Reviewerly/exHarmony huggingface dataset:
Fast and Robust People Detection in RGB Images

People detection in images has many uses today, ranging from face detection algorithms used by social networks to help the users tag other people, to surveillance systems that can create a statistic of the population density in an area, or identify a suspect, or even in the automotive industry as part of the Pedestrian Crash Avoidance Mitigation (PCAM) system. This work focuses on creating a fast and reliable object detection algorithm that will be trained on scenes that depict people in an indoor environment, starting from an existing state-of-the-art approach. The proposed method improves upon the You Only Look Once version 4 (YOLOv4) network by adding a region of interest classification and regression branch such as Faster R-CNN’s head. The candidate bounding boxes proposed by YOLOv4 are ranked based on their confidence score, the best candidates being kept and sent as input to the Faster Region-Based Convolutional Neural Network (R-CNN) head. To keep only the best detections, non-maximum suppression is applied to all proposals. This decreases the number of false-positive candidate bounding boxes, the low-confidence detections of the regression and classification branch being eliminated by the detections of YOLOv4 and vice versa in the non-maximum suppression step. This method can be used as the object detection algorithm in an image-based people tracking system, namely Tracktor, having a higher inference speed than Faster R-CNN. Our proposed method manages to achieve an overall accuracy of 95% and an inference time of 22 ms.
Improvement on a Traffic Data Generator for Networking AI Algorithm Development

Recently, many Artificial Intelligence (AI) based schemes have been proposed to support network measurement and management, such as network traffic classification, intrusion detection, traffic prediction, etc. These AI schemes have demonstrated promising performance in supporting networking. However, the development of these AI schemes requires a massive amount of fresh databases. The scarcity and futility of public datasets are straining the development of the networking AI models. Not to mention that most available datasets are not up-to-date. Collecting new datasets can be time-consuming and restricted by networking capabilities. To address the issues, we have introduced a real-application enabled network traffic generator. In this work, we further enhance the network traffic generator with more functionalities. In particular, a traffic flow segment scheme is proposed for the quick establishment of traffic flow databases. An intelligent generator is implemented to simulate point-to-point communications, including network multiplexing, and network duplexing. The evaluation results demonstrate that the improved intelligent traffic generator can generate a large amount of diverse network traffic with practical settings more efficiently than collecting data in real life. Moreover, a case study is given to demonstrate the quality of the generated traffic data.
Detection of the Floating Objects on the Water Surface Based on Improved YOLOv5

An improved YOLOv5 surface floating object detection algorithm is proposed to solve the problems of low detection accuracy and slow speed in the detection of floating objects on the water surface. Firstly, the smooth label is introduced to suppress the over-fitting phenomenon in the training process of the network model. Secondly, the inception topology is used to enhance the feature extraction of floating objects, under the premise of ensuring network accuracy, the low number of parameters and the amount of calculation are maintained. Finally, the loss function of YOLOv5 is optimized to improve the speed and accuracy of prediction frame regression. Experimental results show that the improved mean Average Precision (mAP) of YOLOv5 reaches 96.48%, which is 16.9% higher than that of YOLOv5, and the detection rate up to 30fps. This detection strategy is effective and feasible.
A low-cost system for real-time measuring of the sunlight incident angle using IoT

The incidence angle of solar irradiance is an important parameter for sizing and locate photovoltaic systems, which affects the installation design and has a high influence in the power production of photovoltaic panels. This angle is traditionally estimated considering the geographical position, however, this approach ignores the existence of local elements that affect the generation, such as weather conditions, topography, constructions with high reflection, among others. Therefore, this work presents the design and construction of a measurement device with nine irradiance sensors, which are located at different angles on two orthogonal axes within a semisphere. Since the angles of the sensors are known, a model to determine the direction of the maximum incidence irradiance, at each instant of time, can be calculated from the on-site measurements. In this way, it is also possible to calculate the panel inclination and orientation producing the maximum power for a particular location. The device acquires the irradiance magnitude in the nine sensors in real time, and it is transmitted using the Internet to simplify data recollection. Finally, the device uses a low-cost platform, which makes possible the adoption of this solution in a wide range of applications, e.g. design, diagnostic or reconfiguration of PV arrays.
Financial development and the redistributive channel of monetary policy

