Reviewerly/exHarmony huggingface dataset:
Time-Aware User Embeddings as a Service

Digital media companies typically collect rich data in the form of sequences of online user activities. Such data is used in various applications, involving tasks ranging from click or conversion prediction to recommendation or user segmentation. Nonetheless, each application depends upon specialized feature engineering that requires a lot of effort and typically disregards the time-varying nature of the online user behavior. Learning time-preserving vector representations of users (user embeddings), irrespective of a specific task, would save redundant effort and potentially lead to higher embedding quality. To that end, we address the limitations of the current state-of-the-art self-supervised methods for task-independent (unsupervised) sequence embedding, and propose a novel Time-Aware Sequential Autoencoder (TASA) that accounts for the temporal aspects of sequences of activities. The generated embeddings are intended to be readily accessible for many problem formulations and seamlessly applicable to desired tasks, thus sidestepping the burden of task-driven feature engineering. The proposed TASA shows improvements over alternative self-supervised models in terms of sequence reconstruction. Moreover, the embeddings generated by TASA yield increases in predictive performance on both proprietary and public data. It also achieves comparable results to supervised approaches that are trained on individual tasks separately and require substantially more computational effort. TASA has been incorporated within a pipeline designed to provide time-aware user embeddings as a service, and the use of its embeddings exhibited lifts in conversion prediction AUC on four audiences.
A dual foveal-peripheral visual processing model implements efficient saccade selection

We develop a visuomotor model that implements visual search as a focal accuracy-seeking policy, with the target’s position and category drawn independently from a common generative process. Consistently with the anatomical separation between the ventral versus dorsal pathways, the model is composed of two pathways that respectively infer what to see and where to look. The “What” network is a classical deep learning classifier that only processes a small region around the center of fixation, providing a “foveal” accuracy. In contrast, the “Where” network processes the full visual field in a biomimetic fashion, using a log-polar retinotopic encoding, which is preserved up to the action selection level. In our model, the foveal accuracy is used as a monitoring signal to train the “Where” network, much like in the “actor/critic” framework. After training, the “Where” network provides an “accuracy map” that serves to guide the eye toward peripheral objects. Finally, the comparison of both networks’ accuracies amounts to either selecting a saccade or keeping the eye focused at the center to identify the target. We test this setup on a simple task of finding a digit in a large, cluttered image. Our simulation results demonstrate the effectiveness of this approach, increasing by one order of magnitude the radius of the visual field toward which the agent can detect and recognize a target, either through a single saccade or with multiple ones. Importantly, our log-polar treatment of the visual information exploits the strong compression rate performed at the sensory level, providing ways to implement visual search in a sublinear fashion, in contrast with mainstream computer vision.
Blockchain in Agriculture by using Decentralized Peer to Peer Networks

In agricultural production, the supply chain transparency and data integration system plays a major role in certifying food products genuinely. Anyway, agriculturist like (sodbuster, granger, husbandman, etc.) right now are facing n number of problems like relationship between supplier and retailer, transparency causes devastating in production, less financial foundations for agriculturist in developing countries, overpriced interceder and hence it is very difficult to maintain the records and a state of satisfaction with a centralized approach. To address the problems arising from the farmers related to agriculture, the blockchain technology plays a major role in the agriculture industry by improving transparency and food provenance in the supply chain, which is featured by the distributed ledger, centralized servers, P2P (Peer to Peer) networks, As in [1] [10] RFID (Radio-Frequency Identification) tag, consensus verification. Hence, the proposed work explores the different problems faced in agriculture production and the solutions to those problems are addressed by using blockchain technology. By applying blockchain technology as in [5] [7] [8] [9] the supply chain and food provenance not only lengthens the practical domain of blockchain but also upkeeps real heroes of the country around agricultural production. The theoretical analysis of the proposed model reveals that the use of blockchain technology in the food tracking system builds trust among different stakeholders at different stages in the process of agriculture production and provides a hundred percent benefit to them.
Variational inference based distributed noise adaptive Bayesian filter

In this paper, we investigate noise covariance adaptive distributed Bayesian filter based on variational Bayesian inference method. In Bayesian filter framework, the joint distribution of state and noise covariance is approximated by variational distributions, where the unknown noise covariance is modeled by inverse-Wishart distribution. With communicating with neighbors, we show that the joint posterior distribution of state and noises can be approximated by recursively performing variational Bayesian expectation (VB-E) and variational Bayesian maximization (VB-M) steps. Then we use the cubature Kalman filter (CKF) to approximate Gaussian interval, and propose a variational Bayesian based distributed adaptive cubature information filter (VB-DACIF). Finally, we illustrate the effectiveness of the proposed estimation algorithm by a cooperative object tracking problem.
Single-server Multi-message Private Information Retrieval with Side Information: the General Cases

