Reviewerly/exHarmony huggingface dataset:
Input Enhanced Logarithmic Factorization Network for CTR Prediction

Factorization-based methods, which can automatically model second-order or higher-order cross features, have been the benchmark models for click-through rate (CTR) prediction. In general, they enumerate all cross features with a predetermined order and filter out useless interactions through model training. However, two significant challenges remain. First, a maximum order for feature interactions needs to be defined in advance, imposing a trade-off between computational cost and the expression ability of models; Second, enumerating all feature interactions may introduce unwanted noise. In this work, we propose a novel model called Input Enhanced Logarithmic Factorization Network (ILFN), which can effectively learn arbitrary-order feature interactions and identify useful cross features. More importantly, ILFN can take full advantage of the original feature distribution in a discriminative way. The core of ILFN is the Input Enhanced Component (IEC), which can represent the impact of each feature in a feature interaction as a trainable coefficient to learn arbitrary-order cross features. Moreover, IEC can reduce the demand for logarithmic neurons by exploiting the essential raw information and does not need to incorporate the deep neural network (DNN) to model high-order interactions. Therefore, ILFN is more effective and efficient and can converge to satisfactory results faster. Extensive experiments on four real-world datasets demonstrate that our ILFN model can outperform start-of-the-art methods. The effectiveness of each proposed component is also verified by hyper-parameter and ablation studies.
Smart Surveillance System

Automated Teller Machine (ATM) is one of the major standard and feasible methods for making any financial transactions and is continuously increasing due to its convenience. But ATMs are always at a threat of being robbed. Even if the CCTV records the robbers, they cover their faces with objects like masks when entering the ATM or trying to destroy the machine. Hence, they will be difficult to identify. Thus, there is an essential need to provide high security. But nowadays most surveillance cameras are ‘dumb’ and cannot analyse the incident in real-time. In this paper, we have tried to build a system for smart video surveillance in the Closed-Circuit Television (CCTV) of ATM and to detect, identify and report any type of potentially suspicious/abnormal/anomalous event that the system might identify. The proposed system involves a deep neural network model for significant detection accuracy. A Convolutional Neural Network (CNN) extracts features from individual frames of a video which are then accumulated using a variant of Long Short-Term Memory (LSTM) that uses convolutional gates. CNN and LSTM are together used for the analysis of local motion in a video.
Pool Compression for Undirected Graphs

We present a new graph compression scheme that intrinsically exploits the similarity and locality of references in a graph by first ordering the nodes and then merging the contiguous adjacency lists of the graph into blocks to create a pool of nodes. The nodes in the adjacency lists of the graph are encoded by their position in the pool. This simple yet powerful scheme achieves compression ratios better than the previous methods for many datasets tested in this paper and, on average, surpasses all the previous methods. The scheme also provides an easy and efficient access to neighbor queries, e.g., finding the neighbors of a node, and reachability queries, e.g., finding if node u is reachable from node v. We test our scheme on publicly available graphs of different sizes and show a significant improvement in the compression ratio and query access time compared to the previous approaches.
Removal of Occlusion in Face Images Using PIX2PIX Technique for Face Recognition

Occlusion of face images is a serious problem encountered by the researchers working in different areas. Occluded face creates a hindrance in extracting the features thereby exploits the face recognition systems. Level of complexity increases with changing gestures, different poses, and expression. Occlusion of the face is one of the seldom touched areas. In this paper, an attempt is made to recover face images from occlusion using deep learning techniques. Pix2pix a condition generative adversarial network is used for image recovery. This method is used for the translation of one image to another by converting an occluded image to a non-occluded image. Webface-OCC dataset is used for experimentation, and the efficacy of the proposed method is demonstrated.
Single Step-Ahead Solar Irradiation Forecasting Based on Empirical Mode Decomposition with Back Propagation Neural Network

Efficient forecasting of solar irradiation is always a challenging task for researchers in the past and at present. In order to effectively forecast solar irradiation, this paper proposed a forecasting model based on Empirical Mode Decomposition (EMD) with Back Propagation Neural Network (BPNN). In this, EMD is used to decompose the original data into several Intrinsic Mode Functions (IMF’s) and one residue; whereas the BPNN model is trained for each IMF with a residue for final forecasting. The forecasted result for each IMF and residue are aggregate to find the ultimate prediction results. For the proposed work, three years of data from 2012–14 has been collected from National Solar Radiation Database (NSRDB) for a Delhi location. The proposed model is trained using two-year data and tested for a duration of one year on different seasons by following the single step ahead point forecast technique. The proposed model performance is determined in terms of Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and Correlation Coefficient (R2). The proposed model performance indicates that it outperforms the standalone BPNN and Naive Predictor model for forecasting solar irradiation in terms of precision and complexity.
Say It Right: AI Neural Machine Translation Empowers New Speakers to Revitalize Lemko

