Reviewerly/exHarmony huggingface dataset:
FLoPAD-GRU: A Flexible, Low Power, Accelerated DSP for Gated Recurrent Unit Neural Network

Recurrent neural networks (RNNs) are efficient for classification of sequential data such as speech and audio due to their high precision on tasks. However, power efficiency, the required memory capacity and bandwidth requirements make them less suitable for battery powered devices. In this work, we introduce FLoPAD-GRU: a system on a chip (SoC) for efficient processing of gated recurrent unit (GRU) networks, that consists of a digital signal processor (DSP), supplemented with an optimized hardware accelerator, which reduces memory accesses and cost. The system is programmable and scalable, which allows for execution of different network sizes. Synthesized in 28 nm CMOS technology, real-time classification is achieved at 4 MHz, with an energy dissipation of 4.1 pJ/classification, an improvement of 15 × compared to a pure DSP realization. The memory requirements are reduced by 75 %, which results in a silicon area of 0.7 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> for the entire SoC.
Maximizing Small Spaces Using Smart Portable Desk for Online Learning Purpose

Limited land needs in the urban area result in a two-storey house and vertical housing method to solve the city’s residents needs. To maximize the space remaining in the house and apartment by placing multifunction furniture to accommodate user activities. The pandemic in early 2020 forced almost everyone to work from home, including lecturers, to give distance learning. Thus, it needs a portable desk that can move from one place to somewhere else at home and be inserted in some parts of the house without building additional space. The methodology in this research departs from an existing movable desk in the office that simplified its shape for home use. Furthermore, supplementing a survey on SOD lecturer of BINUS University to improve the design results on the table. Results of the research can be used for all lecturers who use a particular desk for distance learning purpose.
Recommender Systems for University Elective Course Recommendation

Recommendation system is an ongoing research that is applied in various domains. Students are often confused when it comes to choosing a particular course. They are unable to choose one based on their interest. Course recommendation is considered a complex domain that has not been explored thoroughly. It benefits undergraduate students who need suggestion and also enhances course selection processes during the pre-registration period. Our system introduces a recommendation system for university elective courses, which recommends the courses based on the similarity between the course templates of students. According to our research, there are a few techniques and algorithms introduced for recommendation that can be used in course recommendation system. The techniques and algorithms vary from content-based recommendation, collaborative filtering, and rule-based mining. Our system focuses on designing and developing the recommendation system that is able to recommend elective courses for university students and to assist them to make correct decision for enrolling in that course for that semester. The recommendation system is developed to provide aid to students in suggesting suitable courses for them as well as reducing time to explore courses that they will take. Since there are many elective courses in each semester, students have to spend a lot of time for exploring those courses, and they may not be able to explore all of them. Apart from assisting the student, recommendation systems could help the university also, by suggesting course for the student based on interest, current trend, and need.
Intelligent Visualization System for Big Multi-source Medical Data Based on Data Lake

With the rapid development of information technology, large amounts of multi-source data are constantly being generated in medical field. The automatic visualization system based on them has gained a lot of attention, since the intuitive data presentation can help even non-professional users effectively get the information hidden behind the separate data obtained from different scenarios and make better decisions. In this paper, based on the Data Lake architecture, we improve the performance of an existing novel data visualization recommendation system and resolve three challenges about the processing of multi-source and heterogeneous data. First, we build the framework based on Data Lake to store multi-source and heterogeneous data. Second, we optimize the data manipulation module in the visualization system based on the distributed processing power of Data Lake to get potentially interesting visualization candidates in a short time. Third, we efficiently run exploratory queries on large datasets based on the calculation capability of Data Lake to meet the actual needs of users. According to the experiment results, our system demonstrates a remarkable acceleration effect on the task of automatic visualization of big multi-source medical data.
The Heron Formula Approach for Head Movements Detection for Student Focus Detection during Pandemic Online Class

Head movement are widely used and utilized, there are many things that applying head movement as a main subject such as to control a device, control a smart wheelchair, playing games and many more. In this study, we propose a preliminary study to use the head movements as a focus detector for student in online learning. We use several approaches such as Face Landmark until Heron Triangle approach to detect and recognize the movement of the head. In this study, we utilize the Heron formulas to obtain the large of triangle that shaped using the marking point from Face Landmark method. By using the large of triangle, this proposed method could obtain and recognize the gaze of head movements. Our proposed method can reach 85% of accuracy and has an ability to detect the horizontal and vertical movements. This study is an important as an initial step to create a robust head movement detection system especially for online learning purposes.
RGNET: A Two-stage Low-light Image Enhancement Network Without Paired Supervision

