Reviewerly/exHarmony huggingface dataset:
Decision Analysis and Prediction Based on Credit Card Fraud Data

With the common use of credit cards in today's transactions, the related fraudulent behavior inevitably occurs and causes considerable loss of money. To solve this problem, our work used a dataset that contains legal credit card transactions as well as fraud transactions to find an effective solution. In this paper, through processing and analyzing the transaction data, the data was discovered to be unbalanced, so stratified sampling and oversampling were performed to achieve a more reliable analysis of the unbalanced dataset. Meanwhile, due to the randomness of sampling, the cross-validated were used for the final model selection. Further, we utilized five algorithms to build models which contains statistical machine learning model and deep learning model. To obtain optimal performance of the models, hyperparameter tuning was performed for the five classifiers. Finally, the results indicate that the optimal model was XGBoost, and its performance can be verified in a real-life scenario in the future.
A Novel, Oriented to Graphs Model of Robot Arm Dynamics

Robotics is an interdisciplinary field and there exist several well-known approaches to represent the dynamics model of a robot arm. The robot arm is an open kinematic chain of links connected through rotational and translational joints. In the general case, it is very difficult to obtain explicit expressions for the forces and the torques in the equations where the driving torques of the actuators produce desired motion of the gripper. The robot arm control depends significantly on the accuracy of the dynamic model. In the existing literature, the complexity of the dynamic model is reduced by linearization techniques or techniques like machine learning for the identification of unmodelled dynamics. This paper proposes a novel approach for deriving the equations of motion and the actuator torques of a robot arm with an arbitrary number of joints. The proposed approach for obtaining the dynamic model in closed form employs graph theory and the orthogonality principle, a powerful concept that serves as a generalization for the law of conservation of energy. The application of this approach is demonstrated using a 3D-printed planar robot arm with three degrees of freedom. Computer experiments for this robot are executed to validate the dynamic characteristics of the mathematical model of motion obtained by the application of the proposed approach. The results from the experiments are visualized and discussed in detail.
Features Extraction for Live Streaming Video Classification with Deep and Convolutional Neural Networks

These days, the high-quality live video streaming platforms are very widely used by lot of people all over the world. The demand for such services has increased even more in current pandemic conditions, so it is important to maintain certain level of Quality of Service for live streaming video. This article aims at pre-processing live streaming video-traffic packets for Deep Learning application of classification of different quality mode (Resolution and framerate) based only on statistical properties of network traffic Internet Protocol (IP) packets flow. The traffic intensity is used as a main traffic type descriptor with some additional transforms. The article presents some of classification accuracy analysis depending on multiple factors, such as: type of traffic pre-processing, length of traffic window, traffic intensity sampling time. The results show that there is a set of such parameters that leads to the best classification accuracy. This way, it may be possible for content operators (platforms) to provide differentiated services for their users. According to testing results, for Deep Learning application the direct traffic intensity samples allows achieving good accuracy of 94% and higher without any additional transforms. This, of course, depends on the classifier model parameters, and article provides insight into them as well.
Few-Shots Learning for Fine-Grained Vehicle Model Recognition

Recent vehicle recognition models have focused on using conventional Deep Neural Network approach to solve the problem. The most important limitation for such approach is the scalability issue. They require hundreds or even thousands of image samples per class to train effective models. However, new vehicle models become available yearly, which imposes constraints to source for hundreds of image samples for every new vehicle model and retrain the recognition algorithms on the entire dataset to introduce recognition capability for new classes. This process is data hungry, tedious, computationally intensive, and time consuming. To mitigate this issue, we propose using few-shots learning approach which allows for model scalability to new unseen classes with as few as 1 to 20 image samples per class rather than hundreds. In this paper, we introduce RelationNet++, an improved version of Relation Network, for recognition with few-shots-learning. Our experimental results show that the proposed model outperforms current state-of-the-art few-shot learning methods on the vehicle recognition dataset.
Actions, not gestures: contextualising embodied controller interactions in immersive virtual reality

Modern immersive virtual reality (IVR) often uses embodied controllers for interacting with virtual objects. However, it is not clear how we should conceptualise these interactions. They could be considered either gestures, as there is no interaction with a physical object; or as actions, given that there is object manipulation, even if it is virtual. This distinction is important, as literature has shown that in the physical world, action-enabled and gesture-enabled learning produce distinct cognitive outcomes. This study attempts to understand whether sensorimotor-embodied interactions with objects in IVR can cognitively be considered as actions or gestures. It does this by comparing verb-learning outcomes between two conditions: (1) where participants move the controllers without touching virtual objects (gesture condition); and (2) where participants move the controllers and manipulate virtual objects (action condition). We found that (1) users can have cognitively distinct outcomes in IVR based on whether the interactions are actions or gestures, with actions providing stronger memorisation outcomes; and (2) embodied controller actions in IVR behave more similarly to physical world actions in terms of verb memorization benefits.
Multidimensional periodic discrete wavelets

