Reviewerly/exHarmony huggingface dataset:
Non-parametric Bayesian Isotonic Calibration: Fighting Over-Confidence in Binary Classification

Classifiers can often output a score or a probability indicating how sure they are about the predicted class. Classifier calibration methods can map these into calibrated class probabilities, supporting cost-optimal decision making. Isotonic calibration is the standard non-parametric calibration method for binary classifiers, and it can be shown to yield the most likely monotonic calibration map on the given data, where monotonicity means that instances with higher predicted scores are more likely to be positive. Another non-parametric method is ENIR (ensemble of near-isotonic regression models) which allows for some non-monotonicity, but adds a penalty for it. We first demonstrate that these two methods tend to be over-confident and show that applying label smoothing improves calibration of both methods in more than 90% of studied cases. Unfortunately, label smoothing reduces confidence on the under-confident predictions also, and it does not reduce the raggedness of isotonic calibration. As the main contribution we propose a non-parametric Bayesian isotonic calibration method which has the flexibility of isotonic calibration to fit maps of all monotonic shapes but it adds smoothness and reduces over-confidence without requiring label smoothing. The method introduces a prior over piecewise linear monotonic calibration maps and uses a simple Monte Carlo sampling based approach to approximate the posterior mean calibration map. Our experiments demonstrate that on average the proposed method results in better calibrated probabilities than the state-of-the-art calibration methods, including isotonic calibration and ENIR.
Robot Formation Control Based on Internet of Things Technology Platform

The cooperative control technology of robot formation can sense all kinds of external environment in real time. It is a multi-functional control and management system including visual recognition, task management execution and distribution, behavior decision-making and so on. It can easily adapt to all kinds of harsh environment. In order to meet the efficient response requirements of robot formation control, a real-time transmission system of robot cooperative motion control is built based on the Internet of things platform, which collects and feeds back the trajectory of multiple robots. Through particle swarm optimization deep learning algorithm, more accurate identification, prediction and guidance of the robot's next action. Finally, the simulation of robot formation motion is established by MATLAB software, which verifies the feasibility of particle swarm optimization deep learning neural network algorithm under the Internet of things technology. Compared with the traditional robot formation control method, the optimized control method has faster convergence speed, smaller error and more accurate position, which provides method guidance for the accuracy and efficiency of robot formation control technology.
A Novel Level Set Method for Inhomogeneous SAR Image Segmentation

Segmentation is an essential part in SAR image analysis and application. However, the presence of speckle noise and intensity inhomogeneity regions leads to great difficulty in SAR image segmentation. In this letter, we propose a novel level set method that integrates the local intensity and the global feature information for better segmentation results. To establish the energy function, a new image model is developed to describe the inhomogeneous SAR images and form the local intensity clustering term. Meanwhile, the feature map extracted by invariant moment is used to define the global fitting term through a piecewise constant function. Segmentation is realized by solving the level set function from the minimization of the energy function. Experiments on the synthetic and real SAR images verify the effectiveness of the proposed method.
Website Quality Measurement of Higher Education Services Institution Region IV Using Webqual 4.0 Method

The purpose of this study is to measure and describe the Higher Education Services Institution Region IV website quality using the Webqual 4.0 method. The questionnaire filling, depth interviews, and observation are the methods used in this research. The measurement results showed that the overall of the Higher Education Services Institution Region IV website quality was included in Good criteria. In detail from the measurement results, the dimension criteria that gained the highest score was the usability dimension which is 741,63 with a percentage of 81.05 (good criteria). The information quality dimension obtained an average score of 730,57 with a percentage 79.84 (good criteria). While the dimension that obtained the lowest score was the service interaction dimension which was 710,50 with the percentage of 77.10 (good criteria). In addition, in order to accommodate the needs of visitors, this study found the fact that visitors expected website developers to build the website navigate easily, information updated more, implement a comprehensive recommendation system, and more interactive.
Moving Toward 5G: Significance, Differences, and Impact on Quality of Experience

We witness an explosive growth in the volume, velocity, and variety of the data available on the Internet. Such “data explosion” is mostly caused by the mobile devices, sensors, actuators, and social networks. Despite significant technological advancements, the users quality of experience is barely met. The ever-increasing demands of users related to high storage, fast computation, and processing has led toward the innovation of 5G technology. In this article, we conducted a study to highlight the significance of 5G (compared to the existing standards) and its effect on the user perceived quality of experience attributes. We have constructed a comprehensive taxonomy of quality of experience attributes, discussed their importance and how they ultimately lead toward the good quality of service. Moreover, a small experiment is conducted to demonstrate the effect of 5G on certain quality of experience attributes. Results revealed positive impact of 5G on certain experimented quality of experience attributes.
First-Order Rewritability in Consistent Query Answering with Respect to Multiple Keys

