Miracle-dz/UniFAR-FTUs huggingface dataset:
Infant Action Database: A Benchmark for Infant Action Recognition in Uncontrolled condition

The main focus of our work is to create a database for action recognition of unattended infants in uncontrolled environment with wide variations in surroundings, lighting, interactions with objects, camera motions, etc., Action recognition of infants is emerging as an important and technically challenging computer vision problem as compared with adult action recognition because of their physical appearance. Most of the previous action recognition techniques have focused on the recognition of action captured under a controlled environment in a standard laboratory setting. In this study a novel database is introduced that can be used as a benchmark for surveillance parenting. This database involves nine normal and nine abnormal actions classes which consist of actions and movements of infants occurring in fairly uncontrolled conditions. This database consists of realistic user-uploaded videos which are recorded in the clustered background and different camera motion. After the collection of all videos, they are manually trimmed to form a database. To further evaluate the performance of the database, HOG features were extracted from database set and trained by different Machine Learning classifiers like Multi-class Naive Bayes, Support Vector Machine, Ensemble classifier, Discriminant analysis and Decision tree classifiers. This experimentation shows that the database is complex and robust that can serve as a base for testing action recognition algorithms.
Non – Clinical Pain Assessment System – A Pilot Study

The term ‘Pain’ refers to the sensory response of the body when the reflex tolerance level of the person exceeds the threshold value. Though most of the pain senses are tolerable, it still disturbs the normal anatomical actions of the body, classifying it as tolerable and intolerable pain. Various techniques have evolved in the past few years to quantify pain; however, they are successful only in subjective determination thus leading to inaccuracy in prediction. The proposed paper discusses a few objective methods to quantify non – clinical pain. FLIR spectroscopic images can recognize inflammation and high-temperature points in a given region. Upon using various mechanical algorithms, accurate pain values can be determined. Similarly, by solving the pain-causing equation, we can derive the pain peak values. These methods can replace the conventional scales and help in subsidiary pain assessment.
Descriptive analysis of online mathematic learning implementation that oriented to HOTS during Covid-19 pandemic

This research is qualitative research aims to describe the implementation of online mathematics learning that oriented to Higher Order Thing Skills (HOTS) during covid-19 pandemic. Beside that this study aims to describe students’ HOTS abilities during the implementation of online mathematics learning. This research was conducted at SMK Telkom Malang. The online mathematics learning was implemented in class X RPL 4. There are 36 students that are included in this online learning. Class of X RPL 4 was selected as subject of this study caused by two reasons. The reasons are the material about trigonometry has been studied in this class and the mathematics abilities of students in this class are balanced among low, medium and high ability. The learning model that is implemented in this research is discovery learning. Discovery learning consist of 6 stages, namely stimulation, problem statement, data collection, data processing, verification and generalization. Data collection in this study was carried out by using observations, interviews and students’ work results This study presents description of teaching and learning activities that stimulate students’ HOTS in each stage of discovery learning. Beside that, this study indicates that online mathematics learning using discovery learning can stimulate students’ HOTS.
Fading Artificial Intelligence Theory

: Because of relentless ascent of Artificial Knowledge, there has been an astounding spotlight on the topic of "Will shrewd frameworks be ok for mankind of things to come? " Therefore, numerous scientists have begun to coordinate their takes a shot at managing issues that may cause issues on empowering Artificial Intelligence frameworks to carry on wild or on the other hand take positions risky for people. Such research works are as of now included under the writing of Artificial Intelligence Wellbeing and/or Future of Artificial Intelligence. With regards to the clarifications, this exploration paper proposes a hypothesis on accomplishing safe keen frameworks by considering life-time of an Man-made consciousness based framework as indicated by a few operational factors and wipe out – end a canny framework, which is 'mature enough' to work for offering opportunity to new ages of frameworks, which appear to be more secure. The paper makes a brief prologue to the hypothesis and opens entryways broadly for further research on it.
A Proposed Sustainable Model of Food Systems

—Food security becomes more fragile after a pandemic covid-19 hit the world. Strengthening Food Systems should become more prevalent to avoid food insecurity. This study proposed a combination of social and environmental welfare impact to the sustainable model of food systems that should enhance food security after a crisis. The method used is literature review. Data analysis is using information from previous research. The new model of food system has enlarged the food system activities including: production, processing and packaging, retailing and distribution and consumption. The sustainable food system model uses an approach of Twin Track with digital using based on people's behavior and conditions. Furthermore, the role of cooperation between stakeholders (government, private sector, and society) in all lines is the key to strengthening community food security. Their collaboration is needed to support the implementation of a model for a sustainable food system. The finding should enriched the knowledge of food security under complex and rapid changing of global pressure, especially Covid-19 pandemic.
Verifying higher-order concurrency with data automata

