Reviewerly/exHarmony huggingface dataset:
Multi-Criteria Decision Making using Fuzzy Cognitive Maps – Preliminary Results

In this work a new methodology for Multi-Criteria Decision Making (MCDM) with the ability to address dependence and feedback among criteria and their interrelations is proposed. Although a variety of MCDM methods have been developed during the past years, Fuzzy Cognitive Maps (FCMs) consist an alternative, interesting and promising research direction in decision support. The proposed FCM-based method, exploiting the natural FCMs ability to effectively represent concepts interactions, offers a dynamic and flexible way for ranking alternatives on the basis of different scenarios regarding the nature of causal relationships between criteria. The method is validated on the basis of a robot selection problem.
Machine Learning-based Object Recognition Technology for Bird Identification System

The object recognition system for machine learning has become widely known recently, and each algorithm of the object recognition system has its own advantages and disadvantages. The disadvantages are that the speed of identification is slow and the accuracy of identification is poor. The purpose of this paper is to identify birds through the YOLO and Custom Vision algorithms. When the user uploads bird photos to the two systems through a smart mobile device, the system will identify the objects according to the algorithm. It judges which bird it may be and, after a final comparison, the result with a higher percentage of accuracy is transmitted back to the user, thereby improving the accuracy of the identification system.
FAB Classification based Leukemia Identification and prediction using Machine Learning

Background and Objective: Leukemia identification, detection, & classification has erupted an intriguing field in medical research. Several methodologies are convenient in theprevious work to detect five types WBCs (lymphocytes, eosinophils, monocytes, neutrophils, and basophils). Single cell Blood's smear images used for experiment. Propounded method is used for leukemia recognition, uncovering and distribution based on FAB classification. Methodology: This propounded task has developed French-American and British (FAB) classification-based detection module on blood smearimages (BSIs). Methods like pretreatment, segmentation, feature extraction, distribution are used in detection method. The Propounded algorithm-based propounded model is used for segmentation, which is combination of the segmented results of the Linde-Buzo-Gray (LBG) algorithm, Adaptive canny used for edge identification and Hysteresis and watershed algorithm used for thresholding. The shape, texture features, color of segmented image are picked by neural network and classification is performed by Support Vector Machine (SVM) and prediction by Naïve Bayes Classifier (NBC). Result: Dataset-master and Cellavison dataset is being used for the experimentation. The BSIs are considered for the Evaluation based on ROC curve analysis metrics like TPR, TNR and accuracy. Our propounded solution obtains superior classification performance in the given dataset. The suggested classifier enhanced the classification average accuracy to 99.06% and Mean Square Error (MSE) is 0.0407. Conclusion: The enhanced accuracy had achieved by enhancing performance and classification with comparison with some other methods.
Cross-Layer Profiling of Encrypted Network Data for Anomaly Detection

In January 2017 encrypted Internet traffic surpassed non-encrypted traffic. Although encryption increases security, it also masks intrusions and attacks by blocking the access to packet contents and traffic features, therefore making data analysis unfeasible. In spite of the strong effect of encryption, its impact has been scarcely investigated in the field. In this paper we study how encryption affects flow feature spaces and machine learning-based attack detection. We propose a new cross-layer feature vector that simultaneously represents traffic at three different levels: application, conversation, and endpoint behavior. We analyze its behavior under TLS and IPSec encryption and evaluate the efficacy with recent network traffic datasets and by using Random Forests classifiers. The cross-layer multi-key approach shows excellent attack detection in spite of TLS encryption. When IPsec is applied, the reduced variant obtains satisfactory detection for botnets, yet considerable performance drops for other types of attacks. The high complexity of network traffic is unfeasible for monolithic data analysis solutions, therefore requiring cross-layer analysis for which the multi-key vector becomes a powerful profiling core.
Machine Learning-Based Code Auto-Completion Implementation for Firmware Developers

With the advent of artificial intelligence, the research paradigm in natural language processing has been transitioned from statistical methods to machine learning-based approaches. One application is to develop a deep learning-based language model that helps software engineers write code faster. Although there have already been many attempts to develop code auto-completion functionality from different research groups, a need to establish an in-house code has been identified for the following reasons: (1) a security-sensitive company (e.g., Samsung Electronics) may not want to utilize commercial tools given that there is a risk of leaked source codes and (2) commercial tools may not be applicable to the specific domain (e.g., SSD firmware development) especially if one needs to predict unique code patterns and style. This research proposes a hybrid approach that harnesses the synergy between machine learning techniques and advanced design methods aiming to develop a code auto-completion framework that helps firmware developers write code in a more efficient manner. The sensitivity analysis results show that the deterministic design results in reducing prediction accuracy as it generates output in some unexpected ways, while the probabilistic design provides a list of reasonable next code elements in which one could select it manually to increase prediction accuracy.
eLearning Platform for Adult Learner based on Mobile Instant Messaging for Collaborative Learning

