Reviewerly/exHarmony huggingface dataset:
Secure Machine Intelligence and Distributed Ledger

Modern machine and deep learning systems are becoming part of high-performance cloud services and technologies. It is extremely important to understand that in systems such as recommendation systems, data is stored on local machines, and the trained system (matrix) is located in the cloud vendor, for example, in AWS or Google Cloud. Data on local machines can be updated or deleted. Local machines are often networked, which requires the use of synchronization methods and specialized protocols for the exchange of such information. And the central server is used as a single computer center for machine learning tasks. At the same time, it is necessary to control both the integrity of local data and their relevance with respect to other local machines. An important aspect is that the data center in the cloud should not know about our data, that is, we must be able to transmit them in encrypted form. At the same time, the deep learning model should be able to work with such encrypted data and send us the answers in encrypted form too. All this should be calculated in polynomial time, that is, quickly enough. For encryption purposes, it is proposed to use homomorphic algorithms. This report attempts to combine two promising modern paradigms for solving similar problems: machine intelligence and distributed ledger. For the purposes of distributed deep learning in relation to recommender systems, this symbiosis shows very serious practical prospects.
Disentanglement and Local Directions of Variance

Previous line of research on learning disentangled representations in an unsupervised setting focused on enforcing an uncorrelated posterior. These approaches have been shown both empirically and theoretically to be insufficient for guaranteeing disentangled representations. Recent works postulate that an implicit PCA-like behavior might explain why these models still tend to disentangle, exploiting the structure of variance in the datasets. Here we aim to further verify those hypotheses by conducting multiple analyses on existing benchmark datasets and models, focusing on the relation between the structure of variance induced by the ground-truth factors and properties of the learned representations. We quantify the effects of global and local directions of variance in the data on disentanglement performance using proposed measures and seem to find empirical evidence of a negative effect of local variance directions on disentanglement. We also invalidate the robustness of models with a global ordering of latent dimensions against the local vs. global discrepancies in the data.
A Novel Approach for Morphological Analysis and Inflections Based on POS Tagging

Word processing with tokenization and part-of-speech (POS) tagging is an integral component of computer linguistics. During the process of POS tagging, morphological analysis is crucial for applications like information retrieval, language modelling, machine translation, etc. Generating inflections that relate various forms of a single word is an important step in morphological analysis. In this paper, a novel approach of analysing word structure and generating inflections of nouns and adjectives has been proposed. In this approach, an efficient method using xml-based short notation has been implemented to store the generated inflected forms. A regular expression-based numeral sequencing has been used.
Design and Development Learning Media Application of Presidential Car History at a Museum in Indonesia using Augmented Reality Technology

The writing of this paper discusses an application that helps visitors to see the first presidential car show virtually. Many visitors from various regions wanted to see the exhibition. However, it is hindered by distance, time and especially during the pandemic which requires everyone to do everything with online media. Augmented Reality (AR) is content created digitally on top of a real-world environment. However, users cannot interact with the AR environment. In a car show to show the cars of antiquity, it is impossible to show directly to all the customers down to the details of the car. In this study, it is intended to create an android-based application by displaying car products with real time features. Application development using Android Studio with the Markerless Augmented Reality method. This study involved results and discussions containing (1) Concept, (2) Design, (3) Material Collection, (4) Assembly, and (5) Testing. These five things are done so that the application is designed regularly up to the hands of the user. This application aims to serve as a medium and make it easier for users to get clear and detailed information and 3D car images.
Automated Detection of Side Channels in Cryptographic Protocols

Currently most practical attacks on cryptographic protocols like TLS are based on side channels, such as padding oracles. Some well-known recent examples are DROWN, ROBOT and Raccoon (USENIX Security 2016, 2018, 2021). Such attacks are usually found by careful and time-consuming manual analysis by specialists.
Research on Future Education Development under the trend of Information Technology and Artificial Intelligence in the Sixth Scientific and Technological Revolution

