Reviewerly/exHarmony huggingface dataset:
Integrating Privacy Concerns in the Development and Introduction of New Military or Dual Use Technologies

New and emerging technologies impact the ways in which military operations are conducted. Notable quantum leaps are being achieved in three fields: autonomous weapon systems, military use of cyberspace, and human enhancement. These fields involve dual use digital technologies that have both civilian and military uses: Artificial Intelligence-based autonomous systems are increasingly employed in civilian settings; cyberspace is a domain extensively utilized by civilian users and it comprises a linchpin of the global economic system; and enhancement technologies have an important role in treating, aiding and rehabilitating injured persons and persons with disabilities in non-military contexts. The military application of these three technologies raises legal issues relating to human control over military operations and accountability, and, as we wish to discuss in this article, serious privacy concerns.The legal implications of introducing new technologies into the military should be assessed in accordance to Article 36 of first additional protocol to the Geneva Conventions, which obligates States parties to determine “in the study, development, acquisition or adoption of a new weapon or new means or methods of warfare”, whether their employment would be prohibited under international law. The importance, but also the challenges of conducting proper legality reviews under Article 36 increase in the context of new technologies with unclear impact on civilians and civilian objects. The picture is even more complicated when long-term impacts, including long term privacy violations that could continue to affect them long after they finish their military service. In this article, we examine how Article 36 can serve as a vehicle for integrating privacy concerns in the evaluation of new military technologies, including dual use technologies. We claim that all three technologies bring about to privacy-related implications – the reliance of autonomous weapons on extensive surveillance and data collection, the corroding effects of cyber operations on cyber security and data protection and the potentially dramatic intervention in the bodies and minds of enhanced persons. As such, we suggest that the reviews would need to delineate possible harms, consider safeguard measures and justifying circumstances for use. Ultimately, a new privacy impact assessment methodology would need to be developed, covering difficult issues such as consent, long-term harm and indirect harms.
An Adaptive Incremental Learning Algorithm based on Shared Nearest Neighbors in Fault Detection

Incremental learning technology has been widely used in the field of fault diagnosis. Traditional incremental learning model frequent update to avoid losing knowledge of new samples. In this paper, we propose a adaptive incremental learning algorithm based on shared nearest neighbors (AIL-SNN), which controls the incremental update by detecting the concept drift. The algorithm in this paper is verified by experiments in CWRU dataset. The experimental results show that the algorithm can effectively detect the sample concept drift in the sample, and can use a small amount of training time to obtain better fault diagnosis accuracy.
Spatial-Based Trajectory Planning in Time-Varying Environments

The development of automated driving is progressing rapidly and with it the need for trajectory planning algorithms. In this work we present a method for collision-avoidance on roads and highways in the presence of moving obstacles, like other traffic participants. The concept of the restricted volume as a static representation of a moving obstacles geometry in spatial-temporal space can be applied for a wide variety of trajectory planning algorithms without resorting to quasi-static approximations. We demonstrate a method of calculating the restricted region in a time extended, road-aligned coordinate system for a moving obstacle with polygonal shape. To show the applicability of this approach, the restricted region is incorporated in a spatial-based nonlinear optimal control problem and implemented on an autonomous test-vehicle. With computing times averaging less than 200 milliseconds the presented method shows its potential for many road traffic situations.
Top-Rank Learning Robust to Outliers

Top-rank learning aims to maximize the number of absolute top samples, which are "doubtlessly positive" samples and very useful for the real applications that require reliable positive samples. However, top-rank learning is very sensitive to outliers of the negative class. This paper proposes a robust top-rank learning algorithm with an unsupervised outlier estimation technique called local outlier factor (LoF). Introduction of LoF can weaken the effect of the negative outliers and thus increase the stability of the learned ranking function. Moreover, we combine robust top-rank learning with representation learning by a deep neural network (DNN). Experiments on artificial datasets and a medical image dataset demonstrate the robustness of the proposed method to outliers.
Bangla-ExtraSum: Comparative Analysis of Different Methods in Automated Extractive Bengali Text Summarization

