Reviewerly/exHarmony huggingface dataset:
DFILAN: Domain-Based Feature Interactions Learning via Attention Networks for CTR Prediction

Click-Through Rate (CTR) prediction has become an important part of many enterprise applications, such as recommendation systems and online advertising. In recent years, some models based on deep learning have been applied to the CTR prediction systems. Although the accuracy is improving, the complexity of the model is constantly increasing. In this paper, we propose a novel model called Domain-based Feature Interactions Learning via Attention Networks (DFILAN), which can effectively reduce model complexity and automatically learn the importance of feature interactions. On the one hand, the DFILAN divides the input features into several domains to reduce the time complexity of the model in the interaction process. On the other hand, the DFILAN interacts at the embedding vector dimension level to improve the feature interactions effect and leverages the attention network to automatically learn the importance of feature interactions. Extensive experiments conducted on the two public datasets show that DFILAN is effective and outperforms the state-of-the-art models.
The Use of Augmented Reality to Build Occupational Health and Safety (OHS) Learning Media

Abstract Occupation Health and Safety (OHS) has a purpose to ensure a safe and healthy work environment. Occupation health and safety must be apllied by an industry that have potential hazards arising from the production process carried out. The potential hazards include work accident such as explosion, fire, pollution, and disease. The technological development brought us to the next level of invention. One of the invention that found in 21 st century is Augmented Reality. Augmented Reality is an revolutionaty invention which combines virtual object into reality environment. Augmented Reality widely used as a learning media, promotional tools, and for some special purpose. Augmented Reality can be use in desktop, mobile device, and wearable technology. The purpose of this research is to build Occupational Health and Safety learning media based on Augmented Reality. The learning media will be made mobile based with android devices. The method of software development using the waterfall method. The output of this research is marker card that can display objects in the form of images about Occupation Health and Safety. Images will reveal if card scanned using android mobile device that has the Augmented Reality application installed.
Deep learning pre-trained model as feature extraction in facial recognition for identification of electronic identity cards by considering age progressing

Abstract The usage of the face recognition system is various, one of which is to identify missing people. Cases of missing persons can usually happen to people who do not carry an identity card, such as people with mental disorders. Identity cards have some information, namely name, address, date of birth, and a photo ID of a face. This information has stored at the civil registry office, which is an Indonesian government agency. Therefore to solve this problem, we use a photo ID as a dataset to identify someone’s identity. Photo ID as a dataset has age progressing factors in facial recognition systems. Several previous studies have discussed age progressing factors. Previous research used MORPH, CACD, and FGNET datasets. The dataset has several photos of faces consisting of various age levels for the same subject, but it is different from the photo ID that we use. For the training data process, we only use one data, namely a photo ID used to recognize faces at this point. Therefore we use the pre-trained VGGFace2 model, and then, fine-tuning the data during the training process, we use AM-Softmax loss as a loss function. Then the classification is done using SVM. The method we use than compared with the Moustafa method, which also uses a pre-trained model. The results we obtained have a better accuracy performance of 0.9351 compared to the Moustafa method of 0.733
A weakly supervised representation learning for modulation recognition of short duration signals

Modulation recognition of the short duration signals is considered as a challenging area in various civil and military applications. The calculation of distinct features is a serious problem in recognizing these signals. In this study, a robust feature representation is presented to solve this problem. In addition, our learned feature representation has a considerable potential to recognize different modulation types while facing a limited number of labeled signals. In the proposed method, the dataset is randomly divided into equal size disjoint subsets, and fuzzy c-means clustering is performed on the data in each subset to obtain pseudo cluster labels. A projection function is learned for each subset in order to create an ensemble of projection functions, and then the data are represented using these functions. The learned feature representation is tested in AWGN and flat-fading channels. Experimental results indicated a significant improvement in classification accuracy compared to the conventional features.
Topic Identification from Spanish Unstructured Health Texts

Topic Models allow to extract topics from documents and classify them. In this work, Latent Dirichlet Allocation model was applied to extract topics from documents with medical information. 220 digital documents written in Spanish were used, these documents have information about different health conditions. A pre-processing was carried out, which implies tokenization, stop words elimination and lemmatization, to define the medical data or terms that will represent the documents. Subsequently, a document representation was made through a document-term matrix. An important step was to use a medical glossary based on terminology extracted from Internet to assign weights to the terms. LDA was applied and two new matrices were obtained: a document-topic matrix and a topic-term matrix. 25 topics were identified, they can be visualized by heat maps, word cloud and an interactive tool called PyLDAvis. The application was developed in Phyton using some libraries such as Spacy, Scikit-learn, Tmtoolkit, PyLDAvis among others.
WITHDRAWN: Cloud computing based generic medicine recommendation system for advanced E-Healthcare

