Reviewerly/exHarmony huggingface dataset:
Coping Strategies for Temporal, Geographical and Sociocultural Distances in Agile GSD: A Case Study

Globally distributed software development teams face a number of challenges in their work that are associated with temporal, geographical and sociocultural distances. This case study explores the relationship between agile practices and these three distances in global software development. Data were collected by interviews and secondary data analysis. The results show that the three distances affect agile practices and the case team modifies the agile practices accordingly. Agile practices, in turn, affect the three distances by reducing communication, control and coordination challenges. Non-agile coping strategies such as specialised communication strategies also play an important role to alleviate the effects of the different distances.
Dynamic Sampling Meets Pooling

A team of six assessors used Dynamic Sampling (Cormack and Grossman 2018) and one hour of assessment effort per topic to form, without pooling, a test collection for the TREC 2018 Common Core Track. Later, official relevance assessments were rendered by NIST for documents selected by depth-10 pooling augmented by move-to-front (MTF) pooling (Cormack et al. 1998), as well as the documents selected by our Dynamic Sampling effort. MAP estimates rendered from dynamically sampled assessments using the xinfAP statistical evaluator are comparable to those rendered from the complete set of official assessments using the standard trec_eval tool. MAP estimates rendered using only documents selected by pooling, on the other hand, differ substantially. The results suggest that the use of Dynamic Sampling without pooling can, for an order of magnitude less assessment effort, yield information-retrieval effectiveness estimates that exhibit lower bias, lower error, and comparable ability to rank system effectiveness.
Anomaly Detection for Advanced Driver Assistance Systems Using Online Feature Selection

Context: As we move towards higher levels of automation in autonomous driving, we see an increase in functionality that either assists or takes over in both normal and emergency scenarios. These new functionalities can be intentionally switched off by the user, but can also be deactivated unintentionally by (i) accident, (ii) a software malfunction, (iii) a hardware defect, or (iv) an intrusion. Aim: In addition to already applied methods at design time, we aim to recognise mistimed and/or unintended deactivation of vehicle functions, in particular, driver assistance functions (ADAS), at run-time. Upon recognition of the occurrence, we propose to inform the user and the original equipment manufacturer (OEM) in order to improve both the future and the current system behaviour, to support development processes, and to support already conducted safety measures. Method: Based on a feature subset, selected by streaming feature selection, we learn the nominal behaviour of the driver in the interaction with ADAS functions in order to find deviations. The approach considers the technical challenges of automotive E/E architectures and is optimised to reduce communication and computational complexity. We evaluate this approach with recorded real car data from customers participating in a field study. Results: Based on eight datasets, we traced a total of 17 state-of-the-art ADAS functions per participant, yielding to a total of 136 runs. We observed that during 24 among them, the user deactivated the functions at least once for more than a few seconds. For 13 of these 24 runs, we were able to detect and flag possible non-nominal behaviour. Conclusion: As at least one participant configured a convincingly large number of ADAS functions, we need a dynamic system to monitor the configuration of these functions actively. Our approach was capable of detecting potential non-nominal behaviour in up to 52% (13/24) out of these reconfigurations. This result is promising and will receive further attention in future work.
Network-Induced Temporal Disturbances in Virtual Reality Applications

Virtual Reality (VR) applications put high demands on software and hardware in order to enable an immersive experience for the user and avoid causing simulator sickness. As soon as networks become part of the Motion-To-Photon (MTP) path between rendering and display, there is a risk for extraordinary delays that may impair Quality of Experience (QoE). This short paper provides an overview of latency measurements and models that are applicable to the MTP path, complemented by demands on user and network levels. It specifically reports on freeze duration measurements using a commercial TPCAST wireless VR solution, and identifies a corresponding stochastic model of the freeze length distribution, which may serve as disturbance model for VR QoE studies.
Visual Tracking Based on Multi-Feature and Fast Scale Adaptive Kernelized Correlation Filter

