Miracle-dz/UniFAR-FTUs huggingface dataset:
Survey on Federated Learning Towards Privacy Preserving AI

One of the significant challenges of Artificial Intelligence (AI) and Machine learning models is to preserve data privacy and to ensure data security. Addressing this problem lead to the application of Federated Learning (FL) mechanism towards preserving data privacy. Preserving user privacy in the European Union (EU) has to abide by the General Data Protection Regulation (GDPR). Therefore, exploring the machine learning models for preserving data privacy has to take into consideration of GDPR. In this paper, we present in detail understanding of Federated Machine Learning, various federated architectures along with different privacy-preserving mechanisms. The main goal of this survey work is to highlight the existing privacy techniques and also propose applications of Federated Learning in Industries. Finally, we also depict how Federated Learning is an emerging area of future research that would bring a new era in AI and Machine learning.
Basis-Finding Algorithm for Decoding Fountain Codes with Erroneous Received Symbols

Motivated by the application of fountain codes in the DNA-based data storage systems, in this paper, we consider the decoding of fountain codes for a channel where each received symbol has a fixed probability $p_0$ to be correct. This channel is similar to a binary symmetric channel (BSC). We propose an efficient decoding algorithm, namely the basis-finding algorithm (BFA). The key idea of the BFA is to find a basis of the received symbols, and then use the most reliable basis elements to recover the source symbols with the inactivation decoding. The Gaussian elimination (GE) is used to find the basis and to identify the most reliable basis elements. As a result, the BFA has polynomial time complexity. For random fountain codes, we are able to derive some theoretical bounds for the frame error rate (FER) of the BFA. Extensive simulations with Luby transform (LT) codes show that for the considered channel, the BFA has significantly better FER than the belief propagation (BP) algorithm except for $p_0 < 1$ and extremely large overheads.
Visual-Semantic-Pose Graph Mixture Networks for Human-Object Interaction Detection

Human-Object Interaction (HOI) Detection infers the action predicate on a triplet. Whilst contextual information has been found critical in this task, even with the advent of deep learning, researchers still grapple to understand how to best leverage contextual cues for inference. What is the best way to integrate visual, spatial, semantic, and pose information? Many works have used a subset of cues or limited their analysis to single subject-object pair for inference. Few works have studied the disambiguating contribution of subsidiary relations made available via graph networks. In this work, we contribute a two-stream (multi-branched) network that effectively aggregates a series of contextual cues. In a first study, we propose a dual graph attention network to dynamically aggregate the visual, instance spatial, and semantic cues from primary subject-object relations as well as subsidiary ones to enhance inference. Subsequently, we incorporate human pose features and propose a second network stream that runs a pose-based modular network. The latter is composed of dual branches that run a graph convolutional network and multi-layer perceptrons to improve detection in crowded scenes. The result is a graph mixture network that processes a wide set of contextual cues effectively. We call our model: Visual-Semantic-Pose Graph Mixture Networks (VSP-GMNs). Our final model outperforms state-of-the-art on the challenging HICO-DET dataset by significant margins of almost 10%, especially in long-tail cases that are harder to interpret. We also achieve a competitive performance on the smaller V-COCO dataset. Code, video, and supplementary material information are available at www.juanrojas. net/VSPGMN.
Models that learn how humans learn: the case of depression and bipolar disorders

Computational models of learning and decision-making processes in the brain play an important role in many domains. Such models typically have a constrained structure and make specific assumptions about the underlying human learning processes; these may make them underfit observed behaviours. Here we suggest an alternative method based on learning-to-learn approaches, using recurrent neural networks (RNNs) as a flexible family of models that have sufficient capacity to represent the complex learning and decision-making strategies used by humans. In this approach, an RNN is trained to predict the next action that a subject will take in a decision-making task, and in this way, learns to imitate the processes underlying subjects’ choices and their learning abilities. We demonstrate the benefits of this approach with a new dataset containing behaviour of uni-polar depression (n=34), bipolar (n=33) and control (n=34) participants in a two-armed bandit task. The results indicate that the new approach is better than baseline reinforcement-learning methods in terms of overall performance and its capacity to predict subjects’ choices. We show that the model can be interpreted using off-policy simulations, and thereby provide a novel clustering of subjects’ learning processes – something that often eludes traditional approaches to modelling and behavioural analysis.
Rain Code: Multi-Frame Based Forecasting Spatiotemporal Precipitation Using ConvLSTM

