Reviewerly/exHarmony huggingface dataset:
Mind the (QoE) Gap: On the Incompatibility of Web and Video QoE Models in the Wild

Education Service Providers (ESPs) have a paramount role in the digitization of education, providing reliable devices for students and teachers and high quality Internet access at schools. In this paper, a large-scale, passive, in-device Quality of Experience (QoE) monitoring system is presented, which was deployed into a nationwide network of education-purpose devices. Four months' worth of continuous measurements were conducted by an ESP, covering more than 800 education centers and about 4000 devices, used both in schools and at home. When analyzing the QoE of web sessions in school networks, we identify a fundamental issue with the compatibility of web browsing and video QoE models, which inhibits the successful application of QoE-aware network management for multiple services.
IoT based Smart Small Scale Solar Energy Planning using Evolutionary Fuzzy Association Rule Mining

Along-Track Stereo Sun Glitter (ATSSG) shows Indonesia especially Lombok has high solar energy potential, not only on large scale but also small scale such as for hybrid-based electricity savings. The amount of energy that can be saved through solar power is difficult to predict without measurement and planning. The paper proposed the Smart Small Scale Solar Energy Planning using Internet of Things (IoT) by collaborating Wireless Sensor Network (WSN) as data collector and Evolutionary Fuzzy Association Rule Mining (EFARM) as Decision Support System (DSS). WSN collects data generated solar energy by the solar panel and direct current (DC) energy usage by electrical devices. Then both collected data are processed by EFARM through an interpretation of tree-based fuzzy rule extractor to conclude the potential of energy efficiency. The Evaluation is carried out for two weeks using two solar panels with light intensity, temperature, and humidity sensors as a comparison for environment condition and generated energy. Through evaluation EFARM has shown the capability to Interpreted patterns of generated energy and energy consumption by achieving a high average of supports(0.247,0.236), confidence(0.393,0.219) and scores(0.335,0.127) for full-length rules; describe the rules correlation between generated energy and energy consumption to conclude the potential of energy efficiency, and make decision support for the number of panels and batteries to be added with relatively low mean square error (6.094).
Potentials of Teaching, Learning, and Design with Virtual Reality: An Interdisciplinary Thematic Analysis

Virtual reality (VR) provides simulations and immersive interaction that can potentially enable innovative teaching and learning. The VR environment brings promising potential for teaching and learning as well as challenges associated with cognitive load and sophisticated technologies. A systematic review of recent literature from multiple disciplines lays an analysis and exploration stage for the design of teaching and learning activities with VR, which is typically an education design research. This study presents a thematic analysis of published literature and whitepapers related to VR in teaching and learning in various disciplinary fields. The study utilizes the activity system (AS) framework to support the analysis of the literature and cases due to the complexity of the multisensory simulation and haptic technologies of VR and its interaction with the human in the learning process. Instructional design and application suggestions are provided based on representative components of AS with forward-thinking suggestions.
The University of Helsinki and Aalto University submissions to the WMT 2020 news and low-resource translation tasks

This paper describes the joint participation of University of Helsinki and Aalto University to two shared tasks of WMT 2020: the news translation between Inuktitut and English and the low-resource translation between German and Upper Sorbian. For both tasks, our efforts concentrate on efficient use of monolingual and related bilingual corpora with scheduled multi-task learning as well as an optimized subword segmentation with sampling. Our submission obtained the highest score for Upper Sorbian -> German and was ranked second for German -> Upper Sorbian according to BLEU scores. For English–Inuktitut, we reached ranks 8 and 10 out of 11 according to BLEU scores.
Generalized Chronicles for Temporal Sequence Classification

Discriminant chronicle mining (DCM) [6] tackles temporal sequence classification by combining machine learning and chronicle mining algorithms. A chronicle is a set of events related by temporal boundaries on the delay between event occurrences. Such temporal constraints are poorly expressive and discriminant chronicles may lack of accuracy. This article generalizes discriminant chronicle mining by modeling complex temporal constraints. We present the generalized model and we instantiate different generalized chronicle models. The accuracy of these models are compared with each other on simulated and real datasets.
Quantum broadcasting multiple blind signature protocol based on three-particle partial entanglement