We study consistent query answering with respect to key dependencies. Given a (possibly inconsistent) database instance and a set of key dependencies, a repair is an inclusion-maximal subinstance that satisfies all key dependencies. Consistent query answering for a Boolean query is the following problem: given a database instance as input, is the query true in every repair? In [Koutris and Wijsen, ICDT 2019], it was shown that for every self-join-free Boolean conjunctive query and set of key dependencies containing exactly one key dependency per relation name (also called the primary key), this problem is in FO, L-complete, or coNP-complete, and it is decidable which of the three cases applies. In this paper, we consider the more general case where a relation name can be associated with more than one key dependency. It is shown that in this more general setting, it remains decidable whether or not the above problem is in FO, for self-join-free Boolean conjunctive queries. Moreover, it is possible to effectively construct a first-order query that solves the problem whenever such a query exists.
Co-clustering of multivariate functional data for the analysis of air pollution in the South of France

Nowadays, air pollution is a major threat for public health with clear relationships with many diseases, especially cardiovascular ones. The spatiotemporal study of pollution is of great interest for governments and local authorities when deciding for public alerts or new city policies against pollution increase. The aim of this work is to study spatiotemporal profiles of environmental data collected in the south of France (Région Sud) by the public agency AtmoSud. The idea is to better understand the exposition to pollutants of inhabitants on a large territory with important differences in term of geography and urbanism. The data gather the recording of daily measurements of five environmental variables, namely, three pollutants (PM10, NO2, O3) and two meteorological factors (pressure and temperature) over six years. Those data can be seen as multivariate functional data: quantitative entities evolving along time for which there is a growing need of methods to summarize and understand them. For this purpose a novel co-clustering model for multivariate functional data is defined. The model is based on a functional latent block model which assumes for each co-cluster a probabilistic distribution for multivariate functional principal component scores. A stochastic EM algorithm, embedding a Gibbs sampler, is proposed for model inference as well as a model selection criteria for choosing the number of co-clusters. The application of the proposed co-clustering algorithm on environmental data of the Région Sud allowed to divide the region, composed by 357 zones, into six macroareas with common exposure to pollution. We showed that pollution profiles vary accordingly to the seasons, and the patterns are similar during the six years studied. These results can be used by local authorities to develop specific programs to reduce pollution at the macroarea level and to identify specific periods of the year with high pollution peaks in order to set up specific health prevention programs. Overall, the proposed co-clustering approach is a powerful resource to analyse multivariate functional data in order to identify intrinsic data structure and to summarize variables profiles over long periods of time.
Joint Partial Optimal Transport for Open Set Domain Adaptation

Domain adaptation (DA) has achieved a resounding success to learn a good classifier by leveraging labeled data from a source domain to adapt to an unlabeled target domain. However, in a general setting when the target domain contains classes that are never observed in the source domain, namely in Open Set Domain Adaptation (OSDA), existing DA methods failed to work because of the interference of the extra unknown classes. This is a much more challenging problem, since it can easily result in negative transfer due to the mismatch between the unknown and known classes. Existing researches are susceptible to misclassification when target domain unknown samples in the feature space distributed near the decision boundary learned from the labeled source domain. To overcome this, we propose Joint Partial Optimal Transport (JPOT), fully utilizing information of not only the labeled source domain but also the discriminative representation of unknown class in the target domain. The proposed joint discriminative prototypical compactness loss can not only achieve intra-class compactness and inter-class separability, but also estimate the mean and variance of the unknown class through backpropagation, which remains intractable for previous methods due to the blindness about the structure of the unknown classes. To our best knowledge, this is the first optimal transport model for OSDA. Extensive experiments demonstrate that our proposed model can significantly boost the performance of open set domain adaptation on standard DA datasets.
Would you Rather? A New Benchmark for Learning Machine Alignment with Cultural Values and Social Preferences

Understanding human preferences, along with cultural and social nuances, lives at the heart of natural language understanding.Concretely, we present a new task and corpus for learning alignments between machine and human preferences.Our newly introduced problem is concerned with predicting the preferable options from two sentences describing scenarios that may involve social and cultural situations.Our problem is framed as a natural language inference task with crowd-sourced preference votes by human players, obtained from a gamified voting platform.We benchmark several state-of-the-art neural models, along with BERT and friends on this task.Our experimental results show that current state-ofthe-art NLP models still leave much room for improvement.
Overlap Avoidance of Mobility Models for Multi-UAVs Reconnaissance

