Miracle-dz/UniFAR-FTUs huggingface dataset:
An efficient hybrid similarity measure based on user interests for recommender systems

Recommender systems are used to suggest items to users based on their interests. They have been used widely in various domains, including online stores, web advertisements, and social networks. As part of their process, recommender systems use a set of similarity measurements that would assist in finding interesting items. Although many similarity measurements have been proposed in the literature, they have not concentrated on actual user interests. This paper proposes a new efficient hybrid similarity measure for recommender systems based on user interests. This similarity measure is a combination of two novel base similarity measurements: the user interest–user interest similarity measure and the user interest–item similarity measure. This hybrid similarity measure improves the existing work in three aspects. First, it improves the current recommender systems by using actual user interests. Second, it provides a comprehensive evaluation of an efficient solution to the cold start problem. Third, this similarity measure works well even when no corated items exist between two users. Our experiments show that our proposed similarity measure is efficient in terms of accuracy, execution time, and applicability. Specifically, our proposed similarity measure achieves a mean absolute error (MAE) as low as 0.42, with 64% applicability and an execution time as low as 0.03 s, whereas the existing similarity measures from the literature achieve an MAE of 0.88 at their best; these results demonstrate the superiority of our proposed similarity measure in terms of accuracy, as well as having a high applicability percentage and a very short execution time.
Subtractive gating improves generalization in working memory tasks

It is largely unclear how the brain learns to generalize to new situations. Although deep learning models offer great promise as potential models of the brain, they break down when tested on novel conditions not present in their training datasets. One of the most successful models in machine learning are gated-recurrent neural networks. Because of its working memory properties here we refer to these networks as working memory networks (WMN). We compare WMNs with a biologically motivated variant of these networks. In contrast to the multiplicative gating used by WMNs, this new variant operates via subtracting gating (subWMN). We tested these two models in a range of working memory tasks: orientation recall with distractors, orientation recall with update/addition and distractors, and a more challenging task: sequence recognition based on the machine learning handwritten digits dataset. We evaluated the generalization properties of these two networks in working memory tasks by measuring how well they copped with three working memory loads: memory maintenance over time, making memories distractor-resistant and memory updating. Across these tests subWMNs perform better and more robustly than WMNs. These results suggests that the brain may rely on subtractive gating for improved generalization in working memory tasks.
A Real-time Simulation Platform for Maximum Power Point Tracking Algorithm Study in Solar Photovoltaic System

This paper presents the application of controller hardware-in-the-loop (C-HIL) for real-time study of maximum power point tracking (MPPT) algorithm in solar photovoltaic (PV) system. The testing platform in this study which consist of three series connected PV modules together with a DC/DC boost converter were modelled and implemented using Tyhoon HIL-402. For the MPPT controller, a conventional perturb and observe (P&O) and a well-established particle swarm optimization (PSO) based MPPT algorithm are developed and implemented in a digital signal processor. The performance of both algorithms were examined under real-time working condition with 10 different partial shaded test cases. From the study, the P&O shows a faster convergence speed which it can track for maximum power point rapidly up to 0.5070 s compared to the fastest tracking of PSO at 1.3598 s. However, PSO shows a greater capability in tracking for true maximum power point under partial shaded conditions with average output efficiency up to 99.92% compared to P&O which is only 76.76%.
Moving from Closed to Open Source: Observations from Six Transitioned Projects to GitHub

Open source software systems have gained a lot of attention in the past few years. With the emergence of open source platforms like GitHub, developers can contribute, store, and manage their projects with ease. Large organizations like Microsoft, Google, and Facebook are open sourcing their in-house technologies in an effort to more broadly involve the community in the development of software systems. Although closed source and open source systems have been studied extensively, there has been little research on the transition from closed source to open source systems. Through this study we aim to: a) provide guidance and insights for other teams planning to open source their projects and b) to help them avoid pitfalls during the transition process. We studied six different Microsoft systems, which were recently open-sourced i.e., CoreFX, CoreCLR, Roslyn, Entity Framework, MVC, and Orleans. This paper presents the transition from the viewpoints of both Microsoft and the open source community based on interviews with eleven Microsoft developer, five Microsoft senior managers involved in the decision to open source, and eleven open-source developers. From Microsoft’s perspective we discuss the reasons for the transition, experiences of developers involved, and the transition’s outcomes and challenges. Our results show that building a vibrant community, prompt answers, developing an open source culture, security regulations and business opportunities are the factors which persuade companies to open source their products. We also discuss the transition outcomes on processes such as code reviews, version control systems, continuous integration as well as developers’ perception of these changes. From the open source community’s perspective, we illustrate the response to the open-sourcing initiative through contributions and interactions with the internal developers and provide guidelines for other projects planning to go open source.
A Sliding-Mode Controller with Zero Forced Zero PWM for Full Bridge Inverter

