Reviewerly/exHarmony huggingface dataset:
Hyper-Parameter-Free Generative Modelling with Deep Boltzmann Trees

Deep neural networks achieve state-of-the-art results in various classification and synthetic data generation tasks. However, only little is known about why depth improves a model. We investigate the structure of stochastic deep neural works, also known as Deep Boltzmann Machines, to shed some light on this issue. While the best known results postulate an exponential dependence between the number of visible units and the depth of the model, we show that the required depth is upper bounded by the longest path in the underlying junction tree, which is at most linear in the number of visible units. Moreover, we show that the conditional independence structure of any categorical Deep Boltzmann Machine contains a sub-tree that allows the consistent estimation of the full joint probability mass function of all visible units. We connect our results to $$l_1$$-regularized maximum-likelihood estimation and Chow-Liu trees. Based on our theoretical findings, we present a new tractable version of Deep Boltzmann Machines, namely the Deep Boltzmann Tree (DBT). We provide a hyper-parameter-free algorithm for learning the DBT from data, and propose a new initialization method to enforce convergence to good solutions. Our findings provide some theoretical evidence for why a deep model might be beneficial. Experimental results on benchmark data show, that the DBT is a theoretical sound alternative to likelihood-free generative models.
Empirical Validation of Software Integration Practices in Global Software Development

Software complexity accrues, as modern societies are highly dependent on large-scale, software-intensive systems that increasingly operate within an environment of continuous availability. Global software development (GSD) vendors strive to reduce this complexity by decomposing the target software product into various components that are developed in-house, outsourced or purchased as commercial off-the-shelf (COTS) components. Subsequently, these components are integrated into a final working product. However, there is a lack of specific practices to be followed by GSD vendors in the software integration phase. In our previous study, we identified a list of nine software integration critical success factors (CSFs) for GSD vendors. In order to assist GSD vendors in adoption of the identified CSFs, we conducted another Systematic Literature Review (SLR) for identification of practices and validated the results using questionnaire survey. We identified 116 software integration practices/solutions that will help GSD vendors in trimming down the complexity of their software integration process.
Integrating virtual and physical production processes in learning factories

Scaled learning factories are industrial learning environments that provide production systems and processes for learners on a model scale rather than using actual productive machines. This approach has benefits as for instance lower invest, increased approachability and higher safety levels. At the same time, constraints for implementation of actual production processes and required abstraction levels from industrial processes are limitations. To bridge the gap between benefits and limitation we propose the integration of virtual production processes in a prevalent physical learning factories. Resulting mixed reality solutions bear the potential to combine real and virtual objects at the same time and thus extend the physical model environment with virtually represented processes. Based on an initial analysis we develop a concept using spatial augmented reality and a game engine based simulation to realize a virtual integrated production process. The theoretical concept as well as the technical implementation is described. A first evaluation indicates a high rate of acceptance by trainees and illustrates the benefits for learning performance.
DeepErase: Weakly Supervised Ink Artifact Removal in Document Text Images

Paper-intensive industries like insurance, law, and government have long leveraged optical character recognition (OCR) to automatically transcribe hordes of scanned documents into text strings for downstream processing. Even in 2019, there are still many scanned documents and mail that come into businesses in non-digital format. Text to be extracted from real world documents is often nestled inside rich formatting, such as tabular structures or forms with fill-in-the-blank boxes or underlines whose ink often touches or even strikes through the ink of the text itself. Further, the text region could have random ink smudges or spurious strokes. Such ink artifacts can severely interfere with the performance of recognition algorithms or other downstream processing tasks. In this work, we propose DeepErase, a neural-based preprocessor to erase ink artifacts from text images. We devise a method to programmatically assemble real text images and real artifacts into realistic-looking "dirty" text images, and use them to train an artifact segmentation network in a weakly supervised manner, since pixel-level annotations are automatically obtained during the assembly process. In addition to high segmentation accuracy, we show that our cleansed images achieve a significant boost in recognition accuracy by popular OCR software such as Tesseract 4.0. Finally, we test DeepErase on out-of-distribution datasets (NIST SDB) of scanned IRS tax return forms and achieve double-digit improvements in accuracy. All experiments are performed on both printed and handwritten text.
A Unit Softmax with Laplacian Smoothing Stochastic Gradient Descent for Deep Convolutional Neural Networks

