Reviewerly/exHarmony huggingface dataset:
A Study on User Experience of Smart Glasses for Higher Education Students

This paper reports a study on the User experience (UX) of STEM (e.g., Science, Technology, Engineering, and Mathematics) students using smart glasses and an interactive Augmented Reality (AR) educational app. The results show that the AR app provides a good UX, despite the presence of some form factor issues. Students’ usability ratings for both the AR app and the smart glasses, are positively correlated with the students’ perceived learning. The results of this study can be used as a guideline to design and develop further immersive e-learning technologies.
Cryptographic mixing transformations and message embedding in chaotic systems

The paper focuses on mixing functions for cryptographic applications, pursued in the context of including the message in a dynamic system. The contribution of the dynamic system to the mixing function when the message is included in the dynamic system is highlighted. For the discussion we chose three popular dynamic systems - the Hénon map, the logistic map and the tent map - and we used the mixing functions suggested by C.E. Shannon. The analyzed aspects are illustrated on images, but the applicability is much wider.
Deep Learning-based Object Searching and Reporting for Aerial Surveillance Systems

Multi-class aircraft recognition is important in aerial surveillance applications to make consistent proposals for decision makers. Motivated by the state-of-art object detection methods for aerial sensing images, we explored and achieved satisfactory results based on standard YOLO archi-tectures by providing an analysis of certain aerial scenarios, from chained to scattered or yin-yang pairs objects and model's performance comparison on a typical wild emergency situation. First, we tackle the scarcity of multi-class aircraft detection data by developing of a 50 cm GSD dataset. Then, applying different training strategies and using a series of good practices, we achieved a F1-score of 0.820 and a con-siderable mAP@50 of 0.809 for the more difficult detection class. Experiments have been conducted over a dataset from Google Earth platform. Thus, our final proposal for aerial surveillance systems contains the pre-trained YOLOv5×6 architecture with attention on performance maximization for multiple specific aerial scenarios, processing 50km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> in no more than 6 seconds.
An e-Business Services Composition Framework

E-business offers to economic agents the opportunity to increase the efficiency of their activity, promote and offer their products and services on a large and diversified market. Often a single service, separately, does not meet all the needs of various categories and groups of customers. In such a situation, service composition can provide more technical, commercial, informational facilities than each separate component service can offer. There are many web-based frameworks used for e-business, but still with limitations. The paper presents a Services Composition Framework and a wise algorithm for e-business models, based on REST and SOAP web services. It is intended to be used for the development of e-business models, comprising stock market exchange, e-commerce, e-banking, etc. The customers can access the framework services, through Smartphones, on mobile and wireless communication channels. Within a Quality-of-Service performance analysis of the e-business Service Composition Framework, the response time and throughput for multiuser access simultaneous requests were evaluated. A stock market exchange service is considered, as an e-business service example, while Bucharest Stock Exchange information is the digital content source for data transactions.
Cloud Computing Security and Future

Cloud computing has grown tremendously over the past few years. It creates new ways for information to be stored and processed which bring the growing problem of security. The question at hand is what makes the cloud safe and secure as well as the challenges involved in the process. As modern technology emerges so do the new challenges and issues that come along with this progression. For now, the cloud is highly reliable, provides incredible scalability, and an easy-to-use system. This paper will discuss what cloud computing is, how information is stored, the security behind the scenes, any improvements that can be made, and what the cloud's future looks like.
Reuse of Client Models in Federated Learning

Federated Learning (FL) has attracted a lot of attention from both academia and industry. In FL, user data no more need to be transmitted to the data center and each client device trains the deep model using its personal data so as to protect user privacy from being revealed. There exists two kinds of FL architecture, cloud based and edge based. Researchers have proposed the client-edge-cloud hierarchical FL system combining their advantage together to take the full advantage and avoid the defects. To improve the utilization ratio of local model parameters and data, so that enhance every single edge model accuracy, and ultimately achieve a more outstanding global model, we propose an algorithm Client Model Multiple Access (CMMA). CMMA allows clients associate with a set of edge servers, and uploads its training results to all servers in the set. That said, the local model of one client is reused by multiple edge servers. Such reuse can improve model performance particularly when the number of clients is small. Empirical experiments demonstrate the superiority of our scheme in different datasets, CNN models and data distributions. The results have not just shown the general suppress CMMA against hierarchical FL, but also validate the advantage of CMMA guaranteeing global model accuracy in unstable network or short of clients dilemma.
Bringing Energy into Utility-Privacy Tradeoff in IoT

