Miracle-dz/UniFAR-FTUs huggingface dataset:
The Making and Re-making of The Philadelphia Negro

This article sheds light on the methods and meaning of W. E. B. Du Bois’ 1899 study of the everyday lives of Black residents of Philadelphia’s Seventh Ward. It does so by juxtaposing the way Du Bois conducted his research with our contemporary efforts to recover, recreate, and preserve The Philadelphia Negro using digital and geospatial technologies to document historical and contemporary patterns relating to race and class. Beginning with an exploration of primary source documents that provide new details about how Du Bois went about his original research, we focus on the humanities and social science research methods that he employed. Of note is the color-coded parcel-level map Du Bois created to illustrate Black social class status, which reflected both the influence of the Social Survey Movement and Du Bois’ efforts to present a new understanding of the color line. His findings were groundbreaking, considering that most white scientists of his time assumed Black people to be biologically inferior and socially homogeneous. Instead, he documented the variability and social stratification within Philadelphia’s Black population and the systematic exclusion they faced because of anti-Black racism. Our ongoing project — The WARD: Race and Class in Du Bois’ Seventh Ward , which seeks to recreate Du Bois’ study — includes new technologies and participatory research methods that engage high school and college students. In-depth, intergenerational oral histories conducted with students also add a new dimension to this work and complement our high school curriculum, which incorporates online mapping, documentaries, a board game, a walking tour, and a mural to engage others to create their own primary sources. This research provides a historical context for today’s racial tensions as we seek new ways to address the 21st century color line.
Adaptive ADALINE Robust Training Algorithm Under the Maximum Correntropy Criterion With Variable Center

The problem of training ADALINA in the presence of non-Gaussian interference is considered. The learning algorithm is a gradient procedure for maximizing the functional. In contrast to the commonly used Gaussian kernels, the centers of which are at zero and effective for distributions with zero mean, the paper considers a modification of the criterion suitable for distributions with nonzero mean. The modification is to use correntropy with a variable center. The use of Gaussian kernels with a variable center will allow us to estimate unknown parameters under Gaussian and non-Gaussian noises with zero and non-zero mean distributions. The properties of its convergence in the stationary and non-stationary cases in conditions of Gaussian and non-Gaussian noises are investigated.
Recommendation System for Monitoring the Energy Value of Consumer Food Products Based on Machine Learning

Healthy eating is the cause of longevity. Rational consumption of products will help achieve high efficiency and activity throughout life. To improve the quality of your life, you do not need to read dozens of books and delve into medicine. Just use intelligent systems. Today, there are many different approaches to solving the problem of diet control in a fleeting world. The need for research on healthy eating in modern conditions is crucial to improving the physical condition of different age groups. After all, non-compliance with the basic rules reduces life expectancy and provokes the emergence of non-communicable diseases of various types: cancer, cardiovascular disease, and diabetes. The main goal is to create a system whose main task will help the end-user follow a healthy diet. The primary skills of the system include the ability to recognise dishes, read barcodes, decompose a dish into a set of ingredients, calculate calories, and calculate the energy value consumed per day.
Research Application of the Spam Filtering and Spammer Detection Algorithms on Social Media

There are a bunch of different social networks and messengers today, which in times of pandemic corona-virus and Russian war in Ukraine have take a really big part of our entire live, especially in our work activities. Besides that, the problem with the spam and spammers is the most relevant than ever, the count of spam in the work text stream is continuously increased. Under spam we understand the text content that is not necessary in the particular text stream, in case of spammer it is meant the person that is sending the spam messages in his or her own purposes. The project was design to solve the scientific and applied problem of detecting spammers and identifying spam messages in the text context of any social network or messenger using various spam detection algorithms and spammer detection approaches. We have implemented four algorithms for spam recognition and the complex majority algorithm for spam recognition and spammer detection: an algorithm using naive Bayesian classifier, Support-vector machine, multilayer perceptron neural network and convolution neural network. The developed approach using a complex majority algorithm can be used not only to remove spam and spammer detecting, but also, for example, to antispam bot messages monitoring for chats that are important for a particular user.
Trends in the inter-regional and international research collaboration of the PRC’s regions : 2000-2015. STI Conference, 21st Int. Conf. on Science and Technology Indicators, Valencia

The regional structure of the PRC’s scientific output is analyzed using publications processed for the Web of Science. Over the period 2000-2015 and measured by the Salton Index of the co-publications the scientific collaboration among the PRC’s regions increased only slightly, in stark contrast with the USA’ states and during the most recent years the EU member countries. Only for research with other nations, representing about 30% of the total publication output, inter-regional collaboration is on the rise. For the leading PRC’s regions the USA is the dominant partner co-authoring about 50% of their publications. Germany and especially Japan seems to lose attractiveness to the advantage of the UK, Australia and neighboring Asian countries.
Semibridgeless Interleaved PFC Boost Rectifier for PHEV Battery Chargers

