Miracle-dz/UniFAR-FTUs huggingface dataset:
Coping mechanisms used by the families of mental health care users in Mahikeng sub-district, North West province

Background Families of the mental health care users (MHCUs) face different challenges in dealing with, supporting and caring for MHCUs on a daily basis. The divergent coping mechanisms that the family members use aim to lower the negative, psychological and emotional impact of the stress. These include: escape, avoidance and denial. Aim To explore, describe and contextualise coping mechanisms used by the families of MHCUs and to suggest recommendations for improving their coping mechanisms in Mahikeng sub-district, North West province (NWP), South Africa. Setting The study was conducted in three community health centres in Mahikeng sub-district, NWP, South Africa. Methods A qualitative-exploratory-descriptive and contextual research design was used. Non-probability convenience and purposive sampling techniques were used to select participants. WhatsApp video calls were used to collect data which were analysed following Creswell’s six steps of qualitative data analysis. Results The study established three themes namely; challenges experienced by the family members, coping mechanism used by the family members, and suggestions for improvement in the coping mechanisms for the family members. Conclusion The findings of this study show that the family members of MHCUs are faced with different challenges. Some of the coping mechanisms used by the family members are insufficient and require improvement to enable them to cope effectively. When the coping mechanisms of the family members of MHCUs are improved, their well-being and that of the MHCUs might improve significantly. Contribution The findings of this study provides information that may be used to improve the coping mechanisms of the families of MHCUs in the NWP, South Africa.
Decentralize Log File Storage and Integrity Preservation using Blockchain

— Cloud computing is rapid growing technology as it provides resources to the user's based on their requirements. Users can allocate and de-allocate the services of the cloud. Cloud uses virtual machines to offer assistance. Availably, rapid elasticity and Cost-effectiveness is main key benefits of cloud technology. As today is the digital world, user's uses cloud technology to perform ethical and unethical work. To identify any crime or unethical work in cloud environment forensic team allocated. The Cloud forensic team follows a complete forensic process in a cloud environment to identify any attack and the person behind this attack. Cloud Forensic process completed in four steps that are Identification, Preservation and collection, Examination and presentation. When the user performs single activity in the cloud, it stored in the form of log files. Logs are crucial assets in the legal process. These cloud log files can track all the user's actions. The integrity of log files is essential in cloud computing to reach any attacker. To preserve log file integrity proposed work introduces Decentralize log files storage system to ensure security and integrity of log file—the prototype of the proposed approach based on Ethereum Blockchain and IPFS Peer-to-Peer network protocol. The Proposed prototype is secure, efficient and cost-effective to preserve the integrity of log files with minimum gas value and minimum transaction rate to store log files in Blockchain.
GPT-2C: A GPT-2 parser for Cowrie honeypot logs

Deception technologies like honeypots produce comprehensive log reports, but often lack interoperability with EDR and SIEM technologies. A key bottleneck is that existing information transformation plugins perform well on static logs (e.g. geolocation), but face limitations when it comes to parsing dynamic log topics (e.g. user-generated content). In this paper, we present a run-time system (GPT-2C) that leverages large pre-trained models (GPT-2) to parse dynamic logs generate by a Cowrie SSH honeypot. Our fine-tuned model achieves 89\% inference accuracy in the new domain and demonstrates acceptable execution latency.
Internal reverse-biased p-n junctions: a possible origin of the high resistance in phase change superlattice

Phase change superlattice is one of the emerging material technologies for ultralow-power phase change memories. However, the resistance switching mechanism of phase change superlattice is still hotly debated. Early electrical measurements and recent materials characterizations have suggested that the Kooi phase is very likely to be the as-fabricated low-resistance state. Due to the difficulty in in-situ characterization at atomic resolution, the structure of the electrically switched superlattice in its high-resistance state is still unknown and mainly investigated by theoretical modellings. So far, there has been no simple model that can unify experimental results obtained from device-level electrical measurements and atomic-level materials characterizations. In this work, we carry out atomistic transport modellings of the phase change superlattice device and propose a simple mechanism accounting for its high resistance. The modeled high-resistance state is based on the interfacial phase changed superlattice that has previously been mistaken for the low-resistance state. This work advances the understanding of phase change superlattice for emerging memory applications.
Towards optimized actions in critical situations of soccer games with deep reinforcement learning

Soccer is a sparse rewarding game: any smart or careless action in critical situations can change the result of the match. Therefore players, coaches, and scouts are all curious about the best action to be performed in critical situations, such as the times with a high probability of losing ball possession or scoring a goal. This work proposes a new state representation for the soccer game and a batch reinforcement learning to train a smart policy network. This network gets the contextual information of the situation and proposes the optimal action to maximize the expected goal for the team. We performed extensive numerical experiments on the soccer logs made by InStat for 104 European soccer matches. The results show that in all 104 games, the optimized policy obtains higher rewards than its counterpart in the behavior policy. Besides, our framework learns policies that are close to the expected behavior in the real world. For instance, in the optimized policy, we observe that some actions such as foul, or ball out can be sometimes more rewarding than a shot in specific situations.
Are Group Acknowledgements Worth Anything in IEEE 802.15.4 DSME: A Comparative Analysis