Purpose This paper aims to examine the distributional channel of monetary policy (MP) and evaluate how financial development (FD) affects the transmission mechanism from MP to income inequality. Design/methodology/approach The empirical investigation is implemented for 32 sub-Saharan African countries over the period 2000–2017, with the aid of vector autoregressions and a dynamic panel data model. Findings This study shows that MP has a significant impact on income inequality and the financial system plays an important role by dampening the dis-equalising effects of MP shocks. Both MP and FD directly exert redistributive effects. However, the financial system appears to wield the greatest impact and contribute more to the inequality dynamics. Practical implications The policy-relevant conclusion is that the financial system is crucial for the monetary transmission mechanism and the effects of MP actions. As the economy develops financially, it may require less movement in the policy position to achieve the desired policy outcome. Also, macroeconomic stabilisation policies may not be distributionally neutral and may have a role to play in averting longer-term increases in inequality. Originality/value Contrary to previous studies, this study indicates MP by the structural shocks to purge the MP stance of the issues of endogenous and anticipatory actions. A distinctive finding of this paper is that cross-country differences in monetary regimes and income explain a significant variation in the distributional impacts of monetary policy. Notwithstanding, the evidence shows that the strength of the transmission is more dependent on FD than the nature of the policy regime.
Interpolation and Uniform Interpolation in Quantifier-Free Fragments of Combined First-Order Theories

In this survey, we report our recent work concerning combination results for interpolation and uniform interpolation in the context of quantifier-free fragments of first-order theories. We stress model-theoretic and algebraic aspects connecting this topic with amalgamation, strong amalgamation, and model-completeness. We give sufficient (and, in relevant situations, also necessary) conditions for the transfer of the quantifier-free interpolation property to combined first-order theories; we also investigate the non-disjoint signature case under the assumption that the shared theory is universal Horn. For convex, strong-amalgamating, stably infinite theories over disjoint signatures, we also provide a modular transfer result for the existence of uniform interpolants. Model completions play a key role in the whole paper: They enter into transfer results in the non-disjoint signature case and also represent a semantic counterpart of uniform interpolants.
Curriculum Design of Artificial Intelligence in Middle School - Taking Posture Recognition as an Example

At present, there is still a lack of suitable artificial intelligence curriculum in primary and secondary schools in China. Based on a project-based learning model, an artificial intelligence course was designed for middle school-aged students. Learning objectives were deconstructed according to the five concepts of artificial intelligence, and learning contents were arranged according to the cognitive characteristics of middle school students. In the process of teaching, the cultivation of students&#x0027; computational thinking ability was paid great attention, and teaching example was given at the end of the paper.
Federated Learning with User Mobility in Hierarchical Wireless Networks

Recently, the implementation of federated learning (FL) in wireless networks becomes a hotspot due to its flexible collaborative learning methods and privacy-preserving benefits. However, most of the existing works overlook the impact of user mobility on the learning performance, which is critical. Specifically, the mobile users may roam among multiple edge access points (APs) during the local training procedures, leading to incompletion of inconsistent FL training. In this paper, we theoretically study the impact of user mobility on the FL in hierarchical wireless networks. In our system model, the network consists of one cloud server, several edge APs, and multiple mobile users that have their positions vary over time. During the local training process, users may stay in or move out of the coverage area of the originally attached edge AP. In such a practical context, we analyze the convergence rate of the FL algorithm and provide experiments to evaluate the learning performance under different network parameters. Our results provide insights in further improvements of FL in hierarchical wireless networks.
A Comparison of Support Vector Machine and Naïve Bayes Classifier in Binary Sentiment Reviews for PeduliLindungi Application

COVID-19 statistics in Indonesia show more than 4.2 million active confirmed cases with more than 140 thousand deaths. The Indonesian government has made several policies to reduce the number of COVID-19 cases, one of them is by implementing the PeduliLindungi application. The government has socialized and recommended this application as an effort to fulfill the tracking, tracing, and fencing program. Various kinds of responses appear in the community to this application, therefore sentiment analysis is needed to find out public trends so that the government can evaluate the policies that have been made. This study aims to determine the best model from the comparison of the Naïve Bayes algorithm and the Support Vector Machine, besides that this study will also see whether a simpler model such as Naive Bayes is still good in handling binary sentiment for PeduliLindungi data reviews. The data was obtained by web scraping from the PeduliLindungi application review on the Google Play Store. The Naïve Bayes accuracy value is 81%, smaller than the Support Vector Machine which has an accuracy of 84%, although the Support Vector Machine is the best model we have, Naive Bayes itself can still be used to handle binary sentiment data because the difference in accuracy values is not too far.
Influential Factors for Tourist Profiling for Personalized Tourism Recommendation Systems– A Compact Survey

Over the past few years, recommendation systems have gained immense popularity and are being applied to many domains. This paper discusses the application of recommendation systems for the domain of tourism. In this paper, we aim to present an up-to-date, thorough and compact survey on Tourism Recommendation Systems and the most influential factors which significantly impacts the decision-making of tourists while planning a tour. We have discussed complete architecture and modules of recommendation systems, various types of recommendation approaches and algorithms, the functionalities offered by these systems, the factors used for recommendation and knowledge base, tourist profiling and type of data used in tourism recommendation systems. The paper also mentions the scope of improvement and promising areas of research in this field.
A Note on Holevo Quantity of SU(2)-invariant States

