Miracle-dz/UniFAR-FTUs huggingface dataset:
Load frequency control in the presence of simultaneous cyber-attack and participation of demand response program

Simultaneous investigation of demand response programs and false data injection cyber-attack are critical issues for the smart power system frequency regulation. To this purpose, in this paper, the output of the studied system is simultaneously divided into two subsystems: one part including false data injection cyder-attack and another part without cyder-attack. Then, false data injection cyber-attack and load disturbance are estimated by a non-linear sliding mode observer, simultaneously and separately. After that, demand response is incorporated in the uncertain power system to compensate the whole or a part of the load disturbance based on the available electrical power in the aggregators considering communication time delay. Finally, active disturbance rejection control is modified and introduced to remove the false data injection cyber-attack and control the uncompensated load disturbance. The salp swarm algorithm is used to design the parameters. The results of several simulation scenarios indicate the efficient performance of the proposed method.
Charging and Discharging Strategies for Clustered Regional Energy Storage System

Abstract With the massive expansion of decentralised renewable energy in electricity grid networks, the power supply system has been changed from centralised to decentralised one and from directional to bi-directional one. However, due to the regional energy structure difference in the power imbalance between electricity generation and consumption is becoming more and more serious. A grid-scale energy storage system (ESS) can be one solution to balance the local difference. In this paper, two charging/discharging strategies for the grid-scale ESS were proposed to decide when and with how much power to charge/discharge the ESS. In order to realise the two strategies, this paper focuses on the application of fuzzy logic control system. The proposed strategies aim to reduce the peak power generation, consumption and the grid fluctuation. In particular, this paper analysis the ratio between energy-capacity and rated power of ESS. The performance of the proposed strategies is evaluated from two aspects, the normalised power of ESS itself and the influence on the power grid. Simulation studies were carried out on the rule-based control systems with different energy-to-power (e2p) ratios, and the results show that the proposed charging strategy with combination of extreme situation of power imbalance and the rest capacity of ESS provides a smooth load curve for the regional power grid system while the external power exchange is reduced effectively.
Novel Views on Novels:Embedding Multiple Facets of Long Texts

Novels are one of the longest document types and thus one of the most complex types of texts. Many NLP tasks utilize document embeddings as machine-understandable semantic representations of documents. However, such document embeddings are optimized for short texts, such as sentences or paragraphs. When faced with longer texts, these models either truncate the long text or split it sequentially into smaller chunks. We show that when applied to a fictional novel, these traditional document embeddings fail to capture all its facets. Complex information, such as time, place, atmosphere, style, and plot is typically not represented adequately. To this end, we propose lib2vec which computes and combines multiple embedding vectors based on various facets. Instead of splitting the text sequentially, lib2vec splits the text semantically based on domain-specific facets. We evaluate the semantic expressiveness using human-assessed book comparisons as well as content-based information retrieval tasks. The results show that our approach outperforms state-of-the-art document embeddings for long texts.
Deep Reinforcement Learning with Noisy Exploration for Autonomous Driving

Autonomous driving decision-making is a great challenge in complex traffic environment, and the deep reinforcement learning (DRL) can contribute to the more intelligent strategy. In the autonomous driving scenarios with DRL algorithms, sufficient exploration to the traffic environment is vital for constructing the state spaces, training the driving decision model and transferring to a new environment. In this paper, three different noise modes are presented to investigate the performance of noisy exploration and generalization in self-driving tasks. Extensive experiments indicate that the noisy exploration is not necessary for the easy traffic environments, and the correlated noisy exploration is an effective technique in generalizing to complex traffic environments, while the uncorrelated noisy exploration may result in a counter-productive effect in inertial autonomous driving system.
Portraits of Programmer Behavior in a Frame-Based Language

The frame-based programming language Stride has the potential to simultaneously simplify and accelerate the task of coding for novices. This is facilitated through a combination of reduced cognitive load, assistance when editing and the elimination of certain syntax errors. Stride also offers the opportunity for comparison to Java, another programming language used by novice programmers, as Stride is integrated into the BlueJ (Java) development environment and user data is also captured in the Blackbox dataset. This paper sets out to determine whether there is evidence to support some of these claims. Since compiler error messages are a key mechanism for user feedback, we compare lesser-studied Stride error message data with better understood Java data. Secondly, we identify groups of Stride and Java users in order to characterise their behavior and to discover differences between frame-based and more conventional text-based programming. These groups include cross-sections of random users as well as two sets of Stride and Java programmers that appear to be engaged in similar tasks. We find that the typical Stride user is primarily a Java user and behavior patterns are similar in both languages. However, we also found a small number of Stride users whose programming time was dominated by Stride, and these users exhibit markedly different patterns for generating user-driven events. These results have implications for educators and tool designers, as well as researchers studying Stride, Java, and Blackbox.
Evaluating the Factors Affecting Consumer's Online Shopping Behavior: The Case of Uzbekistan

