Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving Scalability in Multi-Robot Systems with Abstraction and Specialization

Planning and controlling multi-robot systems is challenging due to high dimensionality. When the number of robots increases in the system, the complexity of computation grows exponentially. In this thesis, we examine the scalability problem in planning and control of the multi-robot system. We propose two ideas to improve the scalability of multi-robot systems: abstraction and specialization. For planning with abstraction, we propose a Hybrid Hierarchical Partially Observable Markov Decision Processes (POMDPs) structure for improved scalability and efficiency in an indoor environment that creates abstract states of convex hulls over the grid environment. We focus our application on the problem of pursuitevasion with multiple pursuers and one evader whose location is unknown if not visible to the pursuers. This approach is scalable that it significantly reduces the number of states expanded in the policy tree to solve the problem by abstracting environment structures. Specialization is important for systems with a large number of robots. Connectivity maintenance is essential for collaborative behaviors since robots need to communicate with neighbor robots to share states and information. It is inefficient for a robot to both attend to task behaviors and also maintain connectivity. Therefore, we introduce the idea of connection robots, whose goal is to maintain connectivity of the robot team by correcting the topology of the connectivity graph so as to provide flexibility for the task robots to perform task behaviors. We propose a scalable distributed approach of topology correction that is able to guarantee a faster convergence rate with response to dynamic environment and tasks.
SPACE EFFICIENT IMAGE STEGANOGRAPHY : A NOVEL DATA HIDING SCHEME FOR TAMIL TEXT DOCUMENT

Hiding secret information in a cover, which is of same type or different type is called Steganography. The main goal of the Steganography is that the sharing of secret information should not be shown to the intermediaries during communication. Several algorithms have been developed having different kinds of cover media, such as text, image, audio or video. We propose an approach to convert a secret message, which is available as Tamil text document into a gray-scale image that can be created of any size based on the size of the secret message by the sender, and that is transmitted over communication channel. At the hiding end, the characters in the tamil text document are encoded after encryption, then the image is scanned row by row, and in each non black pixel four characters are hidden. Only if the color of a pixel is not black, then the characters are hidden into it. At the extraction end, the characters are extracted after finding that the pixel is not in black color. This proposed method shows an agreeable experimental result with the cover image chosen. The efficiency of this proposed method is that the size of the stego image is lesser than the size of the secret message. Security can be increased by encrypting the secret text before embedding into the cover image.
SELF-SELECTION BIAS MODEL : AN APPLICATION OF TWO-STAGE SWITCHING REGRESSION TO SFAS NO . 19

Banyak penelitian akuntansi yang melibatkan pemisahan sampel menjadi beberapa grup atas dasar metoda akuntansi. Kemudian peneliti membandingkan karakteristik antargrup dan menguji perbedaannya secara statistis dan regresional. Dalam memecah sampel menjadi beberapa grup, pada umumnya diasumsi bahwa dua sampel atau lebih diambil dari populasi yang homogenus dan penempatan observasi ke dalam grup dianngap terjadi secara random. Dengan asumsi tersebut bias seleksi (selection bias) diabaikan sehingga dapat mengakibatkan estimasi lebih (overestimation) koefisien regresi pada salah satu grup sehingga simpulan dapat salah.
Guidelines to Understanding Design of Experiment and Reliability Prediction

This paper will focus on how to plan experiments effectively and how to analyse data correctly. Practical and correct methods for analysing data from life testing will also be provided. This paper gives an extensive overview of reliability issues, definitions and prediction methods currently used in the industry. It defines different methods and correlations between these methods in order to make reliability comparison statements from different manufacturers’ in easy way that may use different prediction methods and databases for failure rates. The paper finds however such comparison very difficult and risky unless the conditions for the reliability statements are scrutinized and analysed in detail.
Stabilizing switched TS fuzzy systems using a fuzzy Lyapunov function approach ⋆

In this work, sufficient conditions for the existence of switching laws for stabilizing switched T-S fuzzy systems via a fuzzy Lyapunov function are proposed. The conditions are found by exploring properties of the membership functions and are formulated in terms of linear matrix inequalities (LMIs). A state feedback design is also used to extend the applicability of the results. Numerical examples illustrate the effectiveness of the proposed design methods.
Learning Size and Structure of Document Ontologies using Generative Topic Models

We look into the problem of learning the size and structure of an interlinked hierarchy of topics which best describes a given document corpus. With increasing amount of text content being generated today, it has become important to develop techniques for learning ontologies which categorize documents into semantically meaningful classes. The ontology is best represented as a DAG to capture correlations and links between topics. We propose a method of estimating the number of topics in each layer of the topic hierarchy using agglomerative clustering. We use generative topic models (such as Latent Dirichlet Allocation [2]) to discover topics and link them to form an ontology. The links are made by finding subsumption relations between topics in consecutive layers. Our method uses a variant of the algorithm proposed in [10]. We validate our method on real-world document corpora and present the results.
BAM: A combination of deep and shallow models for German Dialect Identification.

