Reviewerly/exHarmony huggingface dataset:
Rule-Based Reasoners in Epistemic Logic

In this paper, we offer a balanced response to the problem of logical omniscience, whereby agents are modeled as non-omniscient yet still logically competent reasoners. To achieve this, we account for the deductive steps that form the epistemic state of an agent. In particular, we introduce operators for applications of inference rules and design a possible-worlds model which is (a) equipped with a syntactic valuation, determining the agent’s (explicit) knowledge, and (b) suitably structured by rule-induced transitions between worlds. As a result, we obtain a detailed analysis of the agent’s reasoning processes. We then offer validities that exemplify how the problem of logical omniscience is avoided and compare our response to others in the literature. A sound and complete axiomatization is also provided. We finally show how simple extensions of this setting make it compatible with tools from Dynamic Epistemic Logic (DEL) and open to the incorporation of empirical findings on human reasoning.
A Distributed Wheel Sieve Algorithm

This paper presents a new distributed approach for generating all prime numbers in a given interval of integers. From Eratosthenes, who elaborated the first prime sieve (more than 2000 years ago), to the current generation of parallel computers, which have permitted to reach larger bounds on the interval or to obtain previous results in a shorter time, prime numbers generation still represents an attractive domain of research and plays a central role in cryptography. We propose a fully distributed algorithm for finding all primes in the interval [2; n], based on the wheel sieve and the SMER (Scheduling by Multiple Edge Reversal) multigraph dynamics which runs in O(√(n)) computational complexity, close to the theoretical lower bound on sieve methods, that is O(n), without making use of preprocessing techniques.
Automated Large-Scale Multi-Language Dynamic Program Analysis in the Wild (Tool Insights Paper)

Today's availability of open-source software is overwhelming, and the number of free, ready-to-use software components in package repositories such as NPM, Maven, or SBT is growing exponentially. In this paper we address two straightforward yet important research questions: would it be possible to develop a tool to automate dynamic program analysis on public open-source software at a large scale? Moreover, and perhaps more importantly, would such a tool be useful? We answer the first question by introducing NAB, a tool to execute large-scale dynamic program analysis of open-source software in the wild. NAB is fully-automatic, language-agnostic, and can scale dynamic program analyses on open-source software up to thousands of projects hosted in code repositories. Using NAB, we analyzed more than 56K Node.js, Java, and Scala projects. Using the data collected by NAB we were able to (1) study the adoption of new language constructs such as JavaScript Promises, (2) collect statistics about bad coding practices in JavaScript, and (3) identify Java and Scala task-parallel workloads suitable for inclusion in a domain-specific benchmark suite. We consider such findings and the collected data an affirmative answer to the second question.
Budgeting for Agile Product Development

Abstract Recent reports and predictions indicate a consistent and continuous growth in the field of Research and Development. Such growth leads to increased resource investments, which have to be managed effectively to eventually achieve value maximization. This management is cohesive with budgeting. In changing environments, said effectiveness can be difficult to attain. Agile development is supposed to provide the necessary flexibility for uncertain situations and has recently seen a stark adoption incline. Unfortunately, budgeting and resource allocation have not yet been resolved for agile approaches: a comprehensive research including recent publications showed a lack of models and frameworks for the adoption and application of budgeting with agile development. Due to this lack of a comprehensive approach, as well as limitations and restrictions of existing research, this paper describes the design of a budgeting approach suitable for and compatible with agile product development. The developed solution, the Structured Agile Budgeting Process, provides a holistic and interdisciplinary way to allocate resources while still allowing the flexibility and benefits of agile development.
Multi-scale Densely Connected Dehazing Network

Single image dehazing is a challenging ill-posed problem. The traditional methods mainly focus on estimating the transmission of atmospheric-light medium with some priors or constraints. In this paper, we propose a novel end-to-end convolutional neural network (CNN) for image dehazing, called multi-scale densely connected dehazing network (MDCDN). The proposed network consists of a parallel multi-scale densely connected CNN network and an encoder-decoder U net. The parallel multi-scale dense-net can estimate transmission map accurately. The encoder-decoder U net is used to estimate the atmospheric light intensity. The all-in-one training can jointly learn the transmission map, atmospheric light, and dehazing images all together with jointly MSE error and a discriminator loss. We also create a dataset with indoor and outdoor data based on the LFSD, NLPR, and NYU2 depth datasets to train our network. Extensive experiments demonstrate that, in most cases, the proposed method achieves significant improvements over the state-of-the-art methods.
The Error Linear Complexity Spectrum as a Cryptographic Criterion of Boolean Functions

