Reviewerly/exHarmony huggingface dataset:
Towards Secure Architecture-Based Adaptations

As any software system, a self-adaptive system is subject to security threats. However, applying self-adaptation may introduce additional threats. So far, little research has been devoted to this important problem. In this paper, we propose an approach for vulnerability analysis of architecture-based adaptations in self-adaptive systems using threat modeling and analysis techniques. To this end, we specify components' vulnerabilities and the system architecture formally and generate an attack model that describes the attacker's strategies to attack the system by exploiting different vulnerabilities. We use a set of security metrics to quantitatively assess the security risks of adaptations based on the produced attack model which enables the system to consider security aspects while choosing an adaptation to apply to the system. We automate and incorporate our approach into the Rainbow framework, allowing for secure architectural adaptations at runtime. To evaluate the effectiveness of our approach, we apply it on a simple document storage system and on the ZNN system.
Current Challenges in Practical Object-Oriented Software Design

According to the extensive 50-year-old body of knowledge in object-oriented programming and design, good software designs are, among other characteristics, lowly coupled, highly cohesive, extensible, comprehensible, and not fragile.However, with the increased complexity and heterogeneity of contemporary software, this might not be enough.This paper discusses the practical challenges of object-oriented design in modern software development.We focus on three main challenges: (1) how technologies, frameworks, and architectures pressure developers to make design decisions that they would not take in an ideal scenario, (2) the complexity of current realworld problems require developers to devise not only a single, but several models for the same problem that live and interact together, and (3) how existing quality assessment techniques for object-oriented design should go beyond high-level metrics.Finally, we propose an agenda for future research that should be tackled by both scientists and practitioners soon.This paper is a call for arms for more reality-oriented research on the objectoriented software design field.
Eclipse-based Stake-Bleeding Attacks in PoS Blockchain Systems

A stake-bleeding attack is a newly proposed form of long-range attacks against Proof of Stake (PoS) based blockchain systems. However, as shown in this paper, with the decrease of transaction fees in current PoS based blockchain systems, adversaries require several decades to complete this attack, which is unrealistic. In this paper, we make an improvement on the stake-bleeding attack. We propose an eclipse-based stake-bleeding attack that reduces the attack completion time significantly, e.g., the proposed attack only requires 2.4 years of the attack completion time, while the original stake-bleeding attack needs 22.9 years for the successful attack. We also present a preliminary analysis result on the minimum time to complete the proposed attack.
A multi-approach to community question answering

In this paper we face the problem of Community Question Answering for the Arabic language. In this setting, a member of the community posts an initial query expressed in Natural Language. Other participants post their own interventions: answers, comments, additional questions, etc. contributing to building a rather tangled thread of nodes containing Natural Language short texts. The task consists in answering the initial query using the thread as the space of possible answers. The task can be approached as a classification, a regression or a ranking problem. In our case we select the set of possible candidates, we assign a relevance score to each candidate and we rank them accordingly. We propose a bunch of unsupervised models and show that a model based on Latent Semantic Indexing approach outperforms state of the art models for this task. We also use transfer learning to power the embeddings layer of various deep learning models and prove that the pairwise approaches outperform their pointwise counterparts. All the proposed models have been evaluated on Semeval 2017 Task 3 Subtask D: Arabic Community Question Answering and achieve state of the art or near performance.
Evaluating Spread of ‘Gasless Send’ in Ethereum Smart Contracts

We present an approach for analyzing Ethereum smart contracts regarding to the issue of `gasless send' [1, p. 8]. Gasless send is about contracts which lose the ability to receive Ether due to gas depletion when executing the contract's fallback function. Our approach is based on the security analysis tool Mythril which is a framework for analyzing security issues in Ethereum smart contracts. Mythril's analysis engine is based on symbolic code execution and features different analyses for security vulnerabilities in smart contracts. We upgraded the analysis engine in order to model the gas usage of Ethereum smart contracts during execution. In order to utilize the newly acquired data, we added an analysis module to detect fallback functions, whose execution may exceed the defined threshold of gas. Our analysis of 167,698 smart contracts currently deployed to the live Ethereum network shows that a total of 96.3 thousand (or 57%) might be affected by the issue of gasless send.
AEM: Attentional Ensemble Model for personalized classifier weight learning

