Reviewerly/exHarmony huggingface dataset:
A lightweight deep learning with feature weighting for activity recognition

Abstract With the development of deep learning, numerous models have been proposed for human activity recognition to achieve state‐of‐the‐art recognition on wearable sensor data. Despite the improved accuracy achieved by previous deep learning models, activity recognition remains a challenge. This challenge is often attributed to the complexity of some specific activity patterns. Existing deep learning models proposed to address this have often recorded high overall recognition accuracy, while low recall and precision are often recorded on some individual activities due to the complexity of their patterns. Some existing models that have focused on tackling these issues are always bulky and complex. Since most embedded systems have resource constraints in terms of their processor, memory and battery capacity, it is paramount to propose efficient lightweight activity recognition models that require limited resources consumption, and still capable of achieving state‐of‐the‐art recognition of activities, with high individual recall and precision. This research proposes a high performance, low footprint deep learning model with a squeeze and excitation block to address this challenge. The squeeze and excitation block consist of a global average‐pooling layer and two fully connected layers, which were placed to extract the flattened features in the model, with best‐fit reduction ratios in the squeeze and excitation block. The squeeze and excitation block served as channel‐wise attention, which adjusted the weight of each channel to build more robust representations, which enabled our network to become more responsive to essential features while suppressing less important ones. By using the best‐fit reduction ratio in the squeeze and excitation block, the parameters of the fully connected layer were reduced, which helped the model increase responsiveness to essential features. Experiments on three publicly available datasets (PAMAP2, WISDM, and UCI‐HAR) showed that the proposed model outperformed existing state‐of‐the‐art with fewer parameters and increased the recall and precision of some individual activities compared to the baseline, and the existing models.
Unmanned Vehicle Path Planning based on Improved Ant Colony Algorithm

To address the matter that the normal ant colony algorithm in path planning cannot meet the problem of planning in three-dimensional environment, and the problem that there are too many turns in the planned path, a path planning strategy based on slope and terrain undulation constraints is proposed for the ant colony algorithm, and the number of turns is improved. In this paper, we present the concept of slope, add the slope and terrain undulation to the heuristic function move forward the state transfer probability, the pheromone update formula was improved by adding the number of turns to the pheromone update formula; in terms of environment modeling, at the same time, a 2.5-dimensional raster map is defined to represent the unevenness of the environment. And finally, it is verified by simulation experiments that the proposed algorithm can satisfy the path planning matter under the constraints of slope and number of turns, and the proposed algorithm is effective.
Low-light Traffic Objects Detection for Automated Vehicles

The low-light condition brings huge challenges to the traffic object detection, limiting its application in complex scenarios such as the round-the-clock traffic monitoring and automatic driving. To address this issue, an accurate traffic object detection method in low-light conditions is proposed. First, the dark channel prior knowledge is utilized to restore the scene radiance via the transmission map and the estimation of the atmospheric light. Meanwhile, the grayscale is adjusted by adaptive gamma transformation to further improve the brightness of the image and reduce the impact of low-light conditions. Second, a LL4PH-Net framework equipped with preset anchors and detection branches is developed to detect low-light traffic objects. The performance of the algorithm is tested on the public BDD100K dataset. Experimental results show that compared with the SOTA methods, the proposed LL4PH-Net acquires the best detection mAP, the most lightweight model and a good time efficiency. It can effectively detect traffic objects even under low-light conditions.
Lexical Complexity Detection and Simplification in Amharic Text Using Machine Learning Approach

Text complexity is the level of difficulty of the document for understanding by the target readers. One common type of this text complexity is Lexical complexity which can cause comprehensibility and understandability problems for second language learners, and children, furthermore it is challenging for NLP applications. To reduce this text complexity for low resourced and morphologically reach language Amharic, we have designed a complexity detection and lexical simplification model using a machine learning approach. For the tasks, we have developed three subsequent models. The first model is used to classify text complexity, trained using 19k sentences. The second model is developed for detecting specific complex terms. The model is developed using 1002 unique complex terms. Lastly, we have trained word2vec (CBOW) model using 57.6k sentences (which contains 9756 unique tokens) for simplification generation and ranking. The experimental result of the classification model scores an accuracy of 88%(LSTM), 88%(BiLSTM), and 91%(BERT). The simplification generation for the identified complex term using cosine similarity results 92% for top-ranked and 61% for lowest-ranked simplest equivalents from five top-generated words.
Multimodal Amharic Hate Speech Detection Using Deep Learning

