Reviewerly/exHarmony huggingface dataset:
HEMP: High-order entropy minimization for neural network compression

We formulate the entropy of a quantized artificial neural network as a differentiable function that can be plugged as a regularization term into the cost function minimized by gradient descent. Our formulation scales efficiently beyond the first order and is agnostic of the quantization scheme. The network can then be trained to minimize the entropy of the quantized parameters, so that they can be optimally compressed via entropy coding. We experiment with our entropy formulation at quantizing and compressing well-known network architectures over multiple datasets. Our approach compares favorably over similar methods, enjoying the benefits of higher order entropy estimate, showing flexibility towards non-uniform quantization (we use Lloyd-max quantization), scalability towards any entropy order to be minimized and efficiency in terms of compression. We show that HEMP is able to work in synergy with other approaches aiming at pruning or quantizing the model itself, delivering significant benefits in terms of storage size compressibility without harming the model's performance.
Enhancing the Utilization of Processing Elements in Spatial Deep Neural Network Accelerators

Equipping mobile platforms with deep learning applications is very valuable. Providing healthcare services in remote areas, improving privacy, and lowering needed communication bandwidth are the advantages of such platforms. Designing an efficient computation engine enhances the performance of these platforms while running deep neural networks (DNNs). Energy-efficient DNN accelerators use skipping sparsity and early negative output feature detection to prune the computations. Spatial DNN accelerators in principle can support computation-pruning techniques compared to other common architectures, such as systolic arrays. These accelerators need a separate data distribution fabric like buses or trees with support for high bandwidth to run the mentioned techniques efficiently and avoid network on chip (NoC)-based stalls. Spatial designs suffer from divergence and unequal work distribution. Therefore, applying computation-pruning techniques into a spatial design, which is even equipped with an NoC that supports high bandwidth for the processing elements (PEs), still causes stalls inside the computation engine. In a spatial architecture, the PEs that perform their tasks earlier have a slack time compared to others. In this article, we propose an architecture with a negligible area overhead based on sharing the scratchpads in a novel way between the PEs to use the available slack time caused by applying computation-pruning techniques or the used NoC format. With the use of our dataflow, a spatial engine can benefit from computation-pruning and data reuse techniques more efficiently. When compared to the reference design, our proposed method achieves a speedup of ×1.24 and an energy efficiency of ×1.18 per inference.
Architecture for Emergency Control of Autonomous UAV Ensembles

Applying unmanned aerial vehicles (UAV) has benefits for many different use-cases. Existing implementations of ground control stations (GCS) to manage UAVs in such scenarios already provide some support for the operation of multi-unit systems, i.e., ensembles. However, since they are usually designed for the operation of only one copter at once, this is often not sufficient to react quickly in dangerous situations, e.g., search and rescue scenarios. To address this problem, we propose an approach for easy observation and control of complete autonomous UAV ensembles: The Intention of our approach is to greatly reduce the number of personnel required for the operation of an UAV ensemble. Thereby, we generate the possibility for rapid intervention in potentially dangerous situations in order to prevent damage to the UAVs and the environment. In this paper, we present a software architecture for this safety-critical multi UAV ground control station including a fully implemented prototype which we also tested in a realistic environment.
UAFSan: an object-identifier-based dynamic approach for detecting use-after-free vulnerabilities

Use-After-Free (UAF) vulnerabilities constitute severe threats to software security. In contrast to other memory errors, UAFs are more difficult to detect through manual or static analysis due to pointer aliases and complicated relationships between pointers and objects. Existing evidence-based dynamic detection approaches only track either pointers or objects to record the availability of objects, which become invalid when the memory that stored the freed object is reallocated. To this end, we propose an approach UAFSan dedicated to comprehensively detecting UAFs at runtime. Specifically, we assign a unique identifier to each newly-allocated object and its pointers; when a pointer dereferences a memory object, we determine whether a UAF occurs by checking the consistency of their identifiers. We implement UAFSan in an open-source tool and evaluate it on a large collection of popular benchmarks and real-world programs. The experiment results demonstrate that UAFSan successfully detect all UAFs with reasonable overhead, whereas existing publicly-available dynamic detectors all miss certain UAFs.
Equilibrium optimizer of interswarm interactive learning strategy

This paper proposes an equilibrium optimiser of interswarm interactive learning strategy to plan the shortest flight path of drones. This paper divide the population into a learning swarm and a learned swarm. When the global best fitness value does not change in consecutive k generations, interswarm interactive learning behaviour is triggered. In addition, this paper also proposes a global optimal disturbance strategy to improve the exploitation capability of particles and balances exploration and exploitation by introducing linearly decreasing inertia weights. The algorithm is tested on 23 benchmark functions, and the test results show that the algorithm has greater advantages.
Homomorphic Properties Within Lattice-Based Encryption Systems

