Reviewerly/exHarmony huggingface dataset:
Modality Representation Learning for Geometry Feature of Skeleton Based Human Action

Existing skeleton-based human action recognition has achieved enormous progress thanks to the accessible 3D skeleton capturing device and deep learning methods. One of the obstacles in action recognition lies in the cross-modal discrepancy when performing modalities fusion. Moreover, intra-modal variations in the same modality of different samples also inferior the representation learning of skeleton sequence. To address the issues, we designed five discriminative geometric representations as input of our approach. By considering the designed geometric representations as modalities, we introduce a Modality Feature Learning (MFL) framework based on Convolutional Neural Network, which is capable of automatically mines the relationships from modalities. The experimental results on the MSR Action3D dataset show that our approach achieves performance improvements over mainstream methods.
An Overview of Enabling Federated Learning over Wireless Networks

In this paper, we provide an overview of enabling federated learning (FL) techniques over wireless networks. More specifically, we present key techniques such as model compression, quantization and sparsification that increase the training accuracy of the distributed learning over the wireless medium. Next, the joint FL, resource allocation and scheduling approach is presented, which is identified in two types: a) both user and network assisted, and b) network assisted only. More specifically, the proposed FL-driven resource allocation and scheduling result in a joint optimization problem, where resource allocation and scheduling are jointly optimized. Finally, the simulation setup is described and the obtained simulation results are discussed, while several key enabling techniques are employed that further highlight the achievable performance of enabling FL over wireless networks in terms of training accuracy and loss.
Analyzing the Effects of Dimensionality Reduction for Unsupervised Domain Adaptation

Deep neural networks are extensively used for solving a variety of computer vision problems. However, in order for these networks to obtain good results, a large amount of data is necessary for training. In image classification, this training data consists of images and labels that indicate the class portrayed by each image. Obtaining this large labeled dataset is very time and resource consuming. Therefore, domain adaptation methods allow different, but semantic-related, datasets that are already labeled to be used during training, thus eliminating the labeling cost. In this work, the effects of embedding dimensionality reduction in a state-of-the-art domain adaptation method are analyzed. Furthermore, we experiment with a different approach that use the available data from all domains to compute the confidence of pseudo-labeled samples. We show through experiments in commonly used datasets that, in fact, the proposed modifications led to better results in the target domain in some scenarios.
Book Reader using Embedded Systems

Different types of technical readers have been experimented precisely for people with poor vision, but at present no designed project can identify text from challenging patterns and backgrounds found on many commercial products. In this fast paced life, many people who love reading are unable to do so due to the time constraints. Hence, with our project we also aim to help them by making it easier to read using a turning mechanism with Raspberry Pi as the main component and no type of interface.
A comparison of machine learning algorithms for forecasting solar irradiance in Eskişehir, Turkey

This work compares the efficiency of 45 different machine learning (ML) algorithms to provide a comprehensive and most accurate model for global horizontal solar irradiance (GHSI) prediction in Eskişehir, Turkey. The dataset is provided by NASA Prediction of Worldwide Energy Resource (POWER) as satellite data that involves some characteristic weather condition variables such as temperature, precipitation, humidity etc. over 35 years. Some ML algorithms such as Extra Trees, LightGBM, HistGB, Random Forest (RF), Bagging and Decision Tree exhibit better performance among the others with commonly used statistical evaluation metrics in literature such as coefficient of determination (R²), root mean squared error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). In addition, Extra Tress regression slightly outperformed the rest of ensemble learning methods with R² of 0.99, RMSE of 8.05, MAE of 5.67, MAPE of 4%. Finally, the outcome demonstrates that the ML algorithms belonging to ensemble learning family achieved great results in GHSI prediction at specific location.
BLSTM-based source code vulnerability detection visualization system

A visualization system with source code vulnerability detection is constructed based on the deep learning BLSTM model. The system firstly has the static analysis and slicing capabilities of the vulnerability statement, and then based on the deep learning model of the recurrent neural network and the attention mechanism technology, it can learn from the vulnerability data set the characteristics of the vulnerability and the realization of vulnerability detection. Firstly, the system analyzes the source code through the deployed open-source static analysis tool Joern according to the code to be tested, and stores the generated code attribute graph in the Neo4J graph database, then slices the program code according to the API call. The sliced code is treated as a text, which is mapped into a vector using word embedding technology, and finally input into the BLSTM model to determine whether there is vulnerability in the code under test. Through the visual interface, the system can interact with the user, and the user can independently select and train the model. Through experimental analysis and testing, the system has achieved good results.
DMM-GAPBS: Adapting the GAP Benchmark Suite to a Distributed Memory Model