The single-server multi-message private information retrieval with side information problem is studied for general cases. In this problem, K independent messages are stored at a single server. One user initially has M messages as side information and wants to download N demand messages while not leaking any information about the indices of demand messages to the server. In our previous work, we characterized the minimum number of required download bits and presented an achievability scheme for this problem with the constraint of linear codes. In this paper, we extend the results to general (non-linear) codes and present the closed-form expression for the minimum number of required download bits. Moreover, we show that linear coding schemes are sufficient to achieve the optimal (minimum) download bits. Additionally, we show that the trivial MDS coding scheme with K − M transmissions is optimal if N > M or N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> +N ≥ K−M. This means if one wishes to privately download more than the square-root of the number of messages in the database, then one must effectively download the full database (minus the side information), irrespective of the amount of side information one has available.
Augmenting Exposure Therapy: Mobile Augmented Reality for Specific Phobia

New technologies, such as virtual reality (VR) and augmented reality (AR), can be used as an add-on to exposure therapy for common anxiety disorders. While the benefits of VR for exposure therapy have already been demonstrated extensively in research, AR applications are only just becoming widely available. Evidence for the added value and effectiveness of augmented reality exposure therapy (ARET) is still scarce. The current study aimed to explore whether a first markerless AR iOS app for specific phobia could induce fear for multiple animal species in a general population sample. In two experiments participants made use of the Phobos AR app in a behavioral approach task (BAT), using animals for which they were anxious, but not phobic. Self-report data and physiological measures were recorded. In Experiment 1, 108 participants chose one of the seven available animal species and were either allocated to a smartphone or tablet condition. Results showed increasing levels of self-reported anxiety with increasing levels of BAT difficulty. However, this increase was smaller in individuals reporting low levels of perceived realism. No effects on heart rate (HR) could be established. In Experiment 2, 52 participants were exposed to virtual spiders. For both self-reported anxiety and the interaction with perceived realism, results were similar to Experiment 1. Skin conductance did increase significantly from baseline to the highest level of difficulty of the BAT and the severity of fear of spiders also appeared to be related to the fear response in the BAT. In conclusion, the study shows that animals presented in AR through a mobile device can evoke anxiety, which is a prerequisite for the implementation of ARET. However, further research should establish the effects of ARET in a clinical sample of people with specific phobias.
Testing the effectiveness of tailored phishing techniques in industry and academia

Organizations are experiencing more and more sophisticated attacks specifically targeting their employees and customers. These attacks exploit tailored information on the victim or organization to increase their credibility. To date, no study has evaluated the role of 'traditional' phishing cognitive effects in these advanced settings. In this paper, we run a field experiment targeting 747 subjects employed in two organizations (a university and a large international consultancy company) to evaluate the interaction between phishing persuasion techniques and the success rate in a highly-tailored setting. For this purpose, we exploit well-established user notification methods to devise enhanced attack delivery techniques, and evaluate how such techniques affect success rate of our phishing campaigns. We find that the effect of 'traditional' attack techniques is widely mitigated in highly-tailored phishing settings, suggesting that current user training and detection techniques may be off-target for more sophisticated attacks. However, we find that the means by which the attack is delivered to the victim matter, and can greatly (up to three times) boost the effect of the base attack.
Smart Image Enhancement Using CLAHE Based on an F-Shift Transformation during Decompression

As technologies for image processing, image enhancement can provide more effective information for later data mining and image compression can reduce storage space. In this paper, a smart enhancement scheme during decompression, which combined a novel two-dimensional F-shift (TDFS) transformation and a non-standard two-dimensional wavelet transform (NSTW), is proposed. During the decompression, the first coefficient s00 of the wavelet synopsis was used to adaptively adjust the global gray level of the reconstructed image. Next, the contrast-limited adaptive histogram equalization (CLAHE) was used to achieve the enhancement effect. To avoid a blocking effect, CLAHE was used when the synopsis was decompressed to the second-to-last level. At this time, we only enhanced the low-frequency component and did not change the high-frequency component. Lastly, we used CLAHE again after the image reconstruction. Through experiments, the effectiveness of our scheme was verified. Compared with the existing methods, the compression properties were preserved and the image details and contrast could also be enhanced. The experimental results showed that the image contrast, information entropy, and average gradient were greatly improved compared with the existing methods.
Improving Recommendation Quality in Google Drive