A world that is constantly changing makes adults have to keep learning, but adults often have difficulty learning because they have other priorities such as work and having a family. eLearning is compatible with adult learning because adults can learn while doing other things. However, to meet adult learning requirements, eLearning must be adapted to adult learning theory. Interaction and collaboration are of key importance to adult learning. In recent years, Mobile Instant Messaging (MIM) has become an important platform for communication and information exchange. MIM has also been widely used in education, but its use in adult education is still unknown. The eLearning platform acts as a liaison between the facilitator and students with information, tools, and resources to engage adult learners in collaborative learning. In this study, a prototype of the eLearning platform was produced with the results of performance testing still far from acceptable response times, and usability testing resulted in 88% effectiveness, 81% efficiency, and satisfaction using SUS 74.3. The results of the evaluation show that the experience of using the eLearning platform has a significant effect on the desire of adult learners to engage in collaborative learning.
Generation of Rectangular Matrix Key for Hill Cipher Algorithm Using Playfair Cipher

Abstract Hill cipher is a cryptographic algorithm that uses matrix as a key by utilizing modulo operations. The key matrix used is usually made randomly as a square matrix. In this study, a key matrix was created using the Playfair Cipher algorithm as a rectangular matrix. The use of the Playfair Cipher algorithm makes it easy for users to remember key matrices, while remaining safe when distributed. Beside the key matrix security, use of a rectangular key matrix produces a longer and more complicated ciphertext to find the linear equation.
Mitigation of Attacks on Email End-to-End Encryption

OpenPGP and S/MIME are two major standards for securing email communication introduced in the early 1990s. Three recent classes of attacks exploit weak cipher modes (EFAIL Malleability Gadgets, or EFAIL-MG), the flexibility of the MIME email structure (EFAIL Direct Exfiltration, or EFAIL-DE), and the Reply action of the email client (REPLY attacks). Although all three break message confidentiality by using standardized email features, only EFAIL-MG has been mitigated in IETF standards with the introduction of AEAD algorithms. So far, no uniform and reliable countermeasures have been adopted by email clients to prevent EFAIL-DE and REPLY attacks. Instead, email clients implement a variety of different ad-hoc countermeasures which are only partially effective, cause interoperability problems, and fragment the secure email ecosystem.
Bounds for the capacity error function for unidirectional channels with noiseless feedback

In digital systems such as fiber optical communications, the ratio between probability of errors of type 1→0 and 0→1 can be large. Practically, one can assume that only one type of error can occur. These errors are called asymmetric. Unidirectional errors differ from asymmetric type of errors; here both 1→0 and 0→1 type of errors are possible, but in any submitted codeword all the errors are of the same type. This can be generalized for the q-ary case. We consider q-ary unidirectional channels with feedback where the errors have magnitude one and give bounds for the capacity error function. It turns out that the bounds depend on the parity of the alphabet size q. Furthermore, we show that for feedback, the capacity error function for the binary asymmetric channel is different from the symmetric channel. This is in contrast to the behavior of the function without feedback.
RETRACTED: New technology application in logistics industry based on machine learning and embedded network

A great deal of rivalry in the coordination business; the conventional coordination reasons are moving towards it in the requirements and the quickly changing innovation of coordination applications that change from second to second. Coordination in business transport and coordination exercises has been improved coordination. In the initial segment of the investigation, inspected the coordination's, the coordination idea of coordination the board and web-based business. In the next part, the arrangement of coordination's business exercises is clarified by the data's impact. In the last aspect of the investigation, I have chosen to utilize the advantages and difficulties of coordination's innovation and handy experience. In such a manner, on account of the profound examination exercises in the installed organization, such an embedded network, such as various underlying organization-based innovation, set up to understand its manageable creation. Accordingly, it is an object of the current investigation, methodically, to break down the utilization of analytical writing and the inherent organization and embedded network of the business Machine Learning (ML). The mechanical implanted organization's presentation and the embedded network are viewed as the main thrust behind the keen manufacturing plant insurgency.
IMMERSIVE TECHNOLOGIES IN A HIGHER SCHOOL IN THE MODERN DIGITAL REALITY

