Reviewerly/exHarmony huggingface dataset:
Similarities Across Libraries: Making a Case for Leveraging Test Suites

Developers may choose to implement a library, despite the existence of similar libraries, considering factors such as computational performance, language or platform dependency, and accuracy. As a result, GitHub is a host to several library projects that have overlaps in the functionalities. These overlaps have been of interest to developers from the perspective of code reuse or preferring one implementation over the other. We present an empirical study to explore the extent and nature of existence of these similarities in the library functions. We have further studied whether the similarity among functions across different libraries and their associated test suites can be leveraged to reveal defects in one another. Applying a natural language processing based approach on the documentations associated with functions, we have extracted matching functions across 12 libraries, available on GitHub, over 2 programming languages and 3 themes. Our empirical evaluation indicates existence of a significant number of similar functions across libraries in same as well as different programming languages where a language can influence the extent of existence of similarities. The test suites from another library can serve as an effective source of defect revealing tests. The study resulted in revealing 72 defects in 12 libraries. Further, we analyzed the source of origination of the defect revealing tests. We deduce that issue reports and pull requests can be beneficial in attaining quality test cases not only to test the libraries in which these issues are reported but also for other libraries that are similar in theme.
A Newcomer's Guide to EICS, the Engineering Interactive Computing Systems Community

Welcome to EICS, the Engineering Interactive Computing Systems community, PACMHCI/EICS journal, and annual conference! In this short article, we introduce newcomers to the field and to our community with an overview of what EICS is and how it positions with respect to other venues in Human-Computer Interaction, such as CHI, UIST, and IUI, highlighting its legacy and paying homage to past scientific events from which EICS emerged. We also take this opportunity to enumerate and exemplify scientific contributions to the field of Engineering Interactive Computing Systems, which we hope to guide researchers and practitioners towards making their future PACMHCI/EICS submissions successful and impactful in the EICS community.
A Comparison of Request Distribution Strategies Used in One and Two Layer Architectures of Web Cloud Systems

Web Cloud systems are becoming more and more popular. In the article we want to examine HTTP request distribution strategies that can be used in one and two layer architectures of Web cloud systems. In particular, we want to compare our intelligent solutions with each other, and with popular and most commonly used in Web cloud systems. We describe modern solutions, present the test-bed and results of conducted experiments. At the end, we discuss results and present final conclusions.
Continuous Key Agreement with Reduced Bandwidth

Continuous Key Agreement (CKA) is a two-party procedure used by Double Ratchet protocols (e. g., Signal). This is a continuous and synchronous protocol that generates a fresh key for every sent/received message. It guarantees forward secrecy and post-compromise security. Alwen et al. have recently proposed a new KEM-based CKA construction where every message contains a ciphertext and a fresh public key. This can be made quantum-safe by deploying a quantum-safe KEM. They mention that the bandwidth can be reduced when using an ElGamal KEM (which is not quantum-safe). In this paper, we generalized their approach by defining a new primitive, namely Merged KEM (MKEM). This primitive merges the key generation and the encapsulation steps of a KEM. This is not possible for every KEM and we discuss cases where a KEM can be converted to an MKEM. One example is the quantum-safe proposal BIKE1, where the BIKE-MKEM saves $$50\%$$ of the communication bandwidth, compared to the original construction. In addition, we offer the notion and two constructions for hybrid CKA.
An Improved Animal Migration Optimization Algorithm Based on Interactive Learning Behavior for High Dimensional Optimization Problem

Animal migration optimization(AMO) algorithm inspired by the behavior of animal migration is proposed recently. AMO shows good performance on the benchmark functions whose dimensionality is no more than 30. However, the performance of AMO is degraded rapidly when the dimensionality is larger than 30. In order to overcome this shortcoming, an improved animal migration algorithm (IAMO) based on interactive learning behavior is proposed in this paper. First, we introduce an interactive learning behavior that individuals will learn from each other by exchanging information. During the search process, the search step is dynamically adjusted. In this case, the intelligence of IAMO is higher than AMO. Second, a refined search method is used to search around the current solutions, and this method can enhance the search ability of the algorithm. Third, a birth-and-death mechanism is designed to avoid local optimum. The effectiveness of IAMO is verified on 100 dimensional benchmark functions, and the empirical results show that the performance of IAMO is promising.
Location Privacy in Heterogeneous Vehicular Networks

Vehicle exchange Floating Car Data (FCD) to increase their awareness beyond their local perception. For this purpose, the context-sensitive FCD is commonly distributed using the cellular network. To receive FCD via the cellular network, the vehicles share their current context (often location) with a backend.
UPS Node based Workload Management for Data Centers considering Flexible Service Requirements

