Reviewerly/exHarmony huggingface dataset:
Edge-Based Transfer Learning for Classroom Occupancy Detection in a Smart Campus Context

Studies and systems that are aimed at the identification of the presence of people within an indoor environment and the monitoring of their activities and flows have been receiving more attention in recent years, specifically since the beginning of the COVID-19 pandemic. This paper proposes an approach for people counting that is based on the use of cameras and Raspberry Pi platforms, together with an edge-based transfer learning framework that is enriched with specific image processing strategies, with the aim of this approach being adopted in different indoor environments without the need for tailored training phases. The system was deployed on a university campus, which was chosen as the case study. The proposed system was able to work in classrooms with different characteristics. This paper reports a proposed architecture that could make the system scalable and privacy compliant and the evaluation tests that were conducted in different types of classrooms, which demonstrate the feasibility of this approach. Overall, the system was able to count the number of people in classrooms with a maximum mean absolute error of 1.23.
A Post-training Quantization Method for the Design of Fixed-Point-Based FPGA/ASIC Hardware Accelerators for LSTM/GRU Algorithms

Recurrent Neural Networks (RNNs) have become important tools for tasks such as speech recognition, text generation, or natural language processing. However, their inference may involve up to billions of operations and their large number of parameters leads to large storage size and runtime memory usage. These reasons impede the adoption of these models in real-time, on-the-edge applications. Field-Programmable Gate Arrays (FPGAs) and Application-Specific Integrated Circuits (ASICs) have emerged as promising solutions for the hardware acceleration of these algorithms, thanks to their degree of customization of compute data paths and memory subsystems, which makes them take the maximum advantage from compression techniques for what concerns area, timing, and power consumption. In contrast to the extensive study in compression and quantization for plain feed forward neural networks in the literature, little attention has been paid to reducing the computational resource requirements of RNNs. This work proposes a new effective methodology for the post-training quantization of RNNs. In particular, we focus on the quantization of Long Short-Term Memory (LSTM) RNNs and Gated Recurrent Unit (GRU) RNNs. The proposed quantization strategy is meant to be a detailed guideline toward the design of custom hardware accelerators for LSTM/GRU-based algorithms to be implemented on FPGA or ASIC devices using fixed-point arithmetic only. We applied our methods to LSTM/GRU models pretrained on the IMDb sentiment classification dataset and Penn TreeBank language modelling dataset, thus comparing each quantized model to its floating-point counterpart. The results show the possibility to achieve up to 90% memory footprint reduction in both cases, obtaining less than 1% loss in accuracy and even a slight improvement in the Perplexity per word metric, respectively. The results are presented showing the various trade-offs between memory footprint reduction and accuracy changes, demonstrating the benefits of the proposed methodology even in comparison with other works from the literature.
Maintaining trust in a technologized public sector

Abstract Emerging technologies permeate and potentially disrupt a wide spectrum of our social, economic, and political relations. Various state institutions, including education, law enforcement, and healthcare, increasingly rely on technical components, such as automated decision-making systems, e-government systems, and other digital tools to provide cheap, efficient public services, and supposedly fair, transparent, disinterested, and accountable public administration. The increased interest in various blockchain-based solutions from central bank digital currencies, via tokenized educational credentials, and distributed ledger-based land registries to self-sovereign identities is the latest, still mostly unwritten chapter in a long history of standardized, objectified, automated, technocratic, and technologized public administration. The rapid, (often) unplanned, and uncontrolled technologization of public services (as happened in the hasty adoption of distance-learning and teleconferencing systems during Corona Virus Disease (COVID) lockdowns) raises complex questions about the use of novel technological components, which may or may not be ultimately adequate for the task for which they are used. The question whether we can trust the technical infrastructures the public sector uses when providing public services is a central concern in an age where trust in government is declining: If the government’s artificial intelligence system that detects welfare fraud fails, the public’s confidence in the government is ultimately hit. In this paper, we provide a critical assessment of how the use of potentially untrustworthy (private) technological systems including blockchain-based systems in the public sector may affect trust in government. We then propose several policy options to protect the trust in government even if some of their technological components prove fundamentally untrustworthy.
Blockchain technology for efficient data management in healthcare system: Opportunity, challenges and future perspectives

