Reviewerly/exHarmony huggingface dataset:
A study on Cloud enabled Applications and their Security Issues

Cloud computing was first established to connect people and data at anytime from anywhere in the 1960s. Today cloud computing is one of the most widely used technologies. Its applications include Multi-cloud, healthcare, testing, finance, data storage and handling big data to increase efficiency and how it differs from traditional systems. We have stated how cloud computing can be used in various domains and have compared its efficiency to traditional systems in terms of data recovery, access control, management, security and cost. However, like any other technology, this one too might have flaws which can be exploited. This paper also lists the security challenges that will have to be kept in mind while implementing these systems.
Supervaluationism, Subvaluationism and the Sorites Paradox

One way in which we might approach the challenge posed by the Sorites Paradox is considering that Sorites-susceptible predicates have several candidate extensions, or several ways in which these expressions can be made precise. For example, a candidate extension for the predicate ‘is a baby’ is the set of humans of less than two years, but also the set of those less than two years and one second, and of those less than two years and two seconds. In this chapter we present and discuss two theories for vague predicates based on this idea: super-valuationism and subvaluationism. The chapter is structured in three parts. The first presents the super- and subvaluationist theories: their similarities and differences in the semantics, the resulting logics with their most characteristic features. The second reviews the super- and subvaluationist solutions to the Sorites Paradox and provides discussion on several controversies surrounding these theories. The third part introduces proof procedures for s’valuationist logics.
Scalable Gaussian Process for Extreme Classification

We address the limitations of Gaussian processes for multiclass classification in the setting where both the number of classes and the number of observations is very large. We propose a scalable approximate inference framework by combining the inducing points method with variational approximations of the likelihood that have been recently proposed in the literature. This leads to a tractable lower bound on the marginal likelihood that decomposes into a sum over both data points and class labels, and hence, is amenable to doubly stochastic optimization. To overcome memory issues when dealing with large datasets, we resort to amortized inference, which coupled with subsampling over classes reduces the computational and the memory footprint without a significant loss in performance. We demonstrate empirically that the proposed algorithm leads to superior performance in terms of test accuracy, and improved detection of tail labels.
Using Situational and Narrative Analysis for Investigating the Messiness of Software Security

Background: Software engineering work and its context often has characteristics of what in social science is termed 'messy'; it has ephemeral and irregular qualities. This puts high demands on researchers doing inquiry and analysis. Aims: This paper aims to show what a combination of situational analysis (SA) and narrative analysis (NA) can bring to qualitative software engineering research, and in particular for situations characterised by mess. Method: SA and NA were applied to a case study on software security. Results: We found that these analysis methods helped us gain new insights and understandings and a broader perspective of the situation we are studying. Additionally, the methods helped collaboration in the analysis. Conclusion: We recommend applying and studying these and similar combinations of analysis approaches further.
Are ‘Flow of Ideas’ and ‘Research Productivity’ in secular decline?

It is widely held true that fundamental scientific knowledge has been accelerating exponentially over the past centuries and will continue to do so for the foreseeable future. Moreover, endogenous growth theory postulates that this exponential accumulation of knowledge is the main source of the ubiquitous exponential economic growth. We test these claims by constructing two new series of knowledge indices, one representing the historical evolution of the Flow of Ideas, the other of the Research Productivity, for the time period between 1750 and 1988. Three different geographical regions are covered: 1) Continental Europe, 2) the United Kingdom, and 3) the United States; and two disciplines: a) the physical sciences, and b) the life sciences. Our main result is that scientific knowledge has been in clear secular decline since the early 1970s for the Flow of Ideas and since the early 1950s for the Research Productivity. We also observe waves coinciding with the three industrial and technological revolutions, in particular in the United Kingdom. Overall, our results support the Kuhnian theory of knowledge creation through scientific revolutions, punctuation and paradigm shifts and falsify the gradualism that lies at the basis of the currently prevailing economic paradigm of endogenous growth.
Multi-modal constraint propagation via compatible conditional distribution reconstruction

In many applications, the pairwise constraint is a kind of weaker supervisory information which can be collected more efficiently than the label information. The constraint propagation is a constrained clustering approach, which has been proved to be a success of exploiting such side-information. Recent years have witnessed many methods of multi-modal constraint propagation. However, the problem of reasonably fusing different modalities under the framework of constraint propagation remains unaddressed. In this paper, we first identify a necessary and sufficient condition for compatible conditional distributions under a specific assumption and address the problem of Compatible Conditional Distributions Reconstruction (CCDR). With the help of CCDR, we propose a multi-modal constraint propagation method dubbed Instance Level Multi-Modal Constraint Propagation (ILMCP). ILMCP fuses the affinity of different modalities at the data instance level instead of the modality level and constructs a unified affinity matrix. Extensive experiments on two publicly available multi-modal datasets show the superior performance of the proposed method.
Controlling the Controllers: What Software People Can Learn From Control Theory

