Reviewerly/exHarmony huggingface dataset:
[Evaluation of Heavy Metal Content, Sources, and Potential Ecological Risks in Soils of Southern Hilly Areas].

In order to explore the characteristics of soil heavy metal pollution and ecological risk status in southern hilly areas, a hilly area of a southern province was selected as the research area, using soil samples collected at 60 points in 2017. The single factor pollution index (Pi), Nemero comprehensive pollution index, and potential ecological risk index (RI) were used to evaluate the content characteristics and potential ecological risks of eight heavy metal elements (As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn), and positive definite matrix factor analysis models (PMF) were used to analyze their sources of pollution. The results were as follows:① the content of eight heavy metal elements in the study area exceeded the standard, and the Nemeiro comprehensive pollution index showed that the proportions of light, moderate, and heavy pollution in the study area were 63%, 8%, and 2%, respectively. Unpolluted and lightly polluted samples accounted for 27% and were thus basically lightly polluted. ②The comprehensive potential ecological risk index showed that the RI of soil heavy metals was 39.58-224.15, the proportions of samples with low and medium ecological risks were 73.33% and 25%, respectively, and the proportion of sites with severe ecological risks was 1.67%. Although this site had the highest ecological risk, the content of heavy metals was lower than the background value of soil elements in the province. ③Six pollution sources were obtained through the PMF model:natural sources, agricultural activity sources, mixed sources composed of copper mining and transportation, industrial activity sources, transportation sources, and domestic waste emission sources. The contribution rates of each source were 24.8%, 17.7%, 17.7%, 17.6%, 12.0%, and 10.2%, respectively.
Shedding Light on the Dark Web: Authorship Attribution in Radical Forums

Online users tend to hide their real identities by adopting different names on the Internet. On Facebook or LinkedIn, for example, people usually appear with their real names. On other standard websites, such as forums, people often use nicknames to protect their real identities. Aliases are used when users are trying to protect their anonymity. This can be a challenge to law enforcement trying to identify users who often change nicknames. In unmonitored contexts, such as the dark web, users expect strong identity protection. Thus, without censorship, these users may create parallel social networks where they can engage in potentially malicious activities that could pose security threats. In this paper, we propose a solution to the need to recognize people who anonymize themselves behind nicknames—the authorship attribution (AA) task—in the challenging context of the dark web: specifically, an English-language Islamic forum dedicated to discussions of issues related to the Islamic world and Islam, in which members of radical Islamic groups are present. We provide extensive analysis by testing models based on transformers, styles, and syntactic features. Downstream of the experiments, we show how models that analyze syntax and style perform better than pre-trained universal language models.
ImAiR: Airwriting Recognition Framework Using Image Representation of IMU Signals

The problem of airwriting recognition is focused on identifying letters written by movement of finger in free space. It is a type of gesture recognition where the dictionary corresponds to letters in a specific language. In particular, airwriting recognition using sensor data from wrist-worn devices can be used as a medium of user input for applications in human–computer interaction (HCI). Recognition of in-air trajectories using such wrist-worn devices is limited in literature and forms the basis of the current work. In this letter, we propose an airwriting recognition framework by first encoding the time-series data obtained from a wearable inertial measurement unit (IMU) on the wrist as images and then utilizing deep learning-based models for identifying the written alphabets. The signals recorded from 3-axis accelerometer and gyroscope in IMU are encoded as images using different techniques such as self-similarity matrix (SSM), Gramian angular field (GAF), and Markov transition field (MTF) to form two sets of 3-channel images. These are then fed to two separate classification models, and letter prediction is made based on an average of the class conditional probabilities obtained from the two models. Several standard model architectures for image classification such as variants of ResNet, DenseNet, VGGNet, AlexNet, and GoogleNet have been utilized. Experiments performed on two publicly available datasets demonstrate the efficacy of the proposed strategy. The code for our implementation will be made available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/ayushayt/ImAiR</uri> .
Representing Interoperability Between Software Systems by Using Pre-Conceptual Schemas

Interoperability is a software property for exchanging and using information between software systems.Some interoperability proposals are intended to use metamodels, models, and frameworks, in which visual representations are included.However, such proposals describe interoperability according to their point of view, focus of analysis, solution techniques, and specific level of interoperability; in addition, some of the visual representations use no standards, which lead to subjective interpretations by the reader.In this paper we propose a representation formalizing interoperability which includes essential elements and structural relationships among them.After a review of previous work, a summary of the analyzed literature is performed by using linguistic analysis in order to recognize the relationships among interoperability essential elements and mapping them to controlled language expressions.Finally, the mapping process is used to represent the interoperability essential elements and their relationships by using pre-conceptual schemas linked to the definition of each essential element.As a result, a pre-conceptual schema of the software system interoperability is proposed.Such a pre-conceptual schema is also used for explaining an interoperability lab study adapted from the literature.The proposed pre-conceptual schema explains the interoperability between two software systems and allows for characterizing interoperability problems and solutions.
Construction of a Prediction Model for the Mortality of Elderly Patients with Diabetic Nephropathy