As avionics technologies have advanced, it is possible to perform many aerial applications which demand cooperative work with multiple Unmanned Aerial Vehicles (UAVs). Since one of the basic applications is reconnaissance, we focus on efficient cooperative reconnaissance. While random mobility models are useful for multi-UAVs reconnaissance, they suffer from overlapped reconnaissance problem that two or more UAVs reconnoiter a region at the same time. The overlapped reconnaissance also leads to imbalanced reconnaissance in which an area scanned by one UAV may be re-visited soon by the other UAV. Thus, we provide overlap avoidance schemes for the existing reconnaissance mobility models and enhance their performance. Throughout the simulations, we evaluate the effect of applying overlap avoidance in the existing models. The simulation results show that overlapped area is reduced by up to 20 times and 90%-coverage reaching time is improved by up to 19%.
A user‐centered behavioral software development model

Abstract The concept of usability, which affects the quality and cost of the software projects, has become essential in recent years. There is a need to integrate usability testing activities and techniques into the development processes; however, integration is difficult. Therefore, this study proposes user‐centered behavioral (UCB) software development model, which aims to bridge the gap between human–computer interaction (HCI) and software engineering (SE) disciplines. A triangulation methodology of mixed methods is used to understand the current situation in software development companies in terms of usability and user‐centered software development by survey and interviews. Their results are then used to inform the development of the proposed model. The UCB software development model proposes a process in which several tools and techniques from SE and HCI are integrated to facilitate the production of highly usable software. In essence, the model proposes an iterative process that combines behavior‐driven development (BDD)—an agile approach that enables a better understanding and analysis of the requirements—with the style guides, usability tests, heuristic evaluation, and Wizard of Oz techniques of HCI. The model was evaluated in a pilot project and then is improved based on feedback gathered. Participants' feedback supported the effectiveness of the proposed model.
Employment of Fizeau fringe disintegration to magnify small phase variations

When an object produces a small phase variation not less than the minimum phase variation detectable by the used system and more than the system’s noise, the object is considered as a tiny (or small) object. The measurement of interferometric phase differences evolved from a tiny optical phase object has gained attention in recent years due to its usefulness in accurate detection of nanometric dimensions. In the current work, a Fizeau wedge interferometer is used as an aberrationless method to amplify phase variations of a tiny object. The phase object is inserted between the two semi-reflecting/partially transmitting plates making a small wedge angle in between. The fast Fourier transformation (FFT) spectrum of the obtained interferograms is characterized by some frequency equidistant peaks. The number of these peaks is related to the number of the interfered rays and each peak represents a set of two-beam interferences. We applied a mask to select one of these peaks and reproduced its corresponding optical phase map via employing inverse fast Fourier transformation. The produced map contains the magnified phase variation with a magnification factor equal to 2 times the selected peak’s order. Experimental verification of two different phase object samples to amplify their phase variations with different amplification factors is performed. An integrated magnified phase by the object is calculated to test the validity of the proposed method. It is found that the magnification according to the selection of different FFT peaks is a little bit deviated from the expected magnification factors which is referred to ignoring refraction of the optical rays by the object. In spite of these deviations, good results are obtained.
Maximum Roaming Multi-Task Learning

Multi-task learning has gained popularity due to the advantages it provides with respect to resource usage and performance. Nonetheless, the joint optimization of parameters with respect to multiple tasks remains an active research topic. Sub-partitioning the parameters between different tasks has proven to be an efficient way to relax the optimization constraints over the shared weights, may the partitions be disjoint or overlapping. However, one drawback of this approach is that it can weaken the inductive bias generally set up by the joint task optimization. In this work, we present a novel way to partition the parameter space without weakening the inductive bias. Specifically, we propose Maximum Roaming, a method inspired by dropout that randomly varies the parameter partitioning, while forcing them to visit as many tasks as possible at a regulated frequency, so that the network fully adapts to each update. We study the properties of our method through experiments on a variety of visual multi-task data sets. Experimental results suggest that the regularization brought by roaming has more impact on performance than usual partitioning optimization strategies. The overall method is flexible, easily applicable, provides superior regularization and consistently achieves improved performances compared to recent multi-task learning formulations.
SLIDE : In Defense of Smart Algorithms over Hardware Acceleration for Large-Scale Deep Learning Systems

