Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Representations for Incomplete Time Series Clustering

Time-series clustering is an essential unsupervised technique for data analysis, applied to many real-world fields, such as medical analysis and DNA microarray. Existing clustering methods are usually based on the assumption that the data is complete. However, time series in real-world applications often contain missing values. Traditional strategy (imputing first and then clustering) does not optimize the imputation and clustering process as a whole, which not only makes per- formance dependent on the combination of imputation and clustering methods but also fails to achieve satisfactory re- sults. How to best improve the clustering performance on incomplete time series remains a challenge. This paper pro- poses a novel unsupervised temporal representation learning model, named Clustering Representation Learning on Incom- plete time-series data (CRLI). CRLI jointly optimizes the im- putation and clustering process to impute more discrimina- tive values for clustering and make the learned representa- tions possessed good clustering property. Also, to reduce the error propagation from imputation to clustering, we introduce a discriminator to make the distribution of imputation values close to the true one and train CRLI in an alternating train- ing manner. An experiment conducted on eight real-world in- complete time-series datasets shows that CRLI outperforms existing methods. We demonstrates the effectiveness of the learned representations and the convergence of the model through visualization analysis. Moreover, we reveal that the joint training strategy can impute values close to the true ones in those important sub-sequences, and impute more discrim- inative values in those less important sub-sequences at the same time, making the imputed sequence cluster-friendly.
Communication-Efficient Frank-Wolfe Algorithm for Nonconvex Decentralized Distributed Learning

Recently decentralized optimization attracts much attention in machine learning because it is more communication-efficient than the centralized fashion. Quantization is a promising method to reduce the communication cost via cutting down the budget of each single communication using the gradient compression. To further improve the communication efficiency, more recently, some quantized decentralized algorithms have been studied. However, the quantized decentralized algorithm for nonconvex constrained machine learning problems is still limited. Frank-Wolfe (a.k.a., conditional gradient or projection-free) method is very efficient to solve many constrained optimization tasks, such as low-rank or sparsity-constrained models training. In this paper, to fill the gap of decentralized quantized constrained optimization, we propose a novel communication-efficient Decentralized Quantized Stochastic Frank-Wolfe (DQSFW) algorithm for non-convex constrained learning models. We first design a new counterexample to show that the vanilla decentralized quantized stochastic Frank-Wolfe algorithm usually diverges. Thus, we propose DQSFW algorithm with the gradient tracking technique to guarantee the method will converge to the stationary point of non-convex optimization safely. In our theoretical analysis, we prove that to achieve the stationary point our DQSFW algorithm achieves the same gradient complexity as the standard stochastic Frank-Wolfe and centralized Frank-Wolfe algorithms, but has much less communication cost. Experiments on matrix completion and model compression applications demonstrate the efficiency of our new algorithm.
Nutritional disorder in Pfaffia glomerata by mercury excess in nutrient solution

The mineral nutritional homeostasis in response to different concentrations of Hg (0, 25 and 50μM) was evaluated in Pfaffia glomerata plant. The exposure to the highest level of Hg (50µM) caused a decreasing in shoot and root fresh weights of 15.5% and 20%, respectively. Both shoot and root Hg concentrations increased linearly with increasing external Hg concentrations. Ca concentration decreased in shoot only at 50µM Hg, whereas shoot K and Mg concentrations decreased at both 25 and 50µM Hg, when compared to the control. A significant decrease in Cu, Zn, Fe and Mn concentrations in plants exposed to Hg was observed, but most Zn, Mn, and Cu in the roots. On the other hand, P concentration increased in both root and shoot of plants exposed at 25 and 50µM Hg, whereas Na concentration increased only in the root at 25 and 50µM Hg exposure. In general, tissue nutrient concentrations in P. glomerata plantlets exposed to Hg were significantly decreased, which indicates that the Hg may cause alteration on the mineral nutritional homeostasis of this species.
Generating optimal distinguishing sequences with a model checker