Recently, flood damage has become a social problem owing to unexperienced weather conditions arising from climate change. An immediate response to heavy rain and high water levels is important for the mitigation of casualties and economic losses and also for rapid recovery. Spatiotemporal precipitation forecasts may enhance the accuracy of dam inflow prediction, more than 6 hours forward for flood damage mitigation. This paper proposes a rain-code approach for spatiotemporal precipitation forecasting. We propose a novel rainy feature that represents a temporal rainy process using multi-frame fusion for timestep reduction. We perform rain-code studies with various term ranges based on spatiotemporal precipitation forecasting using the standard ConvLSTM. We applied to a dam region within the Japanese rainy term hourly precipitation data, under 2006 to 2019 approximately 127 thousands hours, every year from May to October. We apply the radar analysis hourly data on the central broader region with an area of 136 x 148 km2 , based on new data fusion rain code with multi-frame sequences. Finally we got some evidences and capabilities for strengthen forecasting range.
Relationship between Information Literacy Skills Acquisition and Research Self-efficacy of Library and Information Science Postgraduate students in Southeast Nigerian Universities

The study investigated the relationship between information literacy skills acquisition and research self-efficacy of Library and Information Science (LIS) postgraduate students in Southeast Nigerian Universities. A correlational research design was adopted for the study. The population comprised 326 postgraduate students which included all 2017/2018 and 2018/2019 Ph.D. and Masters Degree students from the Departments of LIS in Southeast Nigerian universities that offer the postgraduate programme in LIS. The entire population was studied. Two validated instruments which included cognitive ability test for Information Literacy Skills (ILST) and Research Self-Efficacy Scale (RSES) adopted from Büyüköztürk, Atalay, Sozgunc, and Kebapçı were used for data collection. The internal consistency of ILST and RSES were established using Kudder-Richardson and Cronbach's alpha coefficient which yielded 0.85 for ILST and 0.86 for RSES. Data collected were analyzed using Pearson’s Product Moment Correlation (r). The study found out that there is a negative relationship between information literacy skills and research self-efficacy scores of LIS postgraduate students. Also, no significant relationship exists between information literacy skills and research self-efficacy scores of LIS postgraduate students. Based on the findings, it was recommended among others that the assessment of students’ information literacy skills by the LIS Department, LIS educators, and LIS professionals should be employed frequently. This will help determine their strength and weakness and with that, the students will be able to know their stand which will encourage them to acquire these skills where it is lacking as well as instill confidence in their ability to conduct research.
IMPLEMENTATION OF GAMIFICATION IN COMPUTING SUBJECTS: A COMPARISON STUDY

Gamification, which involves the use of game elements in non-game contexts, has been gaining increasing popularity and is being applied in various domains of studies in ‘Computing Education’. However, not many studies have been carried out in terms of the application of gamification in technical and non-technical subjects within computing education. Taking this in view, this paper will investigate the impact of gamification on student engagement, motivation and student learning in two introductory computing subjects, which are Programming (technical) and System Analysis (non-technical). A comparison study would be carried out to study the effect of implementation of gamification for these two different types of subjects using various software applications. Some best practices in gamification such as using badges, levels, providing feedback, rewards and team collaboration would be applied. In order to measure the implementation results, online surveys, focus group interviews and observations would be conducted to answer the research question.
GENERATIVE MODEL FOR SPACECRAFT IMAGE SYNTHESIS USING LIMITED DATASET

This work presents for the ﬁrst time a conditional Generative Adversarial Network (GAN) to sample arbitrary high-ﬁdelity spacecraft images from the learned distribution of spacecraft texture and illumination conditions (i.e., styles ). The proposed SPEEDGAN utilizes a low-texture template of the spacecraft to empower SPEEDGAN with a priori knowledge of the spacecraft geometry and pose, allowing the model to focus on creating a spacecraft style. The SPEEDGAN also trains with a content loss from style transfer literature to improve the structural ﬁdelity of the generated spacecraft images. Trained on a limited dataset containing images from the computer graphics renderer and the hardware-in-the-loop simulation facility, SPEEDGAN generates samples with remarkable visual qualities, measured by both human and a separate neural network for pose estimation. It is also capable of separated control over different style aspects, such as spacecraft texture and illumination effects.
Characterization of non-ferrous metal powders

With the extended use of additive manufacturing in the past few years, powder metallurgy is quickly acquiring a more important role in world industry. Because the metal powder represents the initial material for most of additive-manufacturing processes, the properties of final products are directly influenced by the powder properties. Besides the product properties, the chosen process parameters are based on the characteristics of the powder. In this work the physical and thermal properties as well as the microstructure of different non-ferrous metal powders were investigated. The flow rate, tap and apparent density, angle of repose, compression factor and Hauser ratio were measured or calculated. The microstructures of separate metal powder particles were observed with a light microscope. The particles’ shape, size and roughness were investigated with a scanning electron microscope and the correlations with the determined physical properties were found. From the heating and cooling cycle curves of different scanning calorimetries the liquid and solidification temperatures were determined and the influence of the chemical composition on them was observed.
A New Context Driven Page Ranking Algorithm