With the rapid rise of big data, cloud computing, artificial intelligence and other emerging technologies and their wide application in various fields of society, the Sixth Scientific and Technological Revolution is quietly infiltrating human habitat, which Includes the field of education. At present, China's education development is also facing the challenges of education reform. Based on the influence of technological revolution on the future, this paper investigates the development of educational technology for China's educational reform. Our education should change the teaching form through multimedia and online classroom tools, and expand the new teaching content under the analysis of Artificial Intelligence (AI). On the one hand, informatization, internationalization and knowledge economy bring severe external challenges to education. On the other hand, China's education is facing such problems as educational injustice, lack of innovation and lack of lifelong education system. Traditional educational concepts, models and methods are no longer able to adapt to the new development situation, and there is an urgent need to promote systematic changes in education as a whole. Based on the future impact of the Sixth Scientific and Technological Revolution, this study aims to clarify the scientific trend and law of education in the future. In view of these problems, this paper puts forward concrete analysis and countermeasures for China's educational reform in combination with the technological development in the field of education.
STCP: An Efficient Model Combining Subject Triples and Constituency Parsing for Recognizing Textual Entailment

Recognizing Textual Entailment (RTE) aims at automatically determining the logical relationship between the given premise and hypothesis, and good recognition ability would be helpful for other natural language understanding tasks. Based on the Bidirectional Encoder Representation from Transformer (BERT), this paper proposes a model combining Subject Triples and Constituency Parsing (STCP) for RTE. Specifically, the model combines the central subject triples to fine-tune BERT, which obtains all hidden layers' features weighted by attention, to capture the global semantic information. The sentences with constituency syntax parsing are encoded by Tree Long Short-Term Memory (Tree-LSTM) to obtain the local structural information. Finally, the global semantic information and the local structural information are incorporated by constructing the matrix splicing fusion module, to enhance the ability to recognize semantic logical relationships. Experimental results show that the STCP model can achieve better recognition performance than the benchmark models on the public datasets SNLI and MNLI, and the effectiveness of the model is verified through ablation experiments and visualization of attention weights.
Facial Recognition System Using DWT, DCT, and Multilayer Sigmoid Neural Network Classifier

Facial recognition systems have seen widespread use in numerous applications, including identity verification for phone security, missing person identification, and forensic investigations. The purpose of this study is to improve both the speed and accuracy of a facial recognition system, thus enhancing its suitability for real-world applications. The proposed system reduces overall computational complexity by using simple algorithms and transforms such as grayscaling, a two-dimensional discrete wavelet transform, and a two-dimensional discrete cosine transform. The classification algorithm increases accuracy by using a straight-forward multilayer sigmoid neural network, which better correlates the input and output data than existing methods. The recognition system is tested with four freely accessible datasets: the ORL, YALE, FERET-c, and FEI. A test set based on the combination of all datasets is also utilized to evaluate the system performance. Results show that the system still maintains high recognition rates despite reducing complexity compared to popular existing methods.
Privacy-Preserving Convex Factorization Machine

Factorization Machine (FM) significantly improves the prediction accuracy of the linear models due to its powerful capability of feature combination and has been widely used in various systems, i.e., in recommendation systems and online advertisement. Convex FM further solves the bad local minima issue, which is incurred by the non-convex optimization problem and achieves better prediction performance. However, convex FM has a serious privacy issue since the learning process may reveal the sensitive information of training data. In order to mitigate the information leakage while achieving good prediction performance, we design a privacy-preserving convex Factorization Machine algorithmic framework that satisfies Rényi Differential Privacy. Our algorithmic framework called Out-CFM introduces noises to the learned optimizer at each round and delicately leverages the privacy amplification theorem to reduce the perturbations. Moreover, we utilize a modified Frank-Wolfe method to eschew the expensive projection operation in the optimization process. In addition, we develop two classical learning tasks, i.e., regression and classification tasks, in the Out-CFM framework to enable privacy-preserving learning. We provide the theoretical proof of the privacy guarantee for the algorithmic framework and derive an RDP-based analytical model for the utility analysis. Extensive experiments are performed on the real datasets for both regression and classification tasks to evaluate the performance of our proposals, which validate our theoretical analysis and demonstrate that our proposed methods outperform the baseline methods.
Diagrammatic Representations of Uncertainty in Meteorological Forecasting

Meteorological prediction is complex, but an important scientific practice to help keep people safe. In the past few decades, forecasts have become much more accurate. However, this accuracy has not removed uncertainty from forecasts. Indeed, it is widely acknowledged that weather forecasting is an inherently uncertain practice. Given that the aims of weather prediction are practical (to help keep people and property safe), it is important that weather forecasts be effectively communicated to the broader public. But, there is an interesting question here about how the uncertainty (itself an interesting epistemological concept) of the forecasts is communicated. One of the ways this is done by the National Weather Service in the United States is to use various forms of graphical and diagrammatic representations to identify and communicate the uncertainty that remains as part of a forecast. In this essay, I will explore some of these diagrammatic and graphical representations to identify the ways in which uncertainty is expressed. I argue that there is an interesting and important way in which the aims of meteorological practice constrain the representations—such that they do not aim at perfect (or true) representations, but rather at products which will help change behavior to save lives.
Making Warning Messages Personal: A Big 5 Personality Trait Persuasion Approach