Text summarization is one of the most exciting fields for modern researchers. Through summarization, the reader can gain a comprehensive understanding of the larger content. Summarization techniques can be applied in every content area. The demand for summarization in news portals is growing day by day. Though several approaches for summarizing English texts have been developed, only a few notable works have been done in Bangla. In addition, there are just a handful of Bangla extractive summarization models and datasets available. In this paper, we introduce different approaches to text summarization that can capture the main context of the text using semantic and contextual relations between sentences. There is also a visual representation of the comprehensive analysis of these approaches and their results. The necessity of semantic relations between sentences while doing summarization is shown here and also demonstrated in transformer model and word2vec model assistance in summarization methodologies. 500 articles are proposed with two human-generated summaries for each article in this research. Finally, the performances of five different types of models in two different datasets are shown. Our designed model gets a 0.68 f-score in the existing dataset and a 0.63 f-score in our dataset.
A way to explore SMS: Social Media Scams for a secure Digi-world

Social media is a revolutionary phenomenon, which deals with how consumers look for information, interact with their work and communicate with one another. There are billions of active users in various social medias like Twitter, WhatsApp, Facebook and Instagram with numbers that go up to one million every day. This provides way for criminals to use a wide range of tactics to connect and commit fraud. Constantly using innovative ways to access the personal data is achieved by Scammers. Social media scams degrade the trust of consumers. This paper focuses on scams and provides different ways to various agencies to improve safety and minimize harmful damages. It helps in upgrading the consumer awareness on social media scams which is the heart and soul for a secure Digi-world.
Stability and Accuracy of Feature Selection Methods on Datasets of Varying Data Complexity

One widespread criterion used to evaluate feature selection techniques is the classifier performance of the selected features. Another criterion that has recently drawn attention in the feature selection community is the stability of feature selection techniques. Our study indicates that using feature selection techniques with different data characteristics may generate different subsets of features under variations to the training data. Our study motivation is that there are significant contributions in the research community from examining the effect of complex data characteristics such as class overlap on classification algorithms performance; however, relatively few studies have investigated the stability and the accuracy of feature selection methods with complex data characteristics. Accordingly, this study aims to conduct empirical study to measure the interactive effects of the class overlap with different data characteristics so we will provide meaningful insights into the root causes for feature selection methods misdiagnosing the relevant features among different data challenges associated with real world data in which will guide the practitioners and researchers to choose the correct feature selection methods that are more appropriate for particular dataset. Also, in this study we will provide a survey on the current state of research in the feature selection stability context.
Blockchain-based Supervised Anonymous Cross-domain Authentication Scheme

With the increasing complexity of the network environment, the traditional authentication technology has exposed its disadvantages such as low efficiency and power concentration. At the same time, the continuous development of information technology also puts forward higher requirements for authentication technology, for example, to realize authentication anonymization while ensuring authentication efficiency and dishonest users can be held accountable. In this paper, cross-domain authentication of heterogeneous networks is analyzed in detail, and a Blockchain-based Supervised Anonymous Cross-domain Authentication (BSA-CA) scheme is proposed. In this scheme, we retain the original infrastructure of the two trust domains. The trust network of users is established in the Blockchain system, and the trust model of heterogeneous cross-domain authentication is constructed by using zero-knowledge proof method. The BSA-CA scheme uses group signature to design anonymous supervisory properties. System analysis results show that the BSA-CA scheme has a good balance between security and authentication efficiency.
IoT-based health care data analytical paradigm using blockchain technology

Internet of Things (IoT) reshapes the incumbent industries into smart industries by bringing more opportunities with efficient decision making. The fundamental characteristics of IoT make the system to face various challenges such as poor interoperability, decentralization, heterogeneous data, diversified devices, network complexity, and security related issues. Blockchain technology overcomes the challenges of IoT by maintaining a digital ledger in the distributed systems. The convergence of IoT and blockchain technology synthesizes a novel paradigm that improves production throughput, operation efficiency, and product quality. During the complete procedure, privacy is preserved using blockchain which manages individual data, data sharing among hospitals, insurance companies, and medical centers. Blockchain acts as an interface or repository among IoT devices for the data which is generated by them. The patient's wearing IoT devices can be monitored in their trajectory and the countermeasures could be provided by safeguarding the privacy of patients via blockchain.
A Scheme of Traceless File Deletion for Windows FAT32 File System

FAT32 file system is a common file system, especially in the USB devices, with good stability and compatibility. When the FAT file system, as an important carrier of data storage, is attacked, data in the file system can be easily stolen by an adversary. Even the deleted file data can be recovered using data recovery technology that is one of the hot topics of computer forensics. A scheme of traceless data deletion for Windows FAT32 file system is presented in this paper to delete a file completely. Experimental results show that the deleted files cannot recover and the deletion operation leaves no trace.
Mapping Chinese Oracle-bone Glyphs to Simplified Glyphs with Generative Adversarial Networks

