Reviewerly/exHarmony huggingface dataset:
Learning Two-Time-Scale Representations For Large Scale Recommendations

We propose a surprisingly simple but effective two-time-scale (2TS) model for learning user representations for recommendation. In our approach, we will partition users into two sets, active users with many observed interactions and inactive or new users with few observed interactions, and we will use two RNNs to model them separately. Furthermore, we design a two-stage training method for our model, where, in the first stage, we learn transductive embeddings for users and items, and then, in the second stage, we learn the two RNNs leveraging the transductive embeddings trained in the first stage. Through the lens of online learning and stochastic optimization, we provide theoretical analysis that motivates the design of our 2TS model. The 2TS model achieves a nice bias-variance trade-off while being computationally efficient. In large scale datasets, our 2TS model is able to achieve significantly better recommendations than previous state-of-the-art, yet being much more computationally efficient.
Visualizing Multilevel Test-to-Code Relations

The test-to-code traceability (TCT) links play an important role in the process of software maintenance and re-engineering. However, the main issue is how to visualize these links efficiently and effectively to support the comprehension and maintenance of these links. In this work, a visualization approach is presented that displays the TCT links in two levels, class-level, and method level. Visualization traceability links at a method-level provide detailed information of the traceability links that support the development of software in different tasks such as, software maintenance, refactoring, and change impact analysis. The visualization approach is implemented using our visualization tool namely TCTracVis. The presented tool is evaluated on a real simple project and the achieved results confirm that the proposed approach and tool are efficient to support several tasks in software development.
Implementasi e-FAKTUR 2.2 dan PMK 62/PMK03/2015 atas Pupuk Bersubsidi Perspektif Distributor

E-fakur 2.2 is a manifestation of Undang-undang No. 42 of 2009 concerning Value Added Tax (VAT) which does not accommodate VAT subsidies for commodity Fertilizer packages which have an impact on SAS VAT for distributors. This study aims to see how the implementation of subsidized fertilizer commodity e-invoices with the government-borne method (PPN DTP). General problems regarding the legal basis which technically only explain down to the producer level. The results of observations in a case study by processing data from 2016 onwards show problems in their implementation, both from administration, validation sistems and developing information on supervision and cross-information, especially on the management of SAS PPN through e-faktur2.2 from the distrbutor perspective.&#x0D; Keywords : E-Faktur; VAT Subsidized; Subsidized Fertilzer.
Sentiment Analysis towards Hotel Reviews

The used of social media analytic towards the information in social media could bring a significant impact toward the hotel business thus making it easier to gain such insight and analysis. A preliminary study showed that the hoteliers in Malaysia are unaware on the sentiment analysis computation in analyzing the customer review through TripAdvisor despite having the website to promote their hotel. This study aims to provide another addition of sentiment analysis study especially on hotel reviews dataset gain from TripAdvisor. The process start by scrapping the reviews from The Majestic Hotel in TripAdvisor. The dataset then undergoes a pre-processing stage using Valence Aware Dictionary for Sentiment Reasoning to identify the sentiment in the text reviews. The text reviews are converted into vector form and fed into machine learning classifier. Three type of machine learning classifier was chosen to classify the review that are Naive Bayes, Support Vector Machine and Maximum Entropy. The classifiers are then evaluated using three measurement from the accuracy, precision and recall value perspectives in order to find the best classifier. Based on this study, Maximum Entropy gained the highest value compared to the other two classifier.
Impact of TETFund Intervention on Quality and Relevance of Research Development in Tertiary Institutions in North Central Nigeria

The study examined the impact of the Tertiary Education Trust Fund (TETFund) intervention on quality and relevant educational research (2015-2019). The study focused on the impact of TETFund interventions on adequate relevant research development in tertiary institutions in Nigeria with a special interest in state-owned Colleges of Education in North Central Nigeria. The descriptive survey research design was adopted for the study with African political economy model and resource-based model laying the theoretical foundation. The total population of the study was Eight thousand, seven hundred and eleven (8711) respondents drawn from five tertiary institutions in North Central Nigeria. A structured questionnaire was administered to a sample of 650 drawn from the population of the study. 612 valid copies of the questionnaire were used for analysis. The hypothesis used for the study was tested with one-way ANOVA at 0.05 levels of significance. The result indicates that TETFund intervention to a high extent has no significant impact on quality and relevant research by employees of state-owned Colleges of Education in North Central Nigeria. This was attributed first, to the fact that funds allocated to these institutions over time were under accessed due to the institutions' inability to meet conditions set by TETFund for continuous access. Secondly, many institutions/staff that access research grants use part of them to take care of their personal needs leaving a paltry sum for research. The study recommends that the concerned institutions should work towards meeting the conditions for assessment by eliminating corrupt tendencies, and adequately remunerate academics so that research grants would be used for what they are meant, TETFund should cut down on some of the procedures to enable lecturers to access the fund in due time, Tertiary Education Trust Fund should make provision to allow a tertiary institution to assess missed funds provided for the previous year instead of forfeiting such allocations.
Modelo de formación constructivista en el proceso Enseñanza-Aprendizaje virtual