In this paper, the definition of a periodic discrete multiresolution analysis is provided. The characterization of such systems is obtained in terms of properties of scaling sequences. Wavelet systems generated by such multiresolution analyses are defined and described. Decomposition and reconstruction formulas for the associated discrete wavelet transform are provided.
Cryptographic Protection of Cyclic Real-Time Communication in Ethernet-Based Fieldbuses: How Much Hardware is Required?

It seems to be a widespread impression that the use of strong cryptography inevitably imposes a prohibitive burden on industrial communication systems, at least inasmuch as real-time requirements in cyclic fieldbus communications are concerned. AES-GCM is a leading cryptographic algorithm for authenticated encryption, which protects data against disclosure and manipulations. We study the use of both hardware and software-based implementations of AES-GCM. By simulations as well as measurements on an FPGA-based prototype setup we gain and substantiate an important insight: for devices with a 100 Mbps full-duplex link, a single low-footprint AES-GCM hardware engine can deterministically cope with the worst-case computational load, i.e., even if the device maintains a maximum number of cyclic communication relations with individual cryptographic keys. Our results show that hardware support for AES-GCM in industrial fieldbus components may actually be very lightweight.
A Chatbot as a Support System for Educational Institutions

The constant advance of technology in recent years has generated that several educational institutions are in the need to implement various technological tools to improve educational services; however, several of these programs have not been able to meet the needs they required. For this reason, the use of a chatbot emerges as a solution, which represents an interactive communication, allowing to perform a series of tasks in a novel, attractive way and finding information efficiently. Therefore, this article aims to provide a chatbot that serves as a support system in educational centers, giving answers in a short time and that are accessible at any time. The methodology used was based on the review of published articles no older than 5 years on the identification of technologies in the architecture of a chatbot that serves as a support system in educational centers. Of the 70 articles collected, after being analyzed, 22 articles were selected that provided the necessary information for the research topic. The results obtained coincide in using a web platform in the user interface component, the Dialogflow platform in the dialog management component and Firebase in the database component as technologies when developing the architecture of a chatbot.
Doing more by doing less

Many organizations employ compute clusters equipped with accelerators such as GPUs and TPUs for training deep learning models in a distributed fashion. Training is resource-intensive, consuming significant compute, memory, and network resources. Many prior works explore how to reduce training resource footprint without impacting quality, but their focus on a subset of the bottlenecks (typically only the network) limits their ability to improve overall cluster utilization. In this work, we exploit the unique characteristics of deep learning workloads to propose Structured Partial Backpropagation(SPB), a technique that systematically controls the amount of backpropagation at individual workers in distributed training. This simultaneously reduces network bandwidth, compute utilization, and memory footprint while preserving model quality. To efficiently leverage the benefits of SPB at cluster level, we introduce Jigsaw, a SPB aware scheduler, which does scheduling at the iteration level for Deep Learning Training(DLT) jobs. We find that Jigsaw can improve large scale cluster efficiency by as high as 28%.
Fast Variable-Grained Resemblance Data Deduplication For Cloud Storage

With the prevalence of cloud storage, data deduplication has been a widely used technology by removing cross users’ duplicate data and saving network bandwidth. Nevertheless, traditional data deduplication hardly detects duplicate data among resemblance chunks. Currently, a resemblance data deduplication, called Finesse, has been proposed to detect and remove the duplicate data among similar chunks efficiently. However, we observe that the chunks following the similar chunk have a high chance of resembling data locality property, and vice versa. Processing these adjacent similar chunks in small average chunk size level increases the metadata, which deteriorates the deduplication system performance. Moreover, existing resemblance data deduplication schemes ignore the performance impact from metadata. Therefore, we propose a fast variable-grained resemblance data deduplication for cloud storage. It dynamically combines the adjacent resemblance chunks or unique chunks or breaks those chunks, located at the transition region between resemblance chunks and unique chunks. Finally, we implement a prototype and conduct a serial of experiments on real-world datasets. The results show that our method dramatically reduces the metadata size while achieving the high deduplication ratio.
Leveraging Open Threat Exchange (OTX) to Understand Spatio-Temporal Trends of Cyber Threats: Covid-19 Case Study

