Reviewerly/exHarmony huggingface dataset:
WARBLE: Programming Abstractions for Personalizing Interactions in the Internet of Things

Advances in sensing and networking along with ubiquitous Internet connectivity have paved the way for today's massive Internet of Thing (IoT) market. Despite the vast potential of connecting to myriad devices across homes, office buildings, and public spaces, there is still a large need to unify the scattered protocols, hubs, and cloud services while personalizing enduser experiences. Enabling personalized IoT experiences requires an expressive and flexible middleware that enables simplified development of applications that address diverse individual needs and seamlessly cross multiple vendors and administrative domains. We introduce Warble: a middleware for such personalized IoT applications; Warble encapsulates device and protocol complexities, represents interaction with IoT devices as flexible programming abstractions, and enables applications to learn from their prior interactions in the IoT on behalf of their users. In this paper, we present Warble's architectural abstractions, API, and implementation. We then evaluate the middleware through a case study application using our Android implementation; this evaluation showcases the novelty of the Warble architecture and its programming abstractions.
Synthesizing differentially private programs

Inspired by the proliferation of data-analysis tasks, recent research in program synthesis has had a strong focus on enabling users to specify data-analysis programs through intuitive specifications, like examples and natural language. However, with the ever-increasing threat to privacy through data analysis, we believe it is imperative to reimagine program synthesis technology in the presence of formal privacy constraints. In this paper, we study the problem of automatically synthesizing randomized, differentially private programs, where the user can provide the synthesizer with a constraint on the privacy of the desired algorithm. We base our technique on a linear dependent type system that can track the resources consumed by a program, and hence its privacy cost. We develop a novel type-directed synthesis algorithm that constructs randomized differentially private programs. We apply our technique to the problems of synthesizing database-like queries as well as recursive differential privacy mechanisms from the literature.
A gradient approximation algorithm based weight momentum for restricted Boltzmann machine

Restricted Boltzmann Machine (RBM) is a powerful generative model in deep learning that can automatically conduct learning of data probability distributions without supervision. Deep architectures can effectively enhance the capability of image feature expression in image recognition. However, their learning time is long or they present poor performance given the same running time in a deep model. To address these problems, we propose a new gradient approximation algorithm called average contrastive divergence (ACD) with weight-decay momentum for training the RBM. It is an improved contrastive divergence (CD) algorithm combined with weight-decay momentum. Different combinations of the weight momentum term are added in the pre-training and fine-tuning phases of the RBM to accelerate the network convergence and improve the classification effect. Finally, the proposed algorithm is evaluated on the MNIST database, Extended Yale B and CMU-PIE face databases. The experimental results show that the proposed learning algorithm is a better approximation of the log-likelihood gradient method and outperforms the other algorithms.
Post-Processing of Word Representations via Variance Normalization and Dynamic Embedding

Language processing becomes more and more important in multimedia processing. Although embedded vector representations of words offer impressive performance on many natural language processing (NLP) applications, the information of ordered input sequences is lost to some extent if only context-based samples are used in the training. For further performance improvement, two new post-processing techniques, called post-processing via variance normalization (PVN) and post-processing via dynamic embedding (PDE), are proposed in this work. The PVN method normalizes the variance of principal components of word vectors, while the PDE method learns orthogonal latent variables from ordered input sequences. The PVN and the PDE methods can be integrated to achieve better performance. We apply these post-processing techniques to several popular word embedding methods to yield their post-processed representations. Extensive experiments are conducted to demonstrate the effectiveness of the proposed post-processing techniques.
Complex Deep Learning with Quantum Optics

The rapid evolution towards future telecommunications infrastructures (e.g., 5G, the fifth generation of mobile networks) and the internet is renewing a strong interest for artificial intelligence (AI) methods, systems, and networks. Processing big data to infer patterns at high speeds and with low power consumption is becoming an increasing central technological challenge. Electronics are facing physically fundamental bottlenecks, whilst nanophotonics technologies are considered promising candidates to overcome the limitations of electronics. Today, there are evidences of an emerging research field, rooted in quantum optics, where the technological trajectories of deep neural networks (DNNs) and nanophotonics are crossing each other. This paper elaborates on these topics and proposes a theoretical architecture for a Complex DNN made from programmable metasurfaces; an example is also provided showing a striking correspondence between the equivariance of convolutional neural networks (CNNs) and the invariance principle of gauge transformations.
Multi-robot Path Planning for Complete Coverage with Genetic Algorithms