&lt;sec&gt;Recently, a quantum broadcasting multiple blind signature scheme based on GHZ state has been proposed, which could be used to settle the problem that a message is so important that it needs to be signed by multiple signatories, in order to guarantee the message privacy: none of signatories can acquire the content of the message they have signed. Maybe it can be applied to an E-bank system. For example, a large amount of money has to be transferred through E-bank system on the internet. The E-bank system operator submits the request to the bank after filling the application form including payment amount, bank transfer account and some other information. When the request arrives, the bank clerk signs to approve. However, it is not enough, it has to ask the manager for authority, and then it needs to be signed by the manager. In the whole process, all the signatories cannot learn what they have signed, but the application form has been recorded in the E-bank system. So, once disagreement takes place, the bank can track the message sender. &lt;/sec&gt;&lt;sec&gt;In this paper, we present a new quantum broadcasting multiple blind signature scheme which is based on a three-particle partial entanglement state. Comparing with the original scheme, the partial entanglement state is utilized in our new scheme in place of the GHZ state, and this does not bring down the security of the scheme. Particularly, using the partial entanglement state can not only save the entanglement resource to some extent, but also make the scheme much easier to be realized. As is well known, It is not easy to keep the maximum entanglement state shared among the participants in the whole quantum communication process. By using the partial entanglement in place of the maximum entanglement can improve the new scheme applicability to make it more practical. It is also indicated that multi-qubit entangled systems which are partially entangled can be efficiently used as a resource in quantum information processing with perfect performance.&lt;/sec&gt;
Deep Transfer Learning for Nucleus and Micronucleus Recognition

Nucleus is the main component in a human cell. Excellent nucleus shape observation methods help endorse scientific research in medical and biological fields. Nucleus shape observation methods are performed manually by humans through medical labs. Abnormal nucleus (Micronucleus) is caused by drugs and other toxical factors. Current methods are only confined in detecting and segmenting nucleuses images from different human tissues. None of the these methods has tackled the problem of automating the nucleus and micronucleus recognition. In this paper, first, we apply a deep transfer learning to automate the recognition of nucleus and micronucleus from microscopy images. Second, we introduce our dataset which consists of manually collected nucleus and micronucleus image examples to train and evaluate the performance of the proposed method. The examples are manually curated by domain of experts in the biology. The nucleus and micronucleus images are used as an input to feed several pre-trained Convolutional Neural Network (CNN) models. The models performance is measured using 2-fold cross validation. Our experimental evaluation shows that GoogLeNet pre-trained model performs better than other models with an 80.06 % of average classification accuracy.
Runtime Deep Model Multiplexing for Reduced Latency and Energy Consumption Inference

We propose a learning algorithm to design a lightweight neural multiplexer that given the input and computational resource requirements, calls the model that will consume the minimum compute resources for a successful inference. Mobile devices can use the proposed algorithm to offload the hard inputs to the cloud while inferring the easy ones locally. Besides, in the large scale cloud-based intelligent applications, instead of replicating the most-accurate model, a range of small and large models can be multiplexed from depending on the input's complexity which will save the cloud's computational resources. The input complexity or hardness is determined by the number of models that can predict the correct label. For example, if no model can predict the label correctly, then the input is considered as the hardest. The proposed algorithm allows the mobile device to detect the inputs that can be processed locally and the ones that require a larger model and should be sent a cloud server. Therefore, the mobile user benefits from not only the local processing but also from an accurate model hosted on a cloud server. Our experimental results show that the proposed algorithm improves mobile's model accuracy by 8.52 % which is because of those inputs that are properly selected and offloaded to the cloud server. In addition, it saves the cloud providers' compute resources by a factor of 2.85 × as small models are chosen for easier inputs.
Impact of Hyperparameters on Model Development in Deep Learning

Deep learning has revolutionized the field ofShaziya, Humera computerZaheer, Raniah vision. To develop a deep learning model, one has to decide on the optimal values of various hyperparameters such as learning rate. These are also called as model parameters which are not learned by the model rather initialized by the user. Hyperparameters control other parameters of the model such as weights and biases. Parameter values are learned effectively by tuning the hyperparameters. Hence, hyperparameters determine the values of the parameters of the model. Manual Tuning is a tedious and time-consuming process. Automating the selection of values for hyperparameters results in the development of effective models. It has to be investigated to figure out which combinations yield the optimum results. This work has considered scikit-optimize library functions to study the impact of hyperparameters on the accuracy of MNIST dataset classification problem. The results obtained for different combination of learning rate, number of dense layers, number of nodes per dense layer, and activation function showed that a minimum of 8.68% and a maximum of 98.96% for gp_minimize function, 8.68% and 98.74% for forest_minimize function and gbrt_minimize generates 9.24% and 98.94% for lowest and highest accuracy, respectively.
Mapping the Web Ontology Language to the OpenAPI Specification