Using a combination of automata-theoretic and game-semantic techniques, we propose a method for analysing higher-order concurrent programs. Our language of choice is Finitary Idealised Concurrent Algol (FICA) due to its relatively simple fully abstract game model.Our first contribution is an automata model over a tree-structured infinite data alphabet, called split automata, whose distinctive feature is the separation of control and memory. We show that every FICA term can be translated into such an automaton. Thanks to the structure of split automata, we are able to observe subtle aspects of the underlying game semantics.This enables us to identify a fragment of FICA with iteration and limited synchronisation (but without recursion), for which, in contrast to the whole FICA, a variety of verification problems turn out to be decidable.
Mechanical Behaviors of Ceramic Particulate Reinforced Aluminium Metal Matrix Composites – A Review

1,2Assistant Professor, Department of Industrial and Production Engineering, Institute of Technology Guru Ghasidas Vishwavidyalaya (A Central University), Bilaspur, Chhattisgarh, India, 495009 3Assistant Professor, Department of Mechanical Engineering, National Institute of Science and Technology, Berhampur, Odisha, India, 761008 4Professor, Department of Industrial and Production Engineering, Institute of Technology Guru Ghasidas Vishwavidyalaya (A Central University), Bilaspur, Chhattisgarh, India, 495009 -----------------------------------------------------------------------***-------------------------------------------------------------------Abstract This review paper is broadly focused on the effect of the different ceramic particulates (SiC, B4C and Al2O3) on the mechanical properties of the Al and Al alloy. Ceramic Particulate Reinforced Aluminium Metal Matrix Composites (PRAMMCs) provide improved properties i.e. improved hardness, improved strength to weight ratio, tensile & compressive strength, creep & fatigue resistance etc. as compared to conventional Al and Al alloy. Aluminium Metal Matrix Composites (AMMCs) are preferred to use in aerospace, automobile, sports, marine application etc due to its lightweight and high strength property. This manuscript reported the pioneering work of most of the researchers in the field of mechanical characterizations of ceramic PRAMMCs. The effects of weight or volume percentages and particles size of the reinforcement on the properties of AMMCs are discussed in this article.
A Journey In Search Of Meaning For Existence In Mary Shelley’s Frankenstein

:All human beings have a desire to be alive and wish to survive or stick on to the environment they belong to. It is actually applicable to all living things in the world. The existence of a species is not solely dependent on the organism, but the habitat, proper living conditions and social relations also plays a pivotal role. In the novel Frankenstein, the creature had a quest for creating a meaning for its existence. We can consider it as a partial human being; it successfully acquires a space for itself in the living conditions of human society. The monster customizes itself and adopts the characteristic traits of human beings for its survival. But the society was not ready to accept the monster as a part of its social set up. Existential philosopher Albert Camus states that “I rebel; therefore I exist” (The Rebel, 15). Most human beings try to create a meaning for their existence in different stages of their life, starting from initial stages of life to till their end, and thinking, accept, and contradict with the worldly powers and ideas. As a result of this continuous evolution, each and every human being find own way and meaning for their existence. This paper tries to dismantle the psyche of the monster with a social as well as existentialistic perspective and attempts to signify the importance of habitat, peer groups and a society which are the integral components of existence.
AI and Autonomous Driving: Key ethical considerations

Researchers and companies around the world are working on advancing autonomous driving technology with a major goal of increasing road safety and comfort of motorized transport. Autonomous vehicles (AVs) are expected to have a global impact that will change society, the safety of roadways and transportation systems in the future. The field is moving quickly, leaving governance and ethical consideration to catch up. However, it is important to consider policy and ethical challenges and trade-offs, as well as potential solutions, already while AVs are being developed. AI and Autonomous Driving: Key ethical considerations
A Novel Method in Attention Measurement for Operators of Radar Devices Based on P300 Event-Related Potential Protocols Detection

Various methods have been proposed to detect the attention and perception of an operator during tasks such as radar monitoring. Due to the high accuracy of electroencephalographic signals, it is utilized for systems based on brain signal. The eventrelated potential (ERP) technique has been widely used for testing theories of perception and attention. Brain-computer Interface (BCI) provides the communication link between the human’s brain and an external device. In this article, we propose a method to investigate the attention of operators of very sensitive monitoring devices, in particular, the operators of navy ships’ radars in detecting fighter aircrafts. Using a Visual Stimuli, which was shown to the subjects prior to the test, the protocol utilized in this paper yielded a very high accuracy (up to 87%), which makes it a robust method to use in such conditions. Linear LDA and non-linear SVM classifiers were utilized in processing the output signal. Although several P300 systems have been used to detect attention using pattern recognition techniques, the novelty of this study is that attention detection is used for the first time for a radar operator which resulted in acceptable accuracy.
It’s a man’s world: An investigation of masculinity and internalised homophobia amongst homosexual men

