Reviewerly/exHarmony huggingface dataset:
Kernelized Unified Domain Adaptation on Geometrical Manifolds

Primitive machine learning algorithms like the k-nearest Neighbor (k-NN) and Support Vector Machine (SVM) are a major challenge for expert and intelligent systems that recognize objects with large-scale variations in lighting conditions, backgrounds, color, size, etc. The variations may be due to the fact that the training and test data may come from related but different domains. Considerable effort has been put into the advancement of domain adaptation methods. However, most of the existing work only concentrates on considering only a few of the following goals or objectives: (i) subspace alignment; (ii) Minimization of distribution divergence by using the Maximum Mean Discrepancy (MMD) criterion; (iii) Preservation of source domain discrimination information; (iv) Preservation of original similarity of the data samples; (v) Maximization of target domain variance. Current approaches to preserve source domain discriminant information can easily mis-classify target domain samples which are distributed near the edge of the cluster. In order to overcome the limitations of existing domain adaptation methods, and expert and intelligent systems, we propose the Unified Domain Adaptation on Geometrical Manifolds (UDAGM) framework. UDAGM optimizes all the aforementioned objectives jointly as well as uses the Regularized Coplanar Discriminant Analysis (RCDA) method for better inter-class separability and intra-class compactness. In addition, we extend our proposed framework UDAGM to a kernelised version in order to deal with non-linear separable datasets. Extensive experimentation on two real-world problems datasets (PIE face recognition and Office-Caltech) has proven that the proposed frameworks outperform several approaches to domain adaptation.
Detecting Ordinal Subcascades

Abstract Ordinal classifier cascades are constrained by a hypothesised order of the semantic class labels of a dataset. This order determines the overall structure of the decision regions in feature space. Assuming the correct order on these class labels will allow a high generalisation performance, while an incorrect one will lead to diminished results. In this way ordinal classifier systems can facilitate explorative data analysis allowing to screen for potential candidate orders of the class labels. Previously, we have shown that screening is possible for total orders of all class labels. However, as datasets might comprise samples of ordinal as well as non-ordinal classes, the assumption of a total ordering might be not appropriate. An analysis of subsets of classes is required to detect such hidden ordinal substructures. In this work, we devise a novel screening procedure for exhaustive evaluations of all order permutations of all subsets of classes by bounding the number of enumerations we have to examine. Experiments with multi-class data from diverse applications revealed ordinal substructures that generate new and support known relations.
Grad

Video streaming commonly uses Dynamic Adaptive Streaming over HTTP (DASH) to deliver good Quality of Experience (QoE) to users. Videos used in DASH are predominantly encoded by single-layered video coding such as H.264/AVC. In comparison, multi-layered video coding such as H.264/SVC provides more flexibility for upgrading the quality of buffered video segments and has the potential to further improve QoE. However, there are two challenges for using SVC in DASH: (i) the complexity in designing ABR algorithms; and (ii) the negative impact of SVC's coding overhead. In this work, we propose a deep reinforcement learning method called Grad for designing ABR algorithms that take advantage of the quality upgrade mechanism of SVC. Additionally, we quantify the impact of coding overhead on the achievable QoE of SVC in DASH, and propose jump-enabled hybrid coding (HYBJ) to mitigate the impact. Through emulation, we demonstrate that Grad-HYBJ, an ABR algorithm for HYBJ learned by Grad, outperforms the best performing state-of-the-art ABR algorithm by 17% in QoE.
GoldenRetriever: A Speech Recognition System Powered by Modern Information Retrieval

Existing Automatic Speech Recognition (ASR) systems usually generate the N-best hypotheses list first, and then rescore them with the language model score and the acoustic model score to find the best one. This procedure is essentially analogous to the working mechanism of modern Information Retrieval (IR) systems, which retrieve a relatively large amount of relevant candidates first, re-rank them, and output the top-N list. Exploiting their commonality, this demonstration proposes a novel system named GoldenRetriever that marries IR with ASR. GoldenRetriever transforms the problem of N-best hypotheses rescoring as a Learning-to-Rescore (L2RS) problem and utilizes a wide range of features beyond the language model score and the acoustic model score. In this demonstration, the audience can experience the great potential of marrying IR with ASR for the first time. GoldenRetriever should inspire more research on transferring the state-of-the-art IR techniques to ASR.
Blockchain-based decentralized workload and energy management of geo-distributed data centers

