Reviewerly/exHarmony huggingface dataset:
A Short Text Classification Approach with Event Detection and Conceptual Information

Text classification is an elementary task in Natural Language Processing (NLP). Existing methods, such as Long Short-Term Memory Networks (LSTM) and Attention Mechanism have recently achieved strong performance on multiple NLP related tasks. However, in the field of text classification, their results are often limited by the quality of feature extraction. This phenomenon is particularly prominent in short text classification tasks, since short text does not have enough contextual information compared to paragraphs and documents. To address this challenge, in this article, we propose a method to enhance the semantic information of short text with two aspects: event-level information extracted from text and conceptual information retrieved from external knowledge base. We take event and conceptual information as a type of supplementary knowledge and incorporate it into deep neural networks. Attention mechanism is utilized to measure the importance of the supplementary knowledge. Meanwhile, we have discussed the granularity selection for Chinese word segmentation, and select char-based models. Finally, we classify a short text with the help of event and conceptual information. The experimental results show that the proposed method outperforms the state-of-the-art methods.
Efficient Facial Landmark Localization Based on Binarized Neural Networks

Facial landmark localization is a significant yet challenging computer vision task, whose accuracy has been remarkably improved due to the successful application of deep Convolutional Neural Networks (CNNs). However, CNNs require huge storage and computation overhead, thus impeding their deployment on computationally limited platforms. In this paper, to the best of our knowledge, it is the first time that an efficient facial landmark localization is implemented via binarized CNNs. We introduce a new network architecture to calculate the binarized models, referred to as Amplitude Convolutional Networks (ACNs), based on the proposed asynchronous back propagation algorithm. We can efficiently recover the full-precision filters only using a single factor in an end-to-end manner, and the efficiency of CNNs for facial landmark localization is further improved by the extremely compressed 1-bit ACNs. Our ACNs reduce the storage space of convolutional filters by a factor of 32 compared with the full-precision models on dataset LFW+Webface, CelebA, BioID and 300W, while achieving a comparable performance to the full-precision facial landmark localization algorithms.
Explaining Failure: Investigation of Surprise and Expectation in CNNs

Ai Convolutional Neural Networks (CNNs) have expanded into everyday use, more rigorous methods of explaining their inner workings are required. Current popular techniques, such as saliency maps, show how a network interprets an input image at a simple level by scoring pixels according to their importance. In this paper, we introduce the concept of surprise and expectation as means for exploring and visualising how a network learns to model the training data through the understanding of filter activations. We show that this is a powerful technique for understanding how the network reacts to an unseen image compared to the training data. We also show that the insights provided by our technique allows us to "fix" misclassifica- tions. Our technique can be used with nearly all types of CNN. We evaluate our method both qualitatively and quantitatively using ImageNet.
The 17th Top Chess Engine Championship, TCEC17

TCEC17 was the 17th season of the Top Chess Engine Championship and ran from January 2nd to 21st April, 2020. TCEC has become the largest Open Computer Chess Championship. It attracts the best engines in the field and provides an opportunity for a comparative analysis of the Shannon-AB and the new Neural-Network engines’ styles of play. LEELA CHESS ZERO took the Grand Champion title from STOCKFISH again with +5 in an exciting Superfinal. The attached files at http://centaur.reading.ac.uk/89999 provide the 1300 games with engine PVs, the detail on them, some summary statistics and the completion to mate of the wins.
Broadcast Complexity and Adaptive Adversaries in Verifiable Secret Sharing

Verifiable secret sharing (VSS) is one of the basic problems in the theory of distributed cryptography and has an important role in secure multiparty computation. In this case, it is tried to share a confidential data as secret, between multiple nodes in a distributed system, in the presence of an active adversary that can destroy some nodes, such that the secret can be reconstructed with the participation of certain size of honest nodes. A dynamic adversary can change its corrupted nodes among the protocol. So far, there is not a formal definition and there are no protocols of dynamic adversaries in VSS context. Also, another important question is, would there exist a protocol to share a secret with a static adversary with at most 1 broadcast round? In this paper, we provide a formal definition of the dynamic adversary. The simulation results prove the efficiency of the proposed protocol in terms of the runtime, the memory usage, and the number of message exchanges. We show that the change period of the dynamic adversary could not happen in less than 4 rounds in order to have a perfectly secure VSS, and then we establish a protocol to deal with this type of adversary. Also, we prove that the lower bound of broadcast complexity for the static adversary is (2,0)-broadcast rounds.
Multi-Stage Trajectory Optimization for Multiple Aircraft Approaching an Airport

