Reviewerly/exHarmony huggingface dataset:
Differential Privacy of Hierarchical Census Data: An Optimization Approach

This paper is motivated by applications of a Census Bureau interested in releasing aggregate socio-economic data about a large population without revealing sensitive information. The released information can be the number of individuals living alone, the number of cars they own, or their salary brackets. Recent events have identified some of the privacy challenges faced by these organizations. To address them, this paper presents a novel differential-privacy mechanism for releasing hierarchical counts of individuals satisfying a given property. The counts are reported at multiple granularities (e.g., the national, state, and county levels) and must be consistent across levels. The core of the mechanism is an optimization model that redistributes the noise introduced to attain privacy in order to meet the consistency constraints between the hierarchical levels. The key technical contribution of the paper shows that this optimization problem can be solved in polynomial time by exploiting the structure of its cost functions. Experimental results on very large, real datasets show that the proposed mechanism provides improvements up to two orders of magnitude in terms of computational efficiency and accuracy with respect to other state-of-the-art techniques.
Characterizing Perception Module Performance and Robustness in Production-Scale Autonomous Driving System

Autonomous driving is a field that gathers many interests in academics and industry and represents one of the most important challenges of next years. Although individual algorithms of autonomous driving have been studied and well understood, there is still a lack of study for those tasks in a production-scale system. In this work, we profile and analyze the perception module of the open-source autonomous driving system Apollo, developed by Baidu, in terms of response time and robustness against sensor errors. The perception module is fundamental to the proper functioning and safety of autonomous driving, which relies on several sensors, such as LIDARs and cameras, for detecting obstacles and perceiving the surrounding environment. We identify the computation characteristics and potential bottlenecks in the perception module. Furthermore, we design multiple noise models for the camera frames and LIDAR cloud points to test the robustness of the whole module in terms of accuracy drop against a noise-free baseline. Our insights are useful for future performance and robustness optimization of autonomous driving system.
Threat‐Based Declassification and Endorsement for Mobile Computing

Declassification and endorsement can efficiently improve the usability of mobile applications. However, both declassify and endorse operations in practice are often ad-hoc and nondeterministic, thus, being insecure. From a new perspective of threat assessments, we propose the Threat-based typed security π-calculus (πTBTS) to model declassification and endorsement in mobile computing. Intuitively, when relaxing confidentiality policies and/or integrity policies, we respectively assess threats brought by performing these two relaxes. If these threats are acceptable, the declassification and/or endorsement operations are permitted; Otherwise, they are denied. The proposed assessments have explicit security conditions, results and less open parameters, so our approach solves the problem of the ad-hoc and nondeterministic semantics and builds a bridge between threat assessments and declassification/endorsement.
A visual model for predicting chromatic banding artifacts

Quantization of images containing low texture regions, such as sky, water or skin, can produce banding artifacts. As the bitdepth of each color channel is decreased, smooth image gradients are transformed into perceivable, wide, discrete bands. Commonly used quality metrics cannot reliably measure the visibility of such artifacts. In this paper we introduce a visual model for predicting the visibility of both luminance and chrominance banding artifacts in image gradients spanning between two arbitrary points in a color space. The model analyzes the error introduced by quantization in the Fourier space, and employs a purpose-built spatio-chromatic contrast sensitivity function to predict its visibility. The output of the model is a detection probability, which can be then used to compute the minimum bit-depth for which banding artifacts are just-noticeable. We demonstrate that the model can accurately predict the results of our psychophysical experiments.
GREEN CLOUD COMPUTING SOLUTION FOR OPERATIONAL COST EFFICIENCY AND ENVIRONMENTAL IMPACT REDUCTION

Cloud computing provides information technology based services based on utilities to users across the globe. Application hosting for business, consumers and scientific domains is made possible with cloud computing. However, the data centres used for this purpose consume large amount of energy. This increases the operational cost and contribute carbon footprint affecting the environment. With increasing energy scarcities and global climate changes, it is essential to control power consumption. This paper presents a green cloud computing solution that addresses the issues of operational cost reduction and decreasing the carbon footprint and its impact on the environment. For this purpose we use data mining tools and auto-scaling with constraint satisfaction problems (CSP).
Nongreedy Unbalanced Huffman Tree Compressor for Single and Multifasta Files

