Reviewerly/exHarmony huggingface dataset:
Investigating Variability-aware Smells in SPLs

Variability-aware smell is a concept referring to artifact shortcomings in the context of highly-configurable systems that can degrade aspects such as program comprehension, maintainability, and evolvability. To the best of our knowledge, there is very little evidence that variability-aware smells exist in Software Product Lines (SPLs). This work presents an exploratory study that investigated (I) evidence that variability-aware smells exist in SPLs and (II) new types of variability-aware smell not yet documented in the literature based on a quantitative study with open source SPL projects. We collected quantitative data to generate reliable research evidence, by performing feature model and source code inspections on eleven open-source SPL projects. Our findings revealed that (1) instances of variability-aware smells exist in open-source SPL projects and (2) feature information presented significant associations with variability-aware smells. Furthermore, (3) the study presented six new types of variability-aware smells.
Secure and Efficient Adjacency Search Supporting Synonym Query on Encrypted Graph in the Cloud

With the popularity of cloud computing, cloud outsourcing has aroused great concern in academia. A lot of graph data are outsourced to the cloud for saving cost. As the cloud server may be not entirely reliable, the outsourced graph data are usually encrypted before sending to the cloud for the security considerations. Adjacency search is a basic operation, and many other operations can be performed based on the adjacency search. Adjacency search supporting synonym query is a more meaningful operation which can make a query user improve the scope of the query. Due to the graph data being encrypted in the cloud, adjacency search supporting synonym query becomes a very challenging task. In this paper, we propose an efficient solution to perform privacy-assured adjacency search supporting synonym query on the encrypted graph in the cloud (PASQ). Our work utilizes a stemming algorithm and an encryption mechanism to perform adjacency search. A query user can get a wider range of search results by this solution, and the privacy of query will not be disclosed. We present a design scheme for the outsourced graph and analyze the security. We demonstrate the efficiency of our scheme by the experiment results on a real graph data.
Practical Application of SPARK to OpenUxAS

This paper presents initial, positive results from using SPARK to prove critical properties of OpenUxAS, a service-oriented software framework developed by AFRL for mission-level autonomy for teams of cooperating unmanned vehicles. Given the intended use of OpenUxAS, there are many safety and security implications; however, these considerations are unaddressed in the current implementation. AFRL is seeking to address these considerations through the use of formal methods, including through the application of SPARK, a programming language that includes a specification language and a toolset for proving that programs satisfy their specifications. Using SPARK, we reimplemented one of the core services in OpenUxAS and proved that a critical part of its functionality satisfies its specification. This successful application provides a foundation for further applications of formal methods to OpenUxAS.
Kerdock Codes Determine Unitary 2-Designs

The binary non-linear Kerdock codes are Gray images of Z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> -linear Kerdock codes of length N = 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> . We show that exponentiating z = √-1 by these Z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> -valued codewords produces stabilizer states, which are the common eigenvectors of maximal commutative subgroups (MCS) of the Pauli group. We use this quantum description to simplify the proof of the classical weight distribution of Kerdock codes. Next, we partition stabilizer states into N + 1 mutually unbiased bases and prove that automorphisms of the Kerdock code permute the associated MCS. This automorphism group, represented as symplectic matrices, is isomorphic to the projective special linear group PSL(2, N) and forms a unitary 2-design. The design described here was originally discovered by Cleve et al. (2016), but the connection to classical codes is new. This significantly simplifies the description of the design and its translation to circuits.
Irradiance distribution model for laser triangulation displacement sensor and parameter optimization

An irradiance distribution model for laser triangulation displacement sensors is proposed, which is more suitable for practical applications than the geometric optical model (GOM). Based on the irradiance distribution model, a convergence algorithm is developed. Compared with the design parameter optimized method based on the GOM, the convergence algorithm can be used for optimization of not only the traditional design parameters including view angle and object distance but also the waist of the source laser beam. Additionally, using this convergence algorithm, the nonlinearity can be evaluated in the design stage. To validate the convergence algorithm, two example cases are performed. The results show that the nonlinearities are reduced to 6.3098 and 5.7946 μm, respectively.
Neighborhood Pyramid Preserving Hashing