In the paper, a multi-stage approach for numerical trajectory optimization of multiple aircraft in the vicinity of an airport is presented. The method for solving the problem consists of 5 stages where the first four solve the problem of ordering, fixing the final times and hierarchical distance separation, and the solution of the hierarchical formulation is then used as the initial guess for the combined problem with pairwise distance constraints. The approach proved to be very efficient in terms of computation speed resulting in computation time of less than 3 minutes for all stages of solving the trajectory optimization problem for 5 aircraft. The numerical experiments were carried out with MUSCOD-II optimization software.
A Case Study: Performance Comparison of Solar Power Generation between GridLAB-D and SAM in Dili Timor Leste

Study of comparison of solar power generation between the GridLAB-D tool and System Advisor Model (SAM) in Dili, Timor Leste is presented in this paper.Weather Research and Forecasting (WRF) model is used to simulate solar radiation for one calendar year from January to December 2014 using six-hourly interval 1˚ × 1˚ NCEP FNL analysis data.The one calendar year results from the WRF model will be used as input data for GridLAB-D and SAM to estimate the solar power generation.GridLAB-D is an open-source and analysis tool designed to operate the distribution power systems with a high-performance algorithm.System Advisor Model version SAM 2017.9.5 is used to estimate solar power performance with Photovoltaics (PVWatts)-Commercial Distributed model.This model is designed to analyze the performance and the financing of renewable energy for electricity generation.The results show the lowest solar radiation is 512 W/m 2 obtained in June with an average monthly power of 20.6 kW and 30.55 kW generated from the SAM model and the GridLAB-D simulator, respectively.Meanwhile, the highest solar radiation is 1100 W/m 2 , 1112 W/m 2 , 1046 W/m 2 , and 1077 W/m 2 obtained in October, November, December, and January with an average monthly power of 55.72 kW, 62.44 kW, 56.65 kW, and 56.97 kW from the SAM model, in the other hand, 48.89 kW, 51.31 kW, 55.51 kW, and 57.18 kW generated by the GridLAB-D.Finally, the results show that the performance of the GridLAB-D and the SAM model was quite good because both model precisely presented values are almost closest to each other.This study proposes that the results of solar output power from both methods, GridLAB-D and SAM can be used to design grid-connected or stand-alone electric power projects to increase the quality of electricity generation in Dili, Timor Leste.
Artificial Bee Colony – Modifications and An Application to Software Requirements Selection

In this chapter, a brief description of the basic Artificial Bee Colony (ABC) is provided and modified versions of the basic algorithm for various kinds of problems including constrained, multi-objective and combinatorial problems are described in terms of representation, local search operator, selection operator, objective function definition. In the second part of the chapter, an application of the ABC algorithm to a real world problem, software requirements selection, is addressed by considering the representation, objective function definition, local search and selection operators. We demonstrate the profit and satisfied cost produced by the software requirements determined by the ABC algorithm.
Range-scattering Information and Range Resolution of Multiple Target Radar

Radar super-resolution technology studies have shown that the resolution of radar systems can exceed the traditional physical limit resolution. However, the super-resolution technology lacks analysis from aspects of information theory. In this paper, we propose a new definition of range resolution according to a series of information- theoretic analysis of the radar system for multiple target detection. With the definition, we prove the achievability of the super-resolution technology from the perspective of information theory. First, we derive a theoretical expression of range information and closed-form expression of scattering information for multiple complex Gaussian scattering targets in the presence of complex additive white Gaussian noise (CAWGN). Then, we define the new range resolution in the view of information theory and derive the upper bound of the range resolution 2/ρβ, which is different from the traditional range resolution 1/B, where ρ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> denotes the signal-to-noise ratio (SNR), B denotes the bandwidth and β <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> denotes the root mean square bandwidth. Finally, We use two-dimensional maximum likelihood estimation (MLE) to verify that the range resolution can be less than 2/ρβ in the high SNR regime, which is different from the one-dimensional MLE used in traditional range resolution.
Exploring a Humanoid Video-Understanding Algorithm Guided by Behavior

To make current artificial intelligence systems flexibly explore causal information between behaviors and understand events based on human language habits, we propose a hybrid 2D/3D convolutional network guided by human behavior, which aims to explore a new machine understanding video model by focusing on human activities.
Distributed Multi-agent Joint Time Alignment and State Estimation