Social media has become a tool of online communication used for discussions, information sharing, and the creation of online content on social networks. Hate speech is one of the detrimental or harmful information spread on social media. Discrimination against individuals results from hate speech. Human rights are also violated by it. As a result, several studies have been attempting to identify hate speech. However, while the majority of studies focus on identifying hate speech in textual data, it is also distributed through films. Amharic is also one of the under-resourced languages that benefits from hate speech detection, so this is another positive. This work seeks to apply a multi-modal, which is a mix of the acoustic and textual elements using a deep learning technique, to address the issue of Amharic hate speech on social media In the beginning, we gathered 1,459 extracted audios from YouTube videos, ranging in length from 30 seconds to 140 minutes. A five-minute audio speech employing silent segmentation techniques follows this. From these, we have a collection of 6497 audios from one to five minutes after the audio segmenting and filtering. Each Audio is annotated by a domain expert for the purpose of performing tests. Then, for our research, we use the Google Speech-to-Text API to transcribe audio speech into text scripts. The features were then extracted, with textual features extracted using word2vec and acoustic features extracted using Mel-Frequency Cepstral Coefficient (MFCC). As a result, this study makes use of four deep learning algorithms: LSTM, BILSTM, GRU, and BIGRU. The results of the multi-modal experiment demonstrate that the multi-modal model with BILSTM outperforms the other experiment for detecting Amharic hate speech with an accuracy of 88.15%. Furthermore, we are working to extend the Amharic hate speech detection taking the video in to account addition to the text and audio.
The Effect of Normalization for Bi-directional Amharic-English Neural Machine Translation

Machine translation (MT) is one of the prominent tasks in natural language processing whose objective is to translate texts automatically from one natural language to another. Nowadays, using deep neural networks for MT task has received a great deal of attention. These networks require lots of data to learn abstract representations of the input and store it in continuous vectors. This paper presents the first relatively large-scale Amharic-English parallel sentence dataset. Using these compiled data, we build bi-directional Amharic-English translation models by fine-tuning the existing Facebook M2M100 pre-trained model achieving a BLEU score of 37.79 in Amharic-English translation and 32.74 in English-Amharic translation. Additionally, we explore the effects of Amharic homophone normalization on the machine translation task. The results show that normalization of Amharic homophone characters increases the performance of Amharic-English machine translation in both directions.
Block-chain based cloud storage integrity verifycation scheme for recoverable data

With the advent of the era of big data, the files that need to be stored in the storage system will increase exponentially. Cloud storage has become the most popular data storage method due to its powerful convenience and storage capacity. However, in order to save costs, some cloud service providers, Malicious deletion of the user's infrequently accessed data causes the user to suffer losses. Aiming at data integrity and privacy issues, a blockchain-based cloud storage integrity verification scheme for recoverable data is proposed. The scheme uses the Merkle tree properties, anonymity, immutability and smart contracts of the blockchain to effectively solve the problems of cloud storage integrity verification and data damage recovery, and has been tested and analyzed that the scheme is safe and effective.
A Context-Driven Merge-Sort Model for Community-Oriented Lexical Simplification

Lexical simplification aims to convert complex words in a sentence into semantic equivalent but simple words. Most existing methods ignore sentence contextual information, which inevitably produces a large number of spurious substitute candidates. To that end, this paper proposes a new context-driven Merge-sort model which leverages contextual information in each step of lexical simplification, and a new merging method to combine ranking results produced by the proposed model. Based on standard datasets, our model outperforms a list of baselines including the state-of-the-art LSBert model, indicating its effectiveness in community-oriented lexical simplification.
A Global-to-Local Thresholding Method for Object Enhancement in SAR Images

Speckle effect exhibited in Synthetic Aperture Radar (SAR) images significantly influences the interpretation performance of the follow-up tasks, such as object recognition. Although many contrast enhancement methods have been proposed for discriminating objects from the background, their performances often degrade in SAR images. To achieve better contrast between objects and the background, a global-to-local thresholding method is proposed for SAR object enhancement. Specifically, we first obtain two global thresholds for object segmentation based on entropy maximization and Ostu methods. Then, detailed object structures can be enhanced based on an adaptive local thresholding. After several iterations of the local processing, we can obtain the enhanced SAR objects with high contrast with respect to the background. Based on the experiments on real SAR images, the proposed method demonstrates better performance than other contrast enhancement algorithms.
Analysis and Design of Oscillator Coupling for Solving Combinatorial Optimization Problems

