Reviewerly/exHarmony huggingface dataset:
Cross-Modal Knowledge Adaptation for Language-Based Person Search

In this paper, we present a method named Cross-Modal Knowledge Adaptation (CMKA) for language-based person search. We argue that the image and text information are not equally important in determining a person’s identity. In other words, image carries image-specific information such as lighting condition and background, while text contains more modal agnostic information that is more beneficial to cross-modal matching. Based on this consideration, we propose CMKA to adapt the knowledge of image to the knowledge of text. Specially, text-to-image guidance is obtained at different levels: individuals, lists, and classes. By combining these levels of knowledge adaptation, the image-specific information is suppressed, and the common space of image and text is better constructed. We conduct experiments on the CUHK-PEDES dataset. The experimental results show that the proposed CMKA outperforms the state-of-the-art methods.
Predicting the condensate viscosity near the wellbore by ELM and ANFIS-PSO strategies

By lowering the pressure beneath the dew point as the result of production in gas condensate (GC) reservoirs, liquid droplets are formed in the borehole zone. Accurate calculation of pressure decline and optimization operations in these reservoirs need to know and predict the specific properties such as liquid viscosity. Empirical models have already been developed to predict this parameter. Due to the peculiar behavior of fluids beneath the dew point pressure (DPP), the prediction of liquid viscosity associates with an error. With the development of machine learning (ML) approaches, studies on fluid properties like other sciences have entered a new phase. In this study, extreme learning machine (ELM) and adaptive neuro-fuzzy inference system with particle swarm optimization (ANFIS-PSO) methods applied to this end. Therefore, a large data bank of reservoir and fluid properties including reservoir temperature and pressure, specific gravity (SG) of gas, API gravity, and gas to oil ratio (Rs) were used. The results showed that R-squared and RMSE for ANFIS-PSO are 0.762 and 0.15, respectively, while these values are 0.941 and 0.06 for ELM which shows that the last model has a better performance in estimating output values. Also, the range of reliable data is determined, and further, a sensitivity analysis was done, which showed that the greatest impact on the viscosity was from SG, and API gravity has the least effect on it. This model can be used as a reference for calculating condensate viscosity and also by expanding the range of datasets, it can be applied in the commercial software.
A Case Study of Educational Games in Virtual Reality as a Teaching Method of Lean Management

(1) At present, it is important to bring the latest technologies from industrial practice into the teaching process of educational institutions, including universities. The presented case study addresses the application of educational games in virtual reality to the teaching process in a university environment. (2) The study took place at the Department of Industrial Engineering of the University of Žilina in Žilina and consisted of two phases. In the first phase, students’ satisfaction with current teaching methods was examined. The second phase focused on an educational game in virtual reality, which introduced a non-traditional approach for teaching lean management, namely the tool 5S. (3) This game was designed by the study authors and created in the Godot game engine. The educational game was provided to students during class. After completing the game, participants were asked to fill out a questionnaire. The aim was to enable students to express their opinion on the educational game and to identify the main benefits of this approach in the teaching process. (4) In the study’s final phase, based on the acquired knowledge, the authors examined the benefits and disadvantages of virtual reality educational games for the teaching process of industrial engineering tools.
Image captioning in Hindi language using transformer networks

Neural encoder–decoder architectures have been used extensively for image captioning. Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) are popularly used in encoder and decoder models. Recurrent Neural Networks are popular architectures in natural language processing used for language modeling, but they are sequential in nature. The transformer model can solve this problem of sequential dependency by using an attention mechanism. Many works are available for image captioning in the English language, but models for generating Hindi captions are limited; hence, we have tried to fill this gap. We have created the Hindi dataset for image captioning by manually translating the popular MSCOCO dataset from English to Hindi. Experimental results show that our proposed model outperforms other models. The proposed model has attained the BLEU-1 score of 62.9, BLEU-2 score of 43.3, BLEU-3 score of 29.1, and BLEU4 score of 19.0.
Estimating the Instantaneous Frequency of Linear and Nonlinear Frequency Modulated Radar Signals—A Comparative Study

Automatic modulation recognition plays a vital role in electronic warfare. Modern electronic intelligence and electronic support measures systems are able to automatically distinguish the modulation type of an intercepted radar signal by means of real-time intra-pulse analysis. This extra information can facilitate deinterleaving process as well as be utilized in early warning systems or give better insight into the performance of hostile radars. Existing modulation recognition algorithms usually extract signal features from one of the rudimentary waveform characteristics, namely instantaneous frequency (IF). Currently, there are a small number of studies concerning IF estimation methods, specifically for radar signals, whereas estimator accuracy may adversely affect the performance of the whole classification process. In this paper, five popular methods of evaluating the IF–law of frequency modulated radar signals are compared. The considered algorithms incorporate the two most prevalent estimation techniques, i.e., phase finite differences and time-frequency representations. The novel approach based on the generalized quasi-maximum likelihood (QML) method is also proposed. The results of simulation experiments show that the proposed QML estimator is significantly more accurate than the other considered techniques. Furthermore, for the first time in the publicly available literature, multipath influence on IF estimates has been investigated.
A hybrid approach for text document clustering using Jaya optimization algorithm