-The size of web is growing day by day. People rely on search engines to find the information on the web. Now it is a challenge for the search engine to provide quality and relevant information to the information seekers. In search engines, the quality and relevancy of the results depends upon the performance of page ranking algorithms. In this paper a new algorithm for page ranking has been proposed. The proposed algorithm is ranking the searched results on the basis of the context of the user query. The algorithm works in two steps, step 1 is to find the context of user query with the help of thesaurus. In step-2 the context of different documents is also determined with the help of thesaurus. Then only those documents which are in the context of user query are ranked and returned for the query result. The number of documents ranked by the ranking algorithm will get reduced by matching the context of the query and context of the documents. This decrease in no of documents ranked will improve the precision and recall values of the search results.
E –BUTTON BASED PERSONAL DIETARY ASSESSMENT

-Image based dietary assessment has recently received much attention in the community of nutrition and obesity research. The food intake of every individual is measured in terms of calorie which caters the energy of food. Recently, the dietary assessment becomes a challenging trend which is majorly based on self-reported data of patients. Manual report of dietary data is time consuming, so image processing holds the promising way to the automatic detection of dietary foods. This study deals the problem of image segmentation on images acquired by E - Button. Segmentation is a challenging task where each image varies in their shape, size and its texture. Initially, the segmentation is applied with input image which determines the regions where a particular food item is located using salient region detection, multi scale segmentation and fast rejection. After the features are obtained, the classification is performed for each segmented region using Sphere Shaped SVM model. Finally, the area and its volume are computed for every food item. It attains better accuracy when compared with the other methods.
Studying genetic antagonisms as drivers of genome evolution

L13 ‘Moved’ seems a bit unspecific in this sentence, I would replace with extended PAR. P3 L12 I’m not aware of any direct evidence that recombination between the primate X and Y stopped instantaneously as ‘at once’ suggests. I would remove this. P3 L14 I am not sure what process is referred to in this sentence. P7 L12 I believe Wright et al showed extension of recombination suppression not additional strata P7 L17
An Automated Mechanism Design Approach for Sponsored Search Auctions with Federated Search Engines

Advertising mechanisms for search engines (i.e., sponsored search auctions) have recently received a lot of attention in the scientiﬁc community. Advertisers bid on keywords and, when a user enters key-words for her search, the search engines uses an auction mechanism to select the list of sponsored links to display alongside the search results. In this paper, we make a ﬁrst attempt to extend the currently available mechanisms for sponsored search auctions to the new paradigms of search computing. According to them, multiple federated domain-speciﬁc search engines are integrated by a special search engine (called integrator). The user can enter a multi-domain query that is decomposed by the integrator in single-domain queries and these are singularly addressed to the most appropriate domain-speciﬁc search engine. The integrator merges the search results. We propose a business model for this scenario and we develop an economic mechanism for it resorting to the automated mechanism design approach.
Morphological and Structural Properties of Zinc Oxide Films by Sol-gel Spin Coating Technique

Zinc oxide (ZnO) is n-type II-VI semiconductor compounds, which have technical applications such as thin film gas sensors [1], transistors [2], light emitting diodes [3] and solar cells. [4] ZnO film was used as a surface acoustic wave (SAW) devices and film bulk acoustic resonator (FBAR) due to its excellent piezoelectric properties [5]. Moreover, ZnO has a large band gap (3.37 eV) and large exciton binding energy (60 meV) and it consists of a hexagonal wurtzite structure with unit cell shape = 0.325 nm and c = 0.521 nm because of their unique properties of semiconductors, ZnO films bone behind the base of the electronics industry and modern technology. Various synthesis techniques have been used to synthesize ZnO films such as chemical vapour deposition (CVD) [6], molecular beam epitaxy (MBE) [7], pulsed laser deposition (PLD) and sol-gel method [8]. Among the methods, sol-gel method is widely adopted for the fabrication of ZnO films due to it is an inherently lowcost and simple liquid-phase deposition method. In addition, the chemicals required for sol-gel is relatively inexpensive, and easy to prepare. On the other hand, synthesis techniques play a significant role in controlling the properties of ZnO films. In fact, the electrical and optical properties of the films strongly depend on the structure and morphology of the films. In order to produce high quality ZnO film, it is necessary to study the parameters that can affect the properties of the films, such as preheating and annealing temperature, thickness, sol concentration and etc. In this present work, ZnO films have been prepared by the sol-gel spin coating using different monoethanolamine (MEA) volume and their surface morphologies and structural properties were studied in detail.
INTER-CORRELATIONS AMONG EMOTIONAL INTELLIGENCE, ROLE CONFLICT, OCCUPATIONAL STRESS, DEPRESSION AND ADJUSTMENT AMONG WOMEN EMPLOYEES OF DIFFERENT PROFESSIONS IN HARYANA