Proper combination of multiple classifiers can achieve better performance than any individual one. As one of the most popular combination methods, weighted combination assigns the same or different but invariant weights to base classifiers without consideration of the specificity of different instances. However, each classifier usually has different predictive ability on different instances. To deal with this problem, we propose a combination method named attentional ensemble model (AEM), which learns personalized classifier weights w.r.t different instances via an attentional mechanism. Specifically, we first embed classifiers and instances into a latent space. Then the latent representations are used to profile the predictive ability of classifiers w.r.t different instances. In addition, we design an explicit diversity measure in the latent space, based on which we can enhance the diversity among base classifiers to further improve the performance of combination. Extensive experiments on 20 public data sets show the effectiveness of the proposed method compared with state-of-the-art methods.
IdenNet: Identity-Aware Facial Action Unit Detection

Facial Action Unit (AU) detection is an important task to enable the emotion recognition from facial movements. In this paper, we propose a novel algorithm which utilizes identity-labeled face images to tackle the identity-based intra-class variation of AU detection that the appearances of the same AU vary significantly among different subjects, which makes existing methods generate low performance under cross-domain scenarios in case that the training and test datasets are dissimilar. The proposed method is based on network cascades consisting of two sub-tasks, face clustering and AU detection. The face clustering network, trained from a large dataset containing numerous identity-annotated face images, is designed to learn a transformation to extract identity-dependent image features, which are used to predict AU labels in the second network. The cascades are jointly trained by AU- and identity-annotated datasets that contain numerous subjects to improve the method's applicability. Experimental results show that the proposed method achieves state-of-the-art AU detection performance on benchmark datasets BP4D, UNBC-McMaster, and DISFA.
Stabilized training of generative adversarial networks by a genetic algorithm

Generative adversarial networks (GAN) facilitate the learning of probability distributions of complex data in the real world, and allow neural networks to generate the distribution. GANs (GAN and its variants) exhibit excellent performance in applications like image generation and video generation. However, GANs sometimes experience problems during training with regard to the distribution of real data. We applied a genetic algorithm to improve and optimize the GAN's training performance. As a result, the convergence speed and stability during the training process improved compared to the conventional GAN.
Assessing the Significant Impact of Concept Drift in Software Defect Prediction

Concept drift is a known phenomenon in software data analytics. It refers to the changes in the data distribution over time. The performance of analytic and prediction models degrades due to the changes in the data over time. To improve prediction performance, most studies propose that the prediction model be updated when concept drift occurs. In this work, we investigate the existence of concept drift and its associated effects on software defect prediction performance. We adopt the strategy of an empirically proven method DDM (Drift Detection Method) and evaluate its statistical significance using the chi-square test with Yates continuity correction. The objective is to empirically determine the concept drift and to calibrate the base model accordingly. The empirical study indicates that the concept drift occurs in software defect datasets, and its existence subsequently degrades the performance of prediction models. Two types of concept drifts (gradual and sudden drifts) were identified using the chi-square test with Yates continuity correction in the software defect datasets studied. We suggest concept drift should be considered by software quality assurance teams when building prediction models.
Forensic Analysis of Wearable Devices: Fitbit, Garmin and HETP Watches

Wearable technology has been on an exponential rise and shows no signs of slowing down. One category of wearable technology is Fitness bands, which have the potential to show a user's activity levels and location data. Such information stored in fitness bands is just the beginning of a long trail of evidence fitness bands can store, which represents a huge opportunity to digital forensic practitioners. On the surface of recent work and research in this area, there does not appear to be any similar work that has already taken place on fitness bands and particularly, the devices in this study, a Garmin Forerunner 110, a Fitbit Charge HR and a Generic low-cost HETP fitness tracker. In this paper, we present our analysis of these devices for any possible digital evidence in a forensically sound manner, identifying files of interest and location data on the device. Data accuracy and validity of the evidence is shown, as a test run scenario wearing all of the devices allowed for data comparison analysis.
Column generation based heuristic for learning classification trees

This paper explores the use of Column Generation (CG) techniques in constructing univariate binary decision trees for classification tasks. We propose a novel Integer Linear Programming (ILP) formulation, based on root-to-leaf paths in decision trees. The model is solved via a Column Generation based heuristic. To speed up the heuristic, we use a restricted instance data by considering a subset of decision splits, sampled from the solutions of the well-known CART algorithm. Extensive numerical experiments show that our approach is competitive with the state-of-the-art ILP-based algorithms. In particular, the proposed approach is capable of handling big data sets with tens of thousands of data rows. Moreover, for large data sets, it finds solutions competitive to CART.
A Secured Biometric-Based Authentication Scheme in IoT-Based Patient Monitoring System

