Miracle-dz/UniFAR-FTUs huggingface dataset:
Integrating Resource Management and Timeline-Based Planning

This paper describes how explicit resource reasoning is added within an existing temporal planning framework that uses timelines as plan representation. The work is grounded on a recent formalization of timeline-based planning that here is extended to model and reason over resources. The formal account is then fully implemented as an extension of the PLATINUm planner and tested to demonstrate its new capabilities.
A Survey of Recent Trends in Multiobjective Optimal Control—Surrogate Models, Feedback Control and Objective Reduction

Multiobjective optimization plays an increasingly important role in modern applications, where several criteria are often of equal importance. The task in multiobjective optimization and multiobjective optimal control is therefore to compute the set of optimal compromises (the Pareto set) between the conflicting objectives. The advances in algorithms and the increasing interest in Pareto optimal solutions have led to a wide range of new applications related to optimal and feedback control which results in new challenges such as expensive models or real-time applicability. Since the Pareto set generally consists of an infinite number of solutions, the computational effort can quickly become challenging which is particularly problematic when the objectives are costly to evaluate or when a solution has to be presented very quickly. This article gives an overview over recent developments in accelerating multiobjective optimal control for complex problems where either PDE constraints are present or where a feedback behavior has to be achieved. In the first case, surrogate models yield significant speed-ups. Besides classical meta-modeling techniques for multiobjective optimization, a promising alternative for control problems is to introduce a surrogate model for the system dynamics. In the case of real-time requirements, various promising model predictive control approaches have been proposed, using either fast online solvers or offline-online decomposition. We also briefly comment on dimension reduction in many-objective optimization problems as another technique for reducing the numerical effort.
LASIMUP: Large scale multi-touch support integration across multiple projections on arbitrary surfaces

Interactive projections account for a great way of user interaction and can be used in applications that need to span a significant amount of space. However, commercial hardware limit the number of the displayed projections and as a result the need for such applications cannot be satisfied out of the box. This paper discusses the inner workings of LASIMUP (LArge Scale Interactive MUlti-Projection), a platform that aims to bypass that limitation by developing the appropriate software and the use of low-cost supplementary hardware which can enable such functionality. The platform presented has the benefits of being cross-platform, easy to install and affordable to acquire. These factors enable the possibility of creating large scale applications, that otherwise would be impractical to deploy, with a very small financial and operating overhead.
Dolphin echolocation algorithm for optimal sizing of grid-connected photovoltaic system

Despite being used as large-scale power plant, a common issue of photovoltaic system being connected to the grid is the system sizing. As numerous models of system components are commercially available, the selection of optimal components has frequently become tedious and time consuming for system designers. Hence, optimization methods are regularly incorporated in the sizing algorithm for such system. This paper presents the introduction of dolphin echolocation for sizing a photovoltaic system. The optimal combination of inverter and PV module was chosen such that the system's net present value was correspondingly optimized. The results showed that the Dolphin Echolocation Algorithm-based sizing algorithm had outperformed sizing algorithms using selected computational intelligence, i.e. Evolutionary Programming, Firefly Algorithm and Cuckoo Search in producing the lowest elapsed time and highest net present value.
Personality and Risk Taking in Sports: A Focus on Unintentional and Intentional Head Impacts in Amateur Soccer Players.

OBJECTIVE In soccer, unintentional and intentional (heading) head impacts are associated with concussive symptoms and cognitive dysfunction. We examined whether personality traits were associated with these behaviors in soccer players. DESIGN Cross-sectional study. SETTING AND PARTICIPANTS Participants completed study visits at the Albert Einstein College of Medicine. A total of 307 adult amateur soccer players, recruited from New York City and the surrounding area, completed 737 HeadCount-2w questionnaires. PREDICTOR VARIABLES Personality traits (intellect/imagination, conscientiousness, extraversion, agreeableness, and neuroticism) were assessed with the Mini-International Personality Item Pool questionnaire at the baseline study visit. MAIN OUTCOME MEASURES Participants completed an online questionnaire (HeadCount-2w) to ascertain frequency of intentional head impacts and occurrence of unintentional head impacts every 3 to 6 months. Generalized estimating equations repeated-measures regressions determined whether personality predicted unintentional and intentional impacts. RESULTS Personality traits were not associated with unintentional head impact(s) or frequency of intentional head impacts. CONCLUSIONS These findings have important clinical implications, suggesting that personality is not driving the association between high levels of unintentional and intentional head impacts and worse neuropsychological functioning and concussive symptoms.
Toward intelligent agriculture service platform with lora-based wireless sensor network