Sliding mode control scheme for the full bridge inverter was analyzed, including modeling the variable structure system, selecting the switching line and control law. To make maximum use of degrees of freedom of the full bridge inverter circuit switching device, the idea of the single polarity sine pulse width modulation (SPWM) is introduced to the sliding mode control in full bridge inverter to reduce the switching loss. Furthermore, zero forced zero sliding mode PWM is used to suppress voltage distortion caused by sliding mode surface passing zero modulation. Simulation results are given to verify the correctness and validity of implemented zero forced zero sliding mode PWM for full bridge inverter.
Attribute-aware Sequence Network for Review Summarization

Review summarization aims to generate a condensed summary for a review or multiple reviews. Existing review summarization systems mainly generate summary only based on review content and neglect the authors’ attributes (e.g., gender, age, and occupation). In fact, when summarizing a review, users with different attributes usually pay attention to specific aspects and have their own word-using habits or writing styles. Therefore, we propose an Attribute-aware Sequence Network (ASN) to take the aforementioned users’ characteristics into account, which includes three modules: an attribute encoder encodes the attribute preferences over the words; an attribute-aware review encoder adopts an attribute-based selective mechanism to select the important information of a review; and an attribute-aware summary decoder incorporates attribute embedding and attribute-specific word-using habits into word prediction. To validate our model, we collect a new dataset TripAtt, comprising 495,440 attribute-review-summary triplets with three kinds of attribute information: gender, age, and travel status. Extensive experiments show that ASN achieves state-of-the-art performance on review summarization in both auto-metric ROUGE and human evaluation.
Optimal scheduling of energy systems incorporating load management schemes

The advent of enabling smart grid technologies has resulted in the proliferation of heterogeneous power generation networks. In this context, the concept of microgrids has gained popularity in recent years due to their ability to integrate renewable energy sources with the power system. As such, many industrial units are increasingly displaying characteristics similar to grid-connected microgrids. Consequently, the traditional day-ahead scheduling (unit commitment) problem solved in power systems needs to account for the increasingly heterogeneous nature of the generators. Furthermore, deregulated electricity market concepts such as load management need to be incorporated in the scheduling problem. As such, there exists a need to formulate optimization models for modern energy systems which can account for the heterogeneity in the generation and the flexibility in the load. This thesis is broadly divided into four parts. The first part develops accurate scheduling models of the components which constitute the energy systems considered in the later chapters of the thesis. The mixed logical dynamical modelling framework is used to develop scheduling models of the gas turbines, steam turbines, boilers, diesel generators, battery energy storage systems, thermal energy storage systems and interruptible loads. The scheduling models of the gas turbines, the steam turbines and the boilers include the power trajectories followed by these components while undergoing the hot, warm and cold start-up processes. A detailed treatment of the modelling of an exemplar conventional fossil fuel based generating unit using the mixed logical dynamical framework is also provided. The second part of this thesis proposes a shipyard energy management system (SEMS) to optimize the cost of operating a typical shipyard drydock. The SEMS comprises three modules load forecasting, contracted capacity optimization and optimal scheduling. The load forecasting module uses artificial neural networks (ANN) to generate short term and medium term load forecasts. Historical load demand data and ship arrival schedules are provided as inputs to the ANN. The inclusion of the ship arrival schedule as an input to the ANN enhances the accuracy ix
Learning Local Search Heuristics for Boolean Satisfiability

We present an approach to learn SAT solver heuristics from scratch through deep reinforcement learning with a curriculum. In particular, we incorporate a graph neural network in a stochastic local search algorithm to act as the variable selection heuristic. We consider Boolean satisfiability problems from different classes and learn specialized heuristics for each class. Although we do not aim to compete with the state-of-the-art SAT solvers in run time, we demonstrate that the learned heuristics allow us to find satisfying assignments in fewer steps compared to a generic heuristic, and we provide analysis of our results through experiments.
The Effect of Gadget on Children’s Social Capability

