Reviewerly/exHarmony huggingface dataset:
On Graph Neural Networks versus Graph-Augmented MLPs

From the angles of expressive power and learning, this work compares multi-layer Graph Neural Networks (GNNs) with a simplified alternative that we call Graph-Augmented Multi-Layer Perceptrons (GA-MLPs), which first augments node features with certain multi-hop operators on the graph and then applies an MLP in a node-wise fashion. From the perspective of graph isomorphism testing, we show both theoretically and numerically that GA-MLP with suitable operators can distinguish almost all non-isomorphic graphs, just like the Weifeiler-Lehman (WL) test. However, by viewing them as node-level functions and examining the equivalence classes that they induce on rooted graphs, we prove a separation in expressive power between GA-MLPs and GNNs that grows exponentially in depth. In particular, unlike GNNs, GA-MLPs are unable to count the number of attributed walks. We also demonstrate via community detection experiments that GA-MLPs can be limited by their choice of operator family, as compared to GNNs with higher flexibility in learning.
On some topological properties in the class of Alexandroff spaces

In the class of Alexandroff spaces we study the properties of being a submaximal, door, Whyburn and weakly Whyburn space. We provide characterizations in order theoretical terms. Connections with posets, counting formulas and numerical results in the class of finite spaces are also given.
Mining Students’ Topics of Interest and Innermost Feelings Through Confession Pages

Capturing psychological changes and aspirations of the young, especially students, is always the essential concerns. However, this is still a big challenge not only for administrators but also for psychologists. Due to the population of sharing inside feeling trends through confessions in student communities, confession pages become a valuable source for identifying the interests and attitudes of communities. In our research, we propose a confession pages mining methodology based on text analysis method. This method is applied to several confession pages of student communities in a year to find out interest issues and their attitude. As a result, the model achieved average accuracy as F-measure = 62.45% for interest issues classification and F-measure = 76.43% for sentiment classification. The model have been applied to mining characteristics of community of four universities.
Sself: Robust Federated Learning against Stragglers and Adversaries

While federated learning allows efficient model training with local data at edge devices, two major issues that need to be resolved are: slow devices known as stragglers and malicious attacks launched by adversaries. While the presence of both stragglers and adversaries raises serious concerns for the deployment of practical federated learning systems, no known schemes or known combinations of schemes, to our best knowledge, effectively address these two issues at the same time. In this work, we propose Sself, a semi-synchronous entropy and loss based filtering/averaging, to tackle both stragglers and adversaries simultaneously. The stragglers are handled by exploiting different staleness (arrival delay) information when combining locally updated models during periodic global aggregation. Various adversarial attacks are tackled by utilizing a small amount of public data collected at the server in each aggregation step, to first filter out the model-poisoned devices using computed entropies, and then perform weighted averaging based on the estimated losses to combat data poisoning and backdoor attacks. A theoretical convergence bound is established to provide insights on the convergence of Sself. Extensive experimental results show that Sself outperforms various combinations of existing methods aiming to handle stragglers/adversaries.
PHONETIC PATTERNS OF WORDS IN THEIR ORDINARY FUNCTIONS AND AS PARASITE WORDS (BASED ON CHINESE)

In the language, lexical units perform different functions and are characterized by different informativeness. Some of them can appear both in their usual meaning and as parasite words and thus perform double function. This paper aims at studying those words. We hypothesize that in Chinese spontaneous speech, the same words that appear in their usual meaning and as parasite words have different phonetic manifestation, particularly, the latter are used in weak forms more often. An acoustic study was performed to prove the hypothesis. The material was obtained from 6 young females Putonghua speakers. Five functional words 然后ranhou, 就是jiushi, 那个nage, 这个zhege, 那种nazhong were chosen for this study as far as they were relatively frequent and appeared in the speech of all the 6 subjects. In general, the obtained data proved the hypothesis. In both functions, weak forms were found with very few exceptions, however, the number of weak forms increased several times when functioning as parasite words. Different degrees of their incompleteness were noticed as a result of various modifications of the syllable elements as well as syllables liaison.
Application of artificial neural networks in the design of biomedical materials

Biomedical science is a scientific field that includes the intersection of multiple technologies, combining the theoretical methods of biology, medicine and engineering. Biomedical materials are now a branch of the body that studies materials that are adapted to the body’s functioning to ensure normal human activity. Because of its closely related to human activities, it has become an important research field in our time. Therefore, the purpose of this paper is to explore the application of artificial intelligence in the design of biological science materials. Therefore, in the case of using high-quality materials, the material design is improved and optimized by using artificial neural networks under the basis of adverse rejection reactions to the properties of raw materials. The experimental results show that artificial neural network can be better connected and reaction, which is beneficial to improve sensitivity and use emergency measures to deal with it.
Quantifying the agreement between computational models and experimental data under uncertainty

