Reviewerly/exHarmony huggingface dataset:
Scalable FPGA Hardware Accelerator for SVM Inference

In this paper we present a scalable hardware architecture implementing the Support Vector Machine (SVM) binary classification in a Xilinx Zynq FPGA. Our design utilizes a hardware friendly version of the RBF kernel running at 175MHz and taking 11.8µs to process 4096 support vectors with 15 features and 12 bit precision. The architecture is pipelined and calculates the L1-norm part of the hardware friendly kernel iteratively. The model is stored in on-chip memory.
Silicon-Based Metastructure Optical Scattering Multiply–Accumulate Computation Chip

Optical neural networks (ONN) have become the most promising solution to replacing electronic neural networks, which have the advantages of large bandwidth, low energy consumption, strong parallel processing ability, and super high speed. Silicon-based micro-nano integrated photonic platforms have demonstrated good compatibility with complementary metal oxide semiconductor (CMOS) processing. Therefore, without completely changing the existing silicon-based fabrication technology, optoelectronic hybrid devices or all-optical devices of better performance can be achieved on such platforms. To meet the requirements of smaller size and higher integration for silicon photonic computing, the topology of a four-channel coarse wavelength division multiplexer (CWDM) and an optical scattering unit (OSU) are inversely designed and optimized by Lumerical software. Due to the random optical power splitting ratio and incoherency, the intensities of different input signals from CWDM can be weighted and summed directly by the subsequent OSU to accomplish arbitrary multiply-accumulate (MAC) operations, therefore supplying the core foundation for scattering ONN architecture.
Applying Real-Time Dynamic Scaffolding Techniques during Tutoring Sessions Using Intelligent Tutoring Systems

An intelligent tutoring system (ITS) is a computer system or software application that is built to replicate human tutors by supporting the theory of “learning by doing.” Even though ITSs have been proven to be successful in academic studies, they still have not found large adoption by the industry due to the complexities of building such systems due to the high technical expertise and domain knowledge requirements. Attempts have been made to build authoring tools that can provide assistance in building tutoring systems; however, most of these tools are targeted toward authors that have considerable programming experience. This research proposes an authoring tool for ITS, which is targeted at novice authors with minimum technical/programming experience and provides real-time scaffolding to learner’s incomplete/incorrect answers using the best scaffolding techniques. Two evaluation techniques were applied for the evaluation of the performance of the proposed authoring tool, e.g., paired t-test analysis and postexperiment survey. The learning gains obtained from paired t-test contend a significant learning gain and improvement in the learning process with enhanced learning performance with multiple scaffolding techniques as compared to single scaffolding technique experience. The postexperiment survey has a notable result that shows the effectiveness of the tutor model that ensures a very user-friendly interface, deploying scaffolding techniques and adequate control of selecting and deploying scaffolding techniques and making the authoring process easy.
Towards the Integration of Security and Safety Patterns in the Design of Safety-Critical Embedded Systems

The design of safety-critical embedded systems is a complex process that involves the reuse of proven solutions to fulfill a set of requirements. While safety is considered as the major requirement to be satisfied in safety-critical embedded systems, the security attacks can affect the security as well as the safety of these systems. Therefore, ensuring the security of the safety-critical embedded systems is as important as ensuring the safety requirements. The concept of design patterns, which provides common solutions to widely recurring design problems, have been extensively engaged in the design of the hardware and software in many fields, including embedded systems. However, there is an inadequacy of experience with security patterns in the field of safety-critical embedded systems. To address this problem, this paper proposes an approach to integrate security patterns with safety patterns in the design of safety-critical embedded systems. Moreover, it presents a customized representation for security patterns to be more relevant to the common safety patterns in the context of safety-critical embedded systems.
Testing reward function choice influence on training performance of Double DQN

This paper compares the influence of different reward functions on the training performance of a Double DQN. Modified reward function were chosen by analysing the training process with a simple base reward function. The results of the simulations can be used for constructing reward function that improve the training speed of Reinforcement Learning algorithms in non-simulated real life applications.
Disruptive GNSS Signal detection and classification at different Power levels Using Advanced Deep-Learning Approach

