Miracle-dz/UniFAR-FTUs huggingface dataset:
Can Humans Be out of the Loop?

Recent advances in Reinforcement Learning have allowed automated agents (for short, agents) to achieve a high level of performance across a wide range of tasks, which when supplemented with human feedback has led to faster and more robust decision-making. The current literature, in large part, focuses on the human’s role during the learning phase: human trainers possess a priori knowledge that could help an agent to accelerate its learning when the environment is not fully known. In this paper, we study an interactive reinforcement learning setting where the agent and the human have different sensory capabilities, disagreeing, therefore, on how they perceive the world (observed states) while sharing the same reward and transition functions. We show that agents are bound to learn sub-optimal policies if they do not take into account human advice, perhaps surprisingly, even when human’s decisions are less accurate than their own. We propose the counterfactual agent who proactively considers the intended actions of the human operator, and proves that this strategy dominates standard approaches regarding performance. Finally, we formulate a novel reinforcement learning task maximizing the performance of an autonomous system subject to a budget constraint over the available amount of human advice.
Case Study on Business Excellence Issues of an Indian Automobile Manufacturer using SAP-LAP Framework

The business excellence of any organization is governed by many factors. The management decisions are responsible for the control of these factors, and thus these decisions are of prime importance. This paper investigates the issues affecting the business excellence of an Indian automobile manufacturer. The organization ABC limited is an Indian automobile manufacturer producing medium and heavy commercial vehicles. The main aim of this study is to propose an interpretive framework that will help in multi-criteria decision making. Therefore, the present study uses an interpretive method, namely ‘‘situation– actor–process–learning– action–performance’’ (SAP–LAP) analysis, to enhance the understanding and analyze the business excellence issues in ABC ltd. The organization was facing some issues in achieving business excellence. SAP-LAP framework methodology has been adopted by the authors for analyzing managerial context to aid up the management decision making.The major challenge towards implementing the SAP-LAP approach is its interpretive nature which is judgmental and highly subjective nature.The organization is analyzed for situation-actor-process. Based on SAP analysis, some learning issues* are identified, actions* are suggested with a proposed set of performance* parameters. For the SAP analysis, the data is collected from the organizational reports, newsletters, success stories, etc. Oral discussions are also carried out with some of the managers of the organization. The findings of the study revealed several issues like the need for formulation of energy liaison policy, focused maintenance approach with adoption of technology management, and use of IT in for cost management.The authors also propose a theoretical LAP framework for the organization.
A Framework for Sentiment Analysis of Online News Articles

: The current innovation era has changed the conventional way of life in many areas. Information Technology has been overflowed with an enormous quantity of data, which is being distributed each moment of consistently, by a large number of users, in the form of online journals, feedbacks, reviews, news blogs, comments, micro-blogging websites, social media and some more. Due to this absolute amount of assessment of precious web assets, a significant part of the research is concentrating on the sentiment analysis. Opinion analysis is the act of implementing text analysis and natural language processing techniques to recognize and extricate abstract data from content. News analysis can be utilized to plot the company's conduct after some time and in this manner, yield significant vital bits of knowledge about companies. Sentiment analysis is additionally helpful in web-based life checking to consequently describe the general inclination or state of mind of purchasers as reflected in web-based life toward a particular brand or organization and decide if they see positively or negatively. Today, it is an everyday exercise for some users to read the news on the internet. Persons' viewpoints, in general, will encounter a variation as per the news they go through. News reports occasions that involve feelings – good, bad, neutral. The human feelings available in the textual information can be studies by using sentiment analysis. Finding the sentiments news articles involves many challenges as it requires going through each and every word of the news text articles to find the particular sentiments. This paper presents a brief study of sentiment analysis and opinion mining and an experimental approach of sentiment analysis of news articles obtained from various news websites.
Studies on Phytochemicals, Antioxidant and Anticancer Potentials of Evolvulus Alsinoides using Lung Cancer Cell Line

