Reviewerly/exHarmony huggingface dataset:
Collection weeding: Innovative processes and tools to ease the burden

Evaluating collections and ultimately removing content poses a variety of difficult issues, including choosing appropriate deselection criteria, communicating with stakeholders, providing accountability, and managing the overall timetable to finish projects on time. The Science and Engineering librarians at Brigham Young University evaluated their entire print collection of over 350,000 items within one year, significantly reducing the number of items kept on the open shelves and the physical collection footprint. Keys to accomplishing this project were extensive preparation, tracking progress and accountability facilitated by Google Sheets and an interactive GIS stacks map, and stakeholder feedback facilitated by a novel web-based tool. This case study discusses guidelines to follow and pitfalls to avoid for any organization that is considering a large- or small-scale collection evaluation project.
A Social Multi-Agent Cooperation System Based on Planning and Distributed Task Allocation

Planning and distributed task allocation are considered challenging problems. To address them, autonomous agents called planning agents situated in a multi-agent system should cooperate to achieve planning and complete distributed tasks. We propose a solution for distributed task allocation where agents dynamically allocate the tasks while they are building the plans. We model and verify some properties using computation tree logic (CTL) with the model checker its-ctl. Lastly, simulations are performed to verify the effectiveness of our proposed solution. The result proves that it is very efficient as it requires little message exchange and computational time. A benchmark production system is used as a running example to explain our contribution.
An Efficient Study of Fraud Detection System Using Ml Techniques

The growing world has the transactions of finance mostly done by the transfer of amount through the cashless payments over the Internet. This growth of transactions led to the large amount of data which resulted in the creation of big data. The day-by-day transactions increase continuously which explored as big data with high speed, beyond the limit of transactions and variety. The fraudsters can also use anything to affect the systematic working of current fraud detection system (FDS). So, there is a challenge to improve the present FDS with maximum possible accuracy to fulfill the need of FDS. When the payment is made by using the credit cards, there is chance of misusing the credit cards by the fraudsters. Now, it is essential to find the system that detects the fraudulent transactions as a real-world challenge for FDS and report them to the corresponding people/organization to reduce the fraudulent rate to a minimal one. This paper gives an efficient study of FDS for credit cards by using the machine learning (ML) techniques such as support vector machine, naïve Bayes, K-nearest neighbor, random forest, decision tree, OneR, AdaBoost. These machine learning techniques evaluate a dataset and produce the performance metrics to find the accuracy of each one. This study finally reported that the random forest classifier outperforms among all the other techniques.
Where should I comment my code?

Programmers should write code comments, but not on every line of code. We have created a machine learning model that suggests locations where a programmer should write a code comment. We trained it on existing commented code to learn locations that are chosen by developers. Once trained, the model can predict locations in new code. Our models achieved precision of 74% and recall of 13% in identifying comment-worthy locations. This first success opens the door to future work, both in the new where-to-comment problem and in guiding comment generation. Our code and data is available at http://groups.inf.ed.ac.uk/cup/comment-locator/.
Joint Availability Enhancement and Traffic Optimization of Virtual Cluster Allocation in Cloud Datacenters

As more and more services are deployed in the cloud datacenter, network traffic is growing exponentially. Virtual machines (VMs) of a virtual cluster (VC) must be allocated on physical machines (PMs) in the datacenter with a certain topology. Each VM needs some resources to run various services. Apparently, allocating VMs in the datacenter as compactly as possible can reduce traffic consumption and avoid bandwidth-related bottlenecks. However, loose allocation scheme can reduce the loss expectation of VMs due to failure possibilities of PMs and switches, and the availability of the VC is increased thereby. To enhance availability and reduce network bandwidth usage, it is significant to determine the scheme of allocating VMs of the VC. In this paper, we first introduce four typical datacenter architectures with network topologies and corresponding cost matrices, and extend to generality. Then we propose a joint optimization function to measure the risk of VC and the core bandwidth usage with a global availability constraint. Subsequently, an evolutionary algorithm is raised to minimize the value of the constrained optimization function. Finally, the evaluation results show the effectiveness of the proposed approach and performance advancement over the existing approaches.
From <i>quick</i> to <i>quick-to</i>-<scp>infinitival</scp>: on what is lexeme specific across paradigmatic and syntagmatic distributions