Homomorphic encryption allows users to perform mathematical operations on open data in encrypted form by performing homomorphically appropriate operations on encrypted data without knowing the decryption function (key). Nowadays such possibilities for cryptoalgorithm are very important in many areas such as data storage, cloud computing, cryptocurrency, and mush more. In 2009 a system of fully homomorphic encryption was constructed, in the future, many works were done based on it. In this work, is performed the implementation of ideal lattices for constructing homomorphic operations over ciphertexts. The idea, presented in this work, allows to separate relations between homomorphic and security parts of a lattice-based homomorphic encryption system.
CompPrehension - Model-Based Intelligent Tutoring System on Comprehension Level

Intelligent tutoring systems become increasingly common in assisting human learners, but they are often aimed at isolated domain tasks without creating a scaffolding system from lower- to higher-level cognitive skills. We designed and implemented an intelligent tutoring system CompPrehension aimed at the comprehension level of Bloom's taxonomy that often gets neglected in favour of the higher levels. The system features plugin-based architecture, easing adding new domains and learning strategies; using formal models and software reasoners to solve the problems and judge the answers; and generating explanatory feedback and follow-up questions to stimulate the learners' thinking. The architecture and workflow are shown. We demonstrate the process of interacting with the system in the Control Flow Statements domain. The advantages and limits of the developed system are discussed.
Administrative Measures Against Far-Right Protesters: An Example of Japan’s Social Control

Abstract Japan’s pre-emptive approach to far-right demonstrations has had a significant impact. Far-right street protests accompanied by hate speech have been rapidly decreasing, although Japan has not introduced penalties. Why did the Japanese approach have such an effect? While the regulation of hate speech in Japan has been discussed mainly in legal studies, Japan’s use of administrative measures against hate speech has not been emphasized. Focusing on the implementation of the 2016 Hate Speech Law, this article examines administrative measures against far-right protestors as an example of Japan’s ‘soft’ approach to social control: not directly banning but discouraging social activities that are deemed harmful to social harmony. These measures have a pre-emptive character and are implemented based on a prior consensus between local officials and far-right activists about appropriate ways to use public spaces and possible expressions. This article also shows that Japan’s treatment of both far-right protesters and counterdemonstrators is guided by the harmony-related concept of kenka ryōseibai, which imposes punishment on both parties in a private quarrel, whether right or wrong. Japan’s approach to far-right protests thereby differs from the conventional American and European approaches in terms of both forms of regulation and central values.
An Analysis of Blessed Friday Sale at a Retail Store Using Classification Models

Today decision-making and business planning are fundamental challenges, especially for retailers. Vendors are paying more attention to find out the age group of customers most affected by and interested in special offers on events such as Blessed Friday (falsely known as Black Friday). Besides, they want to know which strategy or marketing plan should be adopted to grasp more customers. Therefore, to assess customers’ needs or figure out the group more interested in different Blessed Friday deals is challenging. Many solutions to this problem, like analysis of Blessed Friday consumer behaviour, classification of retail stores, etc., have been proposed. Still, such solutions do not consider the age group of customers. In this research, a comparison of techniques for predicting the age group of shoppers on a Blessed Friday is proposed based on machine learning. This work is done on a dataset obtained from a repository published online. During pre-processing of the dataset, missing values are replaced. Algorithms such as Decision Tree, K-Nearest Neighbor, Naïve Bayes, and Neural Network are applied to determine the most suitable algorithm. This study provides a good understanding of classification, targets the age group more interested in Blessed Friday, and provides primary research ground for further analysis in this domain.
Information Theory Based Evaluation of the RC4 Stream Cipher Outputs

This paper presents a criterion, based on information theory, to measure the amount of average information provided by the sequences of outputs of the RC4 on the internal state. The test statistic used is the sum of the maximum plausible estimates of the entropies H(jt|zt), corresponding to the probability distributions P(jt|zt) of the sequences of random variables (jt)t∈T and (zt)t∈T, independent, but not identically distributed, where zt are the known values of the outputs, while jt is one of the unknown elements of the internal state of the RC4. It is experimentally demonstrated that the test statistic allows for determining the most vulnerable RC4 outputs, and it is proposed to be used as a vulnerability metric for each RC4 output sequence concerning the iterative probabilistic attack.
GREN: Graph-Regularized Embedding Network for Weakly-Supervised Disease Localization in X-Ray Images

