Reviewerly/exHarmony huggingface dataset:
Similarity-based Classification: Connecting Similarity Learning to Binary Classification

In real-world classification problems, pairwise supervision (i.e., a pair of patterns with a binary label indicating whether they belong to the same class or not) can often be obtained at a lower cost than ordinary class labels. Similarity learning is a general framework to utilize such pairwise supervision to elicit useful representations by inferring the relationship between two data points, which encompasses various important preprocessing tasks such as metric learning, kernel learning, graph embedding, and contrastive representation learning. Although elicited representations are expected to perform well in downstream tasks such as classification, little theoretical insight has been given in the literature so far. In this paper, we reveal that a specific formulation of similarity learning is strongly related to the objective of binary classification, which spurs us to learn a binary classifier without ordinary class labels---by fitting the product of real-valued prediction functions of pairwise patterns to their similarity. Our formulation of similarity learning does not only generalize many existing ones, but also admits an excess risk bound showing an explicit connection to classification. Finally, we empirically demonstrate the practical usefulness of the proposed method on benchmark datasets.
Metaheuristics in civil engineering: A review

Real-world engineering problems deal with nonlinear, nonconvex, and discontinuous solution space. Due to the high level of complexity, state-of-art methods are required for handling this sort of NP-hard type problem. One of the most efficient strategies is considering metaheuristic optimization algorithms to facilitate them. Civil engineering problems because of the high level of uncertainties and effective parameters have been the subject of many optimization-based studies. In this paper, the main effort was to provide an overview of different applications of optimization algorithms for civil engineering problems. Moreover, we classified a large number of available studies on the implementation of metaheuristics in various fields of civil engineering classified in this study and highlighted the most important features of them to provide an efficient reference for active researchers in this field.
MicaPen: A Pen to Write in Air Using Mica Motes

This paper demonstrates a technique to write in air using Micaz motes. The application, named MicaPen is low-cost and can be used by the disabled who does not have fingers or limbs. Patients can wear the mote as a wrist watch or bracelet and move their hands based on what character they want to write. The mote detects the movement pattern using the accelerometer of MTS310 sensorboard and displays the characters on the screen. The application can also be extended in several applications including virtual touchscreen or remotely interact with different controlled devices.
Constructing a Set of Weak Values for Full-round MD4 Hash Function

In this paper we describe the construction of a set of full-round MD4 compression function values, which are weak against the preimage attack of a special kind. The cardinality estimation for this set, obtained by using an effective probabilistic algorithm, is close to 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">32</sup> . According to this, the fraction of weak outputs in the set of all outputs of the MD4 compression function is about $2^{-96}$. Thus, the probability to obtain an easyinvertible output by choosing a random input is significantly higher than $2^{-128}$. These results demonstrate that the full-round MD4 compression function does not have the properties of a random oracle.
Noise-Response Analysis of Deep Neural Networks Quantifies Robustness and Fingerprints Structural Malware

The ubiquity of deep neural networks (DNNs), cloud-based training, and transfer learning is giving rise to a new cybersecurity frontier in which unsecure DNNs have `structural malware' (i.e., compromised weights and activation pathways). In particular, DNNs can be designed to have backdoors that allow an adversary to easily and reliably fool an image classifier by adding a pattern of pixels called a trigger. It is generally difficult to detect backdoors, and existing detection methods are computationally expensive and require extensive resources (e.g., access to the training data). Here, we propose a rapid feature-generation technique that quantifies the robustness of a DNN, `fingerprints' its nonlinearity, and allows us to detect backdoors (if present). Our approach involves studying how a DNN responds to noise-infused images with varying noise intensity, which we summarize with titration curves. We find that DNNs with backdoors are more sensitive to input noise and respond in a characteristic way that reveals the backdoor and where it leads (its `target'). Our empirical results demonstrate that we can accurately detect backdoors with high confidence orders-of-magnitude faster than existing approaches (seconds versus hours).
The Fermionic Quantum Emulator

The fermionic quantum emulator (FQE) is a collection of protocols for emulating quantum dynamics of fermions efficiently taking advantage of common symmetries present in chemical, materials, and condensed-matter systems. The library is fully integrated with the OpenFermion software package and serves as the simulation backend. The FQE reduces memory footprint by exploiting number and spin symmetry along with custom evolution routines for sparse and dense Hamiltonians, allowing us to study significantly larger quantum circuits at modest computational cost when compared against qubit state vector simulators. This release paper outlines the technical details of the simulation methods and key technical advantages.
Partial Information Decomposition via Deficiency for Multivariate Gaussians