Next-generation sequencing technologies are producing genomic data at ever-increasing rates. It has become a challenge to store, transmit, and process the massive quantity of data, creating a vital need for a tool that compresses genomic data produced in a lossless manner, thus reducing storage space and speeding up data transmission. Data centers are adopting either of the two general-purpose genomic data compressors: gzip or bzip2. Both these use Huffman encoding, although they implement it in different ways. However, neither of these two takes advantage of properties of DNA data, such as the presence of a small alphabet and many repeats. Huffman encoding compression can be improved by exploiting DNA characteristics. Recently, it has been shown that Huffman encoding compression can be improved by creating an unbalanced Huffman tree (UHT), which demonstrates significant advances in compression over a standard Huffman tree used in both gzip and bzip2. However, the UHT created is greedy. This article proposes an improved nongreedy UHT (NUHT), a lossless nonreference-based fasta and multifasta compressor. We compare our algorithm with two well-known general-purpose compressors, gzip and bzip2, as well as with UHT, a DNA-specific compressor based on Huffman tree. Our algorithm outperforms all three in terms of compression ratio and is seven times faster than UHT.
Payment Networks as Creation Games

Payment networks were introduced to address the limitation on the transaction throughput of popular blockchains. To open a payment channel one has to publish a transaction on-chain and pay the appropriate transaction fee. A transaction can be routed in the network, as long as there is a path of channels with the necessary capital. The intermediate nodes on this path can ask for a fee to forward the transaction. Hence, opening channels, although costly, can benefit a party, both by reducing the cost of the party for sending a transaction and by collecting the fees from forwarding transactions of other parties. This trade-off spawns a network creation game between the channel parties. In this work, we introduce the first game theoretic model for analyzing the network creation game on blockchain payment channels. Further, we examine various network structures (path, star, complete bipartite graph and clique) and determine for each one of them the constraints (fee value) under which they constitute a Nash equilibrium, given a fixed fee policy. Last, we show that the star is a Nash equilibrium when each channel party can freely decide the channel fee. On the other hand, we prove the complete bipartite graph can never be a Nash equilibrium, given a free fee policy.
Certifying graph-manipulating C programs via localizations within data structures

We develop powerful and general techniques to mechanically verify realistic programs that manipulate heap-represented graphs. These graphs can exhibit well-known organization principles, such as being a directed acyclic graph or a disjoint-forest; alternatively, these graphs can be totally unstructured. The common thread for such structures is that they exhibit deep intrinsic sharing and can be expressed using the language of graph theory. We construct a modular and general setup for reasoning about abstract mathematical graphs and use separation logic to define how such abstract graphs are represented concretely in the heap. We develop a Localize rule that enables modular reasoning about such programs, and show how this rule can support existential quantifiers in postconditions and smoothly handle modified program variables. We demonstrate the generality and power of our techniques by integrating them into the Verified Software Toolchain and certifying the correctness of seven graph-manipulating programs written in CompCert C, including a 400-line generational garbage collector for the CertiCoq project. While doing so, we identify two places where the semantics of C is too weak to define generational garbage collectors of the sort used in the OCaml runtime. Our proofs are entirely machine-checked in Coq.
Plasmonic-modulated dissipative-driven multiqubit entanglement under asymmetric detuning

A dissipative-driven system can generate steady-state entanglement robust to control parameters, while a quantum plasmonic system has the advantages of being strongly coupled to qubits and manipulation of electromagnetic waves in the subwavelength regime. We propose an effective multiqubit dissipative-driven entanglement model for a plasmonic system which takes into account coupling of the plasmonic cavity and qubits as well as the direct coupling between any two qubits and develop a numerical solution of the effective model on a scalable composite plasmonic structure. In this structure, the multiqubit entanglement under asymmetric detuning, especially for those combinations containing pairs of antisymmetric detuning qubits, achieves stronger steady-state entanglement measured by negativity and pairwise concurrence. The robustness of multiqubit entanglement against variations of the control parameters is also presented. In addition, the phenomenon of entanglement sudden death caused by the driving field in entanglement dynamics is investigated. Our model shows evidence that quantum plasmonics has great potential in multiqubit entanglement.
An Improved Strategy for Predicting Diagnosis, Survivability, and Recurrence of Breast Cancer

Breast Cancer is a common disease among females. Early detection of the Breast Cancer aids in an easier efficient treatment. The application of Machine Learning algorithms can help in the diagnosis of this disease. There are three main problems related to Breast Cancer. The existing works focused only on one problem. In addition, the resulted accuracy still needs improvement. This research paper aims to identify the Breast Cancer diagnosis, predict the recurrence of the disease, and predict the survivability of its patients. This is achieved by using the Feedforward Neural Network (FFN) on the SEER (Surveillance, Epidemiology, and End Results) dataset by using different attributes and preprocessing of data for each problem. The obtained FFN classification accuracy resulted in 99.8% for the Breast Cancer diagnosis, 88.1% for the Breast Cancer recurrence, and 97.3% for the survivability.
Island-based whale optimisation algorithm for continuous optimisation problems