In this research work, the biometric-based authentication scheme for IoT-based patient monitoring system has been studied. IoT-based patient monitoring system helps the patients to enjoy the healthcare-related services sitting at remote location in their homes. Patient's privacy, safety, and security in this case are very much essential. Authentication technique in this regard is the unique selling point for establishing the safe and secure communication between the patient and the medical server. Jiang et al. have proposed and analyzed a biometric-based authentication scheme in 2017. It was seen that Jiang et al.'s scheme fails to protect the communication system against some of the vulnerable attacks like denial-of-service attack, replay attack, man-in-the-middle attack, offline password guessing attack, smart card stolen attack, forward secrecy attack, user anonymity attack, mutual authentication attack, etc. In order to prevent these security attacks, an enhanced biometric-based authentication scheme using biometric hash function and time stamping in the proposed cryptographic algorithm has been proposed. An informal security analysis of the proposed scheme is also done here. The authentication proof using BAN (Burrows-Abadi-Needham) logic is also done in this paper. For hospitals, a close and prompt monitoring might be required for the critical patients who are admitted in respective critical care units. In order to monitor and diagnose the health of the ailing patients more effectively and efficiently, the smart health application using IoT-based infrastructure with biometric-based authentication system would be required. Considering the poor physiological condition of the patients, some wearable wireless sensor devices would be a better option. The system would monitor patient's condition throughout the day and would send the different health-related parameters of the patients to the relevant doctors through sensors and human interfacing system like mobile app. The doctors sitting at the remote location would see and monitor every data of the patients through their app and can suggest relevant medicines to the patients immediately. The inherent nature of the system would reduce the lead time for the doctors to come and visit physically to the patients and diagnose. The whole architecture of IoT in this case would be requiring some biometric-based authentication scheme in order to maximize the security and safety features of the system. This also in turn will reduce any kind of vulnerabilities or risks in the system. As the patient monitoring system deals with the life and death of a patient and which is very critical and sensitive for the patient, hence a robust and secured system is very much required for this architecture to work. In this thesis, a secured and robust biometric-based authentication system for IoT-based patient monitoring system has been discussed. This scheme is suitable, secure, and effective for healthcare applications.
Scheduling in Cloud Computing Environment using Metaheuristic Techniques: A Survey

Cloud computing has become a burning topic for research these days. To reap the full benefits of cloud computing, a wider perspective of thinking and study is required from a researcher. A vital part of this cloud innovation is that it has given the solid development and movement to the computational environment like scheduling. Scheduling tends to the proficient administration and usage of the extensive amount of processing assets. Being an example of N-P category, scheduling maps the tasks to appropriate resources for delivering an optimized solution. Cloud technology paves a way to provides sub-optimal solutions within a short time period. Metaheuristic-based techniques are one of the ways through which solutions can be achieved efficiently even for hard optimization problems. The present work provides an analysis and discussion based on extensive study of various metaheuristic techniques. In addition, various parameters are identified and explored, that plays a vital role in the scheduling of tasks in a cloud environment.
Decentralised signal processing on graphs via matrix inverse approximation

In the processing of signals defined over graph domains, it is highly desirable to have algorithms that can be implemented in a decentralised manner, whereby each node only needs to exchange information within a localized subgraph of nodes. The most common example is when the subgraph consist of immediate neighbors and this allows for distributed processing. Many graph signal operators, in their original forms, are not amenable to distributed processing. To overcome this deficiency, a polynomial approximation is usually applied to the original operator to yield a distributed operator which is a matrix polynomial of the graph shift matrix. This approach however is only applicable when the original operator is a function of the graph shift matrix. In this paper, we propose a generalized approach to approximate graph signal operators that are not necessary functions of the shift matrix. The key idea here is to restrict the approximated matrix inverse to have small geodesic-width so that multiplication with the small geodesic-width matrix can be implemented in a decentralised manner. Furthermore, to increase the accuracy of the approximated operator, an iterative algorithm which also has the decentralised property and low computational complexity, is proposed. We apply the proposed approach to signal inpainting and signal reconstruction in filter banks. Numerical results verify the effectiveness of the proposed algorithm. The proposed algorithms can also be used to efficiently solve linear system of equations.
Identification of some factors influencing soil transfer on shoes