Brauner & Stephens argued that around 9.5% to 14.2% of early childhood children have emotional social problems that have a negative impact on the development and readiness of their schools due to children’s habits in using gadgets. The purpose of this study was to identify the relationship between the use of gadgets for children’s social emotional development in PGRI 2 Rangkasbitung Kindergarten. This research implemented quantitative research approach. This study uses a questionnaire or questionnaire sheet in collecting data, with a total sample of 33 respondents. The results of this study indicate that the majority of children use gadgets with a duration of> 60 minutes per day which is around 60.61%, the type of gadget used in the form of smartphones / mobile phones is 84.85%, and applications that are often used are games (66.67% ) and games played in the form of educational games (60.61%). Based on the results of the study there were 60.61% or as many as 20 children have normal / good social emotional development, this is because the majority of children in PGRI 2 Rangkasbitung kindergarten are more utilizing the sophistication of gadgets towards more positive ones such as knowing numbers, getting to know animals, colors, and to communicate while far apart. It is also related to the characteristics of respondents who stated that around 66.67% of respondents did not work which meant that respondents had more time to accompany their children in using gadgets. The result of Chi-Square correlation test between gadget duration and social emotional development obtained p-value 0.011 (p <0.05) which means that there is a significant relationship between the duration of gadget usage on children’s social emotional development in PGRI 2 Rangkasbitung Kindergarten. Based on the results of the study, it is advisable for parents to accompany their children in using gadgets as well as providing time limits, checking gadgets and choosing games and spectacle applications that can hone child development.
Two Time-scale Off-Policy TD Learning: Non-asymptotic Analysis over Markovian Samples

Gradient-based temporal difference (GTD) algorithms are widely used in off-policy learning scenarios. Among them, the two time-scale TD with gradient correction (TDC) algorithm has been shown to have superior performance. In contrast to previous studies that characterized the non-asymptotic convergence rate of TDC only under identical and independently distributed (i.i.d.) data samples, we provide the first non-asymptotic convergence analysis for two time-scale TDC under a non-i.i. d. \ Markovian sample path and linear function approximation. We show that the two time-scale TDC can converge as fast as O(log t/t^(2/3)) under diminishing stepsize, and can converge exponentially fast under constant stepsize, but at the cost of a non-vanishing error. We further propose a TDC algorithm with blockwisely diminishing stepsize, and show that it asymptotically converges with an arbitrarily small error at a blockwisely linear convergence rate. Our experiments demonstrate that such an algorithm converges as fast as TDC under constant stepsize, and still enjoys comparable accuracy as TDC under diminishing stepsize.
System Design Considerations for a Low-Intensity Hyperspectral Imager of Sensitive Cultural Heritage Manuscripts

Cultural heritage imaging is becoming more common with the increased availability of more complex imaging systems, including multiand hyperspectral imaging (MSI and HSI) systems. A particular concern with HSI systems is the broadband source required, regularly including infrared and ultraviolet spectra, which may cause fading or damage to a target. Guidelines for illumination of such objects, even while on display at a museum, vary widely from one another. Standards must be followed to assure the curator to allow imaging and ensure protection of the document. Building trust in the cultural heritage community is key to gaining access to objects of significant import, thus allowing scientists, historians, and the public to view digitally preserved representations of the object, and to allow further discovery of the object through spectral processing and analysis. Imaging was conducted with a light level of 270 lux at variable ground sample distances (GSD’s). The light level was chosen to maintain a total dose similar to an hour’s display time at a museum, based on the United Kingdom standard for cultural heritage display, PAS 198:2012. The varying GSD was used as a variable to increase signal-to-noise ratios (SNR) or decrease total illumination time on a target. This adjustment was performed both digitally and physically, and typically results in a decrease in image quality, as the spatial resolution of the image decreases.
Leverage Lexical Knowledge for Chinese Named Entity Recognition via Collaborative Graph Network

The lack of word boundaries information has been seen as one of the main obstacles to develop a high performance Chinese named entity recognition (NER) system. Fortunately, the automatically constructed lexicon contains rich word boundaries information and word semantic information. However, integrating lexical knowledge in Chinese NER tasks still faces challenges when it comes to self-matched lexical words as well as the nearest contextual lexical words. We present a Collaborative Graph Network to solve these challenges. Experiments on various datasets show that our model not only outperforms the state-of-the-art (SOTA) results, but also achieves a speed that is six to fifteen times faster than that of the SOTA model.
Semi-High Frequency Portfolio Optimization With Deep Reinforcement Learning

