Reviewerly/exHarmony huggingface dataset:
Face Sketch Recognition-An Overview

Face Sketch recognition has been one of the most studied topics in Forensic literature. The automatic retrieval of suspect photos from the mug-shot police data-base can help them, quickly reduce and deduct potential suspects, but in most cases, the photographic image of a suspect is not available. The best substitute is often sketch based on the memory of an eyewitness or a victim. Generally, this process is slow and really not effective, it does not allow to find and arrest the right suspect. So, a stronger algorithm for even partial face sketch recognition can be useful. Although many methods have been proposed in this scenario, especially the techniques that are applied to face recognition system and ranked among the perfect and most effective. The main objective of this paper is to presents a review of recent and different researches on recognizing face sketches. We wrote this work to discover and analyze multiple researches on face sketch recognition. We wrote it to review the different classes of approaches, of datasets and evaluation protocols used, by analyzing their kernels and identifying their limitations.
Answer Keyword Generation for Community Question Answering by Multiaspect Gamma–Poisson Matrix Completion

Community question answering (CQA) recommends appropriate answers to existing and new questions. Such answer recommendation is challenging since CQA data are often sparse and decentralized, and lacks sufficient information to generate suitable answers to existing questions. Matching answers to new questions is more challenging in modeling Q/A sparsity, generating answers to cold-start/novel questions, and integrating metadata about Q/A into models, etc. This article addresses these issues by a novel statistical model to automatically generate answer keywords in CQA with multiaspect Gamma-Poisson matrix completion (MAGIC). MAGIC is the first trial in CQA to model multiple aspects of Q/A sentence information in CQA by involving Q/A metadata, Q/A sparsity, and both lexical and semantic Q/A information in a hierarchical Gamma-Poisson model. MAGIC can efficiently generate answer keywords for both existing and new questions against nonnegative matrix factorization (MF), probability MF, and relevant Poisson factorization models w.r.t. recommending appropriate and informative answer keywords.
Blockchain and its application fields in both power economy and demand side management

The conventional power system has been confronted stunning revolutions in all main system components, and the architecture has changed entirely thanks to integrated technological innovations from top to bottom. Renewable energy sources (RESs) have been harnessed substantially in generation side since last decade in a distributed manner and prosumers (consumers also with on-site production facilities) have obtained an opportunity to participate energy market to enhance system operational performance. Within the smart grid paradigm, the future communities have supported intelligent infrastructures to provide communication between countless clusters of end users and system operators with incorporating demand response (DR) strategies into the power system management tools. The flexibility resources aid to achieve power quality requirements and to regulate voltage/frequency fluctuations that are one of the most challenging issues. However, the processing of huge amount of data, end-users’ privacy, and secure and transparent operation needs enforce utility decision-makers to develop new approaches that play a critical role. In this sense, blockchain, the brand-new buzzword, is essentially a distributed ledger technology that records all transactions of values, digital assets, and funds between two unrelated participants without trusting any central authority that sounds tremendously strange, innovative, and creative. The highly reliable and distributed structure has been provided by executing particular cryptographic algorithms and protocols. The verification of each transaction has been ensured by consensus of a majority of the participants in the network. In this study the emerging blockchain technology was evaluated from different points of view. The architecture, opportunities, drawbacks, and application fields were all presented in detail. What’s more the potential opportunities of combined DR strategies with blockchain in real-world pilot projects were examined thoroughly.
CHI 2020: Right Here, Right Now?

Over the last twenty years the CHI conference has grown substantially. However, with the reframing of climate change as a climate crisis, environmental concerns have become increasingly pervasive in the community. In 2019 CHI introduced a sustainability role and set a goal to make CHI more sustainable. In 2020 CHI is in Hawaii. This work looks back over the last two decades and estimates what are substantial and growing CO2 emissions from conference travel. First, it posits how, in the short term, potential environmental damage can be minimised. Second, and longer-term, it invites the community to reflect on research dissemination and how the conference experience may need to change.
Tree Illustrator: Interactive Construction of Tree Visualizations

We present Tree Illustrator, an interactive authoring tool of tree visualizations. Tree Illustrator is based on GoTree, a declarative grammar allowing users to create tree visualizations by configuring three aspects: visual elements, layout, and coordinate system. Within the set of all possible tree visualization techniques, we identify a subset of techniques that are both "unit-decomposable" and "axis-decomposable" (terms we define). For tree visualizations within this subset, Tree Illustrator provides the users with flexible and fine-grained control over the parameters of the techniques, supporting not only existing techniques but also undiscovered and hybrid visualizations.
A general exact optimal sample allocation algorithm: With bounded cost and bounded sample sizes