Due to the ability of graphs to model diverse real-world scenarios such as social networks, roads, or biological networks, effective graph processing techniques are of critical importance to a wide array of fields. As a consequence of the growth of data volumes, some graphs have already outgrown the memory capacities of single servers. In such cases, it is desirable to partition and keep the entire graph in a distributed memory space into order to bring the resources of a computing cluster to bear on the problem. This approach introduces a number of challenges, such as communication bottlenecks and low hardware utilization. However, it is difficult to effectively measure the impact of innovations addressing these challenges due to a lack of standardization in the domain of distributed graph processing. This research study was inspired by, and builds off of, the widely-used GAP Benchmark Suite (GAPBS), which was developed to provide an effective baseline and consistent set of evaluation methodologies for shared memory multiprocessor graph processing systems. We design and develop a new benchmark suite called DMM-GAPBS, a distributed-memory-model GAPBS. We adapt the GAPBS graph building infrastructure and algorithms, but utilize OpenSHMEM to enable a distributed memory environment, in the hope of providing a modular, extensible baseline for the distributed graph processing community. In order to showcase our design and implementation for processing graphs that cannot fit within a single server, we present the results of executing the DMM-GAPBS benchmark kernels on two large synthetic graphs distributed across sixteen nodes of an enterprise class system.
An Analysis of Gaze and Operation Differences of FPS Beginners and Experts

Previous studies have shown that in FPS games, the weaker players will feel discouraged, and the stronger players may be bored by the lack of challenges. Our preliminary survey suggests that players of different levels differ vastly in their ability to aim targets and reacting capability, which are particularly prominent in beginners. However, the existing training software touches merely upon tutoring players to react and to aim, without giving beginners any knack of play. Providing appropriate support for beginners may reduce the competitive, interesting and ornamental nature of the game. Therefore, in this study, our goal is to design a piece of software that trains the judgement of game players and cultivates their operational habits quickly. As the first step of our goal, we verify the operational differences between beginners and experts of FPS games in this paper.
Emotion Recognition of Textual Session Record Based on the Voice Dialogue System of Reminiscence Therapy

For this research, we propose a reminiscence therapy dialogue system with an emotion presumption function and demonstrate the effectiveness of our method experimentally. Reminiscence therapy is a technique used to remind patients of their past through conversation. The emotion analysis function of the reminiscence therapy dialogue system can infer the psychological changes of users more effectively and can adjust the dialogue contents in time to achieve reminiscence therapy with better effects.
DERAINIZER for Unpaired Single Image De-raining

Rain streaks are known to cause many performance or accuracy reductions in many computer vision systems. The problem of obstructing rain streaks is addressed within the domain of “De-raining”. De-raining is the process of removing rain streaks. It has two main sub-categories which are video de-raining and single image de-raining. Many works on single image de-raining have taken place using both traditional approaches as well as deep learning approaches. This paper does a critical analysis of credible, novel, and best performing single image de-raining systems. Also, the paper addresses an unpaired training gap which exists within the domain with the novel de-raining system ”DERAINIZER”, enabling all future researchers to employ unpaired training data to train their models. It is followed by results, research limitations and possible future works.
Automated Grading of Breast Cancer Histopathology Images Using Multilayered Autoencoder

Breast cancer (BC) is the most widely recognized cancer in women worldwide. By 2018, 627,000 women had died of breast cancer (World Health Organization Report 2018). To diagnose BC, the evaluation of tumours is achieved by analysis of histological specimens. At present, the Nottingham Bloom Richardson framework is the least expensive approach used to grade BC aggressiveness. Pathologists contemplate three elements, 1. mitotic count, 2. gland formation, and 3. nuclear atypia, which is a laborious process that witness's variations in expert's opinions. Recently, some algorithms have been proposed for the detection of mitotic cells, but nuclear atypia in breast cancer histopathology has not received much consideration. Nuclear atypia analysis is performed not only to grade BC but also to provide critical information in the discrimination of normal breast, non-invasive breast (usual ductal hyperplasia, atypical ductal hyperplasia) and pre-invasive breast (ductal carcinoma in situ) and invasive breast lesions. We proposed a deep-stacked multi-layer autoencoder ensemble with a softmax layer for the feature extraction and classification process. The classification results show the value of the multi-layer autoencoder model in the evaluation of nuclear polymorphisms. The proposed method has indicated promising results, making them more fit in breast cancer grading.
Modern techniques for decentralized key establishment in symmetric cryptographic systems