Locating diseases in chest X-ray images with few careful annotations saves large human effort. Recent works approached this task with innovative weakly-supervised algorithms such as multi-instance learning (MIL) and class activation maps (CAM), however, these methods often yield inaccurate or incomplete regions. One of the reasons is the neglection of the pathological implications hidden in the relationship across anatomical regions within each image and the relationship across images. In this paper, we argue that the cross-region and cross-image relationship, as contextual and compensating information, is vital to obtain more consistent and integral regions. To model the relationship, we propose the Graph Regularized Embedding Network (GREN), which leverages the intra-image and inter-image information to locate diseases on chest X-ray images. GREN uses a pre-trained U-Net to segment the lung lobes, and then models the intra-image relationship between the lung lobes using an intra-image graph to compare different regions. Meanwhile, the relationship between in-batch images is modeled by an inter-image graph to compare multiple images. This process mimics the training and decision-making process of a radiologist: comparing multiple regions and images for diagnosis. In order for the deep embedding layers of the neural network to retain structural information (important in the localization task), we use the Hash coding and Hamming distance to compute the graphs, which are used as regularizers to facilitate training. By means of this, our approach achieves the state-of-the-art result on NIH chest X-ray dataset for weakly-supervised disease localization. Our codes are accessible online.
The Portilla-Simoncelli Texture Model: towards Understanding the Early Visual Cortex

Texture synthesis is a prolific subarea in computer vision where statistical methods are often successful.The Portilla and Simoncelli (PS) texture algorithm is one of such methods that became very popular and has influenced visual perception studies.For many reasons it can still be considered as a state-of-the art texture synthesis algorithm: (i) it generates textures that are often indistinguishable from the original without scrutiny; (ii) it relies on few parameters compared to recent deep learning methods; (iii) recent algorithms often compare to it.Here, we review the scientific impact of this algorithm and give a detailed explanation.Briefly, the PS algorithm synthesizes a new texture by iteratively imposing to a Gaussian white noise image a set of high-order statistics of wavelet coefficients precomputed on a texture example.After few iterations the initial white noise image is transformed into a texture that is similar to the texture example.We provide a fast C++ implementation, evaluate the effect of the algorithm parameters and illustrate its capabilities with many synthesis examples.In addition, we propose two notable new features to the original implementation: (i) the possibility to interpolate between two textures; (ii) the possibility to handle non-periodicity using the "periodic+smooth" decomposition.
Proposal for Customer Identification Service Model Based on Distributed Ledger Technology to Transfer Virtual Assets

Recently, cross-border transfers using blockchain-based virtual assets (cryptocurrency) have been increasing. However, due to the anonymity of blockchain, there is a problem related to money laundering because the virtual asset service providers cannot identify the originators and the beneficiaries. In addition, the international anti-money-laundering organization (the Financial Action Task Force, FATF) has placed anti-money-laundering obligations on virtual asset service providers through anti-money-laundering guidance for virtual assets issued in June 2019. This paper proposes a customer identification service model based on distributed ledger technology (DLT) that enables virtual asset service providers to verify the identity of the originators and beneficiaries.
Automatic Source Camera Identification Technique Based-on Hierarchy Clustering Method

Many existing source camera identification technique depend on a set of images of known origins to train a classifier or to acquire the reference pattern noise of camera, and match the being tested images. However, it is hard to get the natural images which are the same type of tested image as training image library in our actual applications. In this work, we propose the automatic source camera identification technique based-on Hierarchy Clustering method, which can formulate the classification task without any training image library. Experimental results have verified the validity and practicality of the proposed approach at last.
Web Search Personalization based on the Principle of the Ant Colony

The Internet is a global source of Information. The Information present on Internet is growing rapidly. Due to these quick growths of information, it is very hard to locate the relevant information on the search query specified for the user. Thus, it is very tedious task to find relevant information. In this paper, a web search personalization system based on the principle of the ant colony is proposed that prepares user profile of each user to reflect the preferences of the user. It performs query pre-processing before retrieving the search results from the built corpus of web pages. It personalizes search results retrieved from the corpus of web pages based on the user preferences present in user profile. By the principle of the ant colony, users can utilize user profiles of other users for personalizing search results when their profile are not enough capable. The proposed personalization system performs better the search accuracy in terms of precision and recall over existing three personalization search systems.
Incremental computation for structured argumentation over dynamic DeLP knowledge bases