Bivariate partial information decompositions (PIDs) characterize how the information in a "message" random variable is decomposed between two "constituent" random variables in terms of unique, redundant and synergistic information components. These components are a function of the joint distribution of the three variables, and are typically defined using an optimization over the space of all possible joint distributions. This makes it computationally challenging to compute PIDs in practice and restricts their use to low-dimensional random vectors. To ease this burden, we consider the case of jointly Gaussian random vectors in this paper. This case was previously examined by Barrett [1], who showed that certain operationally well-motivated PIDs reduce to a closed form expression for scalar messages. Here, we show that Barrett’s result does not extend to vector messages in general, and characterize the set of multivariate Gaussian distributions that reduce to closed-form. Then, for all other multivariate Gaussian distributions, we propose a convex optimization framework for approximately computing a specific PID definition based on the statistical concept of deficiency. Using simplifying assumptions specific to the Gaussian case, we provide an efficient algorithm to approximately compute the bivariate PID for multivariate Gaussian variables with tens or even hundreds of dimensions. We also theoretically and empirically justify the goodness of this approximation.
An Overview and Analysis of Publications on Locomotion Taxonomies

In immersive virtual environments, locomotion allows users to change their viewpoint in the virtual world and is one of the most common tasks. Locomotion taxonomies can describe relationships between the locomotion techniques and thus represent a common understanding, form the backbone of many studies and publications, and can increase the comparability of studies. Therefore, it is relevant for VR researchers, developers, and designers to get an overview of previous research on taxonomies including benefits, drawbacks, and possible research gaps. Current literature reviews focus on locomotion techniques instead of locomotion taxonomies. Thus, a time-consuming search, evaluation and comparison of many publications is required to get such an overview. We present the design of a currently performed systematic literature review examining taxonomies of locomotion techniques. In addition, we present initial results including an overview of publications introducing locomotion taxonomies, their relationships, and impact. We aim to provide a reference to potential taxonomies to support the choice of a locomotion taxonomy and insights into the research field evolution to aid the design of novel locomotion taxonomies.
“When people come to me for suggestions, I feel like an expert”

This chapter reports on an Information and Communications Technologies for Development (ICT4D) project, PROTIC. The project aims for the socioeconomic empowerment of women farmers in rural Bangladesh through information access. As part of the project, 300 women in three remote locations in Bangladesh received smartphones as well as training and information support to improve local agriculture and social development. In this chapter, empowerment is understood as a process of social and structural change where the capacity to make choices and influence sociocultural and structural challenges are considered important for women. The women’s experiences with smartphones and their journey to overcome social and cultural challenges were researched. Data was collected through episodic interviews of 60 participants in 2016 and 2018 and doctoral research. Findings show that women’s use of smartphones contributed to improved learning and skills in using new technology, created greater participation opportunities in livelihood activities, improved communication and networking, resulted in increased respect for the women, and improved their social position. These are notable achievements in a society where social, cultural, economic, and religious barriers constantly challenge the uptake of new and emerging technologies by women. Insights from PROTIC can guide local, national, and international level development organisations, partners, and other stakeholders in the design and implementation of more appropriate and sustainable ICT initiatives.
Cross Domain Recommendation Systems using Deep Learning: A Systematic Literature Review

Recommender Systems play an inevitable role nowadays as in every field, there are plethora of choices to consider. With the advent of more and more Artificial Intelligence and Machine Learning practices, recommender systems are rising as a blooming area with vast horizon. Cross Domain Recommender Systems are a way to learn user’s interests and choices from source domain(s), transfer the learnt features to a target domain from which recommendations are to be made. Cross Domain Recommender Systems not only make effective recommendations, but also solve Cold Start and Data Sparsity problems. Deep learning based recommender systems are able to make recommendations from large datasets and are in trend. The presented work fulfils the need of a systematic survey of all the Cross Domain Recommender System implemented using Deep Learning Techniques. After excluding all the papers based on only Cross Domain Recommender Systems (without Deep Learning), Deep Recommender Systems (working within one domain only) and all other classical Recommender Systems from the research papers available over all the quality sources from the web, 42 papers were shortlisted and studied for the techniques being used, domains and datasets on which CDRS using Deep Learning have been implemented, research issues and the evaluation parameters on which Deep Learning based CDRS can be verified.
Artificial Intelligence (AI)