Quick Access is a machine-learned system in Google Drive that predicts which files a user wants to open. Adding Quick Access recommendations to the Drive homepage cut the amount of time that users spend locating their files in half. Aggregated over the ~1 billion users of Drive, the time saved up adds up to ~1000 work weeks every day. In this paper, we discuss both the challenges of iteratively improving the quality of a personal recommendation system as well as the variety of approaches that we took in order to improve this feature. We explored different deep network architectures, novel modeling techniques, additional data sources, and the effects of latency and biases in the UX. We share both pitfalls as well as successes in our attempts to improve this product, and also discuss how we scaled and managed the complexity of the system. We believe that these insights will be especially useful to those who are working with private corpora as well as those who are building a large-scale production recommendation system.
Lifted Reed-Solomon Codes with Application to Batch Codes

Guo, Kopparty and Sudan have initiated the study of error-correcting codes derived by lifting of affine-invariant codes. Lifted Reed-Solomon (RS) codes are defined as the evaluation of polynomials in a vector space over a field by requiring their restriction to every line in the space to be a codeword of the RS code. In this paper, we investigate lifted RS codes and discuss their application to batch codes, a notion introduced in the context of private information retrieval and load-balancing in distributed storage systems. First, we improve the estimate of the code rate of lifted RS codes for lifting parameter m ≥ 3 and large field size. Second, a new explicit construction of batch codes utilizing lifted RS codes is proposed. For some parameter regimes, our codes have a better trade-off between parameters than previously known batch codes.
Teaching Blockchain to Accounting Students

ABSTRACT The objective of these teaching notes is to provide an outline for offering blockchain related foundational knowledge to accounting students so they can evaluate and prepare for the effect of blockchain on the accounting profession. The approach is driven by the following principle: Given that accounting students do not want to become programmers or cryptographers, what is the essential blockchain knowledge that they need to know? Based on my own teaching and research experience, I propose and explain how I use a combination of storytelling and scaffolding approach to focus on the delivery of the following topics: (1) why study blockchain? (2) general foundational knowledge (i.e., element of complete transactions and double spending), (3) interactive exercises to explain technical knowledge on topics such as hashing and proof-of-work (mining), and (4) blockchain implementation in a supply chain setting.
Bayesian estimation of extropy and goodness of fit tests

Extropy, a complementary dual of entropy, is considered in this paper. A Bayesian approach based on the Dirichlet process is proposed for the estimation of extropy. A goodness of fit test is also developed. Many theoretical properties of the procedure are derived. Several examples are discussed to illustrate the approach.
Multi-AUV Collaborative Operation Based on Time-Varying Navigation Map and Dynamic Grid Model

In a dynamic and complex environment, to improve the cooperative operation efficiency of multiple AUV groups, a bionic neural wave network (BNWN) algorithm, and a velocity vector synthesis (VVS) algorithm are proposed. A strategy of space decomposition and node space recursion is adopted to provide dynamic navigation maps for AUV monomers and to modularize the tasks. A closed boundary function is introduced to construct a dynamic grid model to autonomously avoid obstacles with multiple moving forms. The results of three sets of simulation experiments show that the number of changes in direction, the total path length, and the collision rate of AUV individuals are greatly reduced. These results prove that the proposed algorithm has high autonomy and strong adaptability.
LOG-Means

Clustering is a fundamental primitive in manifold applications. In order to achieve valuable results, parameters of the clustering algorithm, e.g., the number of clusters, have to be set appropriately, which is a tremendous pitfall. To this end, analysts rely on their domain knowledge in order to define parameter search spaces. While experienced analysts may be able to define a small search space, especially novice analysts often define rather large search spaces due to the lack of in-depth domain knowledge. These search spaces can be explored in different ways by estimation methods for the number of clusters. In the worst case, estimation methods perform an exhaustive search in the given search space, which leads to infeasible runtimes for large datasets and large search spaces. We propose LOG-Means, which is able to overcome these issues of existing methods. We show that LOG-Means provides estimates in sublinear time regarding the defined search space, thus being a strong fit for large datasets and large search spaces. In our comprehensive evaluation on an Apache Spark cluster, we compare LOG-Means to 13 existing estimation methods. The evaluation shows that LOG-Means significantly outperforms these methods in terms of runtime and accuracy. To the best of our knowledge, this is the most systematic comparison on large datasets and search spaces as of today.
Microanalysis of Cl, Br and I in apatite, scapolite and silicate glass by LA-ICP-MS