Deep learning-based methods have achieved remarkable success in low-light image enhancement. However, in the absence of a large number of low/normal light image pairs, it is still a challenge to train the enhancement network with good generalization ability. In this paper, we propose a highly effective unsupervised network for low-light image enhancement (named RGNET). We divide the enhancement task into two stages, complete from coarse to precise. At the first stage, we roughly amplify the input image nonlinearly using an unsupervised network. At the second stage, we build a two-path network to restore image details, one is uesed for residual restoration and the other is used for contextual attention. With the combination of reconstruction and adversarial loss, our enhancement effects are more consistant and natural than other GAN-based methods. Both quantitative and qualitative experiments on challenging datasets demonstrate the advantages of our method in comparison with state-of-the-art methods.
An Automatic 3D Scene Generation Pipeline Based on a Single 2D Image

In the last years, solutions were proposed in the literature to alleviate the complexity of using sophisticated graphic suites for 3D scene generation by leveraging automatic tools. The most common approach based on the processing of text descriptions, however, may not represent the ideal solution, e.g., for fast prototyping purposes. This paper proposes an alternative methodology able to extract information about the objects and the layout of the scene to be created from a single 2D image. Compared to previous works, experimental results reported in this work show improvements in terms of similarity between the 2D and 3D scenes.
Disappeared Face: A Physical Adversarial Attack Method on Black-Box Face Detection Models

Face detection is a classical problem in the field of computer vision. It has significant application value in face recognition and face recognition related applications such as face-scan payment, identity authentication, and other areas. The emergence of adversarial algorithms on face detection poses a substantial threat to the security of face recognition. The current adversarial attacks on face detection have the limitations of the need to fully understand the attacked face detection model’s structure and parameters. Therefore, these methods’ transferability, which can measure the attack’s effectiveness across many other models, is not high. Moreover, due to the consideration of commercial confidentiality, commercial face detection models deployed in real-world applications cannot be accessed, so we cannot directly launch white-box adversarial attacks against these models. Aiming at solving the above problems, we propose a Black-Box Physical Attack Method on face detection. Through ensemble learning, we can extract the public weakness of the face detection models. The attack against the public weakness has high transferability across models and makes escaping black-box face detection models possible. Our method realizes the successful escape of both the white-box and black-box face detection models in both the PC terminal and the mobile terminal, including the camera module, mobile payment module, selfie beauty module, and official face detection models.
Extending the determinants of currency substitution in Nigeria: Any role for financial innovation?

Abstract Existing literature suggests that macroeconomic and institutional factors are the drivers of currency substitution. The persistent and significant incidence of currency substitution during the period of mixed performance of macroeconomic variables suggests the existence of a knowledge gap on the drivers of currency substitution during the era of rapid technological innovation. To contribute to this literature, we augmented the traditional money demand model of the determinants of currency substitution to introduce financial innovation. We use Nigerian data from 2005Q1 to 2019Q4 and Pesaran et al. (2001, https://doi.org/10.1002/jae.616 ) autoregressive distributed lag (ARDL) bound test approach to cointegration to estimate the models. The results confirm the presence of short‐run and long‐run relationships between financial technology and currency substitution in Nigeria. In effect, the deployment of financial technology in developing payment system infrastructure creates additional incentives for economic agents to hold foreign currency deposit. Economic managers must, therefore, mainstream credible monetary and fiscal policies to moderate the effect of financial innovation on currency substitution.
Identifying the support of rectangular signals in Gaussian noise

We consider the problem of identifying the support of the block signal in a sequence when both the length and the location of the block signal are unknown. The multivariate version of this problem is also considered, in which we try to identify the support of the rectangular signal in a hyper-rectangle. In this article, we greatly generalize the requirement in Jeng, Cai, and Li and in particular, we allow the length of the block signal to grow polynomially with the length of the sequence. A statistical boundary above which the identification is possible is presented and an asymptotically optimal and computationally efficient procedure is proposed under the assumption of Gaussian white noise. The problem of block signal identification is shown to have the same statistical difficulty as the corresponding problem of detection, in the sense that whenever we can detect the signal, we can identify the support of the signal. Some generalizations are also considered here: (1) the case of multiple block signals, (2) the robust identification problem where the noise distribution is unspecified, (3) the block signal identification problem under the exponential family setting.
Laplace distribution: Properties, regression model and simulation