To construct a prediction model for all-cause mortality in elderly diabetic nephropathy (DN) patients, in this cohort study, the data of 511 DN patients aged ≥65 years were collected and the participants were divided into the training set (n = 358) and the testing set (n = 153). The median survival time of all participants was 2 years. The data in the training set were grouped into the survival group (n = 203) or the death group (n = 155). Variables with P ≤ 0.1 between the two groups were selected as preliminary predictors and involved into the multivariable logistic regression model and the covariables were gradually adjusted. The receiver operator characteristic (ROC), Kolmogorov-Smirnov (KS), and calibration curves were plotted for evaluating the predictive performance of the model. Internal validation of the performance of the model was verified in the testing set. The predictive values of the model were also conducted in terms of people with different genders and ages or accompanied with chronic kidney disease (CKD) or cardiovascular diseases (CVD), respectively. In total, 216 (42.27%) elderly DN patients were dead within 2 years. The prediction model for the 2-year mortality of elderly patients with DN was established based on length of stay (LOS), temperature, heart rate, peripheral oxygen saturation (SpO2), serum creatinine (Scr), red cell distribution width (RDW), the simplified acute physiology score-II (SAPS-II), hyperlipidemia, and the Chronic Kidney Disease Epidemiology Collaboration equation for estimated glomerular filtration rate (eGFR-CKD-EPI). The AUC of the model was 0.78 (95% CI: 0.73-0.83) in the training set and 0.72 (95% CI: 0.63-0.80) in the testing set. The AUC of the model was 0.78 (95% CI: 0.65-0.91) in females and 0.78 (95%CI: 0.68-0.88) in patients ≤75 years. The AUC of the model was 0.74 (95% CI: 0.64-0.84) in patients accompanied with CKD. The model had good predictive value for the mortality of elderly patients with DN within 2 years. In addition, the model showed good predictive values for female DN patients, DN patients ≤75 years, and DN patients accompanied with CKD.
Design and Implementation of Real-Time Image Acquisition Chip Based on Triple-Hybrid Encryption System

With the improved hardware storage capabilities and the rapid development of artificial intelligence image recognition technology, information is becoming image-oriented. Increasingly sensitive image data needs to be processed. When facing a large amount of real-time sensitive image data encryption and decryption, ensuring both the speed and the security is an urgent demand. This paper proposes an original triple-hybrid encryption system for a real-time sensitive image acquisition chip. This encryption system optimizes the symmetric encryption algorithm AES, asymmetric encryption algorithm ECC, and chip authentication algorithm PUF in pursuit of security, calculation speed, and to ensure that it is lightweight. The three optimized algorithms are further mixed and reused on the circuit level, to ensure mutual protection while making full use of their advantages. Apart from sensitive image protection at the algorithm level, the image chip itself is also protected by an innovative PUF chip authentication method that prevents it from being tampered with and copied. Triple-hybrid encryption system hardware implementation achieves a frequency of 132.5 MHz under the Virtex-5 FPGA with an area of 2834 Slices; with Virtex-7 FPGA, it reaches a frequency of 137.6 MHz with an area of 2716 Slices. The system is also implemented on SMIC 40 nm ASIC, and the clock frequency reaches 480 MHz and the area is 94,812.4 μm2. In terms of computing speed, the peak image encryption speed is 6.15 Gb/s, which meets the real-time image encryption requirement. In terms of hardware resource usage, AES reduced the hardware area by 60.1% compared with the results in other literature, ECC reduced the hardware area by 43.4%, and the PUF hardware area decreased exponentially with the increase in information entropy. The implementation of the three algorithms is reasonable and cost-effective, and the mixture of algorithms does not increase the required capacity of the hardware resource. The triple-hybrid encryption system cooperates with the image acquisition subsystem, storage subsystem, and asynchronous clock subsystem through software control to realize a complete triple-hybrid encryption SoC chip solution, and was successfully taped-out under the SMIC 40 nm process with all constraints passed and a total area of 10.59 mm2.
A Scalable Model Specialization Framework for Training and Inference using Submodels and its Application to Speech Model Personalization