Author(s): Hegde, Arun Shantaram | Advisor(s): Frenklach, Michael | Abstract: Bound-to-bound data collaboration (abbreviated B2BDC) is a deterministic optimization-based approach for uncertainty quantification. The framework combines models and data from multiple sources by formulating inequality constraints over a parameter space. This dissertation explores the following question: how can agreement between computational models and experimental data be quantified while necessarily accounting for uncertainty in both model parameters and observations? In a typical B2BDC application, this is performed by constructing a dataset -- a collection of constraints over an uncertain parameter space involving surrogate models, experimental data, and prior knowledge -- and then assessing its consistency. Our first contribution is a formalization of this procedure within an iterative context. This new strategy effectively extends the applicability of the B2BDC technique and can be viewed as a natural extension of previous work. Oftentimes, demonstrating model-data disagreement is just as important as verifying agreement. In B2BDC, this is manifested through dataset inconsistency. Our second contribution is a new tool for analyzing inconsistency called the vector consistency measure. This measure provides a more thorough diagnosis of an inconsistent dataset by computing minimal constraint corrections that lead to consistency. The inclusion of weights facilitates domain expert knowledge and opinions to be incorporated in the process of resolving an inconsistency. The primary developments in this thesis are methodological. Their application is illustrated on various examples, ranging from the small-scale instances drawn from the literature to larger-scale realistic gas combustion datasets.
A Modified Binary Descriptor for Object Detection

The reliability of computer vision applications highly depends on the extraction of compact, fast, and accurate and robust feature description. This paper presents a better and modified binary descriptor based on ORB (oriented and rotated brief) with the SVM-RBF-RFE (support vector machine-radial basis function-recursive feature elimination) to achieve a better extraction and representation of local binary descriptors. This work presents the extensive comparison of the proposed modified descriptor with the state-of-the-art binary descriptors on various datasets. The results show that the proposed descriptor is highly distinctive and efficient as compared to the other state-of-the-art binary descriptors. The experiments were performed on the four benchmark datasets PASCAL, CALTECH, COIL, and OXFORD to demonstrate the robustness and effectiveness of the proposed descriptor. The robustness and effectiveness of the proposed descriptor is tested under the various transformations like scaling, rotation, noise, intensity variation.
Petrographic and Geochemical Analyses of Kirana Hills Shield Rocks around Sargodha and Economic Potential

: The present study deals with geochemical and petrographic analysis of the Kirana Hill shield rocks of Punjab plains from Buland, Hachi, Shaheen Abad, Shaikh and Machh hills. On basis of the current studies certain modifications have been made in the classification and nomenclature of rocks exposed in the study areas. Chemical analyses have also been carried out in order to calculate Cross Iddings, Pirsson, and Washington (CIPW) norms”, to strengthen nomenclature scheme and finally rocks are classified by using “MAGMA SOFTWARE”. Rhyolites predominate over the basalts/dolerites, andesites, and phyllite/ slate. Rhyolitic rocks are light grey, greenish grey and light brown in color, aphanitic in nature. The observed microscopic textures are aphyric, phyric or porphyritic and micropoikilitc. Moreover, some rhyolitic rocks also show flow texture. They are either cryptocrystalline to microcrystalline or microcrystalline to cryptocrystalline. No glassy material has been observed in any thin section. Mafic rocks are characterized by the presence of ferromagnesian minerals with plagioclase. Andesites exhibit mainly porphyritic texture, but aphyric texture has also been observed in few samples. Hydrothermal alterations are also very common in these rocks. Other rock assemblages identified during laboratory studies from Kirana area include: tuffs i.e. (Lithic Crystal Tuff and Lithic Tuff), basaltic andesite, rhyodacite/ dacite, slate/ phyllite, ankeritic rocks/ veins and quartzofeldspathic veins. Our studies also reveal that no evidence of quartzite has been found in the samples collected from above mentioned areas of Kirana, although it has been reported in previous literature. Iron (Fe) has been observed in rhyolite as well as other volcanic rocks of Kirana hills, its presence suggests magma from deep mantle instead of crustal melting / anatexis. In the present analysis some primary and secondary copper minerals including chalcopyrite, atacamite and malachite have been identified. Some anomalous values of the Rare Earth Elements are also observed. Inclusive geological investigations are recommended for better studies to appraise the potential of Iron (Fe), Copper (Cu) and trace/Rare Earth Elements (REE) in the area.
Efficient and Practical Correlation Filter Tracking