Who takes care of the caretaker, while the caretaker is busy taking care?
An Automatic Approach for Extracting Chinese Entity Synonyms from Encyclopedias

Synonyms play an important role in many entity-based applications. However, most known synonym extraction methods are in English, while Chinese ones are relatively rare. In this paper, we propose a simple yet effective extraction and cleaning framework that automatically extracts Chinese entity synonyms from encyclopedias. In the extraction phase, three extraction methods named direct extraction, pattern-based extraction and neural mining extraction are employed to acquire a large number of candidate entity synonym sets from multiple sources of Chinese online encyclopedias (e.g., title, infobox and abstract). In the cleaning phase, three error detection strategies named lexical and semantic rules, domain filtering and similarity filtering are used to improve the precision of the extracted candidate entity synonym sets. Hence, we build a Chinese synonym knowledge base named CN-EntitySyn. Experimental results show that CN-EntitySyn outperforms the compared baselines in terms of precision and coverage.
On-chip Parallel Photonic Reservoir Computing using Multiple Delay Lines

Silicon-Photonics architectures have enabled high speed hardware implementations of Reservoir Computing (RC). With a delayed feedback reservoir (DFR) model, only one non-linear node can be used to perform RC. However, the delay is often provided by using off-chip fiber optics which is not only space inconvenient but it also becomes architectural bottleneck and hinders to scalability. In this paper, we propose a completely on-chip photonic RC architecture for high performance computing, employing multiple electronically tunable delay lines and micro-ring resonator (MRR) switch for multi-tasking. Proposed architecture provides 84% less error compared to the state-of-the-art standalone architecture in [8] for executing NARMA task. For multi-tasking, the proposed architecture shows 80% better performance than [8]. The architecture outperforms all other proposed architectures as well. The on-chip area and power overhead of proposed architecture due to delay lines and MRR switch are 0.0184mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and 26mW respectively.
Towards Constructing Fully Homomorphic Encryption without Ciphertext Noise from Group Theory

Abstract In CRYPTO 2008, 1 year earlier than Gentry’s pioneering “bootstrapping” technique for the first fully homomorphic encryption (FHE) scheme, Ostrovsky and Skeith III had suggested a completely different approach towards achieving FHE. They showed that the $$\mathsf {NAND}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>NAND</mml:mi> </mml:math> operator can be realized in some non-commutative groups; consequently, homomorphically encrypting the elements of the group will yield an FHE scheme, without ciphertext noise to be bootstrapped. However, no observations on how to homomorphically encrypt the group elements were presented in their paper, and there have been no follow-up studies in the literature. The aim of this paper is to exhibit more clearly what is sufficient and what seems to be effective for constructing FHE schemes based on their approach. First, we prove that it is sufficient to find a surjective homomorphism $$\pi :\widetilde{G} \rightarrow G$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>π</mml:mi> <mml:mo>:</mml:mo> <mml:mover> <mml:mi>G</mml:mi> <mml:mo>~</mml:mo> </mml:mover> <mml:mo>→</mml:mo> <mml:mi>G</mml:mi> </mml:mrow> </mml:math> between finite groups for which bit operators are realized in G and the elements of the kernel of $$\pi $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>π</mml:mi> </mml:math> are indistinguishable from the general elements of $$\widetilde{G}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mover> <mml:mi>G</mml:mi> <mml:mo>~</mml:mo> </mml:mover> </mml:math> . Secondly, we propose new methodologies to realize bit operators in some groups G . Thirdly, we give an observation that a naive approach using matrix groups would never yield secure FHE due to an attack utilizing the “linearity” of the construction. Then we propose an idea to avoid such “linearity” by using combinatorial group theory. Concretely realizing FHE schemes based on our proposed framework is left as a future research topic.
Mining specific and representative information by the attribute‐oriented induction method

Abstract Attribute‐oriented induction (AOI) is a data analysis technique based on induction. The traditional AOI algorithm requires a threshold given by users to determine the number of output tuples. However, it is not easy to set an appropriate tuple threshold, and there is usually noise contained in a dataset. The traditional AOI algorithm can only generate a summary output of a fixed size, but it cannot guarantee that all generalized tuples have sufficient specificity and representativeness. In this article, a new AOI method is proposed to make up for the shortcomings. We introduce the concept of cost to measure the loss of accuracy due to attribute ascension. We also propose two algorithms based on the hierarchical clustering method. By setting cost constraints on each generalized tuple, our method can generate accurate output while eliminating noise, and help users get more informative and clearer results.
Cohesion Measure for Restructuring