With a bound on cost and bounds on stratum sample sizes, a cost weighted objective function is decomposed to reveal a general simple exact optimal sample allocation algorithm for minimizing sampling variances. Some known allocation methods are special cases.
From Rerandomizability to Sequential Aggregation: Efficient Signature Schemes Based on SXDH Assumption

An aggregate signature allows one to generate a short aggregate of signatures from different signers on different messages. A sequential aggregate signature (SeqAS) scheme allows the signers to aggregate their individual signatures in a sequential manner. All existing SeqAS schemes that do not use the random oracle assumption either require a large public key or the security depends upon some non-standard interactive/static assumptions. In this paper, we present an efficient SeqAS scheme with constant-size public key under the SXDH assumption. In the process, we first obtain an optimized (and more efficient) variant of Libert et al.'s randomizable signature scheme. While both the schemes are more efficient than the currently best ones that rely on some static assumption, they are only slightly costlier than the most efficient ones based on some interactive assumption.
Learning Context-Aware Outfit Recommendation

With the rapid development and increasing popularity of online shopping for fashion products, fashion recommendation plays an important role in daily online shopping scenes. Fashion is not only a commodity that is bought and sold but is also a visual language of sign, a nonverbal communication medium that exists between the wearers and viewers in a community. The key to fashion recommendation is to capture the semantics behind customers’ fit feedback as well as fashion visual style. Existing methods have been developed with the item similarity demonstrated by user interactions like ratings and purchases. By identifying user interests, it is efficient to deliver marketing messages to the right customers. Since the style of clothing contains rich visual information such as color and shape, and the shape has symmetrical structure and asymmetrical structure, and users with different backgrounds have different feelings on clothes, therefore affecting their way of dress. In this paper, we propose a new method to model user preference jointly with user review information and image region-level features to make more accurate recommendations. Specifically, the proposed method is based on scene images to learn the compatibility from fashion or interior design images. Extensive experiments have been conducted on several large-scale real-world datasets consisting of millions of users/items and hundreds of millions of interactions. Extensive experiments indicate that the proposed method effectively improves the performance of items prediction as well as of outfits matching.
Things Change: Comparing Results Using Historical Data and User Testing for Evaluating a Recommendation Task

We address a recommendation task for next likely flight destination to customers of a major international airline company. We compare performance using historical flight data and an actual user evaluation. Using two years of historical flight data consisting of tens of millions of flights, an ensemble and a collaborative filtering approach obtained an accuracy of 47% and 20% using a test set of 100,000 customers, respectively, highlighting the challenge of the domain. We then evaluated our recommendations on 10,000 actual customers, with a 45-45-10 split among ensemble, collaborative filtering, and control group. The overall predictive power employed with real users was 23%, with the ensemble method having a predictive power of 19% and 30% for collaborative filtering. Results indicate that, in complex and shifting domains such as this one, one cannot rely solely on historical data for evaluating the impact of user recommendations. We discuss implications for recommendation systems and future research in this and related domains.
Designing Prototype Player Personas from a Game Preference Survey

The competitiveness of the video game market has increased the need for understanding players. We generate player personas from survey data of 15,402 players' 195,158 stated game preferences from 130,495 game titles using the methodology of automatic persona generation. Our purpose is to demonstrate the potential of data-driven personas for segmenting players by their game preferences. The resulting prototype personas provide potential value for game marketing purposes, e.g., targeting gamers with social media advertising, although they can also be used for understanding demographic variation among various game preference patterns.
Low Complexity Deep Learning for Mobile Face Expression Recognition

The problem of Face Expression Recognition (FER) remains a challenging one due to variations in illumination and pose as well as partial occlusion of the face. Deep neural networks have been increasingly applied to this problem and have achieved excellent recognition results, especially on challenging datasets such as FER2013. However, the trend has been towards more complex networks to increase performance. In this paper, we develop a low complexity model, and we experiment with a variety of parameters to determine the performance of these models on the FER2013 dataset relative to the complexity of the models. We show that we are able to obtain an accuracy of 70.86% on the test FER images which approximately matches the winning entry to the FER2013 competition but our model is 5 times smaller in size. We show that we are able to reduce the model size 5 times more, resulting in a model with fewer than 500,000 parameters, and still maintain an excellent accuracy of 68.43% which would make this model ideal for resource constrained environments.
Improving Cost for Data Migration in Cloud Computing Using Genetic Algorithm