Tracking methods based on a correlation filter have attracted much attention because of their high efficiency and strong robustness. However, a tracker based on a single feature is obviously not sufficient to adapt to the complex appearance changes of the target. Besides, rapid and exact scale estimation is still a challenging problem in the field of visual tracking. In this paper, we introduce an independent scale filter for the estimation of the scale of an object and merge two complementary features to further boost the performance of the tracker. At the same time, a dimension reduction strategy is adopted to decrease the computational load. Finally, a dynamic learning rate-based model update mechanism is inserted to effectively alleviate model degradation problem by suppressing the influence of noisy appearance changes. The extensive experiments were conducted on the object tracking benchmark (OTB) dataset and Temple color 128 dataset. The quantitative and qualitative results exhibit that compared with other popular trackers, the tracker proposed in this paper acquires favorable results in tracking accuracy, efficiency, and robustness. On the OTB-2015 benchmark dataset, it obtains precision scores of 0.773, 0.782, and 0.714 and success scores of 0.585, 0.606, and 0.534 in the three indexes of OPE, TRE, and SRE. On the Temple color 128 dataset, it acquires precision scores of 0.641, 0.681, and 0.606 and success scores of 0.478, 0.515, and 0.445 in the three indexes of OPE, TRE, and SRE, surpassing many well-known tracking methods. In terms of tracking efficiency, it runs at a speed of 42.3 frames/s on a single CPU, making it suitable for real-time applications.
DRSQ - A Dynamic Resource Service Quality Based Load Balancing Algorithm

In cloud computing domain, the main problem faced by cloud manager is to handle the huge amount of clients request at a single amount. For this reason, there have different types of load balancing algorithms, some are static algorithm and some are dynamic algorithm. But till, when the question is arrived to maintain service quality with the handling of work load balance than the shortage is occurred. In this paper, we proposed a novel dynamic load balancing algorithm named as Dynamic Resource Service Quality Based Load Balancing Algorithm (DRSQ). Besides supporting the load balance, it’s main aim is to continue with a good performance rate by utilizing resources in proper way. To reach the goal, back-end resource selection is done based on the provided service quality by the resources. The service quality is measured by considering the request type (e.g., READ, WRITE) and the value set of resource metrics. To prove the usefulness, it goes through some experiments using some real temperature sensor generated datasets in private cloud environment. The most famous load balancing algorithm Round-Robin is considered for comparative analysis.
Post-Editing Neural MT in Medical LSP: Lexico-Grammatical Patterns and Distortion in the Communication of Specialized Knowledge

The recent arrival on the market of high-performing neural MT engines will likely lead to a profound transformation of the translation profession. The purpose of this study is to explore how this paradigm change impacts the post-editing process, with a focus on lexico-grammatical patterns that are used in the communication of specialized knowledge. A corpus of 109 medical abstracts pre-translated from English into French by the neural MT engine DeepL and post-edited by master’s students in translation was used to study potential distortions in the translation of lexico-grammatical patterns. The results suggest that neural MT leads to specific sources of distortion in the translation of these patterns, not unlike what has previously been observed in human translation. These observations highlight the need to pay particular attention to lexico-grammatical patterns when post-editing neural MT in order to achieve functional equivalence in the translation of specialized texts.
MSAM: A Multi-Layer Bi-LSTM Based Speech to Vector Model with Residual Attention Mechanism

Word embedding is one of the most popular representation of a document vocabulary. It is capable of capturing the context, semantic and syntactic similarity of words in a document. Word2vec is a well-known technique to learn word embeddings of fixed dimensionality by using shallow neural networks, which can also be used to transform the audio segment of each words into a vector. In this paper, a deep neural network based on speech to vector model is proposed to learn the vector directly from the speech segment, in which the vector can represent some semantic information. Unlike the previous methods, such as speech2vec [1], our proposed model adopts a high-performance parser based on the residual attention mechanism, which uses multi-layer bi-directional long short-term memory (LSTM) network to learn representations of the audio segment. Finally, our proposed speech to vector model is analyzed and evaluated on 12 public datasets, which are widely-used in word similarity and word analogy benchmarks.
Self-regulation Model of Decision-Making

The paper proposes the self-regulation model (SRM) of decision-making, which is based on the self-regulation model of the thinking process developed within the systemic-structural activity theory. SRM includes two sub-models: formation of mental model (FMM), which is executed by the divide and concur algorithm, and formation of the level of motivation (FLM), which is executed by the dynamic programming algorithm, as well as the regulation of their interaction by using feedback and feedforward controls. Feedback control is regulated by the factor of difficulty and feedforward control is regulated by the factor of significance. These two factors determine four general criteria of success in evaluating and regulating the level of motivation. The paper formulates primary rules of self-regulation in decision-making in which the factors of significance and difficulty are designated the leading role. In a real-life example with a Facebook friend request we demonstrate how these rules were implemented in Performance Evaluation Process, which relies on Express Decision, a mobile web application for supporting an individual in making quick decisions in complex problems.
Teaching Where to See: Knowledge Distillation-Based Attentive Information Transfer in Vehicle Maker Classification

