Miracle-dz/UniFAR-FTUs huggingface dataset:
Playing Games with Implicit Human Feedback

We consider the following central question in the field of Deep Reinforcement Learning (DRL): How can we use implicit human feedback to accelerate and optimize the training of a DRL algorithm? State-of-the-art methods rely on any human feedback to be provided explicitly, requiring the active participation of humans (e.g., expert labeling, demonstrations, etc.). In this work, we investigate an alternative paradigm, where non-expert humans are silently observing (and assessing) the agent interacting with the environment. The human’s intrinsic reactions to the agent’s behavior is sensed as implicit feedback by placing electrodes on the human scalp and monitoring what are known as event-related electric potentials. The implicit feedback is then used to augment the agent’s learning in the RL tasks. We develop a system to obtain and accurately decode the implicit human feedback, specifically error-related event potentials (ErrP), for state-action pairs in an Atari-type environment. As a baseline contribution, we demonstrate the feasibility of capturing error-potentials of a human observer watching an agent learning to play several different Atari-games using an electroencephalogram (EEG) cap, and then decoding the signals appropriately and using them as an auxiliary reward function to a DRL algorithm with the intent of accelerating its learning of the game. Building atop the baseline, we then make the following novel contributions in our work: (i) We argue that the definition of ErrP is generalizable across different environments; specifically we show that ErrP of an observer can be learned for a specific game, and the definition used as-is for another game without requiring re-learning of the error-potentials. (ii) In order to improve ErrP data efficiency, we propose a new learning framework to combine recent advances in DRL into the ErrP-based feedback system, allowing humans to provide implicit feedback only prior to the start of RL agent training. (iii) Finally, we scale the implicit human feedback (via ErrP) based RL to reasonably complex environments (games) and demonstrate the significance of our approach through synthetic and real user experiments.
Simulation Studyfor Comparison of Maximum Likelihood and Bayesian Method in Spatial Autoregressive Models with Heteroskedasticity

Generally spatialregressionconsiders onlyone of the spatial effects, namely spatial dependence or heteroskedasticity between areas. Spatial autoregressive(SAR) models take only into account thedependence on the response variable. Most of SAR estimators are valid if there is no violation in the error assumption. Estimation of SAR parameters with heteroskedasticity using maximum likelihood (ML)method gives bias and inconsistent estimators. An alternative method that can be used is Bayesian method. Bayesian method solves heteroskedasticity by modeling the structure of variance-covariance matrix. Simulation data is used to evaluate the Bayesian method in estimating parameters of SAR model with heteroskedasticity. The results indicate that Bayesian method provides bias parameter estimates relatively small and consistent compared to the ML method.
Investigation on Structural, Relative Stable, and Electronic Properties of Binary AlnLin (n = 2 – 12) Clusters through Density Functional Theory

In the present research, the structural, the relative stable, and the electronic properties of AlnLin (n = 2 – 12) clusters were investigated by density functional theory (DFT). By comparing the calculated values of Al2 and Li2 dimers with experimental ones, the reliability of the proposed method was proved. Furthermore, by considering the values of average binding energy (Eb), vertical ionization potential (VIP), vertical electron affinity (VEA), fragmentation energy (△E), second-order energy difference (△2E), HOMO-LUMO (HL) gap, and chemical hardness (η) were calculated. It was found Fragmentation energy, second-order energy difference, VIP, VEA, the chemical stability and HOMO-LUMO gap exhibit odd-even oscillatory behaviours along with cluster size and have extreme values at n = 5 revealed Al5Li5 cluster yielded excellent stability. Those can be well explained by the density of states (DOS) that Li and Al atoms with stronger covalent bonds. Therefore, Al5Li5 cluster can be used as an ideal candidate for calculating Wilson parameters of Al-Li alloys.
Validating the Ready, Willing, and Able Leader Scale of Student Leadership Capacity