In criminal activities, soil can be transferred from a crime scene to items linked with a perpetrator; for example, shoes, cars or tools. Several parameters will influence the quantity of soil transferred in a given scenario. The knowledge of the most influential factors can help the expert to assess the evidence using a logical approach at the activity level or to predict the amount of soil that can be expected in a given scenario. The influence of five chosen parameters, namely the shoe profile, shoe size, walker's weight, soil type and soil humidity were assessed using Design of Experiment (DOE) in order to understand their influence on soil quantity transferred on shoes. The Faced Central Composite Design (FCCD) using a quadratic model was found to be highly significant, thus they could be adequately used to model and to interpret the amount of soil recovered from one shoe. These designs demonstrate that the characteristics of the donor (soil type and soil humidity), as well as a combination of these two factors have a very significant impact on the soil transfer. The characteristics of the receptor (shoe profile, shoe size and walker's weight) also have an impact on the transfer but to a lesser extent. Globally, this research provides valuable information for the forensic scientist both in investigative mode: evaluation of the soil quantity possibly transferred on shoes, and in the evaluative steps: is the quantity of soil found on the suspect shoes in accordance to the proposition/scenario given by the prosecution and the defence?
SENSORY: Leveraging Code Statement Sequence Information for Code Snippets Recommendation

Software developers often have to implement unfamiliar programming tasks. When faced with these problems, developers often search online for code snippets as references to learn how to solve the unfamiliar tasks. In recent years, some researchers propose several approaches to use programming context to recommend code snippets. Most of these approaches use information retrieval based techniques and treat code snippets as a set of tokens. However, in code, the smallest meaningful unit is code statement, in general, the line of code. Since these studies did not consider this issue, there is still room for improvement in the code snippets recommendation. In this paper, we propose a code Statement sEquence iNformation baSed cOde snippets Recommendation sYstem (SENSORY). Different from existing token based approaches, SENSORY performs code snippets recommendation at code statement granularity. It uses the Burrows Wheeler Transform algorithm to search relevant code snippets, and uses the structure information to re-rank the results. To evaluate the effectiveness of our proposed method, we construct a code database with 1000000 real world code snippets which contain more than 15000000 lines of code. The experimental results show that SENSORY outperforms the two strong baseline work in terms of precision and NDCG.
Efficient Person Search via Expert-Guided Knowledge Distillation

The person search problem aims to find the target person in the scene images, which presents high demands for both effectiveness and efficiency. In this paper, we present a unified person search framework which jointly handles the two demands for real-world applications. We explore the technique of knowledge distillation (KD), which allows the student network to share capabilities of the deep expert networks with much fewer parameters and less computing time. To achieve this, we describe an efficient person search network and a set of deep and well-engineered expert networks, to build a tiny and compact model that can approximate the representations of the expert networks in a multitask learning manner. We present extensive experiments on three customized student networks with different scales of networks and show strong performance compared to the state-of-the-art methods on both mean average precision and top-1 accuracies. We further demonstrate the efficiency of the proposed network at 120 frames/s in the feedforward time with only a little sacrifice on the accuracy.
Towards Efficient Privacy-Preserving Auction Mechanism for Two-Sided Cloud Markets

Auction is an efficient trading mechanism for cloud markets and adopted by many major cloud providers, such as Amazon EC2. However, most cloud auction designs only target at economic robustness without considering the bidding privacy leakage, which would dramatically hamper the practical applications of truthful cloud auctions. Existing secure cloud auction mechanisms only work on the single-sided cloud markets rather than more practical two-sided markets, and these schemes are too unwieldy to be practical due to significant computation and communication overheads. To fill these gaps, in this paper we propose a privacy-preserving double auction mechanism for two-sided cloud markets, which would not leak any bidding information beyond the auction results to anyone. Technically, we start by presenting a novel secure sorting protocol in the mixed form, which combines additive secret sharing and garbled circuits together. On this basis, our design for secure cloud auction is implemented given consideration to bidding privacy and auction efficiency. Finally, we use extensive experiments to validate its efficacy and performance.