Several mitigation strategies in form of warning messages against phishing attacks have continued to fail largely due to user negligence. Thus, it is important for researchers to focus not only on the accuracy of the provided recommendations but also on other factors that influence the acceptance of recommendations and the extent to which these recommendations are convincing or persuasive. In this paper, we present our ongoing approach that leverages on the Big 5 Personality trait model and users digital traces harvested from their social networks, thereafter, transformed into a personalized warning message. We argue that stimulating users through personal recommendations evokes an understanding of the implications of their actions or inaction.
The Stabilizer Formalism and the Gottesman–Knill Theorem

The stabilizer formalism provides another method of constructing quantum error-correcting codes. However, we are going to be interested in it primarily for a different reason; it allows us to prove the Gottesman–Knill theorem. That theorem serves as a useful warning. Just because you are manipulating qubits with quantum gates does not guarantee that what you are doing cannot be simulated efficiently on a classical computer.
Semantic segmentation method in family life scene

The application of home service robots is the future development trend of mobile robots. To make the mobile robot perceive the family living environment and serve all kinds of people, based on collecting images and segmenting them into data sets, a semantic segmentation method for the family living environment is proposed. The photographed images of the research objects (bed and stroller) constitute the image dataset which is split into training and test datasets. Two semantic segmentation algorithms, FCN and SegNet, are utilized to learn the training set, and the performance of each semantic segmentation algorithm is compared and evaluated on the test set. Experimental results show that the accuracy of the two semantic segmentation algorithms for strollers is higher than that for beds. Among them, the AP and the MIoU of the FCN algorithm for the stroller are 95.39%, and 91.08% respectively whereas the SegNet algorithm is 88.88%, and 81.34% respectively. The FCN algorithm shown better performance compared to the SegNet algorithm and it can be applied in our daily life.
Raya: A Tangible Exercise Buddy Reminding Oneself of the Commitment to Exercise

Although many people have a positive intention to be more active, a key challenge remains to turn this intention into action. Social support as a motivational strategy can increase adherence in exercise and can be provided by relational agents as a substitute for human coaches. We first conducted an exploratory two-week user study, to explore how emotional design and tangible interaction influences experience and motivation to exercise. We then designed a propositional research object Raya, a tangible exercise buddy that helps one to realize their workout by reminding them of their goals and self-commitment. We invite designers to bridge the gap in the design space of sport-related technologies by designing tangible artefacts embedding supportive and qualitative aesthetics of interaction rather than focusing on performance.
Application of Frame Semantics to Teach Persian Vocabulary to Non-native Speakers

There are different approaches to teach a foreign or second language to non-native speakers in the world of education. Learning vocabulary is the most important one within the language learning process; as a result, improving students’ knowledge about vocabulary in a language has a priority in language teaching. Fillmore’s Frame Semantics Theory (Fillmore CJ, Speech, place and action. John Wiley, London, 1982) is one of the newest methods in the field of Cognitive Semantics that utilizes frame semantics to teach vocabulary. It is believed that the meanings of words are perceived within a system of knowledge arisen from the human cultural experience and semantic frames which display sections of an event used for connecting a group of words to a set of meanings. On the other hand, verbs are the most important elements in events and play a significant role in the interpretation of the meaning, too. This chapter addresses the extent of appropriateness of frame semantics to teach Persian vocabulary to non-native speakers. To this end, we selected the verb /∫odæn/ (to become) as a complex and controversial Persian verb and discuss its semantic properties within frame semantics to determine its senses and create a frame semantic model to be used for teaching.
Recognition of MFR Based Motif Discovery

Multi-function radar (MFR) has pulse-level parameter agility, and under the scheduling of complex procedures, it can adjust resource allocation in an optimal way to perform multiple tasks in different directions. The agility of the parameters makes it difficult to describe MFR statistically, and traditional statistical description methods based on signal characteristics are difficult to support the needs of MFR recognition tasks. Another potential MFR description method is using the high-dimensional temporal pattern hidden in pulse stream, which is represented by the pulse repetition interval (PRI) switch rule and value range. This paper uses motif mining algorithm to find high-dimensional repeated patterns in MFR pulse stream, and the key PRI sequence of MFR is obtained through further screening. Then, multiple key sample sequence matchers are integrated into a matching degree generator, which is used to output the matching degree between the MFR to be identified and the reference MFR. Simulation results show that compared with the deep learning method, it has better performance and stronger interpretability.
Random Deep Neural Network for Melanoma Recognition