As the IT sector is rapidly growing all over the world, more and more energy is consumed by data centers, which leads to the continuous increase of operating costs. Among other factors, the load management among uninterruptible power supply (UPS) nodes should be considered in the energy management model of data center since its energy efficiency is correlated to load ratio. In this paper, an energy management scheme of data center which focuses on the workload scheduling among UPS nodes is proposed to improve its operation efficiency. The flexible service requirement of batch workloads is also modeled in the proposed scheme to illustrate the cost of flexibility. In the proposed scheme, the efficiency of UPS nodes is modeled as a function of load ratio. Meanwhile, the cost of inconvenience is introduced to represent the flexible service requirements of batch workloads. A stochastic formulation is established to address the uncertainties involved in the scheduling process. Simulated case studies are provided and the results demonstrate the effectiveness of this proposed approach.
Graph-based semisupervised and manifold learning for image retrieval with SVM-based relevant feedback

Over the years, many content-based image retrieval (CBIR) methods, which use SVM-based relevant feedback, are proposed to improve the performance of image retrieval systems. However, the performance of these methods is low due to the following limitations: (1) ignore the unlabeled samples; (2) only exploit the global Euclidean structure and (3) not taking advantage of the various useful aspects of the object. In order to solve the first problem, we propose a graph-based semisupervised learning (GSEL), which can add positive samples and construct balanced sets. With the second problem, we propose a manifold learning for dimensional reduction (MAL), which exploits the geometric properties of the manifold data. With the third problem, we propose a combination of classifiers by aspect (CCA), which exploits the various useful aspects of the object. Experimental results reported in the Corel Photo Gallery (with 31,695 images), which demonstrate the accuracy of our proposed method in improving the performance of the content-based image retrieval system.
Heterogeneous Computing for Edge AI

Current artificial intelligence (AI) with human-level accuracy has promising business values in many application fields. However, the price is high computation complexity and memory bandwidth. Therefore it is challenging to deploy AI onto edge devices where the power and hardware resource are limited. In this paper, the design challenges on Edge AI and solutions from Mediatek is introduced. Dedicated parallel AI processor is embedded to gain computation and power efficiency. Memory hierarchy is designed to share and reuse data locally without redundant DRAM accessing. A direct data link is implemented to pass the data between system peripherals and processors to further reduce DRAM bandwidth. The implementation result shows the SoC with all these techniques significantly outperforms other works according to ETHZ AI benchmark.
The impact of Use Cases in real-world software development projects: A systematic mapping study

There is abundant literature on the application of UML Use Cases. However, the impact that these applications have had on real projects (in industry) is not well known. It is necessary to know what the impact of the Use Cases really is, so that both, researchers and professionals can make the most pertinent decisions. To identify and classify the positive and negative impacts of using Use Cases in real-world settings. We conducted a systematic mapping study. The search strategies retrieved a set of 4431 papers out of which 47 were selected as primary studies. We defined four facets to classify these studies: 1) the positive impact (advantages), 2) the negative impact (disadvantages), 3) the industry's domain and 4) the type of research reported. Our study identified eight categories of advantages related to the application of Use Cases. The most mentioned were estimation, analysis and automation. These advantages had a heterogeneous distribution along the years. On the other hand, the granularity of the scenarios described in the Use Cases, the lack of a standardized format for specifying requirements, and the lack of appropriate guidelines for analysing them were the most mentioned disadvantages. We identified a variety of industry domains, grouped into seven categories. As we can expect most of the papers report experiences in the Information Technology domain, followed by financials applications. Almost half the papers applied evaluation research, including an empirical validation. Only one third of the analysed papers reported threats to validity, the most mentioned being generalizability (38%). Use Cases have proven to be a useful tool in software development, particularly during the early stages. The positive effects far outweigh the few negative effects reported, although this may be due to the researchers' tendency of not reporting negative results.
Improved asymptotic estimates for the numbers of correlation-immune and k-resilient vectorial Boolean functions

Abstract We refine local limit theorems for the distribution of a part of the weight vector of subfunctions and for the distribution of a part of the vector of spectral coefficients of linear combinations of coordinate functions of a random binary mapping. These theorems are used to derive improved asymptotic estimates for the numbers of correlation-immune and k -resilient vectorial Boolean functions.
Ally patches for spoliation of adversarial patches