Strength of the symmetric cryptographic systems, in addition to the quality of the applied cryptographic algorithm, is based on the quality and secrecy of the used cryptographic key. The generation and distribution of cryptographic keys is a bottleneck in the exploitation of symmetric cryptographic systems in mass network communications. This paper presents two modern concepts for cryptographic key establishing in symmetric cryptographic systems. Both concepts provide solutions for the decentralized establishment of cryptographic keys in symmetric cryptographic systems without the involvement of a trusted third party. Cryptographic keys generated by the systems based on these concepts possess cryptographically good statistical characteristics. From the security point of view, keys generated by different concepts belong to different security classes. Some results of related projects in which the authors participate are highlighted, as well as some open questions in this area.
A Method for Fast Outlier Detection in High Dimensional Database Log

An easy to implement and effective outlier detection method is proposed in this paper, which is a two-stage process combining the kd-tree structure and the Isolation Forest (Forest) method. We use kd-tree to split high dimensional data into groups, and then apply Forest to each group to calculate anomaly scores which help to identify outliers. This method is fast since it decides anomaly on groups of a dataset instead of the whole dataset, meanwhile the accuracy is assured by Forest. We tested our method with synthetic and real-world data set to illustrates its application to data base access logs.
A pluggable multi-smart contract engine

Blockchain technology is a decentralized, non-tamperable distributed ledger technology, based on blockchain smart contracts, running on the blockchain system, driven by events and capable of automatically executing trustless according to preset conditions agreement between parties. Most of the existing blockchain architectures only support a single smart contract engine, which makes it costly for users to learn smart contracts and cannot quickly switch from familiar contract programming. At the same time, it also affects the versatility of the chain architecture. In response to the above problems, this paper designs a pluggable multi-smart contract engine. The engine has three major characteristics. One is the access to multiple contract virtual machines with engine adapters as the core module, and a standardized engine interface is provided. Developers only need to implement the corresponding functions according to the interface description to complete the access; the second is pluggability, which supports the access and removal of smart contract virtual machines, which is convenient for system management and also facilitates the performance of the contract engine; The third is the engine interface module. Through the design of the interface, the management of a certain contract machine is simplified and the application is easier to implement.
Impact of Implementing Data Balancing Method in Intelligent Thyroid Cancer Detection

Recently, artificial intelligence became an alternative powerful solution in medical cases that assist the medical personnel to be a second opinion in making diagnosis decisions. In thyroid cancer cases, the development of an intelligent system offered valuable benefits since thyroid examination procedures highly depended on the medical personnel skills and experiences. However, the number of training data still became a challenge in the development of the intelligent system. In this study, we developed an intelligent classification method for thyroid cancer completed with a data balancing method. Our proposed solution aimed to maintain the model performance in a small dataset. Our proposed solution successfully increased the classification performance with an average increasing performance of more than 60%. While, the highest accuracy was 87.20%. This experimental result indicated that implementing data balancing method can significantly increase the classification performance.
Super-Resolution Reconstruction of Remote Sensing Images Based on GAN

In recent years, the methods of super-resolution image reconstruction that based on deep learning have become a hot topic in research of computer vision. The methods of super-resolution image reconstruction that based on the Generative Adversarial Network (GAN) are not controlled in network generation, the models are easy to collapse, the generalization ability is undesirable, and the time complexity degree is too high. To fill these gaps, we propose a super-resolution image reconstruction method based on the GAN of encoding and decoding, which improves the quality of image reconstruction. First of all, our approach uses a design network with regularized structure to avoid model collapse. Then we build a generation network structure that based on encoding and decoding to suppress the uncontrollable defects of GAN network generated images. Finally, in the last layer of the generator, $\mathrm{N}^{\star}\mathrm{N}$ convolutional feature layer is included to replace the Softmax layer, which speeds up the training of the model. The experimental results show that the super-resolution remote sensing image reconstructed by the proposed method has higher reconstruction quality and better generalization ability in the DOTA training data sets. At the same time, the image reconstruction process can take much less time.
Recognition of Gestures over Textiles with Acoustic Signatures