For data collection scenarios in the industrial Internet of Things, wireless communication offers a cost-effective and easy-to-deploy alternative to wired networks. Especially when data needs to be forwarded via multiple battery-powered nodes to a gateway for analysis in the cloud, high energy efficiency and reliability are required. IEEE 802.15. 4 DSME acknowledges every packet individually (ACK), imposing an overhead for each transmitted packet and increasing energy consumption. In networks with little interference, it may thus be beneficial to aggregate ACKs for multiple nodes and broadcast them in a compressed format. This paper compares different of these group acknowledgment (GACK) schemes in best-case and worst-case scenarios and proposes novel GACK schemes to examine whether GACKs constitute a viable alternative to regular ACKs. Results indicate that GACKs can provide a 17% higher reliability than regular ACKs in a best-case scenario. On the other hand, they reduced reliability by at least 15% in comparison to regular ACKs in all other scenarios. Experiments on hardware do not indicate lower energy consumption.
Mitigating Catastrophic Forgetting in Scheduled Sampling with Elastic Weight Consolidation in Neural Machine Translation

Despite strong performance in many sequence-to-sequence tasks, autoregressive models trained with maximum likelihood estimation suffer from exposure bias, i.e. a discrepancy between the ground-truth preﬁxes used during training and the model-generated preﬁxes used at inference time. Scheduled sampling is a simple and often empirically successful approach which addresses this issue by incorporating model-generated preﬁxes into the training process. However, it has been argued that it is an inconsistent training objective leading to models ignoring the preﬁxes altogether. In this paper, we conduct systematic experiments and ﬁnd that it ameliorates exposure bias by increasing model reliance on the input sequence. We also observe that as a side-effect, it wors-ens performance when the model-generated preﬁx is correct, a form of catastrophic forgetting. We propose using Elastic Weight Consolidation as trade-off between mitigating exposure bias and retaining output quality. Experiments on two IWSLT’14 translation tasks demonstrate that our approach alleviates catastrophic forgetting and signiﬁcantly improves BLEU compared to standard scheduled sampling.
Simulations in Recommender Systems: An industry perspective

The construction of effective Recommender Systems (RS) is a complex process, mainly due to the nature of RSs which involves large scale software-systems and human interactions. Iterative development processes require deep understanding of a current baseline as well as the ability to estimate the impact of changes in multiple variables of interest. Simulations are well suited to address both challenges and potentially leading to a high velocity construction process, a fundamental requirement in commercial contexts. Recently, there has been significant interest in RS Simulation Platforms, which allow RS developers to easily craft simulated environments where their systems can be analysed. In this work we discuss how simulations help to increase velocity, we look at the literature around RS Simulation Platforms, analyse strengths and gaps and distill a set of guiding principles for the design of RS Simulation Platforms that we believe will maximize the velocity of iterative RS construction processes.
Machine-Learned Prediction Equilibrium for Dynamic Traffic Assignment

We study a dynamic traffic assignment model, where agents base their instantaneous routing decisions on real-time delay predictions. We formulate a mathematically concise model and derive properties of the predictors that ensure a dynamic prediction equilibrium exists. We demonstrate the versatility of our framework by showing that it subsumes the well-known full information and instantaneous information models, in addition to admitting further realistic predictors as special cases. We complement our theoretical analysis by an experimental study, in which we systematically compare the induced average travel times of different predictors, including a machine-learning model trained on data gained from previously computed equilibrium flows, both on a synthetic and a real road network.
Stability of the weak martingale optimal transport problem

While many questions in (robust) finance can be posed in the martingale optimal transport (MOT) framework, others require to consider also non-linear cost functionals. Following the terminology of Gozlan, Roberto, Samson and Tetali this corresponds to weak martingale optimal transport (WMOT). In this article we establish stability of WMOT which is important since financial data can give only imprecise information on the underlying marginals. As application, we deduce the stability of the superreplication bound for VIX futures as well as the stability of stretched Brownian motion and we derive a monotonicity principle for WMOT.
Deep-Unfolding Neural-Network Aided Hybrid Beamforming Based on Symbol-Error Probability Minimization