Although the Global Navigation Satellite System (GNSS) technology provides an excellent benefit in different critical areas such as civilian, aviation, military, and commercial applications, it is highly vulnerable to various signal disruptions causing significant positioning errors. One of the major threats to a GNSS receiver is the intentional interference known as jamming. A Jammer significantly disrupts the normal functioning of a GNSS receiver, at the acquisition, tracking, and positioning stages. The foremost important step to combat against jamming of GNSS signals is the early detection and characterization of the interfering signals to guarantee the Quality of Service (QoS). This paper presents a robust Deep-Learning (DL) based technique using transfer learning to characterize the type of disruption in GNSS signal based on time-frequency analysis. To this end, a pre-trained Convolutional Neural Network (CNN) is used to extract the informative features from the scalogram of the received signals. Further, a fully connected layer followed by a Soft-Max activation function is deployed to classify the signals. In this work, the Signal of Interest (SoI) is a synthetic GPS signal generated by a GNSS simulator. In our experiment, the GPS signal is combined with different kinds of jamming, spoofing, and multipath signals. Moreover, the proposed classification approach can recognize not only the various kinds of jammers such as ones producing Continuous Wave Interference (CWI), Multi-CWI (MCWI), Chirp Interference (CI), and Pulse interference (PI) but also the inclusion of Additive White Gaussian Noise (AWGN). Besides that, the effect of five pre-trained CNNs, namely, AlexNet, GoogleNet, ResNet-18, VGG-16, and MobileNet-V2, is evaluated on classification accuracy. The GNSS signal and its seven disruptive variants are recorded at three different power levels such as low, medium, and high. The medium power level signal is used for training and the testing has been carried out for unseen data set of low, high, and mixed power level. From the simulation results, it has been observed that MobileNet-V2 has performed better than other techniques with an accuracy of 99. 8%. Finally, the trained MobileNet-V2 is used to predict the unseen data type generated at different Jamming to signal Ratios (JSRs).
Evaluation of Pupillary Distance (PD) Measurement using Smartphone-based Pupilometer

Abstract Measurement of the pupillary distance (PD) is one of the important steps of the spectacles-making process. PD measurement can be done using the PD Ruler, Auto-refractometer, and smartphones’ apps. The study aims to evaluate PD measurement apps (Pupil Distance Meter Pro from Vistech Project, PD Pupil Distance for Eye Glasses and VR Headset from Glassify Me, and The Ultimate PD Rule) and compared them with Auto-refractometer as the gold standard. A total of 39 students (age 19-22) were involved in this study. The result of this study indicated that there was no difference between the measurement of the PD using the Glassify me app and the Auto-refractometer ( p = 0.48). However, there was a mean difference of 0.2 mm between them. It could be concluded that the Glassify me app has the best accuracy compared to other apps ( p value 0.48). This app was recommended to be used as an alternative to PD measurement.
Building a content-based recommendation engine model using Adamic Adar Measure; A Netflix case study

The volume of multimedia data on the Internet is constantly increasing. Nowadays, the most challenging task for Internet users is to extract and use these data smoothly and efficiently. The use of data more efficiently is done using the recommendation system to filter information systems from large volumes of data extracting relevant information. This paper uses a text similarity technique to identify similar content using text-based features. This paper aims to build a content-based recommendation model using Adamic Adar Measure. The content-based recommendation model works based on the similarity of movies' content. The focus of this paper will be on the text-based features on which the model is filtered. The text vectorization using TfIdfV ectorizer to detect similarities between movies will be performed. Then, it is used the cosine similarity from the scikit-learn package to calculate the numeric value representing the similarity between the two movies. To compute the closeness of nodes based on their shared neighbors will be used the Adamic Adar measure. Instead of just using the similarity of movies based only on one field, the graphic form will be used to see the similarity of movies based on many fields. Our recommendation model analyzes the content of the movies (actors, directors, countries, and categories) to find out other movies with similar content. This model will rank similar movies according to their similarity scores and will recommend the most relevant movies to the user.
Learning Facial Motion Representation with a Lightweight Encoder for Identity Verification