This study examines the relationship between various factors that affect consumers' online shopping behavior, which is one of the most important issues of e-commerce and marketing. Initially, relevant literature was integrated and clarified which variables play an important role in consumers' decision. Six hypotheses were formulated influencing consumers’ online shopping behavior. Data gathering process in methodology was completed by online survey, which was sent to 150 randomly selected citizens of Uzbekistan. SPSS statistical tool was used to analyze the data and in order to quantitatively explain the hypothesis. According to the analyzed results, the factors such as affordable prices, quality of products, delivery service and the condition of return policy increase the consumer's attitude towards online shopping.
STRETCH: Stress and Behavior Modeling with Tensor Decomposition of Heterogeneous Data

Stress level modeling and predictions are essential in recommending activities and interventions to individuals. While successful stress models have been proposed in the literature, there is still a missing connection between user engagement behaviors, interest in activities, and their stress levels. In this paper, we propose a novel multi-view tensor decomposition method for stress and user behavior modeling with heterogeneous data, which could provide personalized stress tracking and plausible user behavior modeling across time. To the best of our knowledge, it is the first method that could model user stress and behavior at the same time with multiple resources of data, such as stress measurement, activity rating, and engagement. Our experiments show that leveraging multiple resources of data could not only improve predictions with sparse data, but also results in discovering the underlying stress-activity patterns. We demonstrate the effectiveness of our proposed model on the dataset collected via a self-contained stress management mobile application.
Abnormal Behavior Recognition of Underwater Fish Body Based on C3D Model

The behavior of fish is the direct embodiment of fish life. It is of great significance for the management of mariculture to recognize the abnormal behavior of fish. Traditional abnormal behavior monitoring uses manual monitoring, which will cost a lot of manpower and material resources. With the development of science and technology and the progress of the times, deep learning methods have made great progress in the field of video behavior recognition, which provides an opportunity for abnormal behavior recognition and detection. Firstly, in this paper, a two-category video dataset of fish behavior is constructed. Dataset consisting of 200 videos, including 100 videos of abnormal fish behavior and 100 videos of normal fish behavior. Among them, 150 videos are used for training the network model, and 50 videos are used for testing the model performance. Secondly, C3D deep network model is used to conduct an abnormal behavior recognition experiment on the self-made fish behavior dataset in a complex environment. Meanwhile, the softmax cross-entropy loss and the weight attenuation L2 regularization are used to calculate the total loss. For the experimental results of video classification, this paper makes a more intuitive visualization. ROC curve and t-SNE dimensionality reduction algorithm are used to analyze and evaluate the experimental results. The experiment results show that the C3D model we used has a better recognition effect on fish abnormal behavior and can be used to recognize fish abnormal behavior in actual aquaculture video surveillance.
Embracing Machine Learning in EDA

The application of machine learning (ML) in EDA is a hot research trend. To use ML in EDA, it is nature to think from the ML method point of view, i.e. supervised learning, reinforcement learning and unsupervised learning. Based on this point of view, we can roughly classify the ML applications in EDA into three categories: prediction, optimization, and generation. The prediction category applies supervised learning methods to predict design quality of result (QoR) metrics. There are two kinds of QoR metrics that benefit from the prediction. One kind of metrics are those that can be determined at the current design stage but calculating them consumes a lot of computing resources. For ex-ample, [11] [12] leverage ML to predict circuit power consumption without expensive simulations. The other kind of metrics are those that depend on future design stages. For example, [8] predicts post layout parasitics from schematic of analog circuits. The optimization category applies Bayesian Optimization (BO)and reinforcement learning (RL) to directly optimize EDA problems.BO treats the optimization objective as a blackbox function and tries to find optimal solutions by iteratively sampling the solution space. For example, [5] proposes to use BO with graph embedding and neural network-based surrogate model to size analog circuits. RL treats the optimization objective as the reward from an environment, and trains agents to maximize the reward. [7] proposes to use RL to optimize macro placement, and [9] proposes to use RL to optimize parallel prefix circuit structures. The generation category applies generative models such as generative adversarial networks (GANs) to directly generate solutions to EDA problems. Generative models can learn from previous optimized data distribution and generate solutions for a new problem instance without going through iterative processes like BO or RL. For example, [10] builds a conditional GAN model that learns to generate optical proximity correction (OPC) layout from the original mask.
Optimized is Not Always Optimal - The Dilemma of Analog Design Automation