*This is a submission for the Third VarDial Evaluation Campaign* In this paper, we present a machine learning approach for the German Dialect Identification (GDI) Closed Shared Task of the DSL 2019 Challenge. The proposed approach combines deep and shallow models, by applying a voting scheme on the outputs resulted from a Character-level Convolutional Neural Networks (Char-CNN), a Long Short-Term Memory (LSTM) network, and a model based on String Kernels. The first model used is the Char-CNN model that merges multiple convolutions computed with kernels of different sizes. The second model is the LSTM network which applies a global max pooling over the returned sequences over time. Both models pass the activation maps to two fully-connected layers. The final model is based on String Kernels, computed on character p-grams extracted from speech transcripts. The model combines two blended kernel functions, one is the presence bits kernel, and the other is the intersection kernel. The empirical results obtained in the shared task prove that the approach can achieve good results. The system proposed in this paper obtained the fourth place with a macro-F1 score of 62.55%
Scientific Opinion on monitoring procedures at slaughterhouses for poultry

This scientific opinion proposes toolboxes of welfare indicators, and their corresponding outcomes of consciousness, unconsciousness or death, for developing monitoring procedures at slaughterhouses for poultry stunned using electrical waterbaths and gas mixtures or slaughtered without stunning. For waterbath stunning, the opinion proposes a toolbox of indicators for assessing consciousness in poultry at two key stages of monitoring: (a) between the exit from the waterbath stunner and neck cutting and (b) during bleeding. For gas stunning, the opinion proposes a toolbox of indicators for assessing consciousness in poultry at two key stages of monitoring: (a) during shackling and (b) during bleeding. For slaughter without stunning, a toolbox is proposed for confirming death prior to entering scald tanks. Various activities—including a systematic literature review, an online survey and stakeholders‘ and hearing experts‘ meetings—were conducted to gather information about the specificity, sensitivity and feasibility of the indicators. On the basis of such information, a methodology was developed to select the most appropriate indicators to be used in the monitoring procedures. The frequency of checking differs according to the role of each person with responsibility for ensuring poultry welfare. The personnel will have to check all the birds and confirm that they are not conscious following stunning with electrical waterbaths or gas mixtures and that they are dead before entering scald tanks. For the animal welfare officer, a mathematical model for the sampling protocols is proposed, giving some allowance to set the sample size of birds that he/she needs to check at a given throughput rate (total number of birds slaughtered in the slaughterhouses) and threshold failure rate (number of potential failures—birds that are conscious after stunning). Finally, different risk factors and scenarios are proposed to define a ̳normal‘ or a ̳reinforced‘ monitoring protocol, according to the needs of the slaughterhouse. © European Food Safety Authority, 2013
Context-aware food recommendation system

Recommendation systems are commonly used in websites with large datasets, frequently used in e-commerce or multimedia streaming services. These systems effectively help users in the task of finding items of their interest, while also being helpful from the perspective of the service or product provider. However, successful applications to other domains are less common, and the number of personalized food recommendation systems is surprisingly small although this particular domain could benefit significantly from recommendation knowledge. This work proposes a contextaware food recommendation system for well-being care applications, using mobile devices, beacons, medical records and a recommender engine. Users passing near a food place receives food recommendation based on available offers order by appropriate foods for everyone’s health at the table in real time. We also use a new robust recipe recommendation method based on matrix factorization and feature engineering, both supported by contextual information and statistical aggregation of information from users and items. The results got from the application of this method to three heterogeneous datasets of recipe’s user ratings, showed that gains are achieved regarding recommendation performance independently of the dataset size, the items textual properties or even the rating values distribution.
Determinants of Population Growth Trends for Tanzanian Small Towns

The largest share of urbanisation in Sub Saharan Africa is taking place in settlements that are smaller than cities. However, these small urban settlements are conceptually neglected and mostly mistheorised as overriding land governance concepts and institutional procedures were designed for large urban centres or the extreme opposite, villages. As a result, there is either a total lack of specific policies on small urban centres or the policies are designed wrongly thus contributing to challenges of informality, poor servicing and environment degradations. This paper, using the Tanzanian case, is an attempt to contribute in understanding the dynamics of small towns through establishing the population thresholds that can appropriately be classified as small towns and the factors driving their growth. The study analyses census data spatially using geographical information system and statistical software. The results indicate that the typical size of small towns where polarisation forces still gravitate towards the town has a number of people between 10,000 and 50,000 people with population density at the core of the settlements ranging from 40 to 120 people per hectare. The major factors for the development are the presence of economic activities that have value addition options contributing to off-farm employment, and the typical radius of its hinterland for each small town is about a one hour drive. As the number of small towns continues to grow in Sub Saharan Africa due to continued polarisation forces, policies and interventions for the management of small have to be pre-emptive and anticipatory.
Effect of Pasteurization Methods on Enzyme Activities, Microbial and Sensory Evaluations in Ready to Serve Watermelon Juice (Citrullus lanatus)