Evolved from more than 6,000 years ago, Chinese language has a long history. Its glyphs experienced seven major transformations in chronological order: oracle-bone script, bronze script, seal script, clerical script, cursive script, regular script and simplified script. As the earliest Chinese script, many oracle-bone glyphs still remain undecoded. In this paper, we use two types of Generative Adversarial Networks (GANs), Pix2Pix and CycleGAN to map oracle-bone glyphs to simplified glyphs directly. The research results can be used to help decode undecoded oracle-bone glyphs, find the relationships between these scripts, and enrich Chinese language, history and culture education.
Explore Capabilities and Effectiveness of Reverse Engineering Tools to Provide Memory Safety for Binary Programs

Any technique to ensure memory safety requires knowledge of (a) precise array bounds and (b) the data types accessed by memory load/store and pointer move instructions (called, owners) in the program. While this information can be effectively derived by compiler-level approaches much of this information may be lost during the compilation process and become unavailable to binary-level tools. In this work we conduct the first detailed study on how accurately can this information be extracted or reconstructed by current state-of-the-art static reverse engineering (RE) platforms for binaries compiled with and without debug symbol information. Furthermore, it is also unclear how the imprecision in array bounds and instruction owner information that is obtained by the RE tools impacts the ability of techniques to detect illegal memory accesses at run-time. We study this issue by designing, building, and deploying a novel binary-level technique to assess the properties and effectiveness of the information provided by the static RE algorithms in the first stage to guide the run-time instrumentation to detect illegal memory accesses in the decoupled second stage. Our work explores the limitations and challenges for static binary analysis tools to develop accurate binary-level techniques to detect memory errors.
Mechatrobot exercise based on integration of electronic circuit and robot control

This paper reports on a case study of online exercise for undergraduate students at the Department of Mechano-Informatics of the University of Tokyo. In this exercise, the theme was mechatrobot that integrates electronic circuits and robot control as a new curriculum for online exercise. In the first half of the online electronic circuit exercises, the existing offline electronic circuit exercises for learning digital/analog circuits were reorganized using the browser-based electronic circuit prototype tool Tinkercad. Besides, real electronic circuit prototype materials were distributed as an experience of studying with the real thing. In the latter half of robot programming, a ROS-based robot system can be learned through integration of electronic circuit and robot programming.
Design and Manufacture of a Power Wheelchair by Upper-Body Tilt Estimated by a Complementary Filter

Most power wheelchairs have been designed to be operated using a joystick. Power wheelchairs with joystick controls can assist elderly persons to improve their mobility. However, persons with hand disabilities cannot control a power wheelchair using a joystick. This paper proposes a power wheelchair operated by a user’s upper-body tilt information obtained from a gyro sensor and an accelerometer. In this study, a complementary filter is used to estimate a user’s upper-body tilt. Since the developed power wheelchair can be operated without a joystick, it is expected that elderly persons or persons with hand disabilities will find it simple to use. Experiments are performed to demonstrate the performance of the developed power wheelchair.
Method of Extracting Data from Images on a Curved Surface in Data Embedding to Printed Images Using Mobile Devices

A method of embedding data to printed images and retrieving the data using a mobile device has been proposed. This method assumes that images are placed on a flat surface. However, to increase the flexibility of the method, it is desirable to acquire data from an image placed on a curved surface. In this paper, we propose a method of extracting data from a data-embedded image placed on a cylinder surface as an example of a curved surface. We develop a method of correcting the captured image by estimating the distance between the mobile device and the image placed on a curved surface, given the radius of the cylinder, the size of the printed embedded image, and the focal length of the mobile device. The experimental result for an image attached to a cylinder shows the effectiveness of the proposed method.
State-of-the-Art in QoE for Video streaming

Videos have been the most commonly used source of entertainment on the Internet, accounting for 65% of the world’s Internet traffic in 2020. Efficient internet delivery of multimedia with low bandwidth requirements using DASH (Dynamic adaptive streaming HTTP) means better quality for the end-user and lower costs for the server. With HTTP streaming, it is possible to provide a weak quality of service (QoS), but a better quality of experience (QoE), because HTTP offers a stable meaning of transportation. In this paper, a comprehensive survey on the use of Machine learning (ML) for predicate QoE for user progress of this research area. Furthermore, what are the current trending algorithms in frontier ML research, and discuss why you prefer dealing with “HTTP DASH” describes the benefits of using QoE with HTTP webpage.
Multimodal Named Entity Recognition via Co-attention-Based Method with Dynamic Visual Concept Expansion