Model fine-tuning and adaptation have become a common approach for model specialization for downstream tasks or domains. Fine-tuning the entire model or a subset of the parameters using light-weight adaptation has shown considerable success across different specialization tasks. Fine-tuning a model for a large number of domains typically requires starting a new training job for every domain posing scaling limitations. Once these models are trained, deploying them also poses significant scalability challenges for inference for real-time applications. In this paper, building upon prior light-weight adaptation techniques, we propose a modular framework that enables us to substantially improve scalability for model training and inference. We introduce Submodels that can be quickly and dynamically loaded for on-the-fly inference. We also propose multiple approaches for training those Submodels in parallel using an embedding space in the same training job. We test our framework on an extreme use-case which is speech model personalization for atypical speech, requiring a Submodel for each user. We obtain 128x Submodel throughput with a fixed computation budget without a loss of accuracy. We also show that learning a speaker-embedding space can scale further and reduce the amount of personalization training data required per speaker.
Causation and decision: On Dawid’s “Decision theoretic foundation of statistical causality”

Abstract In a recent issue of this journal, Philip Dawid (2021) proposes a framework for causal inference that is based on statistical decision theory and that is, in many aspects, compatible with the familiar framework of causal graphs (e.g., Directed Acyclic Graphs (DAGs)). This editorial compares the methodological features of the two frameworks as well as their epistemological basis.
Sentiment Analysis of User Groups in an Working Environment Using CNN for Streaming Data Analysis

The domain of sentiment analysis is mainly concerned with observing the nature of text with positive, negative, and neutral effects in a given environment. It is also called the process of opinion mining in which the emotion behind the idea, service, or product completely signifies the nature of the environment as observed. The insights from this unorganized textual data can be evaluated along with the methods available in machine learning. This research work completely focuses on the mechanism of observing the project teams with a significant analysis in monitoring the percentage of happiness involved in executing a specified project. In addition to the collection of views from the different groups of employers, we have observed their nature through a Webcam-enabled platform to best determine the work nature of team members. We have used CNN with the available streaming data and captured the nature of workers in a dignified environment. A set of statistical measures has been evaluated to best validate the proposed method which extracts sentiments for the observed data. Future work can be progressed with the extraction of organizational data rather than focusing on working teams in a given environment.
Direct counterfactual quantum-communication protocol beyond a single-photon source

The direct counterfactual quantum-communication protocol involving double-chained Mach-Zehnder interferometers requires a single-photon input. Here, we show that even with multiphoton light sources, including a strong coherent light source as input, the counterfactual communication can be achieved with success probability approaching unity in the ideal asymptotic limit. The path evolution of multiple photons is nonlocally controlled. Thus, information is transmitted without any photons, or any other auxiliary information carriers, appearing in the public transmission channel. The effect is quantum since quantum measurements are an essential requirement for this protocol. Furthermore, a modified scheme is proposed in which the number of interferometers is reduced.
Automatic infrared image recognition method for substation equipment based on a deep self-attention network and multi-factor similarity calculation

Infrared image recognition plays an important role in the inspection of power equipment. Existing technologies dedicated to this purpose often require manually selected features, which are not transferable and interpretable, and have limited training data. To address these limitations, this paper proposes an automatic infrared image recognition framework, which includes an object recognition module based on a deep self-attention network and a temperature distribution identification module based on a multi-factor similarity calculation. First, the features of an input image are extracted and embedded using a multi-head attention encoding–decoding mechanism. Thereafter, the embedded features are used to predict the equipment component category and location. In the located area, preliminary segmentation is performed. Finally, similar areas are gradually merged, and the temperature distribution of the equipment is obtained to identify a fault. Our experiments indicate that the proposed method demonstrates significantly improved accuracy compared with other related methods and, hence, provides a good reference for the automation of power equipment inspection.
Reversible Data Hiding in Palette Images

Reversible data hiding (RDH) has been investigated for over two decades. Depending on the application scenario, it can be divided into RDH and reversible data hiding in encrypted images (RDHEI). Interestingly, almost all studies on RDH/RDHEI have been conducted on gray-scale images, and relatively few studies have been conducted on color images compared to those on gray-scale images. Moreover, very few studies have been undertaken on palette images as a widely used image format. For palette images in which pixels are not gray-scale levels but color table indexes, it is difficult to apply traditional RDH/RDHEI methods directly. Therefore, we propose a new framework for RDH/RDHEI specifically for palette images. This framework adds a route selection algorithm before the conventional RDH/RDHEI approach. To be specific, we propose a method named the shortest route with correlation and frequency selection to reorder the color table, followed by a correlation reconstruction of the remapped index image according to this color table. The experimental results show the improvement brought by our proposed route selection to the existing RDH/RDHEI methods in palette images.
A Framework for the Classification and Evaluation of Game Mechanics for Virtual Reality Games