The Laplace distribution with two parameters is studied here. Some properties were demonstrated, such as, quantile function, media, mode, variance, analytical expressions for generating function of moments and cumulants. The estimators of the method of moments and maximum likelihood were discussed, with a comparison between these two methods using Monte Carlo simulation. Subsequently, we propose the Laplace regression model, where the parameter estimates are made by maximum likelihood. To show the performance of this model, a Monte Carlo simulation was performed.
Parametric Identification of a Linear Time Invariant Model for a Subglottal System

Models of the human body are key in bio-engineering and medical applications. This study presents the identification, in time and frequency domains, of linear time invariant models of the human subglottal system, for the clinical assessment of vocal function. For time domain identification, the input-output data corresponds to the glottal volume velocity and the acceleration registered by a sensor on the neck skin of the patient. For frequency domain identification, the frequency response of the subglottal tract module is used. Maximum likelihood and prediction error methods are applied. Additionally, the Akaike and Bayes Information Criteria are used to select the models order.
ROMAX: Certifiably Robust Deep Multiagent Reinforcement Learning via Convex Relaxation

In a multirobot system, a number of cyber-physical attacks (e.g., communication hijack, observation per-turbations) can challenge the robustness of agents. This robust-ness issue worsens in multiagent reinforcement learning because there exists the non-stationarity of the environment caused by simultaneously learning agents whose changing policies affect the transition and reward functions. In this paper, we propose a minimax MARL approach to infer the worst-case policy update of other agents. As the minimax formulation is computationally intractable to solve, we apply the convex relaxation of neural networks to solve the inner minimization problem. Such convex relaxation enables robustness in interacting with peer agents that may have significantly different behaviors and also achieves a certified bound of the original optimization problem. We eval-uate our approach on multiple mixed cooperative-competitive tasks and show that our method outperforms the previous state of the art approaches on this topic.
Multitask Learning Using BERT with Task-Embedded Attention

Multitask learning helps to obtain a meaningful representation of the data, retaining a small number of parameters needed to train the model. In natural language processing, models often reach as many as a few hundred million trainable parameters, which makes adaptations to new tasks computationally infeasible. Creating shareable layers for multiple tasks allows to spare resources and often leads to better data representation. In this work, we propose a new approach to train a BERT model in a multitask setup, which we call EmBERT. To introduce information about the task, we inject task-specific embeddings to the multi-head attention layers. Our modified architecture requires a minimal number of additional parameters relative to the original BERT model (+0.025% per task) while achieving state-of-the-art results in the GLUE benchmark.
Short-Term Deterministic Solar Irradiance Forecasting Considering a Heuristics-Based, Operational Approach

Solar energy is an economic and clean power source subject to natural variability, while energy storage might attenuate it, ultimately, effective and operationally feasible forecasting techniques for energy management are needed for better grid integration. This work presents a novel deterministic forecast method considering: irradiance pattern classification, Markov chains, fuzzy logic and an operational approach. The method developed was applied in a rolling manner for six years to a target location with no prior data to assess performance and its changes as new local data becomes available. Clearness index, diffuse fraction and irradiance hourly forecasts are analyzed on a yearly basis but also for 20 day types, and compared against smart persistence. Results show the proposed method outperforms smart persistence by ~10% for clearness index and diffuse fraction on the base case, but there are significant differences across the 20 day types analyzed, reaching up to +60% for clear days. Forecast lead time has the greatest impact in forecasting performance, which is important for any practical implementation. Seasonality in data gaps or rejected data can have a definite effect in performance assessment. A novel, comprehensive and detailed analysis framework was shown to present a better assessment of forecasters’ performance.
One down, 699 to go: or, synthesising compositional desugarings