Artificial-intelligence-powered neural machine translation might soon resuscitate endangered languages by empowering new speakers to communicate in real time using sentences quantifiably closer to the literary norm than those of native speakers, and starting from day one of their language reclamation journey. While Silicon Valley has been investing enormous resources into neural translation technology capable of superhuman speed and accuracy for the world's most widely used languages, 98% have been left behind, for want of corpora: neural machine translation models train on millions of words of bilingual text, which simply do not exist for most languages, and cost upwards of a hundred thousand United States dollars per tongue to assemble. For low-resource languages, there is a more resourceful approach, if not a more effective one: transfer learning, which enables lower-resource languages to benefit from achievements among higher-resource ones. In this experiment, Google's English-Polish neural translation service was coupled with my classical, rule-based engine to translate from English into the endangered, low-resource, East Slavic language of Lemko. The system achieved a bilingual evaluation understudy (BLEU) quality score of 6.28, several times better than Google Translate's English to Standard Ukrainian (BLEU 2.17), Russian (BLEU 1.10), and Polish (BLEU 1.70) services. Finally, the fruit of this experiment, the world's first English to Lemko translation service, was made available at the web address www.LemkoTran.com to empower new speakers to revitalize their language. New speakers are key to language revitalization, and the power to "say it right" in Lemko is now at their fingertips.
Public Key Cryptography

As we have seen, a cryptosystem is a system of the form $$\displaystyle \langle {\mathcal M},{\mathcal C},e,d,{\mathcal K}_e,{\mathcal K}_d\rangle , $$ where $${\mathcal M}$$ is the message space, $${\mathcal C}$$ is the space of all possible cryptograms, e is the encryption transformation, d is the decryption transformation, $${\mathcal K}_e$$ is the encryption keyspace, and $${\mathcal K}_d$$ is the decryption keyspace.
Exploring Generic and Immersive Frameworks for Human-Computer Interactions

The research and teaching curriculum for describing the relationship between humans and machines is still based on the human-machine system diagram, which was proposed by Sanders and McCormick in 1982. However, with the evolution of interface technology, from the earliest hardware interfaces (e.g., buttons and knobs) to the software interfaces and touchscreen displays used in the digital age, users have become familiar with the operation of products through the use of complex gestures. Therefore, the framework for human-computer interactions (HCIs) should be reexamined, and a new diagram that is more suited for the contemporary age should be developed. To this end, we established a research group to review interactive interface technology, analyze multiple relevant products, and perform a questionnaire analysis. This study proposes four interaction frameworks, namely 1) a generic computing model for HCIs, 2) a touchscreen display control model for HCIs, 3) an immersive virtual reality model for HCIs, and 4) a metaverse HCI model. This study reveals the characteristics of these four frameworks and compares their advantages and disadvantages. These new frameworks for contemporary interactions can be used as a reference for future research studies and teaching curricula.
A Robust and Efficient Overhead People Counting System for Retail Applications

Counting people with overhead cameras is a feature that is increasingly required by the retail market, for surveillance and business intelligence. However, despite the great advances in modern neural networks, it is far from simple to train effective systems in all possible real-world scenarios. The main problem is that the publicly available datasets are not sufficiently representative or are not annotated for this purpose. To this aim, in this paper we demonstrate that a considerable effort in the collection of heterogeneous data in real scenarios, producing a new dataset of about 30,000 images, allowed to realize an effective and efficient people counting system, able to process at least 10 FPS on board of three different types of smart cameras. In addition, the F-Score higher than 95% over the test set demonstrates the effectiveness of the proposed people counting system and its suitability for real applications.
Similarity Search with Graph Index on Directed Social Network Embedding