Immersive technologies and virtual reality technologies have become a powerful and promising tool in education due to their unique technological characteristics that distinguish them from other it applications. In the higher education system the coronavirus pandemic (COVID-19) has played a role as an accelerator for the use of all forms and methods of e-learning. Mobile learning (m-learning) is characterized in the context of "smart" education, which has become one of the most viable and popular in terms of flexibility in the organization of the educational process and access to educational resources, virtualization in training. Also, mixed (hybrid) educational technologies that allow e-learning at the transition stage from their use to implementation. Providing the latest statistical data on the degree of penetration of Kazakhstan users into the mobile Internet and its development the advantages of an innovative form of education are revealed in the context.
A Hardware Implementation of ZUC-256 Stream Cipher

In this paper, we present a hardware implementation of ZUC-256 stream cipher. ZUC-256 is developed from ZUC-128 to provide higher 256-bit security for 5G communication. The optimized implementation of ZUC-256 uses a five-stage pipeline architecture to shorten the critical path of the cipher. The evaluation results show that this architecture performs better than the other ZUC-128 designs in frequency and throughput, and satisfy the demand of LTE for 5G communication.
Moveillance: Visualizing Electric Machines

We introduce a novel method of researching, understanding, and visualizing fundamentals of electric machines through the use of augmented reality (AR). The otherwise hidden rotating electromagnetic (EM) fields inside electric motors and generators are made visible with Sequential Wave Imprinting Machines (SWIMs). By attaching one or more SWIMs to the machine's rotating frame, the system reveals the electric machine's inner workings. Changes to these waves directly reflect changes in the controller and its parameters, for example, load or timing. Finally, we discuss the useful insights provided by AR for load analysis and motor control, using the visualized Waveform patterns from a three-phase motor.
Quantifiable Interactivity of Malicious URLs and the Social Media Ecosystem

Online social network (OSN) users are increasingly interacting with each other via articles, comments, and responses. When access control mechanisms are weak or absent, OSNs are perceived by attackers as rich environments for influencing public opinions via fake news posts or influencing commercial transactions via practices such as phishing. This has led to a body of research looking at potential ways to predict OSN user behavior using social science concepts such as conformity and the bandwagon effect. In this paper, we address the question of how social recommendation systems affect the occurrence of malicious URLs on Facebook, based on the assumption that there are no differences among recommendation systems in terms of delivering either legitimate or harmful information to users. Next, we use temporal features to build a prediction framework with &gt;75% accuracy to predict increases in certain user group behaviors. Our effort involves the demarcation of URL classes, from malicious URLs viewed as causing significant damage to annoying spam messages and advertisements. We offer this analysis to better understand OSN user sensors reactions to various categories of malicious URLs in order to mitigate their effects.
Naruto

A covert channel is an information channel which is used by computer process to exfiltrate data through bypassing security policies. The DNS protocol is one of the important ways to implement a covert channel. DNS covert channels are easily used by attackers for malicious purposes. Therefore, an effective detection of the DNS covert channels is significant for computer system and network security. Aiming at the difficulty of the DNS covert channel identification, we propose a DNS covert channel detection method based on stacking model. The stacking model is evaluated in a campus network and the experimental results show that the detection based on the stacking model can detect the DNS covert channels effectively. Besides, it can also identify unknown covert channel traffic. The area under the curve (AUC) of the proposed method, reaching 0.9901, outperforms the existed methods.
Deformation representation based convolutional mesh autoencoder for 3D hand generation

Due to its flexible joints and self-occlusion, representation and reconstruction of 3D human hand is a very challenging problem. Although some parametric models have been proposed to alleviate this problem, these representation models have limited representation ability, like not being able to represent complex gestures. In this paper, we presented a new 3D hand model with powerful representation ability and applied it to high accuracy monocular RGB-D/RGB 3D hand reconstruction. To achieve this, we firstly build a large scale high-quality hand mesh data set based on MANO with a novel mesh deformation method. We train a VAE based on this data set, and get the low-dimensional representation of hand meshes. By using our HandVAE model, we can recover a 3D human hand by giving a code within this latent space. We also build a framework to recover 3D hand mesh from RGB-D/RGB data. Experimental results have demonstrated the powerfulness of our hand model in terms of the reconstruction accuracy and the application for RGB-D/RGB reconstruction. We believe that our 3D hand representation could be further used in other related human hand applications.
DecoLive Jacket with Battery-free Dynamic Graphics

We present the design and prototype of the DecoLive Jacket, a dynamic clothing that allows its wearer to switch between opened and closed states of flower patterns as subtle indication of availability for social interactions. The Jacket embodies two electrochromic (EC) displays and NFC-based wireless energy harvesting components, enabling battery-free operation. The wearer controls the garment's patterning by placing a smartphone in one of the two pockets of the jacket, activating either the open or closed flower visualization, and expressing a subtle message about their willingness to interact with others. Our implementation demonstrates the potential for battery-free EC displays on aesthetic wearables to enable dynamic control of the non-verbal messages communicated by clothing.
WordRecommender