Complete coverage path planning (CPP) generates a path following which a robot can cover all free spaces in an environment. Compared with single robot CPP, multi-robot CPP gains both efficiency and challenges. In large scale environments, one robot is not competent to the coverage task, such as doing cleaning work in airports, supermarkets, shopping malls, etc. The proposed approach firstly creates a global map with a simultaneous localization and mapping (SLAM) method, and partitions the map into a set of small sub-regions according to the environment’s topological structure. Then the multi-robot CPP formed a multiple traveling salesman (mTSP) problem, where a genetic algorithm (GA) allocates the sub-regions to each robot and gives the robots their visiting orders to the sub-regions. This paper mainly focuses on how to model the multi-robot CPP problem with mTSP and how to solve the task allocation problem with an improved GA algorithm off-line. Two SLAM-based environmental experiments validated the proposed method’s feasibility and efficiency in terms of time consumption.
Leveraging SecDevOps to Tackle the Technical Debt Associated with Cybersecurity Attack Tactics

Context: Managing technical debt (TD) associated with external cybersecurity attacks on an organization can significantly improve decisions made when prioritizing which security weaknesses require attention. Whilst source code vulnerabilities can be found using static analysis techniques, malicious external attacks expose the vulnerabilities of a system at runtime and can sometimes remain hidden for long periods of time. By mapping malicious attack tactics to the consequences of weaknesses (i.e. exploitable source code vulnerabilities) we can begin to understand and prioritize the refactoring of the source code vulnerabilities that cause the greatest amount of technical debt on a system. Goal: To establish an approach that maps common external attack tactics to system weaknesses. The consequences of a weakness associated with a specific attack technique can then be used to determine the technical debt principal of said violation; which can be measured in terms of loss of business rather than source code maintenance. Method: We present a position study that uses Jaccard similarity scoring to examine how 11 malicious attack tactics can relate to Common Weakness Enumerations (CWEs). Results: We conduct a study to simulate attacks, and generate dependency graphs between external attacks and the technical consequences associated with CWEs. Conclusion: The mapping of cyber security attacks to weaknesses allows operational staff (SecDevOps) to focus on deploying appropriate countermeasures and allows developers to focus on refactoring the vulnerabilities with the greatest potential for technical debt.
Active Vision in the Era of Convolutional Neural Networks

In this work, we examine the literature of active object recognition in the past and present. We note that methods in the past used a notion of recognition ambiguity in order to find a next best view policy that could disambiguate the object with the fewest camera moves. Present methods on the other hand use deep reinforcement learning to learn camera control policies from the data. We show on a public dataset, that reinforcement learning methods are not superior to a policy of adequately sampling the object view-sphere. Instead of focusing on finding the next best view, we examine a recent method of quantifying recognition uncertainty in deep learning as a potential application to active object recognition. We find that predictions with this technique are well calibrated with respect to the performance of a network on a test-set, showing that it could be useful in an active vision scenario.
Closing the Academia-Industry Gap in Undergraduate CS

Previous studies have shown that the transition of recent CS graduates into their first jobs in industry is often not as smooth as one might expect. The difficulties they face are the result of a significant gap between industry expectations and their academic experiences. The presence of the academia-industry gap has been demonstrated repeatedly over the past two decades, but the question remains why it still persists today and what strategies we as CS educators may employ to effectively close it for good. With my thesis I want to take the first steps towards structurally closing this gap.
A Neural Probabilistic outlier detection method for categorical data

Abstract Unsupervised outlier detection for categorical data is important and essential for broad applications in various domains. The complex interactions between attributes and the relevance of attributes make it a stem challenge. Existing methods, including patterns-based and couplings-based methods, either fail to capture the complex interactions or cannot handle the diverse attributes well. In this paper, we propose a novel Neural Probabilistic Outlier Detection method for categorical data, called NPOD. We present a new log-bilinear neural model to learn the categorical distributions, and we observe that the inliers and the outliers can be well separated according to learning loss. Based on this basic observation, we give both empirical and theoretical analysis and present a new neural network architecture that captures the interactions of attributes. Moreover, the discriminative information is used in the proposed bias training process to make the inliers and the outliers more separable. Lastly, to distinguish relevance of attributes, two indicators are proposed for computing ensemble outlier score to get a reliable result. Experimental results show that NPOD significantly outperforms the state-of-the-art competitors on 12 real-world data sets in terms of AUC and P@k.
Enabling Model-Driven Software Development Tools for the Internet of Things