The demand for high-efficiency computing power is steadily increasing. Despite improvements in classical topologies, non-conventional hardware approaches are of growing interest. Oscillator-based Ising Machines (OIMs) implemented on integrated CMOS circuits are a promising emerging technology. OIMs can solve combinatorial optimization problems in a fraction of time while having an energy consumption several orders of magnitude lower than conventional computing architectures. Their operating principle is based on the individually configurable coupling of a multitude of oscillators. Designing such oscillator-based computers poses unique challenges. While the behavior of oscillators including injection locking is well understood, the interaction inside the OIMs for computation purposes has not been investigated extensively. Hence, this paper discusses the coupling of oscillators and draws attention to important design trade-offs. The OIM system is briefly described and the general coupling mechanics are demonstrated using ideal resistors as coupling elements. The unique challenges of transistor-based couplers are extensively discussed and a coupler circuit that balances design trade-offs is proposed.
Detection-based video surveillance using Deep Neural Networks on STM32 microcontroller

Object detection is one of the most active research areas in the computer vision field. Using object detection techniques, nowadays mostly based on deep neural networks, new intelligent camera-based surveillance systems can be designed, capable of generating alerts only in the presence of specific objects, like persons, in the camera field of view. However the memory and computational load required by these techniques makes it challenging to use them on low power, miniaturised and resource constrained surveillance devices designed for harsh environments. In this paper, we show an efficient method to detect the presence of a specific object in surveillance video frames using deep neural networks on an STM32 microcontroller, suitable for harsh environments. Our solution achieved 97% precision and 93% recall, while consuming less than 400 mW.
Architecture for 3D Convolutional Neural Networks Based on Temporal Similarity Removal

Making sense of human actions in video sequences has become an essential task in video surveillance applications. In such applications, 3D CNNs are a prime choice thanks to their excellent performance. However, the performance advantage offered by these networks comes at a significant computational and memory cost. In this paper, a novel 3D CNN accelerator architecture which leverages on temporal similarity to reduce computations is introduced. The architecture is analyzed and validated with video benchmarks for human action recognition. The proposed Temporal Similarity Removal (TSR) accelerator reduces computation in the convolutional layers of a 3D CNN by skipping feature map similarities introduced by Temporal Similarity Tunnels (TST) among adjacent frames. The proposed architecture achieves 2× better area efficiency and 55%-3.5× (45%) better energy efficiency over prior art, based on the C3D network (3D MobileNet network) and the UCF101 dataset.
A Trusted Communication Unit for Secure Tiled Hardware Architectures

Modern hardware and software in smart systems and devices need to provide high performance and energy efficiency and, at the same time, properly address security and privacy goals. <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$M3$</tex> proposed a system architecture that integrates cores and accelerators within a tiled hardware architecture using a security-by-design approach. Each tile includes a hardware component called trusted communication unit (TCD), which isolates all tiles from each other so that no communication is possible by default. In this paper, we designed, developed, and synthesized the hardware components of the M <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> system architecture comprising a network-on-chip, RISC-V cores, and TCDs. Latency measure-ments show that the timing overhead introduced by the data transfer and security features of the TCD is not significant compared to other latencies like cache accesses or software routines. Synthesis results reveal that the area overhead of the trusted system components is only 11 % when the system is scaled to a high number of processing tiles.
Analysis of PV Module Performance and Electrical Parameters Based on Different Tilt Angles

PV panel application and optimization research has exploded in popularity in recent years. There have been several issues with the early phases of design and implementation, particularly for stand-alone solar systems. One of the primary challenges is determining the optimal slope of PV panels in order to optimise absorbed solar radiation. Although some research has been done in Malaysia to determine the best tilt angle, the goal of this study is to begin an examination into the slope of PV panels for two locations with different altitudes. The Liu and Jordan model is used to determine the monthly ideal tilt angle and fixed tilt angle for solar energy use in these areas. Other than that, for a country like Malaysia, finding a suitable installation location remains a challenge. As a result, the ideal quantity of solar panels and their orientation have become critical. HelioScope is a technology that can help answer that issue to some extent. This programme aids in maximizing the use of available space. In this research, a simulation has been developed to compare the irradiance produced by a small residential household with two field segments of roofs. One model consists of the same angle, separated roofs meanwhile the other model consists of different angle, connected roofs. Results generated suggests that the connected roofs produce more energy and captures more irradiance compared to the house with separated roof parts.
Sentiment Analysis for Super Applications in Indonesia: A Case Study of Gov2Go App