• Hybrid Jaya optimization is proposed for text clustering problem. • Silhouette index is used to measure the quality of a candidate solution. • Crossover and mutation strategies enhance the global search ability. • The proposed approach is successfully evaluated on five well known datasets. In this digital era, millions of Internet users are contributing vast amounts of data in the form of unstructured text documents. Organizing this material is a tedious task. The clustering of text document plays a vital role for organizing these unstructured text documents. In our paper, we make use of Hybrid Jaya Optimization algorithm (HJO) for text Document Clustering (DC), referred to as HJO-DC. We have used the Silhouette index as a metric to measure the quality of a solution. The proposed work is compared with partitioning techniques such as K-Means and K-Medoids and metaheuristic techniques such as Genetic algorithm, Cuckoo Search, Particle Swarm Optimizer, Firefly and Grey Wolf Optimizer. Remarkably, the proposed algorithm achieves the highest quality clustering in all benchmark examples.
A Systematic Review of Augmented Reality in Health Sciences: A Guide to Decision-Making in Higher Education

The objective of this study was to investigate the usability of the augmented reality (AR) in higher education in the area of health sciences to describe what type of interventions have been developed, their impact on various psychopedagogical aspects of the students as well as the main advantages, disadvantages and challenges in incorporating AR in the teaching-learning process. A systematic review was carried out in the CINAHL, PsycINFO, MEDLINE, Web of Science databases and the Google Scholar search engine. The search was limited to original research articles written in English, Spanish or Portuguese since 2014. The quality of the selected articles (n = 19) was assessed using the Mixed Methods Appraisal Tool (MMAT). The applications and electronic devices used and the measurement instruments used were described. The use of AR made it easier for students to acquire skills, especially in courses with a high component of three-dimensional visualization, and positively influenced various aspects of the learning process such as motivation, satisfaction or autonomous learning. As an educational technological tool applied to higher education in health sciences, AR improves the teaching-learning process by influencing it in a multidimensional way.
FedServing: A Federated Prediction Serving Framework Based on Incentive Mechanism

Data holders, such as mobile apps, hospitals and banks, are capable of training machine learning (ML) models and enjoy many intelligence services. To benefit more individuals lacking data and models, a convenient approach is needed which enables the trained models from various sources for prediction serving, but it has yet to truly take off considering three issues: (i) incentivizing prediction truthfulness; (ii) boosting prediction accuracy; (iii) protecting model privacy.We design FedServing, a federated prediction serving framework, achieving the three issues. First, we customize an incentive mechanism based on Bayesian game theory which ensures that joining providers at a Bayesian Nash Equilibrium will provide truthful (not meaningless) predictions. Second, working jointly with the incentive mechanism, we employ truth discovery algorithms to aggregate truthful but possibly inaccurate predictions for boosting prediction accuracy. Third, providers can locally deploy their models and their predictions are securely aggregated inside TEEs. Attractively, our design supports popular prediction formats, including top-1 label, ranked labels and posterior probability. Besides, blockchain is employed as a complementary component to enforce exchange fairness. By conducting extensive experiments, we validate the expected properties of our design. We also empirically demonstrate that FedServing reduces the risk of certain membership inference attack.
Telemetry Data-Based Spacecraft Anomaly Detection With Spatial–Temporal Generative Adversarial Networks

The telemetry data obtained from an on-orbit spacecraft contain important information to indicate anomaly of the spacecraft. However, the large number of monitoring variables and the large amount of data points, as well as the lack of prior knowledge about anomaly due to complicated structure of spacecraft and its working conditions, pose great challenge to the anomaly detection. This article proposes an anomaly detection algorithm based on a spatial–temporal generative adversarial network (GAN) for the anomaly detection in telemetry data. This algorithm establishes a GAN-based model combining convolutional neural network (CNN) and long short-term memory (LSTM) to extract spatial and temporal features of the telemetry data, which facilitates the automatic and simultaneous representation of nonnegligible time-related characteristics of a monitoring variable and complex correlation between variables. Using these features, many kinds of anomalies including multivariate anomalies and contextual anomalies can be detected. Moreover, an anomaly score specifically designed to fit the GAN-based algorithm is also proposed to evaluate the possibility of anomaly by weighted fusion of the generator metric and the discriminator metric, which is proved to be significantly helpful to the accuracy of anomaly detection. Finally, experiments on one real telemetry data set and two public telemetry data sets are conducted, by which the proposed anomaly algorithm is demonstrated to be effective and accurate in detecting outliers in telemetry data. Comparison with three other state-of-the-art methods also reveals the advantages of our proposed algorithm.
MAD-HTLC: Because HTLC is Crazy-Cheap to Attack