Minimizing the costs of cloud services is important for cloud service providers to grow their business. In cost calculation of cloud services, power consumption cost is a major factor. The variability present in renewable power generation, electricity price, and power demand variates the power utilization expenditures. Due to high computational requests, datacenters consume enormous energy every day, thereby necessitating the need for energy efficient designs. Therefore, an energy consumption cost reduction mechanism is required to optimize the data center’s power consumption cost locally and to have the option to redistribute the workload on geo-distributed data centers in peak hours for further reduction in power consumption cost. In this paper, the authors investigate the energy consumption cost optimization problem in cloud datacenters and propose a blockchain-based decentralized workload distribution and management model. Moreover, we minimize request scheduler time for transferring any job from one data center to another and secure the energy cost optimization process due to shut down/ communication failure. The proposed work is the primitive to present the energy cost optimization problem in geo-distributed data centers by introducing blockchain-based secure workload scheduling method considering both the temporal and spatial disparities associated with electricity tariffs and workload arrival process. The simulations are evaluated on real-world workload of the Google data center and associated electricity tariffs. The blockchain-based decentralized workload management framework is evaluate based on privacy and security analysis, update overhead, latency, and throughput. Moreover, the results show blockchain model migrate workload in minimum 46 % reduced time compared to conventional model.
A novel doublet extreme learning machines for Delta 3D printer fault diagnosis using attitude sensor

Extreme learning machine (ELM) has better operation efficiency in fault diagnosis. However, the recognition accuracy of ELM algorithm is actually affected by the activation function. Moreover, most of the testing dataset are coming from high precision and expensive sensors. In this paper, raw data are collected by a low-cost attitude sensor, which is installed on the mobile platform of a delta 3D printer. A doublet activation function is proposed to improve the performance of ELM, named doublet ELM (DELM). The proposed method is evaluated using experimental data collected from the 3D printer, and its advantages are demonstrated by comparing with other activation functions. The experimental results indicate that the proposed method leads to the highest accuracy in different hidden nodes and the testing classification rate achieves 93% and 96% using only 8.33% of the dataset for model training, for R75 and R90 sub-datasets, respectively. Moreover, compared with peer methods, such as random forest, echo state network, and so on, the results show that the present DELM exhibits the best performance in small-sample and improves the accuracy of the 3D printer fault diagnosis.
Revocable identity-based encryption with bounded decryption key exposure resistance: Lattice-based construction and more

In general, identity-based encryption (IBE) does not support an efficient revocation procedure. In ACM CCS'08, Boldyreva et al. proposed revocable identity-based encryption (RIBE), which enables us to efficiently revoke (malicious) users in IBE. In PKC 2013, Seo and Emura introduced an additional security notion for RIBE, called decryption key exposure resistance (DKER). Roughly speaking, RIBE with DKER guarantees that the security is not compromised even if an adversary gets (a number of) short-term decryption keys. Therefore, DKER captures realistic scenarios and is an important notion. In this paper, we introduce bounded decryption key exposure resistance (B-DKER), where an adversary is allowed to get a-priori bounded number of short-term decryption keys in the security game. B-DKER is a weak version of DKER, but it seems to be sufficient for practical use. We obtain the following results: We propose a lattice-based (anonymous) RIBE scheme with B-DKER, which is the first lattice-based construction resilient to decryption key exposure. Our lattice-based construction is secure under the learning with errors assumption. A previous lattice-based construction satisfies anonymity but is vulnerable even with a single decryption key exposure. We propose the first pairing-based RIBE scheme that simultaneously realizes anonymity and B-DKER. Our pairing-based construction is adaptively secure under the symmetric external Diffie-Hellman assumption. Our two constructions rely on cover free families to satisfy B-DKER, whereas all the existing works rely on the key re-randomization property to achieve DKER.
An Adaptive Metronome Technique for Mitigating the Impact of Latency in Networked Music Performances

The improvement of digital communication networks increased the interest towards Networked Music Performance both for entertainment and education. One of the main issues that affects the effectiveness of remote performances is the inherent network latency in the communication chain. In this paper we propose a technique that, to the best of our knowledge, is the first work that explores the use of adaptive metronomes in order to track the tempo of the musicians and help them in coping with the latency. We test our proposal with preliminary experiments in the context of chamber music.
Deep Bayesian Multimedia Learning

