Reviewerly/exHarmony huggingface dataset:
In Depth Bayesian Semantic Scene Completion

For autonomous agents moving around in our world, mapping of the environment is essential. This is their only perception of their surrounding, what is not measured is unknown. Humans have learned from experience what to expect in certain environments, for example in indoor offices or supermarkets. This work studies Semantic Scene Completion which aims to predict a 3D semantic segmentation of our surroundings, even though some areas are occluded. For this we construct a Bayesian Convolutional Neural Network (BCNN), which is not only able to perform the segmentation, but also predict model uncertainty. This is an important feature not present in standard CNNs. We show on the MNIST dataset that the Bayesian approach performs equal or better to the standard CNN when processing digits unseen in the training phase when looking at accuracy, precision and recall. With the added benefit of having better calibrated scores and the ability to express model uncertainty. We then show results for the Semantic Scene Completion task where a category is introduced at test time on the SUNCG dataset. In this more complex task the Bayesian approach outperforms the standard CNN. Showing better Intersection over Union score and excels in Average Precision and separation scores.
Face Recognition Using Transfer Learning on Facenet: Application to Banking Operations

Of late, biometric systems are ubiquitous to provide user authentication and security. With the advent of AI aided computer vision, face recognition systems are the new age biometrics gaining attention. They are more robust than fingerprint scanners and provide contactless experience to users. In this paper, we discussed the applications of face recognition and face verification techniques for banking and finance services. We propose an efficient and better approach to train a face recognition model which has potential applications in banking operations among other domains. We performed face detection with Histograms of Oriented Gradients (HOG) face detection. Our approach involves transfer learning on the state-of-the-art face recognition model Facenet to extract face embeddings and a kind of Nearest Neighbors (NN) to label the face. Our approach doesn’t involve large datasets and powerful GPU computations to train the model. We performed our experimentation on the Georgia Tech Face-Database (GTFD) and achieved an accuracy of 96.67%, which is very close to human vision (97.53%) and a significant improvement over other approaches.
Assessing the Impact of Haze on Solar Energy Potential Using Long Term PM2.5 Concentration and Solar Insolation Filed Data in Naples, Italy

Atmospheric fine particulate pollutant affects seriously the human health but also the passage of light through the lower atmosphere, reducing the solar radiation reaching the ground as well as the PV panels. In this study, the solar insolation reduction due to air pollution has been investigated in the city of Naples (Italy). Analyzing local long term field data, we have obtained that the solar insolation reduction is exponentially correlated to the PM2.5 concentration. By using the derived empirical relation, for Naples it was estimated that the solar insolation was reduced around 5% or 66.20 kWh/m−2 within one-year period (May 2018–May 2019), due to air fine particulate pollution. This study provides the theoretical basis to design successful solar PV systems to be mounted on building rooftops or in other suitable sites, taking into account also the local air pollution condition.
Comparative Study of Usability Evaluation Methods on a Hyper Casual Game

There are many usability evaluation criteria proposed in the past, and each has different limitations. To find the best usability evaluation method for a hyper-casual game, we present a comparative study on three usability evaluation methods; Concurrent Thinking Aloud (TA), Retrospective Think Aloud (RTA), and Interviewing. We test each method with 30 students by comparing the number of problems detected, experimenting time, and analysis time. We found that RTA found 80% of usability problems by experimenting with 14 participants. TA needed about 24 participants to find the number of problems. However, even if we experiment the interviewing method with 30 students, we find only 64.70% of problems. Although the interviewing is not useful in finding the maximum number of usability problems, we find that it takes the minimum time to operate. RTA required the longest experimenting time, and TA required the longest analysis time.
Assessment of aero-engine service reliability based on Akaike information criterion

To solve the problem that it is hard to choose the best model when the sample data test satisfies two or more kinds of distribution models, Akaike information criterion(AIC) was used for model selection in aero-engine reliability assessment. And 261 cases of failure data from three types of military aero-engines were chosen to test the effectiveness of the AIC. Results show that the AIC can choose the better fitting model than the traditional model.
Multipartite Entanglement Structure Resolution Analyzer Based on Quantum‐Control‐Assisted Multipartite Uncertainty Relation

We construct a quantum-control-assisted multi-observable variance-based uncertainty relation, and the uncertainty relation obtained indicates that we can prepare a quantum state, in which the measurement results of any observables can be predicted precisely with the help of quantum control and entanglement resource. The new variance-based uncertainty relation provides a concept of "multiparticle entanglement resolution lines" for multiparticle entangled pure states, that can delineate different multiparticle entanglement classes, and thus it can be considered as an analyzer of multiparticle entanglement classes. The analyzer is used to identify different multiparticle entanglement classes with a suitable number of incompatible measurements, without a complete knowledge of the quantum state.
Combining Deep and Ad-hoc Solutions to Localize Text Lines in Ancient Arabic Document Images

