Reviewerly/exHarmony huggingface dataset:
The Path to a Consensus on Artificial Intelligence Assurance

LEARNING To ensure the wide-scale adoption of intelligent algorithms, artificial intelligence (AI) engineers must offer assurances that an algorithm will function as intended.Providing such guarantees involves quantifying capabilities and the associated risks across multiple dimensions, including data quality, algorithm performance, statistical considerations, trustworthiness, and security as well as explainability.
Automating Coordination Efforts for Reviewing Construction Contracts with Multilabel Text Classification

Construction projects involve multiple company departments and disciplines. The departments follow certain rules in implementing a project, also referred to as requirements in a construction contract. Current administration practices do not show which discipline or department is related to any requirement in the contracts. Thus, all departments need to review contract requirements but typically only from their perspective and with minimal communication with one another. In addition to the tendency of this manual process to error, time and money are lost in evaluating irrelevant departmental requirements. This study concentrates on one aspect of contract interpretation, coordination of the contract requirement review. Automating a classification of the contract requirements by relevant departments can increase the efficiency of contract reviews. This study proposes a robust approach to automating contract sentence classification by relevance to the company department. The approach comprises both natural language processing (NLP) and supervised machine learning techniques to train an algorithm. Training data are selected from an internationally and widely used standard form of construction contract. Precision metric results as high as 0.952 and recall metric results as high as 0.786 are acquired by support vector classifiers (SVCs). These are considered sufficient within the context of multilabel classification of construction contract sentences for construction professionals to operate without further training. The developed methodology reduces time spent on contract review, reliably and accurately predicts classification of contract sentences for departmental relevance, and also removes the dependence on expert participation in coordination efforts contract review.
Construction of an English Research Learning Model Based on Constructivism and Data Mining under a Cloud Computing Platform

This paper attempts to explore and summarize the teaching activities of English research learning based on constructivism and data mining under the guidance of advanced teaching concepts and the support of cloud computing platforms and to explain the problems reflected in the practice of English research learning in the current environment. Constructing a teaching model based on constructivism and data mining on the cloud computing platform is a beneficial attempt to combine the new teaching design theory with teaching practice. Based on constructivist learning theory and data mining technology, combined with current English teaching practice, this paper discusses the integration of English research-based learning and data mining technology. Try to implement it in English teaching to improve English learners’ autonomous inquiry ability. The purpose is to promote students to form a positive learning attitude and good learning strategies and cultivate students’ innovative spirit and practical ability to adapt to the requirements of the times for talents. The combination of cloud computing technology and data mining has created a new English learning model, which can greatly improve the efficiency of English learning and provide a good reference for the English learning model.
FuseKin: Weighted image fusion based kinship verification under unconstrained age group

Kinship verification using facial images is mainly performed with a single face sample per person. To perform with a single sample, it is very difficult to specify an age group where kin pairs may have higher similarities. To address the above problem, we propose a novel weighted multi sample fusion (WMSF) method. The proposed WMSF method combines kin signals present in multiple samples per person (MSPP) to form a FuseKin image. To select the most discriminant features from the extracted feature vector, we propose a patch based discriminative analysis (PDA) method. Weights are calculated using the PDA method so as to reduce the discrimination between positive FuseKin pairs. Experiments were conducted on two different datasets which contain multiple face image samples per person, namely Family101 and Family in the Wild (FIW) to validate the performance of the proposed methods. Our method achieves competitive results as compared to other state-of-the-art methods.
Virtual restoration of paintings based on deep learning

Over time, crack pattern (craquelure) inevitably develops in paintings as a sign of their ageing, sometimes accompanied by larger losses of paint (lacunas). In restoration treatments, cracks are typically not filled in, and virtual restoration is often the only option to “reverse” the ageing of paintings, simulating their original appearance. Moreover, virtual restoration can serve as an important supporting step in decision making during the physical restoration. In this research, we investigate the possibility of applying deep learning-based methods for virtual restoration. In particular, our crack detection method is based on a convolutional autoencoder (U-Net), and we employ a generative adversarial neural network (GAN) to virtually inpaint the detected cracks. We propose an original way of training the GAN model for painting restoration, which improves its practical performance. A series of experiments shows encouraging results in comparison with known methods, and indicates huge potential of deep learning for virtual painting restoration.
Cloud security framework and key management services collectively for implementing DLP and IRM

