Reviewerly/exHarmony huggingface dataset:
Generating Poisson-Distributed Differentially Private Synthetic Data

The dissemination of synthetic data can be an effective means of making information from sensitive data publicly available while reducing the risk of disclosure associated with releasing the sensitive data directly. While mechanisms exist for synthesizing data that satisfy formal privacy guarantees, the utility of the synthetic data is often an afterthought. More recently, the use of methods from the disease mapping literature has been proposed to generate spatially-referenced synthetic data with high utility, albeit without formal privacy guarantees. The objective for this paper is to help bridge the gap between the disease mapping and the formal privacy literatures. In particular, we extend an existing approach for generating formally private synthetic data to the case of Poisson-distributed count data in a way that allows for the infusion of prior information. To evaluate the utility of the synthetic data, we conducted a simulation study inspired by publicly available, county-level heart disease-related death counts. The results of this study demonstrate that the proposed approach for generating differentially private synthetic data outperforms a popular technique when the counts correspond to events arising from subgroups with unequal population sizes or unequal event rates.
The Power of D-hops in Matching Power-Law Graphs

This paper studies seeded graph matching for power-law graphs. Assume that two edge-correlated graphs are independently edge-sampled from a common parent graph with a power-law degree distribution. A set of correctly matched vertex-pairs is chosen at random and revealed as initial seeds. Our goal is to use the seeds to recover the remaining latent vertex correspondence between the two graphs. Departing from the existing approaches that focus on the use of high-degree seeds in $1$-hop neighborhoods, we develop an efficient algorithm that exploits the low-degree seeds in suitably-defined D-hop neighborhoods. Specifically, we first match a set of vertex-pairs with appropriate degrees (which we refer to as the first slice) based on the number of low-degree seeds in their D-hop neighborhoods. This approach significantly reduces the number of initial seeds needed to trigger a cascading process to match the rest of graphs. Under the Chung-Lu random graph model with n vertices, max degree Θ(√n), and the power-law exponent 2&lt;β&lt;3, we show that as soon as D&gt; 4-β/3-β, by optimally choosing the first slice, with high probability our algorithm can correctly match a constant fraction of the true pairs without any error, provided with only Ω((log n)4-β) initial seeds. Our result achieves an exponential reduction in the seed size requirement, as the best previously known result requires n1/2+ε seeds (for any small constant ε&gt;0). Performance evaluation with synthetic and real data further corroborates the improved performance of our algorithm.
IMPLICATIONS OF COMPUTER-AIDED LEARNING IN ELT FOR SECOND LANGUAGE LEARNERS AND TEACHERS DURING COVID-19

Purpose of the study: The primary purpose of this research study was to get detailed insights about numerous aspects of the online ELT for second language learners and teachers in Faisalabad. Methodology: Our study was based on an online survey. Respondents were students and teachers of the English department from different universities in Faisalabad. Data was collected and analyzed to find answers to our research questions. Other characteristics like focus, comfort, understanding, communication, and expense pinpoint the differences between online and physical ELT methods. Main Findings: We found that students and teachers most commonly faced internet connectivity and audio-visual issues. The overall opinion of students about teachers was encouraging. However, teachers claimed that students were not that much serious in online classes. Online ELT has improved the technical capabilities of respondents, and it has made them proficient in using smartphones, online storage, word processing, and computer troubleshooting. Faculty respondents showed interest in learning new tools despite the burden faced and psychological fears. Finally, social media and solution-oriented discussion forums can prove effective and efficient in addressing future pandemic outbreaks. Applications of this study: This study provided us with valuable insights that can help design an effective online educational framework for efficient and result-oriented English language learning. Novelty/Originality of this study: Our study explored faculty and students' psychological and technological readiness at postgraduate institutes to cope with future pandemics.
Stone-Industry in Palestine: Bridging the Gap between Environmental Sustainability and Economical Value

Stone -also referred to locally as ‘white gold’- is one of the most important natural resources and one of the main extractive industries in Palestine. It is one of many traditional industries with a long history of production and a growing economic value. Despite its authenticity and high value, this industry and its facilities, such as quarries, crushers, cutting firms and stone workshops, are spreading randomly throughout the West Bank-Palestine, thus posing a threat to both natural and built environments and negatively impacting human health. This paper assessed these negative impacts on the environment caused by the stone industry’s facilities in the West Bank by developing a Reality Assessment Model using Geographic Information Systems (GIS) which take into consideration stone industry endemism. The model was also used to categorize the stone industry’s facilities based on a comprehensive criteria. As a conclusion, a categorization of four levels (Level 1 to 4) based on their environmental impact and land suitability. It is found that half of the various types of stone industry’s facilities have a negative environmental impact that causes damage ranging from medium to very high, while the other half do not cause significant damage. Thus, the first-class facilities must be closed, second-class facilities may be closed or remain if their negative impacts are reduced, while both third-class and fourth-class facilities may be kept in their current state by minimizing their major negative impact.
Work in Progress: Power-aware Scheduling Strategy for Multiple DAGs in the Heterogeneous Cloud