Text line localization in document images is still considered an open research task. The state-of-the-art methods in this regard that are only based on the classical image analysis techniques mostly have unsatisfactory performances especially when the document images i) contain significant degradations and different noise types and scanning defects, and ii) have touching and/or multi-skewed text lines or overlapping words/characters and non-uniform inter-line space. Moreover, localizing text in ancient handwritten Arabic document images is even more complex due to the morphological particularities related to the Arabic script. Thus, in this paper, we propose a hybrid method combining a deep network with classical document image analysis techniques for text line localization in ancient handwritten Arabic document images. The proposed method is firstly based on using the U-Net architecture to extract the main area covering the text core. Then, a modified RLSA combined with topological structural analysis are applied to localize whole text lines (including the ascender and descender components). To analyze the performance of the proposed method, a set of experiments has been conducted on many recent public and private datasets, and a thorough experimental evaluation has been carried out.
Advancing formative assessment in MCAD education: The visual analytics of parametric feature-based solid models

Advancing formative assessment in MCAD education is an important but difficult problem. Success in this endeavor requires feature-based MCAD model assessment to consider the quality of a model from various viewpoints. That includes the absolute criteria that are associated with technical domain knowledge and also criteria related to model deficiencies. For the latter, this entails assessing the results of wrong or inappropriately applied system commands, and of partial or entire modeling strategies. Here, an approach that combines the perceptual abilities, creativity, and domain knowledge of the human user with the computational power of current desktop computing has great potential to contribute to solving the problem. The aim of the current paper is two-fold. Firstly, it presents a novel approach to analyzing feature-based characteristics of MCAD models, an approach that is aimed at advancing formative assessment in the educational context. This approach is based on visual analytics and efforts to combine visualization, human factors, and data analytics. Secondly, it reports on the technical architecture and concrete implementation of a newly developed visualization environment for a software tool to enable and put into practice this novel MCAD model assessment approach. The development of this new visualization environment is based on an advanced visualization pipeline that employs radial visualization, while supporting dedicated user interaction techniques to facilitate analytical processes.
SecurePilot: Improving Wireless Security of Single-Antenna IoT Devices

With the arrival of the Internet of Things era, IoT devices and the services built on them make our lives more convenient and also raise public concerns on their vulnerability to attacks. Recent literature advocates physical-layer solutions to help IoT devices detect attacks instead of using sophisticated cryptographic methods. However, there is still no satisfying solutions for IoT devices with a single antenna and sparse traffic. Thus, we introduce SecurePilot to fill this gap. SecurePilot is an unsupervised and plug-and-play solution which works without an attacker’s knowledge in advance. It leverages the strengths of two orthogonal physical-layer information, propagation signatures and device signatures embedded in pilot signals to enable effective attack detection. It could work on single-antenna IoT devices with sparse traffic and also work compatibly with communication protocols. The experimental results show that SecurePilot can successfully detect 99.6% of attacks, triggering false alarms on 3.1% of legitimate traffic in a typical office environment.
Corporate Relative Valuation using Heterogeneous Multi-Modal Graph Neural Network

Corporate relative valuation (CRV) refers to the process of comparing a company's value from company products, core staff and other related information, so that we can assess the company's market value, which is critical for venture capital firms. Traditionally, relative valuation methods heavily rely on tedious and expensive human efforts, especially for non-publicly listed companies. However, the availability of information about company's invisible assets, such as patents, talent, and investors, enables a new paradigm for learning and evaluating corporate relative values automatically. Indeed, in this paper, we reveal that, if the companies and their core members are formed as a heterogeneous graph and the attributes of different nodes include semantically-rich multi-modal data, it is able to extract a latent embedding for each company. Along this line, we develop an end-to-end heterogeneous multi-modal graph neural network method, named HM <formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex>$^2$</tex></formula> . Specifically, HM <formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex>$^2$</tex></formula> firstly perform the representation learning for heterogeneous neighbors of input company by taking relationships among nodes into consideration, which aggregates node attributes via linkage-aware multi-head attention mechanism, rather than multi-instance based methods. Then, HM <formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex>$^2$</tex></formula> adopts the self-attention network to aggregate different modal embeddings for final prediction, and employs dynamic triplet loss with embeddings of competitors as the constraint.
A Semantic-based Feature Extraction Method Using Categorical Clustering for Persian Document Classification