This chapter is a use case on how artificial intelligence (AI) can be leveraged to improve Customer Experience (CX) by organizations. Artificial intelligence can be used to provide an excellent customer experience across the customer journey. From need identification to searching for multiple options to the purchase decision and then to in-life experience to managing loyal customers, organizations can leverage AI to improve the experience in a very cost-effective manner. The chapter provides a high-level insight into the customer journey when the customer decides to go for a purchase and how AI can improve the customer experience starting from the pre-purchase phase to purchase and then when the customer is using the product. The chapter talks about details on how various AI-based technology options like Chatbots, Voicebots, analytics, and speech recognition, etc. can be used to be improve customer experience. The chapter ends with a summary section which provides mapping of customer journey and how AI-based technologies can be used by organizations to improve customer experience.
Testing of Actual Scanner Performance in a High-loaded UNIM Laboratory Environment

Scanners are the main tool in digital pathology. The technical abilities of scanners determine the workflow logic in the pathology laboratory. Its performance can be restricted by the divergence between the scanning time presented by the manufacturer and the actual scanning time. This could lead to critical deviations from the established business processes in a 24/7 laboratory.Our investigation is focused in exploring the performance of three main models of high-performance scanners available on the Russian market: 3DHistech, Hamamatsu и Leica.We compared the performance of the scanners on the samples of a given size with the manufacturer's stated specifications and evaluated the speed of the scanners on the reference and routine laboratory material.We examined 3DHistech Pannoramic 1000, Hamamatsu NanoZoomer s360 and Leica AT2 with default settings and automatic mode. Two sets of glasses were used (glass slide): Group 1 included 120 slides with 15 mm × 15 mm slices, Group 2 included 120 workflow slides.The average slide scan times in Groups 1 and 2 for the C13220 (156 ± 1.25 s and 117 ± 4.17 s) and Pannoramic 1000 (210 ± 1.64 s and 183 ± 3.78 s) differ statistically significantly (P < 0.0001). Total scanning time including rack reloading was shorter for the workflow slide set group for the modern C13220 and Pannoramic 1000 scanner models.The scanner specifications provided by manufacturers are not sufficient to evaluate the performance. The guidelines and regulations concerning scanner selection should be consented by the digital pathology community. We suggest discussing criteria for evaluating scanner performance.
Mind the Scaling Factors: Resilience Analysis of Quantized Adversarially Robust CNNs

As more deep learning algorithms enter safety-critical application domains, the importance of analyzing their resilience against hardware faults cannot be overstated. Most existing works focus on bit-flips in memory, fewer focus on compute errors, and almost none study the effect of hardware faults on adversarially trained convolutional neural networks (CNNs). In this work, we show that adversarially trained CNNs are more susceptible to failure due to hardware errors when compared to vanilla-trained models. We identify large differences in the quantization scaling factors of the CNNs which are resilient to hardware faults and those which are not. As adversarially trained CNNs learn robustness against input attack perturbations, their internal weight and activation distributions open a backdoor for injecting large magnitude hardware faults. We propose a simple weight decay remedy for adversarially trained models to maintain adversarial robustness and hardware resilience in the same CNN. We improve the fault resilience of an adversarially trained ResNet56 by 25% for large-scale bit-flip benchmarks on activation data while gaining slightly improved accuracy and adversarial robustness.
Library technology: Innovating technologies, services, and practices

Library technology is a broad concept that encompasses infrastructure and spaces, services, access and more. This special issue of College & Undergraduate Libraries is entitled Library Technology: Innovating Technologies, Services, and Practices and reflects the range of technology services and practices in academic libraries. These articles were grouped into narrower technology themes: innovative services, immersive technologies, makerspaces, web and application (re)design, and research practices. May these articles inspire another wave of innovation.
Rich Structural Index for Stereoscopic Image Quality Assessment

The human visual system (HVS), affected by viewing distance when perceiving the stereo image information, is of great significance to study of stereoscopic image quality assessment. Many methods of stereoscopic image quality assessment do not have comprehensive consideration for human visual perception characteristics. In accordance with this, we propose a Rich Structural Index (RSI) for Stereoscopic Image objective Quality Assessment (SIQA) method based on multi-scale perception characteristics. To begin with, we put the stereo pair into the image pyramid based on Contrast Sensitivity Function (CSF) to obtain sensitive images of different resolution. Then, we obtain local Luminance and Structural Index (LSI) in a locally adaptive manner on gradient maps which consider the luminance masking and contrast masking. At the same time we use Singular Value Decomposition (SVD) to obtain the Sharpness and Intrinsic Structural Index (SISI) to effectively capture the changes introduced in the image (due to distortion). Meanwhile, considering the disparity edge structures, we use gradient cross-mapping algorithm to obtain Depth Texture Structural Index (DTSI). After that, we apply the standard deviation method for the above results to obtain contrast index of reference and distortion components. Finally, for the loss caused by the randomness of the parameters, we use Support Vector Machine Regression based on Genetic Algorithm (GA-SVR) training to obtain the final quality score. We conducted a comprehensive evaluation with state-of-the-art methods on four open databases. The experimental results show that the proposed method has stable performance and strong competitive advantage.
Using Augmented Reality in Language Classrooms: The Case of EFL Elementary Students