Structured argumentation systems, and their implementation, represent an important research subject in the area of Knowledge Representation and Reasoning. Structured argumentation advances over abstract argumentation frameworks by providing the internal construction of the arguments that are usually defined by a set of (strict and defeasible) rules. By considering the structure of arguments, it becomes possible to analyze reasons for and against a conclusion, and the warrant status of such a claim in the context of a knowledge base represents the main output of a dialectical process. Computing such statuses is a costly process, and any update to the knowledge base could potentially have a huge impact if done naively. In this work, we investigate the case of updates consisting of both additions and removals of pieces of knowledge in the Defeasible Logic Programming (DeLP) framework, first analyzing the complexity of the problem and then identifying conditions under which we can avoid unnecessary computations—central to this is the development of structures (e.g. graphs) to keep track of which results can potentially be affected by a given update. We introduce a technique for the incremental computation of the warrant statuses of conclusions in DeLP knowledge bases that evolve due to the application of (sets of) updates. We present the results of a thorough experimental evaluation showing that our incremental approach yields significantly faster running times in practice, as well as overall fewer recomputations, even in the case of sets of updates performed simultaneously.
Meta-Learning Enhanced Neural ODE for Citywide Next POI Recommendation

Recommending citywide POIs where users would visit in the next future benefits many location-based businesses and individuals. To train a decent recommendation model, adequate historical data is usually a prerequisite. However, historical check-ins are usually distributed unevenly, which leads to many cities suffering from data scarcity. To make matters worse, transferring knowledge from data sufficient cities is challenging due to the varying distribution of POIs and city structures. Most of existing next POI recommendation methods assume that the training data is adequate and can not solve these problems. In this paper, we propose a novel meta-learning enhanced neural ordinary differential equation (ODE) method, namely METAODE, which models city-invariant information and city-specified information separately to achieve accurate citywide next POI recommendation. For transferring knowledge from data sufficient cities, METAODE learns city-invariant information including the representation of POIs categories and user groups to extract user preference. Basing on that, METAODE employs a GRU-ODE-Bayes model for city-specified information modeling. It can not only capture the sequential relationships within the historical check-ins but also model the irregular-sampled timestamp in the continuous timeline. Moreover, METAODE leverages meta-learning mechanism to optimize the parameters on various data sufficient cities and train a well-generalized initialization, which can be effectively adapted to data insufficient cities to enhance recommendation performance. Extensive experiments on real-world datasets demonstrate the effectiveness of METAODE. Comparing with the state-of-the-art baselines, METAODE achieves 6.21% and 14.77% improvements on HR and NDCG, respectively.
System Modes - Digestible System (Re-)Configuration for Robotics

High-level deliberation in robotic systems has to handle three different but closely interwoven aspects simultaneously: intended tasks, contingencies, and system-level errors. To reduce the complexity, we propose the system modes concept, to abstract runtime state information and reconfiguration of the software components of the underlying layers by a model-based approach. The proposed concept introduces a notion of hierarchically composed, virtual subsystems as well as a notion of modes that determine their configuration. It features an inference engine to deduce the modes of the subsystems from the components and top-down reconfiguration mechanisms. Repetitive and fine-grained communication between high-level deliberation and underlying software components can thereby be reduced, decreasing unwanted coupling between system parts.
A Kelvin Wake Avoidance Scheme for Autonomous Sailing Robots Based on Orientation-Restricted Dubins Path

Sailboats have a wide range of applications on account of their energy-saving, environmentally benign and low-noise characteristics. This has led to the increasing popularity of research in autonomous sailing robots, with concomitant attention paid to the development of methods that ensure their safety during sailing. Sailboats are affected by obstacles and by nonlinear aerodynamic and hydrodynamic factors. In particular, sailboats are affected by the wake generated by a moving vessel, which in this letter is denoted as “moving boat”. Wakes are swift and can have severe adverse effects on a sailboat by causing it to veer off course, stall or capsize. Moreover, sailboats have low mobility and large inertia and will therefore struggle to avoid a wake. Thus, in this letter, we describe our efforts to develop a method to enable a sailboat to perform wake avoidance. We model the wake as a Kelvin wake and generate waypoints for an orientation-restricted Dubins path-based Kelvin wake-avoidance method. A V-stability-based control method has proven suitable for generating the path, after which the desired heading angle for sailboat control is obtained. The resulting wake-avoidance method is verified in simulations and experiments. It enables the sailboat to avoid alongside and toward wakes, and the qualitative results have revealed that the maximum range of roll in alongside scenario and maximum range of surge acceleration in toward scenario are reduced by 57.4% and 23.4%, respectively, relative to a situation when no wake-avoidance method was used. The maximum range of roll in toward scenario is acceptable.