Reviewerly/exHarmony huggingface dataset:
DeepContext: Parameterized Compatibility-Based Attention CNN for Human Context Recognition

The ubiquity of sensor-rich smartphones has increased interest in mobile context-aware sensing applications in domains such as ambient assisted living, remote health care, and sports injury detection. Recognizing the user's current context by analyzing their smartphone's sensor data is a critical problem for such applications. One of the major technical challenges for context recognition is reliable feature extraction due to coarse-grained labeling. In sensor data coarse-grained labeling, only certain parts of smartphone sensor data are truly representative of the assigned label, while their exact duration and location within the segment are unknown. To address this, we propose DeepContext, a deep learning based network architecture for recognizing a smartphone user's current context. DeepContext uses a Convolutional Neural Network (CNN) with parameterized compatibility-based attention to discover and focus on important parts of smartphone sensor data, mitigating coarse-grained weak labels and extracting salient discriminative features. DeepContext uses a joint-learning fusion strategy that utilizes both domain-specific handcrafted features and features that are autonomously generated by a Convolutional NeuralNetwork (CNN) . We demonstrate that DeepContext consistently outperforms prior state-of-the-art context recognition and human activity recognition deep learning models on smartphone context sensor data gathered from 100 participants by nearly 5% in Balanced Accuracy.
Language Detection in Sinhala-English Code-mixed Data

Language identification in text data has become a trending topic due to multiple language usage on the internet and it becomes a difficult task when it comes to bilingual and multilingual communication data processing. Accordingly, this study introduces a methodology to detect Sinhala and English words in code-mixed data and this is the first research done on such scenario at the time of this paper is written. In addition to that, the data set which is used for this research was newly built and published for similar research users. Even though there are well known models to identify Singlish Unicode characters which is a straightforward study; there are no proper language detection models to detect Sinhala words in a sentence which contains English words (code-mixed data). Therefore, this paper presents a language detection model with XGB classifier with 92.1% accuracy and a CRF model with a Fl-score of 0.94 for sequence labeling.
Fundamentals of Feature Selection: An Overview and Comparison

Tremendous efforts have been put into the development of Feature Selection (FS) methods by the machine learning community. In this paper, we present basics surrounding this topic, providing its general process, evaluation procedure and metrics. In addition, we thoroughly discuss major application aspects. We also provide a comprehensive overview and comparison of some existing feature selection methods. We conclude this work by highlighting some critics, challenges, and future research directions of feature selection.
Deep Convolutional Neural Network Using Triplet Loss to Distinguish the Identical Twins

In face recognition, distinguishing identical twins faces is a challenging task because of the high level of correlation in facial appearance.Generally, facial recognition is easy to make mistakes when it comes to twins or similar faces. To deal with the high level of correlation in similar faces, we proposed a deep convolutional neural network (CNN) using triplet loss function to differentiate the identical twins. We applied a hybrid strategy by combining the deep CNN model, which learns an embedding from facial images to Euclidean space and triplet loss function to evaluate the L2 distance between facial images into Euclidean space, Obtained L2 distance shows the level of similarity between corresponding faces. We implemented two different CNN models on our raw pixel images; additionally, we used different techniques to reduce the overfitting problem such as dropout and batch normalization, additionally L2 regularization. Our method achieves the best mean validation accuracy above 87.2%.
AgroVita using Blockchain

Food touches everyone and everywhere. Food Transparency is a major problem this world has. When you pickup packed chicken and chocolate or avocado at your grocery store how can you be sure about its origin, "Where did it grow?", "How was it packed?", and "For how long was it shipped?", "Is it really organic and local?". These questions must be asked as food fraud is rampant in the largely unregulated organic and farmer market industry and there aren't much rules protecting shoppers from being duped by false claims and fraudulent products sold at premium prices. With the increasingly global nature of food and trade it is difficult to know where the food came from. This proposed system empowers customers, farmers and distributors with the blockchain. Blockchain records all transactions with each record being permission based with trust and transparency built in. This proposed system can achieve absolute trust and food traceability like never before.
Convolutional Neural Network U-Net for Trypanosoma cruzi Segmentation

Chagas disease is a mortal silent illness caused by the parasite Trypanosoma cruzi that affects many people worldwide. A blood test is one of the preferred methods to get an accurate diagnosis of the disease but takes a long time and requires too much effort from the experts to analyze blood samples in the search of the parasites presence. Therefore, it is very useful to have an automatic system to detect the parasite in blood sample microscopic images. In this paper we present a deep learning method to segment the T. cruzi parasite on blood sample images. We implemented a convolutional neural network based on the U-Net model and we trained it with different loss functions to get accurate results. We report an F2 value of 0.8013, a recall value of 0.8702, a precision value of 0.6304 and a Dice score value of 0.6825.
NTTU: An Area-Efficient Low-Power NTT-Uncoupled Architecture for NTT-Based Multiplication