This paper presents an approach for the automatic generation of shortest Distinguishing Sequences (DS) with the Uppaal model checker. The presented method is applicable to a large number of extended finite state machines and it will find an optimal result, if a DS sequence exists for the considered automaton. Our approach is situated in an integrated testing environment that is used to generate checking sequences. The generation method is based on a DS model, which is derived from the same test model that is used for generating test cover sets. The problem of generating DS is reduced to the definition of a DS model and for this reason the complexity of our approach depends mainly on the used model checking algorithm. This means, that the presented method is automatically improved, when the model checking algorithm is improved. This includes the generation of optimal DS depending on the ability of the model checker to produce optimal results.
Quantifying and Optimizing Performance of Distributed Deep Learning with Cloud Storage Buckets

Cloud environments provide a powerful, low-cost environment for running distributed deep learning workloads. As problems scale up, the methods of storing and loading training data become a signiﬁcant concern. While cloud storage buckets seem like a cost-eﬀective option for large data storage, their bandwidth limitations can impose a non-trivial performance overhead for distributed training. We propose two approaches to compensate for this bandwidth limitation: caching and pre-fetching. Our project quantiﬁes the performance and cost of these approaches, and discusses their usefulness in existing cloud-based distributed deep learning systems. With these approaches, we achieve performance close to that of storing data on each node and potentially lower cost, especially with models with long training times—all while storing only a fraction of the data on disk at a time.
Predictive Modelling of Femur Fracture from DXA Scans Using Radiomics and Machine Learning

Predictive modeling of bone fractures at the hip using DXA images is an important research field due to the capability of this imaging modality to capture osteoporosis related changes in the bone tissues. There exist many available techniques providing fracture risk assessment, such as bone mineral density measurements and biomechanical models. However, these methods do not use the wealth of information provided by the DXA scans and thus lack the accuracy to enable their translation to clinical practice. In this study, a radiomics and machine learning approach is proposed for a more comprehensive predictive modelling of femur fracture using DXA. Our main hypothesis is that integrating heterogeneous and complex characteristics of the bone tissue through radiomics at both the global and local scales will lead to improved prediction of fracture risk. In the proposed technique, the optimal radiomics indices of different types (shape, intensity and texture based) are selected using feature selection methods to identify the most relevant ones for discriminating low-risk and high-risk cases. Furthermore, advanced machine learning is applied to integrate the selected radiomics features into a unified risk classification model based on different learning models (Support Vector Machines, Decision Tree and Random Forest). The proposed predictive model was validated using 63 cases including to patients with and without femur fracture. In this preliminary study, all cases were correctly classified using the proposed model, indicating great potential of radiomics-based classification for predicting fractures of the femur.
User-Oriented Preference Toward a Recommender System

في الوقت الحاضر، من الملائم لنا استخدام محرك بحث للحصول على المعلومات المطلوبة. لكن في بعض الأحيان يسيء فهم المعلومات بسبب التقارير الإعلامية المختلفة. نظام التوصية (RS) شائع الاستخدام في كل الأعمال لأنه يمكن أن يوفر معلومات للمستخدمين التي ستجذب المزيد من الإيرادات للشركات. ولكن أيضا ، في بعض الأحيان ، يوصي النظام المستخدمين بالمعلومات غير الضرورية. لهذا السبب ، قدم هذا البحث بنية لنظام التوصية التي يمكن أن تستند إلى التفضيل الموجه للمستخدم. هذا النظام يسمى UOP-RS. لجعل UOP-RS بشكل كبير، ركزهذا البحث على معلومات السينما وتجميع قاعدة بيانات الأفلام من موقع IMDb الذي يوفر معلومات متعلقة بالأفلام والبرامج التلفزيونية ومقاطع الفيديو المنزلية وألعاب الفيديو والمحتوى المتدفق الذي يجمع أيضا العديد من التقييمات والمراجعات من المستخدمين. حلل البحث أيضا بيانات المستخدم الفردي لاستخراج ميزات المستخدم. بناء على خصائص المستخدم ، وتقييمات / درجات الفيلم ، ونتائج الأفلام ، تم بناء نموذج UOP-RS. في تجربتنا ، تم استخدام 5000 مجموعة بيانات أفلام IMDb و 5 أفلام موصى بها للمستخدمين. تظهر النتائج أن النظام يمكنه إرجاع النتائج في 3.86 ثانية ولديه خطأ 14٪ على السلع الموصى بها عند تدريب البيانات على أنها K = 50. في نهاية هذه الورقة خلص إلى أن النظام يمكن أن يوصي بسرعة مستخدمي السلع التي يحتاجون إليها. سوف يمتد النظام المقترح للاتصال بنظام Chatbot بحيث يمكن للمستخدمين جعل الاستعلامات أسرع وأسهل من هواتفهم في المستقبل.
Self-supervised Learning on Point Clouds using Discriminators on Approximate Convex Decompositions