Smart Contracts and transactions allow users to implement elaborate constructions on cryptocurrency blockchains like Bitcoin and Ethereum. Many of these constructions, including operational payment channels and atomic swaps, use a building block called Hashed Time-Locked Contract (HTLC).In this work, we distill from HTLC a specification (HTLC-Spec), and present an implementation called Mutual-Assured-Destruction Hashed Time-Locked Contract (MAD-HTLC). MAD-HTLC employs a novel approach of utilizing the existing blockchain operators, called miners, as part of the design. If a user misbehaves, MAD-HTLC incentivizes the miners to confiscate all her funds. We prove MAD-HTLC’s security using the UC framework and game-theoretic analysis. We demonstrate MAD-HTLC’s efficacy and analyze its overhead by instantiating it on Bitcoin’s and Ethereum’s operational blockchains.Notably, current miner software makes only little effort to optimize revenue, since the advantage is relatively small. However, as the demand grows and other revenue components shrink, miners are more motivated to fully optimize their fund intake. By patching the standard Bitcoin client, we demonstrate such optimization is easy to implement, making the miners natural enforcers of MAD-HTLC.Finally, we extend previous results regarding HTLC vulnerability to bribery attacks. An attacker can incentivize miners to prefer her transactions by offering high transaction fees. We demonstrate this attack can be easily implemented by patching the Bitcoin client, and use game-theoretic tools to qualitatively tighten the known cost bound of such bribery attacks in presence of rational miners. We identify bribe opportunities occurring on the Bitcoin and Ethereum main networks where a few dollars bribe could yield tens of thousands of dollars in reward (e.g., $2 for over $25K).
Cassava Leaf Disease Detection Using Convolutional Neural Networks

Cassava is a plant that is widely found in Indonesia with various benefits. One of the benefits of cassava is as a substitute for rice. According to data from the Indonesian Central Statistics Agency in 2015, cassava production in Indonesia was 21,801,415 tons a year. Lampung Province is the largest producer of cassava in Indonesia. In 2016, its production decreased due to disease attacking the cassava plant. One of the deep learning methods currently being developed is Convolutional Neural Network (CNN). This network is built with the assumption that the input used is an image. This technique can make the image learning function more efficient to implement. Therefore, this study will take advantage of the advantages of CNN, namely being able to classify an object intended for image data so that the CNN model will be used as an introduction to the four types of healthy cassava leaf and cassava leaf diseases that can be found in Indonesia. By using the Tensorflow library, the results of model trials and evaluations of cassava leaf images show an accuracy of 0.8538 for training and 0.7496 for data validation. So it can be concluded that the implementation of Deep Learning with the Convolutional Neural Network (CNN) method can detect cassava leaf disease images.
Auto-Icon: An Automated Code Generation Tool for Icon Designs Assisting in UI Development

Approximately 50% of development resources are devoted to UI development tasks [8]. Occupied a large proportion of development resources, developing icons can be a time-consuming task, because developers need to consider not only effective implementation methods but also easy-to-understand descriptions. In this study, we define 100 icon classes through an iterative open coding for the existing icon design sharing website. Based on a deep learning model and computer vision methods, we propose an approach to automatically convert icon images to fonts with descriptive labels, thereby reducing the laborious manual effort for developers and facilitating UI development. We quantitatively evaluate the quality of our method in the real world UI development environment and demonstrate that our method offers developers accurate, efficient, readable, and usable code for icon images, in terms of saving 65.2% developing time.
A deep attention model to forecast the Length Of Stay and the in-hospital mortality right on admission from ICD codes and demographic data