Several techniques were designed during last few years to improve the performance of deep architecture by means of appropriate loss functions or activation functions. Arguably, softmax is the traditionally convenient to train Deep Convolutional Neural Networks (DCNNs) for classification task. However, the modern deep learning architectures have exposed its limitation towards feature discriminability. In this paper, we offered a supervision signal for discriminative image features through a modification in softmax to boost up the power of loss function. Amending the original softmax loss and motivated by the A-softmax loss for face recognition, we fixed the angular margin to introduce a unit margin softmax loss. The improved alternative form of softmax is trainable, easy to optimize and stable for usage along with Stochastic Gradient Descent (SGD) and Laplacian Smoothing Stochastic Gradient Descent (LS-SGD) and applicable to classify the digits in image. Experimental results demonstrate a state-of-the-art performance on famous database of handwritten digits the Modified National Institute of Standards and Technology (MNIST) database.
Towards the Detection of Malicious URL and Domain Names Using Machine Learning

Malicious Uniform Resource Locator (URL) is an important problem in web search and mining. Malicious URLs host unsolicited content (spam, phishing, drive-by downloads, etc.) and try to lure uneducated users into clicking in such links or downloading malware which will result in critical data exfiltration. Traditional techniques in detecting such URLs have been to use blacklists and rule-based methods. The main disadvantage of such problems is that they are not resistant to 0-day attacks, meaning that there will be at least one victim for each URL before the blacklist is created. Other techniques include having sandbox and testing the URLs before clicking on them in the production or main environment. Such methods have two main drawbacks which are the cost of the sandboxing as well as the non-real-time response which is due to the approval process in the test environment. In this paper, we propose a method that exploits semantic features in both domains and URLs as well. The method is adaptive, meaning that the model can dynamically change based on the new feedback received on the 0-day attacks. We extract features from all sections of a URL separately. We then apply three methods of machine learning on three different sets of data. We provide an analysis of features on the most efficient value of N for applying the N-grams to the domain names. The result shows that Random Forest has the highest accuracy of over 96% and at the same time provides more interpretability as well as performance benefits.
Provenance‐based data flow control mechanism for Internet of things

Abstract In the Internet of things (IoT), the flow and sharing of data between different devices has become a trend. However, determining how to ensure the controlled flow and sharing of data is an urgent problem to address to ensure data security and privacy in the IoT environment. To address the above problem, we propose a provenance‐based data flow control mechanism (PDFC) that includes flow control and further control to realize data flow control and further control after flow. The mechanism implements both direct and indirect control of data flow based on provenance data, which reduces the risk of indirect leakage. As data flow and are shared between different devices, the provenance data become increasingly large, consequently, indirect control on the provenance data takes increasing amounts of time, seriously affecting control efficiency. To improve the control efficiency of data flow, the provenance data are simplified according to the type of operation generating the data and the association degree of data and their provenance data. To reduce the storage space occupied by the provenance data and improve the efficiency of querying the provenance data, we propose a provenance tree that records the relationship between data and their provenance data. A theoretical analysis demonstrates the security and effectiveness of PDFC, which can solve the fine‐grained controlled sharing problem in IoT. Using the data flow in a smart healthcare system as an example, we demonstrate the implementation and optimization method of PDFC, and the result shows PDFC has obvious flexibility and performance advantages.
Soft Computing Approaches to Investigate Software Fault Proneness in Agile Software Development Environment

This chapter presents framework which makes use of product and process metrics to predict faulty modules in agile software development environment. The proposed model makes use of soft computing methods, like Mandani and Takagi–Sugeno style fuzzy inference system, artificial neural network, and adaptive neuro fuzzy inference system, for building fault prediction models. For achieving the goal of the study, several experiments are performed on the ‘Ant dataset project version’ of the ‘PROMISE’ data repository. Additionally, to assess outcomes of the proposed prediction model, evaluation criteria based upon receiver operating characteristics with the area under curve (AUC (ROC)) is applied. The application of the proposed model can help the developers not only in the design phase but also in testing and maintenance phases. It also reduces the time effort in code and review process.
Online Signature Verification using Deep Descriptors

Signature verification is a technique used to counter signature forgery. In the past, the process began with staff at a bank, who is an expert, would confirm if a signature is genuine or forged. With the development of technology, now people no longer sign on paper, rather on a digital pad which can take more data which is recorded on paper, for example, pressure, azimuth and altitude angles. Examples of details captured from a digital pen include pen pressure, azimuth and altitude angles. This data is now used in various dynamic signature verification systems that achieve high accuracy on evaluation tests using different forms of artificial intelligence. This paper investigates using artificial intelligence in the form of a Convolutional Neural Network (CNN) followed by a Recurrent Neural Network (RNN) to verify signatures using the SVC 2004 and SigComp2009 online datasets and it achieved a testing accuracy of 97.05%.
A Unified Framework for Stress Forecasting Using Machine Learning Algorithms