The Holevo quantity and the SU(2)-invariant states have particular importance in quantum information processing. We calculate analytically the Holevo quantity for bipartite systems composed of spin-j and spin- $\frac {1}{2}$ subsystems with SU(2) symmetry, when the projective measurements are performed on the spin- $\frac {1}{2}$ subsystem. The relations among the Holevo quantity, the maximal values of the Holevo quantity and the states are analyzed in detail. In particular, we show that the Holevo quantity increases in the parameter region F < Fd and decreases in region F > Fd when j increases, where F is function of temperature in thermal equilibrium and Fd = j/(2j + 1), and the maximum value of the Holevo quantity is attained at F = 1 for all j. Moreover, when the dimension of system increases, the maximal value of the Holevo quantity decreases.
Bert for Question Answering applied on Covid-19

In response to the COVID-19 pandemic, a lot of scholarly articles have been published recently and made freely available. At the same time, there is emerging need to provide reliable and adequate information, which can reinforce the efforts made in raising awareness, to carry out infallible actions to prevent the worsening of the pandemic situation. With that said the current work tackles the perennial problem of creating a deep learning system allowing answering with a high precision to questions on a determined subject. To do this, we will use open source scientific and academic articles concerning Covid-19, then we will proceed to the selection of suitable documents which will allow us to feed a question answering system based on BERT fine-tuned on the SQuAD benchmark.
Deprewriter: On the fly rewriting method deprecations.

Deprecations are a common way to indicate that a given feature or API will not be available in subsequent versions of a library or framework.While raising deprecation warnings lets developers of libraries evolve their APIs, the developers of client applications often have to manually rewrite their applications to adapt to the deprecation (removal, new APIs. . .).Some may use static analysis tools to support the rewriting.However, dynamically-typed languages or the use of reflective features often produce incorrect rewrite candidates.This is a costly activity that can lead to bug introductions.In this article, we present a method deprecation approach and a tool called DEPREWRITER that can automatically rewrite the callers of deprecated methods during program execution.Clients of a deprecated API execute their program and associated tests, and DEPREWRITER dynamically rewrites the source code of methods that called a deprecated API to use the new API.The implementation of DEPREWRITER is based on dynamic program transformation: when a deprecated method is executed, a program transformation engine rewrites and recompiles the caller's code before continuing the execution.The approach presented in this article has been developed by the Pharo consortium.Since 2016, DEPREWRITER is used in production in multiple distributions of the Pharo programming language: Pharo 6, 7, 8, and 9 alpha.This article presents and validates this approach.The validation is done in two steps: first with an analysis of deprecations available in Pharo 8 and second with an open survey of software developers about DEPREWRITER.We studied 367 Pharo 8 deprecations, among which we analyzed the 218 rewriting deprecations that use transformation rules.We identified the validity conditions and reported defects to the community.We also proposed 33 transformation rules to be added to the non-rewriting deprecations.Both contributions were accepted into Pharo 9 alpha.We classified the rules and identified possible points of improvement.In addition, we performed a user survey and collected information from 46 software developers: some of them used existing DEPREWRITER' rules and executed them on their code, others used DEPREWRITER to create rewriting deprecations, and finally, some were not aware of DEPREWRITER.28 of 46 developers (60%) reported that the rewriting deprecations helped them, while 10 stated the inverse and 8 were uncertain.After discussing the current implementation, we sketch possible implementations for other languages than Pharo, showing that the approach is general enough to be applied to other languages.
Mandarin Electrolaryngeal Speech Voice Conversion with Sequence-to-Sequence Modeling

The electrolaryngeal speech (EL speech) is typically spoken with an electrolarynx device that generates excitation signals to substitute human vocal fold vibrations. Because the excitation signals cannot perfectly characterize sound sources generated by vocal folds, the naturalness and intelligibility of the EL speech are inevitably worse than that of the natural speech (NL speech). To improve speech naturalness, statistical models, such as Gaussian mixture models and deep-learning-based models, have been employed for EL speech voice conversion (ELVC). The ELVC task aims to convert EL speech into NL speech through an ELVC model. To implement a frame-wise ELVC system, accurate feature alignment is crucial for model training. However, the abnormal acoustic characteristics of the EL speech cause misalignments and accordingly limit the ELVC performance. To address this issue, we propose a novel ELVC system based on sequence-to-sequence (seq2seq) modeling with text-to-speech (TTS) pretraining. The seq2seq model involves an attention mechanism to concurrently perform representation learning and alignment. Meanwhile, TTS pretraining provides efficient training with limited data. Experimental results show that the proposed ELVC system yields notable improvements in terms of standardized evaluation metrics and subjective listening tests over a well-known frame-wise ELVC system.
Foreground-guided textural-focused person re-identification