The heterogeneity and complexity of Internet of Things (IoT) applications present new challenges to the software development process. Model-Driven Software Development (MDSD) is increasingly being recognized as a key paradigm in tackling many of these challenges, as evident by the emergence of a significant number of MDSD frameworks targeting IoT in the past couple of years. At the heart of IoT applications are embedded and realtime systems, a domain where model-driven development is well-established and many existing tools have a proven track record. Unfortunately, only a handful of these tools support out-of-the-box integration with the IoT. In this work, we discuss the different design and implementation decisions for enabling existing actor-oriented MDSD tools for the IoT. Moreover, we propose an integration approach based on the use of proxy actors and system interfaces. The approach offers seamless and flexible integration of external IoT devices into the user's model. We implement and evaluate our approach using the MDSD tool Papyrus for Realtime as a testbed.
Digitization, Digital Technology, and Importance of Digital Technology in Teaching

This study focuses on digitization in the education process and on the importance of using digital technologies in the teaching process. The introduction sketches out the need of leading young people towards developing their digital competences and digital as well as information literacy. The teaching process should lead towards gaining key and practically oriented knowledge and skills according to Industry 4.0. The importance of the role of the teacher in the education process is stressed who is a managing adviser influencing the effectiveness of the education process. The first chapter is dedicated to digital literacy and other literacies and connected terminology. The next chapter focuses on case studies and evaluating the opinions of students on the importance of using digital technologies during class. The results were gathered via a questionnaire survey. The objective is to point out specifics of the education process. Gathered data can be valuable and inspirational for the decision-making process when planning and preparing teachers for the education process. The conclusion of the entry stresses the importance of innovative study programs and introducing new study programs, which focus on supporting the development of digital literacy and working with digital technologies.
Future-Ready Strategic Oversight of Multiple Artificial Superintelligence-Enabled Adaptive Learning Systems via Human-Centric Explainable AI-Empowered Predictive Optimizations of Educational Outcomes

Artificial intelligence-enabled adaptive learning systems (AI-ALS) have been increasingly utilized in education. Schools are usually afforded the freedom to deploy the AI-ALS that they prefer. However, even before artificial intelligence autonomously develops into artificial superintelligence in the future, it would be remiss to entirely leave the students to the AI-ALS without any independent oversight of the potential issues. For example, if the students score well in formative assessments within the AI-ALS but subsequently perform badly in paper-based post-tests, or if the relentless algorithm of a particular AI-ALS is suspected of causing undue stress for the students, they should be addressed by educational stakeholders. Policy makers and educational stakeholders should collaborate to analyze the data from multiple AI-ALS deployed in different schools to achieve strategic oversight. The current paper provides exemplars to illustrate how this future-ready strategic oversight could be implemented using an artificial intelligence-based Bayesian network software to analyze the data from five dissimilar AI-ALS, each deployed in a different school. Besides using descriptive analytics to reveal potential issues experienced by students within each AI-ALS, this human-centric AI-empowered approach also enables explainable predictive analytics of the students’ learning outcomes in paper-based summative assessments after training is completed in each AI-ALS.
Privacy-Preserving Proof of Storage for the Pay-As-You-Go Business Model

Proof of Storage (PoS) enables a cloud storage provider to prove that a client's data is intact. However, existing PoS protocols are not designed for the pay-as-you-go business model in which payment is made based on both storage volume and duration. In this paper, we propose two PoS protocols suitable for the pay-as-you-go storage business model. The first is a time encapsulated Proof of Retrievability (PoR) protocol that ensures retrievability of the original file upon successful auditing by a client. Considering the large size of outsourced data, we then extend the protocol to a privacy-preserving public auditing protocol which allows a third party auditor to audit outsourced data on behalf of its clients without sacrificing the privacy of the data or the timestamp (i.e., time of storage). We formalize the definition, system model and security model of the proposed PoS system and prove the security of the proposed protocols by a sequence of games in the algebraic group model with a random oracle. We analyze the performance of the protocols both theoretically and experimentally and show that the protocols are practical.
Bare Bones Fireworks Algorithm for Feature Selection and SVM Optimization