The whale optimisation algorithm (WOA) is a newly proposed evolutionary algorithm that uses a simulation model based on the bubble-net hunting mechanism of humpback whales to find solutions for different classes of optimisation problems. WOA may occasionally converge to suboptimal solutions because of the loss of diversity in its population of candidate solutions. The island model is a distributed approach that is commonly used to control the population diversity in evolutionary algorithms. This paper introduces an improved version of WOA namely island-based whale optimisation algorithm (iWOA) that incorporates the island model into WOA. The iWOA algorithm was compared to well-known optimisation algorithms using 18 standard benchmark functions. The simulation results indicate that iWOA improves the accuracy of results compared to WOA and other popular evolutionary algorithms. In addition, the sensitivity analysis of iWOA to its parameters indicates that its convergence behaviour is sensitive to the parameters of the island model.
Restoration of Non-Rigidly Distorted Underwater Images Using a Combination of Compressive Sensing and Local Polynomial Image Representations

Images of static scenes submerged beneath a wavy water surface exhibit severe non-rigid distortions. The physics of water flow suggests that water surfaces possess spatio-temporal smoothness and temporal periodicity. Hence they possess a sparse representation in the 3D discrete Fourier (DFT) basis. Motivated by this, we pose the task of restoration of such video sequences as a compressed sensing (CS) problem. We begin by tracking a few salient feature points across the frames of a video sequence of the submerged scene. Using these point trajectories, we show that the motion fields at all other (non-tracked) points can be effectively estimated using a typical CS solver. This by itself is a novel contribution in the field of non-rigid motion estimation. We show that this method outperforms state of the art algorithms for underwater image restoration. We further consider a simple optical flow algorithm based on local polynomial expansion of the image frames (PEOF). Surprisingly, we demonstrate that PEOF is more efficient and often outperforms all the state of the art methods in terms of numerical measures. Finally, we demonstrate that a two-stage approach consisting of the CS step followed by PEOF much more accurately preserves the image structure and improves the (visual as well as numerical) video quality as compared to just the PEOF stage. The source code, datasets and supplemental material can be accessed at \cite{GitRepo}, \cite{ProjectPage}.
PREPARATION OF WOOD, CHARCOAL AND BONE COLLAGEN MICRO SAMPLES USING AUTOMAT FOR AMS RADIOCARBON DATING

For the pretreatment of wood, charcoal and collagen from bone micro samples using the Acid-Base-Acid (ABA) method, we have assembled an automated computer-controlled unit in our laboratory CRL. The sample is placed in a glass single-necked cuvette. The machine consists of prepared solutions which are guided through capillaries, switching valve and peristaltic pump into the cuvette with the sample according to the currently selected program. The automat can be used for the pretreatment of charcoal, wood and also collagen from bones.
Making Sense of Reinforcement Learning and Probabilistic Inference

Reinforcement learning (RL) combines a control problem with statistical estimation: the system dynamics are not known to the agent, but can be learned through experience. A recent line of research casts 'RL as inference' and suggests a particular framework to generalize the RL problem as probabilistic inference. Our paper surfaces key shortcomings in that approach, and clarifies the sense in which RL can be coherently cast as an inference problem. In particular, an RL agent must consider the effects of its actions upon future rewards and observations: the exploration-exploitation tradeoff. In all but the most simple settings, the resulting inference is computationally intractable so that practical RL algorithms must resort to approximation. We show that the popular 'RL as inference' approximation can perform poorly in even the simplest settings. Despite this, we demonstrate that with a small modification the RL as inference framework can provably perform well, and we connect the resulting algorithm with Thompson sampling and the recently proposed K-learning algorithm.
Model Learning and Knowledge Sharing for Cooperative Multiagent Systems in Stochastic Environment

An imposing task for a reinforcement learning agent in an uncertain environment is to expeditiously learn a policy or a sequence of actions, with which it can achieve the desired goal. In this article, we present an incremental model learning scheme to reconstruct the model of a stochastic environment. In the proposed learning scheme, we introduce a clustering algorithm to assimilate the model information and estimate the probability for each state transition. In addition, utilizing the reconstructed model, we present an experience replay strategy to create virtual interactive experiences by incorporating a balance between exploration and exploitation, which greatly accelerates learning and enables planning. Furthermore, we extend the proposed learning scheme for a multiagent framework to decrease the effort required for exploration and to reduce the learning time in a large environment. In this multiagent framework, we introduce a knowledge-sharing algorithm to share the reconstructed model information among the different agents, as needed, and develop a computationally efficient knowledge fusing mechanism to fuse the knowledge acquired using the agents’ own experience with the knowledge received from its teammates. Finally, the simulation results with comparative analysis are provided to demonstrate the efficacy of the proposed methods in the complex learning tasks.
FPGA Implementation of Prefix-Free Code Distribution Matching for Probabilistic Constellation Shaping