The healthcare data management system faces critical challenges related to data transparency, traceability, immutability, auditing, secure data provenance, and trust. This study aimed to provide a prospective overview of how blockchain technology can efficiently improve medical record management. Blockchain technology, an emerging and disruptive decentralized technology, is poised to impact the way data is handled in the healthcare industry significantly. As a result of this technology, we can fill out a patient's medical record efficiently and validated. Blockchain helps form a single network in which different organizations can safely share their records by forming a chain by linking it privately to store records like radiographic or different images. Blockchain-based digital platforms allow patients to interact with their data providers, including healthcare providers, in a seamless, fast and simple way. Numerous open research challenges, which we identify and explore, are inhibiting the successful implementation of blockchain in the healthcare sector. In this chapter, we have discussed the way that Electronic Medical Records is managed by blockchain, and this novel technology can be used in creating an efficient as well as interoperation infrastructure that helps in maintaining the record of patients that deals in improving the healthcare outcomes at the same time it also maintains its integrity and privacy of the essential data. We also discuss both existing and recent developments of management data in healthcare based on the blockchain model. We highlight the primary benefits of implementing blockchain technology, as well as the potential for healthcare companies. We highlight a range of healthcare applications have been demonstrated using recent research and case studies. The present study identifies and explains several outstanding issues that slow the adoption of blockchain technology in health care. Finally, we propose many more research topics for discussion and investigation.
Learner Autonomy and Learning Strategy Use before and during the COVID-19 Pandemic

This study aims to explore the autonomy level and use of language learning strategies in a preparatory school of a state university before and during the COVID-19 pandemic. One hundred fifty-five preparatory school students from a state university participated in the research. For the data collection, Oxford’s Language Learning Strategy (LLS) and a learner autonomy questionnaire developed by Zhang and Li were used. Means, standard deviation, t-test, and Pearson’s r-correlation were used to analyze the data. The results showed that while participants’ level of learner autonomy before the pandemic was high, during the pandemic it was moderate. Additionally, the results from the LLS questionnaires showed that students used a moderate proportion of language learning strategies before and after the pandemic. Finally, the correlation analysis used to determine the relationship between the level of learner autonomy and LLS use before and during the COVID-19 pandemic indicated that there is a positive and linear relationship between the level of learner autonomy and LLS use.
SIDNet: A single image dedusting network with color cast correction

Dust degrades image content and causes image color cast, which negatively impacts on many high-level computer vision tasks. In this paper, we proposed a dedusting network with color cast correction for a single dusty image (SIDNet). The SIDNet contains several dust-aware representation extraction (DustAre) modules with the same structure. Each DustAre module contains two branches. The first branch encodes the input to estimate global veiling-light and local spatial information. The second branch generates a dust-aware map and fuses the global veiling-light, the local spatial information and the dust-aware map to generate the output. To further improve real dusty image dedusting performance, the SIDNet introduces a color cast correction scheme to our neural network. After considering that the average chromaticity values of a dusty image in CIELAB color space are usually larger than those of a clean (dust-free) image, the SIDNet defines a new loss function to better guide the network training. Additionally, we also construct a new synthetic dusty image dataset for network training, which additionally considers the scene depth relationship between real dusty image and dust-free image. Experiments on synthetic and real dusty images show that the SIDNet achieves better dedusting performance compared to state-of-the-art image restoration methods.
Digital Pathology and Artificial Intelligence Applications in Pathology

Digital pathology is revolutionizing pathology. The introduction of digital pathology made it possible to comprehensively change the pathology diagnosis workflow, apply and develop pathological artificial intelligence (AI) models, generate pathological big data, and perform telepathology. AI algorithms, including machine learning and deep learning, are used for the detection, segmentation, registration, processing, and classification of digitized pathological images. Pathological AI algorithms can be helpfully utilized for diagnostic screening, morphometric analysis of biomarkers, the discovery of new meanings of prognosis and therapeutic response in pathological images, and improvement of diagnostic efficiency. In order to develop a successful pathological AI model, it is necessary to consider the selection of a suitable type of image for a subject, utilization of big data repositories, the setting of an effective annotation strategy, image standardization, and color normalization. This review will elaborate on the advantages and perspectives of digital pathology, AI-based approaches, the applications in pathology, and considerations and challenges in the development of pathological AI models.
Are primary education teachers trained for the use of the technology with disabled students?