Machine learning algorithms are used in various application and the need for faster and more accurate algorithms is urgent. Classification problem, as one of the most common machine learning tasks, has numerous proposed algorithms for solving it. One of the main factors that affects the classification accuracy, regardless of the used classifier, is the chosen feature set. Due to the fact that the classification quality depends on the features, feature selection represents an important task in machine learning. In this paper we propose adjusted bare bone fireworks algorithm for feature selection. Support vector machine is used as classifier, thus we additionally added SVM parameter optimization. The proposed method is tested on standard benchmark classification datasets from the UCI repository and the results are compared with other swarm intelligence methods. The results show that the proposed method achieves higher accuracy compared to the other methods even without SVM parameters tuning. As expected, parameter tuning has additionally increased the classification accuracy.
Feature Selection of High Dimensional Data by Adaptive Potential Particle Swarm Optimization

For high-dimensional data classification, feature selection has played a significant role in removing the redundant or irrelevant features and improving the performances of classifiers. Particle Swarm Optimization (PSO), an evolutionary computational tool, has been widely and successfully applied in the field of feature selection. A recent pioneer work, Potential PSO (PPSO) which utilizes feature discretization process based on cut-points, provides better overall performance in classification and lower amount of selected features compared with some existing approaches. In this paper, we proposed an adaptive PPSO (APPSO) to reduce the number of features further and increase the classification accuracy. In APPSO, the pre-filtering method, ReliefF, is incorporated into the discretization process to help screen the potential cut-points effectively, and a new updating strategy is proposed to strengthen the searching scope of particles and avoid being trapped into local optima. Also, APPSO follows an adaptive way to alter the random level of the selection of cutpoints. The random level depends on the mean instance number per class of each dataset. Experiments on 10 datasets are carried out, and APPSO is proven to select less than 2% of the number of features while maintaining a very competitive accuracy compared with some state-of-the-art methods.
Kinship Verification based on Local Binary Pattern features coding in different color space

Kinship verification is one of the interesting and challenging topics which is related to face analysis. Most of existing methods have been trying to find out the relevant information between two persons (edge nose, lip, eyes) to increase the verification rate. In this paper, we propose to apply the Local Binary Pattern for Kinship on different color space. Then the features are computed by χ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> distance and apply feature selection to reduce the noisy and irrelevant features. The Support Vector Machine is used to train and predict the model. We analyze the effectiveness of our method by carrying out experiments on two popular databases such as KinFaceW-I and KinFaceW-II.
Security in Autonomous Systems

Autonomous systems promise solutions to a wide range of technical and societal problems, and their use appears especially attractive in safety-critical domains, like transportation or factory automation. This paper focuses on an underestimated aspect of autonomous systems: their security implications. Many approaches to design traditional secure systems do not readily transfer to autonomous systems, due to their high complexity and exposure to a broad spectrum of threats. Moreover, autonomous systems are often designed to be extremely long-living, and any security solutions should anticipate future threats to some extent. This paper starts with an overview of security threats applicable to autonomous systems and today's countermeasures to address these threats. Then, two representative techniques are elucidated in more detail: the use of post-quantum cryptography to achieve secure communication, and remote attestation as one essential building block for platform security.
Calibration of portable X-ray fluorescence equipment for the geochemical analysis of carbonate matrices

A methodology was developed for the calibration of carbonate matrices using portable X-ray fluorescence equipment (pXRF), which involved the analysis of 11 solid mixtures prepared with certified reference material PACS-3 and an analytical CaCO3 reagent as diluent. Of the 31 elements detected in the solid mixtures, significant linear regressions (P < 0.001) were obtained for 20 of them (Al, As, Ca, Cu, Fe, Hg, Mn, Mo, P, Pb, S, Si, Sr, Ti, Zn, K, V, Rb, Y, Zr). The first 17 yielded the best fit with a correlation coefficient of >0.90. The elements Rb, Zr and Y could have been reliably measured with the pXRF if their certified concentrations in PACS-3 were reported, given that they showed a highly significant correlation (P < 0.001) with their respective percentages in the solid mixture. The main advantages of the calibration method include (1) its low cost resulting from the use of a single certified reference material, (2) an excellent linear fit (R = 0.924 to 1.000, for Mo and Zn, respectively), obtained in the calibration, and (3) the possibility of combination with other calibration techniques reported in the literature. The new calibration technique was used to determine the concentrations of major and trace elements in carbonate sediments of the Yucatan continental shelf. Finally, the spatial sampling resolution of the pXRF, equivalent to the diameter of the X-ray beam, was determined to be 6.25 ± 0.55 mm. This methodology allows rapid analysis, low cost and shows an excellent application to carbonate matrices.