Numerous difficulties exist in employing psychometrically sound quantitative measures of leadership development that are both generalizable and brief. Here, we highlight our efforts in creating the Ready, Willing, and Able Leader (RWAL) Scale. This instrument of leadership capacity: (a) has been psychometrically validated for use with college student populations; (b) includes measures of leadership capacity that are known to be essential to effective leadership practice (leader self-efficacy, motivation, and skill); (c) is broad enough for use across diverse educational and developmental contexts; and (d) is brief and concise.
Diverse User Selection for Opinion Procurement

Many applications maintain a repository of user pro(cid:28)les with semantically rich information on each user. Such repositories have a potential of allowing active opinion procurement: reaching out to users to ask for their opinions on di(cid:27)erent topics. An important desideratum of the pro-curement process is that it targets a diverse set of users. To realize this potential, we present Podium : a (cid:28)rst framework, to our knowledge, that supports the selection of diverse representatives in presence of high-dimensional, semantically rich user pro(cid:28)les. We demonstrate that data dimensionality is a challenge for both de(cid:28)ning and achieving diversi(cid:28)cation. We address these challenges by proposing a lightweight, (cid:29)exible notion of diversity that in turn allows explanations and customization of diversi(cid:28)cation results. We show that the problem of (cid:28)nding an optimally diverse user subset is intractable, and provide a greedy algo-rithm that computes an approximate solution. We have implemented our solution in a system prototype and tested it on real-world crowdsourcing platform data. Our experimental results show that Podium is e(cid:27)ective in selecting users with diverse properties, and in turn that the opinions of these users are diverse according to multiple metrics.
Overview of Multi- Criteria Decision Making Techniques

Today’s competitive market set challenges to modern industries for adopting newer and economic manufacturing processes. Basically market value is active and existing only on demand and supply rule specifically on customer demand. Increased prospective of today’s customer is more challenging for now a day’s manufacturing industry. Customer demand and competitive market can be optimized by new manufacturing systems. So, there is need to analyse the implementation process of new manufacturing systems. Therefore, in this paper four main analysing MCDM techniques have been described i.e. Fuzzy Agility Index (FAI), Interpretive Structural Modelling (ISM), Total Interpretive Structural Modelling (TISM) Approach and Fuzzy Performance Importance Index Approach.
A review and comparison of ontology-based approaches to robot autonomy

Abstract Within the next decades, robots will need to be able to execute a large variety of tasks autonomously in a large variety of environments. To relax the resulting programming effort, a knowledge-enabled approach to robot programming can be adopted to organize information in re-usable knowledge pieces. However, for the ease of reuse, there needs to be an agreement on the meaning of terms. A common approach is to represent these terms using ontology languages that conceptualize the respective domain. In this work, we will review projects that use ontologies to support robot autonomy. We will systematically search for projects that fulfill a set of inclusion criteria and compare them with each other with respect to the scope of their ontology, what types of cognitive capabilities are supported by the use of ontologies, and which is their application domain.
Forecasting Energy Commodity Prices: A Large Global Dataset Sparse Approach

This paper focuses on forecasting quarterly energy prices of commodities, such as oil, gas and coal, using the Global VAR dataset proposed by Mohaddes and Raissi (2018). This dataset includes a number of potentially informative quarterly macroeconomic variables for the 33 largest economies, overall accounting for more than 80% of the global GDP. To deal with the information on this large database, we apply a dynamic factor model based on a penalized maximum likelihood approach that allows to shrink parameters to zero and to estimate sparse factor loadings. The estimated latent factors show considerable sparsity and heterogeneity in the selected loadings across variables. When the model is extended to predict energy commodity prices up to four periods ahead, results indicate larger predictability relative to the benchmark random walk model for 1-quarter ahead for all energy commodities. In our application, the largest improvement in terms of prediction accuracy is observed when predicting gas prices from 1 to 4 quarters ahead.
Clinical and Neuropsychological Predictors of Methylphenidate Response in Children and Adolescents with ADHD: A Naturalistic Follow-up Study in a Spanish Sample