The paper presents a new method for building the CNN architecture with improved generalization capabilities. The network is formed from two deep structures differing by the activation functions. The dense layer signals are also separated into two branches and the verdicts of both are fused randomly into final decision using the classical cross-entropy loss formulation and softmax approach. In the learning process, the parameters of the locally connected CNN layers are adapted based on random combinations of the signals of these two parallel CNN subnetworks. The randomness introduced in all stages of the network structure fulfills the role of automatic implicit regularization at a very small number of learning data. The positive role of such regularization has been confirmed in numerical experiments of image recognition representing melanoma or non-melanoma cases. The results obtained by using the proposed method showed a significant improvement in numerical quality measures, including accuracy, sensitivity, F1 measure, and the area under the ROC curve.
A Novel Facial Expression Recognition Method Based on AMSSD Model

Facial expression recognition is a technology that uses computers to understand human emotions to provide a good human-computer interaction experience. To further improve the accuracy and efficiency of facial expression recognition, we propose a lightweight model AMSSD based on SSD reconstruction. In this paper, we use the MobileNetV2 lightweight network to replace the SSD base net to reduce the amount of parameters. And use CBAM to improve feature extraction capabilities from the two dimensions of channel and space. This method reduces the number of parameters and improves the efficiency and accuracy of recognition. The AMSDD model achieves 0.733 mAP on the FER2013 facial expression data set, which fully verified the effectiveness of the method.
Density-Based Evaluation Metrics in Unsupervised Anomaly Detection Contexts

From cybersecurity to life sciences, anomaly detection is considered crucial as it often enables the identification of relevant semantic information that can help to prevent and detect events such as cyber attacks or patients heart-attacks. Although anomaly detection is a prominent research area it still encompasses several challenges, namely regarding results evaluation in real-world unlabelled and imbalanced datasets. This work contributes to understand and compare the behaviour of different evaluation metrics, namely classic metrics based on positive and negative rates, and density based metrics without classes information. We experiment five state-of-art anomaly detection approaches over two datasets with contrasting characteristics regarding dimensionality or contamination. Each metrics' ability to give trustful results is analysed regarding different datasets or approaches properties focusing on the possibility of evaluating real-world unsupervised learning models using density metrics.
Designing and performance analysis of solar tracker system: a case study of Çukurova region

Today, solar energy has an important place among the renewable energy sources in the world due to its high energy potential. Therefore the share of solar energy is gradually increasing in electricity generation. It is difficult to get maximum efficiency from the solar photovoltaic (PV) panels installed permanently because they can’t benefit from solar energy continuously. Therefore one of the most effective ways to prevent loss of energy efficiency is the solar tracking systems that provide up to 40% efficiency. In this study, it is aimed to increase the efficiency of solar PV plants by following the sun throughout the day and to maximize the power produced by solar PV panels by exposing it to more light. Therefore a single-axis passive-controlled solar tracker system design is recommended for 42,000 kWp solar PV power plants in the Çukurova region. The efficiency effect of the proposed tracker system for high-power solar PV plants is examined. By comparing the performance analysis of the proposed solar PV system having a solar tracker system with a fixed angle solar PV system, the effectiveness of the proposed system is revealed for high-power solar PV plants.
Representation Learning on Multi-layered Heterogeneous Network

Network data can often be represented in a multi-layered structure with rich semantics. One example is e-commerce data, containing user-user social network layer and item-item context layer, with cross-layer user-item interactions. Given the dual characters of homogeneity within each layer and heterogeneity across layers, we seek to learn node representations from such a multi-layered heterogeneous network while jointly preserving structural information and network semantics. In contrast, previous works on network embedding mainly focus on single-layered or homogeneous networks with one type of nodes and links. In this paper we propose intra- and cross-layer proximity concepts. Intra-layer proximity simulates propagation along homogeneous nodes to explore latent structural similarities. Cross-layer proximity captures network semantics by extending heterogeneous neighborhood across layers. Through extensive experiments on four datasets, we demonstrate that our model achieves substantial gains in different real-world domains over state-of-the-art baselines.