We implement rate-adaptable prefix-free code distribution matching in an FPGA, demonstrating its real-time feasibility with substantially less hardware resources than low-density parity-check coding.
Typographic-Based Data Augmentation to Improve a Question Retrieval in Short Dialogue System

Many questions posed by users to particular customer service with a short dialog (such as a chatbot) cause difficulties to answer. These reduce the user satisfaction level to the service. A question answering (QA) system can be developed to solve this problem by providing relevant answers to the user questions. One of the commonly used methods to build a QA is a question retrieval (QR) that provides answers based on the most relevant stored- questions. However, interpreting two questions those are essentially the same but in different words is quite challenging. Besides, the limitation of the data set to learn is also interesting. This paper investigates a data augmentation based on typographic and synonym as well as evaluates the use of sub-word (instead of word) features to get the best word-embedding in the question. The word-embedding is then used to search the cosine similarity between a query and the stored-questions. Finally, the user receives an answer based on the question with the highest cosine similarity. Evaluation on a quite low data set shows that the proposed data augmentation is capable of significantly improving the system performance. Besides, the sub-word feature is better for word-embedding in the short conversation than the whole-word one.
Training of Generative Adversarial Networks with Hybrid Evolutionary Optimization Technique

This paper proposes a new method for training Generative Adversarial Networks(GANs), named as Hybrid Evolutionary Optimization. The proposed method trains GAN networks by evolving generator set to reduce Frechet Inception Distance. To overcome the problems such as non-convergence and mode collapse which are associated with conventional GANs, this paper uses an evolutionary algorithm to train in the initial iterations to stabilize the weights and followed by conventional optimization for remaining iterations. The efficiency of proposed method is evaluated with MNIST and celebA dataset. The results show that the proposed training algorithm converges faster than conventionally trained GANs. Moreover, the training with CPU and GPU is compared and analyzed.
Combining trust and reputation as user influence in cross domain group recommender system (CDGRS)

Group recommender system provides suggestions for a group of users by exploring the choices of individual users of the group. Popularity of group recommender systems is increasing because many activities such as listening to music, watching movies, traveling, etc. are normally performed in groups r ather individually. Group recommender systems like personal recommender systems also suffer from cold start and sparsity issues. The cold start and sparsity issues result into inaccurate recommendation computation which degrades the recommendation quality. To handle the cold start and sparsity issues in a Group Recommender System (GRS), this paper proposes to use cross domain approach and introduces Cross Domain Group Recommender System (CDGRS). The recommendations provided by trustworthy and reputed users in the group enhance the acceptance towards the presented recommendations as compared to the other individuals in the group. We have combined the social factors e.g. trust and reputation to get influential user in the group recommendation. A prototype of the system is developed for tourism domain that incorporates four sub-domains i.e. restaurants, hotels, tourist places and shopping places. The performance of CDGRS is compared with GRS. Spearman’s Correlation Coefficient, MAE, RMSE, Precision, Recall and F-measure are used to find the accuracy of the generated recommendations.
Global search in single-solution-based metaheuristics

Purpose Metaheuristic algorithms are classified into two categories namely: single-solution and population-based algorithms. Single-solution algorithms perform local search process by employing a single candidate solution trying to improve this solution in its neighborhood. In contrast, population-based algorithms guide the search process by maintaining multiple solutions located in different points of search space. However, the main drawback of single-solution algorithms is that the global optimum may not reach and it may get stuck in local optimum. On the other hand, population-based algorithms with several starting points that maintain the diversity of the solutions globally in the search space and results are of better exploration during the search process. In this paper more chance of finding global optimum is provided for single-solution-based algorithms by searching different regions of the search space. Design/methodology/approach In this method, different starting points in initial step, searching locally in neighborhood of each solution, construct a global search in search space for the single-solution algorithm. Findings The proposed method was tested based on three single-solution algorithms involving hill-climbing (HC), simulated annealing (SA) and tabu search (TS) algorithms when they were applied on 25 benchmark test functions. The results of the basic version of these algorithms were then compared with the same algorithms integrated with the global search proposed in this paper. The statistical analysis of the results proves outperforming of the proposed method. Finally, 18 benchmark feature selection problems were used to test the algorithms and were compared with recent methods proposed in the literature. Originality/value In this paper more chance of finding global optimum is provided for single-solution-based algorithms by searching different regions of the search space.