With billions of the Internet of Things (IoT) world-wide, the proliferation of IoT technologies will increase the global energy footprint. To date, much of the focus has been on addressing privacy exposure with approaches that remove sensitive information and, at the same time, ensure that users still derive utility from the data. In this paper, we bring the energy aspects of privatizers into the modeling and analysis of utility-privacy (UP) tradeoffs in IoT. We present a framework that enables users to examine (a) the energy cost of privatizing data, (b) the effect on the utility of the data, and (c) the privacy achieved. We evaluate our model with audio and image data and show that we can reduce energy consumption while retaining the user's chosen UP tradeoff point.
CoDEm: Conditional Domain Embeddings for Scalable Human Activity Recognition

We explore the effect of auxiliary labels in improving the classification accuracy of wearable sensor-based human activity recognition (HAR) systems, which are primarily trained with the supervision of the activity labels (e.g. running, walking, jumping). Supplemental meta-data are often available during the data collection process such as body positions of the wearable sensors, subjects' demographic information (e.g. gender, age), and the type of wearable used (e.g. smartphone, smart-watch). This information, while not directly related to the activity classification task, can nonetheless provide auxiliary supervision and has the potential to significantly improve the HAR accuracy by providing extra guidance on how to handle the introduced sample heterogeneity from the change in domains (i.e positions, persons, or sensors), especially in the presence of limited activity labels. However, integrating such meta-data information in the classification pipeline is non-trivial - (i) the complex interaction between the activity and domain label space is hard to capture with a simple multi-task and/or adversarial learning setup, (ii) meta-data and activity labels might not be simultaneously available for all collected samples. To address these issues, we propose a novel framework Conditional Domain Embeddings (CoDEm). From the available unlabeled raw samples and their domain meta-data, we first learn a set of domain embeddings using a contrastive learning methodology to handle inter-domain variability and inter-domain similarity. To classify the activities, CoDEm then learns the label embeddings in a contrastive fashion, conditioned on domain embeddings with a novel attention mechanism, enforcing the model to learn the complex domain-activity relationships. We extensively evaluate CoDEm in three benchmark datasets against a number of multi-task and adversarial learning baselines and achieve state-of-the-art nerformance in each avenue.
Account Takeover Detection on E-Commerce Platforms

Account takeover is a type of malicious attack where a fraudster steals accounts and passwords from normal users, causing the loss of money and the exposure of personal information. Existing solutions either rely on extensive manual labeling, or require behavior sequences and context graphs of accounts. In this paper, I propose a Siamese neural Network-based Multi-Relation Graph Embedding method (MRGE-SiameseNet) to detect stolen accounts. The key idea of MRGE-SiameseNet is that the two inputs from the same users are similar. I adopt the idea of the siamese neural network to judge whether two different inputs are from the same user. To get a powerful representation of each account, I integrate several embeddings of multiple relationships of accounts and profile feature embedding for each account with multi-head attention mechanism. The fully connected module is employed to obtain the similarity score, which can be utilized to identify whether the account is stolen by account takeover.
SpecTextor: End-to-End Attention-based Mechanism for Dense Text Generation in Sports Journalism

Language-guided smart systems can help to design next-generation human-machine interactive applications. The dense text description is one of the research areas where systems learn the semantic knowledge and visual features of each video frame and map them to describe the video's most relevant subjects and events. In this paper, we consider untrimmed sports videos as our case study. Generating dense descriptions in the sports domain to supplement journalistic works without relying on commentators and experts requires more investigation. Motivated by this, we propose an end-to-end automated text-generator, SpecTextor, that learns the semantic features from untrimmed videos of sports games and generates associated descriptive texts. The proposed approach considers the video as a sequence of frames and sequentially generates words. After splitting videos into frames, we use a pre-trained VGG-16 model for feature extraction and encoding the video frames. With these encoded frames, we posit a Long Short-Term Memory (LSTM) based attention-decoder pipeline that leverages soft-attention mechanism to map the semantic features with relevant textual descriptions to generate the explanation of the game. Because developing a comprehensive description of the game warrants training on a set of dense time-stamped captions, we leverage two available public datasets: ActivityNet Captions and Microsoft Video Description. In addition, we utilized two different decoding algorithms: beam search and greedy search and computed two evaluation metrics: BLEU and METEOR scores.
Random Matrix-Based Group Target Tracking Using Nonlinear Measurement

Random matrix (RM) can estimate the dynamic and extension state jointly for group target (GT) tracking. In this paper, to improve the tracking performance for the rotation, the rotation matrix is added to the original random matrix in group target tracking algorithm based on variational Bayesian (VB) approach for nonlinear measurement. Different from the existing method, the model of nonlinear measurement is established, where the prediction and update process are given. Simulation results show that the proposed method have better performance in the estimation of group state and extension than the original method.
Text Matching Model with Multi-granularity Term Alignment