Many health institutes manually record and collect patients' data may be misunderstood and at times, inconsistent. Also, patients' alienation from medical details leaves them unaware of the necessary medical costs and inevitably causes them to pay long and ineffective bills. Therefore, it is very important and important to consider the consumers of generic medicinal products – medicinal products of the same chemical composition, but available at low cost. This device proposes a method for sensing the physical wellbeing of patients using sensors after they are admitted to the hospital. This is added to a “cloud” server that stores the health parameters of patients and accesses them whenever possible. The patient's cloud-based databases also include the medicine prescribed by physicians, the diagnosed condition of the patient and the tests suggested by the doctors. The software interfaced between the cloud and the patient data entry displays the results of 'cost-effective generic drugs,' the necessary tests needed for the patient and the hospital to be qualified for further treatment, which are shown to the patients or their associates. Few well-equipped healthcare facilities follow data centralization and database approaches, but their accuracy remains a matter of reliability. Our proposed system offers a way for patients to track their medical costs within their boundaries, using sensors and cloud computers as their technological resources.
Sparse oblique decision trees: a tool to understand and manipulate neural net features

The widespread deployment of deep nets in practical applications has lead to a growing desire to understand how and why such black-box methods perform prediction. Much work has focused on understanding what part of the input pattern (an image, say) is responsible for a particular class being predicted, and how the input may be manipulated to predict a different class. We focus instead on understanding which of the internal features computed by the neural net are responsible for a particular class. We achieve this by mimicking part of the neural net with an oblique decision tree having sparse weight vectors at the decision nodes. Using the recently proposed Tree Alternating Optimization (TAO) algorithm, we are able to learn trees that are both highly accurate and interpretable. Such trees can faithfully mimic the part of the neural net they replaced, and hence they can provide insights into the deep net black box. Further, we show we can easily manipulate the neural net features in order to make the net predict, or not predict, a given class, thus showing that it is possible to carry out adversarial attacks at the level of the features. These insights and manipulations apply globally to the entire training and test set, not just at a local (single-instance) level. We demonstrate this robustly in the MNIST and ImageNet datasets with LeNet5 and VGG networks.
Recognition of Fingers movement Using Fiber Bragg Gratings in Silicon Elastomer Packing

This work presents preliminary results from a single FBG sensor used to recognize fingers movement pattern. Six subjects had their movements acquired to signal processing through Linear Discriminant Analysis (LDA) further. Firstly, the index and middle fingers movements are discriminated within a training group of four subjects. To apply the LDA result, a test group of two subjects is used. Despite the non-symmetrical Gaussian samples distribution, the LDA technique was able to differentiate between the two fingers within all the subjects.
One and Two‐Weight ℤ <sub>2</sub> <i>R</i> <sub>2</sub> Additive Codes

This paper is devoted to the construction of one and two-weight additive codes, where . It is a generalization towards another direction of codes (S.T. Dougherty, H.W. Liu and L. Yu, “One weight additive codes”, Applicable Algebra in Engineering, Communication and Computing, Vol.27, No.2, pp.123-138, 2016). A MacWilliams identity which connects the weight enumerator of an additive code over and its dual is established. Several construction methods of one-weight and two-weight additive codes over are presented. Several examples are presented to illustrate our main results and some open problems are also proposed.
A Digital Currency Architecture for Privacy and Owner-Custodianship

In recent years, electronic retail payment mechanisms, especially e-commerce and card payments at the point of sale, have increasingly replaced cash in many developed countries. As a result, societies are losing a critical public retail payment option, and retail consumers are losing important rights associated with using cash. To address this concern, we propose an approach to digital currency that would allow people without banking relationships to transact electronically and privately, including both internet purchases and point-of-sale purchases that are required to be cashless. Our proposal introduces a government-backed, privately-operated digital currency infrastructure to ensure that every transaction is registered by a bank or money services business, and it relies upon non-custodial wallets backed by privacy-enhancing technology such as blind signatures or zero-knowledge proofs to ensure that transaction counterparties are not revealed. Our approach to digital currency can also facilitate more efficient and transparent clearing, settlement, and management of systemic risk. We argue that our system can restore and preserve the salient features of cash, including privacy, owner custodianship, fungibility, and accessibility, while also preserving fractional reserve banking and the existing two-tiered banking system. We also show that it is possible to introduce regulation of digital currency transactions involving non-custodial wallets that unconditionally protect the privacy of end-users.
Underwater Image Enhancement Using Neighbourhood Based Two Level Contrast Stretching and Modified Artificial Bee Colony

