Reviewerly/exHarmony huggingface dataset:
Absolutely <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>k</mml:mi></mml:math> -incoherent quantum states and spectral inequalities for the factor width of a matrix

We investigate the set of quantum states that can be shown to be $k$-incoherent based only on their eigenvalues (equivalently, we explore which Hermitian matrices can be shown to have small factor width based only on their eigenvalues). In analogy with the absolute separability problem in quantum resource theory, we call these states "absolutely $k$-incoherent", and we derive several necessary and sufficient conditions for membership in this set. We obtain many of our results by making use of recent results concerning hyperbolicity cones associated with elementary symmetric polynomials.
Transformer-Based Joint Learning Approach for Text Normalization in Vietnamese Automatic Speech Recognition Systems

In this article, we investigate the task of normalizing transcribed texts in Vietnamese Automatic Speech Recognition (ASR) systems in order to improve user readability and the performance of downstream tasks. This task usually consists of two main sub-tasks: predicting and inserting punctuation (i.e., period, comma); and detecting and standardizing named entities (i.e., numbers, person names) from spoken forms to their appropriate written forms. To achieve these goals, we introduce a complete corpus including of 87,700 sentences and investigate conditional joint learning approaches which globally optimize two sub-tasks simultaneously. The experimental results are quite promising. Overall, the proposed architecture outperformed the conventional architecture which trains individual models on the two sub-tasks separately. The joint models are furthered improved when integrated with the surrounding contexts (SCs). Specifically, we obtained 81.13% for the first sub-task and 94.41% for the second sub-task in the F1 scores using the best model.
Investigation of time impact on electrocoagulation process to treat ablution wastewater

Abstract The ablution ritual performed by Muslims before praying requires large amounts of clean water (3.9 L - 4.42 L/person). This is a challenge that needs to be addressed, as in some places, water is a scarce commodity. This study aims to treat ablution wastewater at Masjid Al-Bukhari UIN Raden Mas Said Surakarta using electrocoagulation. The treated water is expected to be reused for ablution rituals in the mosque. Laboratory-scale research methods were performed to treat ablution wastewater. The effect of electrolysis time was evaluated to determine efficiency of the method on the parameters of pH, temperature, total dissolved solid (TDS), conductivity, and ammonia level. Interestingly, ablution wastewater treated using electrocoagulation meets clean water quality standards. This means that the water is safe to reuse for ablution activities in mosques. The electrolysis time has a significant effect on the increase of pH value and the decrease of ammonia level. TDS levels decreased to 65.3% when treated for 40 min. Meanwhile, the conductivity value decreased to 526 µS/cm from the initial value of 1108 µS/cm when the water was treated for 60 min. Statistically, electrolysis time has a significant effect on the decrease of TDS, conductivity, and ammonia levels (α&lt;0.05).
NSGA-II-AMO: A Faster Genetic Algorithm for QWSCP

The task of the QoS-driven web service composition problem (QWSCP) is to find the optimal combination of atomic web services to meet the high demand for QoS parameters. However, different scenarios have various sensitivities to each parameter, researchers prefer algorithms that solve the problem to output non-dominated solution sets. NSGA-II, a genetic algorithm, has become one of the most contacted algorithms for solving the QWSCP problem due to its ability to search for non-dominated solution sets. However, NSGA-II algorithm still suffers from the challenge of slow convergence and the tendency to fall into local optimum solutions. In adjusting the NSGA-II mutation probabilities, it was found that the setting of the mutation probabilities affects the effectiveness of the algorithm, and that the optimal mutation probabilities for different loci are related to the distribution of data for that locus. Therefore, this paper proposes an improved NSGA-II-AMO algorithm, which uses an adaptive mutation operator to complete the mutation process when generating children in the NSGA-II algorithm, so that the mutation probability adaptively changes with different distributions of data, effectively improving the search speed at the beginning of population iteration; at the same time, in order to avoid the problem of oscillatory search at a later stage, AMO reduces the mutation probability as the population converges, preventing the re-introduction of inferior genes into the population. We compare the NSGA-II-AMO algorithm cross-sectionally with the PSO algorithm, the NSGA-II algorithm and the ABC algorithm, and the adaptive mutation operator achieves improved convergence with guaranteed distributivity.
Analysis of Water Quality Change in Tourist Attractions to Reduce Ecological Destruction: Perspectives on Science and Environmental Theology