The error linear complexity spectrum constitutes a well-known cryptographic criterion for sequences, indicating how the linear complexity of the sequence decreases as the number of bits allowed to be modified per period increases. In this paper, via defining an association between <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2^{n}$ </tex-math></inline-formula> -periodic binary sequences and Boolean functions on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> variables, it is shown that the error linear complexity spectrum also provides useful cryptographic information for the corresponding Boolean function <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$f$ </tex-math></inline-formula> - namely, it yields an upper bound on the minimum Hamming distance between <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$f$ </tex-math></inline-formula> and the set of functions depending on fewer number of variables. Therefore, the prominent Lauder-Paterson algorithm for computing the error linear complexity spectrum of a sequence may also be used for efficiently determining approximations of a Boolean function that depend on fewer number of variables. Moreover, it is also shown that, through this approach, low-degree approximations of a Boolean function can be also obtained in an efficient way.
Efficient algorithmic evaluation of correlation power analysis: Key distinguisher based on the correlation trace derivative

Correlation power analysis (CPA) is one of the most common side-channel attacks today, posing a threat to many modern ciphers, including AES. In the final step of this attack, the cipher key is usually extracted by the attacker by visually examining the correlation traces for each key guess. The naïve way to extract the correct key algorithmically is selecting the key guess with the maximum Pearson correlation coefficient. We propose another key distinguisher based on a significant change in the correlation trace rather than on the absolute value of the coefficient. Our approach performs better than the standard maximization, especially in the noisy environment, and it allows to significantly reduce the number of acquired power traces necessary to successfully mount an attack in noisy environment, and in some cases make the attack even feasible.
Measuring Class Cohesion Using Internal and External Class Relationships

Class cohesion is a design quality that has a great impact on posterior development and maintenance of object-oriented systems. There have been much works on measuring class cohesion based on internal class relationships such as method-attribute referencing and internal method invocations. However, object-oriented systems involve many external class relationships carrying a functional context which should be also taken into account in measuring class cohesion. However, the existing works using internal relationships cannot measure cohesion of classes involving many external relationships. In this paper, we present a novel metric for measuring class cohesion based on both internal and external class relationships through an iterative process. We evaluate the metric by applying it to five applications — ArgoUML, jEdit, JHotDraw, JMeter, and Refactor, and validate the metric theoretically and statistically. The theoretical validation shows that the metric exhibits the required properties for cohesion metrics and the statistical validation shows that the metric captures a new aspect of class cohesion that has not been addressed by the existing works.
Precipitation Modeling and Prediction Based on Fuzzy-Control Multi-cellular Gene Expression Programming and Wavelet Transform

Accurate and timely precipitation prediction is very important to development and management of regional water resources, flood disaster prevention/control and people’s daily activities and production plans. However, the prediction accuracy is greatly affected by nonlinear and non-stationary features of precipitation data and noise. Many researches show that Multi-cellular Gene Expression Programming (MC-GEP) algorithm has strong function mining ability. This paper designed a Fuzzy-control Multi-cellular Gene Expression Programming algorithm (FMC-GEP) based on Multi-cellular Gene Expression Programming, which used fuzzy control theory to dynamically adjust the probability of genetic manipulation. Then we coupled Fuzzy-control Multi-cellular Gene Expression Programming algorithm with wavelet transform to develop a novel algorithm for precipitation prediction (abbreviated as WT_FMC-GEP). To verify the prediction performance, we conducted experiments using RMSE and MAE as evaluation metrics on the real precipitation data sets in three regions of different continents where climate vary widely. The results show that the WT_FMC-GEP algorithm outperforms other existing prediction algorithms including BP neural network, support vector regression and Gene Expression Programming. It also outperforms such algorithm based on MC-GEP and wavelet transform, thus having a good application prospect.
Survey on Sketch Based Image and Data Retrieval

Sketch Based Image Retrieval (SBIR) is one of the efficient way of image mining. By considering growth in multimedia technologies, demand of image retrieval increased nowadays. CBIR work on shape, color, texture like properties of an image, where as SBIR works on query by sketch input. This paper presents survey about different image retrieval techniques and data retrieval methods. This paper includes various approaches for image and data retrieval from an image.
Continuous Path Planning for Free-Floating Space Manipulator Based on Genetic Algorithm