The Teaching-Learning process with the implementation of virtual resources, introduces new challenges in the assimilation of knowledge. The novel methods facilitate a creative Teaching-Learning process. However, in virtual learning environments, the use of constructivism as an essential method has been little addressed. The present research proposes a model for constructivist training in the virtual Teaching-Learning process. It nurtures its operation through four basic components that manage the flow of proposed activities. To validate the contribution of the model, an expert consultation was carried out, from which it was possible to verify that the proposal favors constructivist learning through virtual learning
Pre-prints: a more affordable and quicker way to share research results

The growing availability of a wide range of subject-based pre-print servers such as bioRxiv and ChemRxiv is driving the research funders' and institutions' policies towards promoting the deposit of early versions of research works in such platforms. This trend has only been reinforced by the emergence of the Covid-19 pandemic, which has reassured these key stakeholders that this is the right way forward in the domain of scholarly communications. Pre-prints not only allow a much earlier dissemination of research results, but they do not prevent the publication of the work in a well-respected journal following the standard peer-review process and they critically ensure the open availability of these usually publicly-funded research results at a very early stage. The talk will examine the workflows associated to pre-print deposit and later publication in a journal, the associated usage stats and citation advantages offered by these pre-print servers, the policies issued by specific research funders regarding pre-prints and the permissions offered by publishers for posting early, still unreviewed versions of a research work. A prominent Strathclyde researcher who regularly posts pre-prints on these servers will feature alongside the Open Access Advocacy Librarian to discuss these topics.
Applied Cyber Security for Applied Software Engineering Undergraduate Program

In the current landscape where a constantly growing number of cyber threats is accompanied by the increasing shortage of cyber security professionals, it is essential to provide a well thought-out hands-on cyber security education as a part of all Computer Science and Software Engineering degrees. This paper described the experience of designing and delivering a Cyber Security module to Level 5 students on a three-year BSc Applied Software Engineering program. The key goal of the module is to instil the importance of cyber security in software development, and to teach in practice modern security techniques. While being predominantly focused on web-application security, the module also covers foundational cyber security concepts, cryptography and network security, and discusses non-technical topics including security frameworks and security economics. The paper presents the outline of the module, the configuration of the virtual machine used, the structure and content of sessions.
Pareto-Frontier-aware Neural Architecture Search

Designing feasible and effective architectures is essential for deploying deep models to real-world scenarios. In practice, one has to consider multiple objectives (e.g., model performance and computational cost) and diverse constraints incurred by different computation resources. To address this, most methods seek to find promising architectures via optimizing a well pre-defined utility function. However, it is often non-trivial to design an ideal function that could well trade-off different objectives. More critically, in many real scenarios, even for the same platform, we may have different applications with various latency budgets. To find promising architectures under different budgets, existing methods may have to perform an independent search for each budget, which is very inefficient and unnecessary. Nevertheless, it would be fantastic if we can produce multiple promising architectures to fulfill each budget in the same search process. In this paper, we propose a Pareto-Frontier-aware Neural Architecture Search (PFNAS) method which seeks to learn the Pareto frontier (i.e., the set of Pareto optimal architectures) w.r.t. multiple objectives. Here, we formulate the Pareto frontier learning problem as a Markov decision process (MDP). Relied on the MDP, we transform and absorb the objectives other than model performance into the constraints. To learn the whole Pareto frontier, we propose to find a set of Pareto optimal architectures which are uniformly distributed on the range of budget to form a frontier. Based on the learned frontier, we are able to easily find multiple promising architectures to fulfill all considered constraints in the same search process. Extensive experiments on three hardware platforms (i.e., mobile, CPU, and GPU) show that the searched architectures by our PFNAS outperform the ones obtained by existing methods under different budgets.
Κατασκευή και αξιολόγηση αυτομάτου συστήματος μηχανικής μάθησης για το παιχνίδι stratego