Text matching is one of the fundamental research tasks in the field of natural language processing. It can be applied to a large number of NLP tasks, such as information retrieval, question, and answer systems and text repetition. In this paper, we propose a text-matching model with multi-granularity term alignment (MGTA). The model extracts word information at different granularities through convolutional neural networks and enhances the model effect by aligning the original location features at different word granularities, enabling the model to obtain multiple granularities of information during text matching. We conduct experiments on the Q&A dataset, the text-implication dataset, and the paraphrase recognition dataset, respectively, and compare them with current mainstream models in terms of accuracy, MAP and MRR evaluation metrics, and has fewer parameters, which greatly improves the inference speed.
A General Framework for Finding the Optimal Imbalance Ratio in Sampling Methods

How to obtain better classification results from imbalance data has always been a research hot spot in the neighborhood of machine learning and data mining. At present, there are many techniques such as sampling and cost-sensitive learning algorithms to reduce the negative impact of imbalance on classification performance. Some scholars start with the relationship between imbalance ratio and classification performance, hoping to improve classification performance. In this paper, the classification performance is mainly improved by improving the sampling method. Considering that many invalid samples may be synthesized, this paper defines a metric of distribution difference in the sampling process. Then, by analyzing the relationship between the distribution difference and the classification performance, the optimal imbalance ratio in the sampling process can be found. Based on some classic general sampling methods, experimental results on some real data sets prove the effectiveness of the framework.
Research on Human Pose Estimation

We propose a convolutional neural network to estimate human pose. Firstly, a lightweight image scaling network is designed to scale the input image to a standard size image. This network would effectively suppress image feature loss caused by traditional interpolation method. Secondly, non-local convolution operator is introduced into the convolutional neural network, thereby increasing the receptive field. Finally, the resolution attention mechanism is introduced in the process of multiresolution feature fusion to improve the scale invariance of the network. Experimental results show that the scale invariance and accuracy for the proposed network is improved effectively.
A Lightweight Scalable Blockchain Architecture for IoT Devices

With the development of Internet of Things (IoT) technology, the transaction behavior of IoT devices has gradually increased, which also brings the problem of transaction data security and transaction processing efficiency. As one of the research hotspots in the field of data security, blockchain technology has been widely applied in the maintenance of transaction records and the construction of financial payment systems. However, the proportion of microtransactions in the Internet of Things poses challenges to the coupling of blockchain and IoT devices. This paper proposes a three-party scalable architecture based on “IoT device-edge server-blockchain”. In view of the characteristics of micropayment, the verification mechanism of the execution results of the off-chain transaction is designed, and the bridge node is designed in the off-chain architecture, which ensures the finality of the blockchain to the transaction. According to system evaluation, this scalable architecture improves the processing efficiency of micropayments on blockchain, while ensuring its decentration equal to that of blockchain. Compared with other blockchain-based IoT device payment schemes, our architecture is more excellent in activity.
Inspection of Steel Strip Surface Defects Based on Visual Attention Mechanism

Aiming at the low contrast and small defects on the surface of steel strip, a defect detection method based on human visual attention mechanism is proposed. On the one hand, the improved Itti model is used to obtain the bottom-up saliency map, that is, the Gaussian pyramids of intensity, orientations and edges are constructed by multi-scale filtering of the image, and the “center-surround differences” operation and normalization are used to form the intensity, orientations and edges conspicuity maps. Then, each kind of conspicuity maps is combined into bottom-up saliency maps by weighting method. On the other hand, the multi-channel and high-level features are extracted by convolutional neural network (CNN), and the high-level saliency map is formed by fusion and up sampling. Next, the comprehensive saliency maps are generated by weighting bottom-up saliency maps and high-level saliency maps, so as to realize the comprehensive fusion of image features. The results show that this method has good effect on low contrast and small defects on the surface of steel strip.
Road Target Detection Method Based on Improved Tiny-YOLOV3

In this paper, the Tiny-YOLOV3 algorithm has made improvements to the detection speed and accuracy of vehicles and pedestrians on the road. On the basis of TinyYOLOV3, the model is compressed by reducing the size of the convolution kernel of each convolution layer; the feature extraction ability of the model is improved by deepening the depth of the model, and the detection of small targets is enhanced by adding feature fusion modules. In the first layer of the yolo layer, the Spatial pyramid pooling module is used to eliminate the effect of inconsistent effective feature information caused by the large difference of road targets, and the loss function is adjusted according to the condition of road vehicles to reduce the false detection rate. According to the experimental results, the size of the improved model is only 1.8M, and the average accuracy reaches 67.8%, which is 7.7% higher than Tiny-YOLOV3.
Image Super-Resolution Method Based on Improved Generative Adversarial Network