Deep learning has been successfully developed as a complicated learning process from source inputs to target outputs in presence of multimedia environments. The inference or optimization is performed over an assumed deterministic model with deep structure. A wide range of temporal and spatial data in language and vision are treated as the inputs or outputs to build such a domain mapping for multimedia applications. A systematic and elaborate transfer is required to meet the mapping between source and target domains. Also, the semantic structure in natural language and computer vision may not be well represented or trained in mathematical logic or computer programs. The distribution function in discrete or continuous latent variable model for words, sentences, images or videos may not be properly decomposed or estimated. The system robustness to heterogeneous environments may not be assured. This tutorial addresses the fundamentals and advances in statistical models and neural networks for domain mapping, and presents a series of deep Bayesian solutions including variational Bayes, sampling method, Bayesian neural network, variational auto-encoder (VAE), stochastic recurrent neural network, sequence-to-sequence model, attention mechanism, end-to-end network, stochastic temporal convolutional network, temporal difference VAE, normalizing flow and neural ordinary differential equation. Enhancing the prior/posterior representation is addressed in different latent variable models. We illustrate how these models are connected and why they work for a variety of applications on complex patterns in language and vision. The word, sentence and image embeddings are merged with semantic constraint or structural information. Bayesian learning is formulated in the optimization procedure where the posterior collapse is tackled. An informative latent space is trained to incorporate deep Bayesian learning in various information systems.
CHAT: a Cultural Heritage Adaptive Tutor

The world of distance education is enriching with new tools able to improve the training process. Modern eLearning environments do not simply aim to reproduce the typical processes of the world of traditional education but seek to enrich them thanks to the support of new technologies. In this scenario, the introduction of the Chatbots is interesting. Chatbots are software designed to simulate a conversation with a human being, through sophisticated natural language processing systems, and can support students making the training process more adaptive. In this paper an educational chatbot, CHAT, is presented. This chatbot is able to support students in the study of Cultural Heritage. Through natural language processing techniques and inferential approaches, the proposed Chatbot provides learning contents having a conversation with the students and understanding what their main training needs are. The Chatbot was used in a real context, the archaeological parks of Paestum, Herculaneum and Pompeii where the students can use it obtaining training contents contextualized to their training needs. The results obtained, in terms of student satisfaction, were very satisfactory.
Development of Mobile Learning Application Based on Augmented Reality with Index Card Match Method

The following topics are dealt with: computer aided instruction; educational institutions; educational courses; vocational training; teaching; object detection; Internet; power supply quality; power system stability; and pattern classification.
The Impact of a Virtual Agent's Non-Verbal Emotional Expression on a User's Personal Space Preferences

Virtual-reality-based interactions with virtual agents (VAs) are likely subject to similar influences as human-human interactions. In either real or virtual social interactions, interactants try to maintain their personal space (PS), an ubiquitous, situative, flexible safety zone. Building upon larger PS preferences to humans and VAs with angry facial expressions, we extend the investigations to whole-body emotional expressions. In two immersive settings-HMD and CAVE-66 males were approached by an either happy, angry, or neutral male VA. Subjects preferred a larger PS to the angry VA when being able to stop him at their convenience (Sample task), replicating previous findings, and when being able to actively avoid him (Pass By task). In the latter task, we also observed larger distances in the CAVE than in the HMD.
Blockchain-based Building Information Modeling

Building Information Modeling (BIM) is a 3D real time and dynamic building modeling software that is used to enhance productivity in designing and constructing buildings. BIM is also a process to create and manage building information throughout building life cycle. BIM is used by many parties such as building owner, contractor, architect, engineer, site manager and facility manager. Thus to enhance security of BIM, we propose a new technology called blockchain. Blockchain has security features that can benefit BIM such as immutable and impossibility to be tampered with and forged, The features makes blockchain have incomparable advantages in ensuring information credibility, security and traceability. Thus, the objective of this research is to assess application of blockchain technology on Building Information Modeling (BIM). Construction management based on blockchain-based BIM could enhance information system security.
Expansion of Regularized Kmeans Discretization Machine Learning Approach in Prognosis of Dementia Progression