In this paper, we devote our efforts to the approximate nearest neighbour (ANN) search problem and propose a new unsupervised binary hashing method, i.e., Neighbourhood Pyramid preserving Hashing (NPH). We represent the nearest neighbours of each data point in a pyramid, and as the learning objective, we impose that the pyramid neighbourhood in each level is consistently preserved across the original Euclidean space and the transformed Hamming space. The neighbourhood is quantitatively characterized by its size, defined as the average distance from the involved nearest neighbours to the referred data point. Our approach is consistent with the distance-preserving principle of binary hashing and achieves stricter neighbourhood structure preserving over previous graph hashing algorithms. The experiments on several large-scale benchmark datasets demonstrate that NPH achieves promising performances compared with those of the existing state-of-the-art unsupervised binary hashing methods.
Perfect quantum state transfer on the Johnson scheme

For any graph X with the adjacency matrix A, the transition matrix of the continuous-time quantum walk at time t is given by the matrix-valued function HX(t)=eitA. We say that there is perfect state transfer in X from the vertex u to the vertex v at time τ if |HX(τ)u,v|=1. It is an important problem to determine whether perfect state transfers can happen on a given family of graphs. In this paper we characterize all the graphs in the Johnson scheme which have this property. Indeed, we show that the Kneser graph K(2k,k) is the only class in the scheme which admits perfect state transfers. We also show that, under some conditions, some of the unions of the graphs in the Johnson scheme admit perfect state transfer.
Multiple Impossible Differentials Attack on AES-192

The security of AES-192 against multiple impossible differentials attack is studied in this paper.Based on two types of impossible differentials for 4-round AES, two 7-round attack trails of AES-192 with the same plaintext and ciphertext difference structure are proposed.A new optimum combination of these two attack trails is applied in our attack so that the plaintext pairs can be reused and data complexity can be reduced.Furthermore, this new optimum combination also can reduce the time complexity in the master key recovering phase.Our attack can also reduce the number of subkeys by key schedule considerations.For each attack trail, only 15-byte subkeys need to be guessed.Combined with the master key recovering technique based on the key schedule algorithm, the early abort technique for plaintext pairs, and the sieve method for plaintext pairs based on quick sort etc., we have obtained the best result so far in terms of time complexity for impossible differential cryptanalysis of AES-192.The time, memory, and data complexities are 2 109.27-round AES encryptions, 2 86.5 bytes and 2 106.3 chosen plaintexts, respectively.
Information Theory-Based Quantitative Evaluation Method for Countermeasures Against Fault Injection Attacks

The integrated circuits are faced with the threats of fault injection attacks (FIAs), which employ the faulty results and differential fault analysis to retrieve the vital information processed by the circuits.Countermeasures should be taken to protect the circuits against FIAs.This paper proposes a security evaluation method to quantitatively analyze the resistance of block ciphers with a Substitution-Permutation Network structure against FIAs from an information-theoretic perspective.A quantitative security factor is defined based on the amount of information leakage.Specifically, an extended cipher model is proposed to numerically analyze the theoretical amount of information leakage, and an efficient approach is proposed to obtain the actual amount of information leakage.Experiments by applying the quantitative evaluation method to AES circuit validate feasibility, efficiency and scalability of the method.The experimental results show that the security factor can quantify the effects of different fault models and countermeasures.2000 fault injections are sufficient to complete the evaluation under the considered fault models within 10 microseconds.The proposed method can be used during the circuit design stage as well as chip testing stage.
Discern Depth Under Foul Weather: Estimate PM$_{2.5}$ for Depth Inference

Nowadays, haze is a common and serious problem and PM <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$_{2.5}$</tex-math></inline-formula> is a main measurement for air quality. Current methods estimate the level of primary pollutant with professional instruments, which is expensive and inconvenient. Moreover, with haze, the captured images will be unclear and are difficult to estimate the depth of the scene using passive methods. This article proposes a cheap, fast, and convenient PM <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$_{2.5}$</tex-math></inline-formula> estimation method that only need a captured image using daily-life devices, and further, discerns the depth of the scene using the estimated PM <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$_{2.5}$</tex-math></inline-formula> . We learn haze-relevant classified mapping via the hybrid convolutional neural network and combine the high-level features extracted from the convolutional layer with ground-truth PM <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$_{2.5}$</tex-math></inline-formula> to train support vector regression. The transmission map is computed using nonlocal sparse priors, and the depth map is inferred using the estimated PM <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$_{2.5}$</tex-math></inline-formula> value through the atmospheric scattering model. Experimental results demonstrate that our method achieves accurate PM <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$_{2.5}$</tex-math></inline-formula> estimation and depth inference. This could be very useful in many applications, for both clean and foul weather.
Restoring distribution entanglement for two-qubit transmission using environment-assisted measurement and quantum measurement reversal