Since annotating 3D data is expensive, several recent works have proposed pretraining models on self-supervised tasks before transferring representations to smaller datasets of downstream applications. Recent work [6] proposed a simple self-supervised task of ﬁrst decomposing an object into approximate convex polyhedra followed by metric learning on this approximate segmentation. They showed encouraging transfer learning results and a focus on higher-level semantics of an object with this approach. Inspired by the success of [6], we proposed a new self-supervised algorithm which applies discriminators on point clouds segmented as approximate convex polyhedra. Our key idea is to perform a random perturbation (such as rotation, scaling, dropping) of a single approximate polyhedra, and teach a model to discriminate between the perturbed and original 3D objects. We conduct several experiments testing our approach on unsupervised shape classiﬁcation & few-shot part segmentation, and notice competitive performance of individual perturbations compared to [6]. Our best method interpolates the losses from our proposed task and the method in [6], outperforming re-runs of [6].
SandTrap: Securing JavaScript-driven Trigger-Action Platforms

Trigger-Action Platforms (TAPs) seamlessly connect a wide variety of otherwise unconnected devices and services, ranging from IoT devices to cloud services and social networks. TAPs raise critical security and privacy concerns because a TAP is effectively a “person-in-the-middle” between trigger and action services. Third-party code, routinely deployed as “apps” on TAPs, further exacerbates these concerns. This paper focuses on JavaScript-driven TAPs. We show that the popular IFTTT and Zapier platforms and an open-source alternative Node-RED are susceptible to attacks ranging from exﬁltrating data from unsuspecting users to taking over the entire platform. We report on the changes by the platforms in response to our ﬁndings and present an empirical study to assess the implications for Node-RED. Motivated by the need for a secure yet ﬂexible way to integrate third-party JavaScript apps, we propose SandTrap, a novel JavaScript monitor that securely combines the Node.js vm module with fully structural proxy-based two-sided membranes to enforce ﬁne-grained access control policies. To aid developers, SandTrap includes a policy generation mechanism. We instantiate SandTrap to IFTTT, Zapier, and Node-RED and illustrate on a set of benchmarks how SandTrap enforces a variety of policies while incurring a tolerable runtime overhead.
Search-Based Drama Management

A major obstacle to creating compelling story-based video games is shaping the player’s experience into a narrative structure without impinging on the player’s sense of agency (interactive freedom). Story-based video games (e.g. adventure games, many RPGs) tend to prescript linear or mildly branching stories, alternating periods of relatively open-ended and story-free interaction with non-interactive story events (cut-scenes) or discrete story choices. Simulation world games (e.g. Grand Theft Auto , some RPGs) present the player with many possible courses of action at every moment, at the expense of global story structure. We propose a system, a Search-Based Drama Manager (SBDM), which can help guide the player’s experience in more open-ended story worlds along narratively pleasing story arcs. SBDM rests on two assumptions: that an evaluation function can encode an author’s aesthetic for a story, and that search can be used to effectively guide a user’s experience in a story world. We present preliminary work on applying SBDM to the Interactive Fiction piece Anchorhead.
From Human Grading to Machine Grading: Automatic Diagnosis of e-Book Text Marking Skills in Precision Education