We demonstrate a method capable of turning textured surfaces (in particular textile patches) into opportunistic input interfaces thanks to a machine learning model pre-trained on acoustic signals generated by scratching different fabrics. A single short audio recording is then sufficient to characterize both a gesture and the textures substrate. The sensing method does not require intervention on the fabric (no special coating, additional sensors or wires). It is passive (no acoustic or RF signal injected) and works well using regular microphones, such as those embedded in smartphones. Our prototype yields 93.86% accuracy on simultaneous texture/gesture recognition on a test matrix formed by eight textures and eight gestures as long as the microphone is close enough (e.g. under the fabric), or when the patch is attached to a solid body transmitting sound waves. Preliminary results also show that the system recognizes the manipulation of Velcro straps, zippers, or the taping or scratching of plastic cloth buttons over the air when the microphone is in personal space. This research paves the way for a fruitful collaboration between wearables researchers and fashion designers that could lead to an interaction dictionary for common textile patterns or guidelines for the design of signature-robust stitched patches not compromising aesthetic elements.
Not all Kripke models of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:mi mathvariant="sans-serif">HA</mml:mi></mml:math> are locally <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si2.svg"><mml:mi mathvariant="sans-serif">PA</mml:mi></mml:math>

Let K be an arbitrary Kripke model of Heyting Arithmetic, HA. For every node k in K, we can view the classical structure of k, Mk as a model of some classical theory of arithmetic. Let T be a classical theory in the language of arithmetic. We say K is locally T, iff for every k in K, Mk⊨T. One of the most important problems in the model theory of HA is the following question: Is every Kripke model of HA locally PA? We answer this question negatively. We introduce two new Kripke model constructions to this end. The first construction actually characterizes the arithmetical structures that can be the root of a Kripke model K⊩HA+ECT0 (ECT0 stands for Extended Church Thesis). The characterization says that for every arithmetical structure M, there exists a rooted Kripke model K⊩HA+ECT0 with the root r such that Mr=M iff M⊨ThΠ2(PA). One of the consequences of this characterization is that there is a rooted Kripke model K⊩HA+ECT0 with the root r such that Mr⊭IΔ1 and hence K is not even locally IΔ1. The second Kripke model construction is an implicit way of doing the first construction which works for any reasonable consistent intuitionistic arithmetical theory T with a recursively enumerable set of axioms that has the existence property. We get a sufficient condition from this construction that describes when for an arithmetical structure M, there exists a rooted Kripke model K⊩T with the root r such that Mr=M. As applications of this sufficient condition, we construct two new Kripke models. The first one is a Kripke model K⊩HA+¬θ+MP (θ is an instance of ECT0 and MP is Markov's principle) which is not locally IΔ1. The second one is a Kripke model K⊩HA such that K forces exactly the sentences that are provable from HA, but it is not locally IΔ1. Also, we will prove that every countable Kripke model of intuitionistic first-order logic can be transformed into another Kripke model with the full infinite binary tree as the Kripke frame such that both Kripke models force the same sentences. So with the previous result, there is a binary Kripke model K of HA such that K is not locally IΔ1.
A Real Time Automatic Composition Algorithm for Mobile Terminal

Photo composition is one of the most basic skills in photography and image processing. Different composition methods often lead to different perceptual effects. Different application scenarios need different composition methods. For the scenes that need to extract main information such as website blog, good composition method is to select the most important information in the image, and for photography and other application scenes, good composition method is to keep the aesthetic feeling of the image and enhance the expressive power. In view of the need to keep the main body in composition and reduce the background area, this paper proposes an automatic composition algorithm based on lightweight salient object detection model and minimum information loss. The test is carried out on the CUHK composition data set and the Flick cropping composition data set. The IOU, model size and model running time are used as quantitative evaluation indexes, Compared with the mainstream aesthetic composition model, the experimental results show that the proposed method can achieve the same performance as the mainstream aesthetic based composition model, while the operation time is only one tenth of the aesthetic model, the model volume is only one percent of the aesthetic model, and the composition results are more stable, which is very suitable for the mobile terminal real-time automatic composition algorithm.
Using Text Semantic Mining to Calculate Emotional Heat of Social Network Nodes

Social network is an important channel for users to vent their emotions, such as microblog community. This paper proposes a method to calculate the emotional heat of social network nodes based on text sentiment analysis and text classification model. Firstly, the sentiment tendency of the text content is analyzed by sentiment dictionary and semantic analysis, and the emotional value of each text content is calculated. Secondly, the text classification model is used to distinguish the different text topics of each node. And finally, the weighted algorithm is used to calculate the comprehensive evaluation value of different topic texts published by the node as the emotional heat. Taking the microblog community as an example, we use this method to collect text information published by social network nodes. The results show that integration of sentiment analysis, topic analysis, semantic analysis of text mining is conducive to the intuitive display of users' emotions in social networks, and helps regulators guide netizens' emotions.