ABSTRACT In this paper, a new semi-bridgeless interleaved (SBLIL) boost rectifier topology as front end converter for plug-in hybrid electric vehicle (PHEV) is proposed. The proposed rectifier topology achieves higher efficiency, with high input power factor and less power drawn from the utility. A detailed principle of operation and analytical modelling of the converter is presented. The converter prototype is designed for 300 W with a high switching frequency of 80 kHz and tested for a universal input voltage of (110–240) V to get the desired output voltage of 400 V. The modified converter topology achieves a high efficiency of 97% as it eliminates the bridge rectifier and addition of slow diodes. The performance of the proposed converter is compared with the existing standard topologies. The experimental results infer that the power factor at the supply end is 0.99 with THD less than 5% when the load is varied from half load to full load condition.
Design of prototype decision support system for flood detection based on ultrasonic sensor

The system of awareness of the overflow of river water that causes flooding cannot work automatically and in real time in providing a warning about the elevation of river surface that potentially flood. It's caused residents who live around the river area do not know the situation when the river overflow. There are several alternatives to detect river overflow, one of them is by using microcontroller ultrasonic sensor based. The research aims to design prototype decision support system based on the ultrasonic sensor for flood detection that works automatically detects the river overflow. The software development model used is Prototype Model. The result of this research is a prototype in the form of microcontroller ultrasonic sensor based which can be used as flood detection decision support system that will know the height of river surface designed to detect certain level about flood potency with a low-level error.
COTS – Harsh Condition Effects Considerations from Technology to User Level

A R T I C L E I N F O A B S T R A C T Article history: Received: 15 June, 2017 Accepted: 23 July, 2017 Online: 24 August, 2017 Radiation hardened devices are mostly extremely expensive. The continuously downscaling of microelectronic structures and the unavoidable presence of particle radiation on ground and in space leads to unwanted failures in electronic devices. Furthermore it is expected that in the next few years around 8000 new satellites will be launched around the world. Due to the enormous increasing need for Rad-Hard devices, there will be more focus on Commercial Of The Shelf (COTS) devices, which costs are lower. Also nowadays microelectronics for automotive systems are tested to withstand radiation especially SEUsingle event upsets. It is clear that SEU cannot be ignored anymore especially in the application of unmanned autonomous vehicles and systems. Reliability testing is expensive and extremely time consuming. The use of COTS-Commercials of the shelf is the ultimate goal to reach. In this paper, an overview of radiation effects on different CMOS technologies used in COTS devices is given. These effects can be considered while selecting different functional equivalent COTS devices implemented with different technologies. Moreover, an overview of software techniques used in programmable commercial devices to reduce the radiation effects is also described.
Computer Vision Self-supervised Learning Methods on Time Series

Self-supervised learning (SSL) has had great success in both computer vision. Most of the current mainstream computer vision SSL frameworks are based on Siamese network architecture. These approaches often rely on cleverly crafted loss functions and training setups to avoid feature collapse. In this study, we evaluate if those computer-vision SSL frameworks are also effective on a different modality (\textit{i.e.,} time series). The effectiveness is experimented and evaluated on the UCR and UEA archives, and we show that the computer vision SSL frameworks can be effective even for time series. In addition, we propose a new method that improves on the recently proposed VICReg method. Our method improves on a \textit{covariance} term proposed in VICReg, and in addition we augment the head of the architecture by an iterative normalization layer that accelerates the convergence of the model.
Shedding light on low-mass subhalo survival and annihilation luminosity with numerical simulations

In this work, we carry out a suite of specially-designed numerical simulations to shed light on dark matter (DM) subhalo survival at mass scales relevant for gamma-ray DM searches, a topic subject to intense debate nowadays. We have employed an improved version of DASH, a GPU N-body code, to study the evolution of low-mass subhaloes inside a Milky Way-like halo with unprecedented accuracy, reaching solar-mass and sub-parsec resolution. We simulate subhaloes with varying mass, concentration, and orbital properties, and consider the effect of baryons in the host. We analyse the evolution of the bound mass fraction and annihilation luminosity, finding that most subhaloes survive until present, yet losing in some cases more than 99 per cent of their initial mass. Baryons induce a much greater mass loss, especially when the subhalo orbit is more parallel to the galactic disk. Many of these subhaloes cross the solar galactocentric radius, making it easier to detect their annihilation fluxes from Earth. We find subhaloes orbiting a DM-only halo with a pericentre in the solar vicinity to lose 70-90 per cent of their initial annihilation luminosity at present, which increases up to 99 per cent when including baryons. We find a strong relation between subhalo’s mass loss and the effective tidal field at pericentre. Indeed, much of the dependence on all considered parameters can be explained through this single parameter. In addition to shedding light on the survival of low-mass galactic subhaloes, our results can provide detailed predictions that will aid current and future quests for the nature of DM.
Applying incremental answer set solving to product configuration