: Precision education is a new challenge in leveraging artificial intelligence, machine learning, and learning analytics to enhance teaching quality and learning performance. To facilitate precision education, text marking skills can be used to determine students ’ learning process. Text marking is an essential learning skill in reading. In this study, we proposed a model that leverages the state-of-the-art text summarization technique, Bidirectional Encoder Representations from Transformers (BERT), to calculate the marking score for 130 graduate students enrolled in an accounting course. Then, we applied learning analytics to analyze the correlation between their marking scores and learning performance. We measured students ’ self-regulated learning (SRL) and clustered them into four groups based on their marking scores and marking frequencies to examine whether differences in reading skills and text marking influence students ’ learning performance and awareness of self-regulation. Consistent with past research, our results did not indicate a strong relationship between marking scores and learning performance. However, high-skill readers who use more marking strategies perform better in learning performance, task strategies, and time management than high-skill readers who use fewer marking strategies. Furthermore, high-skill readers who actively employ marking strategies also achieve superior scores of environment structure, and task strategies in SRL than low-skill readers who are inactive in marking. The findings of this research provide evidence supporting the importance of monitoring and training students ’ text marking skill and facilitating precision education.
Convolutional Audio Source Separation Applied to Drum Signal Convolutional Audio Source Separation Applied to Drum Signal Separation Separation

This study examined the task of drum signal separation from full music mixes via both classical methods (Independent Component Analysis) and a combination of Time-Frequency Binary Masking and Convolutional Neural Networks. The results indicate that classical methods relying on predefined computations do not achieve any meaningful results, while convolutional neural networks can achieve imperfect but musically useful results. Furthermore, neural network performance can be improved by data augmentation via transposition – a technique that can only be applied in the context of drum signal separation
Activity Theory

Activity is currently one of the most fundamental concepts in HCI research (Moran, 2006). Early HCI was predominantly concerned with understanding and supporting tasks, which people do to achieve clear predetermined goals (such as making certain changes in a document). The issues of why a person carries out a task and what the task means to the person were typically outside the scope of analysis, evaluation, and design. However, with interactive technology becoming a part of our everyday environments the focus on tasks proved to be insufficient. Understanding and designing technology in the context of purposeful, meaningful activities is now a central concern of HCI research and practice. Virtually all significant recent developments in interactive technologies — think about, for instance, social media, smartphones, and bookreaders — owe their success to helping us live fuller lives rather than merely supporting new types of tasks.
Interactive comment on “How rainfall event characteristics affect the applicability of I30 as an index of intense or erosive rainfall: a brief review with proposed new rainfall index” by David L. Dunkerley et al

soil erosion. Rainfall event data comprise the evidence employed in the analyses of I30 that I present. This is all completely normal practice. Short-term rainfall intensity variation can certainly linked to climatic conditions but this is frequently not considered in analysis of indices such as I30. For instance, the seasonal and diurnal variation of I30 and similar parameters are commonly overlooked, and the mean value of I30 reported merely as an index of storm rainfall. This is sometimes done using data from only selected months, rather than entire years. Much could potentially be learned by examining the seasonal and other variations that are associated with I30 and related indexes, in order to build a more complete picture of the climatology of rainfall events. As an example, the annual maxima in short-period rainfall rates most frequently occur in the summer months, and indeed often in the late afternoon of summer months. Consequently, indices of short-term rainfall intensity cannot be regarded in any sense as reflecting conditions through the other periods of the year, and certainly not the overall climate of a place. Nevertheless, daily maxima, or monthly means, or annual maxima, of indices such as I30 can validly be calculated.
Combined hydrological, rainfall–runoff, hydraulic and sediment transport modeling in Upper Acheloos River catchment