Cloud computing is gradually increasing its popularity in enterprise-wide organizations. Information technology organizations e.g., IBM, Microsoft, and Amazon have already shifted towards Cloud computing. Cloud-based offerings such as Software as a Service, Platform as a Service and Infrastructure as a Service (IAAS) are the most famous offerings. Most of the existing enterprise applications are deployed using an on-premise model. Organizations are looking for Cloud based offerings to deploy or upgrade their existing applications. SAP, Microsoft Dynamics, and Oracle are the most famous ERP or CRM application OEMs. These enterprise applications generate lots of data are hosted in an organization or on client data centers. Moving data from one data center to the Cloud is always a challenging tasks which cost a lot and takes much effort. This study proposes an efficient approach to optimize cost for data migration in cloud computing. This study also proposes the approach to optimize cost for data collection from multiple locations which can be processed centrally and then migrate to Cloud Computing.
Promise: Leveraging Future Gains for Collateral Reduction

Collateral employed in cryptoeconomic protocols protects against the misbehavior of economically rational agents, compensating honest users for damages and punishing misbehaving parties. The introduction of collateral, however, carries three disadvantages: (i) requiring agents to lock up substantial amount of collateral can be an entry barrier, limiting the set of candidates to wealthy agents; (ii) affected agents incur ongoing opportunity costs as the collateral cannot be utilized elsewhere; and (iii) users wishing to interact with an agent on a frequent basis (e.g., with a service provider to facilitate second-layer payments), have to ensure the correctness of each interaction individually instead of subscribing to a service period in which interactions are secured by the underlying collateral. We present Promise, a subscription mechanism to decrease the initial capital requirements of economically rational service providers in cryptoeconomic protocols. The mechanism leverages future income (such as service fees) prepaid by users to reduce the collateral actively locked up by service providers, while sustaining secure operation of the protocol. Promise is applicable in the context of multiple service providers competing for users. We provide a model for evaluating its effectiveness and argue its security. Demonstrating Promise’s applicability, we discuss how Promise can be integrated into a cross-chain interoperability protocol, XCLAIM, and a second-layer scaling protocol, NOCUST. Last, we present an implementation of the protocol on Ethereum showing that all functions of the protocol can be implemented in constant time complexity and Promise only adds USD 0.05 for a setup per user and service provider and USD 0.01 per service delivery during the subscription period.
IECT: A methodology for identifying critical products using purchase transactions

Identifying critical products and key customers to strengthen company performance is vitally important in the digital transformation era. Critical products are the itemsets that are preferred by vip customers and yet not popular among ordinary customers. As a result, critical products should be kept on the shelf despite its sales volumes may be lower than other popular items. However, few studies have considered identifying critical products or their potentially valuable patterns. Therefore an innovative algorithm taking advantage of vertical databases to identify critical products was designed. The proposed algorithm is applied to a transaction database of a midsize supermarket to verify the performance. The result showed that precision can reach 80.55% and 82.15% for two different filtering criteria. To the best of our knowledge, this study is the first to apply the concept of critical products to real retail industry transaction records.
Loanword Identification in Low-Resource Languages with Minimal Supervision

Bilingual resources play a very important role in many natural language processing tasks, especially the tasks in cross-lingual scenarios. However, it is expensive and time consuming to build such resources. Lexical borrowing happens in almost every language. This inspires us to detect these loanwords effectively, and to use the “loanword (in receipt language)”-“donor word (in donor language)” to extend the bilingual resource for NLP tasks in low-resource languages. In this article, we propose a novel method to identify loanwords in Uyghur. The most important advantage of this method is that the model only relies on large amount of monolingual corpora and only a small scale of annotated data. Our loanword identification model includes two parts: loanword candidate generation and loanword prediction. In the first part, we use two large-scale monolingual corpora and a small bilingual dictionary to train a cross-lingual embedding model. Since semantic unrelated words often cannot be treated as loanword pairs, a loanword candidate list will be generated according to this model and a word list in Uyghur. In the second part, we predict from the preceding candidates based on a log-linear model that integrates several features such as pronunciation similarity, part-of-speech tags, and hybrid language modeling. To evaluate the effectiveness of our proposed method, we conduct two types of experiments: loanword identification and OOV translation. Experimental results showed that (1) our proposed method achieved significant F1 improvements compared to other models in all four loanword identification tasks in Uyghur, and (2) after extending the existing translation models with loanword identification results, OOV rates in several language pairs reduced significantly and the translation performance improved.
Eye-Tracking as a Method for Enhancing Research on Information Search