Underwater images usually endure from colour deformation and low contrast, since light is dispersed and absorbed when it passes through water. In this paper, a new two-level, neighbourhood based contrast adjustment method is proposed for underwater image enhancement, which can be effectively applied to put back and enhance the quality of underwater images. Several factors typically influence to reduce the lighting in water, that may lead to poor visibility of objects in underwater images. In this method, initially, the pixels are segregated into two halves from the image histogram. A Modified version of Artificial Bee Colony (ABC) algorithm is proposed to identify points in the histogram, based on which, the pixels are stretched in both sides to increase its construct level. The supremacy of the proposed method is visually and quantitatively established in reference to the optimum fitness, mean fitness, Structural Similarity Index Measure (SSIM) and Underwater Colour Image Quality Evaluation (UCIQE), average SSIM and average UCIQE values for all test instances. Finally, Friedman test and two tailed t-test have also been conducted to statistically establish the efficiency of the proposed method. Experimental results demonstrate that the proposed method outperforms others.
Human Activity Recognition Based on Residual Network

Abstract With the development of information technology and the blooming of artificial intelligence, human activity recognition (HAR) has become a hot research topic. HAR has a certain prospect and practical value for mobile medicine and security monitoring, which has attracted the attention of many researchers. However, most of the existing HAR methods rely on the shallow feature learning mechanism, and the features learned by these methods are not completely consistent with the actual activities. To solve this problem, a human motion recognition method based on residual network (ResNet) is proposed. In addition, we modified the internal structure of ResNet by limiting the number of its network layers within a certain range to prevent over fitting and degradation. Our proposed method has better recognition performance than methods using pure convolutional neural networks(CNN), and it achieves 95.66% recognition accuracy.
Extracting Socio-Economic Indicators from Chinese Text with a BERT-based Model

Socio-economic indicators are powerful instruments for measuring economic conditions. Extracting them can help people grasp the economy trend and make decisions. Traditional machine learning methods for indicator extraction rely heavily on handcrafted features, which costs a large amount of human effort. While, deep learning methods can solve this problem but require a huge amount of labeled data, which is the trickiest challenge as the labeled data in indicator extraction task is quite rare. In this paper, we use a BERT-based model to deal with the challenges in this task. The model firstly represents input text with BERT, taking advantage of the strong ability of BERT to capture generic language features. Then, it fine-tunes the pre-trained model through the labeled data in our indicator extraction task to learn the specific features. Finally, they go through a conditional random field (CRF) layer to get the predicted tags across output token labels. In this way, our model does not require too much labeled data but it can automatically and sufficiently capture the language features of input text. Additionally, this paper also constructs a middle scale dataset for fine-tuning process and evaluates our model on it. The results demonstrate that the BERT-based model is superior to some strong baselines.
Post-Quantum Secure Identity-Based Encryption from Multivariate Public Key Cryptography

In this paper, we develop an identity-based encryption (IBE) scheme, namely $$\mathsf{MU\text{-}IBE}$$ , that achieves post-quantum security. Our scheme relies on multivariate public key cryptography, which is one of the most promising candidates of post-quantum cryptography. The proposed IBE is efficient as it incurs low communication and computation costs. Our design is proven to be IND-ID-CCA (believed to be the right security model for IBE) secure in the random oracle model under the hardness of the MQ problem. Moreover, the proposed $$\mathsf{MU\text{-}IBE}$$ is resistant to the collusion attack. In particular, our scheme is the first to achieve IND-ID-CCA in the context of multivariate identity-based encryption systems.
Derin Sinir Ağları ve Uzun Kısa Süreli Bellek Hiperparametrelerinin Konuşma Tanıma Tabanlı Sistemler Üzerindeki Etkisinin İncelenmesi için Türkçe Yayın Haberleri Konuşma Veri Tabanı

Speech recognition is the transformation of spoken words and sentences into text.There have been many studies on speech recognition in many countries recently.However, studies on speech recognition applications in our country are very few, one of the reasons is the lack of voice dataset.In this study, a Turkish speech database has been developed for Turkish speech recognition based systems.Sound recordings were obtained from news broadcasted by Turkish news tv channels at different times.The created data set was shared on the web in a way that everyone can access in order to set a precedent for other studies.Additionally, the effects of number of layers and number of cells hyperparameters of Long Short Term Memory (LSTM) and Deep Neural Network (DNN) models were investigated on the Turkish Broadcast News Speech Database.
A classification model for student exchange using CART algorithm