Abstract This study aims to determine changes in water quality due to water tourism activities in Umbul Ponggok and Jumog Waterfall, Central Java, Indonesia. This research uses a mixed-method approach that combines science (laboratory tests) and environmental theology study. Water samples were taken from tourism locations and then analyzed in the laboratory. Environmental theology studies were conducted by interviewing managers &amp; visitors to find out about the care and utilization of water sources. Based on statistical tests, the pH, Temperature, TDS, and conductivity values of the water at the research location did not change significantly; both the quality of the water from the source, the water when it was used and the water after it was used for water tourism activities. The water pH at the Umbul Ponggok tourist and Jumog Waterfall is between 8.7-9.1 and 8-8.1, respectively. Water temperature ranges between 20-21°C and 24- 25°C. TDS levels ranged from 67-121 mg/L, and water in the Umbul Ponggok ranged from 126-129 mg/L. Jumog waterfall's water conductivity is 123-217 mg/L and Umbul Ponggok 235-240 mg/L. The implementation of environmental theology was shown by the existence of preventive and curative actions against water pollution that water tourism managers have carried out.
In the Identification of Arabic Dialects: A Loss Function Ensemble Learning Based-Approach

The automation of a system to accurately identify Arabic dialects many natural language processing tasks, including sentiment analysis, medical chatbots, Arabic speech recognition, machine translation, etc., will greatly benefit because it's useful to understand the text's dialect before performing different tasks to it. Different Arabic-speaking nations have adopted various dialects and writing systems. Most of the Arab countries understand modern standard Arabic (MSA), which is the native language of all other Arabic dialects. In this paper we propose a method for identifying Arabic dialects Using the Arabic Online Commentary dataset (AOC), which includes three Arabic dialects-Gulf, Levantine, and egyptian-alongside MSA. Our approach includes two ensemble learning strategies using two BERT-based models and different loss functions such as focal loss, dice loss, and weighted cross-entropy loss. The first strategy is between the two proposed models using the loss function that performed best on the models, and the other is between the same model but using different loss functions, which resulted in 83.3%, 80.1%, 85.8%, 81.45%, Precision, Recall, Accuracy and Macro-F1 on the test set respectively.
Develop a Horizontal Virtual Frame by Adding Field of View Restrictions to Reduce VR Sickness

VR sickness is a symptom that similar to motion sickness such as strong discomfort, nausea, dizziness, and headache, and 3D sickness when using VR. As VR becomes more and more widely used, the accompanying VR sickness also needs to be paid attention to. This research is based on the previous development of the virtual horizontal frame. On the basis of confirming that the virtual horizontal frame has a good effect on VR sickness, we will further study the effect of restricting part of the field of view. The effect was visualized by using SSQ (Simulator Sickness Questionnaire) to see the degree of sickness. Through experiments, it was found that different visual field limitations can alleviate various symptoms to different degrees. We can also formulate different visual specialization strategies according to different results of each SSQ questionnaire.
Climbing Keyboard: A Tilt-Based Selection Keyboard Entry for Virtual Reality

Text input is one of the common interaction tasks in virtual environments. However, current inputting methods (e.g., laser-input: aim-and-shoot technique) have many limitations, such as inefficiency, lack of precision, and fatigue of long-text inputting. We propose a Climbing Keyboard method to allow easier, faster, and more accurate text input, and use tilt instead of precise aiming. The selected target changes from a specific letter to a group of letters with such a tilt-based interaction based on the QWERTY layout, which aims to reduce the learning cost, especially for novice users. Meanwhile, expert users can focus on the screen without looking at the keyboard. We designed three user studies to evaluate the performance of proposed method, including the verification of the usability of the tilt interaction method in the first study, optimization of the tilt angle range in the second study, and evaluation of the learning curve of Climbing keyboard in the last study. Our results showed that participants can reach 16.48 words per minute after an hours of training.
Super-resolving compressed images via parallel and series integration of artefacts removal and resolution enhancement

In real-world applications, such as sharing photos on social media platforms, images are always not only sub-sampled but also heavily compressed thus often containing various artefacts. Simple methods for enhancing the resolution of such images will exacerbate the artefacts, rendering them visually objectionable. In spite of its high practical values, super-resolving compressed images is not well studied in the literature. In this paper, we propose a novel compressed image super resolution (CISR) framework based on parallel and series integration of artefacts removal and resolution enhancement. Based on a mathematical inference model for estimating a clean low-resolution (LR) image and a clean high-resolution (HR) image from a down-sampled and compressed observation, we have designed a CISR architecture consisting of two deep neural network modules: the artefacts removal module (ARM) and the resolution enhancement module (REM). The ARM and the REM work in parallel with both taking the compressed LR image as their inputs, at the same time they also work in series with the REM taking the output of the ARM as one of its inputs and the ARM taking the output of the REM as its other input. A technique called unfolding is introduced to recursively suppress the compression artefacts and restore the image resolution. A unique feature of our CISR system is that it exploits the parallel and series connections between the ARM and the REM, and recursive optimization to reduce the model's dependency on specific types of degradation thus making it possible to train a single model to super-resolve images compressed by different methods to different qualities. Experiments are conducted on a mixture of JPEG and WebP compressed images without assuming apriori compression type and compression quality factor. To demonstrate our technique's real-world application value, we have also applied the trained models directly to restore social media images which have undergone scaling and compression by unknown algorithms. Visual and quantitative comparisons demonstrate the superiority of our method over state-of-the-art super resolution methods, especially for heavily compressed images.
Verifiable quaternion fully homomorphic encryption scheme for mitigating false data injection attacks by privacy preservation in fog environment