A free and open-source framework management solution can help organizations as well as end-users. An agentless, completely controlled, centrally administered, and freely distributed source, cloud-based data leak control includes enhanced data loss protection management for both corporations and end-users. An Open-Source Framework in the cloud zone was used to construct the data loss prevention tool. This framework can be released under an Open License or a Free License, making it freely usable by end-users throughout the world. You can run it on any of these platforms: Linux, Windows, or UNIX. It relies on open database connectivity and MSSQL credentials for its database connectivity, although MySQL Server is used for database connectivity. As many as hundreds of individual files or thousands of multiple files can be accessed simultaneously by the Open User Data Loss Management Console, which can identify the case-sensitive information among them.
Data security in e-health monitoring system

Our world has become fully reliant on machines nowadays. Across the board, machines have reduced human labor and simplified our lives. When it comes to the medical field, forget about the long lines that people must endure in order to have their opportunity or obtain their report. Everything is right in front of you with only one touch. Thousands of hospitals have medical data kept in their systems or in the cloud, which clinicians can use for comparison. However, intruders may gain access to sensitive data at times, jeopardizing security and privacy. The goal of this study is to apply the Modified Blowfish Algorithm to make patient data safe and secure when stored on diverse platforms. The algorithm takes 72 percent of the time to encrypt the files and 48 percent of the time to decrypt them.
Query-based black-box attack against medical image segmentation model

With the extensive deployment of deep learning, the research on adversarial example receives more concern than ever before. By modifying a small fraction of the original image, an adversary can lead a well-trained model to make a wrong prediction. However, existing works about adversarial attack and defense mainly focus on image classification but pay little attention to more practical tasks like segmentation. In this work, we propose a query-based black-box attack that could alter the classes of foreground pixels within a limited query budget. The proposed method improves the Adaptive Square Attack by employing a more accurate gradient estimation of loss and replacing the fixed variance of adaptive distribution with a learnable one. We also adopt a novel loss function proposed for attacking medical image segmentation models. Experiments on a widely-used dataset and well-known models demonstrate the effectiveness and efficiency of the proposed method in attacking medical image segmentation models. The implementation code and extensive analysis are available at https://github.com/Ikracs/medical_attack.
Instance selection for big data based on locally sensitive hashing and double-voting mechanism

The increasing data volumes impose unprecedented challenges to traditional data mining in data preprocessing, learning, and analyzing, it has attracted much attention in designing efficient compressing, indexing and searching methods recently. Inspired by locally sensitive hashing (LSH), divide-and-conquer strategy, and double-voting mechanism, we proposed an iterative instance selection algorithm, which needs to run p rounds iteratively to reduce or eliminate the unwanted bias of the optimal solution by double-voting. In each iteration, the proposed algorithm partitions the big dataset into several subsets and distributes them to different computing nodes. In each node, the instances in local data subset are transformed into Hamming space by l hash function in parallel, and each instance is assigned to one of the l hash tables by the corresponding hash code, the instances with the same hash code are put into the same bucket. And then, a proportion of instances are randomly selected from each hash bucket in each hash table, and a subset is obtained. Thus, totally l subsets are obtained, which are used for voting to select the locally optimal instance subset. The process is repeated p times to obtain p subsets. Finally, the globally optimal instance subset is obtained by voting with the p subsets. The proposed algorithm is implemented with two open source big data platforms, Hadoop and Spark, and experimentally compared with three state-of-the-art methods on testing accuracy, compression ratio, and running time. The experimental results demonstrate that the proposed algorithm provides excellent performance and outperforms three baseline methods.
RETRACTED ARTICLE: Coronavirus herd immunity optimizer to solve classification problems

Classification is a technique in data mining that is used to predict the value of a categorical variable and to produce input data and datasets of varying values. The classification algorithm makes use of the training datasets to build a model which can be used for allocating unclassified records to a defined class. In this paper, the coronavirus herd immunity optimizer (CHIO) algorithm is used to boost the efficiency of the probabilistic neural network (PNN) when solving classification problems. First, the PNN produces a random initial solution and submits it to the CHIO, which then attempts to refine the PNN weights. This is accomplished by the management of random phases and the effective identification of a search space that can probably decide the optimal value. The proposed CHIO-PNN approach was applied to 11 benchmark datasets to assess its classification accuracy, and its results were compared with those of the PNN and three methods in the literature, the firefly algorithm, African buffalo algorithm, and β-hill climbing. The results showed that the CHIO-PNN achieved an overall classification rate of 90.3% on all datasets, at a faster convergence speed as compared outperforming all the methods in the literature.The online version contains supplementary material available at 10.1007/s00500-022-06917-z.
Hotspot-Aware Workload Scheduling and Server Placement for Heterogeneous Cloud Data Centers