Gender norms often dictate areas of daily living which individuals ought to adopt and abide by. However, as a consequence of the heterosexist ideals associated with gender norms, sexual minority individuals often experience negative attitudes towards their own sexuality— internalised homophobia. As a result, gay men often feel the compulsion to compensate for their perceived lack of masculinity. The current thesis contains a series of two manuscripts which explore and critique hegemonic masculinity and its attributing role in the lives of homosexual males. The thesis draws attention to both hegemonic—heterosexual— masculinity and homosexual—straight-acting—masculinity. INVESTIGATING MASCULINITY AND INTERNALISED HOMOPHOBIA 2
A Reinforcement Learning Based Approach for Robotic Grasping Tasks

—Robots are nowadays increasingly required to deal with (partially) unknown tasks and situations. The robot has, therefore, to adapt its behavior to the speciﬁc working conditions. Reinforcement learning (RL) holds the promise of autonomously learning new control policies through trial-and-error. However, RL approaches are prone to learning with high samples, particularly for vision-based continuous control tasks. In this paper, a learning based method is presented that uses simulation data and kinematic state information to learn an object manipulation task through RL. Unlike vision, the input modality based on kinematic information allows learning with less samples and facilitates transfer to the real-world without additional training requirements. The control policy is parameterized by a neural network and learned using modern Proximal Policy Optimization (PPO) algorithm. A dense reward function has been designed for the task to enable efﬁcient learning of an agent. The proposed approach is trained entirely in simulation (exploiting the MuJoCo environment) from scratch without any prior demonstrations of the task. A grasping task involving a Franka Emika Panda manipulator has been considered as the reference task to be learned. The proposed approach has been demonstrated to be generalizable across multiple object geometries and initial robot/parts conﬁgurations. Furthermore, it is shown that the learned policy can transfer the past learning experience and quickly adapt to new variations of the environment by ﬁne tuning through on-policy RL, having the robot able to re-execute the target task in modiﬁed setting.
Hidrogenación de Aceite Crudo de Jatropha para Aplicaciones Industriales

espanolSe estudio la hidrogenacion de aceite crudo de Jatropha usando dos catalizadores comerciales 3 niquel/silice y niquel/alumina. Se realizo un diseno experimental 2 , comprendiendo las variables temperatura, presion y contenido de catalizador. El aceite y sus productos de reaccion fueron caracterizados mediante la medicion de composicion y los indices de acidez, saponificacion y yodo. Los catalizadores se caracterizaron por area BET, FT-IR, TPR y TGA. Dentro de los resultados mas relevantes del presente trabajo estan: a) el catalizador niquel/silice mostro mayor actividad, debido posiblemente a su mayor area superficial, lo cual pudo favorecer la dispersion del niquel; b) se encontro una alta estabilidad a la oxidacion del catalizador niquel/silice, lo que le confiere un alto grado de reutilizacion; c) se obtuvo un producto con una hidrogenacion mayor al 98% con potenciales aplicaciones en lubricantes textiles, aditivos suavizantes, entre otros. EnglishHydrogenation of crude Jatropha oil using two commercial catalysts nickel/silica and nickel/alumina was studied. An experimental design 23 was carried out, including the variables temperature, pressure and content of catalyst. Oil and their reaction products were characterized by measuring composition and acid value, saponification and iodine. The catalysts were characterized by BET area, FT-IR, TPR and TGA. Among the most relevant results are: a) The catalyst nickel/silica showed higher activity, possibly due to its larger surface area, which could favor the dispersion of nickel; b) high stability to oxidation by catalyst nickel/silica was found, which gives a high degree of reuse; c) a product with hydrogenation greater than 98% with the potential to apply in textile lubricants, softening additives, among others.
Charles A. Dice Center for Research in Financial Economics Does Retrenchment Boost Performance? Evidence from Fallen Angels

We study restructuring by firms whose stock prices experience a sharp decline to a low price level– fallen angels. In response to a price decline, firms can retrench by reducing investments and cutting the workforce, or increase leverage and investments hoping for lottery-like payoffs. We find that relative to a matched sample, fallen angels retrench. While retrenchment helps boost stock prices, reducing fixed assets and employment also increase firm risk, lower growth opportunities, and reduce the probability a firm remains listed. We find no consistent evidence that retrenchment actions undertaken by fallen angels affect future operating performance. JEL Classifications: G10, G30, G34
A Continuized View on Nesterov Acceleration