Visual tracking is a basic task in many applications. However, the heavy computation and low speed of many recent trackers limit their applications in some computing power restricted scenarios. On the other hand, the simple update scheme of most correlation filter-based trackers restricts their robustness during target deformation and occlusion. In this paper, we explore the update scheme of correlation filter-based trackers and propose an efficient and adaptive training sample update scheme. The training sample extracted in each frame is updated to the training set according to its distance between existing samples measured with a difference hashing algorithm or discarded according to tracking result reliability. In addition, we expand our new tracker to long-term tracking. On the basis of the proposed model updating mechanism, we propose a new tracking state discrimination mechanism to accurately judge tracking failure, and resume tracking after the target is recovered. Experiments on OTB-2015, Temple Color 128 and UAV123 (including UAV20L) demonstrate that our tracker performs favorably against state-of-the-art trackers with light computation and runs over 100 fps on desktop computer with Intel i7-8700 CPU(3.2 GHz).
Accuracy and confidence in estimation of speaker age

This article describes two experiments investigating listeners’ accuracy in estimation of speaker age as well as the listeners’ confidence that their estimates were correct. In Experiment 1, listeners made age estimates based on spontaneous speech. In Experiment 2, the estimates were based on read speech. The purpose of the study was to explore differences in accuracy and confidence depending on speech material, speaker characteristics (gender and age) and listener gender. Another purpose was to examine the realism in the listeners’ confidence ratings in estimations of spontaneous versus read speech. No differences in accuracy or confidence were found due to speech material type. Although accuracy was higher in estimates of male speakers, confidence was higher in estimates of female speakers, effects that were also dependent on speaker age. Possible acoustic and linguistic explanations behind the age and gender effects are discussed. As the correlation between confidence and accuracy was weak, it was concluded that confidence should not be relied on as an indicator of accuracy in estimation of speaker age.
Edge-preserving efficient dense stereo matching

Edge accurate dense disparity estimation is of great importance to applications, such as augmented reality where the geometry relationships among objects in a scene need to be presented precisely. Binocular stereo is a promising approach to get 3D depth information of a real scene from 2D images, but faces the issue of difficult to achieve high accurate disparity edges with reasonable computation complexity. A depth edge preserving dense stereo matching method is presented in this paper aiming to alleviate this problem. By taking a sparse-to-dense route for disparity estimation, depth edges corresponding to object boundaries are distinguished from the texture edges based on sparse disparities which can be obtained efficiently. With a designed disparity filling strategy, these extracted edges are used to refine the dense disparities and align the depth discontinuity edges with corresponding object boundaries. Disparities obtained by this work can faithfully conform to the scene geometry recorded in the input images only with a relative small increase about 13% in computational complexity. The effectiveness of the proposed method is verified through experiments and contrastive analysis.
On Disentangled Representations Extracted from Pretrained GANs

Constructing disentangled representations is known to be a difficult task, especially in the unsupervised scenario. The dominating paradigm of unsupervised disentanglement is currently to train a generative model that separates different factors of variation in its latent space. This separation is typically enforced by training with specific regularization terms in the model's objective function. These terms, however, introduce additional hyperparameters responsible for the trade-off between disentanglement and generation quality. While tuning these hyperparameters is crucial for proper disentanglement, it is often unclear how to tune them without external supervision. This paper investigates an alternative route to disentangled representations. Namely, we propose to extract such representations from the state-of-the-art GANs trained without disentangling terms in their objectives. This paradigm of post hoc disentanglement employs little or no hyperparameters when learning representations, while achieving results on par with existing state-of-the-art, as shown by comparison in terms of established disentanglement metrics, fairness, and the abstract reasoning task. All our code and models are publicly available.
The Impact of Audit Quality on Financial Performance in Jordan

Investors and capital providers are expressing growing concern about the information quality provided in annual reports. To date, however, few studies have been conducted related to audit quality and its impact on the performance of companies particularly in Jordan and developing countries in general. That was the motivation to conduct the current study and provide empirical evidence in this area. This study relies on data collected from the annual reports of 88 non-financial Jordanian companies listed on the Amman Stock Exchange (ASE) from 2009 to 2018. In total, the study contained 880 observations. The panel data approach was employed in this study due to the nature of the data collected. The current study measured the independent variable, which is audit quality (AQ) measured by the log of audit fees, and the independent variable, financial performance, measured by TOBIN'S Q (TQ). A multiple regression method was used to analyze panel data. The results showed there is significant and positive impact of AQ and financial performance. Moreover, cash flow from financing activities (CASHF) and Liquidity (LIQ) have a positive impact on the financial performance of companies listed on ASE, while Leverage (LEV) and firm size (SIZ) have an insignificant relationship with financial performanceKeywords- audit quality, Agency Theory, financial performance
Data Association between Perception and V2V Communication Sensors