High energy consumption has become a major problem of cloud platform. Most of the current task scheduling methods neglect the heterogeneity of cloud platform, which may consume more power consumption of heterogeneous cloud platform. In this paper, we propose a power-aware scheduling strategy (PASS) for multiple DAGs workflow in the heterogeneous cloud with the goal of minimizing the energy consumption. First, we predict the PM energy consumption considering VM status after scheduling tasks, and then we formulate the power-aware DAGs task scheduling as a NP-hard problem, which tries to minimize the energy consumption of heterogeneous cloud platform. Second, we propose a multiple DAGs workflow scheduling algorithm to solve the formulated NP-hard problem. We consider the combination of the coarse-grained sorting for DAGs and the fine-grained sorting for sub-tasks to obtain the optimal sorting of DAG workflows. We then assign tasks to the appropriate computing nodes considering the heterogeneity of the cloud platform to minimize its energy consumption. Third, experiments are conducted to evaluate PASS, and the experimental results verify its efficiency.
Holistic Analytics of Digital Artifacts

Investigation of every crime scene with digital evidence is predominantly required in identifying almost all atomic files behind the scenes that have been intentionally scrubbed out. Apart from the data generated across digital devices and the use of diverse technology that slows down the traditional digital forensic investigation strategies. Dynamically scrutinizing the concealed or sparse metadata matches from the less frequent archives of evidence spread across heterogeneous sources and finding their association with other artifacts across the collection is still a horrendous task for the investigators. The effort of this article via unique pockets (UP), unique groups (UG), and unique association (UA) model is to address the exclusive challenges mixed up in identifying incoherent associations that are buried well within the meager metadata field-value pairs. Both the existing similarity models and proposed unique mapping models are verified by the unique metadata association model.
A multi domains short message sentiment classification using hybrid neural network architecture

Sentiment analysis of short texts is challenging because of its limited context of information. It becomes more challenging to be done on limited resource language like Bahasa Indonesia. However, with various deep learning techniques, it can give pretty good accuracy. This paper explores several deep learning methods, such as multilayer perceptron (MLP), convolutional neural network (CNN), long short-term memory (LSTM), and builds combinations of those three architectures. The combinations of those three architectures are intended to get the best of those architecture models. The MLP accommodates the use of the previous model to obtain classification output. The CNN layer extracts the word feature vector from text sequences. Subsequently, the LSTM repetitively selects or discards feature sequences based on their context. Those advantages are useful for different domain datasets. The experiments on sentiment analysis of short text in Bahasa Indonesia show that hybrid models can obtain better performance, and the same architecture can be directly used in another domain-specific dataset.
A View of Artificial Neural Network Models in Different Application Areas

Neural network is a web of million numbers of inter-connected neurons which executes parallel processing. An Artificial neural network is a nonlinear mapping structure; an information processing pattern is stimulated by the approach as biological nervous system (brain) process the information. It is used as a powerful tool for modeling the data in the application domains where incomplete understanding of the data relationship to be solved with the readily available trained data. The basic element for this processing pattern is the structure of the data which is the collection of densely interconnected neurons to elucidate the problems. A prominent part of these network is their adaptive nature to “learn by example” just like human substitutes “programming” in resolving the problems. Through learning process, neural net is designed for data classification and prediction where statistical techniques and regression model have been employed. This report is an overview of artificial neural networks in different application areas and it also illustrate the architecture structure formed for the applications. It also provides information about the training algorithm used for certain application.
ON DECODING ALGORITHMS FOR GENERALIZED REED — SOLOMON CODES WITH ERRORS AND ERASURES

The article is devoted to the decoding algorithms for generalized Reed Solomon codes with errorsand erasures. These algorithms are based on Gao algorithm, Sugiyama algorithm, Berlekamp Massey algorithm (Peterson Gorenstein Zierler algorithm). The first of these algorithms belongs to syndrome-free decoding algorithms, the others to syndrome decoding algorithms. The relevance of these algorithms is that they are applicable for decoding Goppa codes, which are the basis of some promising post-quantum cryptosystems. These algorithms are applicable for Goppa codes over an arbitrary field, as opposed to the well-known Patterson decoding algorithm for binary Goppa codes.
Formation of a complete stress-strain curve of concrete using digital image corellation