A comparative evaluation of the use of thermal, microwave and irradiation treatments for pasteurization of ready to serve watermelon juice was undertaken to study their relative impact on residual enzyme (polyphenol oxidase and peroxidase) activities microbial population, and sensorial changes during its refrigerated storage for a period of three months. Pasteurization using thermal and microwave treatments could effectively control the microbial load within the acceptable limit (<1 log cfu mL−1) over the entire three months of storage. Irradiation treatment of 0.5kGy resulted in the lowest residual polyphenol oxidase (RAPPO) activity followed by the microwave (1.56W/ml) and thermal treatments. However, maximum reduction in peroxidase activity (RAPOD) was achieved using thermal and microwave treatments. At the end of the three months storage, watermelon juice pasteurized using microwave energy was sensorially found to be the most acceptable product.
Comparative Plasma Proteomics in Muscle Atrophy Induced by Cancer Cachexia and Hindlimb Unloading

Introduction: Muscle atrophy results from a dysfunction in protein turnover that leads to loss of mass and function and occurs concurrently with multiple pathologies such as cancer and extended bed rest. Atrophy reduces overall quality of life while increasing morbidity and mortality. Currently, efficacious therapeutic interventions to treat and prevent muscle wasting in all its forms are lacking, however if conserved mechanisms can be identified between wasting conditions, this would aid in the development of multipurpose therapeutics to ameliorate this pathology. Purpose: To examine circulating factors present across atrophic pathologies. Methods: 35 male C57BL/6J mice were assigned to hindlimb unloading (HU), Lewis Lung Carcinoma (LLC), or control groups. HU animals were hindlimb-unloaded for 1 week to induce disuse atrophy. LLC cells were injected into the right hind-flank to induce cancer cachexia. Tumors developed for approximately 4 weeks. Age-matched mice were humanely euthanized after experimental intervention. Muscle and plasma were collected for analysis. Plasma was analyzed by tandem mass tag (TMT) proteomics. Results: Raw plantaris weight was significantly lower in LLC (22.7%) and HU (18.6%) groups compared to control (p 0.05). Equivalent tibia sizes across groups suggest no significant differences in body size. In the LLC-HU comparison, 104 proteins were differentially expressed (DE). 44 were up-regulated and 60 were down-regulated. In the LLC-CON comparison, 91 DE proteins were observed. Of these, 39 were up-regulated and 52 were down-regulated. In the HU-CON comparison, 5 DE proteins were up-regulated and 4 were down-regulated; a total of 9 DE proteins were observed. Several signaling pathways were implicated in this analysis including the acute phase response signaling system and liver X receptor/retinoid X receptor (LXR/RXR) and farnesoid X receptor/retinoid X receptor (FXR/RXR) activation. Conclusion: Plasma proteomic profiles are altered in cancer cachexia and disuse-induced atrophy. Modulated protein profiles suggest the possibility of multi-tissue involvement in muscle atrophy. Future experiments are required to understand how plasma proteins and muscle interact and to determine the mechanisms of possible novel atrokines observed herein.
Semi Supervised Collaborative Image Retrieval Using Binary Classifier

Content Based Image Retrieval (CBIR) is commonly used in image processing. But the accuracy of CBIR system is not very high. To improve the performance of CBIR system Relevance feedback system can be used. In relevance feedback system the user refines the search results progressively by marking images in the results as "relevant", "irrelevant", or "neutral" to the search query and then repeating the search with the new information. In many cases there may be large number of images to label. Most of the times user would not like to label a large number of images. So in this paper we are using semi-supervised method. This means the user needs to label only few most informative images. These labeled images are then used as training set for SVM classifier. Then images in database are resorted based on new similarity metric. If the user is satisfied with the results, Relevance feedback is no longer required and the system gives the final results. These results are most semantically relevant to the query image. Thus the image retrieval process is ended. Otherwise, Relevance Feedback will be performed iteratively. Key WordsContent Based Image Retrieval(CBIR), Semantic Gap, Relevance Feedback(RF), Feature Selection, Binary Classifier, Precision, Recall.
Supplementary Material for ‘ Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning ’