With the rapid development of the Internet and the Internet of Things (IoTs), various convenient service applications are being used in various fields. In recent years, a new agricultural information and communication technology (ICT), called intelligent agriculture, which meets the needs of farmers for information collection, signal processing, data analysis and equipment control, has been developed. This work proposes an intelligent agricultural service platform that is based on a wireless sensor network and LoRa communication technology. This work uses LoRa as a network transmission interface to solve problem of communication failure and save energy. An agricultural intelligent agriculture service platform is developed to support environmental monitoring and to improve the efficiency of agricultural management.
Narrative Bytes: Data-Driven Content Production in Esports

Esports - video games played competitively that are broadcast to large audiences - are a rapidly growing new form of mainstream entertainment. Esports borrow from traditional TV, but are a qualitatively different genre, due to the high flexibility of content capture and availability of detailed gameplay data. Indeed, in esports, there is access to both real-time and historical data about any action taken in the virtual world. This aspect motivates the research presented here, the question asked being: can the information buried deep in such data, unavailable to the human eye, be unlocked and used to improve the live broadcast compilations of the events? In this paper, we present a large-scale case study of a production tool called Echo, which we developed in close collaboration with leading industry stakeholders. Echo uses live and historic match data to detect extraordinary player performances in the popular esport Dota 2, and dynamically translates interesting data points into audience-facing graphics. Echo was deployed at one of the largest yearly Dota 2 tournaments, which was watched by 25 million people. An analysis of 40 hours of video, over 46,000 live chat messages, and feedback of 98 audience members showed that Echo measurably affected the range and quality of storytelling, increased audience engagement, and invoked rich emotional response among viewers.
Comparison of the effect of the Dietary Approaches to Stop Hypertension diet with usual dietary advice on expression of peroxisome proliferators-activated receptor gamma gene in women: A randomized controlled clinical trial

BACKGROUND Peroxisome proliferator-activated receptor gamma (PPAR-γ) which controls body weight, glucose homeostasis, and adipocyte differentiation is a valuable candidate gene for insulin resistance (IR). The present study aimed to compare the effects of the Dietary Approaches to Stop Hypertension (DASH) diet and usual dietary advice (UDA) on PPAR-γ gene expression in women at risk for cardiovascular disease (CVD). METHODS This randomized controlled trial was performed on 44 women aged 20-50 years at risk for CVD (BMI > 25 kg/m2 and low physical activity). Participants were randomly assigned to the UDA (n = 22) or DASH (n = 22) diets for 12 weeks. The DASH diet was rich in fruits, vegetables, whole grains and low-fat dairy products and low in saturated fat, total fat, cholesterol, refined grains and sweets, with a total of 2400 mg/day sodium. The UDA diet was a regular diet with healthy dietary advice. Anthropometric indices and PPAR-γ gene expression were measured and compared between the two groups at the end of the study. RESULTS After the intervention, body mass index (BMI) and waist circumference (WC) significantly decreased in the DASH group (P < 0.050) but the results showed no significant differences between the two groups. At the end of the trial, PPAR-γ gene expression was significantly different between the UDA and the DASH diet groups (P = 0.040) and this difference remained significant after adjustment for BMI, and physical activity (P = 0.030). CONCLUSION The result of the study showed that the DASH diet significantly decreased the expression of PPAR-γ. This finding was unexpected and future studies on the current topic are therefore recommended.
Oral Glucose Tolerance Test Glucose Peak Time Is Most Predictive of Prediabetes and Hepatic Steatosis in Obese Girls

Abstract Obese adolescent girls are at increased risk for type 2 diabetes, characterized by defects in insulin secretion and action. We sought to determine if later glucose peak timing (>30 minutes), 1-hour glucose >155 mg/dl, or monophasic pattern of glucose excursion during an oral glucose tolerance test (OGTT) reflect a worse cardiometabolic risk profile. Post-pubertal overweight/obese adolescent girls without diabetes were studied (N = 88; age, 15.2 ± 0.2 years; body mass index percentile, 97.7 ± 0.5). All participants completed an OGTT and body composition measures. Thirty-two girls had a four-phase hyperinsulinemic euglycemic clamp with isotope tracers, vascular imaging, and muscle mitochondrial assessments. Participants were categorized by glucose peak timing (≤30 min = early; >30 min = late), 1-hour glucose concentration (±155 mg/dL) and glucose pattern (monophasic, biphasic). Girls with a late (N = 54) vs earlier peak (n = 34) timing had higher peak glucose (P < 0.001) and insulin (P = 0.023), HbA1c (P = 0.021); prevalence of hepatic steatosis (62% vs 26%; P = 0.003) and lower oral disposition index (P < 0.001) and glucagon-like peptide-1 response (P = 0.037). When classified by 1-hour glucose, group differences were similar to peak timing, but minimal when classified by glucose pattern. In the >155 mg/dL group only, peripheral insulin sensitivity and fasting free fatty acids were worse. A later glucose peak or >155 mg/dL 1-hour glucose predicts metabolic disease risk in obese adolescent girls. This may defect incretin effects and first phase insulin response, and muscle and adipose insulin resistance.
Sparse feature auto-combination deep network for video action recognition