Person re-identification (Re-ID) is challenging in computer vision. It is crucial to strengthen discriminative features and suppress irrelevant ones for high-performance. Existing Re-ID approaches have won significant progress by attention mechanisms or introducing key-point/part priors. However, these methods suffer the high cost of building models. Besides, potential feature false alarm problems may happen due to background interference and inaccurate priors. Moreover, they usually ignore the degradation of texture clues which are identity sensitive. In this paper, we propose the Foreground-Guided Textural-Focused Network (FTN) to address these problems. Specifically, our FTN is an end-to-end Re-ID framework, which consists of a Semantic Encoder (S-Enc), a Compact Foreground Attention (CFA) module, and a Texture-Focused Decoder (TF-Dec). First, based on CFA and 2D Gaussian kernel, a coarse foreground-guided learning strategy is developed to suppress the feature false alarms at the source. Its core idea lies in constructing foreground guidance, which forces S-Enc to pay more attention to person instance-level features. Second, the TF-Dec is designed as a lightweight reconstruction task. It is trained via a novel gradient loss, and further forces S-Enc to maintain texture-wise details. Our method is computationally efficient as TF-Dec is abandoned in the inference phase. Extensive experiments are conducted on three large-scale Re-ID datasets Market1501, CUHK03, MSMT17, and two other occluded datasets. The results indicate that FTN achieves superior performance against the state-of-the-art methods, e.g., Rank-1 of 96.2% and 59.0% on Market1501 and Occluded-Duke respectively.
Dual Sensor Indian Masked Face Dataset

With the advancements in deep learning technologies, real-world applications like face detection, gender prediction, and face recognition have achieved human-level performance. However, the emergence of the COVID-19 pandemic brought new challenges to existing deep learning algorithms. People are forced to wear a mask to limit the spread of COVID-19. These face masks occlude a significant portion of the face, thereby posing multiple challenges to existing algorithms. Images captured using surveillance cameras have a low resolution which hinders the model performance. Along with this, skin tone, ethnicity and attire also play a significant role in detection and recognition performance. India is a large country with huge diversity in skin tone and attire of the people. To address the challenges due to masks in the Indian context, we propose a novel Dual Sensor Indian Masked Face (DS- IMF) dataset, which contains images captured in constrained environmental settings with a variety of masks and degrees of occlusion. Multiple experiments are performed on the DS- IMF dataset at different resolutions. Experimental results demonstrate the limitations of existing algorithms on low-resolution masked face images. The proposed dataset can be found at http://www.iab-rubric.org/resources/dsimf.html.
How commodity prices and technical development has influenced exploration strategies: a case study from Sweden

Abstract In Sweden, mining of metallic mineral resources goes back at least 1000 years, possibly much longer. This paper reviews the development of exploration over the past 500 years and discusses exploration based on 1) demand and price, 2) technical development, and 3) legislation. Without doubt, exploration efforts have been driven by an early demand, especially from the Swedish crown and well-known mines such as Falun copper mine and Sala silver mine sustained the Swedish crown and financed several wars during the seventeenth and eighteenth centuries for several centuries. The fluctuation in metal prices, basically a function of demand, due to technical development and industrialization, has been an important driver for the focus of exploration over the last 100 years, and even more so during the last 50 years when exploration, besides base metals and iron, has focussed on more critical raw materials for the energy transition and green technology. The technical development has also meant that new methods have become available for exploration, not the least deep exploration methods and Swedish innovations have played a major role in drilling technology, geophysical instrument development and other areas. This is discussed based on development in drilling technology, geological, geophysical and geochemical methods. Finally, a review of the how the mining legislation has changed over the last 500 years is provided where this is placed in the context of how it has influenced private investment in exploration and what role the Swedish state has played.
Quantum technology to expand soft computing

Soft computing was founded to unify and advance computing methods beyond the limits of binary variables, Boolean logic and Turing machines. David Deutsch invented a way to use massive quantum parallelism to massively improve the power of Turing machines in some applications. Here we give a roadmap to use quantum parallelism to massively improve the power of soft computing, focusing on tasks in optimization ranging from function optimization, finding a needle in a haystack, optimal control, energy technology and reinforcement learning (Quantum artificial general intelligence, AGI) in a path leading up to optimization of physical plants themselves put into quantum superpositions. The path begins with design and test of prototypes built on platforms such as Nuclear Magnetic Resonance, sparse quantum optics, superconductors (sQuID), quantum dots and new high speed electronics.