This paper reports the development and verification of a new procedure for formation of a complete stress-strain curve of concrete with a downward region of strain by using a digital image correlation method. A new technique to build spectle patterns on the surface of concrete is described. That makes it possible to accurately enough reproduce the spectle patterns on the surface of concrete and perform a high-quality analysis of strains involving digital image correlation. The advantages of this research technique have been established when predicting the formation of internal cracks in concrete followed by their propagation. In addition, using the digital image correlation methodology makes it possible to obtain strains of the entire studied plane of the sample at each stage of loading. This procedure provides an opportunity to investigate a change in strains and the movement of individual points or areas when studying concrete surfaces. That is a relevant issue as it enables more detailed diagnostics of existing reinforced concrete structures. To check the accuracy of this procedure application, a mechanical gauge with an accuracy of 0.001 mm was additionally installed. 2 high-speed monochrome CCD cameras with different lenses were used in determining concrete strains involving the digital image correlation technique. The deformations were controlled with a period of time every 250 ms. The load was controlled by an additional third camera with a speed of 50 frames/second. The result of the experimental study is the formed full concrete destruction diagram with a downward region of strain. The deviation of the results of strains based on the mechanical gauge with an accuracy of 0.001 mm with a base of 200 mm from those acquired by the digital image correlation procedure was mainly up to 10 %, which confirms the reliability of the results. The results of this work allow a more accurate calculation of reinforced concrete structures in the practice of design, inspection, or reinforcement of existing structures
Deep learning for the standardized classification of Ki-67 in vulva carcinoma: A feasibility study

The aim of this study is to demonstrate the feasibility of automatic classification of Ki-67 histological immunostainings in patients with squamous cell carcinoma of the vulva using a deep convolutional neural network (dCNN).For evaluation of the dCNN, we used 55 well characterized squamous cell carcinomas of the vulva in a tissue microarray (TMA) format in this retrospective study. The tumor specimens were classified in 3 different categories C1 (0-2%), C2 (2-20%) and C3 (>20%), representing the relation of the number of KI-67 positive tumor cells to all cancer cells on the TMA spot. Representative areas of the spots were manually labeled by extracting images of 351 × 280 pixels. A dCNN with 13 convolutional layers was used for the evaluation. Two independent pathologists classified 45 labeled images in order to compare the dCNN's results to human readouts.Using a small labeled dataset with 1020 images with equal distribution among classes, the dCNN reached an accuracy of 90.9% (93%) for the training (validation) data. Applying a larger dataset with additional 1017 labeled images resulted in an accuracy of 96.1% (91.4%) for the training (validation) dataset. For the human readout, there were no significant differences between the pathologists and the dCNN in Ki-67 classification results.The dCNN is capable of a standardized classification of Ki-67 staining in vulva carcinoma; therefore, it may be suitable for quality control and standardization in the assessment of tumor grading.
Supporting, tinkering, adjusting and resisting: a typology of user translations of the built environment

Purpose Architectural theorists have a long tradition of acknowledging the centrality of building users to architectural production. This article contributes to the discourse on architecture, actor–network theory (ANT), and users by proposing a typology of user translations ranging from supporting to tinkering to adjusting to resisting. Design/methodology/approach The research utilises an ANT-inspired ethnography of sustainable lighting scripts at the Masdar Institute of Science and Technology (MIST). It comprises semi-structured interviews with MIST designers and students, and site visits and participant observation to understand how the users interpret the scripts and how they interact and change them on a daily basis. Findings There is a shared understanding that users do not simply receive architectural designs but interpret and change them to suit their preferences. The findings reveal the multiple ways that users interpret and respond to the assumptions of designers and in the process, recast the relations between themselves and their material surroundings. Originality/value The research contributes to acknowledging the centrality of users to architectural design processes and the interpretation of design scripts, addressing the limitation in current literature in demonstrating the diversity of ways that users react to such scripts. The research suggests that user actions have significant implications on long-term building performance. It accordingly points to the need for devising multiple means of user involvement in the design process and allowing greater flexibility in design scripts to improve the alignment with user preferences.
The Dynamic Extensions of Fuzzy Grey Cognitive Maps