The present study is oriented towards finding out relationship between Emotional Intelligence of the women employees and the degree of Role Conflict, Occupational Stress, Depression and Adjustment being faced by them. As emotional intelligence plays a dominant role in deciding degree of Role Conflict, Occupational Stress, Depression and Adjustment among working women. The study was conducted involving 210 working women in the age group of 3045 years of Govt. sector of Haryana. Tests were administered in three session. Correlation Analysis has been used to interpret the data collected. The correlation of Emotional Intelligence was found negative with variable of Role Conflict, Occupational Stress, Depression and Adjustment was negative in case of all the categories of women employees. The correlation between the variables of Role conflict and Occupational Stress was positive in case of all the categories of women employees. The correlation between the variables of Role Conflict and Depression was positive in case of administrators and doctors excepting lecturers The correlation between the variables of Role Conflict and Adjustment was positive in case of all the categories of women employees. The correlation between the variables of Occupational Stress and Depression was positive in case of all the categories of women employees. The correlation between the variables of Occupational Stress and Adjustment was positive in case of all the categories of women employee. The correlation between the variables of Depression and Total Adjustment was positive in case of all the categories of women employees.
Cognitive Intelligent Equipment Design In Context-Aware Cloud Robotics For Material Handling Using In Cognitive Industrial Internet Of Things

In the context of Industry 4.0, industrial robotics, for example, AGVs (Automated Guided Vehicles) have attracted increased consideration because of their mechanization capacities and minimal effort. With the help of cognitive advances for industrial Internet of Things (IoT), creation procedures can be essentially streamlined and progressively intelligent manufacturing can be actualized for brilliant production lines. In this paper, for cutting edge material handling, a cognitive industrial substance called Context-Aware Cloud Robotics (CACR) are introduced and dissected. Contrasted and the One-Time On-Demand Delivery (OTODD), CACR is portrayed by two highlights: context-aware administrations, and compelling burden balancing. In the first place, the system design, focal points, difficulties, and applications for CACR are introduced. At that point, essential capacities for material handling are verbalized, in particular, basic leadership instruments and cloud-empowered synchronous restriction and mapping. Finally, a CACR contextual investigation is performed to feature its vitality effective and cost-saving material handling abilities. Reproductions indicate the predominance of cognitive industrial IoT and demonstrate that using CACR for material handling can altogether enhance vitality effectiveness and spare expense.
Patientself-Controllable and Multilevel Protection Safeguarding Confirmation in Distributed Computing

Presently a days the could computing. in this framework Distribution w-human services clinics or focuses in distributed computing framework is more useful for taking care of the issue encourages proficient patient treatment for therapeutic focuses and other meeting accomplices by sharing individual wellbeing data or individual medical issues through the distributed storage framework, in that among social insurance officers or representatives they are more useful for as. Be that as it may, it achieves the test of keeping both the information productively or greater security by the significant data and patients' personality protection at the same time they are giving area capable or related answers for the patient or clients. Numerous old accomplices get to control and interesting verification process can't be straight forwardly settled. in this paper we are giving their district capable or capable data who are enduring or utilizing this framework. a worldwide approved open security demonstrate (GAPM) is built up and presented for all the entrance control accomplices . Clients can approve specialists by setting an entrance tree giving outcast or related data. At that point, in view of it, by erasing another technique for trait based approach eerier mark, a patient can act naturally controllable multi-level or greater availability protection saving helpful validation process getting more measure of highlights and process or three levels of security and protection prerequisite in dispersed w-social insurance distributed computing framework is presented. We are giving new open key administration framework, which items the consistent size significant messages to such an extent that tried and true distribution of unscrambling for any arrangement of modules writings is likely or competent for getting the data . In this paper we presenting some viable secure examination of our creations or related data can be dissected toss the clients issue with compelling way . by this standard model and giving greater security to encode information records. which was
Methodology to aid in the decision-making of retrofit solutions in the residential sector using calibrated building energy simulation models

This work involves firstly, the calibration of the Building Energy Simulation (BES) model of one Portuguese residential household representative of a large share of households in Lisbon, which is known by the occupants as having poor thermal conditions (i.e. thermal comfort). After the uncertain parameters of the model are calibrated with a singleobjective optimization using the genetic algorithm, more accurate predictions of the indoor air temperature can be performed, increasing the confidence on the thermal comfort.