Abstract Incorporating information and communication technology (ICT) in inclusive classrooms requires competent teachers, both technological and pedagogical. To contrast these theoretical assumptions, this study aims to identify the level of training and technical knowledge of primary school teachers in Spain regarding the use of ICTs for supporting students with special needs. The research methodology used was a mixed research design (quantitative and qualitative method), analysing 777 questionnaires supplied to primary school teachers and 723 interviews conducted with key informants (members of management teams, ICT coordinators, directors and technological advisors of teacher training centres). The results informed teachers' knowledge about ICT and disability and barriers or obstacles to their training. Among the conclusions, teachers' inadequate training regarding ICTs for students with special needs stands out and the lack of training experiences in this field.
Investigating information processing paradigm to predict performance in emerging firms: the mediating role of technological innovation

Purpose This paper aims to investigate the influence of the information processing paradigm (information sharing and quality) on firm performance using organizational information processing theory (OIPT) in emerging marketing. This research also pursues to explore the underpinning mediation mechanism of technological innovation in the association between the information processing paradigm and firm performance. Design/methodology/approach Using the survey method, this study recruited 331 textile SME entrepreneurs and validated the proposed model and hypotheses in AMOS. Findings The outcomes reveal that information sharing, information quality and technological innovation are positively related to firm performance, while technological innovation mediates the association between the information processing paradigm (i.e. information sharing and information quality) and firm performance. Practical implications The findings enable the firms to bring technological innovation and realize the best performance by seeking and sharing valuable information across the firm. The managers should ensure a culture of quality information sharing, improve coordination among departments and discourage irrelevant/fake information. Such practices are beneficial for firm decision-makers when formulating business plans and focusing on communication mechanisms that will assist them in attaining higher innovation levels and firm performance. Originality/value This paper theoretically and empirically contributes to examining the influence of the information processing paradigm on firm performance, which was ignored in information and performance management literature. Moreover, to the best of the author’s knowledge, this is the first study that investigates the mediation mechanism between the information processing paradigm and firm performance.
Touchless Control of Heavy Equipment Using Low-Cost Hand Gesture Recognition

Human-machine interaction using remote hand gestures is becoming increasingly prevalent across various industries. However, their potential application to heavy construction equipment is often overlooked. This article presents a robust and inexpensive hand gesture recognition system that was implemented and tested on a robotic 1-tonne wheel loader. The system uses an RGB camera paired with a laptop to process, in real time, hand gestures to control the loader. We first design four unique gestures for controlling the loader and then collect 26,000 images to train and test a neural network for hand gesture recognition. Our system uses robust landmark detection using an off-the-shelf system prior to gesture recognition. We successfully controlled the loader to excavate in a rock pile by using the proposed hand gesture recognition system.
A Closer Look at Blind Super-Resolution: Degradation Models, Baselines, and Performance Upper Bounds

Degradation models play an important role in Blind super-resolution (SR). The classical degradation model, which mainly involves blur degradation, is too simple to simulate real-world scenarios. The recently proposed practical degradation model includes a full spectrum of degradation types, but only considers complex cases that use all degradation types in the degradation process, while ignoring many important corner cases that are common in the real world. To address this problem, we propose a unified gated degradation model to generate a broad set of degradation cases using a random gate controller. Based on the gated degradation model, we propose simple baseline networks that can effectively handle non-blind, classical, practical degradation cases as well as many other corner cases. To fairly evaluate the performance of our baseline networks against state-of-the-art methods and understand their limits, we introduce the performance upper bound of an SR network for every degradation type. Our empirical analysis shows that with the unified gated degradation model, the proposed baselines can achieve much better performance than existing methods in quantitative and qualitative results, which are close to the performance upper bounds.
A shape-guided deep residual network for automated CT lung segmentation