The object-oriented programming is widely adopted in recent software developments. The development of a well-designed software system is needed that reduces software maintenance costs. On the other hand, the internal structure of the software system is deteriorating due to prolong maintenance operations. In such cases, restructuring is one of the strategies to strengthen the system’s overall internal structure without changing its external behavior. Another restructuring strategy is to use refactoring on the current system. Code refactoring is an effective approach to software development to improve the internal structure of the program. Through refactoring, the quality of the program can be enhanced by maintenance and improvement in reliability. Code refactoring is done without any modification in its features. Cohesion is used to assess a software system’s design quality and is the main pillar of object-oriented software development in good software design. Using software metrics, the quality of object-oriented classes that require code refactoring is assessed. This work proposes the need for refactoring and focuses on exploring how to use object-oriented metrics as guidance where code refactoring may be used. We present object-oriented software metric, i.e., cohesion metric and analyzes the need of metric for restructuring.
STREAMER

With the proliferation of continuous data generation, data stream processing has become a key topic in research. As a consequence, the need for dedicated tools to apply continuous learning in streams emerges. This paper presents STREAMER, a flexible, scalable, and cross-platform machine learning experimenter with a realistic operational stream environment and visualization capabilities. Oriented to data scientists, this framework provides a set of machine learning algorithms and an API to easily integrate new ones. In order to illustrate how STREAMER works, we show a demonstration of an unsupervised anomaly detection of electrocardiograms (ECG) tested in a streaming context.
Bilinear Fusion of Commonsense Knowledge with Attention-Based NLI Models

We consider the task of incorporating real-world commonsense knowledge into deep Natural Language Inference (NLI) models. Existing external knowledge incorporation methods are limited to lexical-level knowledge and lack generalization across NLI models, datasets, and commonsense knowledge sources. To address these issues, we propose a novel NLI model-independent neural framework, BiCAM. BiCAM incorporates real-world commonsense knowledge into NLI models. Combined with convolutional feature detectors and bilinear feature fusion, BiCAM provides a conceptually simple mechanism that generalizes well. Quantitative evaluations with two state-of-the-art NLI baselines on SNLI and SciTail datasets in conjunction with ConceptNet and Aristo Tuple KGs show that BiCAM considerably improves the accuracy the incorporated NLI baselines. For example, our BiECAM model, an instance of BiCAM, on the challenging SciTail dataset, improves the accuracy of incorporated baselines by 7.0% with ConceptNet, and 8.0% with Aristo Tuple KG.
More Than One: A Cluster-Prototype Matching Framework for Zero-Shot Learning

Zero-shot learning (ZSL) aims to recognize unseen categories whose data is unavailable during the training stage. Most existing ZSL algorithms focus on learning an embedding space and determine the classes of test samples according to sample-prototype similarities in this space. However, we observe that, in contrast to the single sample-prototype relationship, an ensemble criterion usually benefits the final classification, just as the saying "more than one". Inspired by this, we introduce a novel cluster-prototype matching (CPM) strategy and propose a ZSL framework based on CPM. Firstly, we learn a mapping between the visual space and the semantic space utilizing a well-established ZSL algorithm. Via the learned mapping, all test samples are projected into the embedding space and clustered in this space. Secondly, two CPM methods, soft-CPM and hard-CPM, are proposed to match clusters and class prototypes, along with cluster-prototype similarities calculated. Finally, the label of each sample is determined by the combination of the sample-prototype similarity and the cluster-prototype similarity. We apply our framework to five basic ZSL methods and compare them with several advanced baselines of ZSL. The experimental results demonstrate that the proposed framework can significantly improve the performance of the basic ZSL models and help them achieve or beyond the state-of-the-art.
Combining Influence Maps with Heuristic Search for Executing Sneak-Attacks in RTS Games

Real-Time Strategy (RTS) games have become a popular domain for AI research due to their large state and action spaces, as well as complex sub-problems. One popular strategy in RTS games is the idea of a "Sneak-Attack", in which one player attempts to sneak enemy units into the base of their opponent without being seen, in order to gain the element of surprise. In this paper we will present initial results on combining influence maps with heuristic search to produce a path-finding system which allows us to guide StarCraft drop ships in order to execute a sneak attack. Our preliminary results show that by combining these two techniques, we can efficiently and automatically produce paths that guide our drop ships in a stealthy manner toward the enemy base, minimizing distance traveled and avoiding enemy vision of our army.
Touchless Typing Using Head Movement-based Gestures