Many organizations maintain knowledge graphs that are organized according to ontologies. However, these ontologies are implemented in languages (e.g. OWL) that are difficult to understand by users who are not familiar with knowledge representation techniques. In particular, this affects web developers who want to develop ontology-based applications but may find challenging accessing ontology-based data in knowledge graphs through SPARQL queries. To address this problem, we propose an accessible layer for ontology-based knowledge graphs through REST APIs. We define a mapping specification between the Web Ontology Language (OWL) and the OpenAPI Specification (OAS) to provide ontology-based API definitions in a language well-known to web developers. Our mapping specification identifies key similarities between OWL and OAS and provides implementation guidelines and examples. We also developed a reference mapping implementation that automatically transforms OWL ontologies into OpenAPI specifications in a matter of seconds.
Automatic Fetal Head Circumference Measurement in 2D Ultrasound Images Based On Optimized Fast Ellipse Fitting

Gestational age (GA) monitoring from fetal ultrasound imaging is one method to observe pre-birth risk factors and to prepare early treatment for neonatal problems. There are several parameters in an ultrasound image that can be used to estimate GA, one of which is the fetal head circumference (HC). However, fetal HC measurement is prone to error since it relies on manual annotation by sonographer or obstetrician. This research aims to design an algorithm to automatically calculate the fetal HC based on optimized ellipse fitting on a localized region of interest (RoI) previously defined as fetal head candidate area. Our optimization method consists of pre-processing steps to exclude noise within the RoI and to select the optimum representation of fetal head pixels to be processed by the ellipse fitting algorithm. We managed to perform ellipse fitting on 699 and 141 ultrasound images representing respectively the second and third trimester pregnancies; with the average dice similarity coefficient (DSC) of 95.27%±6.25%, hausdorff distance (HD) of 3.51 mm±5.54 mm, a difference in fetal HC (DF) of -3.42 mm±13.66 mm, and an absolute difference in fetal HC (ADF) of 6.53 mm±12.5 mm. The results demonstrated that the presented method performed comparably to other systems published in the literature. Moreover, our results represent an evaluation of a significantly larger number of data compared to most of the previous works.
Climbing Activity Recognition and Measurement with Sensor Data Analysis

The automatic detection of climbers activities can be the basis of software systems able to support trainers to assess the climbers performance and to define more effective training programs. We propose an initial building block of such a system, for the unobtrusive identification of the activity of someone pulling a rope after finishing the ascent. We use a novel type of quickdraw, augmented with a tri-axial accelerometer sensor. The acceleration data generated by the quickdraw during the climbs are used by a Machine Learning classifier for detecting the rope pulling activity. The obtained results show that this activity can be detected automatically with high accuracy, particularly by a Random Forest classifier. Moreover, we show that data acquired by the quickdraw sensor, as well as the detected rope pulling, can also be used to benchmark climbers.
The Effect of Code Smells on the Relationship Between Design Patterns and Defects

The relationship between design patterns and defects was investigated in the literature, but with mixed results.While the majority of studies found the presence of patterns to be positively correlated with defects, other works reported the opposite conclusions.This may suggest that contextual factors affect this relationship.In this study we analyze the role of code smells as a confounding variable in the relationship between design patterns and defects in Java classes.To investigate this, we applied statistical tests to capture the difference in the impact on defects between pattern classes with/without code smells in 10 Java systems from the PROMISE dataset, with respect to 13 design patterns and 10 code smells.The presence of code smells in patterns appears to be a valid factor affecting defect-proneness.Pattern classes with smells attract more defects than non-smelly pattern classes, and in most systems smelly design patterns are positively correlated with the presence of defects, while non-smelly patterns tend to have no impact, or a slightly negative impact on the presence of defects.As a result, the presence of code smells in design pattern classes appears to be a contextual factor affecting the defect-proneness of the subject code.
Tractability frontiers in probabilistic team semantics and existential second-order logic over the reals

Probabilistic team semantics is a framework for logical analysis of probabilistic dependencies. Our focus is on the axiomatizability, complexity, and expressivity of probabilistic inclusion logic and its extensions. We identify a natural fragment of existential second-order logic with additive real arithmetic that captures exactly the expressivity of probabilistic inclusion logic. We furthermore relate these formalisms to linear programming, and doing so obtain PTIME data complexity for the logics. Moreover, on finite structures, we show that the full existential second-order logic with additive real arithmetic can only express NP properties. Lastly, we present a sound and complete axiomatization for probabilistic inclusion logic at the atomic level.
TVOR: Finding Discrete Total Variation Outliers Among Histograms