The human eye plays an essential role in information acquisition from external world, and much of our contemporary information technology relies on visual processing. The eye-mind hypothesis suggests that human attention is connected to where our eyes are looking (Just and Carpenter 1980). Taken together with the continual movement of our eyes and the limited area of high-acuity human vision, eye-tracking methods are considered to offer theoretically reliable measures of visual attention and search task activities. We first briefly review cognitive factors of interest to information search and the “traditional” methods of their measurement. We then present examples of eye tracking tools and how they capture data before examining how eye-tracking data has been used to assess select cognitive factors in information search.
Whole-slide Imaging: Clinical Workflows and Primary Diagnosis

Digital pathology has made great strides in recent years culminating with the approval to market devices from the Food and Drug Administration. The pathology community is now poised to begin using these systems for diagnostic purposes. This article will discuss the preparatory steps needed to implement digital pathology as well as some implementation styles that may be sufficient for a pathology department.
Glissade: Generating Balance Shifting Feedback to Facilitate Auxiliary Digital Pen Input

This paper introduces Glissade, a digital pen that generates balance shifting feedback by changing the weight distribution of the pen. A pulley system shifts a brass mass inside the pen to change the pen's center of mass and moment of inertia. When the mass is stationary, the pen delivers a constant yet natural sensation of weight, which can be used to convey a status. The pen can also generate a variety of haptic clues by actuating the mass according to the tilt or rotation of the pen, two commonly-used auxiliary pen input channels. Glissade demonstrates new possibilities that balance shifting feedback can bring to digital pen interactions. We validated the usability of this feedback by determining the recognizability of six balance patterns – a mix of static and dynamic patterns chosen based on our design considerations – in two controlled experiments. The results show that, on average, the participants could distinguish between the patterns with a 94.25% accuracy. At the end, we demonstrate a set of novel interactions enabled by Glissade and discuss the directions for future research.
How's Your Day Look? The (Un)Expected Sociolinguistic Effects of User Modeling in a Conversational Agent

We performed an ex situ Wizard of Oz study of young adults interacting with an idealized digital personal assistant to discuss daily scheduling concerns and stress levels. We further varied rates of "learning" and personalization with the system to test user preferences and changes in participants' linguistic and psychological interactions with an unadapted versus adapted user model, and to determine whether those changes were attributable to acclimatization with the system or to the modeling capabilities, seeking to address 3 research questions: What are the psycholinguistic characteristics of user interactions with a dialogue system designed to act as a scheduling assistant? How does a system's ability to learn about a user and maintain a user model affect these interactions? Are changes in interaction styles uniquely attributable to user modeling ability rather than simply user familiarity or acclimation with the system? We present a linguistic analysis of the results using summary measures generated by a widely used psycholinguistic text analysis tool. Some of the measures seem to present the slightly paradoxical effect of reduced user engagement when a conversational agent explicitly discloses information about its user model to the user. These results suggest that future studies should take care to consider the degree to which the user model is directly exposed to the user. That is, being overly forthcoming about what has been learned about a user may undermine attempts to tailor conversational agents to actively engage and relate to users.
Gaze Estimation in the Dark with Generative Adversarial Networks

In this paper, we propose to utilize generative adversarial networks (GANs) to achieve successful gaze estimation in interactive multimedia environments with low light conditions such as a digital museum or exhibition hall. The proposed approach utilizes a GAN to enhance user images captured under low-light conditions, thereby recovering missing information for gaze estimation. The recovered images are fed into the CNN architecture to estimate the direction of user gaze. The preliminary experimental results on the modified MPIIGaze dataset demonstrated an average performance improvement of 6.6 under various low light conditions, which is a promising step for further research.
Towards Usable Protection Against Honeypots

The Ethereum blockchain enables the execution of so-called smart contracts. These are programs that facilitate the automated transfer of funds according to a given business logic without the participants requiring to trust one another. However, recently attackers started using smart contracts to lure users into traps by deploying contracts that pretend to give away funds but in fact contain hidden traps. This new type of scam is commonly referred to as honeypots. In this paper, we propose a system that aims to protect users from falling into these traps. The system consists of a plugin for MetaMask and a back-end service that continuously scans the Ethereum blockchain for honeypots. Whenever a user is about to perform a transaction through MetaMask, our plugin sends a request to the back-end and warns the user if the target contract is a honeypot.