In massive multiple-input multiple-output (MIMO) systems, hybrid analog-digital (AD) beamforming can be used to attain a high directional gain without requiring a dedicated radio frequency (RF) chain for each antenna element, which substantially reduces both the hardware costs and power consumption. While massive MIMO transceiver design typically relies on the conventional mean-square error (MSE) criterion, directly minimizing the symbol error rate (SER) can lead to a superior performance. In this article, we first mathematically formulate the problem of hybrid transceiver design under the minimum SER (MSER) optimization criterion and then develop an MSER-based iterative gradient descent (GD) algorithm to find the related stationary points. We then propose a deep-unfolding neural network (NN). The iterative GD algorithm is unfolded into a multi-layer structure wherein trainable parameters are introduced to accelerate the convergence and enhance the overall system performance. To implement the training stage, we derive the relationship between adjacent layers' gradients based on the generalized chain rule (GCR). The deep-unfolding NN is developed for both quadrature phase shift keying (QPSK) and $M$-ary quadrature amplitude modulated (QAM) signals, and its convergence is investigated theoretically. Furthermore, we analyze the transfer capability, computational complexity, and generalization capability of the proposed deep-unfolding NN. Our simulation results show that the latter significantly outperforms its conventional counterpart at a reduced complexity.
Do Female Loan Officers Mitigate Social-Financial Trade-Offs in Microfinance?

This paper revisits social-financial trade-offs in microfinance. We theorize that workforce diversity mitigates the divergence between social and financial performance levels. We test our prediction by performing fixed-effects generalised least squares regressions on a global sample of 1257 microfinance institutions (MFIs) in 107 countries over the period 2010–2018. Confirming our prediction, the empirical results show that the proportion of female loan officers attenuates the negative relationship between the social performance and the financial performance of MFIs. We attribute our findings to the personal characteristics of female loan officers and the high repayment rates of loans that are disbursed and monitored by them. Our findings draw attention to the uneven manifestation of social-financial trade-offs among MFIs.
Exploring the Connection between Knowledge Distillation and Logits Matching

Knowledge distillation is a generalized logits matching technique for model compression. Their equivalence is previously established on the condition of $\textit{infinity temperature}$ and $\textit{zero-mean normalization}$. In this paper, we prove that with only $\textit{infinity temperature}$, the effect of knowledge distillation equals to logits matching with an extra regularization. Furthermore, we reveal that an additional weaker condition -- $\textit{equal-mean initialization}$ rather than the $\textit{zero-mean normalization}$ already suffices to set up the equivalence. The key to our proof is we realize that in neural networks with the cross-entropy loss and softmax activation, the mean of back-propagated gradient on logits always keeps zero.
The possible mediatory role of adipokines in the association between low carbohydrate diet and depressive symptoms among overweight and obese women

Background Previous studies showed the possible association between obesity, dietary pattern, and depressive symptoms. Due to the lack of enough data to confirm the association of obesity and depression in the Middle East, here, we aimed to explore the possible mediatory role of adipokines Galectin-3, transforming growth factor-beta (TGF-β), and endothelial plasminogen activator inhibitor (PAI-1) in the association between low carbohydrate diet (LCD) and depressive symptoms. Methods A total of 256 women aged 17–56 years old were grouped based on their LCD score. Depression anxiety stress scales-21 (DASS-21) self-administered questionnaire was used to evaluate the three negative emotional states of stress, depressive symptoms, and anxiety. Body composition and dietary intake were assessed. Enzyme-linked immunosorbent assay (ELISA) was used to measure the serum levels of Galectin-3, TGF-β, and PAI-1. Results No significant difference was observed regarding Galectin-3, TGF-β, and PAI-1 levels between the groups with dissimilar adherence to LCD or the groups with different levels of depressive symptoms (P>0.05). However, there was a negative association between LCD score as a covariant and depressive symptoms as an independent variable (P = 0.02) and remarkably, a regression model linear analysis using Galectin-3, TGF-β, and PAI-1 as confounding variables indicated the mediatory role of these adipokines in this association (P>0.05). In other words, adipokines eliminated the significance of the relationship between adherence to LCD and depressive symptoms. Conclusion It seems that higher adherence to LCD is probably associated with a lower prevalence of depressive symptoms in obese adults through the mediatory role of adipokines.
KroneckerBERT: Learning Kronecker Decomposition for Pre-trained Language Models via Knowledge Distillation

The development of over-parameterized pre-trained language models has made a significant contribution toward the success of natural language processing. While over-parameterization of these models is the key to their generalization power, it makes them unsuitable for deployment on low-capacity devices. We push the limits of state-of-the-art Transformer-based pre-trained language model compression using Kronecker decomposition. We use this decomposition for compression of the embedding layer, all linear mappings in the multi-head attention, and the feed-forward network modules in the Transformer layer. We perform intermediate-layer knowledge distillation using the uncompressed model as the teacher to improve the performance of the compressed model. We present our KroneckerBERT, a compressed version of the BERT_BASE model obtained using this framework. We evaluate the performance of KroneckerBERT on well-known NLP benchmarks and show that for a high compression factor of 19 (5% of the size of the BERT_BASE model), our KroneckerBERT outperforms state-of-the-art compression methods on the GLUE. Our experiments indicate that the proposed model has promising out-of-distribution robustness and is superior to the state-of-the-art compression methods on SQuAD.
Identifying Untrustworthy Samples: Data Filtering for Open-domain Dialogues with Bayesian Optimization