In this paper, a path planning method based on Genetic algorithm for free-floating space manipulator is proposed. Considering the non-holonomic characteristics of free-floating space manipulator, the kinematics model and dynamics coupling model are established first. In order to ensure the smoothness and continuity of the joint trajectory, the joint angle is parameterized by sinusoidal function with its arguments in fifth-order polynomial form. And then objective function is designed considering the end-effector position, the obstacle avoidance, and the trajectory cost. Finally, the Genetic algorithm is employed to search for the suitable values of the polynomial coefficients to optimize the objective function. The simulation verifies the effectiveness of the proposed method.
Speeding up Gaussian Belief Space Planning for Underwater Robots Through a Covariance Upper Bound

Existing belief space motion planning methods are not efficient for underwater robots that are subject to spatially varying motion and sensing uncertainties arising from the non-uniform current disturbances and landmark populations, respectively. Based on a closed-loop stochastic control framework, we propose a fast Gaussian belief space planning approach for coupled optimization of trajectory, localization and control, resulting in a non-linear programming problem (NLP). In particular, as opposed to advancing the covariance by a Kalman filter in the existing literature, we utilize an upper bound of the trace propagation of the covariance, thereby avoiding to solve Riccati equations and thus, reducing the computational complexity. The NLP is then efficiently solved by sequential quadratic programming based on the initial solutions obtained from RRT-connect. These initials lie in multiple homotopy classes guaranteed by H-signature discrimination, leading to global optimality with probability one as the number of samples in RRT-connect goes to infinity. Numerical simulations on holonomic and non-holonomic autonomous underwater vehicles (AUVs) and an Intervention-AUV with a manipulator in cluttered underwater environments demonstrate that optimal and collision-free trajectories with low localization uncertainty are obtained efficiently.
An Efficient Image Quality Assessment Guidance Method for Unmanned Aerial Vehicle

More and more advanced unmanned aerial vehicles (UAVs) equipped with different kinds of sensors can acquire images of various scenes from tasks. Some of them have to assess the obtained images first and then decide the subsequent actions like humans. Accurate and fast image quality assessing capability is critical to UAV. One or more objective quality indexes are usually selected by UAV to assess all the whole image, which may lead to inefficient evaluation performance. In order to further link human cognition pattern with intelligent vision system and provide useful guidance to shorten the image quality assessment time for UAV, a new experimental method of subjective image assessment based on local image is proposed in this paper. 60 participants are invited to conduct subjective image quality assessment experiment, in which 15 original images including people, scenery and animals are distorted by four methods, i.e., Gaussian additive white noise, Gaussian blur, jpeg compression and jp2k compression. Moreover, a new local image segmentation method is designed to segment each image into 6 local areas. For the subjective scores, global-local correlation is analyzed by Spearman Rank Order Correlation Coefficient (SROCC). The experimental results show that the global subjective assessment has the strongest correlation with the local subjective assessment having the best image quality. Further analysis shows that the local images with the best quality often have sufficient color information and rich texture details. Assessing the local images instead of the global ones provides a shortcut to design objective evaluation algorithms, which is a practical guidance for UAV to perform efficient images quality assessment.
Alleviating the New Item Cold-Start Problem by Combining Image Similarity

Cold-start scenarios in recommender systems are situations in which no historical behavior, like ratings or clicks, are known for certain users or items. Aiming at the cold-start problem caused by the addition of the new item in the recommender system, this paper proposed a collaborative filtering recommendation model (USPMF-CFIA) based on matrix factorization model, which combines the similarity of item image and category attributes. First, it used the matrix factorization model based on users' preference to predict and fill the missing rating items. Then, it used the VGG16 neural network to extract the features of the item images and combined category attributes to calculate the similarity between the new item and the historical items, then got the item's neighbors. Finally, the new item's score was predicted based on the similarity between the new item and the neighbors, and the top N items with high scores are recommended to the user. The experiment on the dataset provided by GroupLens proved that this model is more accurate.
Continuous Thinking Aloud

Thinking Aloud is a method that allows the collection of expressive user feedback for software improvement. However, its frequent application in a rapid development processes such as Continuous Software Engineering (CSE) is challenging, since repetitively performing manual observations and evaluations demand high effort. We propose the Continuous Thinking Aloud (CTA) approach for conducting Thinking Aloud during CSE. CTA records speech feedback for a user who starts using a new feature increment. The recordings are automatically transcribed and classified into one of four feedback categories that differentiate between insecure, neutral, positive, and negative sentiments. CTA visualizes these feedback classifications on a sentence level, next to its related high-level change of the feature increment. This supports developers in problem discovery, in particular regarding usability. CTA integrates with CSE processes and represents a scalable approach enabling repeated application during the software development lifecycle.
Hypothesis Testing Based Side-Channel Collision Analysis