Understanding the properties exhibited by Spatial-temporal evolution of cyber attacks improve cyber threat intelligence. In addition, better understanding on threats patterns is a key feature for cyber threats prevention, detection, and management and for enhancing defenses. In this work, we study different aspects of emerging threats in the wild shared by 160,000 global participants form all industries. First, we perform an exploratory data analysis of the collected cyber threats. We investigate the most targeted countries, most common malwares and the distribution of attacks frequency by localisation. Second, we extract attacks’ spreading patterns at country level. We model these behaviors using transition graphs decorated with probabilities of switching from a country to another. Finally, we analyse the extent to which cyber threats have been affected by the COVID-19 outbreak and sanitary measures imposed by governments to prevent the virus from spreading.
Application of Computer Intelligent Proofreading System in English Phrase Translation

Abstract To solve the problem of poor coherence, an intelligent proofreading system based on improved phrase translation model is proposed. Firstly, semantic ontology model is created by creating semantic ontology model and phrase translation combined translation algorithm. In addition, the overall design of the system. Through the overall architecture of the system, the software and hardware of the system are designed. The hardware design is mainly the search module, so as to improve the accuracy of the search. In the software module process, it mainly includes the system application framework, network topology, client, translator organization and management, online collaborative translation and other modules, and can support the translation and proofreading of multiple languages. Finally, the system is tested. The test results show that the system can meet the needs of users and improve the efficiency of proofreading.
Teaching Robotics in Pandemic Times Through Remote Education

During the COVID-19 pandemic, several challenges appeared before everyone. One of the biggest impacts that were observed was on education as a whole. The need for social distance and remote classes directly impacted teaching. Especially in matters of technology there were some more difficulties that needed to be overcome. In this article we will see an experience of how to teach robotics remotely at the IFSP/RGT in an accessible way to students and as a motivating tool during the period of remote classes.
Text Sentiment Support Phrases Extraction based on RoBERTa

It is significant to distinguish which words or phrases actually determine the emotional tendency. This paper presents a generative method based on the RoBERTa model to extract sentiment support words or phrases. Although it is not necessarily better than the extraction method in common indicators, the generative method can truly understand sentence semantics. RoBERTa is a Transformer-based pretraining model. It is pre-trained on a large-scale corpus and able to seize the semantic information in the corpus. It performs well in many text generation and text understanding tasks. We train and test based on the Twitter comment data set. Compared with previous models, our model's test score is 0.031~0.1 points higher, which proves that our proposed model has better performance.
Coding Theorems on Digital Fingerprinting Coding under Informed and Uninformed Setups

Digital fingerprinting codes are used to protect copyrighted contents from unauthorized redistribution. In this paper we focus on a digital fingerprinting code that can identify both of two malicious users and investigate coding theorems. We give a new definition of an identifier of the malicious users which enables us to give an explicit formula of the joint capacity, the supremum achievable rate of the number of users. Two coding theorems are given under the two kinds of setups depending on the knowledge of an attack model of the malicious users.
BROFY: Towards Essential Integrity Protection for Microservices

Trusted computing has emerged as one of the main components in a critical microservice application. A powerful adversary such as the cloud provider could harm its integrity by altering the application&#x0027;s code, behavior, and memory. Numerous attempts to preserve application integrity have been made, especially using Trusted Execution Environments (TEE). However, recent studies show that a CPU bitflip, which both adversary or faulty hardware can trigger, may invalidate its integrity despite being executed inside TEE. In the form of Silent Data Corruption (SDC), this bitflip may come undetected and shamble the trust built in a distributed system. We present BROFY, a toolchain that makes the program reliably perform correct computation inside the Intel SGX enclave that already provides code and memory integrity protection out-of-the-box. BROFY is compatible with multiple programming languages, needs no specific requirements or changes on the codebase, and offers a configurable trade-off between recovery ability and performance. We tested BROFY against actual bitflips by undervolting CPU, and our results show a significant decrease in irrecoverable failure rate from 96.7&#x0025; to 0.5&#x0025;, with a 100&#x0025; detection rate inside an SGX enclave. Our experiment shows that programs armored by BROFY, compared to native execution, have 84&#x0025; overhead on average based on the computation-intensive Starbench benchmark and only 3&#x0025; overhead on a multithreaded HTTP server application written in C.
Real-Time Implementation of an Interval Type-2 Fuzzy Logic Controller for the Trajectory Tracking of an UAV