Dementia, a fatal and progressive neurodegenerative disease, poses a huge challenge to health care fraternity. A timely diagnosis of this intricate disorder can help in designing and formulating strategies helpful in effective regulation of the disease. There exists a huge amount of multi-variate heterogeneous clinical data which can be developed by examining MRI of the brain of the inflicted patients. Discretization is a widely employed method which transforms continuous attributes into discrete attributes by building a group of contiguous intervals. This helps in extending the range of the attribute's values. It is employed to handle the outlier problem that may have a noteworthy effect on the generalization performance of the model. In this study, we propose a machine learning-based probabilistic approach aimed to evaluate the dementia progression in cross-sectional Magnetic Resonance Imaging (MRI) data of demented and nondemented adults. We analyzed a dataset based on 416 subjects, aged between 18 to 96 years. Initially, we built a ML model employing discretization and non-discretization on the MRI dataset. Next, we employed kmeans strategy and three encoding techniques viz. ordinal, onehot-dense and onehot. Clinical Dementia Rating (CDR) score aided in determining the classes of dementia. Besides overcoming inherent adverse outlier issues, the proposed approach helped in spreading the values of a skewed attribute throughout a set of bins along with the same number of observations. We compared the performance of 18 employed ML classifiers. Experimental results on the MRI data showed noteworthy relative improvement in the pattern analysis of both discretization and non-discretization approach.
The Multiplicative-Additive Lambek Calculus with Subexponential and Bracket Modalities

We give a proof-theoretic and algorithmic complexity analysis for systems introduced by Morrill to serve as the core of the CatLog categorial grammar parser. We consider two recent versions of Morrill’s calculi, and focus on their fragments including multiplicative (Lambek) connectives, additive conjunction and disjunction, brackets and bracket modalities, and the ! subexponential modality. For both systems, we resolve issues connected with the cut rule and provide necessary modifications, after which we prove admissibility of cut (cut elimination theorem). We also prove algorithmic undecidability for both calculi, and show that categorial grammars based on them can generate arbitrary recursively enumerable languages.
SR-TPP: Extending IPv6 Segment Routing to enable Trusted and Private Network Paths

A trusted network path is a desired property of the Internet. Previous works introduced new protocol headers based on source routing for source authentication and path verification. It is obvious that any extra protocol headers will increase the network burden, and network path privacy deserves attention, especially when we use source routing. The emergence of IPv6 Segment Routing (SRv6) may bring the opportunity to assemble trusted network paths with a lightweight header. In this paper, we propose SR-TPP, a novel mechanism based on SRv6 to support network path verification meanwhile hides both-end and path information. Different from existing works, SR-TPP extends SRv6 function instead of introducing a new protocol header to meet the requirement of path compliance. Path information is sequentially encoded into the segment list in SR-TPP so that path information is partially visible to each intermediate router. The distributed verification of SR-TPP also makes it easier to locate faults. Finally, the security analysis and evaluation show that SR-TPP can assemble private and trusted network paths with acceptable performance.
Teamwork Quality and Team Success in Software Development

Background: Teamwork is central component of any software development organization. Therefore, the assessment of teamwork quality is important for team management in practice. TWQ (Teamwork Quality) is a measure of the quality of intra-team interactions developed specifically for software development teams. Aims: To perform a differentiated replication of previous studies on TWQ to expand the contexts in which TWQ has been applied, to refine the measurement instrument, increasing its reliability and validity, and to identify possibilities for future research on software teams. Method: We performed a cross-sectional survey collecting data from all 18 teams in one single software organization and from all members of each team, totaling 123 participants. Results: First, we refined the measurement instrument, achieving a more reliable and parsimonious instrument. Second, our results confirmed findings from previous studies when individual level data was used, but almost no confirmation was found when data was aggregated at team level. Conclusions: Our results partially supported previous studies but raised questions about the validity of the aggregation of individual data to team level measures of the studied constructs.
Efficient arithmetic in (pseudo-)mersenne prime order fields

&lt;p style='text-indent:20px;'&gt;Elliptic curve cryptography is based upon elliptic curves defined over finite fields. Operations over such elliptic curves require arithmetic over the underlying field. In particular, fast implementations of multiplication and squaring over the finite field are required for performing efficient elliptic curve cryptography. The present work considers the problem of obtaining efficient algorithms for field multiplication and squaring. From a theoretical point of view, we present a number of algorithms for multiplication/squaring and reduction which are appropriate for different settings. Our algorithms collect together and generalize ideas which are scattered across various papers and codes. At the same time, we also introduce new ideas to improve upon existing works. A key theoretical feature of our work is that we provide formal statements and detailed proofs of correctness of the different reduction algorithms that we describe. On the implementation aspect, a total of fourteen primes are considered, covering all previously proposed cryptographically relevant (pseudo-)Mersenne prime order fields at various security levels. For each of these fields, we provide 64-bit assembly implementations of the relevant multiplication and squaring algorithms targeted towards two different modern Intel architectures. We were able to find previous 64-bit implementations for six of the fourteen primes considered in this work. On the Haswell and Skylake processors of Intel, for all the six primes where previous implementations are available, our implementations outperform such previous implementations.&lt;/p&gt;