Adversarial attacks represent a serious evolving threat to the operation of deep neural networks. Recently, adversarial algorithms were developed to facilitate hallucination of deep neural networks for ordinary attackers. State-of-the-arts algorithms could generate offline printable adversarial patches that can be interspersed within fields of view of the capturing cameras in an innocently unnoticeable action. In this paper, we propose an algorithm to ravage the operation of these adversarial patches. The proposed algorithm uses intrinsic information contents of the input image to extract a set of ally patches. The extracted patches break the salience of the attacking adversarial patch to the network. To our knowledge, this is the first time to address the defense problem against such kinds of adversarial attacks by counter-processing the input image in order to ravage the effect of any possible adversarial patches. The classification decision is taken according to a late-fusion strategy applied to the independent classifications generated by the extracted patch alliance. Evaluation experiments were conducted on the 1000 classes of the ILSVRC benchmark. Different convolutional neural network models and varying-scale adversarial patches were used in the experimentation. Evaluation results showed the effectiveness of the proposed ally patches in reducing the success rates of adversarial patches.
Function and Pattern Extrapolation with Product-Unit Networks

Neural networks are a popular method for function approximation and data classification and have recently drawn much attention because of the success of deep-learning strategies. Artificial neural networks are built from elementary units that generate a piecewise, often almost linear approximation of the function or pattern. To improve the extrapolation of nonlinear functions and patterns beyond the training domain, we propose to augment the fundamental algebraic structure of neural networks by a product unit that computes the product of its inputs raised to the power of their weights. Linearly combining their outputs in a weighted sum allows representing most nonlinear functions known in calculus, including roots, fractions and approximations of power series. We train the network using stochastic gradient descent. The enhanced extrapolation capabilities of the network are demonstrated by comparing the results for a function and pattern extrapolation task with those obtained using the nonlinear support vector machine (SVM) and a standard neural network (standard NN). Convergence behavior of stochastic gradient descent is discussed and the feasibility of the approach is demonstrated in a real-world application in image segmentation.
Mining Order-Preserving Submatrices Under Data Uncertainty: A Possible-World Approach

Given a data matrix D, a submatrix S of D is an order-preserving submatrix (OPSM) if there is a permutation of the columns of S, under which the entry values of each row in S are strictly increasing. OPSM mining is widely used in real-life applications such as identifying coexpressed genes, and finding customers with similar preference. However, noise is ubiquitous in real data matrices due to variable experimental conditions and measurement errors, which makes conventional OPSM mining algorithms inapplicable. No previous work has ever combated uncertain value intervals using the possible world semantics. We establish two different definitions of significant OPSMs based on the possible world semantics: (1) expected support based and (2) probabilistic frequentness based. An optimized dynamic programming approach is proposed to compute the probability that a row supports a particular column permutation, and several effective pruning rules are introduced to efficiently prune insignificant OPSMs. These techniques are integrated into our two OPSM mining algorithms, based on prefix-projection and Apriori respectively. Extensive experiments on real microarray data demonstrate that the OPSMs found by our algorithms have a much higher quality than those found by existing approaches.
Gesture-based incident reporting through smart watches

With the capabilities and autonomy of smart watches constantly increasing, there is the need for introducing applications which can exploit their full potential, establishing the use of smart watches in our daily routine. The field of personal safety and security provides an excellent basis over which applications can be developed, enabling the use of wrist worn devices as tools for easy, discreet and efficient reporting of incidents or suspicious behavior. However, current practices in report creation using smart watches rely on methods and interfaces, such as taking pictures and writing text, without taking into account gesture-based input. In this paper, we present the design a smart watch-based approach, which utilizes a Deep Learning model, to recognize specific user gestures that could result in reporting hazardous situations and could alert the authorities for assistance. We evaluated the performance of the model by training it to distinguish 5 predefined gestures from a set of random gestures performed by 9 subjects wearing a smart watch on their dominant arm. Our Deep Learning model surpasses the performance of conventional classifiers that rely on hand-crafted features and produced gesture recognition accuracies above 98% in 26,061 motion signal samples by fusing the automatically extracted features of 3-axial accelerometer signals. We conclude by discussing the related issues we have encountered by using the proposed application in real-time use and providing future directions.
Recurrent Neural Network for Classifying of Hpc Applications

This paper presents a novel approach using deep neural networks in HPC software application domain. The approach uses Recurrent Neural Network (RNN) and more specifically Long Short-Term Memory (LSTM) models to classify jobs running on HPC clusters into different application families. This is a mandatory step in order to optimize their run time environment for our future work. Our approach was inspired by the recent advancement in Deep Learning in the AI area. We implemented our models on a HPC cluster and took advantage of the GPU to accelerate the training process. The evaluation of our models on a database of synthetic jobs (with more than 500 runs) gave good results for the classification of short duration jobs (less than 1 hour). In comparison with the conventional approaches such as Support Vector Machine and Hidden Markov Model, the stateful LSTM implementation improves significantly the classification accuracy for long duration jobs (more than 2 hours).
Linear Cryptanalysis Reduced Round of Piccolo-80