In this paper, we propose a novel touchless typing interface that makes use of an on-screen QWERTY keyboard and a smartphone camera. The keyboard was divided into nine color-coded clusters. The user moved their head toward clusters, which contained the letters that they wanted to type. A front-facing smartphone camera recorded the head movements. A bidirectional GRU based model which used pre-trained embedding rich in head pose features was employed to translate the recordings into cluster sequences. The model achieved an accuracy of 96.78% and 86.81% under intra- and inter-user scenarios, respectively, over a dataset of 2234 video sequences collected from 22 users.
Augmented Reality in learning Malay Language

Learning Malay language is challenging especially to international students. Augmented reality is one of an attractive technology, and has been identified as one of the solutions for people to learn foreign language. Although, many augmented reality mobile based applications have been developed, however, it is not for those who want to learn Malay language. In addition, most of the existing applications are limited to students in elementary level, and it is also lack of attractive features. Therefore, in this work, the main aim is to develop the augmented reality mobile based application for UPM international students to learn Malay language by implementing university elements in the proposed application. The application was developed in android platform, and were developed by using Unity 3D and Vuforia. For the evaluation purpose, the target users were identified and the questionnaire was used to evaluate users' satisfaction. Based on the survey, it shows that about 88% target users are satisfied with the proposed application. This shows that the proposed application could be used to learn Malay language for international students.
Detection of Various Speech Forgery Operations Based on Recurrent Neural Network

Most existed algorithms of speech forensics have been proposed to detect specific forgery operations. In realistic scenes, however, it is difficult to predict the type of the forgery. Since the suspicious speech might have been processed by some unknown forgery operation, it will give a confusing result based on a classifier for a specific forgery operation. To this end, a forensic algorithm based on recurrent neural network (RNN) and linear frequency cepstrum coefficients (LFCC) is proposed to detect four common forgery operations. The LFCC with its derivative coefficients is determined as the forensic feature. An RNN frame with two-layer LSTM is designed with preliminary experiments. Extensive experiments on TIMIT and UME databases show that the detection accuracy for the intra-database evaluation can achieve about 99%, and the detection accuracy for the cross-database can achieve higher than 88%. Finally, compared with the previous algorithm, better performance is obtained by the proposed algorithm.
Variational satisfiability solving

Incremental satisfiability (SAT) solving is an extension of classic SAT solving that allows users to efficiently solve a set of related SAT problems by identifying and exploiting shared terms. However, using incremental solvers effectively is hard since performance is sensitive to a problem's structure and the order sub-terms are fed to the solver, and the burden to track results is placed on the end user. For analyses that generate sets of related SAT problems, such as those in software product lines, incremental SAT solvers are either not used at all, used but not explicitly stated so in the literature, or used but suffer from the aforementioned usability problems. This paper translates the ordering problem to an encoding problem and automates the use of incremental SAT solving. We introduce variational SAT solving, which differs from incremental SAT solving by accepting all related problems as a single variational input and returning all results as a single variational output. Our central idea is to make explicit the operations of incremental SAT solving, thereby encoding differences between related SAT problems as local points of variation. Our approach automates the interaction with the incremental solver and enables methods to automatically optimize sharing of the input. To evaluate our methods we construct a prototype variational SAT solver and perform an empirical analysis on two real-world datasets that applied incremental solvers to software evolution scenarios. We show, assuming a variational input, that the prototype solver scales better for these problems than naive incremental solving while also removing the need to track individual results.
Measuring Similarity in CCTV Systems for a Real-time Assessment of Traffic Jams

Traffic jams are inevitable on the roads that many of us use every day. Their number and scale are generally increasing, especially in cities where economic activities are flourishing. The causes of these traffic jams are numerous and generally have economic and socio-environmental consequences. Many solutions have been proposed for detecting traffic jams without considering mathematical tools. In this article, we propose to provide solutions based on mathematical tools which make it possible to measure the similarity between two successive images acquired via closed circuit television (CCTV) systems. This similarity measure will allow us to assess the state of traffic jams in a CCTV system in order to prevent them. By analyzing the transmission of images through a variable sliding window, the implementation of the SSIM (Structural Similarity Index Measure) and the cross-correlation metrics which make possible to measure the similarity between two successive images in transmission in standardized Performance Evaluation of Tracking and Surveillance (PETS) datasets. The comparison between these two metrics based on the processing time and the probability distributions reveals that the SSIM metric provides better performance to prevent traffic jams.