Adjectives are paradigmatically versatile: they combine with many different items in the same syntactic configuration. They are also syntagmatically versatile: they occur in many different syntactic configurations. Given this versatility, how and to what extent can lexeme-specific preferences and features of the adjectives be identified? With the adjective quick as its starting point, this article answers this question by using corpus data, contrasting the behavior of quick with that of its semantic neighbors. Case study 1 investigates quick 's attributive usage. It is shown that quick in its default usage combines with eventive heads, and that there are clear differences in combinatorial preferences across its semantic neighbors. Case study 2 investigates the quick-to - infinitival construction. Here, direct combination with eventive heads is impossible. It behaves differently from other adj - to - infinitival constructions as well as the competing quickly constructions. Comparison of the availability of this construction for quick 's semantic neighbors, and linking this to the results of study 1, shows a clear connection between paradigmatic and syntagmatic distributions.
Jointly Trained Image and Video Generation using Residual Vectors

In this work, we propose a modeling technique for jointly training image and video generation models by simultaneously learning to map latent variables with a fixed prior onto real images and interpolate over images to generate videos.The proposed approach models the variations in representations using residual vectors encoding the change at each time step over a summary vector for the entire video.We utilize the technique to jointly train an image generation model with a fixed prior along with a video generation model lacking constraints such as disentanglement.The joint training enables the image generator to exploit temporal information while the video generation model learns to flexibly share information across frames.Moreover, experimental results verify our approach's compatibility with pre-training on videos or images and training on datasets containing a mixture of both.A comprehensive set of quantitative and qualitative evaluations reveal the improvements in sample quality and diversity over both video generation and image generation baselines.We further demonstrate the technique's capabilities of exploiting similarity in features across frames by applying it to a model based on decomposing the video into motion and content.The proposed model allows minor variations in content across frames while maintaining the temporal dependence through latent vectors encoding the pose or motion features.
Analysis of Models of Security to Mitigate the Risks, Vulnerabilities and Threats in a Company of Services of Telecommunications

Different models in Information and Communication Technologies were analyzed for the creation of an alternative model for mitigating information security risks within telecommunications organizations. The main objective is to define a concept model prototype for the management of process and information security in this type of organizations. Was used the deductive and exploratory research method to analyze the information of the references. It is research in an information security prototype through a risk matrix, a conceptual security prototype. It was concluded that the prototypes presented as results are an alternative to improve the security of the processes and the flow of information and that these can be used as a reference in this type of organizations.
Rhythmic proprioceptive stimulation improves embodiment in a walking avatar when added to visual stimulation

Synchronicity between user’s movements and that of their self-avatar leads to a subjective illusion of embodiment over the virtual avatar. Tendon vibrations can be used to produce a perception of movement, in the absence of any voluntary movements. Such stimulations have been combined with virtual reality to induce the ownership illusion. In this study we investigate the ability of using a virtual self-avatar combined with tendon vibrations to give standing users the impression of walking physically. The vibrations, applied on 24 participants, had different levels of complexity and congruency with the avatar’s movements. Overall, results suggest that the pattern of vibrations is not crucial to produce ownership illusion.
An analysis of two-way equi-join algorithms under MapReduce

Large-scale datasets collected from heterogeneous sources often require a join operation to extract valuable information. MapReduce is an efficient programming model for large-scale data processing. However, it has some limitations in processing heterogeneous datasets. In this study, we review the state-of-the-art strategies for joining two datasets based on an equi-join condition and provide a detail implementation for each strategy. We also present an in-depth analysis of the join strategies and discuss their feasibilities and limitations to assist the reader in selecting the most efficient strategy. Concluding, we outline interesting directions for future join processing systems.
Addressing Deaf or Hard-of-Hearing People in Avatar-Based Mixed Reality Collaboration Systems

Automatic Speech Recognition (ASR) technologies can be used to address people with auditory disabilities by integrating them in an interpersonal communication via textual visualization of speech. Especially in avatar-based Mixed Reality (MR) remote collaboration systems, speech is an important additional modality and allows natural human interaction. Therefore, we propose an easy to integrate ASR and textual visualization extension for an avatar-based MR remote collaboration system that visualizes speech via spatial floating speech bubbles. In a small pilot study, we achieved word accuracy of our extension of 97% by measuring the widely used word error rate.
Ensemble of Handcrafted and Deep Learned Features for Cervical Cell Classification