AbstractThe downstream impact of dams may solve but may also cause problems in catchment management. The present study assesses the hydrodynamic and sand transport regime of Acheloos River, focusing in the area upstream of the Kremasta Dam. Calibration and validation of water discharge presented very satisfactory coefficients of efficiency (Nash–Sutcliffe up to 85%). The proposed and applied in this study system of models and methods may be used as a water and sediment management tool in dammed or undammed catchments. The results from the present study are deemed to contribute towards improving the existing knowledge of the Acheloos hydrodynamic regime and better comprehending the sediment transport mechanisms.
Longitudinal Associations of Global and Daily Support with Marital Status

Abstract The provision and receipt of emotional support demonstrates benefits for relationships; however, little research has investigated how either global or day-to-day spousal support influences marital stability. This project assessed how global perception of support from a partner and daily provision and receipt of emotional support over 1 week contributed to divorce 10 years later, accounting for demographic covariates. There were no significant associations of support variables with future divorce. Greater education attainment was the only factor significantly associated with decreased risk for divorce. Results suggest that despite potential short-term benefits, global and daily support may not predict long-term marital stability.
In silico Approach on Ribavirin Inhibitors for COVID-19 Main Protease

It has been well-known that COVID-19 has serious negative impacts on all sides of human life worldwide since the end of the year 2019, and exploring possible treatments for it is a must. To this aim, this computer-based in silico work was performed to investigate the impact of structural modification of Ribavirin (RBV) on its potential interaction with the COVID-19 main protease (MPO). Fourteen RBV compounds, including the original RBV and thirteen modified structures regarding the triazole ring, were investigated in this work based on quantum computations of structural chemistry features and molecular docking simulations (MDs) of RBV…MPO complex formations. Interestingly, six compounds were better than the original RBV for potent interaction with the MPO target, in which R08 (-NH2) was the best one among fourteen compounds. In conclusion, modification of RBV raised binding affinity against the COVID-19 MPO target.
Nickel–Iron Alloy Supported over Iron–Alumina Catalysts for Steam Reforming of Biomass Tar Model Compound

Iron–alumina-supported nickel–iron alloy catalysts were tested in a fixed-bed reactor for steam reforming of toluene as a biomass tar model compound. The influence of the calcination temperature of the iron–alumina support was also explored for the steam reforming reaction. Ni supported on an Fe2O3–Al2O3 support calcined at 500 °C [NFA(500)] gave superior catalytic performance in terms of activity and stability over other catalysts. NFA(500) gave a toluene conversion of more than 90% for a period of 26 h with a H2/CO value of 4.5. According to XRD analysis, the Ni–Fe alloys were formed and stable throughout the reforming reaction. It was observed from XPS results that the surface of the reduced NFA(500) catalyst was enriched with Fe species, where the other catalysts were enriched with Ni species. These surface Fe species play the role of cocatalysts by increasing the coverage of oxygen species during the reforming reaction to enhance the reaction of toluene and suppresses coke formation. The presence of .
The mediated and mediatised justice-seeking: Chinese digital vigilantism from 2006 to 2018

Abstract The Chinese Internet has developed rapidly in the past decade and given rise to many online phenomena, including digital vigilantism (DV). It refers to citizens’ practice of weaponising online visibility for retaliation when collectively offended. In China, since the Cat Torture Case in 2006, DV has been widely adopted by citizens to defend social norms and values. With recent technological developments and socio-political changes in China, how Chinese citizens conduct DV and its influence have also changed along various dimensions. This research, therefore, identifies the historical changes of DV in China and situates these changes in relation to contemporary Chinese technological and socio-political development. The study constructs a database of 1265 Chinese DV cases that receive media coverage between 2006 to 2018 and conducts a thematic analysis to identify characteristics, changes, and trends of DV in China. The author argues that these developments demonstrate the mediation and more importantly, the mediatisation of justice-seeking on the Chinese Internet conditioned by the ubiquitous state power.