Being able to reply with a related, fluent, and informative response is an indispensable requirement for building high-quality conversational agents. In order to generate better responses, some approaches have been proposed, such as feeding extra information by collecting large-scale datasets with human annotations, designing neural conversational models (NCMs) with complex architecture and loss functions, or filtering out untrustworthy samples based on a dialogue attribute, e.g., Relatedness or Genericness. In this paper, we follow the third research branch and present a data filtering method for open-domain dialogues, which identifies untrustworthy samples from training data with a quality measure that linearly combines seven dialogue attributes. The attribute weights are obtained via Bayesian Optimization (BayesOpt) that aims to optimize an objective function for dialogue generation iteratively on the validation set. Then we score training samples with the quality measure, sort them in descending order, and filter out those at the bottom. Furthermore, to accelerate the "filter-train-evaluate'' iterations involved in BayesOpt on large-scale datasets, we propose a training framework that integrates maximum likelihood estimation (MLE) and negative training method (NEG). The training method updates parameters of a trained NCMs on two small sets with newly maintained and removed samples, respectively. Specifically, MLE is applied to maximize the log-likelihood of newly maintained samples, while NEG is used to minimize the log-likelihood of newly removed ones. Experimental results on two datasets show that our method can effectively identify untrustworthy samples, and NCMs trained on the filtered datasets achieve better performance.
Adaptive Constrained Kinematic Control Using Partial or Complete Task-Space Measurements

Recent advancements in constrained kinematic control make it an attractive strategy for controlling robots with arbitrary geometry in challenging tasks. Most of the current works assume that the robot kinematic model is precise enough for the task at hand. However, with increasing demands and safety requirements in robotic applications, there is a need for a controller that compensates online for kinematic inaccuracies. We propose an adaptive constrained kinematic control strategy based on quadratic programming, which uses partial or complete task-space measurements to compensate online for calibration errors. Our method is validated in experiments that show increased accuracy and safety compared to a state-of-the-art kinematic control strategy.
Machine-learning-based identification for initial clustering structure in relativistic heavy-ion collisions

$\alpha$-clustering structure is a significant topic in light nuclei. A Bayesian convolutional neural network (BCNN) is applied to classify initial non-clustered and clustered configurations, namely Woods-Saxon distribution and three-$\alpha$ triangular (four-$\alpha$ tetrahedral) structure for $^{12}$C ($^{16}$O), from heavy-ion collision events generated within a multi-phase transport (AMPT) model. Azimuthal angle and transverse momentum distributions of charged pions are taken as inputs to train the classifier. On multiple-event basis, the overall classification accuracy can reach $95\%$ for $^{12}$C/$^{16}$O + $^{197}$Au events at $\sqrt{S_{NN}} =$ 200 GeV. With proper constructions of samples, the predicted deviations on mixed samples with different proportions of both configurations could be within $5\%$. In addition, setting a simple confidence threshold can further improve the predictions on the mixed dataset. Our results indicate promising and extensive possibilities of application of machine-learning-based techniques to real data and some other problems in physics of heavy-ion collisions.
An Inverse Procedural Modeling Pipeline for SVBRDF Maps

Procedural modeling is now the de facto standard of material modeling in industry. Procedural models can be edited and are easily extended, unlike pixel-based representations of captured materials. In this article, we present a semi-automatic pipeline for general material proceduralization. Given Spatially Varying Bidirectional Reflectance Distribution Functions (SVBRDFs) represented as sets of pixel maps, our pipeline decomposes them into a tree of sub-materials whose spatial distributions are encoded by their associated mask maps. This semi-automatic decomposition of material maps progresses hierarchically, driven by our new spectrum-aware material matting and instance-based decomposition methods. Each decomposed sub-material is proceduralized by a novel multi-layer noise model to capture local variations at different scales. Spatial distributions of these sub-materials are modeled either by a by-example inverse synthesis method recovering Point Process Texture Basis Functions (PPTBF) [30] or via random sampling. To reconstruct procedural material maps, we propose a differentiable rendering-based optimization that recomposes all generated procedures together to maximize the similarity between our procedural models and the input material pixel maps. We evaluate our pipeline on a variety of synthetic and real materials. We demonstrate our method’s capacity to process a wide range of material types, eliminating the need for artist designed material graphs required in previous work [38, 53]. As fully procedural models, our results expand to arbitrary resolution and enable high-level user control of appearance.