The aim of this work is to propose an ensemble of descriptors for Cervical Cell Classification. The system proposed here achieves strong discriminative power that generalizes well thanks to the combination of multiple descriptors based on different approaches, both learned and handcrafted. For each descriptor, a separate classifier is trained, then the set of classifiers is combined by sum rule. The system we propose here also presents a simple and effective method for boosting the performance of trained CNNs by combining the scores (using sum rule) of multiple CNNs into an ensemble. Different types of ensembles and different CNN topologies with different learning parameter sets are evaluated. Moreover, features extracted from tuned CNNs are used for training a set of Support Vector Machines (SVM). First, we validate our method on two cervical cell-related datasets; then, for more in-depth validation, we test the same system on other bioimage classification problems. Results show that the proposed system obtains state-of-the-art performance in all datasets, despite not being tuned on a specific dataset, i.e. the same descriptors with the same parameters are used in all the datasets. The MATLAB code of the descriptors will be available at https://github.com/LorisNanni.
Visually grounded paraphrase identification via gating and phrase localization

Visually grounded paraphrases (VGPs) describe the same visual concept but in different wording. Previous studies have developed models to identify VGPs from language and visual features. In these existing methods, language and visual features are simply fused. However, our detailed analysis indicates that VGPs with different lexical similarities require different weights on language and visual features to maximize identification performance. This motivates us to propose a gated neural network model to adaptively control the weights. In addition, because VGP identification is closely related to phrase localization, we also propose a way to explicitly incorporate phrase-object correspondences. From our evaluation in detail, we confirmed our model outperforms the state-of-the-art model.
UAE Cybersecurity Perception and Risk Assessments Compared to Other Developed Nations

Understanding perception and risk behavior of citizens in a particular country can influence policies that leads to creation of appropriate cyber laws that guarantees citizens' security and privacy in cyber related transactions. UAE cyber activity and perception data is compared with data from nine developed nations. Results obtained have shown that with respect to monetary loss due to cybercrime versus relative risk, UAE is at par with Canada. Combination of results obtained suggests a need for aggressive promotion of cybersecurity awareness program in UAE. Although digital maturity, access to Internet and cybersecurity awareness in UAE may be low compared to other developed nations, cyber risk behavior is comparatively the same as in other nine developed nations. One major contribution of this research is that methodologies are presented that can be used in developing cybersecurity assessment of cybersecurity risk behaviors, and comparison of cyber related risk behavior among countries.
A particular directional multilevel transform based method for single-image rain removal

In this work, the wavelet based sparse optimization approach is introduced to process rain removal problem from a single image by considering the direction and shape of rain streaks. Firstly, a new kind of wavelet, the uni-directional multilevel system is construct to describe the singularities of noisy in particular direction and scales, like rain streaks and stripes in radar images. Compared with total variation, the uni-directional multilevel transform of rainy images gives the sparse representation of singularities in different scales and frequency bands due to its multiscale structure, which includes more rain details. Secondly, a convex optimization rain removal model is proposed by considering the intrinsic directional and structure information of the rain streak and the background image. The model involves three sparse priors, including the sparse regularizer on rain streaks and two sparse regularizers on the uni-directional multilevel transform of background layer in the rain drop’s direction and the multilevel transform of rain streaks across the rain direction. The split Bregman algorithm is utilized to solve the proposed convex optimization model which ensures the global optimal solution. Thirdly, comparison tests with four stat-of-the-art methods are implemented on synthetic and real rainy images, which suggests that the proposed method is efficient both in rain removal and details preservation of the background layer.
The concept of integrated communication under close scrutiny: A study on the effects of congruity-based tactics

Abstract This study examines how consumers react to a Facebook post that is completely, moderately or not at all in conflict with a brand-typical TV clip. It thus analyzes the need for congruity between the offline and online advertising activities of a brand and draws on the literature of integrated communication, schema incongruity theory and advertising effectiveness. Results of an online experiment with 131 participants and a 2 (content: congruent vs. incongruent) by 2 (stylistic devices: congruent vs. incongruent) between-subjects design provide first insights into the modeling of (in)congruity based on integrated communication. Thus, the article presents a first attempt to conceptualize (in)congruity in advertising through a combination of content and stylistic devices. In addition to this research agenda, the manuscript offers implications for advertising practice.
Video Synopsis Based on Attention Mechanism and Local Transparent Processing

The increased number of video cameras makes an explosive growth in the amount of captured video, especially the increase of millions of surveillance cameras that operate 24 hours a day. Since video browsing and retrieval is time consuming, while video synopsis is one of the most effective ways for browsing and indexing such video that enables the review of hours of video in just minutes. How to generate the video synopsis and preserve the essential activities in the original video is still a costly and labor-intensive and time-intensive work. This paper proposes an approach to generating video synopsis with complete foreground and clearer trajectory of moving objects. Firstly, the one-stage CNN-based object detecting has been employed in object extraction and classification. Then, combining integrating the attention-RetinaNet with Local Transparency-Handling Collision (LTHC) algorithm is given out which results in the trajectory combination optimization and makes the trajectory of the moving object more clearly. Finally, the experiments show that the useful video information is fully retained in the result video, the detection accuracy is improved by 4.87% and the compression ratio reaches 4.94, but the reduction of detection time is not obvious.
Financial Fraud Detection using Bio-Inspired Key Optimization and Machine Learning Technique