We introduce the continuized Nesterov acceleration, a close variant of Nesterov acceleration whose variables are indexed by a continuous time parameter. The two variables continuously mix following a linear ordinary differential equation and take gradient steps at random times. This continuized variant benefits from the best of the continuous and the discrete frameworks: as a continuous process, one can use differential calculus to analyze convergence and obtain analytical expressions for the parameters; and a discretization of the continuized process can be computed exactly with convergence rates similar to those of Nesterov original acceleration. We show that the discretization has the same structure as Nesterov acceleration, but with random parameters. We provide continuized Nesterov acceleration under deterministic as well as stochastic gradients, with either additive or multiplicative noise. Finally, using our continuized framework and expressing the gossip averaging problem as the stochastic minimization of a certain energy function, we provide the first rigorous acceleration of asynchronous gossip algorithms.
Constrained Motion Planning of a Cable-Driven Soft Robot With Compressible Curvature Modeling

A cable-driven soft robot with redundancy can perform the tip trajectory tracking task and in the meanwhile fulfill some extra constraints, such as tracking with a designated tip orientation, or avoiding obstacles in the environment. These constraints require proper motion planning of the soft material-based body that can be axially compressed. In this letter, we derive the compressible curvature kinematics of a cable-driven soft robot which takes the undesirable axial compression caused by the cable-driven mechanism into account. Robot motion planning for tip trajectory tracking tasks in constrained conditions, including fixed orientation tip and manipulator–obstacle collision avoidance, are investigated. The inverse solution of cable actuation is formulated as a damped least-square optimization problem and iteratively computed off-line. The performance of path and trajectory tracking, and the obedience to constraints are evaluated via the simulation we make open-source, as well as prototype experiments. The method can be generalized to similar multisegment cable-driven soft robotic systems by customizing the robot parameters for the prior motion planning in tip trajectory following tasks.
Bilateral Personalized Dialogue Generation with Dynamic Persona-Aware Fusion

—Generating personalized responses is one of the major challenges in natural human-robot interaction. Current researches in this ﬁeld mainly focus on generating responses consistent with the robot’s pre-assigned persona, while ignoring the user’s persona. Such responses may be inappropriate or even offensive, which may lead to the bad user experience. Therefore, we propose a bilateral personalized dialogue generation (BPDG) method with dynamic persona-aware fusion via multi-task transfer learning to generate responses consistent with both personas. The proposed method aims to accomplish three learning tasks: 1) an encoder is trained with dialogue utterances added with corresponded personalized attributes and relative position (language model task), 2) a dynamic persona-aware fusion module predicts the persona presence to adaptively fuse the contextual and bilateral personas encodings (persona prediction task) and 3) a decoder generates natural, ﬂuent and personalized responses (dialogue generation task). To make the generated responses more personalized and bilateral persona-consistent, the Conditional Mutual Information Maximum (CMIM) criterion is adopted to select the ﬁnal response from the generated candidates. The experimental results show that the proposed method outperforms several state-of-the-art methods in terms of both automatic and manual evaluations.
Challenges and Considerations with Code-Mixed NLP for Multilingual Societies

Multilingualism refers to the high degree of proficiency in two or more languages in the written and oral communication modes. It often results in language mixing, a.k. a. code-mixing, when a multilingual speaker switches between multiple languages in a single utterance of a text or speech. This paper discusses the current state of the NLP research, limitations, and foreseeable pitfalls in addressing five real-world applications for social good crisis management, healthcare, political campaigning, fake news, and hate speech for multilingual societies. We also propose futuristic datasets, models, and tools that can significantly advance the current research in multilingual NLP applications for the societal good. As a representative example, we consider English-Hindi code-mixing but draw similar inferences for other language pairs
Weakly-Supervised Photo-realistic Texture Generation for 3D Face Reconstruction

Although much progress has been made recently in 3D face reconstruction, most previous work has been devoted to predicting accurate and fine-grained 3D shapes. In contrast, relatively little work has focused on generating high-fidelity face textures. Compared with the prosperity of photo-realistic 2D face image generation, high-fidelity 3D face texture generation has yet to be studied. In this paper, we propose a novel UV map generation model that predicts the UV map from a single face image. The model consists of a UV sampler and a UV generator. By selectively sampling the input face image's pixels and adjusting their relative locations, the UV sampler generates an incomplete UV map that could faithfully reconstruct the original face. Missing textures in the incomplete UV map are further full-filled by the UV generator. The training is based on pseudo ground truth blended by the 3DMM texture and the input face texture, thus weakly supervised. To deal with the artifacts in the imperfect pseudo UV map, multiple UV map and face image discriminators are leveraged.