In the fast-emerging world, the population is rapidly increasing where healthcare provider is constantly decreasing. As a result, there is a predominant need to develop new technological systems to monitor the health status of people over their life span. In particular, stress is a major frequently occurring health issue around the globe which may affect everyday activities of lives, develops a negative mental illness, depression and anxieties. Precise and well-timed diagnosis of stress is important for improving the quality of life which is impossible through traditional methodologies and that is considered as unreliable under many facets. In order to classify healthy subjects and subjects with stress, machine learning algorithms are found to be consistent and efficient. This research work focuses on developing a unified machine learning framework for forecasting the stress. The framework includes two feature selection algorithms and six classification algorithms which have been evaluated under various metrics. The proposed framework identifies and classifies the subjects with stress from healthy subjects efficiently.
Combining chemical and lead isotope analyses with 3-D geometric–morphometric shape analysis: A methodological case study of socketed bronze arrowheads from the southern Levant

In this study, the shape of socketed bronze arrowheads is analysed and expressed as a series of mathematical trends which are then compared to chemical and lead isotope composition, as well as to the categorization of traditional non-computerized typology. It is shown that while traditional typology has statistical validation, additional important information can be gleaned from 3-D geometric morphometric shape analysis (3DGM), particularly when combined with material analyses. For example, arrowheads that are traditionally categorized as a single type demonstrate minute shape differences that correlate with the sites where they were found. This micro-variability, detected only through 3DGM, has potential cultural, chronological and regional implications. Most importantly, this pilot study shows that chemical and isotopic composition is correlated to a specific shape trend, revealed through computerized analysis, rather than to the traditional typological classification. This opens up new vistas for a more advanced analysis of archaeological finds.
GAIM: Game Action Information Mining Framework for Multiplayer Online Card Games (Rummy as Case Study)

We introduce GAIM, a deep-learning analytical framework that enables benchmarking and profiling of players, from the perspective of how the players react to the game state and evolution of games. In particular, we focus on multi-player, skill-based card games, and use Rummy as a case study. GAIM framework provides a novel and extensible encapsulation of the game state as an image, and uses Convolutional Neural Networks (CNN) to learn these images to calibrate the goodness of the state, in such a way that the challenges arising from multiple players, chance factors and large state space, are all abstracted. We show that our model out-performs well-known image classification models, and also learns the nuances of the game without explicitly training with game-specific features, resulting in a true state model, wherein most of the misclassifications can be attributed to user mistakes or genuinely confusing hands. We show that GAIM helps gather fine-grained insights about player behavior, skill, tendencies, and business implications, that were otherwise not possible, thereby enabling targeted services and personalized player journeys.
A visibility-based approach to computing non-deterministic bouncing strategies

Inspired by motion patterns of some commercially available mobile robots, we investigate the power of robots that move forward in straight lines until colliding with an environment boundary, at which point they can rotate in place and move forward again; we visualize this as the robot “bouncing” off boundaries. We define bounce rules governing how the robot should reorient after reaching a boundary, such as reorienting relative to its heading prior to collision, or relative to the normal of the boundary. We then generate plans as sequences of rules, using the bounce visibility graph generated from a polygonal environment definition, while assuming we have unavoidable non-determinism in our actuation. Our planner can be queried to determine the feasibility of tasks such as reaching goal sets and patrolling (repeatedly visiting a sequence of goals). If the task is found feasible, the planner provides a sequence of non-deterministic interaction rules, which also provide information on how precisely the robot must execute the plan to succeed. We also show how to compute stable cyclic trajectories and use these to limit uncertainty in the robot’s position.
Is it REALLY All That? The Impact of the Digital Textbook in Introductory Political Science Classes

In recent years, great enthusiasm has been expressed about the benefits of the digital textbook, which is capturing more and more of the academic book market. However, questions remain about the extent to which e-textbooks actually improve educational outcomes, especially with respect to the impact for students in political science. Using a quasi-experimental design employed in introductory political science courses, this research begins to address these limitations by directly assessing the impact of the online text platform on several different educational outcomes. The results indicate that the digital format has a positive impact on both students' self-report of engagement with the textbook and on reading compliance levels, although a student's interest in politics was found to be an important mediating factor. Text format is not a significant predictor for most measures of academic performance, although assignment of the e-textbook is associated with substantially lower average course grades. Greater student involvement with supplemental resources provided by the digital platform, however, has a strong and positive impact on performance on all learning measures. The results therefore suggest that if students are properly encouraged or required to interact with the digital text platform, the benefits of using the e-textbook outweigh the potential drawbacks.
Distributed Continuous Home Care Provisioning through Personalized Monitoring &amp; Treatment Planning