Side-channel collision analysis has become a research hotspot since its first publication in 2003. Compared with differential power analysis (DPA) and correlation power analysis (CPA), collision analysis does not need to know the intermediate value and does not depend on a specific power model. Collision analysis includes collision detection phase and key recovery phase. After collisions have been detected, the cryptographic key can be recovered from these collisions using such cryptanalytic techniques as linear collision attack and algebraic collision attacks. Therefore, the success rate of the entire attack depends largely on the success rate of collision detection. In this paper, we propose an efficient collision detection method, which combines side channel information with hypothesis testing in statistics. With the traces gathering by repeatedly encrypting a certain plaintext that is unnecessary to be known, we are able to evaluate the cumulative distance between two collision S-boxes, then, define a null hypothesis H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> and calculate the rejection region accordingly. Given some traces collected from repeatedly encrypting any identical plaintext, collisions can be detected by determining whether the cumulative distance between the corresponding two S-boxes falls into the rejection region or not. We compare our work with those related to side channel collision attack in recent years from perspective of four hot issues. Comparing with current methods, our proposed collision detection method is simpler and more feasible. Furthermore, the experiments show that the more accurate the constant power is, the higher the success rate of collision detection is.
A Novel Compression Algorithm for Hardware-Oriented Gradient Boosting Decision Tree Classification Model

Gradient boosting decision tree is a widely used machine learning algorithm. As the big data era comes, this algorithm has been applied to the multimedia fields. However, this algorithm suffers a lot when it comes to the mobility of multimedia application. In this paper, we propose a compression algorithm, GBDT Compression, to enhance the storage adaptability of a gradient boosting decision tree classification model. GBDT Compression introduces a new rule to pruning, addressing and encoding for a well trained gradient boosting decision tree model. By conduct GBDT Compression on small data sets, we show that this algorithm considerably reduces the space cost of original model up to dozens of times. Furthermore, as data increases and the original model grows, the compression rate also enhances. Which is meaningful for mobile multimedia application with limited memory.
Semi-Supervised Anti-Fraud Models for Cash Pre-Loan in Internet Consumer Finance

This exploratory study aims to address the problem that cash loan fraud customers are difficult to detect manually. Cash loan is a new consumption model in the concept of Internet consumer finance(ICF). Manual detection of fraudulent customers requires a lot of manpower and time, and often causes great losses to financial institutions, so our group did the research mentioned above. In this paper, we proposed a Semi-supervised Pre-loan Fraud Detection (SPFD) system via investigating various supervised and unsupervised learning algorithms on basis of 285,771 applicants' desensitized data from MUCFC (a Chinese ICF company). In SPFD, feature selection methods consist of KL Divergence, Wasserstein Distance and Manual Selection, while the clustering algorithms we adopted was K-constrained seed clustering. Final result demonstrates good performance with the Adjusted Rand Index(ARI) reaching 81.7%. Such method would help financial institution to reduce financial losses.
Cross-modal Learning by Hallucinating Missing Modalities in RGB-D Vision

Diverse input data modalities can provide complementary cues for several tasks, usually leading to more robust algorithms and better performance. However, while a (training) dataset could be accurately designed to include a variety of sensory inputs, it is often the case that not all modalities are available in real life (testing) scenarios, when the model is to be deployed. This raises the challenge of how to learn robust representations leveraging multimodal data in the training stage, while considering limitations at test time, such as noisy or missing modalities. This chapter presents a new approach for multimodal video action recognition, developed within the unified frameworks of distillation and privileged information, named generalized distillation. We consider the particular case of learning representations from depth and RGB videos, while relying on RGB data only at test time. Our approach consists in training a hallucination network that learns to distill depth features through multiplicative connections of spatiotemporal representations, leveraging soft labels and hard labels, and the euclidean distance between feature maps. We report state-of-the-art or comparable results on video action recognition on the largest multimodal dataset available for this task, the NTU RGB+D, as well as on the UWA3DII and Northwestern-UCLA. Our code is available at https://github.com/ncgarcia/modality-distillation.