The vast majority of state-of-the-art integrated circuits are mixed-signal chips. While the design of the digital parts of the ICs is highly automated, the design of the analog circuitry is largely done manually; it is very time-consuming; and prone to error. Among the reasons generally listed for this is often the attitude of the analog designer. The fact is that many analog designers are convinced that human experience and intuition are needed for good analog design. This is why they distrust the automated synthesis tools. This observation is quite correct, but this is only a symptom of the real problem. This paper shows that this phenomenon is caused by very concrete technical (and thus very rational) issues. These issues lie in the mode of operation of the typical optimization processes employed for the synthesizing tasks. I will show that the dilemma that arises in analog design with these optimizers is the root cause of the low level of automation in analog design. The paper concludes with a review of proposals for automating analog design.
Balancing Team Membership and Independent Activity for Robots in Dangerous Domains

Dangerous domains are a challenge for teams of heterogeneous robots, since robot losses may involve the loss of particular skills that might be rare in the domain. Our work considers situations where agents may at times provide more useful work globally by not joining a team, or situations where it might be discovered that types of work might be associated with a given locality. Our framework gives agents the ability to refuse to join teams and work for times on their own, by considering current satisfaction in the use of their skills, the likely rarity of their skills, and the distribution of places those skills are used in the environment. We examine this work in a simulated Urban Search and Rescue domain. In this paper, we describe extensions that we have made to existing research that has resulted in a framework that allows teams to rebalance and recruit from the environment.
Bargaining Chips: Coordinating One-to-Many Concurrent Composite Negotiations

This study presents Bargaining Chips: a framework for one-to-many concurrent composite negotiations, where multiple deals can be reached and combined. Our framework is designed to mirror the salient aspects of real-life procurement and trading scenarios, in which a buyer seeks to acquire a number of items from different sellers at the same time. To do so, the buyer needs to successfully perform multiple concurrent bilateral negotiations as well as coordinate the composite outcome resulting from each interdependent negotiation. This paper contributes to the state of the art by: (1) presenting a model and test-bed for addressing such challenges; (2) by proposing a new, asynchronous interaction protocol for coordinating concurrent negotiation threads; and (3) by providing classes of multi-deal coordinators that are able to navigate this new one-to-many multi-deal setting. We show that Bargaining Chips can be used to evaluate general asynchronous negotiation and coordination strategies in a setting that generalizes over a number of existing negotiation approaches.
Analyzing Features of Passive Twitter’s Users to Estimate Passive Twitter-User’s Interests

User modeling based on the contents of social network services has been developed to recommend information related to each user’s preference. Most of the previous studies have analyzed active users’ tweets and estimated their interests. Meanwhile, although more than a certain number of passive users do not tweet but only gather information, little research has been conducted on interest estimation due to the lack of clues for estimating their interests. These studies have achieved the estimation method using cues other than users’ tweets without understanding the behavior of passive Twitter users. Therefore, in this study, I analyzed the Twitter data with the user features used in the previous studies by using statistical methods to clarify the clue for extracting the interest of the passive user. To do so, a dataset including features of Twitter passive user and the active user was generated. The features of the passive user were clarified by statistical methods, such as Support Vector Machine, Principal Component Analysis, and Decision Tree Analysis. The results showed that it was possible to identify the passive user with an accuracy of 0.93 using features regarding user profiles, followers, and followed users. It was also found that most passive users had fewer than 8 followers and tended to be friendly connected to celebrities without self-disclosure. The results of this study identified types of Twitter passive users using the features. It contributes to the development of an interest estimation for the targeted types of a passive user.
An Empirical Investigation on the Factors Affecting Smartphone Addiction in Digital Era: Effect of Social Networking Services and Instant Messaging Applications Usage