The image super-resolution algorithm based on generative adversarial network has the problem of imperfect extraction of detailed texture features, and the network is difficult to converge in the training process. Therefore, a new image super-resolution method: MR-SRGAN (Multi-branch receptive field dense block improved super-resolution generative adversarial network) is proposed. MR-SRGAN extracts image detail texture features through the new MBRS residual block and MRB module, then adjusts the loss function of the model to make the training easier to converge. Finally, in the super-resolution comparison experiment on Set5 and Set14 datasets, under the magnification of 2 and 4 times, the PSNR value of this algorithm is 0.36dB (Set $5 \times 2)$, 0.15dB (Set $14 \times 2)$, 0.63dB (Set $5 \times 4)$, 0.57dB (Set $14 \times 4)$ higher than SRGAN algorithm.
Model Aplikasi Sistem Appraisal pada Kantor Jasa Penilai Publik

&lt;p&gt;&lt;strong&gt;Abstrak.&lt;/strong&gt; Kantor Jasa Penilai Publik berperan penting dalam menentukan nilai ekonomis sebuah aset atau properti. Penilaian yang dilakukan oleh perusahaan KJPP (Kantor Jasa Penilai Publik) selama ini dilakukan dengan manual di nilai kurang efisien. Pembuatan Aplikasi Sistem Appraisal berbasis komputer bertujuan untuk membantu dalam penentuan nilai, identifikasi properti, analisa data dan rekonsiliasi. Harapannya dapat mempercepat proses pembuatan laporan serta mencegah terjadinya kesalahan rumus dan mengurangi manipulasi data. Model pengembangan sistem mengikuti tahapan-tahapan dalam model &lt;em&gt;Systems Development Life Cycle&lt;/em&gt;, sedangkan teknik pengumpulan data menggunakan metode &lt;em&gt;field research&lt;/em&gt; yang dilakukan secara langsung di lapangan dengan teknik observasi dan wawancara. Hasil penelitian menunjukkan sistem aplikasi ini mampu mempermudah dalam melakukan proses appraisal yang lebih akurat, cepat dan menghindari kesalahan rumus dan manipulasi data.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Kata kunci&lt;/strong&gt;&lt;strong&gt;:&lt;/strong&gt; &lt;em&gt;Sistem Informasi; Penilaian Properti; Penilaian aset;&lt;/em&gt;&lt;em&gt;&lt;/em&gt;&lt;em&gt;Systems Development Life Cycle&lt;/em&gt;&lt;em&gt;&lt;/em&gt;&lt;/p&gt;&lt;p align="center"&gt;&lt;strong&gt;&lt;em&gt; &lt;/em&gt;&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;em&gt;&lt;strong&gt;Abstract.&lt;/strong&gt; The Public Appraisal Service Office has an important role in determining the economic value of an aset or properti. The assessment carried out by the KJPP (Public Appraisal Service Office) company so far has been carried out manually, which is considered less efficient. Development of a computer-based Appraisal System Application aims to assist in determining values, identifying porperty, data analysis and reconciliation. The hope is that it can speed up the reporting process and prevent formula errors and reduce data manipulation. The systems development model follows the stages in the Systems Development Life Cycle model, while &lt;/em&gt;&lt;em&gt;t&lt;/em&gt;&lt;em&gt;he data collection technique used the field research method which was carried out directly in the field with observation and interview techniques. The results show that this application system makes it easier to carry out an appraisal process that is more accurate, faster and avoids formula errors and data manipulation.&lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;&lt;em&gt;Keywords&lt;/em&gt;&lt;/strong&gt;&lt;strong&gt;&lt;em&gt;:&lt;/em&gt;&lt;/strong&gt;&lt;em&gt; &lt;/em&gt;&lt;em&gt;Information System; &lt;/em&gt;&lt;em&gt;Properti Valuation;&lt;/em&gt;&lt;em&gt; &lt;/em&gt;&lt;em&gt;Aset Valuation; &lt;/em&gt;&lt;em&gt;Systems Development Life Cycle&lt;/em&gt;&lt;em&gt;&lt;/em&gt;&lt;/p&gt;
Actualizing Affordances of Audiobooks for Elective Readings in a University Course

This study explored actualization of affordances of audiobooks for mobile learning, as well as constraints encountered by students engaging with elective readings in a university course. Participants often tried learning in different situations before settling on a preferred learning practice. Students reported having listened on-the-go as well as in their traditional learning spaces. Some students took advantage of hands-free, eyes-free, and screen-free listening along another task, such as a commute, note-taking, or a sports activity. Participants also leveraged audiobooks for incidental and intentional learning of second language (L2). Co-listening was identified as an affordance stemming from unique properties of audiobooks and students' initiative. Some students combined listening and reading to address challenges related to mLearning in L2. Despite having had little or no prior experience of learning with audiobooks, most participants discovered new ways to learn - using audiobooks to customize their mobile learning practice.