This paper deals with distributed state estimation (DSE) over a peer-to-peer (P2P) multi-agent system (MAS) with time-misaligned agents. A Bayes filter, which recursively computes the state posterior, is run at each agent and the Generalized Covariance Intersection (GCI) fusion is employed to aggregate the local posteriors of agents. However, due to the lack of a common time reference, time alignment (TA) among agents needs to be carried out jointly with state estimation. To this end, a cost function of the TA parameters is constructed by exploiting an information-theoretic interpretation of GCI fusion, so that TA can be accomplished by minimizing such cost. Further, an on-line optimization strategy is designed so as to recast the cost function into a quadratic form which can be easily minimized in real-time. The performance of the proposed distributed joint state estimation and TA algorithm is assessed by simulation experiments.
LEARNING WITH REAL-WORLD AND ARTIFICIAL DATA FOR IMPROVED VEHICLE DETECTION IN AERIAL IMAGERY

Abstract. Detecting objects in aerial images is an important task in different environmental and infrastructure-related applications. Deep learning object detectors like RetinaNet offer decent detection performance; however, they require a large amount of annotated training data. It is well known that the collection of annotated data is a time consuming and tedious task, which often cannot be performed sufficiently well for remote sensing tasks since the required data must cover a wide variety of scenes and objects. In this paper, we analyze the performance of such a network given a limited amount of training data and address the research question of whether artificially generated training data can be used to overcome the challenge of real-world data sets with a small amount of training data. For our experiments, we use the ISPRS 2D Semantic Labeling Contest Potsdam data set for vehicle detection, where we derive object-bounding boxes of vehicles suitable for our task. We generate artificial data based on vehicle blueprints and show that networks trained only on generated data may have a lower performance, but are still able to detect most of the vehicles found in the real data set. Moreover, we show that adding generated data to real-world data sets with a limited amount of training data, the performance can be increased significantly, and in some cases, almost reach baseline performance levels.
Mathematical Models of Formation and Functioning of Teams of Software Systems Developers

A systematic review was conducted and a number of original results were presented, which are not exhaustive in the study of mathematical models of formation and functioning of members of software development teams using cloud technology. However, they reflect the general methodology of construction and study of applied mathematical models of dynamic functioning of firm’s developers of software systems, and also can be effectively used in solving problems of wide class of management of social and economic systems. From the point of view of the theory of research of mathematical models it is necessary to recognize that various results of research of commands of developers of the program systems received in psychology and sociology, today in formal models find insufficiently full reflection. For many models, there are certain difficulties in obtaining analytical solutions. For example, such a common in practice class of formation and existence of teams, taking into account the psychological of each team member in program engineering teams, is not yet a subject of deep systemic, formal, theoretical research.
Local projections for high-dimensional outlier detection

Abstract A novel approach for outlier detection is proposed, called local projections, which is based on concepts of the Local Outlier Factor (LOF) (Breunig et al. in Lof: identifying density-based local outliers. In: ACM sigmod record, ACM, volume 29, pp. 93–104, 2000) and ROBPCA (Hubert et al. in Technometrics 47(1):64–79, 2005). By using aspects of both methods, this algorithm is robust towards noise variables and is capable of performing outlier detection in multi-group situations. The idea is to focus on local descriptions of the observations and their neighbors using linear projections. The outlyingness of an observation is determined by a weighted distance of the observation to all identified projection spaces, with weights depending on the appropriateness of the local description. Experiments with simulated and real data demonstrate the usefulness of this method when compared to existing outlier detection algorithms.
Lightweight Ciphers on a 65 nm ASIC A Comparative Study on Energy Consumption

Low energy consumption is an important factor in today's technologies as many devices run on a battery and there are new applications which require long runtimes with very small batteries. As many of these devices are connected to some kind of network, they require encryption/decryption to securely transmit data. Hence, the energy consumption of the cipher is an important factor for the battery life. We evaluate the energy consumption of lightweight ciphers implemented on a custom 65 nm ASIC. Since the energies to measure are very small, we first introduce, compare and evaluate two techniques to precisely measure the energy consumption of a real cryptographic core. In our comparative investigations, using the PRINCE block cipher we examine the effect of the design architecture (round-based versus unrolled) on the amount of energy consumption. In addition to considering other effects (like fixed key versus random key), we compare round-based implementations of different block ciphers (PRINCE, MIDORI and SKINNY) under similar settings providing first such practical investigations.
Investigating Best Practices in the Design of Automated Feedback to Improve Students' Performance and Learning

Timely feedback is essential for students to learn and improve their performance. However, it is hard for computing instructors to provide real-time feedback for every student, particularly during homework or online classes. While researchers have put tremendous effort into developing algorithms to generate automated feedback, little work has evaluated how this feedback actually helps in practice, or what specific design choices make it more or less effective. In my dissertation, I am designing and evaluating different features of automated feedback, specifically next-step hints. Inspired by educational theories and effective human feedback, this work has the goal of discovering automated feedback design choices that can improve students' performance and learning.
Bio-inspired Collaborative and Content Filtering Method for Online Recommendation Assistant Systems