Deep learning became an important image classification and object detection technique more than a decade ago. It has since achieved human-like performance for many computer vision tasks. Some of them involve the analysis of human face for applications like facial recognition, expression recognition, and facial landmark detection. In recent years, researchers have generated and made publicly available many valuable datasets that allow for the development of more accurate and robust models for these important tasks. Exploiting the information contained inside these pretrained deep structures could open the door to many new applications and provide a quick path to their success. This research focuses on a unique application that analyzes short facial motion video for identity verification. Our proposed solution leverages the rich information in those deep structures to provide accurate face representation for facial motion analysis. We have developed two strategies to employ the information contained in the existing models for image-based face analysis to learn the facial motion representations for our application. Combining with those pretrained spatial feature extractors for face-related analyses, our customized sequence encoder is capable of generating accurate facial motion embedding for identity verification application. The experimental results show that the facial geometry information from those feature extractors is valuable and helps our model achieve an impressive average precision of 98.8% for identity verification using facial motion.
Art Product Recognition Model Design and Construction of VR Model

The artwork embodies a profound human history and carries the essence of human civilization. Its content is complex and covers a wide range. How to use advanced technology to quickly and accurately classify and retrieve is an important research topic in the field. In our study, we first according to the requirements of practical application scenarios and existing data conditions proposed an overall scheme of artwork identification and retrieval. Through the functional analysis of the software required and the comparison of various databases, we present the system architecture design and data conceptual design, and complete the system-level planning and design. Then, the crawler grabbing process is designed to obtain artwork graphic data, the artwork dataset production process and labeling status required for the target application scenario were introduced, and the category imbalance state of the target dataset was analyzed. Moreover, the database table structure design of the artwork identification and retrieval system, design and development of each functional module of the server, and the web client was introduced. Finally, according to the organization, structure, and characteristics of virtual reality system, a product design evaluation system based on virtual reality technology was constructed. A theoretical model VR-PDES was designed for the application of virtual reality technology in product design evaluation. The results of this research are of great significance for people to search for images of unknown artworks and improve the service capabilities and service levels of scenic spots.
Reconstruction of Iberian ceramic potteries using generative adversarial networks

Several aspects of past culture, including historical trends, are inferred from time-based patterns observed in archaeological artifacts belonging to different periods. The presence and variation of these objects provides important clues about the Neolithic revolution and given their relative abundance in most archaeological sites, ceramic potteries are significantly helpful in this purpose. Nonetheless, most available pottery is fragmented, leading to missing morphological information. Currently, the reassembly of fragmented objects from a collection of thousands of mixed fragments is a daunting and time-consuming task done almost exclusively by hand, which requires the physical manipulation of the fragments. To overcome the challenges of manual reconstruction and improve the quality of reconstructed samples, we present IberianGAN, a customized Generative Adversarial Network (GAN) tested on an extensive database with complete and fragmented references. We trained the model with 1072 samples corresponding to Iberian wheel-made pottery profiles belonging to archaeological sites located in the upper valley of the Guadalquivir River (Spain). Furthermore, we provide quantitative and qualitative assessments to measure the quality of the reconstructed samples, along with domain expert evaluation with archaeologists. The resulting framework is a possible way to facilitate pottery reconstruction from partial fragments of an original piece.
2PESNet: Towards online processing of temporal action localization

Existing online video processing methods such as online action detection focus on a frame-level understanding for high responsiveness. However, it has a fundamental limitation in that it lacks instance-level understanding of videos, making it difficult to be applied to higher-level vision tasks. The instance-level action detection, known as Temporal Action Localization (TAL), have limitations when applying to the online settings. In this work, we introduce a new task that aims to detect action instances of videos in an online setting, named Online Temporal Action Localization (OnTAL). To tackle this problem, we propose a 2-Pass End/Start detection Network (2PESNet) that detects action instances by effectively finding the start and end of an action instance. Additionally, we propose a two-stage action end detection method to further improve the performance. Extensive experiments on THUMOS'14 and ActivityNet v1.3 demonstrate that our model is able to take both accuracy and responsiveness when predicting action instances from streaming videos.
Identifying and Coding Behavioral Indicators of Social Presence With a Social Presence Behavioral Coding System