Multimodal named entity recognition (MNER) that recognizes named entities in text with the help of images has become a popular topic in recent years. Previous studies on MNER only utilize visual features or detected concepts from a given image directly without considering implicit knowledge among visual concepts. Taking the concepts not detected but relevant to those in the image into consideration provides rich prior knowledge, which has been proved effective on other multimodal tasks. This paper proposes a novel method to effectively take full advantage of external implicit knowledge, called Co-attention-based model with Dynamic Visual Concept Expansion (CDVCE). In CDVCE, we adopt the concept co-occurrence matrix in a large-scale annotated image database as implicit knowledge among visual concepts and dynamically expand detected visual concepts conditioned on the concept co-occurrence matrix and the input text. Experiments conducted on two public MNER datasets prove the effectiveness of our proposed method, which outperforms other state-of-the-art methods in most cases.
An Explainable XGBoost-based Approach for Assessing Product Presentation Video

Product presentation video, an essential medium for online product presentation, mainly conveys product information to consumers. The quality of product information expression will affect consumer's purchase experience, even purchase decisions. But many video production novices often have difficulty assessing the effectiveness of their videos and doing optimization. Thus, we adopt information mediums' perceived usefulness and perceived ease of use as assessment indicators, and propose an automatic explainable assessment method. The method is based on the combined use of the extreme gradient boosting algorithm and SHapley Additive exPlanations method, which is implemented to assess video samples and generate explanations on the assessment results. We create a product presentation video dataset for development and evaluation. The results show that our method can assess video effectively, and provide insights into the shortcomings of videos' production like experts.
Identification of Unmasked Faces: Protection in Public Places from COVID-19 and Other Infections

The $3^{\mathrm{r}\mathrm{d}}$ wave of Coronavirus is hitting the world. The doctors and scientists are also in their last phase of developing a vaccine. But it is challenging to get the vaccine for everyone due to several factors such as production, supply chain, cold chain and price. Till then, the only way to prevent infection is to wear a mark and follow social distancing. This paper describes how to identify mask and no-mask faces in public areas using image processing algorithms. One of the significant steps taken by people around the world to stay safe is wearing masks. This research-based project gets a camera-based solution to detect faces and recognize them if they exist in the database. If the live stream has unmasked faces, which aren’t present in the existing database, give out an alarm to alert the administrator or user. It is a significant project for times like these because now, with the lockdown ending in the world because of financial meltdowns, most public places like malls, temples, shops, grocery stores, and medical stores are opening. A concern for the safety and well-being of everyone around us because the virus can spread at a logarithmic rate and cause mass damage in the form of large bills and some cases, death. To avoid this, we always have to wear masks when in public places, and this is what the project does. The crucial technologies used in our manuscript are Face Detection, Face Recognition & Identification, and Feature Detection in the face.
Online Latent Dirichlet Allocation Model Based on Sentiment Polarity Time Series

The Product Sensitive Online Dirichlet Allocation model (PSOLDA) proposed in this paper mainly uses the sentiment polarity of topic words in the review text to improve the accuracy of topic evolution. First, we use Latent Dirichlet Allocation (LDA) to obtain the distribution of topic words in the current time window. Second, the word2vec word vector is used as auxiliary information to determine the sentiment polarity and obtain the sentiment polarity distribution of the current topic. Finally, the sentiment polarity changes of the topics in the previous and next time window are mapped to the sentiment factors, and the distribution of topic words in the next time window is controlled through them. The experimental results show that the PSOLDA model decreases the probability distribution by 0.160 1, while Online Twitter LDA only increases by 0.069 9. The topic evolution method that integrates the sentimental information of topic words proposed in this paper is better than the traditional model.
CT Scan Image Segmentation of Asphalt Mixture Based on Improved U-Net

In the CT scan image of asphalt mixture, there are common factors such as dense mixture area and uneven illumination, which result in low accuracy of local feature segmentation. Through the introduction of the attention mechanism in U-Net, before fusing the features of each resolution in the encoder with the relating features in the decoder, an attention mechanism is added to make the encoder readjust its output features, which is similar to imitating human attention to achieve the effect of paying attention to multiple details at the same time. Achieve channel enhancement of the local characteristic area of the asphalt mixture, and improving the segmentation ability of the network model to the local characteristic area. Those test outcomes indicate that, compared for the universal segmentation algorithm and the classic U-Net model segmentation algorithm, the methodology of segmentation in this paper has better performance in terms of MPA coefficient, MIoU coefficient and Dice coefficient.