The main objective of this study was to evaluate the phytochemicals, antioxidant potentials and anticancer property of Evolvulus alsinoides. Initially, plant extract was prepared using different solvents includes petroleum ether, ethanol and ethyl acetate. The antioxidant potential of the selected plant extract was assessed by Super oxide radical assay, Hydroxyl radical assay and DPPH assays and the results were analyzed. The anticancer property was observed against lung cancer cell line, A-549. From the results it was found that, Alkaloids, Steroids, Flavonoids, Tannins/Phenol, Proteins, Carbohydrate and Glycosides are present in the ethanolic extract of the plant. No significant compounds could be detected in other extracts including petroleum ether and ethyl acetate. DPPH assay and Ferric thiocyanate method clearly reveals that, this plant extract has significant antioxidant potential in dose dependent manner. It also showed notable inhibitory potential against the growth of the lung cancer cells.
Open Source Face Recognition API

2 Resumo . 3 Acknowlegement . 4 Index . 5 Table Index. 7 Figure Index . 8 List of Abbreviations and Acronyms . 10
Rebel with a cause: When does employee rebelliousness relate to creativity?

Although the literature implies that rebelliousness can be a precursor of creative behaviour, this assumption has rarely been tested empirically. In the present study, we hypothesized that trait-level rebelliousness may have an inverted U-shaped relationship with creativity. Additionally, we expected that the effect is pronounced under two conditions, namely when individuals strive for success (i.e., high promotion focus) or when they are not failure-avoidant (i.e., low prevention focus). We conducted a threewave weekly survey study among a heterogeneous sample of 156 employees. The results suggested that the expected non-linear relation rebelliousness–creativity occurred under high promotion focus, but we did not find a direct link between rebelliousness and creativity. Furthermore, prevention focus did not moderate the non-linear link. Additional analyses revealed that rebelliousness has a linear link with creativity when promotion focus is high and, at the same time, prevention focus is low. Our study reveals that rebelliousness in itself is not sufficient to unlock creativity. Instead, we uncover promotion focus as the condition that amplifies the link betweenmoderate rebelliousness and creativity. Additionally, when employees simultaneously focus on promotion and refrain from prevention, the more rebellious they are, the more creativity they report.
M ODELING C ONVERSATION C ONTEXT BY A DAPTING C OGNITIVE A RCHITECTURES

In this paper, we present an approach for language generation for open-domain dialog systems inspired by neurocognitive memory processes. We overcome the drawbacks of the traditional seq2seq architecture by augmenting the architecture with two types of memory, namely, long-term and working memory based on the Standard Model of cognition. We also implement a novel action selection mechanism that helps identify the relevant utterances containing salient information from long-term memory to working memory. To evaluate our model, we compare our action selection mechanism with the state-of-the-art baseline and observe improvements in the identiﬁcation of most salient utterances over long conversations and our mechanism shows a higher correlation to human rankings
Decentralised Learning with Distributed Gradient Descent and Random Features

We investigate the generalisation performance of Distributed Gradient Descent with Implicit Reg-ularisation and Random Features in the homogenous setting where a network of agents are given data sampled independently from the same unknown distribution. Along with reducing the memory footprint, Random Features are particularly convenient in this setting as they provide a common parameterisation across agents that allows to overcome previous difﬁculties in implementing Decentralised Kernel Regression. Under standard source and capacity assumptions, we establish high probability bounds on the predictive performance for each agent as a function of the step size, number of iterations, inverse spectral gap of the communication matrix and number of Random Features. By tuning these parameters, we obtain statistical rates that are minimax optimal with respect to the total number of samples in the network. The algorithm provides a linear improvement over single machine Gradient Descent in memory cost and, when agents hold enough data with respect to the network size and inverse spectral gap, a linear speed-up in computational runtime for any network topology. We present simulations that show how the number of Random Features, iterations and samples impact predictive performance.
Parameter-free, Dynamic, and Strongly-Adaptive Online Learning