This thesis explores machine learning techniques in algorithmic trading. We implement a trading computer program that balances a portfolio of cryptocurrencies. We try to outperform an equally weighted strategy. As our machine learning technique, we use deep reinforcement learning. Cryptocurrencies are digital mediums of exchange that use cryptography to secure transactions. The most well-known example is Bitcoin. They are interesting to analyze due to high volatility and lack of previous research. The availability of data is also exceptional. We introduce an algorithmic trading agent – a computer program powered by machine learning. The agent follows some pre-determined instructions and executes market orders. Traditionally a human trader determines these instructions by using some technical indicators. We instead give the trading agent raw price data as input and let it figure out its instructions. The agent uses machine learning to figure out the trading rules. We evaluate the performance of the agent in seven different backtest stories. Each backtest story reflects some unique and remarkable period in cryptocurrency history. One backtest period was from December 2017 when Bitcoin reached its all-time high price. Another one is from April 2017 when Bitcoin almost lost its place as the most valued cryptocurrency. The stories show the market conditions where the agent excels and reveals its risks. The algorithmic trading agent has two goals. First, it chooses initial weights, and then it rebalances these weights periodically. Choosing proper initial weights is crucial since transaction costs make trade action costly. We evaluate the trading agent’s performance in these two tasks by using two agents: a static and a dynamic agent. The static agent only does the weight initialization and does not rebalance. The dynamic agent also rebalances. We find that the agent does a poor job in choosing initial weights. We also want to find out the optimal time-period for rebalancing for the dynamic agent. Therefore, we compare rebalancing periods from 15 minutes to 1 day. To make our results robust, we ran over a thousand simulations. We found that 15 – 30 minutes rebalancing periods tend to work the best. We find that the algorithmic trading agent closely follows an equally weighted strategy. This finding suggests that the agent is unavailable to decipher meaningful signals from the noisy price data. The machine learning approach does not provide an advantage over equally weighted strategy. Nevertheless, the trading agent excels in volatile and mean reverting market conditions. On these periods, the dynamic agent has lower volatility and a higher Sharpe ratio. However, it has a dangerous tendency of following the looser. Our results contribute to the field of algorithmic finance. We show that frequent rebalancing is a useful tool in the risk management of highly volatile asset classes. Further investigation is required to extend these findings beyond cryptocurrencies.
Improved Security of Data in Cloud Network to Prevent Detect Key Recovery Attacks Using Node Creation for Authentication

The use of internet in the past decade has increased rapidly people using it for transmission of data saving their works, storing all the private information in a system and transmitting through internet. Due to high usage of internet there is also possibility of losing data by unauthorized access and getting hacked and misuse of data. Previously there was a method used known as kids, but the problem with this method was the method used for generation of key and keeping it secrecy, the attackers could easily get access to the key after black box attack. In this paper the proposed system helps to provide more security and prevents it from attacks and also protect the data stored in the system. The proposed schema can be implemented in hospitals for health care domain, for saving the data of Patience by using cloud storage by using it in the detection system. Many developers and researched have introduced schemes in Machines learning and one of this scheme was key based detection system, But this system required more security to prevent from the attackers, in the proposed module am using cryptographic algorithm for creating secret key and for saving the data and Bowl Fish algorithm to hide the data from the attackers and recover the key from the AES algorithm which is saved in encrypted format which has steps of verification to recover the key and give prevention against unauthorized usage and it provides security from the attackers by saving all the data of patience in a hospital using different domains kept in cloud platform.
Who Is Speaking to Whom? Learning to Identify Utterance Addressee in Multi-Party Conversations

Previous research on dialogue systems generally focuses on the conversation between two participants, yet multi-party conversations which involve more than two participants within one session bring up a more complicated but realistic scenario. In real multi- party conversations, we can observe who is speaking, but the addressee information is not always explicit. In this paper, we aim to tackle the challenge of identifying all the miss- ing addressees in a conversation session. To this end, we introduce a novel who-to-whom (W2W) model which models users and utterances in the session jointly in an interactive way. We conduct experiments on the benchmark Ubuntu Multi-Party Conversation Corpus and the experimental results demonstrate that our model outperforms baselines with consistent improvements.
DIVINE: A Generative Adversarial Imitation Learning Framework for Knowledge Graph Reasoning

Knowledge graphs (KGs) often suffer from sparseness and incompleteness. Knowledge graph reasoning provides a feasible way to address such problems. Recent studies on knowledge graph reasoning have shown that reinforcement learning (RL) based methods can provide state-of-the-art performance. However, existing RL-based methods require numerous trials for path-finding and rely heavily on meticulous reward engineering to fit specific dataset, which is inefficient and laborious to apply to fast-evolving KGs. To this end, in this paper, we present DIVINE, a novel plug-and-play framework based on generative adversarial imitation learning for enhancing existing RL-based methods. DIVINE guides the path-finding process, and learns reasoning policies and reward functions self-adaptively through imitating the demonstrations automatically sampled from KGs. Experimental results on two benchmark datasets show that our framework improves the performance of existing RL-based methods while eliminating extra reward engineering.