Automatic lung segmentation is an effective method for the precise computer-aided diagnosis of lung diseases. However, CT lung scans are always complex due to issues such as weak texture, poor contrast, and variation of appearances and positions, which will affect the lung segmentation accuracy. Recently, due to strong feature expression ability, many deep convolution neural networks (DCNNs) have been proposed for application in medical image segmentation to provide an end-to-end segmentation scheme, especially the U-shape network (U-Net) and its variants. However, accurate lung segmentation methods based on DCNNs still face a certain challenge because of the insufficient process of boundary information, restricted receptive field, etc. To address these issues, with the encoder–decoder framework, a novel shape-guided deep residual network is proposed in this paper for automatic CT lung segmentation. The proposed network is composed of two stream networks: the main stream network and the shape stream network. An effective deep attention residual network is built to act as the mainstream network for lung segmentation. Meanwhile, an attention fusion block is proposed to embed into the mainstream network for multiscale feature extraction of local feature maps. Based on the mainstream network, a shape stream network is proposed to serve as significant guidance for the mainstream network to accurately compute lung shape boundaries. Multiple public CT lung image sets are adopted to qualitatively and quantitatively analyze the segmentation performance on CT scans. Experimental results indicate that the proposed shape-guided deep residual network outperforms related advanced image segmentation methods on medical image analysis.
Deep learning and synthetic media

Deep learning algorithms are rapidly changing the way in which audiovisual media can be produced. Synthetic audiovisual media generated with deep learning—often subsumed colloquially under the label "deepfakes"—have a number of impressive characteristics; they are increasingly trivial to produce, and can be indistinguishable from real sounds and images recorded with a sensor. Much attention has been dedicated to ethical concerns raised by this technological development. Here, I focus instead on a set of issues related to the notion of synthetic audiovisual media, its place within a broader taxonomy of audiovisual media, and how deep learning techniques differ from more traditional approaches to media synthesis. After reviewing important etiological features of deep learning pipelines for media manipulation and generation, I argue that "deepfakes" and related synthetic media produced with such pipelines do not merely offer incremental improvements over previous methods, but challenge traditional taxonomical distinctions, and pave the way for genuinely novel kinds of audiovisual media.
Atmospheric attenuation measurement system for commercial solar plants based on optical spectrum analysis

The measurement of the solar radiation attenuation is one of the main challenges in concentrating solar power technologies. This work presents a new strategy for this measurement, based on the analysis of the VIS and IR spectra, and its related system. The optical design of the system and its implementation in a central receiver solar power plant is described, and the experimental results are detailed. We present, to the best of our knowledge, the first measurements of the atmospheric attenuation spectrum in a solar power plant. This system provides direct measurements of real atmospheric attenuation values, which opens the way to analyze the impact of aerosols and meteorological conditions at surface level in this attenuation.
CANDLES: Channel-Aware Novel Dataflow-Microarchitecture Co-Design for Low Energy Sparse Neural Network Acceleration

Several deep neural network (DNN) accelerators have been designed to exploit the sparsity exhibited by DNN activations and weights. State-of-the-art sparse accelerators can be described as either Pixel-first or Channel-first accelerators, each with its unique dataflow and compression format aiding its dataflow. The former expends significant energy updating neuron partial sums, while the latter expends significant energy in handling the index metadata. This work introduces a novel microarchitecture and dataflow that reconciles these trade-offs by adopting a Pixel-first compression and Channel-first dataflow. The proposed microarchitecture has a simpler index-generation logic combined with an accumulator buffer hierarchy and crossbar with low wiring overhead. The compression format and dataflow promote high temporal locality in neuron updates, further lowering energy. Finally, we introduce work partitions across processing elements that naturally lead to load balance without offline analysis. Compared to four state-of-the-art baselines, the proposed architecture, CANDLES, significantly outperforms three and matches the performance of the fourth. In terms of energy, CANDLES is between 2.5&#x00D7; and 5.6&#x00D7; more energy-efficient than these four baselines.
Vector based sentiment and emotion analysis from text: A survey

As a primary means of communication, texts are used to implicitly or explicitly reflect emotions. Emotion or sentiment detection from text has emerged as an important and expanding research area to more clearly understand the actual feelings of humans. Most of the word representation models, such as Word2Vec or GloVe, project the words in vector space such that if words have similar context, then their representations are also very similar. However, according to the recent studies, this approach limits the success of studies in areas such as emotion detection. For instance, love and happy are emotionally similar words, but they may have a lower similarity score than emotionally dissimilar word such as happy and sad which have high co-occurrence frequency, as they are in similar contexts. Recently, researchers propose some methods based on the addition of emotional or sentimental information to the original word vectors. These have improved the vector representation of words and achieved better results in emotion detection or classification tasks. In this survey, we analyze in detail such recent text-based studies in the literature. We summarize their methods used, emotion models, data sources, findings, and performances.