Leveraging the Electronic Health Records (EHR) longitudinal data to produce actionable clinical insights has always been a critical issue for recent studies. Non-forecasted extended hospitalizations account for a disproportionate amount of resource use, the mediocre quality of inpatient care, and avoidable fatalities. The capability to predict the Length of Stay (LoS) and mortality in the early stages of the admission provides opportunities to improve care and prevent many preventable losses. Forecasting the in-hospital mortality is important in providing clinicians with enough insights to make decisions and hospitals to allocate resources, hence predicting the LoS and mortality within the first day of admission is a difficult but a paramount endeavor. The biggest challenge is that few data are available by this time, thus the prediction has to bring in the previous admissions history and free text diagnosis that are recorded immediately on admission. We propose a model that uses the multi-modal EHR structured medical codes and key demographic information to classify the LoS in 3 classes; Short Los (LoS⩽10 days), Medium LoS (10<LoS⩽30 days) and Long LoS (LoS>30 days) as well as mortality as a binary classification of a patient’s death during current admission. The prediction has to use data available only within 24 h of admission. The key predictors include previous ICD9 diagnosis codes, ICD9 procedures, key demographic data, and free text diagnosis of the current admission recorded right on admission. We propose a Hierarchical Attention Network (HAN-LoS and HAN-Mor) model and train it to a dataset of over 45321 admissions recorded in the de-identified MIMIC-III dataset. For improved prediction, our attention mechanisms can focus on the most influential past admissions and most influential codes in these admissions. For fair performance evaluation, we implemented and compared the HAN model with previous approaches. With dataset balancing techniques HAN-LoS achieved an AUROC of over 0.82 and a Micro-F1 score of 0.24 and HAN-Mor achieved AUC-ROC of 0.87 hence outperforming the existing baselines that use structured medical codes as well as clinical time series for LoS and Mortality forecasting. By predicting mortality and LoS using the same model, we show that with little tuning the proposed model can be used for other clinical predictive tasks like phenotyping, decompensation,re-admission prediction, and survival analysis.
Proof-of-Learning: Definitions and Practice

Training machine learning (ML) models typically involves expensive iterative optimization. Once the model’s final parameters are released, there is currently no mechanism for the entity which trained the model to prove that these parameters were indeed the result of this optimization procedure. Such a mechanism would support security of ML applications in several ways. For instance, it would simplify ownership resolution when multiple parties contest ownership of a specific model. It would also facilitate the distributed training across untrusted workers where Byzantine workers might otherwise mount a denial-ofservice by returning incorrect model updates.In this paper, we remediate this problem by introducing the concept of proof-of-learning in ML. Inspired by research on both proof-of-work and verified computations, we observe how a seminal training algorithm, stochastic gradient descent, accumulates secret information due to its stochasticity. This produces a natural construction for a proof-of-learning which demonstrates that a party has expended the compute require to obtain a set of model parameters correctly. In particular, our analyses and experiments show that an adversary seeking to illegitimately manufacture a proof-of-learning needs to perform at least as much work than is needed for gradient descent itself.We also instantiate a concrete proof-of-learning mechanism in both of the scenarios described above. In model ownership resolution, it protects the intellectual property of models released publicly. In distributed training, it preserves availability of the training procedure. Our empirical evaluation validates that our proof-of-learning mechanism is robust to variance induced by the hardware (e.g., ML accelerators) and software stacks.
An empirical study on the co-occurrence between refactoring actions and Self-Admitted Technical Debt removal

Technical Debt (TD) concerns the lack of an adequate solution in a software project, from its design to the source code. Its admittance through source code comments, issues, or commit messages is referred to as Self-Admitted Technical Debt (SATD). Previous research has studied SATD from different perspectives, including its distribution, impact on software quality, and removal. In this paper, we investigate the relationship between refactoring and SATD removal. By leveraging a dataset of SATD and their removals in four open-source projects and by using an automated refactoring detection tool, we study the co-occurrence of refactoring and SATD removals. Results of the study indicate that refactoring is more likely to co-occur with SATD removals than with other commits, however, in most cases, they belong to different quality improvement activities performed at the same time. Moreover, if looking closely at refactoring actions co-occurring with SATD removal in the same code entities, a relationship between these activities can be found. Finally, we found how both source code quality metrics and SATD removals play a statistically significant role in the likelihood that the commit applies a refactoring action.
DeepRec: On-device Deep Learning for Privacy-Preserving Sequential Recommendation in Mobile Commerce

Sequential recommendation techniques are considered to be a promising way of providing better user experience in mobile commerce by learning sequential interests within user historical interaction behaviors. However, the recently increasing focus on privacy concerns, such as the General Data Protection Regulation (GDPR), can significantly affect the deployment of state-of-the-art sequential recommendation techniques, because user behavior data are no longer allowed to be arbitrarily used without the user’s explicit permission. To address the issue, this paper proposes DeepRec, an on-device deep learning framework of mining interaction behaviors for sequential recommendation without sending any raw data or intermediate results out of the device, preserving user privacy maximally. DeepRec constructs a global model using data collected before GDPR and fine-tunes a personal model continuously on individual mobile devices using data collected after GDPR. DeepRec employs the model pruning and embedding sparsity techniques to reduce the computation and network overhead, making the model training process practical on computation-constraint mobile devices. Evaluation results show that DeepRec can achieve comparable recommendation accuracy to existing centralized recommendation approaches with small computation overhead and up to 10x reduction in network overhead.