Fuzzy Cognitive Map is recognized as an important model in soft computing.The Fuzzy Cognitive Map theory studies often treat uncertainty modeling and dynamic modeling separately.So far, there is no single Fuzzy Cognitive Map that can deal with uncertain data and dynamic environments simultaneously.This paper proposed an environment model to describe the link between changing weights and the dynamic environment.As the extensions of the classic Fuzzy Grey Cognitive Map, two dynamic models were designed and implemented in this work: Dynamic Fuzzy Grey Cognitive Map and DynamicFuzzy General Grey Map.In this work, we also analyze the characteristics of the two models and evaluate their performance using industrial control problems.The results showed that the proposed models could well handle uncertain data in dynamic environments.
A Method of Combining Hidden Markov Model and Convolutional Neural Network for the 5G RCS Message Filtering

As one of the 5G applications, rich communication suite (RCS), known as the next generation of Short Message Service (SMS), contains multimedia and interactive information for a better user experience. Meanwhile, the RCS industry worries that spammers may migrate their spamming misdeeds to RCS messages, the complexity of which challenges the filtering technology because each of them contains hundreds of fields with various types of data, such as texts, images and videos. Among the data, the hundreds of fields of text data contain the main content, which is adequate and more efficient for combating spam. This paper first discusses the text fields, which possibly contain spam information, then use the hidden Markov model (HMM) to weight the fields and finally use convolutional neural network (CNN) to classify the RCS messages. In the HMM step, the text fields are treated differently. The short texts of these fields are represented as feature weight sequences extracted by a feature extraction algorithm based on a probability density function. Then, the proposed HMM learns the weight sequence and produces a proper weight for each short text. Other text fields with fewer words are also weighted by the feature extraction algorithm. In the CNN step, all these feature weights first construct the RCS message matrix. The matrices of the training RCS messages are used as the CNN model inputs for learning and the matrices of testing messages are used as the trained CNN model inputs for RCS message property prediction. Four optimization technologies are introduced into the CNN classification process. Promising experiment results are achieved on the real industrial data.
Optimisation of the rational proportion of intelligent technologies application in service organisations

Abstract Background and Purpose: The growing role and involvement of Artificial Intelligence in modern digital enterprises leads to a considerable reduction of personnel and reorientation of the remaining staff to new responsibilities. However, in many areas like services and support the total elimination of the employed human resources still remains impossible. It is proposed to study the organisational problem of finding the optimal proportion of computer agents and human actors in the mixed collaborative environment. Methods: Using the technology of semantic and statistical analysis, we developed an original model of computer agents’ and human actors’ cooperative interaction and an optimization method, which is novel in considering the focus of the executors while calculating the compliance indicators. Results: The problem was studied by an example of service desk automation. Considering the semantics of the problem domain in the form of ontology introduces the logic for better distribution and automation of tasks. Conclusion: In a modern digital enterprise there exists and can be estimated a rational balance between the computer agents and human actors, which becomes a significant indicator of its performance. In general, human actors are preferable for processing unpredictable events in real time, while agents are better at the modelling and simulation.
Introduction to the Special Section on Artificial Intelligence Security: Adversarial Attack and Defense

Artificial intelligence (AI) has been widely adopted in various applications such as face detection, speech recognition, machine learning, etc. Due to the lack of theoretical explanation, recent works show that AI is vulnerable to adversarial attacks, especially deep neural networks could be easily fooled by adversarial examples that are in the form of subtle perturbations to the inputs. The intrinsic vulnerability of AI might incur severe security problems in areas like automatic driving, face payment, voice command control, etc. Adversarial learning is one typical defense method, which can migrate such security risk of AI by training with generated adversarial examples. However, this method cannot defend the growing number of adversarial attacks. The special section of “Artificial Intelligence Security: Adversarial Attack and Defense” focused on the state-of-art adversarial attack and defense methods, and explored how these security problems could affect other areas such as cyberspace security, and internet-of-things.
Improving Text-to-Image Synthesis Using Contrastive Learning

The goal of text-to-image synthesis is to generate a visually realistic image that matches a given text description. In practice, the captions annotated by humans for the same image have large variance in terms of contents and the choice of words. The linguistic discrepancy between the captions of the identical image leads to the synthetic images deviating from the ground truth. To address this issue, we propose a contrastive learning approach to improve the quality and enhance the semantic consistency of synthetic images. In the pretraining stage, we utilize the contrastive learning approach to learn the consistent textual representations for the captions corresponding to the same image. Furthermore, in the following stage of GAN training, we employ the contrastive learning method to enhance the consistency between the generated images from the captions related to the same image. We evaluate our approach over two popular text-to-image synthesis models, AttnGAN and DM-GAN, on datasets CUB and COCO, respectively. Experimental results have shown that our approach can effectively improve the quality of synthetic images in terms of three metrics: IS, FID and R-precision. Especially, on the challenging COCO dataset, our approach boosts the FID signifcantly by 29.60% over AttnGAN and by 21.96% over DM-GAN.