Regarding recommender systems, there has been a historically dominance on the literature in favor of collaborative algorithms over content-based ones. However, the latter can work better with users on applications and be more transparent. Therefore, in this paper, we propose WordRecommender, an explainable content-based algorithm that calculates similarity by semantic proximity. On its preprocessing step, reviews in the movie context are analyzed to obtain aspects, defined as words with high sentimental value. After that, the recommendations are generated with a neighborhood algorithm that calculates the films' similarity, based on the semantic proximity of the aspects, ordered by their emotional score. Finally, to produce textual explanations, the algorithm can also consider the semantic comparison of metadata by utilizing the most related aspects from the recommended movie and an enjoyed one by the user. As a result, it was found that the algorithm has competitive accuracy when compared with other baseline neighborhood methods, in conclusion that the semantic data of items can be the source of both representative information and reasoning on recommender systems.
Detecting Degradation of Web Browsing Quality of Experience

Quality of Experience (QoE) inference, and particularly the detection of its degradation is an important management tool for ISPs. Yet, this task is made difficult due to widespread use of encryption on the data-plane on the one hand so that measuring QoE is hard, and to the ephemeral properties of the web content on the other hand so that changes in QoE indicators may be rooted in changes in properties of the content itself, more than being caused by network-related events. In this paper, we phrase the QoE degradation detection issue as a change point detection problem, that we tackle by leveraging a unique dataset consisting on several hundreds thousands browsing sessions spanning multiple months. Our results, beyond showing feasibility, warn about the exclusive use of QoE indicators that are very close to content, as changes in the content space can lead to false alarms that are not tied to network-related problems.
MMUU-Net: A Robust and Effective Network for Farmland Segmentation of Satellite Imagery

Abstract Aiming at the multi-scale characteristics of satellite imagery, and the adhesion phenomenon in the farmland segmentation results which is caused by the close distance between different farmland blocks, this paper proposes a robust and effective network based on U-Net for farmland segmentation of satellite imagery, which is called MMUU-Net. On the basis of adopting the encoder with higher classification accuracy network, adding ASPP (Atrous Spatial Pyramid Pooling) layer in the middle, and designing the multi-scale feature fusion module in the decoder, so that the multi-scale feature information is fully utilized; in order to better fuse multi-scale information, a more robust loss function is designed; finally, we propose a segmentation strategy of the coarse and refined two-stage to eliminate the adhesion phenomenon. Through the comparative experiments, it is verified that MMUU-Net is better than other segmentation networks, and can be effectively applied to the task of farmland segmentation of satellite imagery.
ISOMAP-KL: a parametric approach for unsupervised metric learning

Unsupervised metric learning consists in building data-specific similarity measures without information of the class labels. Dimensionality reduction (DR) methods have shown to be a powerful mathematical tool for uncovering the underlying geometric structure of data. Manifold learning algorithms are capable of finding a more compact representation for data in the presence of non-linearities. However, one limitation is that most of them are pointwise methods, in the sense that they are not robust to the presence of outliers and noise in data. In this paper, we present ISOMAP-KL, a parametric patch-based algorithm that uses the KL-divergence between local Gaussian distributions learned from neighborhood systems along the KNN graph. We use this non-Euclidean measure to compute the weights and define the entropic KNN graph, whose shortest paths approximate the geodesic distances between patches of points in a parametric feature space. Results obtained in several datasets show that the proposed method is capable of improving the classification accuracy in comparison to other DR methods.
Matching Research Publications to the United Nations’ Sustainable Development Goals by Multi-Label-Learning with Hierarchical Categories

In 2015 the United Nations proposed the Sustainable Development Goals (SDGs), a set of universal goals for meeting the urgent environmental, political and economic challenges in the world. Universities play an important role to support and contribute to the SDGs mainly through education and research. To evaluate the contributions through research, universities aim at relating their scientific publications to SDGs, and automatically quantify the connectedness of these publications to the detailed targets and the unique indicators under SDGs. In this paper, we apply deep learning techniques to estimate the unknown indicators (third level) and targets (second level) for each publication, and output all its possible goals (first level). Specifically, we first exploit the dependency of categories at different levels (goals, targets, and indicators) to extract the dependent label features. Then we calculate the degree of matching between categories and publications in a bottom-up way and design a hierarchical structure to transfer such matching information level by level until obtaining the predicted SDGs of the publications. This is the first application of a deep learning method on this SDG prediction task and our experiments clearly demonstrate the good performance of our model on this real-world SDGs matching task, the extraction of key information as well as the prediction of potential sub-categories. As auxiliary analysis, we visualize the extraction of key semantic information and the probability of the hierarchical SDG categories.