For dealing with action recognition for large-scale video data, we need to fully study temporal contextual correlation of corresponding pixels among successive video frames, we present a spatio-temporal convolution neural network which is beneficial to extract deep features from short video segments. Based on conventional sparse encoding, we further consider the representation features of adjacent nodes of the hidden layer according to similar activation states. Thus we propose a sparse auto-combination strategy for multiple input maps in each convolution stage. A topology information constraint of representation features of hidden layer nodes is imposed to handle the adaptive sparse encoding of the topology, so that the learned features can represent the spatio-temporal change relationship better and the number of hidden nodes can be limited in a certain range. We carry out a series of experiments on WEIZMAN data set. Experimental results show that compared with traditional methods, our approach is more effective and robust for video action recognition.
Time Reveals All Wounds: Modeling Temporal Characteristics of Cyberbullying

Cyberbullying is a critical socio-technical problem that seriously limits the use of online interaction spaces by different individuals. Emerging literature identifies cyberbullying as a continuous temporal phenomena rather than one-off incidents. However, as of yet, little computational work has been done to model the temporal dynamics of cyberbullying in online sessions. In this work, we model the temporal dynamics of commenting behavior as point processes and validate it over a crowd-labeled cyberbullying data-set of Instagram media sessions. We define several temporal features to model the distinguishing characteristics between cyberbullying and regular media sessions. We find that our approach is successfully able to identify significant differences between cyberbullying and regular media sessions, and provide a performance increase in cyberbullying detection. This paves the way for more nuanced work on the use of temporal modeling to detect and mitigate the occurrence of cyberbullying.
Clinical, behavioral and biochemical alterations due to shearing stress in Ossimi sheep

The effects of shearing on behavioral patterns, antioxidants, and inflammatory and stress biomarkers was investigated in Ossimi sheep. Clinical parameters and behavioral patterns were recorded, and serum samples were collected pre-shearing and 1, 5, 10 and 15 days post-shearing from 60 Ossimi sheep. The results revealed that grooming and standing idle frequencies were significantly (P<0.01) increased post-shearing. There were significant (P<0.01) increases in IL-6, cortisol, and MDA detected from the 1st to the 10th day post-shearing compared to pre-shearing values. IL-2 and TNF-α significantly (P<0.01) increased from the 1st until the 5th day post-shearing compared to pre-shearing values, while significant (P<0.01) decreases in the values of catalase from the 1st until the 5th day post-shearing compared to pre-shearing values were recorded. Regarding to glutathione reductase, there was a significant (P<0.01) decrease from the 1st until the 10th day post-shearing compared to pre-shearing values. Shearing leads to significant changes in antioxidants, inflammatory, stress biomarkers, and some behaviors in sheep.
A hybrid data gravitation based classification algorithm applied to gene expression data

Gene expression data classification problem has caught many researchers' attention in recent years. The main challenge mentioned in such problem is to overcome the high dimensionality in the limited sample sizes. A novel classification algorithm, data gravitation based classification model (DGC), shows significant performance in many general classification problems. Also, there is an important character of DGC to deal with gene expression data classification problem, feature weighing procedure which measures the importance of a feature by applying weight. In this study, we design a hybrid classifier based on the basic DGC model namely ADGC for Gene expression data classification problem. We use ANOVA as a filter to quickly decrease irrelevant, redundant and nuisance genes before apply DGC and improved simplified swarm optimization algorithm to optimize the feature weight for DGC. To practically evaluate the performance of the proposed method, a total ten gene expression datasets are used to test the performance of the proposed method, and corresponding results are compared with up-to-date works. Experimental results present that ADGC is effective for gene expression data classification problems.
Long-Term Evolution of Metabolic Status in Patients with Schizophrenia Stably Maintained on Second-Generation Antipsychotics

Objective Second-generation antipsychotics (SGAs) increase the risk of metabolic syndrome (MetS). Despite the risk of MetS, SGAs may have to be continued with change in some patients. The aim of this study was to trace the evolution of MetS in these patients. Methods Patients with schizophrenia who had been maintained on a fixed SGA regimen for more than a year were followed-up without changing the regimen. Metabolic indicators were evaluated at baseline and at follow-up. Prevalence, incidence and spontaneous normalization rate of MetS were estimated. Risk factors that might have influenced the evolution were scrutinized. Results A total of 151 subjects were included. During the mean observation period of 389.9±162.4 days, the prevalence of MetS was increased from 35.1 to 45.0%. The incidence rate was 29.6%, while the normalization rate was 26.4%, risk factors affecting incidence were age (OR=1.09, 95% CI: 1.03–1.17), baseline continuous values of metabolic syndrome risk scores (cMetS, OR=1.77, 95% CI:1.29–2.55) and baseline body weight (OR=1.06, 95% CI: 1.01–1.13). Normalization was influenced by age (OR=0.74, 95% CI: 0.57–0.89) and baseline body weight (OR=0.85, 95% CI: 0.72–0.95). Conclusion The prevalence of MetS steadily increased with the continuous use of SGAs. However, individual difference was extensive and about a quarter of the patients were able to recover naturally without specific measurements.
The impact of digital health interventions on health-related outcomes in the workplace: A systematic review