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;The connectivity between vehicles, infrastructure, and other traffic participants brings a new dimension to automotive safety applications. Soon all the newly produced cars will have Vehicle to Everything (V2X) communication modems alongside the existing Advanced Driver Assistant Systems (ADAS). It is essential to identify the different sensor measurements for the same targets (Data Association) to use connectivity reliably as a safety feature alongside the standard ADAS functionality. Considering the camera is the most common sensor available for ADAS systems, in this paper, we present an experimental implementation of a Mahalanobis distance-based data association algorithm between the camera and the Vehicle to Vehicle (V2V) communication sensors. The implemented algorithm has low computational complexity and the capability of running in real-time. One can use the presented algorithm for sensor fusion algorithms or higher-level decision-making applications in ADAS modules.&lt;/div&gt;&lt;/div&gt;
Conceptual characteristics and analysis of typical application scenarios of energy blockchain

Abstract In recent years, blockchain has gradually moved from a technical concept to reality, and has increasingly shown its important potential to empower industrial innovation and boost economic construction. Blockchain technology has shown strong application potential in the field of energy and power with its technical characteristics such as traceability and nontampering. At present, many energy companies at home and abroad have used the energy blockchain to implement various scenarios, but they have not yet formed a target systematic research on energy blockchain. This research deeply analyzes the conceptual and technical characteristics of energy blockchain, proposes a screening model for application scenarios, and analyzes typical application scenarios of energy blockchain based on this, aiming to provide a useful reference for energy companies to develop blockchain applications.
Automated detection of plasmodium species using Machine-Learning technique

Background: Malaria is one of the widespread mosquito-borne diseases. The WHO report of year 2018 confirms a recorded disease rate of 219 million and a death rate of 435,000. Accurate diagnosis of malaria infection is essential for appropriate management of cases. WHO's Global Technical Strategy for Malaria 2016–2030 recommends that surveillance strategies should be integrated as core interventions to accelerate progress towards elimination. The proposed work aims to develop a Machine-Learning (ML) system for the assessment and classification of the parasite existing in the stained thin blood smear images recorded with a digital microscope. This work implements a multi-class classifier to categorize the plasmodium according to its shape and texture features. Methods and materials: In this work, an automated system is proposed to extract, evaluate and classify the plasmodium species available in the considered test images. The proposed system initially implements a multi-level thresholding to enhance the image and plasmodium in enhanced image is extracted using morphological segmentation, later the texture/shape features are extracted and finally a multi class classifier is implemented to categorize the test images based on the parasite and its growth phase. In order to attain better classification accuracy, this work implements prevailing feature selection and multi-class classification based on Support-Vector-Machine (SVM). Results: The developed system was tested on 2000 numbers of RGB scale thin blood smear pictures of size 512 x 512 pixels and the results obtained in the proposed technique was evaluated to validate the performance of proposed ML system. The results of this study provides an overall classification accuracy of 97.05% with the SVM and relative assessment with the comparable procedures in the literature also verifies that, ML technique offers superior values of accuracy, precision, sensitivity, specificity and F1score. Conclusion: This work implements a Machine-Learning tool to asses and classifies the plasmodium species using the stained thin blood smear images. The outcome of the proposed tool confirms that, it works well on considered microscopic image database and helps to attain better classification accuracy. In future, the proposed approach can be used to examine the clinical grade thin blood smear images of malaria infected patients.
Fast and robust fringe projection profilometry for surface with hole discontinuities via backlighting

Abstract In fringe projection profilometry (FPP), objects with hole discontinuities can lead to shadows and even phase unwrapping morbidity. To solve this problem, we propose an enhanced FPP system by employing the backlighting technique in this paper. This method consists of shadow compensation (SC) and high dynamic range (HDR) imaging; the SC is implemented using the optimal mask, which is obtained by fusing two high-contrast images from different backlight intensities; the HDR imaging is performed by a multi-exposure technique to tackle the issue of multi-colored or specular reflections. Since the proposed method does not require complex phase-unwrapping algorithms, it is fast and robust for three-dimensional shape measurement of objects with hole discontinuities. To validate the method, morphological measurements of a wheel rim and a multi-colored human mask were performed. The results show that the proposed method can overcome discontinuity problems without degrading performance.