Financial fraud (FF) is a serious issue, a big concern for financial institutes and requires more attention.Financial frauds are a variety of theft or credit card stealing that occurs when someone takes money with the purpose of personal gain.In the current scenario, a highly accurate system is required to detect financial fraud and prevent financial loss.In this paper, new FF detection model has been proposed for accurate detect financial frauds using one of the Bio-inspired optimization algorithms as particle swarm optimization (PSO), feature selection (CFS) method and adaptive three machine learning techniques (as SVM, KNN, and AdaboostM1).The proposed model is capable to reduce the false-positive rate, training & testing time and enhanced accuracy.Therefore, the proposed PSOS algorithm applied as a features optimization method for selecting the best subset features from the highly imbalanced dataset to increased detection accuracy.The experiment results point out that the PSOS algorithm is the most excellent features optimization method and selected 7 features out of 15 features.The proposed model has been enhanced accuracy (from 82.90% to 85.51%), TPR (from 80.50 % to 90.20 %), FPR (from 0.151% to 0.191%), and decreased Error Rate (14.92 % to 17.10%) compared to RIG method.The findings of this study clearly illustrate that the Bioinspired algorithm (PSOS) gives more appropriate results compared with the non-Bio-inspired algorithm (RIG).
Maximum Separated Distribution with High Interpretability Found Using an Exhaustive Search Method —Application to Magnetocrystalline Anisotropy of Fe/Co Films—

We extended the decision tree and frequent itemset mining to find the separated distribution condition in the magnetocrystalline anisotropy of films using an exhaustive search method. We also performed subgroup relevance analysis on the importance of descriptors and descriptor groups and developed a graphical representation to show the importance of the search conditions.
Weight Compression MAC Accelerator for Effective Inference of Deep Learning

Many studies of deep neural networks have reported inference accelerators for improved energy efficiency. We propose methods for further improving energy efficiency while maintaining recognition accuracy, which were developed by the co-design of a filter-by-filter quantization scheme with variable bit precision and a hardware architecture that fully supports it. Filter-wise quantization reduces the average bit precision of weights, so execution times and energy consumption for inference are reduced in proportion to the total number of computations multiplied by the average bit precision of weights. The hardware utilization is also improved by a bit-parallel architecture suitable for granularly quantized bit precision of weights. We implement the proposed architecture on an FPGA and demonstrate that the execution cycles are reduced to 1/5.3 for ResNet-50 on ImageNet in comparison with a conventional method, while maintaining recognition accuracy.
Comparison of Recurrent Neural Networks for Slovak Punctuation Restoration

The paper proposes a punctuation restoration system based on a recurrent neural network that supplements a Slovak automatic speech recognition system. It compares methods based on long short-term memory, gated recurrent units, and bidirectional networks on the same training and evaluation set to discover which method is the best for this task. Experiments show that there are significant differences among recurrent neural network methods. Performance of the classification strongly depends on parameters of learning and size of training data. Each neural network inclines to overfitting. The best setup was found with bi-directional networks with gated recurrent units.
Nondestructive identification of a jet bead from the Changle Cemetery in Ningxia, China

Some bright black beads with dense surface and poor light transmittance are generally considered as jet artifacts in China. However, little scientific investigation has been applied to support this judgment. This study focuses on the nondestructive identification of a jet bead, unearthed from the Changle Cemetery, Han Dynasty (202 BC-AD 220), Ningxia, China through multi nondestructive approach. The results of Gas Chromatography-Mass Spectrometry (GC/MS) analysis indicate that the bead belongs to coal products, based on the typical coal biomarkers including n-alkanes, acyclic isoprenoids and terpenoids. FTIR analysis further confirms that the bead is made of jet in comparison with modern jet samples in China. The bead is also scanned with Synchrotron Radiation Micro-computed Tomography (SR-μCT) to obtain the structure and drilling technology information, which suggests that the bead is uniform and was drilled from both sides. The nondestructive identification of ancient jet artifacts is first reported in China up to date, which not only reveals the varied utilizations of coal products in ancient society but also suggests that the popularization of jet in the Han Dynasty might be related to the first large-scale exploitation of coal, and offers an effective method suitable for archaeological jet samples.