We examine the validity of the environment-assisted measurement (EAM) and quantum measurement reversal (QMR) operations in the restoration of distributed entanglement for two-qubit transmission under three typical kinds of noisy quantum channels, i.e. an amplitude damping channel, a phase damping channel and a depolarizing quantum channel. Given the parameters of the Bell-like initial qubits' state and , we derive exact expressions for the optimal final quantum entanglement, showing explicitly that combining the EAM and QMR operations does indeed help in recovering the distributed entanglement from the various decoherence sources. Comparing with the results obtained using the weak measurement and QMR method, our results show notable superiorities in adaptation scope and success probability.
Autoencoder based API Recommendation System for Android Programming

As a typical example of modern Information Technologies, Android platform and Apps are widely used by smartphone users all over the world. Thus, the research of designing models for assisting programmers in writing Android codes is of great importance and value, and recommending API usages is a stereotype task in this aspect. This paper applies Autoencoder neural networks into the model of API recommendation system for Android programming, and designs new Autoencoder based Android API recommendation system. This paper carries out experiments on the collected Android code dataset and verifies the effectiveness of the newly designed models compared with classical recommendation models.
Twenty-Five Years of MALL Experimental Implementation Studies

This chapter offers a comprehensive, up-to-date overview of Mobile-Assisted Language Learning (MALL) studies, from the first in 1994 through 2018, relating to the experimental implementation of mobile-based affordances and applications in the teaching and learning of languages, L1 as well as L2. It derives from the analysis of over 2000 MALL studies that have appeared in almost 500 distinct journals and presentations in conferences given by nearly 300 different professional organizations, the great majority of both sources being unrelated to language learning and teaching. It provides background information relating to these studies, documents their profoundly atheoretical nature, details their pedagogical features, and assesses their learning outcome effectiveness.
ABC: A Cryptocurrency-Focused Threat Modeling Framework

Cryptocurrencies are an emerging economic force, but there are concerns about their security. This is due, in part, to complex collusion cases and new threat vectors, that could be missed by conventional security assessment strategies. To address these issues, we propose ABC, an Asset-Based Cryptocurrency-focused threat modeling framework capable of identifying such risks. ABC's key innovation is the use of collusion matrices. A collusion matrix forces a threat model to cover a large space of threat cases while simultaneously manages this process to prevent it from being overly complex. We demonstrate that ABC is effective by presenting real-world use cases and by conducting a user study. The user study showed that around 71% of those who used ABC were able to identify financial security threats, as compared to only 13% of participants who used the popular framework STRIDE.
Re-determinizing MCTS in Hanabi

We introduce Re-determinizing IS-MCTS, a novel extension of Information Set Monte Carlo Tree Search (IS-MCTS) that prevents a leakage of hidden information into opponent models that can occur in MCTS, and is particularly severe in Hanabi. When combined with a learned evaluation function to estimate leaf node values and avoid full simulations during MCTS this won the Hanabi competition at the Computation Intelligence in Games (CIG) 2018.
Robust Visual Tracking Using Structural Patch Response Map Fusion Based on Complementary Correlation Filter and Color Histogram

A part-based strategy has been applied to visual tracking with demonstrated success in recent years. Different from most existing part-based methods that only employ one type of tracking representation model, in this paper, we propose an effective complementary tracker based on structural patch response fusion under correlation filter and color histogram models. The proposed method includes two component trackers with complementary merits to adaptively handle illumination variation and deformation. To identify and take full advantage of reliable patches, we present an adaptive hedge algorithm to hedge the responses of patches into a more credible one in each component tracker. In addition, we design different loss metrics of tracked patches in two components to be applied in the proposed hedge algorithm. Finally, we selectively combine the two component trackers at the response maps level with different merging factors according to the confidence of each component tracker. Extensive experimental evaluations on OTB2013, OTB2015, and VOT2016 datasets show outstanding performance of the proposed algorithm contrasted with some state-of-the-art trackers.
On the Role of Blockchain Technology in the Internet of Things

Blockchain is a database of records, which tracks Querythe history of all transactions and communications between different nodes of a network. It provides a decentralized platform to execute transactions with mutual trust among the participants, while at the same time, eliminating the presence of a central mediating authority. Record of every transaction is stored in the Blockchain and is entirely tamper-proof. Blockchain creates a peer-to-peer network where all nodes get to verify transactions occurring in the network, through a consensus-based governance system. It has been used to implement the world’s most popular cryptocurrency Bitcoin. This is a highly promised technology, which is being adopted and implemented in several domains, such as Internet-of-Things-based systems, health care, energy systems, education systems, banking, and many more.
Research and Development of Augmented Reality Children’s Puzzle Game Based on Vuforia