Data center servers located in thermal hotspot regions receive inlet air at a higher than the set temperature and thus generate comparatively high outlet temperature. Consequently, there is a rise in energy that is consumed to cool down the servers that otherwise would undergo reliability hazards. The workload deployment across the servers should be resilient to thermal hotspots to ensure smooth performance. In a heterogeneous data center environment, an equally important fact is the placement of the servers in a thermal hotspot-aware manner to lower the peak outlet temperatures. These approaches can be applied proactively with the help of outlet temperature prediction. This paper presents the hotspot adaptive workload deployment algorithm (HAWDA) and hotspot aware server relocation algorithm (HASRA) based on thermal profiling regarding outlet temperature prediction. HAWDA deploys workload on servers in a thermal-efficient manner and HASRA optimizes the server location in thermal hotspot regions to lower the peak outlet temperatures. Performance comparison is carried out to analyze the efficacy of HAWDA against the TASA and GRANITE algorithms. Results suggest that HAWDA provides average peak utilization of the servers similar to GRANITE and TASA without additional burden on the cooling mechanism, with and without server relocation, as HAWDA minimizes the peak outlet temperature.
The anatomy of a fee change — evidence from cryptocurrency markets

We analyze the impact on liquidity and trading activity of the introduction of maker fees (and simultaneous reduction of taker fees) on a leading cryptocurrency trading platform. Cryptocurrency markets are special because they are non-intermediated and highly fragmented. Furthermore, for the currency pair we analyze (BTC–USD) the relative minimum tick size is negligible, a feature which allows us to derive predictions from the Colliard and Foucault (2012) model which assumes a zero minimum tick size. Consistent with the model we find that quoted spreads increase after the fee change. However, the increase is overcompensated by the decrease in taker fees. Quoted depth and the number of transactions decrease while the average trade size increases.
Review of Fake Product Review Detection Techniques

In the present-day world, reviews available on online websites play a vital role in the sale of products and resources. People using an online platform for purchasing different products very often read reviews of the product before making a decision on which type of product to which platform to buy and whether to buy or not. Basically, reviews are the statements that express the opinions, suggestions, or experiences of a person about any product available in the market. These reviews have their impact directly on the manufacturers as they are interested about the opinion received by the consumer because customer service is the key. But nowadays spam/fake/pseudo reviews are written deliberately on the products to build/break their reputation and attract more consumers. This practice of giving falls truth untruthful deceptive reviews on the products to promote or devaluate the commodities and services is called opinion spam or review spam. Because of these reasons, it is the need of the hour to bring in new techniques for solving the problem of detecting fake reviews. With respect to this, our review paper compares the performance of different techniques Sentimental Analysis, different Boosting techniques in ML.
Interactivized: Visual Interaction for Better Decisions With Interactive Multiobjective Optimization

In today's data-driven world, decision makers are facing many conflicting objectives.Since there is usually no solution that optimizes all objectives simultaneously, the aim is to identify a solution with acceptable trade-offs.Interactive multiobjective optimization methods are iterative processes in which a human decision maker repeatedly provides one's preferences to request computing new solutions and compares them.With these methods, the decision maker can learn about the problem and its limitations.However, advanced optimization software usually offer simple visualization tools that can be significantly improved.On the other hand, current approaches for multiobjective optimization from the visualization community provide superior visualization tools but lack advanced optimization.In this paper, we introduce a new term, interactivize, for integrating interactive multiobjective optimization and interactive visualization and present an interactivized approach supporting decision makers in visually steering interactive multiobjective optimization methods.We integrate state-of-the-art interactive visualization with the process of interactive multiobjective optimization in a visual analytics solution that significantly improves the analysis workflow of decision makers, like comparing selected solutions and specifying new preferences during the iterative solution process.To realize the new interactivized approach, we combine a coordinated multiple views system with DESDEO, an open-source software framework for interactive multiobjective optimization.We demonstrate our interactivized approach on a river pollution problem.
Towards bi-directional skip connections in encoder-decoder architectures and beyond