Deep neural networks (DNNs) have been applied to various fields and achieved high performances.However, they require significant computing resources because of their numerous parameters, even though some of those parameters are redundant and do not contribute to the DNN performance.Recently, to address this problem, many knowledge distillation-based methods have been proposed to compress a large DNN model into a small model.In this paper, we propose a novel knowledge distillation method that can compress a vehicle maker classification system based on a cascaded convolutional neural network (CNN) into a single CNN structure.The system uses mask regions with CNN features (Mask R-CNN) as a preprocessor for the vehicle region detection and has a structure to be used in conjunction with a CNN classifier.By the preprocessor, the classifier can receive the background-removed vehicle image, which allows the classifier to have more attention to the vehicle region.With this cascaded structure, the system can classify the vehicle makers at about 91% performance.Most of all, when we compress the system into a single CNN structure through the proposed knowledge distillation method, it demonstrates about 89% accuracy, in which only about 2% of the accuracy is lost.Our experimental results show that the proposed method is superior to the conventional knowledge distillation method in terms of performance transfer.
A Scalable Blockchain Approach for Trusted Computation and Verifiable Simulation in Multi-Party Collaborations

In high-stakes multi-party policy making based on machine learning and simulation models involving independent computing agents, a notion of trust in results is critical in facilitating transparency, accountability, and collaboration. Using a novel combination of distributed validation of atomic computation blocks and a blockchain-based immutable audit mechanism, this work proposes a framework for distributed trust in computations. In particular we address the scalability problem by reducing the storage and communication costs using a lossy compression scheme. This framework guarantees not only verifiability of final results, but also the validity of local computations, and its cost-benefit tradeoffs are studied using a synthetic example of training a neural network.
Model-Based Clustering and Classification Using Mixtures of Multivariate Skewed Power Exponential Distributions

Families of mixtures of multivariate power exponential (MPE) distributions have already been introduced and shown to be competitive for cluster analysis in comparison to other mixtures of elliptical distributions, including mixtures of Gaussian distributions. A family of mixtures of multivariate skewed power exponential distributions is proposed that combines the flexibility of the MPE distribution with the ability to model skewness. These mixtures are more robust to variations from normality and can account for skewness, varying tail weight, and peakedness of data. A generalized expectation-maximization approach, which combines minorization-maximization and optimization based on accelerated line search algorithms on the Stiefel manifold, is used for parameter estimation. These mixtures are implemented both in the unsupervised and semi-supervised classification frameworks. Both simulated and real data are used for illustration and comparison to other mixture families.
Researcher Profile Ontology for Academic Environment

Universities must know the research profiles and professional competences of professors and researchers to be more competitive and know how to offer creative solutions (through courses, diplomas, research projects, etc.) to complex needs of society. Therefore the representation, management, and exploitation of researcher profiles are important tasks that must be addressed. In this paper we investigate on the use of ontologies as the main solution approach to support the representation of researcher profiles in a given academic environment. We describe the ontology model design, the automatic ontology population process, and the discovery and enrichment of interesting semantic relations between researcher profiles. The functional competency of the resulting ontology is evaluated utilizing a set of inference rules and queries.
Shor’s Algorithm for Integer Factorization

Shor’s algorithm is used to find the integer factors of a large number with exponential speed-up when compared to the best known classical algorithm. The idea behind this algorithm is to reduce the original problem to that of finding the order of an integer, and then to apply the Fourier transform.
Forecasting future student mastery

Considerable attention has been given to methods for knowledge estimation, a category of methods for automatic assessment of a student’s degree of skill mastery or knowledge at a specific time. Knowledge estimation is frequently used to make decisions about when a student has reached mastery and is ready to advance to new material, but there has been little work to forecast how far a student is from mastery or predict how much more practice the student will need before he or she will reach mastery. This article presents a method for predicting the point at which a student will reach skill mastery within an adaptive learning system, based on current approaches to estimating student knowledge. We apply this technique to two popular methods of modeling student learning – Bayesian knowledge tracing and performance factors analysis – and compare prediction correctness. Potential applications and future steps for improving the method are discussed.
Automatic Learning Algorithms for Local Support Vector Machines