Programming or scripting languages used in real-world systems are seldom designed with a formal semantics in mind from the outset. Therefore, developing well-founded analysis tools for these systems requires reverse-engineering a formal semantics as a first step. This can take months or years of effort. Can we (at least partially) automate this process? Though desirable, automatically reverse-engineering semantics rules from an implementation is very challenging, as found by Krishnamurthi, Lerner and Elberty. In this paper, we highlight that scaling methods with the size of the language is very difficult due to state space explosion, so we propose to learn semantics incrementally. We give a formalisation of Krishnamurthi et al.'s desugaring learning framework in order to clarify the assumptions necessary for an incremental learning algorithm to be feasible. We show that this reformulation allows us to extend the search space and express rules that Krishnamurthi et al. described as challenging, while still retaining feasibility. We evaluate enumerative synthesis as a baseline algorithm, and demonstrate that, with our reformulation of the problem, it is possible to learn correct desugaring rules for the example source and core languages proposed by Krishnamurthi et al., in most cases identical to the intended rules. In addition, with user guidance, our system was able to synthesize rules for desugaring list comprehensions and try/catch/finally constructs.
Privacy-Aware Anomaly Detection Using Semantic Segmentation

This paper sets out to investigate if semantic segmentation can be used to achieve anonymity in video surveillance while maintaining the ability to perform anomaly detection. The paper is centered around finding the best model for segmenting the anomaly detection dataset UCHK Avenue without semantic ground truth available. To do this, a series of segmentation models pre-trained on ADE20K and Cityscapes are evaluated against a custom semantic annotation of selected frames from Avenue and the segmentation results are compared both quantitatively and qualitatively. The segmented dataset is then tested on a series of different anomaly detection baselines and the results are compared both in terms of global and anomaly specific accuracy. When comparing the anomaly detection accuracy for RGB and segmented data it was found that anonymity in anomaly detection can be achieved at a small cost in global accuracy but with better accuracy for some specific anomalies.
Extraction of Malignant Tumor Diagnostic Text Information Based on Named Entity Recognition

In order to extract named entity recognition from malignant tumor medical medical text data, an algorithm that combines the bidirectional long short-term memory network and the conditional random field (Bi-LSTM-CRF) is proposed. This method adds the CRF layer processing after Bi-LSTM network output, so that the model has better comprehensive performance since the CRF can take orders of words into consideration. The experimental results show that comparing to the algorithm that combines the maximum entropy Markov model and the conditional random field (MEMM+CRF) and the algorithm of the bidirectional long short-term memory network (Bi-LSTM), our method is more excellent in entity recognition for comprehensive practical applications, and can basically identify the corresponding medical entity.
DDBN: Dual detection branch network for semantic diversity predictions

It is well known that detail features and context semantics are conducive to improving object detection performance. However, the current single-prediction detectors do not well incorporate these two types of information together. To alleviate the limitation of single-prediction on the use of multiple types of information, we propose a dual detection branch network (DDBN) with adjacent feature compensation and customized training strategy for semantic diversity predictions. Different from the conventional single-prediction models, our DDBN is in the form of a single model with dual different semantic predictions. In particular, two types of adjacent feature compensations are designed to extract detail and context information from different perspectives. Also, a specialized training strategy is customized for our DDBN to well explore the diversity of predictions for improving the performance of object detection. We conduct extensive experiments on three datasets, i.e., DOTA, MS-COCO, and Pascal-VOC, and the experimental results strongly demonstrate the efficacy of our proposed model.
Image Encryption Using Diffusion and Confusion Properties of Chaotic Algorithm

Based on Chaos theory, cryptographic techniques show several new and successful ways to build reliable image encryption schemes. We present an image encryption in this paper using a logistic map of 1D. The proposed model framework is based on a main stream generator for the mechanism of uncertainty. A secret key of 256 bits, which is itself created by a logistic map, initiates the confusion process. In order to make the cipher more dynamic against any attack, after encrypting each block of the image, the secret key is changed. The experimental results show that the proposed approach offers an effective and safe way to encrypt and transfer images in real-time.
A Deep Learning Tool to Classify Vehicles in Real Time

The problem of vehicular congestion is transversal to all the cities worldwide. To deal with it, it can be done from the construction of new roads or the management of these as in the case of exclusive roads for public transport. In the latter case, information is necessary. Currently, in Chile this collection process is manually done, so it does not respond to current information needs, because the long time the process takes. The use of deep learning models for automatic counting has shown outstanding results, but based on a scheme for sending video streaming to remote servers. The present proposal aims to not depend on fiber optic connectivity. It is based on a network of modules based on Edge Artificial Intelligence (Edge AI) in order to build a scalable, real-time data capture platform.