Background The impact of employee health on productivity in the workplace is generally evidenced through absenteeism and presenteeism. Multicomponent worksite health programmes, with significant online elements, have gained in popularity over the last two decades, due in part to their scalability and low cost of implementation. However, little is known about the impact of digital-only interventions on health-related outcomes in employee groups. The aim of this systematic review was to assess the impact of pure digital health interventions in the workplace on health-related outcomes. Methods Multiple databases, including MEDLINE, EMBASE, PubMed and PsycINFO, were used to review the literature using PRISMA guidelines. Results Of 1345 records screened, 22 randomized controlled trial studies were found to be eligible. Although there was a high level of heterogeneity across these studies, significant improvements were found for a broad range of outcomes such as sleep, mental health, sedentary behaviours and physical activity levels. Standardized measures were not always used to quantify intervention impact. All but one study resulted in at least one significantly improved health-related outcome, but attrition rates ranged widely, suggesting sustaining engagement was an issue. Risk of bias assessment was low for one-third of the studies and unclear for the remaining ones. Conclusions This review found modest evidence that digital-only interventions have a positive impact on health-related outcomes in the workplace. High heterogeneity impacted the ability to confirm what interventions might work best for which health outcomes, although less complex health outcomes appeared to be more likely to be impacted. A focus on engagement along with the use of standardized measures and reporting of active intervention components would be helpful in future evaluations.
Retweet Us, We will Retweet You: Spotting Collusive Retweeters Involved in Blackmarket Services

Twitter has increasingly become a popular platform to share news and user opinion. A tweet is considered to be important if it receives high number of affirmative reactions from other Twitter users via Retweets. Retweet count is thus considered as a surrogate measure for positive crowd-sourced reactions - high number of retweets of a tweet not only help the tweet being broadcasted, but also aid in making its topic trending. This in turn bolsters the social reputation of the author of the tweet. Since social reputation/impact of users/t weets influences many decisions (such as promoting brands, advertisement etc.), several blackmarket syndicates have actively been engaged in producing fake retweets in a collusive manner. Users who want to boost the impact of their tweets approach the blackmarket services, and gain retweets for their own tweets by retweeting other customers' tweets. Thus they become customers of blackmarket syndicates and engage in fake activities. Interestingly, these customers are neither bots, nor even fake users - they are usually normal human beings; they express a mix of organic and inorganic retweeting activities, and there is no synchronicity across their behaviors. In this paper, we make a first attempt to investigate such blackmarket customers engaged in producing fake retweets. We collected and annotated a novel dataset comprising of customers of many blackmarket services and characterize them using a set of 64 novel features. We show how their social behavior differs from genuine users. We then use state-of-the-art supervised models to detect three types of customers (bots, promotional, normal) and genuine users. We achieve a Macro Fl-score of 0.87 with SVM, outperforming four other baselines significantly. We further design a browser extension, SCoRe which, given the link of a tweet, spots its fake retweeters in real-time. We also collected users' feedback on the performance of SCoRe and obtained 85% accuracy.
EBCRO: Hybrid chemical reaction with employed bee operator

His article proposes a new hybrid algorithm EBCRO, which combines the local search operator of chemical reaction optimization algorithm (CRO) with modified employed bee operator (EB) main part of artificial bee colony algorithm (ABC), and the introduction of probability-based selection of molecular method. This algorithm creates new solutions not only by local search operation of CRO but also mechanisms of EB. CRO is a newly, effectively swarm intelligence optimization algorithm. However, owing to random characteristics of CRO and not make use of other solutions information, its search ability and convergence speed are inefficient. EB as a part of ABC has simplicity and efficiency global search ability, but the solution is updated only by other solutions information which leading to lack of local search ability. Therefore, this paper presents EBCRO, which can balance the exploitation and exploration of algorithm of numerical function optimization. In this study, EBCRO is tested on 23 benchmark numerical functions, and the simulation results of EBCRO, CRO, orthogonal chemical reaction optimization (OCRO), hybrid particle swarm and chemical reaction optimization (HP-CRO2), and ABC showed that EBCRO outperforms other algorithms in convergence performance.