Recently, the Government of the Republic of Indonesia assessed that the number of applications (approximately 24,000) spread across various ministries and regional institutions were less effective and efficient. Therefore, the government will prepare a super application to replace thousands of applications to support electronic government and will use 2,700 data centers that can produce data integration according to the one-data policy concept. To support this initiative, our research proposes a sentiment analysis based on Expectation-Confirmation Theory (ECT). The proposed framework consists of several stages, namely, data preprocessing (cleaning of input data), tokenization, clustering (using a combination of K-Means and Elbow methods to determine the best K value), TF-IDF feature extraction, and classification based on two classification models for comparison, namely Long Short-Term Memory (LSTM) and Naïve Bayes. The ECT constructs used are expectation, confirmation, and satisfaction. We used comment data (review) from the Gov2Go application on the Google Play Store as a case study. The experimental results show that the LSTM model achieves an accuracy value of 97.49%, 3% better than the Naïve Bayes method. This indicates that the proposed framework can effectively detect the polarity of user sentiment and thus can be used as a recommendation to improve application features.
A Method Comparison on Multi-Label Questions Classification for Assessment-Based Personalised Scaffolding Adaptive Learning Path

Classification of the topic of a question item is one of the fundamental problems in e-learning systems. Unlike single-label classification, the multi-label classification method simultaneously predicts more than one-class label. This research is a series of process development for a Personal Diagnostic system based on assessment. This system needs annotated question bank because multi-label question items can be used to build a Concept Effect Relationship (CER). The purpose of building CER is to track the failed concept of students who fail the formative tests. Hence, there is necessary in looking for a multi-label question classification method. Therefore, this paper compares several multi-label classification methods in determining subject topics associated with questions in a formative test question bank. This study investigates the non-neural-based and neural-based multi-label classification. The test results for the non-neural show that Term Frequency– Inverse Document Frequency (TF-IDF) with Random Forest classifier produces the best hamming loss value (16,3%) while on neural, TF-IDF with convolutional neural network (CNN) produces a hamming loss value (21,2%) that is better than Long Short Term Memory (LSTM).
Masked Faces Classification using Deep Convolutional Neural Network with VGG-16 Architecture

Recent years have seen a significant increase in attention given to object detection techniques, particularly in the field of face mask detection, classification, and masked face recognition. Due to the contact-based nature of other biometric methods and the possible outbreak of a pandemic, facial biometrics is now the most secure option for authentication and access management. Experts have advised that adequate preparation must be made to tackle any occurrence of another respiratory-related pandemic in the future. One of the areas worthy of seeking and securing absolute technological breakthroughs is the aspect of face mask detection and classification. The current face mask detection and identification technologies were created using the principles of fair-skinned individuals. This study was carried out with a view to improving the existing systems to perform brilliantly in real- time on dark-skinned faces using a convolutional neural network with VGG-16 architecture. The system was evaluated, and the results show a better performance.
Image Retrieval of Indonesian Batik Clothing Based on Convolutional Neural Network

Indonesian Batik is best-known for unique and distinct pattern. Searching Indonesian Batik clothing images is a challenging problem due to its wide pattern variations. In this paper, proposed image retrieval model of Indonesian Batik clothing image searching based on Convolutional Neural Network (CNN). Autoencoder proposed as CNN model that trained to reconstructed original input batik clothing image. So, the visual features can be extracted from CNN Autoencoder. Based on the experimental results, the proposed method can reach 90.8% in retrieval accuracy, 58.8% in mean average precision, and 61.9% in average recall.
Decoding of stimuli time series by neural activity patterns of recurrent neural network

Abstract The study is concerned with question whether it is possible to identify the specific sequence of input stimuli received by artificial neural network using its neural activity pattern. We used neural activity of simple recurrent neural network in course of “Even-Odd” game simulation. For identification of input sequences we applied the method of neural network-based decoding. Multilayer decoding neural network is required for this task. The accuracy of decoding appears up to 80%. Based on the results: 1) residual excitation levels of recurrent network’s neurons are important for stimuli time series processing, 2) trajectories of neural activity of recurrent networks while receiving a specific input stimuli sequence are complex cycles, we claim the presence of neural activity attractors even in extremely simple neural networks. This result suggests the fundamental role of attractor dynamics in reflexive processes.