The emergence of mobile augmented reality children's puzzle games conforms to the development of the information age. One augmented reality game named “Baby Running” was researched and developed, based on Unity engine and the Vuforia, which improves the interaction between the game and the player, and exercises the child's concentration and reaction. The paper will discuss the game planning, game system design, key technologies, game testing, and the system's conclusion and some future directions.
Blind Unwrapping of Modulo Reduced Gaussian Vectors: Recovering MSBs from LSBs

We consider the problem of recovering n i.i.d samples from a zero mean multivariate Gaussian distribution with an unknown covariance matrix, from their modulo wrapped measurements, i.e., measurement where each coordinate is reduced modulo Δ, for some Δ > 0. For this setup, which is motivated by quantization and analog-to-digital conversion, we develop a low-complexity iterative decoding algorithm. We show that if an informed decoder that knows the covariance matrix can recover each sample with small error probability, and n is large enough, the performance of the proposed blind recovery algorithm closely follows that of the informed one. We complement the analysis with numeric results that show that the algorithm performs well even in non-asymptotic conditions.
Entropy Bounds for Grammar-Based Tree Compressors

The definition of kth-order empirical entropy of strings is extended to node-labeled binary trees. A suitable binary encoding of tree straight-line programs (that have been used for grammar-based tree compression before) is shown to yield binary tree encodings of size bounded by the kth-order empirical entropy plus some lower order terms. This generalizes recent results for grammar-based string compression to grammar-based tree compression. A long version of this paper can be found in [11].
Cooperative processing based on posture change detection and trajectory estimation for unknown multi-object tracking

Tracking of moving objects is a very important step in building an intelligent video surveillance system. The movement of non-rigid objects, appearance variations and luminosity changes make tracking even more difficult. This paper proposes a new automatic multi-target tracking system that can deal with the most confronted problems without any prior knowledge of the characteristics of objects. The system is a combination between classification, learning and tracking in a parallel architecture that allows the three tasks to be performed separately and efficiently to make the most of this combination. The permanent learning of the classifier guarantees the efficiency of the latter compared to the posture changes of moving objects. The classifier sends the new posture changes with a high degree of confidence as a new learning data. This cyclic aspect forces the system to adapt to all possible posture changes. In the case of occlusion, the system uses the estimated information of the trajectories to correct or cancel the learning process. The filtering process prevents the classifier from falling into a false classification, which significantly increases the system adaptability to the environment. Tests carried out on the CAVIAR and MOT16 datasets showed the efficiency and effectiveness of the proposed system.
Energy efficient VM scheduling strategies for HPC workloads in cloud data centers

In virtualized environments, virtual machine (VM) scheduling strategies incorporated with energy efficient techniques are needed to reduce the operational cost of the system while delivering high Quality of Service (QoS). It is widely accepted that the cost of the energy consumption in the environment is a dominant part of the owner’s budget. However, when considering energy efficiency, VM scheduling decisions become more constrained, leading in the violation of job deadlines and hence compromising QoS. This paper studies energy efficient VM scheduling strategies in virtualized environments to minimize the queue time and makespan under the fulfillment of SLA requirements (i.e., deadline). Specifically, six energy efficient VM scheduling strategies are investigated incorporated with the dynamic voltage and frequency scaling (DVFS) power management technique. They consist of user-oriented and system-oriented policies. The strategies are extensively simulated and compared with three power management governing methods provided at hypervisor level (i.e., userspec, ondemand, and performance). To conduct simulation experiments, we employ real-world high performance computing (HPC) workloads collected from a production data center. For comparison and evaluation, we analyze the: (a) energy consumption, (b) runtime, (c) queue time, (d) makespan, and (e) slowdown ratio. Lastly, we highlight the strengths and weaknesses of the VM scheduling strategies that can help to choose the most appropriate VM scheduling strategy for a given scenario.
Deep Reinforcement Learning to train agents in a multiplayer First Person Shooter: some preliminary results

Training agents to play in contemporary multiplayer actions game is a challenging task, especially when agents are expected to cooperate in a hostile environment while performing several different actions at the same time. Nonetheless, this topic is assuming a growing importance due to the rampaging diffusion of this game genre and its related e-sports. Agents playing in a multiplayer survival first person shooter game should mimic a human player, hence they should learn how to: survive in unexplored environment, improve their combat skills, deal with unexpected events, coordinate with allies and reach a good ranking among the players community. Our aim has been to design, develop and test a preliminary solution that exploits Proximal Policy Optimization algorithms to train agents without the need of a human expert, with the final goal of creating teams composed only by artificial players.