Large integer multiplication, or large degree polynomial multiplication, is the most time-consuming operation in fully homomorphic encryption (FHE). Low area and power consumption are difficult to maintain while achieving high performance for a large size multiplier. To address this issue, an area-efficient low-power architecture for multiplication, named NTTU, is proposed in this article. First, a combined number theoretic transform (NTT) method consisting of decimation-in-time (DIT) NTT for input in natural order and bit-reversed order is proposed to eliminate the steps of zero padding, scramble, and the first stage in NTT, thereby achieving a reduction of 7N/2 clock cycles compared with the single-type NTT method. Second, the NTT-uncoupled architecture is proposed to uncouple the multiplication components, decreasing the storage space for coefficients by 1/2 compared with state-of-the-art designs. Third, a parallel computing architecture based on a crossed memory access scheme is proposed, therein reducing the corresponding execution time by one-half compared with serial execution. Synthesized using 65 nm technology, the proposed architecture can multiply two 1024k/768k integers in 1.7 ms at 500 MHz at a cost of 13.66/7.67 million gates and 726.7/550.2 mW, and a 71.17 percent/ 30.37 percent area time product (ATP) reduction is achieved compared with the state-of-the-art ASIC designs.
A Layer-Partitioning Approach for Faster Execution of Neural Network-Based Embedded Applications in Edge Networks

As embedded systems become more prominent in society, the technologies that run on them must be used efficiently.One such technology is the Neural Network (NN).NN's, combined with the Internet of Things (IoT), can utilize the massive amounts of data produced to optimize, control, and automate embedded systems, giving them more functionality than ever before.However, the status quo of offloading all NN functionality onto external devices has many flaws.It forces the embedded system to entirely rely on networks that may have high latency or connection issues.Networks may also expose them to security risks.To reduce the reliance of IoT devices on networks, we examined several solutions, such as delegating some NN's to run solely on the IoT device or splitting the NN and distributing the subnetworks into different devices.It was found that, for shallow NN's, the IoT device itself could run the NN at a rate faster than offloading it to an external device, but the IoT device needed to offload its inputs once the NN's started to increase in layers and complexity.When splitting the NN, it was found that the number of messages sent between devices could be reduced by up to 97% while only reducing the accuracy of the NN by 3%.
Author Profiling: Prediction of Gender and Language Variety from Document

With advancement of technology and social media, textual data is also growing day by day. Thus, Automatic Authorship Identification (AAI), which is majorly divided into to author identification, authorship attribution and author profiling, is a growing field of research. Specifically identifying author characteristics from the content also called author profiling is gaining pace as it contributes in the areas of marketing and forensics. The PAN labs every year provides a platform for scholars to contribute by organizing the challenge of author profiling tasks. In this paper, our attempt is to predict gender and language variety for authors using English data of PAN 2017 dataset.
Pre-Service Teachers’ Declarative Knowledge of Wave-Particle Dualism of Electrons and Photons: Finding Lexicons by Using Network Analysis

Learning the wave-particle dualism of electrons and photons plays a central role in understanding quantum physics. Teaching it requires that the teacher is fluent in using abstract and uncommon terms. We inspect the lexical structures of pre-service teachers’ declarative knowledge about the wave-particle dualism of electrons and photons in the context of double-slit interference. The declarative knowledge is analyzed in the form of a lexical network of terms. We focus on lexical structures because, in teaching and learning, knowledge is communicated mostly through lexical structures, i.e., by speaking and writing. Using the lexical networks, we construct the lexicons used by pre-service teachers to express their knowledge of electrons and photons in the context of double-slit interference. The lexicons consist of eight different key terms, each representing a set of closely-related or synonymous terms. The lexicons by 14 pre-service teachers reveal remarkable variation and differences, and are strongly context-dependent. We also analyzed lexicons corresponding to two didactically-oriented research articles on the same topic and found that they also differ. Lexicons paralleling both texts are found among the pre-service teachers’ lexicons. However, only some of the pre-service teachers use such rich vocabulary as would indicate multi-faceted understanding of quantum entities.
On-device Training for Breast Ultrasound Image Classification