In this paper, we propose automatic learning algorithms (called auto-kSVM-1p, autokSVM-dp) being possible to automatically tune hyper-parameters of k local support vector machines (SVM) for classifying large data sets. The autokSVM algorithms are able to determine the number of clusters k to partition the large training data, followed which they auto-learn the non-linear SVM model in each cluster to classify the data locally in the parallel way on multi-core computers. The autokSVM algorithms combine the grid search, the .632 bootstrap estimator, the hill climbing heuristic to optimize hyper-parameters in the local non-linear SVM training. The numerical test results on four data sets from UCI repository and three benchmarks of handwritten letters recognition showed that our autokSVM algorithms give very competitive classification results compared to the standard LibSVM and the original kSVM. An example of autokSVM-1p’s effectiveness is given with an accuracy of 96.74% obtained in the classification of Forest cover-type data set having 581,012 datapoints in 54-dimensional input space and 7 classes in 334.45 seconds using a PC Intel(R) Core i7-4790 CPU, 3.6 GHz, 4 cores.
The Dawn of GDPR: Implications for the Digital Business Landscape

Digital-enabled business paradigms (e.g. Industry 4.0 in Germany, Industrial Internet in the US) allow, amongst others, real-time end-to-end value chain visibility, enhanced customization and quality delivery. In this context, data management and analytics are the determinant constituents in driving growth to a plethora of economic activities, from distributed manufacturing and pharmaceutical processing to bio-based applications and e-commerce services. However, as the global business landscape increasingly relies on data, cybersecurity issues emerge with regards to confidentiality, integrity, and availability. To this end, in the light of recent data-related scandals and the enacted EU General Data Protection Regulation, the purpose of this session is threefold: (1) to increase our understanding with regards to business-related barriers and challenges stemming from data protection regulations, (2) to identify emerging growth opportunities that can drive sustainable development of businesses, and (3) to articulate meaningful business practices with regards to data collection, processing and utilization.
Fast-to-Finalize Nakamoto-Like Consensus

As the fundamental component of blockchains, proof-of-work (PoW) scheme has been widely leveraged to provide consensus for maintaining a distributed public ledger. However, the long confirmation time, and hence the slow finality rate, is far from satisfactory. Alternative paradigms with performance improvement emerge. Nevertheless, there are fewer attempts in modifying the PoW mechanism itself. We find that the slow finality rate in PoW is caused by using only one bit to measure the computational power, namely, whether the attained hash value is smaller than a given target. In this paper, we first propose Demo-of-Work (DoW), a generalized PoW which assigns the computational work with a score depending on the hash value. We also treat the bitcoin blockchain as a global "clock" to attain synchronization for ensuring that each participant takes part in DoW for roughly the same time interval for ensuring fairness. With these two tools, we construct an alternative blockchain called AB-chain which provides a significantly faster finality rate when compared with the existing PoW-based blockchains, without sacrificing communication complexity or fairness.
Human Action Recognition Using Convolutional Neural Networks with Symmetric Time Extension of Visual Rhythms

Despite the expressive progress of deep learning models on the image classification task, they still need enhancement for efficient human action recognition. One way to achieve such gain is to augment the existing datasets. With this goal, we propose the usage of multiple Visual Rhythm crops, symmetrically extended in time and separated by a fixed stride. The symmetric extension preserves the video frame rate, which is crucial to not distort actions. The crops provide a 2D representation of the video volume matching the fixed input size of the 2D Convolutional Neural Network (CNN) employed. In addition, multiple crops with stride guarantee coverage of the entire video. Aiming to evaluate our method, a multi-stream strategy combining RGB and Optical Flow information is extended to include the Visual Rhythm. Accuracy rates fairly close to the state-of-the-art were obtained from the experiments with our method on the challenging UCF101 and HMDB51 datasets.
Turing Machine-Based Cross-Network Isolation and Data Exchange Theory Model

Due to the confidentiality of the classified information system, it is isolated from the external network, but it is necessary to exchange data with an external network. This makes data exchange security between classified networks an important problem. Previous research focused on using access control policies to limit data access at different security levels, using some security checks to detect the security of the data itself. The security threat of the access control policy itself will threaten the data exchange between the classified networks. Moreover, the data should be performed security check isolated from user during the physical transmission to prevent the attack behavior which bypass the data security check from the user side. At present, the cross-network isolation and data exchange models are designed based on fixed business scenarios and lack generality. Therefore, this paper proposes a Turing machine-based theoretical model for cross-network isolated data exchange. This model consists of two parts: the proof system module and the physical transmission channel module. The proof system module uses an interactive Turing machine to model the part of the operational security and data security check of the communicating party’s authority to provide security and versatility. The physical transmission channel module isolates different networks and provides a detection mechanism independent of the communication parties to ensure the security and efficiency of the transmission. The proof given shows that the theoretical model can exchange data efficiently and safely. Finally, based on the given theoretical model, an intuitive cross-network isolation and data exchange function model is constructed in the form of a functional tree to illustrate the model versatility.