We provide a new online learning algorithm that for the ﬁrst time combines several disparate notions of adaptivity. First, our algorithm obtains a “parameter-free” regret bound that adapts to the norm of the comparator and the squared norm of the size of the gradients it observes. Second, it obtains a “strongly-adaptive” regret bound, so that for any given interval of length N , the regret over the interval is ˜ O ( √ N ) . Finally, our algorithm obtains an optimal “dynamic” regret bound: for any sequence of comparators with path-length P , our algorithm obtains regret ˜ O ( √ PN ) over intervals of length N . Our primary technique for achieving these goals is a new method of combining constrained online learning regret bounds that does not rely on an expert meta-algorithm to aggregate learners.
Mapping the echo-chamber: detecting and characterizing partisan networks on Twitter.

The phrase “echo-chamber effect” has been widely used to describe the way modern media, especially social networks, have reshaped political discourse. Instead of diversifying the political landscape, modern media is blamed for facilitating the formation of like-minded cliques that have little exposure to alternative opinions. This phenomenon has been studied prior to the rise of social networks. For instance, Gilbert et al. [4] showed that the majority of comments posted on blogs tended to agree with the content of the blog post. Social networks only seem to have aggravated the problem. A study by the think tank Demos showed that politically vocal accounts on Twitter largely interacted with like-minded accounts, and shared content from domains that were agreeable to their party [10]. Benkler et al. [1] showed that link-sharing behavior reflected a polarization of political debates during the 2016 presidential elections in the U.S. They also argued that there were differences between the way rightand left-wing media motivated discussions on Twitter and Facebook. Partisan networks are often oblivious to, or openly hostile towards, alternative perspectives. This renders them prone to spreading misinformation. The guarded nature of the networks helps sustain the propagation of false rumors even after they have been debunked by authoritative sources [14, 15], or even when the rumors’ initiators admit to their mistake [12]. They are also susceptible to deliberate attempts by fake news websites, professional trolls and bots who spread misinformation [5, 16]. Identifying such networks can help researchers and analysts locate potential sources of false rumors and conspiracy theories. Studying these networks can also uncover the dynamics of the evolution and operation of the so-called professional fake news industry [6, 3].
Visual Analytics for Domain Experts: Challenges and Lessons Learned

In many fields, domain experts can benefit from applying visual analytics solutions to their tasks and problems. Visual approaches are often necessary for finding anomalies or structural changes in the data, providing comprehensive overviews, formulating and testing hypotheses, and many more. However, domain experts are rarely visualization experts themselves. Off-the-shelf visualization software is usually too powerful and complicated or too simple for their professional needs. In this paper, we present a strategy for creating powerful yet easy-to-use visual analytics solutions for specific problem domains and users. We also discuss challenges and report lessons learned from developing visual analytics dashboards in a variety of domains for almost three years.
THE ETHICAL PROBLEMS OF MIDDLE MANAGERS AND THEIR PERCEIVED ORGANISATIONAL CONSEQUENCES

Minna-Maaria Hiekkataipale MSc. (Admin.) is a PhD student in management and leadership at the School of Business and Economics, University of Jyväskylä, Finland. Currently she is working as a Head of Department in the School of Business at JAMK University of Applied Sciences, Finland. Her career includes both working as a university lecturer in the field of management and leadership in three different universities in Finland and management positions in the School of Business, JAMK University of Applied Sciences.
AN IMPROVED SYSTEM FOR DCASE 2019 CHALLENGE TASK 4 Technical

In this technical report, we present an improved system for DCASE2019 challenge task 4, with the goal to evaluate systems for the detection of sound events using real data either weakly labeled or unlabeled and simulated data that is strongly labeled . We use the multi-scale Mel-spectra as the feature and do the detection with the 3 layers convolutional neural network(CNN) and 2 layers recurrent neural network (RNN), after each layer of CNN, we apply a Res-Net (Residual Neural Network) block to increase learning depth. Aim to use data without labels or with weak labels, we apply the mean–teacher model to do the sound event detection.
Apigenin Mitigates Intervertebral Disc Degeneration through the Amelioration of Tumor Necrosis Factor α (TNF-α) Signaling Pathway