Social presence, the sense of connection with another, is more important than ever as teachers, healthcare providers, and other professionals are using immersive tools to facilitate the social interaction for education, training, therapy and collaboration between geographically distributed humans and surrogates (avatars, agents, or robots). Leading researchers cite the subjective nature of the traditional self-report measures of social presence and the absence of a standardized approach to measuring social presence as a constraint to gaining deeper understanding of user’s experiences of emerging and existing tools. This discourse highlights behavioral indicators of social presence that have been identified over decades across disciplines from psychology, communication, computer science, education, and engineering. The authors explicate the behavioral themes of social presence and describe a classification system grounded in exogenic and endogenic themes of social presence. This article goes on to describe the design of a social presence behavioral coding system (SPBCS) instrument that provides a structure to coding behaviors associated with a users’ experience of social presence. The behavioral coding system described in this paper is the first step in creating a robust standardized approach to quantifying social presence through behavioral, physiological, and subjective indicators that ultimately may replace the current standard subjective approaches to describing the user’s experience in all realities.
Scenario-based Exploration of Integrating Radar Sensing into Everyday Objects for Free-Hand Television Control

We address gesture input for TV control, for which we examine mid-air free-hand interactions that can be detected via radar sensing. We adopt a scenario-based design approach to explore possible locations from the living room where to integrate radar sensors, e.g., in the TV set, the couch armrest, or the user’s smartphone, and we contribute a four-level taxonomy of locations relative to the TV set, the user, personal robot assistants, and the living room environment, respectively. We also present preliminary results about an interactive system using a 15-antenna ultra-wideband 3D radar, for which we implemented a dictionary of six directional swipe gestures for the control of dichotomous TV system functions.
Error analysis and correction equations for a real-time phase-shifting method using circular polariscope-based photoelasticity

The use of a polarization camera to achieve real-time phase shifting in circular polariscope-based photoelasticity is limited due to quantitative error analysis and correction. In particular, the primary factor limiting this method is the mismatch between the quarter-wave plate and the wavelength of the light source. In this regard, this study proposes error correction equations for the instantaneous phase-shifting method based on theoretical analysis and simulation of a diametrically stressed disc. First, the error distributions of the stress direction angle and phase retardation are discussed, and the correction equations of the isochromatic and isoclinic lines are deduced. Subsequently, we consider the phase ambiguity problem caused by the phase absolutization. Finally, the error analysis and correction methods are verified experimentally on the diametrically compressed disc.
To Share or Not to Share: On Location Privacy in IoT Sensor Data

Data sharing is crucial for building large datasets which in return are essential for developing and training accurate models in many contexts including smart cities, agriculture, and medical applications. However, shared data may leak private information, such as personal identifiers or location. Past research provides evidence that solely removing these identifiers through pseudonymization is not enough to ensure data privacy protection, since even the pseudonymized data may still contain information about the data provider. In this paper, we show that sensor data may leak a sensor's location even if the latter is not explicitly shared. Sensors are localized by linking sensor data with publicly available environmental data such as local weather. The proposed localization method relies on a machine learning model to predict weather data from sensor observations. Subsequently, the localization algorithm determines the sensor's location from the predicted weather trace using Bayesian filtering. We apply our approach to three real-world datasets where we (1) localize an ozone sensor given its readings, (2) localize a cow from activity parameters recorded with a tracker in the cow's reticulum, (3) localize solar panels based on their solar generation data. The achieved average localization accuracy of 5.68 km, 19.91 km, and 13.68 km on the above tasks, respectively, using data traces with a length of 365 days is remarkable. In addition, we introduce a mechanism, referred to as teleport, to protect location information in sensor data. The mechanism is based on deep models and masks the location by replacing the weather dependency with a different weather signature.
Automatic Software Engineering Position Resume Screening using Natural Language Processing, Word Matching, Character Positioning, and Regex

Screening candidates' resumes manually is a tedious job, with possibilities of sometimes missing good candidates due to human errors, nepotism, and bias. However, these kinds of mismanagement don’t apply to machines. Instead, automatic screening of candidates reduces a lot of effort, time, and cost. Hence this work specifically focuses on extracting technical skills using natural language processing specifically resume label character positioning, data set consisting of software engineering candidate requirements, regular expressions, and word and phrase matching for candidate information retrieval. Character positioning a new technique for information extraction is introduced, which perceives needed data and pulls it out. This methodology creates a summary of the resume from the extracted information. And computes count scores from based recognized skills, and education plus experience level. Finally, upon testing on five random software engineering positions resume correct extraction rate of 33.59% was obtained.