Background: Methylphenidate (MPH) is the most commonly used medication for Attention-Deficit/Hyperactivity Disorder (ADHD), but to date, there are neither consistent nor sufficient findings on conditions differentiating responsiveness to MPH response in ADHD. Objective: To develop a predictive model of MPH response, using a longitudinal and naturalistic follow-up study, in a Spanish sample of children and adolescents with ADHD. Methods: We included all children and adolescents with ADHD treated with MPH in our outpatient Clinic (2005 to 2015), evaluated with the K-SADS interview. We collected ADHD-RS-IV.es and CGI-S scores at baseline and at follow up, and neuropsychological testing (WISC-IV, Continuous Performance Test (CPT-II) & Stroop). Clinical response was defined as >30% reduction from baseline of total ADHD-RS-IV.es score and CGI-S final score of 1 or 2 maintained for the previous 3 months. Results: We included 518 children and adolescents with ADHD, mean (SD) age of patients was 11.4 (3.3) years old; 79% male; 51.7% had no comorbidities; and 75.31% had clinical response to a mean MPH dose of 1.2 mg/kg/day. Lower ADHD-RS-IV.es scores, absence of comorbidities (oppositional-defiant symptoms, depressive symptoms and alcohol/cannabis use), fewer altered neuropsychological tests, higher total IQ and low commission errors in CPT-II, were significantly associated with a complete clinical response to methylphenidate treatment. Conclusion: Oppositional-defiant symptoms, depressive symptoms, and a higher number of impaired neuropsychological tests are associated with worse clinical response to methylphenidate. Other stimulants or non-stimulants treatment may be considered when these clinical and neuropsychological variables converged in the first clinical interview.
Singleton Property Graph: Adding A Semantic Web Abstraction Layer to Graph Databases

Property graph databases provide eﬃcient implementations of graph traversal operations, while Semantic Web technologies provide expressive symbolic representation, querying, and reasoning tasks. De-spite the diﬀerences between the goals of the two data models, they do share similar graph characteristics. In this paper, we attempt to combine the beneﬁts of each model into a single graph abstraction layer called Singleton Property Graph (SPG). The SPG layer sits on top of the RDF and simulates the property graph model. We describe the SPG model and its queries, which are Semantic Web-compliant, to be executed inside property graph databases such as TinkerPop. We have tested the prototype and evaluated the experiments with the two datasets BKR and PubChem.
Enhanced Dynamic Bandwidth Techniques, protocol standards and policies in IoT network technologies to Improve QoS in IoT

3246 Published By: Blue Eyes Intelligence Engineering & Sciences Publication Retrieval Number: A9164119119/2019©BEIESP DOI: 10.35940/ijitee.A9164. 119119 Abstract--Internet of Things (IoT) is efficiently plays vital role in development of several sectors by offering many opportunities to grow the economy and improve the life standard through connecting billions of “Things” which provides business opportunities in different sectors and encounter many technical and application challenges. This paper emphasizes the role of Dynamic bandwidth allocation and protocols standards in various IoT sectors such as healthcare, education, agriculture, industrial, transportation, smart cities etc., and focuses on the challenges in providing uninterrupted bandwidth to all IoT devices with existing infrastructure, which depends on standardized protocols and network devices to establish connection with heterogeneous IoT devices. This paper covers Enhanced Dynamic Bandwidth Techniques, protocol standards and policies in IoT network technologies to Improve QoS in IoT devices.
University of Birmingham Ultimate response of stainless steel continuous beams