In this paper, we apply incremental answer set solving to product configuration. Incremental answer set solving is a step-wise incremental approach to Answer Set Programming (ASP). We demonstrate how to use this technique to solve product configurations problems incrementally. Every step of the incremental solving process corresponds to a predefined configuration action. Using complex domain-specific configuration actions makes it possible to tightly control the level of non-determinism and performance of the solving process. We show applications of this technique for reasoning about product configuration, like simulating the behavior of a deterministic configuration algorithm and describing user actions.
GATE: Gated Additive Tree Ensemble for Tabular Classification and Regression

We propose a novel high-performance, parameter and compu- tationally efﬁcient deep learning architecture for tabular data, Gated Additive Tree Ensemble(GATE). GATE uses a gating mechanism, inspired from GRU, as a feature representation learning unit with an in-built feature selection mechanism. We combine it with an ensemble of differentiable, non-linear decision trees, re-weighted with simple self-attention to predict our desired output. We demonstrate that GATE is a competitive alternative to SOTA approaches like GBDTs, NODE, FT Transformers, etc. by experiments on several public datasets (both classiﬁcation and regression).
Monitoring Vegetation From Space at Extremely Fine Resolutions via Coarsely-Supervised Smooth U-Net

Monitoring vegetation productivity at extremely fine resolutions is valuable for real-world agricultural applications, such as detecting crop stress and providing early warning of food insecurity. Solar-Induced Chlorophyll Fluorescence (SIF) provides a promising way to directly measure plant productivity from space. However, satellite SIF observations are only available at a coarse spatial resolution, making it impossible to monitor how individual crop types or farms are doing. This poses a challenging coarsely-supervised regression (or downscaling) task; at training time, we only have SIF labels at a coarse resolution (3km), but we want to predict SIF at much finer spatial resolutions (e.g. 30m, a 100x increase). We also have additional fine-resolution input features, but the relationship between these features and SIF is unknown. To address this, we propose Coarsely-Supervised Smooth U-Net (CS-SUNet), a novel method for this coarse supervision setting. CS-SUNet combines the expressive power of deep convolutional networks with novel regularization methods based on prior knowledge (such as a smoothness loss) that are crucial for preventing overfitting. Experiments show that CS-SUNet resolves fine-grained variations in SIF more accurately than existing methods.
A self‐censoring model for multivariate nonignorable nonmonotone missing data

We introduce an itemwise modeling approach called “self‐censoring” for multivariate nonignorable nonmonotone missing data, where the missingness process of each outcome can be affected by its own value and associated with missingness indicators of other outcomes, while conditionally independent of the other outcomes. The self‐censoring model complements previous graphical approaches for the analysis of multivariate nonignorable missing data. It is identified under a completeness condition stating that any variability in one outcome can be captured by variability in the other outcomes among complete cases. For estimation, we propose a suite of semiparametric estimators including doubly robust estimators that deliver valid inferences under partial misspecification of the full‐data distribution. We also provide a novel and flexible global sensitivity analysis procedure anchored at the self‐censoring. We evaluate the performance of the proposed methods with simulations and apply them to analyze a study about the effect of highly active antiretroviral therapy on preterm delivery of HIV‐positive mothers.
A Review of the Operational Use of UAS in Public Safety Emergency Incidents

The domain of public safety in the form of search & rescue, wildland firefighting, structure firefighting, and law enforcement operations have drawn great interest in the field of aerospace engineering, human-robot teaming, autonomous systems, and robotics. However, a divergence exists in the assumptions made in research and how state-of-the-art technologies may realistically transition into an operational capacity. To aid in the alignment between researchers, technologists, and end users, we aim to provide perspective on how small Uncrewed Aerial Systems (sUAS) have been applied in 114 real world incidents as part of a technical rescue team from 2016 to 2021. We highlight the main applications, integration, tasks, and challenges of employing UAS within five primary use cases including searches, evidence collection, SWAT, wildland firefighting, and structure firefighting. Within these use cases, key incidents are featured that provide perspective on the evolving and dynamic nature of UAS tasking during an operation. Finally, we highlight key technical directions for improving the utilization and efficiency of employing aerial technology in all emergency types.
Predictive Neural Speech Coding