In healthcare, the aging of the population is resulting in a gradual shift from residential care to home care, requiring reliable follow-up of elderly people by a whole network of care providers. The environment of these patients is increasingly being equipped with different monitoring devices, which allow to obtain insight into the current condition of patient & environment. However, current monitoring platforms that support care providers are centralized and not personalized, reducing performance, scalability, autonomy and privacy. Because the available data is only exposed through custom APIs, profile knowledge cannot be efficiently exchanged, which is required to provide optimal care. Therefore, this paper presents a distributed data-driven platform, built on Semantic Web technologies, that enables the integration of all profile knowledge in order to deliver personalized continuous home care. It provides a distributed smart monitoring service, which allows to locally monitor only the relevant sensors according to the patient’s profile, and infer personalized decisions when analyzing events. Moreover, it provides a medical treatment planning service, which composes treatment plans tailored to each patient, including personalized quality of service parameters allowing a doctor to select the optimal plan. To illustrate how the platform delivers these services, the paper also presents a demonstrator using a realistic home care scenario.
Mandarin Prosody Boundary Prediction based on Sequence-to-sequence Model

The prediction of prosodic structure of sentences is the key for improving the naturalness of Mandarin speech synthesis. In this paper, we proposed a sequence-to-sequence (seq2seq) model-based method to improve the predictive accuracy of the prosodic boundaries from Chinese sentence. A large-scale text corpus including 100,000 Chinese sentences is collected that is manually labelled the part-of-speech and the boundaries of the prosodic words and prosodic phrases under the guidance of a linguistic expert. By analyzing the text corpus, the shallow features such as part-of-speech, word length and word embedding are selected as the input features of the seq2seq model. At the same time, a new deep feature named syntactic hierarchical number (SHN) is proposed to predict the boundary of prosodic phrases, which describes the relationship between syntactic structure and prosodic structure. Finally, we get the seq2seq model by training the labelled text corpus to predict the boundaries of prosodic words and prosodic phrases. The experimental results show that the seq2seq model achieves F1-score of 97.15% in prosodic word and 82.98% in prosodic phrase boundary prediction. Compared to the other models, our proposed method are more effective on the prediction of prosodic structure, which can be applied to the front-end of speech synthesis.
A Two-Directional BigData Sorting Architecture on FPGAs

Sorting is pivotal data analytics and becomes challenging with intensive computation on drastically growing data volume. Sorting on FPGA has shown superior throughput, but the limited in-system memory causes vast data transferring to/from external storage when handling a large dataset. We propose a two-directional sorting (2DSort) architecture which sorts data sequences on both horizontal and vertical directions. 2DSort significantly reduces the costly data transmission by tracking the data on FPGA and writes back the data to external storage when the final sorting position is determined. 2DSort shows average 38.5 percent runtime enhancement in comparison to state-of-the-art bigdata sorting engine on FPGA.
CSBIIST: cuckoo search-based intelligent image segmentation technique

Optimization has become the most prominent area of research in recent years. Nature-inspired algorithms can be employed to extract the most optimal solution among computed solutions. There exist plenty of nature-inspired algorithms, such as the genetic algorithm, particle swarm optimization, bumble bee mating, artificial bee colony, elephant herding, the firefly algorithm, and the cuckoo search algorithm, which can be employed to procure optimal solutions in less time. Image segmentation employs an unsupervised learning model and is recognized as a most demanding task to recollect nonconverging analogous regions. The most important challenge is to compute count of the same. There is an immense requisite to commence an intelligent technique which fulfills both aforementioned challenges. We have employed the cuckoo search algorithm as it has emerged as the simplest and most prolific algorithm to resolve real optimization issues that are extremely nonlinear. We have compared the technique with state-of-the-art techniques to evince its efficacy.
Dynamics of a nonlinear time-dependent two two-level atoms in a two-mode cavity

In this paper, we present some properties through two two-level atoms interacting with a two-mode quantized cavity field. We study this system in the presence of detuning parameter, Kerr nonlinearity, Stark shift, relative phase and intensity-dependent atoms-field coupling. Also, the coupling parameter is modulated to be time dependent. The exact solution of this model is given by using the Schrődinger equation when the atoms and the field are initially prepared in superposition states and coherent states, respectively. We employed the results to calculate some aspects such as linear entropy, total atomic inversion and cross-correlation function.