An experimental study of stainless steel continuous beams is reported in this paper and the applicability of plastic design methods to such structures is considered. A total of 18 two-span continuous beams were tested. Three cross-section types cold-formed square hollow sections (SHS), cold-formed rectangular hollow sections (RHS) and welded I-sections, and two material grades austenitic EN 1.4301/1.4307 and lean duplex EN 1.4162, were considered. The geometric and material properties of the continuous beam test specimens were carefully recorded and supplemented by tests on simply-supported specimens of the same cross-sections. The test specimens covered a wide range of cross-section slendernesses and two different loading positions were adopted. The experimental results were used to assess the degree of moment redistribution in indeterminate stainless steel structures and the applicability of both conventional and novel plastic design methods, including an extension of the Continuous Strength Method (CSM). Comparisons indicated that conventional plastic design is applicable to stainless steel structures, while greater efficiency can be achieved by considering strainhardening through the CSM. Further research, considering a wider range of indeterminate structural configurations is ongoing.
Comparing Power-System and User-Oriented Battery Electric Vehicle Charging Representation and Its Implications on Energy System Modeling

Battery electric vehicles (BEV) provide an opportunity to balance supply and demand in future power systems with high shares of fluctuating renewable energy. Compared to other storage systems such as pumped-storage hydroelectricity, electric vehicle energy demand is highly dependent on charging and connection choices of vehicle users. We present a model framework of a utility-based stock and flow model, a utility-based microsimulation of charging decisions, and an energy system model including respective interfaces to assess how the representation of battery electric vehicle charging affects energy system optimization results. We then apply the framework to a scenario study for controlled charging of nine million electric vehicles in Germany in 2030. Assuming a respective fleet power demand of 27 TWh, we analyze the difference between power-system-based and vehicle user-based charging decisions in two respective scenarios. Our results show that taking into account vehicle users’ charging and connection decisions significantly decreases the load shifting potential of controlled charging. The analysis of marginal values of equations and variables of the optimization problem yields valuable insights on the importance of specific constraints and optimization variables. Assumptions on fleet battery availability and a detailed representation of fast charging are found to have a strong impact on wind curtailment, renewable energy feed-in, and required gas power plant flexibility. A representation of fleet connection to the grid in high temporal detail is less important. Peak load can be reduced by 5% and 3% in both scenarios, respectively. Shifted load is robust across sensitivity analyses while other model results such as curtailment are more sensitive to factors such as underlying data years. Analyzing the importance of increased BEV fleet battery availability for power systems with different weather and electricity demand characteristics should be further scrutinized.
Conflicts of interest in randomised controlled surgical trials: systematic review and qualitative and quantitative analysis

Abstract Conflicts of interest may lead to biased trial designs and unbalanced interpretation of study results. We aimed to evaluate the reporting of potential conflicts of interest in full publications of surgical randomised controlled trials (RCTs). A systematic literature search was performed in CENTRAL, MEDLINE and EMBASE (1985–2014) to find all surgical RCTs of medical devices and perioperative pharmacological or nutritional interventions. The information on conflicts of interest was evaluated both quantitatively and qualitatively, and the development of stated conflicts over time was studied. Of 7934 articles, 444 met the inclusion criteria. In 93 of 444 trials (20.9%), conflicts of interest were disclosed. In half of the cases, the information provided was insufficient to permit conclusions regarding possible influence on the trials. Information about conflicts of interest has increased continuously during the last decades (1985–1994: 0%, 1995–2004: 2.8% and 2005–2014: 33.0%; p<0.001). Among the 115 industry-funded trials, industry participation was considered as a potential conflict of interest in 24 cases (20.9%). Over the past three decades, only every 10th trial has provided appropriate information on conflicts of interest. However, transparency is crucial for the reliability of evidence-based medicine. There is an urgent need for the full disclosure of all conflicts of interest in surgical publishing and for transparency regarding cooperation between academia and industry.
An examination of compassion fatigue risk in certified child life specialists