Background Intervertebral disc degeneration (IDD) is a common spinal disease affected by environmental and lifestyle factors that has a significant pathological cascade toward inflammation and partial disability. There is currently no therapy that can completely restore the cellular derangement in IDD. Hence, in this study, the therapeutic effects of apigenin on IDD were evaluated using a rat model. Material/Methods Animals were separated into 4 groups: Grp 1, sham-operated control; Grp 2, IDD-induced; Grp 3, IDD-induced+apigenin treatment; Grp 4, apigenin control. The animals were assessed for inflammatory cytokines, chemokines, and prostaglandin signaling. Results There were significant increases in the inflammatory cytokines IL-1β, IL-2, IL-6, IL-8 and IL-17 in the IDD-induced group compared to that of control. Moreover, with increased levels of MMP-3, MMP-9, ADAMTS-4, and syndecan-4, the levels of TNF-α, IFN-γ, prostaglandin E2, and cyclooxygenase 2 were directly increased in the IDD-induced group. In contrast, apigenin protectively restored levels of prostaglandin signaling and reduced cytokine levels. In addition, nucleus pulposus cells cultured separately with either TNF-α inhibitor or apigenin significantly attenuated the levels of extracellular matrix proteins. Conclusions The reduction of cytokine levels under apigenin treatment suggests it may be a promising target drug therapy for the treatment of deleterious IDD conditions.
A ↵ ective Computing of Constitutional States for Human Information Interaction

In this paper, a model for processing the a ↵ ective properties of interaction between human and information is presented. During the interaction process between the human and the information system, various a ↵ ects could be transferred from human to system and vice versa. Such transitions can be modeled by considering each a ↵ ect as a combination of basic constitutional values over time. This model can facilitate bridging the gap of human and information during interaction.
STUCK IN THE WRONG PLACE: LONG-TERM CONSEQUENCES OF PIRATE ATTACKS IN ITALY

From the VIII to early XIX century, coastal areas in Italy (especially, the South) were raided by pirates coming from the shores of Northern Africa. This paper documents that to protect themselves, residents of coastal places relocate inwards to mountainous and rugged areas. It also shows that the relocation had persistent effects, as it constrained local economic development for a long period after the pirates’ threat was over. We also present evidence that – by lowering the human capital endowment and by hampering the growth of major urban centers – mislocation had negative aggregate effects on the economy. JEL Classification: R1, N9, O1
Data-driven predictions of a multiscale Lorenz 96 chaotic system using machine-learning methods: reservoir computing, artificial neural network, and long short-term memory network

Abstract. In this paper, the performance of three machine-learning methods for predicting short-term evolution and for reproducing the long-term statistics of a multiscale spatiotemporal Lorenz 96 system is examined. The methods are an echo state network (ESN, which is a type of reservoir computing; hereafter RC–ESN), a deep feed-forward artificial neural network (ANN), and a recurrent neural network (RNN) with long short-term memory (LSTM; hereafter RNN–LSTM). This Lorenz 96 system has three tiers of nonlinearly interacting variables representing slow/large-scale (X), intermediate (Y), and fast/small-scale (Z) processes. For training or testing, only X is available; Y and Z are never known or used. We show that RC–ESN substantially outperforms ANN and RNN–LSTM for short-term predictions, e.g., accurately forecasting the chaotic trajectories for hundreds of numerical solver's time steps equivalent to several Lyapunov timescales. The RNN–LSTM outperforms ANN, and both methods show some prediction skills too. Furthermore, even after losing the trajectory, data predicted by RC–ESN and RNN–LSTM have probability density functions (pdf's) that closely match the true pdf – even at the tails. The pdf of the data predicted using ANN, however, deviates from the true pdf. Implications, caveats, and applications to data-driven and data-assisted surrogate modeling of complex nonlinear dynamical systems, such as weather and climate, are discussed.
University of Birmingham A systematic quality assessment of Environmental Impact Statements in the oil & gas industry