Most on-device AI pre-trained a neural network model in cloud-based server then deployed to edge device for inference. On-device training not only can build personalized model, but also can do distributed training like federated learning to train accurate models from scratch using small updates from many devices. In this work, we implement the semi-supervised convolutional neural network based on successive subspace learning and use a dataset of breast ultrasound (BUS) images to demonstrate a proof of concept of true on-device training. An important advantage of such network is that we can extract the key feature vectors with CNN network architectures without the need of backpropagation computation made it suitable for portable ultrasound. So it can acquire the ultrasound image and train the CNN classifier on the portable device without cloud-based server. We evaluate the model by using a set of BUS images that includes benign and malignant breast tumors. We obtain 94.8% accuracy with this study and demonstrate the applicablility of the proposed on-device training model to improve the diagnosis of BUS images.
Analyzing Role of Joint Subset Selection in Human Action Recognition

Joints of human pose play an important role in human action recognition (HAR) due to their invariant to subject appearance, low requirement on computation and storage, as well as containing informative data. Psychologists [1] showed that human actions (e.g., walking, running, dancing) could be distinguished by using only a subset of joints rather than using full-joint model. In this paper, we tackle role of joint selection in HAR performance by proposing a pre-determined configuration of joints combined with temporal derivatives of joint positions. To analyze role of joint selection, our proposed scheme is compared with full-joint as well as automatic joint selection schemes. Proposed method is evaluated on three public datasets: MSR-Action3D, UTKinect-Action and Florence3D-Action. Experiments show that our proposed method achieves very competitive results compared with state-of-the-art methods. For MSR-Action3D dataset, our proposed method achieves better result than existing methods with accuracy of up to 95.53%. For Florence3D-Action and UTKinect-Action datasets, accuracy of our proposed method ranks in the second position while computation is 10-20 times faster than top-ranking method.
ISeeColor: Method for Advanced Visual Analytics of Eye Tracking Data

Recent advances in head-mounted eye-tracking technology have allowed researchers to monitor eye movements during locomotion in real-world environments, increasing the ecological validity of research on human gaze behavior. While collecting eye-tracking data is becoming more accessible, visual analytics of eye-tracking data remains difficult and time-consuming. As such, there is a significant need for developing efficient visualization and analysis tools for large-scale eye-tracking data. This work develops a first-of-its-kind eye-tracking data visualization and analysis system that allows for automatic recognition of independent objects within field-of-vision, using deep-learning-based semantic segmentation. This system recolors the fixated objects-of-interest by integrating gaze fixation information with semantic maps. The system effectively allows researchers to automatically infer what objects users view and for how long in dynamic contexts. The contributions are 1) a data visualization and analysis system that uses deep-learning technology along with eye-tracking data to automatically recognize objects-of-interest from head-mounted eye-tracking video recordings, and 2) a graphical user interface that presents objects-of-interest annotation along with eye-tracking data information. The architecture is tested with an outdoor case study of users walking around the Tufts University campus as part of a navigation study, which was administered by a team of research psychologists.
Biometric Systems Utilizing Neural Networks in the Authentication for E-learning Platforms

This paper focuses on the introduction of biometrics and its application in authentication. We will also focus on the applications of these biometrics in authentication, especially concerning today's e-learning platforms and using of neural networks for modern biometrics solutions. We will talk about most of the current biometric technologies and systems, regardless of its cost or necessary equipment. However, this paper assumes that the reader has a basic understanding of neural networks, which are the basis for many modern biometric systems.
Dichotomies in Ontology-Mediated Querying with the Guarded Fragment

We study ontology-mediated querying in the case where ontologies are formulated in the guarded fragment of first-order logic (GF) or extensions thereof with counting and where the actual queries are (unions of) conjunctive queries. Our aim is to classify the data complexity and Datalog rewritability of query evaluation depending on the ontology O , where query evaluation w.r.t. O is in PT ime (resp. Datalog rewritable) if all queries can be evaluated in PT ime w.r.t. O (resp. rewritten into Datalog under O ), and co NP-hard if at least one query is co NP-hard w.r.t. O . We identify several fragments of GF that enjoy a dichotomy between Datalog-rewritability (which implies PT ime ) and co NP-hardness as well as several other fragments that enjoy a dichotomy between PT ime and co NP-hardness, but for which PT ime does not imply Datalog-rewritability. For the latter, we establish and exploit a connection to constraint satisfaction problems. We also identify fragments for which there is no dichotomy between PT ime and co NP. To prove this, we establish a non-trivial variation of Ladner’s theorem on the existence of NP-intermediate problems. Finally, we study the decidability of whether a given ontology enjoys PT ime query evaluation, presenting both positive and negative results, depending on the fragment.
The long road from performing word sense disambiguation to successfully using it in information retrieval: An overview of the unsupervised approach