In broad terms, UX design is concerned with minimizing the workload neccessary for the user to interact with the system. In gaming, however, the system is supposed to provide a level of challenge to keep the player entertained, along with offering specific tools and mechanisms of interaction that are not commonly found across other use-cases. When talking about virtual reality (VR), numerous sources have discussed the optimization of interaction techniques, but there is a gap in research on the subject of gaming-specific VR interaction design, which we aimed to address in this paper. Focusing on the diversity of VR gaming, we introduced the term “interaction mechanics” and provided a taxonomy of interaction mechanics based on several criteria. Based on this taxonomy, we highlighted multiple target-related, task-related, and tool-related parameters that may influence the quality of interaction mechanics. Lastly, we presented the INTERACT framework, which was created to serve as a conceptual foundation for creating applications to be used as tools for user research, and used it to design an application aimed at facilitating the evaluation of interaction mechanics quality.
Obstacles facing women-owned enterprises: a case for sub-Sahara African women

This study identified and ranked the obstacles facing women-owned enterprises in Sub-Saharan Africa (SSA). It analysed secondary data from the World Bank Enterprise Survey that includes 2341 questionnaires filled in by women entrepreneurs from 40 countries within SSA. The findings showed that the lack of access to finance is the biggest obstacle facing women-owned enterprises in SSA. This is followed by the absence of electricity, practices of the informal sectors, tax-related issues and political volatility. This finding contributes to the literature on entrepreneurship by ranking the obstacles that confront women-owned enterprises in a developing country region. It contributes to the knowledge that policymakers require for policy development and effective resource allocation within SSA and other developing countries. This study unravels endogenous barriers that are within the scope of women entrepreneurs who need to take innovative steps to resolve such barriers rather than waiting for external assistance, which frequently never arrives.
Unsupervised feature selection based on incremental forward iterative Laplacian score

Feature selection facilitates intelligent information processing, and the unsupervised learning of feature selection has become important. In terms of unsupervised feature selection, the Laplacian score (LS) provides a powerful measurement and optimization method, and good performance has been achieved using the recent forward iterative Laplacian score (FILS) algorithm. However, there is still room for advancement. The aim of this paper is to improve the FILS algorithm, and thus, feature significance (SIG) is mainly introduced to develop a high-quality selection method, i.e., the incremental forward iterative Laplacian score (IFILS) algorithm. Based on the modified LS, the metric difference in the incremental feature process motivates SIG. Therefore, SIG offers a dynamic characterization by considering initial and terminal states, and it promotes the current FILS measurement on only the terminal state. Then, both the modified LS and integrated SIG acquire granulation nonmonotonicity and uncertainty, especially on incremental feature chains, and the corresponding verification is achieved by completing examples and experiments. Furthermore, a SIG-based incremental criterion of minimum selection is designed to choose optimization features, and thus, the IFILS algorithm is naturally formulated to implement unsupervised feature selection. Finally, an in-depth comparison of the IFILS algorithm with the FILS algorithm is achieved using data experiments on multiple datasets, including a nominal dataset of COVID-19 surveillance. As validated by the experimental results, the IFILS algorithm outperforms the FILS algorithm and achieves better classification performance.
Compute-in-Memory Technologies and Architectures for Deep Learning Workloads

The use of deep learning (DL) to real-world applications, such as computer vision, speech recognition, and robotics, has become ubiquitous. This can be largely attributed to a virtuous cycle between algorithms, data and computing, and storage capacity, which has driven rapid advances in all these dimensions. The ever-increasing demand for computation and memory from DL workloads presents challenges across the entire spectrum of computing platforms, from edge devices to the cloud. Hence, there is a need to explore new hardware paradigms that go well beyond the current mainstays such as graphical processing units (GPUs), tensor processing units (TPUs), and neural processing units (NPUs). A key bottleneck of current platforms is the so-called memory wall, which arises from the need to move large amounts of data between memory and compute units, expending considerable time and energy. One promising solution to this challenge is to move some computations either closer to memory, within the memory subsystem, or even within individual memory arrays. This approach, which is broadly referred to as compute-in-memory (CiM), has the potential to break the memory wall, and thereby greatly improve speed and power consumption. In this article, we provide an overview of CiM techniques used at different levels of the memory hierarchy and based on different memory technologies, including static random access memories (SRAMs), nonvolatile memories (NVMs), and DRAMs. We also discuss architectural approaches to designing CiM-based DL accelerators. Finally, we discuss the challenges associated with adopting CiM in future DL accelerators.