Natural Language Processing (NLP) is one of the promising fields of artificial intelligence. In recent decades, high volume of text data has been generated through the Internet. This kind of data is a valuable source of information which can be used in various fields such as information retrieval, search engines, recommender systems, etc. One practical task of text mining is document classification. In this paper, we mainly focus on Persian document classification. We introduce a new feature extraction approach derived from the combination of K-means clustering and Word2Vec to acquire semantically relevant and discriminant word representations. We call our proposed approach CC-Word2Vec (Categorical Clustering-Word2Vec) since we retrain the Word2Vec model using the word clusters of each category obtained by K-Means algorithm. We use 200 documents of 5 most frequent categories of Hamshahri news dataset to evaluate our method. We pass the extracted word vectors to Multi-Layer Perceptron (MLP) and Gradient Boosting (GB) classifiers to compare the performance of the proposed approach with Term Frequency Inverse Document Frequency (TF-IDF) and Word2Vec methods. Our new approach resulted in an improvement in the obtained accuracy of Gradient Boosting and Multi-Layer Perceptron models in comparison with TF-IDF and Word2Vec techniques.
CLD: An Accurate, Cost-Effective and Scalable Run-Time Cache Leakage Detector

Cache logical side channel attacks pose a significant threat to the security of modern computer systems. This is a result of exploitation of cache information leakages arising from cache contention. Detection of such leakages can be inferred from cache behavior and processes' access patterns during run time. To achieve this, a detection template that uses available information on cache outputs and process accesses at run-time is required. In this work, such template is proposed and implemented as a hardware monitor called Cache Leakage Detector (CLD). CLD is a high-accuracy, cost-effective and scalable run-time cache information leakage detector. CLD uses cache signals and process IDs to detect exploitable cache access patterns. It does so by identifying potential information leakage patterns. Accuracy of CLD is evaluated by using several benchmarks and injecting attacks into a 128-bit key AES algorithm. The experiments demonstrate that CLD has far higher detection accuracy (0.7964 vs 0.3195) and lower percentage of false positive detections (1.2% vs 30.6%) compared to a state-of-the-art hardware detector. Moreover, CLD introduces a very low area overhead of 0.002% to the total area of the cache. Experimental result section reports the above claims in detail.
Hindi-English Code Mixed Hate Speech Detection using Character Level Embeddings

Hinglish is a portmanteau word for 'Hindi' and 'English', and refers to the informal "language" predominantly used in the South-Asian (Indian) Sub-Continent, a blend of the two languages it derives its name from. It considerably differs from the English language in grammar, syntax, punctuations, phonetics and accent, as well as in sentiments.As it is more convenient to use English for certain technical words, sports events, scientific phenomena, and other things, mixed usage of English and regional languages has gained considerable prominence in day-to-day conversations and Social Media. This research aims to create an independent and self-sufficing model that classifies Hingish texts as Hate Speech, Abusive or Non-Offensive.The prevalent use of code-mixed language in the subcontinent, the sensitive nature of hate speeches, and the need of a self-sufficient model for Hinglish, together serve as the motivation for this research.We have used character level embeddings for Hinglish Language which has the potential to most efficiently extract the context from Hinglish sentences given the level of variation in syntax and semantics of the code-mixed (a language that is a combination of two or more languages) language. Later we trained various deep learning classifier models. Hybridisation of GRU with Attention Model performed best among more than 12 models experimented with. The use of Character Level Embeddings, GRU, and attention layer are novel to Hate Speech Detection in Hinglish Code-Mixed Language.
MBD-GAN: Model-based image deblurring with a generative adversarial network

This paper presents a methodology to tackle inverse imaging problems by leveraging the synergistic power of imaging model and deep learning. The premise is that while learning-based techniques have quickly become the methods of choice in various applications, they often ignore the prior knowledge embedded in imaging models. Incorporating the latter has the potential to improve the image estimation. Specifically, we first provide a mathematical basis of using generative adversarial network (GAN) in inverse imaging through considering an optimization framework. Then, we develop the specific architecture that connects the generator and discriminator networks with the imaging model. While this technique can be applied to a variety of problems, from image reconstruction to super-resolution, we take image deblurring as the example here, where we show in detail the implementation and experimental results of what we call the model-based deblurring GAN (MBD-GAN).
SATGAN: Augmenting Age Biased Dataset for Cross-Age Face Recognition