Constraining the abundance and distribution of halogens in geological materials has the potential to provide novel insights into a broad range of earth system processes (e.g. metasomatism, melting, volatile cycling and ore formation). In this contribution we develop analytical protocols for the in situ measurement of Cl, Br and I in widely distributed standard reference materials (apatite, scapolite, silicate glass) using readily available laser ablation ICP-MS instrumentation. Ablations were performed at a range of square spot sizes (30–80 μm) using a high repetition rate (25 Hz) and extended analyte dwell times (up to 250 ms) to improve sensitivity and signal stability. A comparison of LA-ICP-MS results with published halogen data was used to calculate the following theoretical limits of quantification; Cl = 360 μg/g, Br = 8 μg/g, I = 0.75 μg/g. A detailed assessment of raw signal intensities for different matrices with known halogen contents, combined with high resolution mass scans, provides new constraints on the origin of apparent halogen signals: on mass 35Cl signal excesses are likely 16O18OH and/or 17O18O; 79Br is influenced by peak shoulder overlap from 40Ar40Ar (a diargon cation, Ar2+2) and a matrix-based interference (159Tb2+) for samples with Br/Tb < 0.6; 127I signals are similar for all but the highest I materials analysed here, suggesting the presence of ubiquitous gas-based interferences. The observation that false positive halogen signals only occur during sample ablation suggests that they are either matrix derived or related to the process of sample introduction. During ablation, matrix loading may reduce plasma energy, resulting in a greater proportion of polyatomic interferences in the system. For Cl, we provide a new time dependent excess apparent Cl spline correction defined by analysis of halogen-free olivine via a modified version of the Iolite Data Reduction Scheme 'X_Trace_Elements_IS'. The correction improves the limit of linearity to ~100 μg/g for Cl in glasses down to a 38 μm spot size. We test our methodology on apatite from Permian alkaline lamprophyres in the Pyrenees (Spain) and quartz-hosted melt inclusions from rhyolitic deposits at the Taupo volcanic zone (New Zealand), obtaining results comparable to electron microprobe and SIMS data. We provide recommendations for analytical best practice and highlight the need for well characterised matrix matched SRMs spanning a broad range of concentrations to allow for the identification and removal of non-analyte related contributions to measured signals.
Detection and recognition of cursive text from video frames

Abstract Textual content appearing in videos represents an interesting index for semantic retrieval of videos (from archives), generation of alerts (live streams), as well as high level applications like opinion mining and content summarization. The key components of such systems require detection and recognition of textual content which also make the subject of our study. This paper presents a comprehensive framework for detection and recognition of textual content in video frames. More specifically, we target cursive scripts taking Urdu text as a case study. Detection of textual regions in video frames is carried out by fine-tuning deep neural networks based object detectors for the specific case of text detection. Script of the detected textual content is identified using convoluational neural networks (CNNs), while for recognition, we propose a UrduNet, a combination of CNNs and long short- term memory (LSTM) networks. A benchmark dataset containing cursive text with more than 13,000 video frame is also developed. A comprehensive series of experiments is carried out reporting an F-measure of 88.3% for detection while a recognition rate of 87%.
From skeuomorphism to flat design: age-related differences in performance and aesthetic perceptions

The design of graphical user interfaces has been evolving from skeuomorph interfaces – which use elements that mimic the aesthetics and functionality of their real-world counterparts – to minimalist and flat designs. Despite the growing popularity of these new design approaches, they can be challenging for older adults who experience a decline in visual and cognitive abilities. Still, little is known about user performance, aesthetic perception, and preference of older adults, particularly in comparison to younger users and traditional skeuomorph interfaces. In this paper, we examine the performance and aesthetic perception of older (65–77 years old) and younger (20–40) adults with three design approaches: skeuomorph, skeuominimalist, and flat design. Results show flat design is either slower or less accurate than traditional skeuomorph interfaces for older adults across three tasks: visual search, identifying clickable objects, and multiple page navigation. Younger adults were less susceptible to performance differences between design approaches, but still subject to 'click uncertainty' with flat interfaces. Skeuominimalism did not show clear performance benefits over flat design or skeuomorphism, while the latter reduced the performance gap between age groups. Finally, younger adults preferred the simplicity of skeuominimalism, while older adults preferred skeuomorph interfaces because of the perceived usability, beauty, and trustiness.