This thesis has as a goal the creation and evaluation of a machinelearning system for the board game stratego using neural networks. Theneural networks have been trained using deep reinforcement learningtechniques and through the creation of agents capable of learning dueto constant and repetitive interaction with the environment. Themachine learning space has utilized neural networks to combatproblems of increased size and complexity. Specifically, the game ofstratego presents the peculiarity of imperfect information which sets itapart from other board games such as chess and go that have beenmeticulously studied. To complete this project algorithms such as Policygradient and PPO were used.
LibGuides: Current Library Services: Remote dissertation students help

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Success-Rate Targeted Reinforcement Learning by Disorientation Penalty

Current reinforcement learning generally uses discounted return as its learning objective. However, real-world tasks may often demand a high success rate, which can be quite different from optimizing rewards. In this paper, we explicitly formulate the success rate as an undiscounted form of return with {0, 1}-binary reward function. Unfortunately, applying traditional Bellman updates to value function learning can be problematic for learning undiscounted return, and thus not suitable for optimizing success rate. From our theoretical analysis, we discover that values across different states tend to converge to the same value, resulting in the agent wandering around those states without making any actual progress. This further leads to reduced learning efficiency and inability to complete a task in time. To combat the aforementioned issue, we propose a new method, which introduces Loop Penalty (LP) into value function learning, to penalize disoriented cycling behaviors in the agent's decision-making. We demonstrate the effectiveness of our proposed LP on three environments, including grid-world cliff-walking, Doom first-person navigation and robot arm control, and compare our method with Q-learning, Monte-Carlo and Proximal Policy Optimization (PPO). Empirically, LP improves the convergence of training and achieves a higher success rate.
I've set up Delta.Chat on Windows 10 ...

I've set up Delta.Chat on Windows 10 and my Android phone (F-Droid) and got one person to test this with. Why did I not hear
How Servitization Establishes Field Service Success for Manufacturers

It’s important for manufacturers to provide customers with the support they need. Start supplying your products and their solutions. With ...
Laplacian Eigenspaces, Horocycles and Neuron Models on Hyperbolic Spaces

We use hyperbolic Poisson kernel to construct the horocycle neuron model on hyperbolic spaces, which is a spectral generalization of the classical neuron model. We prove a universal approximation theorem for horocycle neurons. As a corollary, this theorem leads to a state-of-the-art result on the expressivity of neurons of the hyperbolic MLR. Our experiments get state-of-the-art results on the Poincare-embedding tree classification task and the two-dimensional visualization of images.
Multi-FAN: multi-spectral mosaic super-resolution via multi-scale feature aggregation network

This paper introduces a novel method to super-resolve multi-spectral images captured by modern real-time single-shot mosaic image sensors, also known as multi-spectral cameras. Our contribution is twofold. Firstly, we super-resolve multi-spectral images from mosaic images rather than image cubes, which helps to take into account the spatial offset of each wavelength. Secondly, we introduce an external multi-scale feature aggregation network (Multi-FAN) which concatenates the feature maps with different levels of semantic information throughout a super-resolution (SR) network. A cascade of convolutional layers then implicitly selects the most valuable feature maps to generate a mosaic image. This mosaic image is then merged with the mosaic image generated by the SR network to produce a quantitatively superior image. We apply our Multi-FAN to RCAN (residual channel attention network), which is the state-of-the-art SR algorithm. We show that Multi-FAN improves both quantitative results (PSNR and SSIM) and inference time.
LLBoost: Last Layer Perturbation to Boost Pre-trained Neural Networks

While deep networks have produced state-of-the-art results in several domains from image classification to machine translation, hyper-parameter selection remains a significant computational bottleneck. In order to produce the best possible model, practitioners often search across random seeds or use ensemble methods. As models get larger, any method to improve neural network performance that involves re-training becomes intractable. For example, computing the training accuracy of FixResNext-101 (829 million parameters) on ImageNet takes roughly 1~day when using 1~GPU. In this work, we present LLBoost, a theoretically-grounded, computationally-efficient method to boost the validation accuracy of pre-trained over-parameterized models without impacting the original training accuracy. LLBoost adjusts the last layer of a neural network by adding a term that is orthogonal to the training feature matrix, which is constructed by applying all layers but the last to the training data. We provide an efficient implementation of LLBoost on the GPU and demonstrate that LLBoost, run using only 1 GPU, improves the test/validation accuracy of pre-trained models on CIFAR10, ImageNet32, and ImageNet. In the over-parameterized linear regression setting, we prove that LLBoost reduces the generalization error of any interpolating solution with high probability without affecting training error.
Monotonic Kronecker-Factored Lattice