Deep Learning (DL) algorithms are the central focus of modern machine learning systems. As data volumes keep growing, it has become customary to train large neural networks with hundreds of millions of parameters to maintain enough capacity to memorize these volumes and obtain state-of-the-art accuracy. To get around the costly computations associated with large models and data, the community is increasingly investing in specialized hardware for model training. However, specialized hardware is expensive and hard to generalize to a multitude of tasks. The progress on the algorithmic front has failed to demonstrate a direct advantage over powerful hardware such as NVIDIA-V100 GPUs. This paper provides an exception. We propose SLIDE (Sub-LInear Deep learning Engine) that uniquely blends smart randomized algorithms, with multi-core parallelism and workload optimization. Using just a CPU, SLIDE drastically reduces the computations during both training and inference outperforming an optimized implementation of Tensorflow (TF) on the best available GPU. Our evaluations on industry-scale recommendation datasets, with large fully connected architectures, show that training with SLIDE on a 44 core CPU is more than 3.5 times (1 hour vs. 3.5 hours) faster than the same network trained using TF on Tesla V100 at any given accuracy level. On the same CPU hardware, SLIDE is over 10x faster than TF. We provide codes and scripts for reproducibility.
Anomaly Detection in Mobile Networks

With the widespread usage of 4G technologies and the upcoming promise of 5G networks, there is a strong need for increased network performance and reliability. However, as these networks become bigger and faster, so does their complexity. Currently, network operators detect most of the network failures manually. This is a very time consuming and tedious task for them, oftentimes taking up to several hours. Thereby arises a need for an automated Anomaly Detection and Correction system. Such a system would be a step towards the ultimate goal of a cognitive self-organizing network. We here take the case of a mobile network with hundreds of key performance indicators, which generates huge amount of network logs every hour. Since user behavior has patterns in usage, e.g. weekdays network traffic will be higher than weekendâ€™s traffic near office areas, we analyze a Time Series (TS) Decomposition based approach, which takes into consideration of trends and seasonality in data. We also explore the use of a seasonal auto-regressive technique, SARIMA, for anomaly detection. Assuming that an anomalous behaviour is continuous in time, we evaluate a recurrent encoder-decoder based approach, MSCRED for Anomalous Window Detection. We do this analysis to find the KPI and the respective network element, whose behavior is abnormal. Our results show that while Time Series Decomposition outperforms SARIMA over single point anomaly detection, MSCRED significantly performs well in predicting anomalous time windows.
A Distributed Covert Channel of the Packet Ordering Enhancement Model Based on Data Compression

Covert channel of the packet ordering is a hot research topic. Encryption technology is not enough to protect the security of both sides of communication. Covert channel needs to hide the transmission data and protect content of communication. The traditional methods are usually to use proxy technology such as tor anonymous tracking technology to achieve hiding from the communicator. However, because the establishment of proxy communication needs to consume traffic, the communication capacity will be reduced, and in recent years, the tor technology often has vulnerabilities that led to the leakage of secret information. In this paper, the covert channel model of the packet ordering is applied into the distributed system, and a distributed covert channel of the packet ordering enhancement model based on data compression (DCCPOEDC) is proposed. The data compression algorithms are used to reduce the amount of data and transmission time. The distributed system and data compression algorithms can weaken the hidden statistical probability of information. Furthermore, they can enhance the unknowability of the data and weaken the time distribution characteristics of the data packets. This paper selected a compression algorithm suitable for DCCPOEDC and analyzed DCCPOEDC from anonymity, transmission efficiency, and transmission performance. According to the analysis results, it can be seen that DCCPOEDC optimizes the covert channel of the packet ordering, which saves the transmission time and improves the concealment compared with the original covert channel.
Automatic Grading System Using Sentence-BERT Network

The integration of digital learning technologies into higher education enhances students' learning by providing opportunities such as online examinations. However, many online examinations tend to have multiple-choice questions, as the marking of text-based questions can be a tedious task for academic staff, especially in large classes. In this study, we utilised SBERT, a pre-trained neural network language model to perform automatic grading of three variations of short answer questions on an Introduction to Networking Computer Science subject. A sample of 228 near-graduation Information Science students from one research-intensive tertiary institution in West African participated in this study. The course instructor manually rated short answers provided by the participants, using a scoring rubric and awarded scores ranging from 0 to 5. Some of the manually graded students' answers were randomly selected and used as a training set to fine-tune the neural network language model. Then quadratic-weighted kappa (QWKappa) was used to test the agreement level between the ratings generated by the human rater compared with that of the language model, on three variations of questions, including description, comparison and listing. Further, the accuracy of this model was tested on the same questions. Overall results showed that the level of the inter-rater agreement was good on the three variety of questions. Also, the accuracy measures showed that the model performed very well on the comparison and description questions compared to the listing question.