In today's digital era severe smartphone usage is increased by several smartphone applications such as social networking services and instant messaging applications. In this study, we argue that use of social networking services and instant messaging applications may cause an individual's inevitable dependence to the smartphone which consequently leads to addiction. Thus, the current research examines use of both social networking services and instant messaging applications as predictors of smartphone addiction with some other antecedents of social networking services and instant messaging applications usage. Results show that use of social networking services and use of instant messaging applications are important predictors of smartphone addiction. Findings also represent that use of social networking services applications is affected by social networking services network size and use of instant messaging applications is affected by instant messaging network size and the number of instant messaging applications actively being used on smartphone. Moreover, social anxiety and peer influence are examined as the moderating effects between relationships of usage of social networking services and instant messaging applications and smartphone addictions. Results of these tests show that social anxiety moderates the relationship between use of social networking services applications and smartphone addiction and peer influence moderates the relationship between use of instant messaging applications and smartphone addiction. This study has contributions to the addiction research, and also implications to governments and health institutions pertaining to the effect of smartphone addiction on society and individual's health.
Machine Learning for Botnet Detection: An Optimized Feature Selection Approach

Technological advancements have been evolving for so long, particularly Internet of Things (IoT) technology that has seen an increase in the number of connected devices surpass non IoT connections. It has unlocked a lot of potential across different organisational settings from healthcare, transportation, smart cities etc. Unfortunately, these advancements also mean that cybercriminals are constantly seeking new ways of exploiting vulnerabilities for malicious and illegal activities. IoT is a technology that presents a golden opportunity for botnet attacks that take advantage of a large number of IoT devices and use them to launch more powerful and sophisticated attacks such as Distributed Denial of Service (DDoS) attacks. This calls for more research geared towards the detection and mitigation of botnet attacks in IoT systems. This paper proposes a feature selection approach that identifies and removes less influential features as part of botnet attack detection method. The feature selection is based on the frequency of occurrence of the value counts in each of the features with respect to total instances. The effectiveness of the proposed approach is tested and evaluated on a standard IoT dataset. The results reveal that the proposed feature selection approach has improved the performance of the botnet attack detection method, in terms of True Positive Rate (TPR) and False Positive Rate (FPR). The proposed methodology provides 100% TPR, 0% FPR and 99.9976% F-score.
Day-ahead electricity load forecasting based on hybrid model of EEMD and Bidirectional LSTM

Load forecasting has always played a particularly important role in the power industry. In this article, we proposed a hybrid model based on Ensemble Empirical Mode Decomposition (EEMD) and Bidirectional Long Short-Term memory (Bi-LSTM). The original time series of load demand was decomposed into several sub-series called Intrinsic Mode Functions (IMFs) using the EEMD method. Then each IMF was divided into training, validation and testing datasets, and predicted using Bi-LSTM. Finally, the forecasting of the load demand would be obtained by composing the predictions of all IMFs. This model was applied to forecast the electrical demand in Hanoi using the data in 2018. The data was first decomposed based on Seasonal-Trend decomposition using the LOESS (STL) method to find down if there was a seasonal characteristic or not. Then 2 cases were analyzed, forecasting the load with and without considering the seasons. The performance of the proposed model in both cases were compared with other traditional models. The results showed that the proposed approach outperformed the other methods with minimal mean absolute percentage error (MAPE) of just over 2% when considering the whole year data as one time series. Meanwhile, the results of the seasonal division are slightly better in 3 seasons and more biased in the summer due to more variability.
Assessing Human-AI Interaction Early through Factorial Surveys: A Study on the Guidelines for Human-AI Interaction

This work contributes a research protocol for evaluating human-AI interaction in the context of specific AI products. The research protocol enables UX and HCI researchers to assess different human-AI interaction solutions and validate design decisions before investing in engineering. We present a detailed account of the research protocol and demonstrate its use by employing it to study an existing set of human-AI interaction guidelines. We used factorial surveys with a 2 × 2 mixed design to compare user perceptions when a guideline is applied versus violated, under conditions of optimal versus sub-optimal AI performance. The results provided both qualitative and quantitative insights into the UX impact of each guideline. These insights can support creators of user-facing AI systems in their nuanced prioritization and application of the guidelines.
Bi-objective lion swarm optimization based on teaching and learning algorithm

To address the problem that it is difficult to obtain a good quality and uniformly distributed Pareto optimal solution set for a complex bi-objective system model, this paper proposes a Teaching-Learning-based Bi-objective Lion Swarm Optimization algorithm (TLBLSO) for solving the bi-objective system uniform Pareto solution set problem. The idea and mechanism of teaching and learning optimization algorithm are introduced into the Lion Swarm algorithm. That is, the knowledge level of the whole group is improved by the way of individual teachers imparting knowledge and students exchanging knowledge, which effectively improves the spatial searching ability of the lion swarm. By comparing with other optimization algorithms, the experimental results show that the proposed TLBLSO can obtain well-distributed optimization solutions, which verifies the superiority of the proposed algorithm in this paper.