Abstract The university has a cooperative relationship with other universities including abroad. The cooperation program covers various fields, one of which is the academic field. Students have the opportunity to exchange information, science, and culture through student exchange programs. However, not all students are eligible to join this program because there are terms and conditions that must be met from various aspects such as academics, attitudes, and even financial conditions. The purpose of this study is to analyse and preprocess the training data and then model it in the form of classification using CART. Based on the test results, the proposed model provides satisfactory results with an accuracy percentage of 90%.
Impact of COVID-19 on Cyber Security Threat Landscape in New Reality

COVID-19 has changed the way the world operates, the way we communicate, the mode of doing business and the functioning of governments. The world as we know it will never be the same. COVID-19 had a massive impact on society as a whole at the start of 2020. Doing projections on what is currently seen and what we expect to happen within the coming months also has the advantage of reflection upon in the future. Currently, the only known fact is that there is no end-goal insight for the pandemic and that the world needs to do something about it. This has led to a sudden surge in demand for online activities. Society has a heightened dependency on digital infrastructure; In these coming days, most organisations will and have already requested their workforce to work from home where possible. Several states, in India has gone into full lock down forcing people to stay indoors. This has caused people to become fully reliant on technology for both communication, news, entertainment and social interaction. The increase in virtual activities such as remote work and online shopping have made enterprise networks and popular websites a breeding ground for cybercrime. There is currently massive uncertainty amongst the general public on what is accurate news and what is fake news. There are also corporations trying to push their own agenda, amidst this hysteria, whilst cyber criminals are trying to profiteer out of this pandemic. The focus of this paper is to explore the exact impact the COVID-19 had within the realm of the cyber security threat landscape of the world.
Number Entities Recognition in Multiple Rounds of Dialogue Systems

As a representative technique in natural language processing (NLP), named entity recognition is used in many tasks, such as dialogue systems, machine translation and information extraction. In dialogue systems, there is a common case for named entity recognition, where a lot of entities are composed of numbers, and are segmented to be located in different places. For example, in multiple rounds of dialogue systems, a phone number is likely to be divided into several parts, because the phone number is usually long and is emphasized. In this paper, the entity consisting of numbers is named as number entity. The discontinuous positions of number entities result from many reasons. We find two reasons from real-world dialogue systems. The first reason is the repetitive confirmation of different components of a number entity, and the second reason is the interception of mood words. The extraction of number entities is quite useful in many tasks, such as user information completion and service requests correction. However, the existing entity extraction methods cannot extract entities consisting of discontinuous entity blocks. To address these problems, in this paper, we propose a comprehensive method for number entity recognition, which is capable of extracting number entities in multiple rounds of dialogues systems. We conduct extensive experiments on a real-world dataset, and the experimental results demonstrate the high performance of our method.
Inferring Deterministic Regular Expression with Unorder and Counting

Schema inference has been an essential task in database management, and can be reduced to learning regular expressions from positive finite-samples. In this paper, schemata are inferred from unordered XML documents. We extend the single-occurrence regular expressions (SOREs) to single-occurrence regular expressions with unorder and counting (SOREUCs), and give an inference algorithm for SOREUCs. First, we present a finite automaton with unorder and counting (FAUC). Then, we construct an FAUC for recognizing a given finite sample. Next, the FAUC runs on the given finite sample to obtain counting operators. Finally we transform the FAUC to a SOREUC by introducing unordered concatenations and counting operators. Experimental results demonstrate that, SOREUCs have stronger expressive powers for modeling unordered schemata than existing works, and our algorithm can efficiently infer a concise SOREUC with better generalization ability.
Geofencing technology implementation for pet tracker using Arduino based on Android

Abstract The risk of losing pets, especially pets without a leash, usually occurs because animals get lost or stolen by irresponsible people. Owners can look for themselves to the nearest environment, ask people around, to make announcement posters at strategic places. Searching in this way takes a lot of time and effort and the pets you are looking for are not necessarily found immediately. So we need an application and an animal tracking device in order to find out the location of the animal’s whereabouts, one of which is by using a tracking device that utilizes the Global Positioning System (GPS). This study aims to implement geofencing technology for pet tracking using Android-based Arduino. The application development stage includes analysis, system design, implementation, and testing. This program is implemented using U-Blox Neo 6M to track location with GPS network, SIM800L is used to send coordinate point data to trackers using GPRS network, and Arduino pro mini to manage input and output processes from the system. The test results show the tools and applications can run well. The android based pet tracker that is made can receive notification when a pet goes out of a predefined geofence limit.