Piccolo is a 64-bit lightweight block cipher suitable for constrained environments such as wireless sensor networks. In this paper we evaluate the security of Piccolo-80 against linear cryptanalysis, we present a 6-round linear approximation of Piccolo-80 with probability $$1/2 + 2^{-29.04}$$ . We use this approximation to attack 7-round Piccolo-80 (with whitening keys) with data complexity of $$2^{61}$$ known plaintexts and time complexity of $$2^{61}$$ . Its extension to an 8-round attack merely increases the time complexity to $$2^{70}$$ . This is the best linear attack against Piccolo-80 and it is also applicable to Piccolo-128 as the difference between the two variates is only the number of rounds and the key schedule algorithm. Moreover, we show that the bias in the approximation of the F-function, in some cases, is related to the MSB of the input. We utilize this relation to efficiently extract the MSBs of the whitening keys in the first round.
An Annotation Protocol for Collecting User-Generated Counter-Arguments Using Crowdsourcing

Constructive feedback is important for improving critical thinking skills. However, little work has been done to automatically generate such feedback for an argument. In this work, we experiment with an annotation protocol for collecting user-generated counter-arguments via crowdsourcing. We conduct two parallel crowdsourcing experiments, where workers are instructed to produce (i) a counter-argument, and (ii) a counter-argument after identifying a fallacy. Our analysis indicates that we can collect counter-arguments that are useful as constructive feedback, especially when workers are first asked to identify a fallacy type.
Time-varying Data Visual Analysis method based on Parallel Coordinate System

With the advent of the era of big data, there are more and more ways to obtain data. The collected data generally is large-scale, multi-dimensional and time-varying. To explore and analyse time-varying data comprehensively, this paper proposes a visual method for processing multidimensional time-varying data based on parallel coordinate system. Firstly, we use MDS algorithm to turn the original multidimensional time data to distribute in one-dimension space in chronological order, constructing the time-varying parallel coordinate system. Then, we cluster the projection coordinate points of each time axis, and each type of data is drawn in different colours to differentiate. At last, edge bundling method is used to cluster data polylines in each time segment, helping to reduce visual confusion and improve information expression efficiency. The experimental analysis of the air quality dataset from Krakow Poland shows that the method can comprehensively analyse the multi-dimensional time-varying data, helping users to explore the implied regularity hidden in the dataset effectively.
Survey on Mathematical Word Problem Solving Using Natural Language Processing

Natural language processing (NLP) is typically used for analyzing a large set of data. It has traditional applicability like sentiment analysis, spam mail detection, summarizing a large text. However, word problem solving is challenging if it is to be done with NLP. There are some approaches which have been proposed. Some could solve basic arithmetic problems like addition/subtraction. Knowledge representation is the main task to be done by NLP. Each kind of problem can be solved by a specific approach. This survey paper is the overview of the work done in this area till date and also discuss what can be done to improve and extend the work.
The Female Effect

A key aspect when designing experiments is the subjects' selection. Many experiments fail to select a gender-balanced set of subjects and there is often a lower proportion of females. In fact, a significant ratio of papers that include user studies don't even mention the gender distribution. Probably, this is due to the low ratio of females in engineering student's population that normally is used in HCI user studies. In this paper we show that a biased selection of subjects can lead to incorrect results when we rely on users to validate a system addressed to general public. We designed two experiments to obtain a benchmark value of throughput of a head-tracker for mobile devices and we selected a gender-balanced population. To assess the validity of our claim we choose gender as the primary independent variable of a between-subjects study design. We found that gender has a direct impact on throughput; therefore, a gender-unbalanced set of subjects is a threat to the external validity of the results obtained.
Saliency Prediction for Omnidirectional Images Considering Optimization on Sphere Domain

There are several formats to describe the omnidirectional images. Among them, equirectangular projection (ERP), represented as 2D image, is the most widely used format. There exist many outstanding methods capable of well predicting the saliency maps for the conventional 2D images. But these works cannot be directly extended to predict the saliency map of the ERP image, since the content on ERP is not for direct display. Instead, the viewport image on demand is generated after converting the ERP image to the sphere domain, followed by rectilinear projection. In this paper, we propose a model to predict the saliency maps of the ERP images using existing saliency predictors for the 2D image. Some pre-processing and post-processing are used to manage the problem mentioned above. In particular, a smoothing based optimization is realized on the sphere domain. A public dataset of omnidirectional images is used to perform all the experiments and competitive results are achieved.