Fog computing as an extension of cloud computing has attracted maximum attention over the recent years due to its suitability and applicability. The Fog computing is identified to handle the issues of inadequate mobility support and location sensitiveness and maximized latency. In the Internet of Things (IoT), a collection of IoT devices needs to be connected to the Fog nodes for supporting the center of the cloud service to process and store partial data part in prior. This possibility of connecting IoT devices with Fog nodes has the maximum possibility of enhancing the service quality and minimize the pressure involved in data processing in real time. However, Fog nodes used in data processing are considered from a diversified number of challenging issues introduced by the privacy violation of IoT devices, modification attacks and false data injection attack. In this paper, a Verifiable Quaternion Fully Homomorphic Encryption (VQFHE) scheme is proposed for mitigating False Data Injection Attacks by means of privacy preservation and fault tolerance in Fog computing scenario. This proposed VQFHE scheme derives the benefits of Quaternions for ensures that data injection always happens from the legitimate devices, inspite to the honest or curious behavior of the cloud control center (CCC) and Fog nodes. The simulation results conducted using iFogSim simulator confirmed its predominance over the benchmarked schemes with respect to communication overhead and computation overhead.
HKGAIL: Policy shaping via integrating human knowledge with generative adversarial imitation learning

Abstract Generative adversarial imitation learning (GAIL) has shown good results in several research areas by taking advantage of generative adversarial networks. However, GAIL lacks a reward mechanism and usually adopts a model‐free approach based on stochastic policies, which is not ideal for solving complex, dynamically uncertain population intelligence problems, especially in the face of autonomous driving environments. In this paper, a policy framework is shaped by combining the human knowledge with GAIL (HKGAIL). HKGAIL embeds human decision models into the learning process to infer the underlying structure of expert demonstrations. The skills learned from expert demonstrations can directly guide the actions (policies) of the learning process of the agents, and the policies can be optimized through the feedback function of the discriminator. Experiments on both driving and landing tasks show that HKGAIL was able to better fit the policy close to the expert, and was 16.2% safer than GAIL for the driving task.
Chapter 18. “These cameras won’t show the crowds”

This chapter uses a linguistically-based critical discourse analytic framework to identify, isolate, and contextualize the discursive strategies involved in the construction of conspiracy theories in former US President Donald Trump’s campaign-style rally speeches. Unlike simple falsehoods, conspiracy theories are discursively constructed, repeated, and must involve (specific or vague) accusations about certain agents acting against another party surreptitiously. This chapter analyzes the discursive strategies Trump uses to construct the “crowd size” conspiracy theory: the oft-repeated claim in Trumpian discourse that Trump’s opponents (especially the media) routinely manipulate, mask, and underestimate the number of Trump’s supporters who come to see him at events. Five discursive strategies are identified: the iconization of stock phrases, the repetition of numbers, insults, intertextuality, and recursivity. I also demonstrate how the application of these strategies is necessarily intradiscursively intertextual, i.e., characterized by repetition and reference across both speech genre (campaign rallies) and discursive context (Trumpian discourse) more broadly.
DCF: Disparity computing framework for stereo vision systems

<h2>Abstract</h2> Disparity maps are vital components of stereo vision systems as they encode the displacement of two or more images. However, previous works provide only a few implementation details, suggest processing steps that are not very well defined, and the software design is rarely discussed. Conversely, DCF applies the main components of a stereo vision system and integrates them to promote disparity map construction. As a result, DCF algorithms can be parameterized or executed with previously defined configurations. Thus, DCF outputs can be directed to different applications, such as benchmarking proposals, computer and robotic applications, triangulation, and 3D reconstruction.
Rich‐scale feature fusion network for salient object detection

Fully convolutional neural networks-based salient object detection has recently achieved great success with its performance benefits from the effective use of multi-layer features. Based on this, most of the existing saliency detectors designed complex network structures to fuse the multi-level features generated by the backbone network. However, the variable scale and complex shape of the target are always a great challenge for saliency detection tasks. In this paper, the authors propose a Rich-scale Feature Fusion Network (RFFNet) for salient object detection. The authors design a rich-scale feature interactive fusion module to obtain more efficient features from the multi-scale features. Moreover, the global feature enhance module is used to extract features with better characterization for the final saliency prediction. Extensive experiments performed on five benchmark datasets demonstrate that the proposed method can achieve satisfactory results on different evaluation metrics compared to other state-of-the-art salient object detection approaches.
Trace metal geochemistry of metalliferous black shales in the Mamfe basin (South West Cameroon): Implication for heavy metal assessment