Pearson's chi-squared test can detect outliers in the data distribution of a given set of histograms. However, in fields such as demographics (for e.g. birth years), outliers may be more easily found in terms of the histogram smoothness where techniques such as Whipple's or Myers' indices handle successfully only specific anomalies. This paper proposes smoothness outliers detection among histograms by using the relation between their discrete total variations (DTV) and their respective sample sizes. This relation is mathematically derived to be applicable in all cases and simplified by an accurate linear model. The deviation of the histogram's DTV from the value predicted by the model is used as the outlier score and the proposed method is named Total Variation Outlier Recognizer (TVOR). TVOR requires no prior assumptions about the histograms' samples' distribution, it has no hyperparameters that require tuning, it is not limited to only specific patterns, and it is applicable to histograms with the same bins. Each bin can have an arbitrary interval that can also be unbounded. TVOR finds DTV outliers easier than Pearson's chi-squared test. In case of distribution outliers, the opposite holds. TVOR is tested on real census data and it successfully finds suspicious histograms. The source code is given at https://github.com/DiscreteTotalVariation/TVOR.
Word-Level Uncertainty Estimation for Black-Box Text Classifiers using RNNs

Estimating uncertainties of Neural Network predictions paves the way towards more reliable and trustful text classifications.However, common uncertainty estimation approaches remain as black-boxes without explaining which features have led to the uncertainty of a prediction.This hinders users from understanding the cause of unreliable model behaviour.We introduce an approach to decompose and visualize the uncertainty of text classifiers at the level of words.Our approach builds on top of Recurrent Neural Networks and Bayesian modelling in order to provide detailed explanations of uncertainties, enabling a deeper reasoning about unreliable model behaviours.We conduct a preliminary experiment to check the impact and correctness of our approach.By explaining and investigating the predictive uncertainties of a sentiment analysis task, we argue that our approach is able to provide a more profound understanding of artificial decision making.
PrivSyn: Differentially Private Data Synthesis

In differential privacy (DP), a challenging problem is to generate synthetic datasets that efficiently capture the useful information in the private data. The synthetic dataset enables any task to be done without privacy concern and modification to existing algorithms. In this paper, we present PrivSyn, the first automatic synthetic data generation method that can handle general tabular datasets (with 100 attributes and domain size $>2^{500}$). PrivSyn is composed of a new method to automatically and privately identify correlations in the data, and a novel method to generate sample data from a dense graphic model. We extensively evaluate different methods on multiple datasets to demonstrate the performance of our method.
CSPM

Security and privacy in cloud systems are critical. To address security and privacy concerns, many security patterns, privacy patterns, and non-pattern-based knowledge have been reported. However, knowing which pattern or combination of patterns to use in a specific scenario is challenging due to the sheer volume of options and the layered cloud stack. To deal with security and privacy in cloud services, this study proposes the cloud security and privacy metamodel (CSPM). CSPM uses a consistent approach to classify and handle existing security and privacy patterns. In addition, CSPM is used to develop a security and privacy awareness process to develop cloud systems. The effectiveness and practicality of CSPM is demonstrated via several case studies.
DCC-Uchile at SemEval-2020 Task 1: Temporal Referencing Word Embeddings

We present a system for the task of unsupervised lexical change detection.Given a target word and two corpora spanning different periods of time, automatically detects whether the word has lost or gained senses from one corpus to another.Our system employs the temporal referencing method to obtain compatible representations of target words in different periods of time.This is done by concatenating corpora of different periods and performing a temporal referencing of target words i.e., treating occurrences of target words in different periods as two independent tokens.Afterwards, we train word embeddings on the joint corpus and compare the referenced vectors of each target word using cosine similarity.Our submission was ranked 6th among 33 teams for subtask 1, obtaining an average accuracy of 0.637, only 0.050 points behind the first ranked system.
Dispersed Exponential Family Mixture VAEs for Interpretable Text Generation

Deep generative models are commonly used for generating images and text. Interpretability of these models is one important pursuit, other than the generation quality. Variational auto-encoder (VAE) with Gaussian distribution as prior has been successfully applied in text generation, but it is hard to interpret the meaning of the latent variable. To enhance the controllability and interpretability, one can replace the Gaussian prior with a mixture of Gaussian distributions (GM-VAE), whose mixture components could be related to hidden semantic aspects of data. In this paper, we generalize the practice and introduce DEM-VAE, a class of models for text generation using VAEs with a mixture distribution of exponential family. Unfortunately, a standard variational training algorithm fails due to the mode-collapse problem. We theoretically identify the root cause of the problem and propose an effective algorithm to train DEM-VAE. Our method penalizes the training with an extra dispersion term to induce a well-structured latent space. Experimental results show that our approach does obtain a meaningful space, and it outperforms strong baselines in text generation benchmarks. The code is available at this https URL.