2) Eliminate redundant context by modifying network structure. Extensive experiments on DISFA and CK+ datasets demonstrate that AU-DCN is superior to the state-of-the-art methods for facial AU detection.
Human Activity Recognition Using Deep Learning-Based Approach

There is a considerable demand for human activity recognition techniques in the area of human perception and also encompasses many other purposes like healthcare monitoring, assisted living for elders, and intelligent video surveillance.Rahman, Maruf Das, Tanuja There are different approaches to machine learning that have been adapted for the purpose of activity recognition. But these techniques depend heavily on hand-crafted feature extraction which is unable to perform well when dealing with complex scenarios. Deep learning techniques have great potential for human activity recognition. In this paper, a neural network (NN) based approach for classification and evaluation of human activities has been explored. In this method, a convolutional neural network (CNN) is put together with long short-term memory (LSTM). The dataset experimented in this system is the classic Human Activity Recognition (HAR) dataset for classifying the six human activities, viz., walking, walking-upstairs, walking-downstairs, sitting, standing, and laying. Results show that the proposed model is very efficient for recognizing human activity.
Research on Code Plagiarism Detection Based on Code Clone Detection Technologies

At present, assignment plagiarism is common in higher education at home and abroad especially in the coursework of computer majors. How to detect code plagiarism in programming course assignments effectively is a significant problem. The classic methods of code plagiarism detection mainly use the attribute or structure information to measure the similarity of source codes. In this dissertation, the definition and general process of clone plagiarism detection, different code clone detection technologies and the related tools are introduced.
A Literature review of Routing and Cloud Deployment Methodologies used in an Air Vehicle

We present a complete overview of routing protocols, routing algorithms, path planning, and cloud deployment for vehicle navigation in several fields of study in this article. In this article, we compare several approaches and algorithms with the goal of identifying the best feasible ones based on the type of application being utilized. In general, navigation of vehicles will be based on models and methods. Hence in this paper each characteristics are examined in detail and the research has been done accordingly. Under each characteristic, performance evaluation criteria are separately analysed. Questions are also provided for which the literature review serves as a form of discussion, according to the research challenge and criteria. For path planning, node-based as well as traditional algorithms are considered as the best choices. Similarly, the performance is significantly improved when using hybrid routing protocols and route planning methodologies that prefer graph based techniques. It has been observed that, a number of future research directions such as routing algorithm with queuing theory and path planning with critical link methods also serve the probable domains. This work is a concise comprehensive study of the various characteristics of a vehicle with respect to navigation. A comparison of techniques, algorithms and methods by using the standard performance criteria has also been elaborated.
The problem of identifying key semantic components in the abstracting of a scientific text

The ever-increasing volume of scientific publications determines the high interest of researchers in abstracting as one of the ways to present the content of the source text in a concise form. This determines the relevance of this work, which discusses the problem of transition from the primary text to its abstract presentation. In particular, using the example of an English-language scientific article on social psychology, an attempt is made to study the criteria for identifying key semantic components in the process of abstracting at the stage of understanding the source text. A theoretical review of scientific papers covering the problem of abstracting a scientific text, as well as a comparative analysis of the source and secondary texts of the article under consideration, allowed us to formulate the main patterns of interpretation of the logical-semantic and discursive structure of the source text as one of the stages of the abstracting process. It was found that in the process of understanding the text, the recipient, analyzing the connections between statements, integrates them into a holistic semantic formation, based on his own assumptions about the possible functional purpose of this text fragment. At the end of the process of understanding the text, the recipient deduces knowledge about the communicative intention of the author of the text and the role of each of the sections of the text in its implementation. It seems that this knowledge becomes the basis for the formation of a propositional model of the source text, which serves as the basis for creating the text of the abstract.
Application of Offset Averaging Filters to enhance the tracking information for small moving targets

Object tracking has become one of significant tasks in the area of computer vision, it has been widely applied for robot navigation and localization, automated surveillance, traffic monitoring, etc. vision sensors have been a favored choice for these tasks because of the simplicity of installation, cost effectiveness and the ability to operate in unstructured environments. However, obtaining accurate tracking information via vision sensors is not an easy task. In this paper, we deal with two types of tracking problems. The first is when a low resolution camera is used as a tracking system, and the second when the target object has different motion patterns, therefore, in order to cope with the aforementioned problems, a super-resolution technique, namely an Offset Averaging Filter (OAF) will be applied for improving the spatial resolution of images, it has previously proved its capability to enhance the spatial resolution up to four times. In this paper, an OAF technique will be used for enhancing the visual information about a target object moving in different motion patterns which are: translation, rotation and rotation plus translation. Moreover, a feature extraction algorithm, namely Two-Stage Circular Hough Transform (CHT) will be applied on the reconstructed images in order to detect and track the moving object. The results showed the capability of OAFs in improving the spatial resolution for the low resolution captured frames, and thus enhancing the visual tracking information for the small moving target objects with the three different motion scenarios.
Progressive Data Mining and Adaptive Weighted Multi-Model Ensemble for Vehicle Re-Identification

In this paper, we introduce our solution to the vehicle re-identification (vehicle ReID) track2 in AI City Challenge 2021. As the key point of intelligent Traffic System, vehicle ReID has been a challenging task due to the higher intra-class and inter-class errors which are owing to variable vehicle orientation, camera and lighting. To reduce this error, at first, we innovatively propose a progressive data mining method to obtain more valid data from testing set. Then, we use the image to the mean of each tracklet method in the matching stage which can ensure the precision of image matching by reducing the error with the information of tracklets. Besides, we propose an adaptive weighted ensemble method which effectively improve the model capability. Finally, our method achieves 0.6533 in the mAP score which yields 4th place in the competition.
An Energy-Efficient Hardware Accelerator for Hierarchical Deep Reinforcement Learning

Reinforcement Learning (RL) has shown great performance in solving sequential decision-making and control in dynamic environments problems. Despite its achievements, training Deep Neural Network (DNN) based RL is expensive in terms of time and power because of the large number of episodes required to train agents with high dimensional image representations. At the deployment also, the massive energy footprint of deep neural networks can be a major drawback. Embedded devices as the main deployment platform, are intrinsically resource-constrained and deploying DNN on them is challenging. Consequently, reducing the number of actions taken by the RL agent to learn desired policy, along with the development of efficient hardware architectures for RL is crucial. In this paper, we propose a novel hardware architecture for RL agents based on the learning hierarchical policies method. We show that hierarchical learning with several levels of control improves RL agents training efficiency and the agent converges faster compared to a none hierarchical model and therefore using less power. This is especially true as the environment becomes more complex with multiple objective sub-goals. Our method is important for efficient learning of policies for RL agent, especially when the target platform is a resource constraint embedded device. By performing a systematic neural network architecture search and hardware design space exploration, we implemented an energy-efficient scalable hardware accelerator for the hierarchical RL. Hardware factors of merit such as the latency, throughput, and energy consumption of the accelerator are evaluated with the various processing elements, and model parameters. The most energy-efficient configuration achieves 139 fps throughput with 5.8 mJ energy consumption per classification on Xilinx Artix-7 FPGA. Compared to similar works our design shows up to 3x better energy efficiency.