In this study, a Type-2 Fuzzy Logic Controller that directly generates the necessary control signals is used to achieve the trajectory tracking control of an unmanned aerial vehicle (UAV). The proposed control scheme takes the quadcopter’s divergence from the given trajectory and its derivative as inputs. A type-1 fuzzy logic controller is also designed and implemented on the quadcopter to assess the performance of the type-2 controller. Real-time flight experiments are conducted for a skewed circular trajectory. The results of these experiments show that the type-2 fuzzy controller provides better transient and steady-state response compared to a conventional fuzzy controller.
Face Swapping Consistency Transfer with Neural Identity Carrier

Deepfake aims to swap a face of an image with someone else’s likeness in a reasonable manner. Existing methods usually perform deepfake frame by frame, thus ignoring video consistency and producing incoherent results. To address such a problem, we propose a novel framework Neural Identity Carrier (NICe), which learns identity transformation from an arbitrary face-swapping proxy via a U-Net. By modeling the incoherence between frames as noise, NICe naturally suppresses its disturbance and preserves primary identity information. Concretely, NICe inputs the original frame and learns transformation supervised by swapped pseudo labels. As the temporal incoherence has an uncertain or stochastic pattern, NICe can filter out such outliers and well maintain the target content by uncertainty prediction. With the predicted temporally stable appearance, NICe enhances its details by constraining 3D geometry consistency, making NICe learn fine-grained facial structure across the poses. In this way, NICe guarantees the temporal stableness of deepfake approaches and predicts detailed results against over-smoothness. Extensive experiments on benchmarks demonstrate that NICe significantly improves the quality of existing deepfake methods on video-level. Besides, data generated by our methods can benefit video-level deepfake detection methods.
The Context of Caring and Concern for Falling Differentiate Which Mobile Fall Technology Features Chinese Family Caregivers Find Most Important

Falls are not only a leading cause of death and disability, but also a strain on the capacity for caregivers to provide care. This study examined how the context of caregiving relates to the importance of caregiver-defined mobile fall prevention feature sets. A sample of 266 family caregivers, recruited from a Chinese social media platform, reported care for an older adult and interest in mobile fall prevention technology features. Factor analysis identified three caregiver-defined feature sets: automatic fall response, digitized fall prevention tools, and social features. Multiple regression showed caregivers' concern about falling was the most robust predictor of a feature set's importance. Poisson regression revealed that caregiver concern and assistance with instrumental activities of daily living were associated with rating more features as important. Our findings suggest that caregivers are interested in mobile fall prevention technologies that support older adults' independence while also alleviating concerns about falling.
Low-Light Image Enhancement Based on Generative Adversarial Network

Image enhancement is considered to be one of the complex tasks in image processing. When the images are captured under dim light, the quality of the images degrades due to low visibility degenerating the vision-based algorithms’ performance that is built for very good quality images with better visibility. After the emergence of a deep neural network number of methods has been put forward to improve images captured under low light. But, the results shown by existing low-light enhancement methods are not satisfactory because of the lack of effective network structures. A low-light image enhancement technique (LIMET) with a fine-tuned conditional generative adversarial network is presented in this paper. The proposed approach employs two discriminators to acquire a semantic meaning that imposes the obtained results to be realistic and natural. Finally, the proposed approach is evaluated with benchmark datasets. The experimental results highlight that the presented approach attains state-of-the-performance when compared to existing methods. The models’ performance is assessed using Visual Information Fidelitysse, which assesses the generated image’s quality over the degraded input. VIF obtained for different datasets using the proposed approach are 0.709123 for LIME dataset, 0.849982 for DICM dataset, 0.619342 for MEF dataset.
Measuring the User Experience of Computer-Aided Translation Systems: A Comparative Study

Computer-aided translation (CAT) has become the mainstream mode of translation in the language industry. In the process of translation, the CAT systems’ irritating features often cause considerable cognitive friction on the part of translators. In this context, this study evaluates and compares, qualitatively and quantitatively, the user experience of four CAT systems, namely, SDL Trados Studio, memoQ, Snowman CAT, and YiCAT. Over 100 graduate students in translation from a Chinese university completed a battery of tasks. They then filled out a questionnaire, which included the standardised System Usability Scale, preferences for the four CAT systems based on key functions, and open-ended questions regarding each CAT system's weaknesses and strengths. The study seeks to raise CAT vendors’ awareness of UX and find out what usability problems translators have encountered. It has implications for CAT tool development, pedagogy as well as cognitive translation studies.