It is computationally challenging to learn flexible monotonic functions that guarantee model behavior and provide interpretability beyond a few input features, and in a time where minimizing resource use is increasingly important, we must be able to learn such models that are still efficient. In this paper we show how to effectively and efficiently learn such functions using Kronecker-Factored Lattice (KFL), an efficient reparameterization of flexible monotonic lattice regression via Kronecker product. Both computational and storage costs scale linearly in the number of input features, which is a significant improvement over existing methods that grow exponentially. We also show that we can still properly enforce monotonicity and other shape constraints. The KFL function class consists of products of piecewise-linear functions, and the size of the function class can be further increased through ensembling. We prove that the function class of an ensemble of M base KFL models strictly increases as M increases up to a certain threshold. Beyond this threshold, every multilinear interpolated lattice function can be expressed. Our experimental results demonstrate that KFL trains faster with fewer parameters while still achieving accuracy and evaluation speeds comparable to or better than the baseline methods and preserving monotonicity guarantees on the learned model.
AttackDist: Characterizing Zero-day Adversarial Samples by Counter Attack

Deep Neural Networks (DNNs) have been shown vulnerable to adversarial attacks, which could produce adversarial samples that easily fool the state-of-the-art DNNs. The harmfulness of adversarial attacks calls for the defense mechanisms under fire. However, the relationship between adversarial attacks and defenses is like spear and shield. Whenever a defense method is proposed, a new attack would be followed to bypass the defense immediately. Devising a definitive defense against new attacks~(zero-day attacks) is proven to be challenging. We tackle this challenge by characterizing the intrinsic properties of adversarial samples, via measuring the norm of the perturbation after a counterattack. Our method is based on the idea that, from an optimization perspective, adversarial samples would be closer to the decision boundary; thus the perturbation to counterattack adversarial samples would be significantly smaller than normal cases. Motivated by this, we propose AttackDist, an attack-agnostic property to characterize adversarial samples. We first theoretically clarify under which condition AttackDist can provide a certified detecting performance, then show that a potential application of AttackDist is distinguishing zero-day adversarial examples without knowing the mechanisms of new attacks. As a proof-of-concept, we evaluate AttackDist on two widely used benchmarks. The evaluation results show that AttackDist can outperform the state-of-the-art detection measures by large margins in detecting zero-day adversarial attacks.
Integrating Systems and Theories in the SAL Hybrid Architecture

The Synthesis of ACT-R and Leabra (SAL) hybrid cognitive architecture is the integration of two theories of cognitive functioning, each itself a highly integrative theory of cognition, ACT-R being predominantly a symbolic production-rule based architecture and Leabra a neural modeling architecture. The combination of the two architectures allows for richer dynamics that take advantage of neural and symbolic aspects and provides mutual constraints that promote convergence towards models that are both neurophysiologically and psychologically valid. We present a hybrid model that makes use of multi-level and multi-system integration to allow an instructed assembly task to be carried out in way that is noise and error robust. Specifically, the model shows how higher-level error recovery routines can interface with lower-level sensory, motor, and error detection processes and result in a robustness to noise and noise-induced errors. Multiple systems and processes operating at multiple levels are recruited to provide a way around the limitations of simpler systems composed of isolated modules that do not allow information to be propagated as easily. The benefits of this approach provide motivation for the adoption of a generally integrated approach to cognitive systems.
INTELLIGENT CLUSTERING TECHNIQUE BASED ON GENETIC ALGORITHM

This paper focuses on the problems of data clustering where the similarity between different objects is estimated with the use of the Euclidean distance metric. Also, K-Means is used to remove data noise, genetic algorithms are used for finding the optimal set of features and the Support Vector, Machine (SVM) is used as a classifier. The experimental results prove that the proposed model has attained an accuracy of 94.79 % when using three datasets taken from the UCI repository.
Unsupervised Anomaly Detection by Robust Collaborative Autoencoders

Unsupervised anomaly detection plays a crucial role in many critical applications. Driven by the success of deep learning, recent years have witnessed growing interests in applying deep neural networks (DNNs) to anomaly detection problems. A common approach is to use autoencoders to learn a feature representation for the normal (non-anomalous) observations in the data. The reconstruction error of the autoencoder is then used as outlier scores to detect anomalies. However, due to the high complexity brought upon by over-parameterization of DNNs, the reconstruction error of the anomalies could also be small, which hampers the effectiveness of these methods. To alleviate this problem, we propose a robust framework using collaborative autoencoders to jointly identify normal observations from the data while learning its feature representation. We investigate the theoretical properties of the framework and empirically show its outstanding performance as compared to other DNN-based methods. Our experimental results also show the resiliency of the framework to missing values compared to other baseline methods.