U-Net, as an encoder-decoder architecture with forward skip connections, has achieved promising results in various medical image analysis tasks. Many recent approaches have also extended U-Net with more complex building blocks, which typically increase the number of network parameters considerably. Such complexity makes the inference stage highly inefficient for clinical applications. Towards an effective yet economic segmentation network design, in this work, we propose backward skip connections that bring decoded features back to the encoder. Our design can be jointly adopted with forward skip connections in any encoder-decoder architecture forming a recurrence structure without introducing extra parameters. With the backward skip connections, we propose a U-Net based network family, namely Bi-directional O-shape networks, which set new benchmarks on multiple public medical imaging segmentation datasets. On the other hand, with the most plain architecture (BiO-Net), network computations inevitably increase along with the pre-set recurrence time. We have thus studied the deficiency bottleneck of such recurrent design and propose a novel two-phase Neural Architecture Search (NAS) algorithm, namely BiX-NAS, to search for the best multi-scale bi-directional skip connections. The ineffective skip connections are then discarded to reduce computational costs and speed up network inference. The finally searched BiX-Net yields the least network complexity and outperforms other state-of-the-art counterparts by large margins. We evaluate our methods on both 2D and 3D segmentation tasks in a total of six datasets. Extensive ablation studies have also been conducted to provide a comprehensive analysis for our proposed methods.
Facial expression recognition using fuzzified Pseudo Zernike Moments and structural features

Facial expression recognition (FER) is an important part of emotional computing that can be useful in many applications for people's behavior analysis. Recently, some methods have been suggested to recognize facial expressions, but they do not offer a strong approach to facial expression recognition. In this paper, we propose a fuzzy-based approach that incorporates two different types of features to increase the recognition rate of facial expression. These features include locally weighted Pseudo Zernike Moments (LWPZM) and structural features (mouth and eye-opening, teeth existence, and eyebrow constriction). To classify facial expressions, the proposed fuzzy inference system uses fuzzified features. The performance of our proposed method has been assessed using the well-known RaFD database. The experimental results show that the proposed method is not only robust in terms of age, ethnicity, and gender changes that would make our contribution, but also improve the recognition rate of facial expression compared to several state-of-the-art methods.
Enhanced performance of a reservoir computing system based on a dual-loop optoelectronic oscillator

Time-delayed reservoir computing (RC) is a brain inspired paradigm for processing temporal information, with simplification in the network's architecture using virtual nodes embedded in a temporal delay line. In this work, a novel, to the best of our knowledge, RC system based on a dual-loop optoelectronic oscillator is proposed to enhance the prediction and classification. The hardware is compact and easy to implement, and only a section of fiber compared to the traditional optoelectronic oscillator reservoir is added to conform the dual-loop scheme. Compared with the traditional reservoir, a remarkable performance of the proposed RC system is demonstrated by simulation on three well-known tasks, namely the nonlinear auto regressive moving average (NARMA10) task, signal waveform recognized task, and handwritten numeral recognition. The parameter optimization in the NARMA10 task is presented with influenced factors. The novel RC system finally obtains a normalized mean square error at 0.0493±0.007 in NARMA10 task, 6.172×10-6 in signal waveform recognized task, and a word error rate at 9% in handwritten numeral recognition with suitable parameters.
Deep learning-based digitalization of a part catalog book to generate part specification by a neutral reference data dictionary

As the manufacturing sector enters the Industry 4.0 era, a higher level cooperative system must be established between manufacturers. Therefore, seamless sharing of information is required between companies, for instance, between an original equipment manufacturer and a parts manufacturer. However, books in PDF or image format that cannot be modified are still commonly used in the field to convey information. Moreover, locating the necessary information in documents, drafted based on unstructured data, is challenging. To overcome these drawbacks, this study proposes an end-to-end digitalization method to convert an image format catalog book into structured digital part specifications. The proposed method also defines a neutral reference data dictionary to ensure consistent digitalization to facilitate data interoperability, classifying catalog pages per part and identifying part numbers, detecting specification tables and recognizing texts in a table, and building part objects and their property objects from the texts extracted from the table. To validate our method, we conducted an experiment where catalog books for motor parts were digitalized. The experiment results exhibited excellent accuracy performance with 96.97% and 90.59%, in part object and property object conversion, respectively, when considering specifications.