This study sought to measure the effectof using augmented reality (AR) on developing language learning. It also explored teachers’ perceptions about the use of augmented reality in general, and its effecton students’ autonomy. A quasi-experimental research design using a pre-test and a post-test with control and experimental groups was followed to measure the effectof using augmented reality on elementary students’ language learning. Further, two questionnaires were designed to explore EFL teachers’ perceptions about AR, and its effecton students’ autonomy. The sample consisted of 72 Saudi EFL elementary students and 80 EFL teachers. The results revealed that the use of augmented reality enhanced EFL students’ language learning significantl. The results also showed that teachers perceived AR technology as valuable and effective.They acknowledged its multiple benefitsfor EFL elementary students. It enabled better learning of essential skills, increased students’ motivation and positive attitudes, and fostered students’ autonomy. It is expected that these findingswill help learners, teachers, curriculum developers, and administrators.
Role of Blockchain Technology in Digital Forensics

Blockchain is a progression of associated information structures called blocks, which contain or track all that occurs in disseminated frameworks in a distributed organization. Each block is connected to the previous block with an uncommon pointer called a hash pointer, forming a chain and resulting in a framework consisting of annexes: A perpetual and irreversible history that can be utilized as a constant review trail by any member to check the precision of the records by essentially surveying information itself. The chapter will discuss the role of blockchain in digital forensics with an introduction to blockchain technology and its applications and challenges. It also explores the architecture and protocols related to blockchain technology. The chapter also highlights the managing of digital evidence by maintaining the chain of custody with the help of Ethereum and Hyperledger. It will also enlighten readers about the application of blockchain for distributed cloud storage in digital forensics. It will reveal the role of blockchain in digital forensics, which will be helpful for cybercrime investigation, blockchain technology, and digital forensics enthusiasts, students, PhD scholars and researchers.
Cognitive and Metacognitive Predictors of Financial Exploitation:

At the intersection of psychology, neuroscience, and economics an emerging field of fraud susceptibility has begun to illustrate a detailed picture of factors from structural brain changes to social functioning declines that can lead to an increased risk for financial exploitation. While aging populations garner much of the focus of this research given their assumed increased susceptibility to financial exploitation, the literature has also greatly contributed to informing a more general understanding of fraud risks in adulthood. This chapter will provide an overview of cognitive markers across the adult lifespan that have been associated with declines in our ability to detect exploitation/fraud risk: namely numeracy, processing speed, attention, and executive functioning as well as global cognitive functioning. We will also present new findings from our laboratory indicating that an array of social/affective cognitive skills considered prerequisite for higher-level social analysis and interactions may be particularly salient when extended to the capacity to detect fraud/exploitation. Finally, this chapter will also highlight how such findings are creating avenues for earlier, more accurate identification of vulnerable groups with respect to fraud and informing the development of new tools to detect and reduce exploitation risk based on known cognitive markers.
Product Recommendation Based on Embeddings: People Who Viewed This Product Also Viewed These Products

Nowadays, recommendation systems are getting more attention from both academia and industry. However, most industrial recommendation systems are still using non-embedding models such as TF-IDF-based machine learning models. We also have had an existing model implemented using TF-IDF. This current model served as the baseline for our performance evaluations. In this paper, we implemented an embedding-based “People Who Viewed This Product Also Viewed These Products” recommendation. The purpose of this recommendation is to be able to recommend alternative products to customers that are similar to the product they are viewing. In this way, users will not only be able to see their own search and filtering results but also view the alternatives based on the experience of the users who view similar products. The product view sequences to be used in model training are created using 6 months of product view data. The number of daily product view events is over 20 million. We used offline and online metrics to compare the new model results with the existing model. We have achieved a significant lift in product coverage with 286%, order count with 29%, and distinct ordered product count with 30% without a decrease in CTR value.