The authors present a hybrid model of a recommender system. The system includes the characteristics of collaborative and content filtering. Also, the article describes a population filtering algorithm and the architecture of a recommendation system based on it. The results of experimental studies on an array of benchmarks and an estimation of filtering efficiency based on a hybrid model and a population algorithm are presented. The results are compared with the traditional method of collaborative filtering using the Pearson correlation coefficient.
Face recognition based on selection approach via Canonical Correlation Analysis feature fusion

Face matching is an active research topic in the last decade due to various applications in pattern recognition. Rather than using a single feature type, the fusion of many distinct features might decrease the error rate of facial recognition systems. This also increases the time processing and data storage. In this paper, we first employ feature fusion extracted from HOG and GIST descriptor from facial image and use Canonical Correlation Analysis (CCA) to combine into a single feature. Then, a feature selection approach based on Fisher ranking is considered to remove irrelevant and noisy features. The experiment is evaluated on three common datasets (AR, Georgia Tech and MUCT) which have been shown the improvement of the proposed approach.
Analysis, Study and Optimization of Chaotic Bifurcation Parameters Based on Logistic/Tent Chaotic Maps

In recent years, chaotic sequences attracted much attention due to its attractive features. It is found that chaotic sequences are very sensitive to the initial conditions. This feature gives the ability to generate large number of sequences with special correlation properties. Chaotic sequences are generally generated from specific chaotic maps with specific control parameters. These control parameters are the initial conditions in addition to the bifurcation parameters. Actually, it’s found that the bifurcation parameters have a series effect on the attitude of the output sequences and its correlation properties, especially in the multiple access applications. The contribution of this paper is to study, analyze and optimize the effect of the bifurcation parameters of the chaotic map. The applied chaotic maps are the logistic and tent map as a model of the 1D chaotic maps. The study, analysis and optimization process are performed based on the correlation properties in presence of different number of chaotic sequences.
Offering ‘Home’ Protection to Private Digital Storage Spaces

The law classically provides strong protection to whatever is inside a home.That protection is lost now that our photo albums, notes and other documents have become digital and are increasingly stored in the cloud.Even if their owner never intended these documents to be shared, their copies in the cloud may be accessed by law enforcement, under possibly lower conditions than apply to home searches.In this paper, we study this problem from a theoretical perspective, asking whether it is possible to establish home-equivalent legal protection of those private digital storage spaces (smartphones, private cloud storage accounts) that most closely resemble the home as a storage environment for private things.In particular, we study whether it is possible, using technological design, to clearly separate digital storage spaces that are used privately versus storage spaces used to share data with others.We sketch a theoretical architecture for such a 'digital home' that most closely resembles the physical home in terms of the space that is the most personal storage environment for private files.The architecture guarantees the data are indeed only stored for private use, and can never be shared with others unless the device used for storage itself is shared.We subsequently argue
Introduction to machine learning: Support vector machines, tree-based models, clustering, and explainability

The previous chapter introduced several foundational concepts in machine learning and provided a general overview of what modeling from data and “learning” really entails. As part of that discussion, we introduced two key machine learning models in the form of linear regression and logistic regression. We also covered a breadth of additional topics such as cost, regularization, and optimization. In this chapter, we will build upon those concepts to discuss additional important model families: support vector machines, clustering, and decision trees. Finally, we will conclude this chapter with a brief discussion of model explainability, which is critical both for understanding the behavior of a trained model and in cultivating trust around the use of that model on previously unseen data. After reading this chapter, you should have a broad understanding of the common forms machine learning takes as well as many of the most common considerations around model design and evaluation. This will not only help you perform analysis and create models, but this knowledge will also greatly enhance your understanding of the research literature in the space of healthcare analytics, which commonly invokes these concepts. Lastly, the content of these two chapters lays the foundation for understanding the coming chapter regarding deep learning.
Sector-Selective Hybrid Scheme Facilitating Hardware Supportability Over Image Compression

Image compression is one of the inevitable operation demands for any form of transmission as well as storage optimization services. Review of existing literatures towards the compression scheme shows that there are further scope of improvement to be carried out to ensure an effective realization of hardware implementation of the cost effective image compression. Therefore, this paper presents a computational model that is constructed for facilitating an effective hardware realization of an effective hybrid compression operation. The proposed system introduces a selective sector of an image to be subjected to the lossless image compression while the other parts of the image are subjected to the lossy image compression scheme. Adopting analytical research based scheme, the outcome of the study is found to offer a better signal quality for the reconstructed image and effective compression performance.