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Inhibitory Effect of Jatrorrhizine-Platinum(II) Complex on Prostate Cancer Cells via PI3K/AKT and STA3/JAK2 Phosphorylation Downregulation

Background: Prostate cancer is common in men worldwide and its incidence in China has increased over the last 2 decades. The present study assessed the cytotoxicity of jatrorrhizine-platinum(II) complex [JR-P(II)] against prostate cancer and investigated the associated mechanism. Material/Methods: MTT assay was used to assess the anti-proliferative potential and flow cytometry was used to assess apoptosis induction ability of JR-P(II). The protein expression was determined using Western blot assay. JR-P(II)induced changes in Akt mRNA were assessed by RT-PCR assay and MMP was evaluated by flow cytometry using Rhodamine 123 staining. Results: JR-P(II) inhibited 22Rv1 cell and LNCaP cell viability by 17% and 24%, respectively, after treatment with 16 μM JR-P(II). In JR-P(II)-treated 22Rv1 cells and LNCaP cells, the levels of cleaved-PARP and caspase-3 were elevated by 4.0, 8.0, and 16 μM JR-P(II). JR-P(II) treatment increased 22Rv1 and LNCaP cell populations in S phase, with reduction of G1/G0 and G2/M phase cell count. Treatment of 22Rv1 and LNCaP cells with JR-P(II) caused reduction of cyclin E1/A1/D1, pRb, and E2F1 proteins. Moreover, JR-P(II) treatment elevated p53 expression in 22Rv1 and LNCaP cells. JR-P(II) treatment raised ROS level and suppressed MMP in 22Rv1 and LNCaP cells. JRP(II) treatment increased cytochrome c and Bax expression, and reduced Bcl-2 expression in 22Rv1 and LNCaP cells. In JR-P(II)-treated 22Rv1 and LNCaP cells, PI3K/Akt/ERK activation was downregulated relative to the control group. JAK2 and STAT3 phosphorylation gradually decreased with increased JR-P(II) concentration, from 4.0 to 16 μM. Conclusions: JR-P(II) inhibits prostate cancer cell proliferative potential via oxidative damage-induced apoptosis, and it downregulated PI3K/AKT and STA3/JAK2 pathway activation in prostate cancer cells. Therefore, JR-P(II) shows promise for use in treatment of prostate cancer.
A Comparative Study Between The Application Of The Existing MDP And The Enhanced MDP Model In Modeling Students Comprehension In A Multimedia Learning Environment

: The analysis of an existing process or model in order to discover the weakness and then developing an improved version of that same process or model has become one of the best practices of software engineering in recent times. The (Markov Decision Process) MDP is an effective mathematical modeling tool used for Sequential Decision Making (SDM) under uncertainty and has been used in many other fields such as manufacturing, industrial, medicine and so on but has been underutilized in the Decision Making Process (DMP) of the educational sector. Therefore, this research paper provides a comparative study on the application of the existing (standard) MDP and the improved MDP models in modeling the student’s comprehension in a multimedia learning environment under uncer tainty. The improved Markov decision processes integrate the decision making process in the model to ensure that multiple decisions are made in a shorter time. Hence, the significant advantages over the standard MDP. This paper compares the improved MDPs model to the standard MDP models by solving the problem of modeling students in a multimedia modeling system in order to assign the optimal learning style to the learning student using both models. Both models result, when compared to each other, is the same in providing the optimal policy for obtaining and assigning the optimal learning style to the learning students but different in the time of arriving at a decision. The result obtained from the experiment performed shows that the computation time for solving the improved MDP model is significantly smaller than that of solving with the standard MDP model.