This study is focused on assessing the contamination levels of heavy metal elements (Cu, Cd, Cr, Ni, and Zn) in black metalliferous shales of the Mamfe basin in South Western Cameroon and identifying the possible pollution sources. Forty (40) black shale samples collected from seven outcrops from different sites in the Mamfe basin were subjected to geochemical analysis by inductively coupled plasma-mass spectrometry (ICP-MS). The results obtained from these analyses were used to calculate the contamination factor (CF), geo-accumulation index (Igeo), and Pollution Load Index (PLI). Person's correlation coefficient was applied in this work to assess the metals origin. The values of heavy metals in the shales were compared with experimental values of TEL (Threshold Effects Level), PEL (Probable Effects Level), ERL (Effects Range Low), and ERM (Effects Range Medium) to assess the long-term adverse biota effect if the shales weathers and release the hosted heavy metals. The average heavy metal concentrations in the studied shales were 35 mg/kg, 89 mg/kg, 0.24 mg/kg, 82 mg/kg and 37.3 mg/kg for Cu, Zn, Cd, Cr and Ni respectively with some above the threshold values of United State Environment Protection Authority (USEPA), average shale (ASV), toxicity reference values (TRV) and Indian River system (IRS). The shales are poorly to moderately enriched in heavy metals which may cause a long-term effect on the surrounding soils and waters after degradation. The heavy metal values of the studied shales compared with the experimental results of TEL, PEL, ERL, and ERM indicate that a little adverse biota effect of Ni, Cu and Cr to the ecosystem after degradation of the studied shales. The geo-accumulation index ranged between 0 and 0.06 indicating that the black Mamfe shales are uncontaminated to moderately contaminated and may have adverse effects on the freshwater ecology and surrounding soils. The pollution load index (between 0.0 to 0.05) and contamination factor (0.18-1.84) indicate that the shales have a low level of contamination and their long-term degradation and weathering will have a limited effect on the soils and water ecosystems. The positive correlation between Cu, and Zn, suggest the same source of contamination input. The positive correlation between Zn, Cu, Cr, and Ni indicates a natural origin of these elements in the shales. A rapid weathering/degradation of large quantity of theses shales may increase the concentration of heavy metals in the surrounding rivers and soils in the near future.
Unsupervised knowledge transfer for nonblind image deconvolution

Nonblind image deconvolution restores the clear image from a blurred one under a known blur kernel, whose recent development has been boosted by supervised deep learning. Motivated by the inaccessibility of ground-truth images for supervised learning in many application domains, such as scientific imaging, this paper studies the unsupervised knowledge transfer problem for nonblind image deconvolution, which aims at adapting a deep model pre-trained on a source domain, to a ground-truth-scare target domain where image contents or blur kernels are distinct from that of the source domain. We propose to conduct the knowledge transfer regarding both images and kernels, by leveraging the model being adapted itself to generate pairs of a pseudo ground-truth image and a blurred image for self training. The proposed method neither accesses source-domain data, which avoids privacy issues, nor accesses target-domain ground-truths, which avoids ground-truth collection. Its effectiveness is demonstrated with the experiments on three deblurring tasks in different domains.
Locality sensitive hashing-driven multifactorial evolutionary algorithms for multitask optimization

In recent years, evolutionary multitasking optimization (EMTO) has attracted considerable attention. Different from the traditional algorithms that solve one optimization problem at a time, EMTO aims at solving multiple problems simultaneously, through transferring useful information among individuals. The most well-known algorithm in this area is the multifactorial evolutionary algorithm (MFEA). However, when designing the information sharing mechanism, MFEA and its variants focus solely on the properties of individuals in the objective space, while neglecting the valuable information in the decision space. To make full use of the inherent knowledge of the evolving population, this paper presents a novel EMTO framework by using the locality sensitive hashing (LSH) method to analyze the population distribution in the decision space and then guide the information sharing. Specifically, LSH uses hash functions to map the individuals in the decision space into hash codes, ensuring that similar individuals have a higher probability to be mapped into the same code. Then, based on the matching relationships of the hash codes and skill factors between individuals, different reproduction and evaluation strategies are exerted to satisfy the specific requirements under different conditions. This way, the knowledge from both the decision space and the objective space are utilized for the purpose of better manipulating the individuals. Additionally, an opposition-based learning method is integrated in this algorithm to enhance exploration. The comprehensive experimental studies confirm the efficacy and efficiency of the proposed method on both single-objective multitask benchmarks and multi-objective multitask benchmarks. According to the statistic tests, the proposed single-objective and multi-objective algorithms significantly outperform their best competitors on 14/18 and 12/18 tasks, respectively.