In the V0-LSTM and V1-LSTM BAD agents, all observations were first processed by an MLP with a single 256-unit hidden layer and ReLU activations, then fed into a 2-layer LSTM with 256 units in each layer. The policy π was a softmax readout of the LSTM output. The baseline network was an MLP with a single 256-unit hidden layer and ReLU activations, which then projected linearly to a single value. Since the baseline network is only used to compute gradient updates, we followed Foerster et al. (2018) in feeding each agent’s own hand (i.e., the other agent’s private observation) into the baseline by concatenating it with the LSTM output; thus we make the common assumption of centralised training and decentralised execution. We note that the V0 and V1-LSTM agents differed only in their public belief inputs.
Virtual Machine Extrospection : A Reverse Information Retrieval in Clouds 1

In a virtualized environment, it is not difficult to retrieve guest OS information from its hypervisor. However, it is very challenging to retrieve information in the reverse direction, i.e., retrieve the hypervisor information from within a guest OS, which remains an open problem and has not yet been comprehensively studied before. In this paper, we take the initiative and study this reverse information retrieval problem. In particular, we investigate how to determine the host OS kernel version from within a guest OS. We observe that modern commodity hypervisors introduce new features and bug fixes in almost every new release. Thus, by carefully analyzing the seven-year evolution of Linux KVM development (including 3485 patches), we can identify 19 features and 20 bugs in the hypervisor detectable from within a guest OS. Building on our detection of these features and bugs, we present a novel framework called Hyperprobe that for the first time enables users in a guest OS to automatically detect the underlying host OS kernel version in a few minutes. We implement a prototype of Hyperprobe and evaluate its effectiveness in six real world clouds, including Google Compute Engine (a.k.a. Google Cloud), HP Helion Public Cloud, ElasticHosts, Joyent Cloud, CloudSigma, and VULTR, as well as in a controlled testbed environment, all yielding
The Effects of Biopesticide on the Antioxidant Enzyme Activities of Lemna minor

It has become inevitable to use pesticides in order to get more efficiency from the unit area in agricultural activities all over the world. Initially, the use of these chemicals was welcomed and later revealed that these compounds had many negative effects on the environment and human health. Pesticides can stay around for years without degradation. Pesticides that are once transmitted to the ecosystem, cause accumulation through the aquatic nutrient chain and this accumulation is increasing in the upper levels of the food chain [1]. The use of pesticides causes serious problems on human health and environment. Pesticides effect the organism by blocking cellular processes and causing changes in the cell. Some pesticides’ residual effect lasts for days, weeks or months. These effects of the pesticides vary according to the chemical properties of the compound, the amount of concentration, temperature, humidity, pH and microorganism activities.
Subjective Experience of Plan-Based Intention Revision

Creating a story branch in response to unexpected user actions in an interactive narrative is considered a desirable feature of an experience manager. Adapting to the user in this manner aspires to improve his or her experience. However, few robust mechanisms exist to create story branches and there is a lack of studies that measure a story branch’s effect on experience. Our work addresses these limitations by using plan-based intention revision as a branching mechanism and evaluates it using established measures of narrative experience. Our results show that when measuring narrative transportation, we observe a significant main effect for group F(1, 652) = 10.74, p < . 01 where individuals in the treatment condition (M = 50.70; SD = 12.81) who read narrative texts that included intention revisions reported higher levels of narrative transportation compared to individuals in the control group (M = 47.43; SD = 12.92). When measuring perceived interest, we observed a significant main effect of group F(1, 652) = 6.43, p < . 05, where individuals in the treatment condition (M = 28.51; SD = 8.97) who read narrative texts that included intention revisions reported higher levels of perceived interest compared to individuals in the control group (M = 26.67; SD = 9.22). These results establish the efficacy of using intention revision as a branching mechanism for intentional planning and invites its extension to interactive narrative.
Benchmarking Sampling-based Probabilistic Object Detectors

This paper provides the first benchmark for sampling-based probabilistic object detectors. A probabilistic objectdetector expresses uncertainty for all detections that reli-ably indicates object localisation and classification perfor-mance. We compare performance for two sampling-baseduncertainty techniques, namely Monte Carlo Dropout andDeep Ensembles, when implemented into one-stage andtwo-stage object detectors, Single Shot MultiBox Detectorand Faster R-CNN. Our results show that Deep Ensemblesoutperform MC Dropout for both types of detectors. We alsointroduce a new merging strategy for sampling-based tech-niques and one-stage object detectors. We show this novelmerging strategy has competitive performance with previ-ously established strategies, while only having one free pa-rameter
Liquidity Risks and Expected REIT Returns

Liquidity measure such as bid-ask spread is an important factor in determining the required rate of return of a security. Since the investment targets of REITs are mostly related to real estates, the REIT markets, in particular, are subject to the influence of asset liquidity to a greater extent. This study investigates whether the liquidity factor is a significant variable in pricing the REIT investment instruments. The findings herein should be helpful for understanding and further developing the REIT markets.