In this paper, we propose a Stable Age Translation GAN (SATGAN) to generate fake face images at different ages to augment age biased face datasets for Cross-Age Face Recognition (CAFR). The proposed SATGAN consists of both generator and discriminator. As a part of the generator, a novel Mask Attention Module (MAM) is introduced to make the generator focus on the face area. In addition, the generator employs a Uniform Distribution Discriminator (UDD) to supervise the learning of latent feature map and enforce the uniform distribution. Besides, the discriminator employs a Feature Separation Module (FSM) to disentangle identity information from the age information. The quantitative and qualitative evaluations on Morph dataset prove that SATGAN achieves much better performance than existing methods. The face recognition model trained using dataset (VGGFace2 and MS-Celeb-lM) augmented using our SATGAN achieves better accuracy on cross age dataset like Cross-Age LFW and AgeDB-30.
Bronze Inscriptions Classification Algorithm On Imbalanced Dataset

Bronze inscriptions are important data for the study of ancient Chinese history. We obtained a dataset included about 100000 images of bronze inscriptions organized by professionals. However, the data distribution is imbalanced--minority classes occupy most of the sample. In this paper, we designed a variety of models based on Convolutional Neural Network(CNN) to classify the dataset. First of all, we added Convolutional Block Attention Module(CBAM) to ResNet. Secondly, we added the sample weight when calculating the train loss. At last, we used Two-Phases-Training method in order to get better results. It has two different ways-only fine-tuning the fully connected(FC) layer and adjusting all layers. According to experimental results, the Two-Phases-Training method not only improves the average accuracy(Avg_acc), but also reduces the probability that the model's output is biased towards major classes. Among them, fine-tuning the FC layer has the best performance during the second phase. Not only the training time was the shortest, but also the Avg_acc and Fl_score are the best.
Coded Gradient Aggregation: A Tradeoff Between Communication Costs at Edge Nodes and at Helper Nodes

Increasing amount of data generated at edge nodes and quest for privacy have resulted in learning at the edge. Computations are performed at edge devices and outputs are communicated to a central node for updating the model. The edge nodes are available intermittently and are connected via low-bandwidth links. The edge nodes communicate local gradients to helper nodes, and these helpers forward messages to the central node after possible aggregation. Recently, schemes using repetition codes and maximum-distance-separable (MDS) codes, respectively known as aligned repetition coding (ARC) and aligned MDS coding (AMC) schemes, were proposed. It was observed that the communication cost at edge nodes becomes optimal in the AMC scheme, at the expense of an increased cost of communication incurred by helpers. An upper bound on the communication cost at helpers for the AMC scheme was known in literature. In this paper, a tradeoff between communication costs at edge nodes and at helper nodes is established with the help of newly proposed pyramid scheme. The scheme makes use of well-known class of pyramid codes, thus expanding the realm of application of locally repairable codes to distributed learning. The communication costs both at helper nodes and at edge nodes are exactly characterized. Using the developed technique, the exact communication cost at helper nodes can be computed for the AMC scheme as well. Next, we come up with a technique to improve the aggregation strategy of both pyramid and AMC schemes, that yields significant reduction in communication cost at helpers without changing parameters of the code used by edges. Finally, we present a greedy algorithm to improve the aggregation strategy of the ARC scheme, achieving significantly reduced communication cost at helpers.
A High-Frame-Rate Eye-Tracking Framework for Mobile Devices

Gaze-on-screen tracking, an appearance-based eye-tracking task, has drawn significant interest in recent years. While learning-based high-precision eye-tracking methods have been designed in the past, the complex pre-training and high computation in neural network-based deep models restrict their applicability in mobile devices. Moreover, as the display frame rate of mobile devices has steadily increased to 120 fps, high-frame-rate eye tracking becomes increasingly challenging. In this work, we tackle the tracking efficiency challenge and introduce GazeHFR, a biologic-inspired eye-tracking model specialized for mobile devices, offering both high accuracy and efficiency. Specifically, GazeHFR classifies the eye movement into two distinct phases, i.e., saccade and smooth pursuit, and leverages inter-frame motion information combined with lightweight learning models tailored to each movement phase to deliver high-efficient eye tracking without affecting accuracy. Compared to prior art, Gaze-HFR achieves approximately 7x speedup and 15% accuracy improvement on mobile devices.
Detection and Estimation of Parameters of a Random Process Set in Multi-Scanning Radar Observation Based on the "Track - Before- Detect" Methods

In this paper we consider methods of long-term (multi-scanning) radar observation based on "track-before-detect" technologies. We analyze a two-stage procedure for joint detection-estimation of trajectories. In the paper we prove the advisability of the use of a relatively simple a priori model of target movement at the traj ectory detection stage. We show that it is possible to use a more complex model at the stage of trajectory parameters estimation. The analysis of the well-known block algorithm for estimating trajectory parameters using the least squares method is carried out. We offer a methodology for testing the accuracy of traj ectory parameters measurements when implementing the block method of multi-scanning observation.