Summary The issue of whether or not word sense disambiguation (WSD) can improve information retrieval (IR) results has been intensely debated over the years, with many inconclusive or contradictory results and a majority of skeptical opinions. All three classes of WSD methods (supervised, unsupervised, and knowledge‐based) have been considered by the literature with respect to IR. We hereby survey the unsupervised approach which, although relatively rarely used, has provided positive results at a large scale. Unsupervised WSD has already made proof of its utility in IR and it is our belief that it still holds a promise for this field. The two main existing types of unsupervised methods for IR, which are of completely different natures, are presented, within the scientific context in which they were born, and are compared. Regardless of the gap in time between these central approaches, we are of the opinion that the unsupervised solution to the discussed problem remains the most significant for IR applications. By surveying what we consider the most promising existing approach to usage of WSD in IR, and by discussing its possible extensions, we hope to stimulate continuation of this line of research, possibly at an even more successful level.
A secure data transmission protocol for cloud‐assisted edge‐Internet of Things environment

Abstract Cloud‐edge (CE) is revolutionizing our modern world with a greater user experience through the Internet of Things (IoT). However, the edge devices, communication bridge between the cloud and users, are repeatedly exhibiting many security flaws which will inevitably lead to massive cyber attacks. Thus, providing a proper security, specifically, the confidentiality and authenticity of sensitive data become a prime focus for many of the researchers. In cryptography, certificateless signcryption (CLSC) is one of the recent public key techniques that meets the requirements of confidentiality and authenticity of sensitive data between parties with minimal overhead. This paper aims to present a new data sharing protocol where the safety of data is achieved through a new CLSC scheme. The proposed CLSC is designed using the bilinear pairing and modular exponentiation operations. Besides, the CLSC is secure based on the intractability of Diffie‐Hellman inversion (DHI) and bilinear‐DHI assumptions without considering the random oracle model (ROM). Performance assessment of proposed CLSC gives satisfactory results after comparing with other related CLSC schemes. Therefore, proposed CLSC can be installed in the cloud‐based edge‐IoT environment where both the authenticity and confidentiality with minimal computational overhead are the essential factors.
The Use of Mobile Learning Technologies in Primary Education

This research explains the rationale behind the utilization of mobile learning technologies. It involves a qualitative study among children to better understand their opinions and perceptions toward the use of educational applications (apps) that are available on their mobile devices, including smartphones and tablets. The researchers organized semi-structured, face-to-face interview sessions with primary school students who were using mobile technologies at their primary school. The students reported that their engagement with the educational apps has improved their competencies. They acquired relational and communicative skills as they collaborated in teams. On the other hand, there were a few students who were not perceiving the usefulness and the ease of use of the educational apps on their mobile device. This study indicates that the research participants had different skillsets as they exhibited different learning abilities. In conclusion, this contribution opens avenues for future research in this promising field of study.
Wheelchair Control System based on Gyroscope of Wearable Tool for the Disabled

Abstract A wheelchair control system based on Gyroscope of wearable tool can serve the disabled, especially in helping them move freely. The recent evolution of new technology means that unassisted, free movement has become possible. For this purpose, human–machine interface hands-free command of an electric-powered wheelchair can be achieved. In this paper, an electroencephalogram instrument, namely the EMOTIV Insight, was implemented in a human–computer interface to acquire the user’s head motion signals. The system can be operated based on the user’s head motions to carry out motion orders and control the motor of the wheelchair. The proposed system consists of an EMOTIV Insight brain-based gyroscope to sense head tilt, a DC motor driver to control wheelchair speed and directions, an eclectic-powered wheelchair, microcontroller, and laptop. We implemented the system in practice and tested it on smooth and rough surfaces in indoor/outdoor settings. The experimental results were greatly encouraging: disabled users were able to drive the wheelchair without any limitations. We obtained a significant average response time of 2 seconds. In addition, the system had accuracy, sensitivity, and specificity of 99%, 99.16%, and 98.83%, respectively.
The Wearables Development Toolkit

Although the last two decades have seen an increasing number of activity recognition applications with wearable devices, there is still a lack of tools specifically designed to support their development. The development of activity recognition algorithms for wearable devices is particularly challenging because of the several requirements that have to be met simultaneously (e.g., low energy consumption, small and lightweight, accurate recognition). Activity recognition applications are usually developed in a series of iterations to annotate sensor data and to analyze, develop and assess the performance of a recognition algorithm. This paper presents the Wearables Development Toolkit, an Integrated Development Environment designed to lower the entrance barrier to the development of activity recognition applications with wearables. It specifically focuses on activity recognition using on-body inertial sensors. The toolkit offers a repository of high-level reusable components and a set of tools with functionality to annotate data, to analyze and develop activity recognition algorithms and to assess their recognition and computational performance. We demonstrate the versatility of the toolkit with three applications and describe how we developed it incrementally based on two user studies.