ABSTRACT Certified child life specialists (CCLSs) provide psychosocial interventions for children and families to manage potentially traumatic experiences in health care settings. Research on related professions indicates engaging in therapeutic relationships in high stress environments may place workers at risk for compassion fatigue. Results of an online survey using the Professional Quality of Life (ProQOL-5), completed by 154 CCLSs, showed that compassion fatigue risk in CCLSs was comparable to other helping professions. This risk was lower with peer support to debrief after patient fatality, and higher when working in intensive care and hematology/oncology units. Findings suggest need for compassion fatigue prevention, early identification, and support resources for CCLSs.
Efficacy Observation of Shenfu Injection on the Cancer-related Fatigue in Lung Cancer Patients after Chemotherapy

OBJECTIVE:To observe the effects of Shenfu injection on the cancer-related fatigue(CRF)in lung cancer patients after chemotherapy.METHODS:182 patients with lung cancer were randomly divided into simple chemotherapy group(control group,58 cases),low dosage group(62 cases) and high dosage group(62 cases). Low dosage group and high dosage group received Shenfu injection 30 mL·d-1 and 60 mL·d-1 via i.v. gtt in addition to conventional chemotherapy.In the study,3 groups all receined chemotherapy for 3 weeks.Treatment course lasted for 4 weeks.The level and degree of CRF and ADR in each group were determined after chemotherapy.RESULTS:After the 4 cycles,the CRF in high dosage group decreased significantly,compared with control group and low dosage group(P0.05);the low dosage group compared with control group had no difference(P0.05). No ADR related to drug use was found in the research process.CONCLUSION:Shenfu injection can reduce the CRF of chemotherapeutical drugs with low ADR in patients with lung cancer.
Digital Commons @ Michigan Tech Digital Commons @ Michigan Tech GOODD, a global dataset of more than 38,000 georeferenced GOODD, a global dataset of more than 38,000 georeferenced dams. dams.

By presenting the most comprehensive GlObal geOreferenced Database of Dams to date containing more than 38,000 dams as well as their associated catchments, we enable new and improved global analyses of the impact of dams on society and environment and the impact of environmental change (for example land use and climate change) on the catchments of dams. This paper presents the development of the global database through systematic digitisation of satellite imagery globally by a small team and highlights the various approaches to bias estimation and to validation of the data. The following datasets are provided (a) raw digitised coordinates for the location of dam walls (that may be useful for example in machine learning approaches to dam identification from imagery), (b) a global vector file of the watershed for each dam.
Forecasting of Revenue, Number of Plane Movements and Number of Passenger Movements at Sultan Iskandar Muda International Airport Using the VARIMA Method

Forecasting has become a necessity in various fields. One of the companies that do the forecasting is PT. Angkasa Pura II Sultan Iskandar Muda (SIM) International Airport. The amount of income, the number of aircraft movements, and the number of passenger movements are some of the interesting things to study from an airport. This kind of problem can be solved by Vector Autoregressive Integrated Moving Average (VARIMA) method. The variables involved in this data analysis can be modeled and forecasted. The analysis shows that the three variables studied tend to have a flat trend, where the amount of income, the number of aircraft movements, and the number of passenger movements has increased over time. The best model obtained is in the form of VARIMA (1,1,0) model. Percentage of forecast errors for income variables, aircraft movements, and passenger movements amounted to 14.028%, 11.003% and 13.330%, respectively.
Virtual Field Studies: Conducting Studies on Public Displays in Virtual Reality

Field studies on public displays can be difficult, expensive, and time-consuming. We investigate the feasibility of using virtual reality (VR) as a test-bed to evaluate deployments of public displays. Specifically, we investigate whether results from virtual field studies, conducted in a virtual public space, would match the results from a corresponding real-world setting. We report on two empirical user studies where we compared audience behavior around a virtual public display in the virtual world to audience behavior around a real public display. We found that virtual field studies can be a powerful research tool, as in both studies we observed largely similar behavior between the settings. We discuss the opportunities, challenges, and limitations of using virtual reality to conduct field studies, and provide lessons learned from our work that can help researchers decide whether to employ VR in their research and what factors to account for if doing so.