Similarity search on directed social networks (DSNs) could help users find the K nearest neighbors (KNNs). The graph index based similarity search does not have to compare query node against every node in DSNs, since the neighbor relationship of the nodes is captured by the edges. Nevertheless, the performance of similarity search is still unsatisfactory, such as not supporting the end-to-end search or taking unnecessary detours, etc. In this paper, we propose the method of Graph Index on Directed Social Network Embedding (GI-DSNE) to facilitate the approximate KNN search on DSNs. First, the DSNE is proposed to embed the DSN into a low-dimensional vector space to achieve the embeddings for efficient calculation of similarities on the search path. Then, the nearest neighbor descent algorithm is adopted to calculate the KNN graph. Subsequently, to construct the graph index efficiently, the direction guided strategy for edge selection, maximum out-degree of GI-DSNE and the depth-first-search tree for guaranteeing the connectivity of GI-DSNE are proposed. Experimental results show that our proposed method outperforms the state-of-the-art competitors on both execution time and precision.
Improving Smartphone Reading Experience for Middle-Aged and Elderly Users: The Effect of Font Size, Line Spacing and Stroke Weight

Aging and digitization are two major trends in modern society, resulting in a rapid increase in the number of middle-aged and elderly smartphone users. Research into these user’s mobile reading experience has significant value in industry application, as well as social value. Based on the subjective experience evaluation and eye movement data of 60 Chinese adults aged 50–70 years, we investigated the impact of different font sizes, line spacing, and stroke weight on the smartphone reading experience of Chinese characters in three typical scenarios: searching, overview reading, and long-text reading. Results show that font size has a significant impact on reading experience in all scenarios. Line spacing and stroke weight have different impacts in different reading scenarios. Line spacing has a significant effect on long-text reading (such as the news details page), while stroke weight has a significant impact on search scenarios (such as news headlines). Bold type fonts can highlight key information on a page with multiple levels of information. This study also identifies the optimal font size, line spacing and stroke weight for different scenarios and different levels of information. This is the first study that provides smartphone application developers with design specifications for optimal font size, line spacing and stroke weight in different scenarios for middle-aged and elderly Chinese users. These findings can be utilized to improve the reading experience of Chinese news apps in a comprehensive manner.
RSA Key Recovery from Digit Equivalence Information

The seminal work of Heninger and Shacham (Crypto 2009) demonstrated a method for reconstructing secret RSA keys from partial information of the key components. In this paper we further investigate this approach but apply it to a different context that appears in some side-channel attacks. We assume a fixed-window exponentiation algorithm that leaks the equivalence between digits, without leaking the value of the digits themselves. We explain how to exploit the side-channel information with the Heninger-Shacham algorithm. To analyse the complexity of the approach, we model the attack as a Markov process and experimentally validate the accuracy of the model. Our model shows that the attack is feasible in the commonly used case where the window size is 5.
A Comparison of Type 1 and Type 2 Fuzzy Logic Controller for DC Motor System

In today’s modern world application, there are high-level uncertainties that are faced and affecting every kind of operation in various industries. Thus, researchers, today are on the rise to find solutions that will able help to reduce these uncertainties in many types of situations especially control system applications. The type-1 Fuzzy Logic Controller is shown not to be able to handle a high level of uncertainties and the new type-2 Fuzzy Logic Controller is now being said to be able to do a better performance than the type-1 especially in controlling a DC motor system. This can be seen by the simulation graph that clearly observes the comparison of both types. The result where FLC type 2 outperforms FLC type 1 with reduced settling time and rising time can be seen. In conclusion, the new type-2 FLC is now able to overcome the limits of what type-1 FLC are able to do and this will give birth to better and improved performance of new Fuzzy Logic Controllers that is well suited as controllers for DC motor system. This paper will briefly discuss the comparison between both of these types of FLC and the benefits.
An IoT Method for Telemedicine: Lossless Medical Image Compression Using Local Adaptive Blocks

Image compression gains prominence in picture archiving and communication systems for storage and transmission of data. The advancements in technology enable the usage of computer-assisted algorithms for disease diagnosis and therapeutic planning. In this research work, a prediction-based compression algorithm was proposed to compress DICOM images. The local adaptive block-based predictor compression technique utilizes neighborhood pixel gray values for prediction and the Huffman coder was employed for the encoding of predicted coefficients. The compression results were considered effective, in contrast with the JPEG lossless, Kernel Least Mean Square, Context Adaptive Block-Based Prediction, and Least Mean square Prediction approaches. The performance validation by metrics reveals the effectiveness of the proposed local adaptive block prediction-based approach. The proposed compression algorithm was implemented in Raspberry Pi B+ embedded processor as IoT application. The compressed images are transferred through the cloud to other nodes in the network, thus facilitating the teleradiology for disease diagnosis by physicians.
Text-Based VQA