—Neural audio/speech coding has shown its capability to deliver a high quality at much lower bitrates than traditional methods recently. However, existing neural audio/speech codecs employ either acoustic features or learned blind features with a convolutional neural network for encoding, by which there are still temporal redundancies inside encoded features. This paper introduces latent-domain predictive coding into the VQ-VAE framework to fully remove such redundancies and proposes the TF-Codec for low-latency neural speech coding in an end-to-end way. Speciﬁcally, the extracted features are encoded conditioned on a prediction from past quantized latent frames so that temporal correlations are further removed. What’s more, we introduce a learnable compression on the time-frequency input to adaptively adjust the attention paid on main frequencies and details at different bitrates. A differentiable vector quantization scheme based on distance-to-soft mapping and Gumbel-Softmax is proposed to better model the latent distributions with rate constraint. Subjective results on multilingual speech datasets show that with a latency of 40ms, the proposed TF-Codec at 1kbps can achieve a much better quality than Opus 9kbps and TF-Codec at 3kbps outperforms both EVS 9.6kbps and Opus 12kbps. Numerous studies are conducted to show the effectiveness of these techniques.
Troll Tweet Detection Using Contextualized Word Representations

In recent years, numerous troll accounts that manipulate social media sentiment have emerged. Due to the use of fake and automated accounts by businesses, abusers, and nation-state-sponsored troll farms, detecting and eliminating trolling is a crucial issue for social networking platforms. Various NLP techniques are used to extract information from social networking text, such as tweets on Twitter, to identify the messages originating from fake accounts. In this context, this paper implements and compares nine deep learning-based architectures for troll tweet detection, with three models for each BERT, ELMo, and GloVe word embedding model. The majority of BERT-based architectures improve the detection of trolling tweets, as demonstrated by experimental results. An ELMo-based architecture with a GRU classifier has the highest AUC for detecting troll messages. Future socially-based systems can utilize the proposed architectures to identify troll messages.
Human Keypoint Detection for Close Proximity Human-Robot Interaction

We study the performance of state-of-the-art hu-man keypoint detectors in the context of close proximity human-robot interaction. The detection in this scenario is specific in that only a subset of body parts such as hands and torso are in the field of view. In particular, (i) we survey existing datasets with human pose annotation from the perspective of close proximity images and prepare and make publicly available a new Human in Close Proximity (HiCP) dataset; (ii) we quantitatively and qualitatively compare state-of-the-art human whole-body 2D keypoint detection methods (OpenPose, MMPose, AlphaPose, Detectron2) on this dataset; (iii) since accurate detection of hands and fingers is critical in applications with handovers, we evaluate the performance of the MediaPipe hand detector; (iv) we deploy the algorithms on a humanoid robot with an RGB-D camera on its head and evaluate the performance in 3D human keypoint detection. A motion capture system is used as reference. The best performing whole-body keypoint detectors in close proximity were MMPose and AlphaPose, but both had difficulty with finger detection. Thus, we propose a combination of MMPose or AlphaPose for the body and MediaPipe for the hands in a single framework providing the most accurate and robust detection. We also analyse the failure modes of individual detectors-for example, to what extent the absence of the head of the person in the image degrades performance. Finally, we demonstrate the framework in a scenario where a humanoid robot interacting with a person uses the detected 3D keypoints for whole-body avoidance maneuvers.
MULTICHANNEL MAC UNIT FOR DSP APPLICATIONS

In most of the DSP (Digital Signal Processing) and multimedia communication applications the important operations are multiplication and accumulation. Real time signal processing requires a low power consumption, lower delay and highspeed MAC (Multiplier-Accumulator) unit. Multiply and Accumulator (MAC) unit is the backbone of DSP processors. So its design and performance will define the efficiency of the overall system working and its accuracy. The proposed system is to design Multichannel MAC unit which will achieve high speed multiplication and accumulation by CSA (Carry Save Adder) and Multiplication using Modified Radix 4 Booth Algorithm. Here 4 MAC unit of each 32 bit is integrated parallelly and the input data is given from control logic. Carry Save adder is used to reduce the delay time and Modified Radix 4 Booth multiplication algorithm is used to reduce the partial products with this overall helps in increasing the speed of the MAC. The proposed design is developed, simulated and synthesized using Xilinx ISE showing the results in terms of reduced delay, less power and lower delay