VQA requires reasoning regarding the visual content of an image. However, in a large proportion of images, visual content is not the only information. Texts that can be recognized by optical character recognition (OCR) tools provide considerably more useful and high-level semantic information, such as the street name, product brand and prices, which is not available in any other forms in the scene. Interpreting this written information in human environments is essential for performing most everyday tasks like making a purchase, using public transportation and finding a location in a city. Hence, the new task TextVQA has been proposed. In this chapter, we briefly introduce the main datasets that benchmark progress in this field, including TextVQA [29], ST-VQA [2] and OCR-VQA [25]. Subsequently, we describe an important tool (OCR) that is a prerequisite for the reasoning process, as texts must be first recognized. Next, we select 3 representative and effective models to address this problem and describe them in a sequential manner.
Amalgamation of blockchain, IoT, and 5G to improve security and privacy of smart healthcare systems

Smart healthcare systems have become smarter compared to traditional systems due to the advancement of communication technologies such as 5G and with the wide range of Internet of Things (IoT) devices such as sensors with blockchain technologies, evolving to the current trend of Healthcare 4.0. It is decided that 75% of the company will start using the blockchain concept within 2022 and the IoT adaptors are planning to implement blockchain for around 90% based on the Gartner report. Healthcare systems, which are vital for any society, store information about the patients and their diagnoses. So, it is important to secure and maintain the privacy of patient information in smart healthcare. An amalgamation of 5G, IoT, and blockchain in a smart healthcare system provides the necessary security and privacy of the data generated. 5G provides a reliable high-performance communication network with high throughput and large network coverage for data transfer. IoT provides remote patient monitoring and real-time status for quick reactions while blockchain provides the security and privacy of the data. The framework of the smart healthcare system is discussed along with comparisons of various framework models. The layer-wise security issues and applications in IoT are also discussed. A case study is discussed to provide a wide range of information about the techniques and plan used to provide security and privacy along with a comparison of the various technologies used.
Spatial Augmented Reality (SAR) System for Agriculture Land Suitability Maps Visualization

The purpose of this Study is to visualize Agriculture land suitability maps using Spatial Augmented Reality (SAR). This research has three main stages. The first stage collects map data from open data sites and processes it with GIS software. The second stage is the land suitability evaluation using the weighted overlay method. The Last stage is the visualization of the land suitability map. The visualization process begins with the creation of a 3D map taken from DEM data. Then the land suitability map generated in the previous stage is projected onto the 3D map model. SAR visualizes a land suitability map to a 3D object with projectors. The calibration process uses the Procam library using Open CV. This study compares the projection map with the land suitability map in the form of a raster file (Tiff file format) to evaluate the projection results. Then used qualitative methods to get user responses to the system. This study concludes that SAR provides a new way to visualize maps. SAR makes it easy for map users to obtain information on the map. This system is expected to assist farmers in understanding the resulting land suitability map.
A Sensor-Independent Multimodal Fusion Scheme for Human Activity Recognition

Human Activity Recognition is a field that provides the fundamentals for Ambient Intelligence and Assisted Living Applications. Multimodal methods for Human Activity Recognition utilize different sensors and fuse them together to provide higher-accuracy results. These methods require data for all sensors employed to operate with. In this work we present a sensor-independent, in regards to the number of sensors used, scheme for designing multimodal methods that operate when sensor-data are missing. Furthermore, we present a data augmentation method that increases the fusion model’s accuracy (up to $$11\%$$ increases) when operating with missing sensor-data. The proposed method’s effectiveness is evaluated on the ExtraSensory dataset, which contains over 300,000 samples from 60 users, collected from smartphones and smartwatches. In addition, the methods are evaluated for different number of sensors used at the same time. However, the max number of sensors must be known beforehand.
Emerging Technologies: Blockchain and Smart Contracts

This chapter begins by briefly covering the history of Blockchain and introducing its core elements, continuing to explain the fundamentals of Blockchain technology and Smart Contracts. A discussion is made on nodes, consensus mechanisms, digital signatures and cryptographic hashes, types of blockchains, Ethereum, and Smart Contracts benefits. After that, it explores the distributed ledger technology (DLT) and blockchain as a subset of DLT in greater detail, discussing the benefits and challenges of DLT with Blockchain. It then presents us with some of the interesting use cases of Blockchain within various industries including financial, healthcare, manufacturing, and agriculture. Furthermore, it provides a roadmap for successfully implementing Blockchain in modern business, with recommendations on preparation, design and planning, implementation, and review. Finally, it explores future trends in DLT, blockchain and Smart Contracts.