Miracle-dz/UniFAR-FTUs huggingface dataset:
Motion planning for a redundant manipulator by genetic algorithm

A motion planning method is proposed for cutting a three dimensional workpiece by a redundant manipulator with six degrees of freedom. The method applies a genetic algorithm to optimize the rotational angles of the end-effector on a path. For a fitness function, an evaluation function is defined based on references from skilled operators. Using the proposed method the operator only has to determine a path without considering the redundant parameters. Simulations show the effectiveness of the proposed method. <<ETX>>
Other-Condemning Anger = Blaming Accountable Agents for Unattainable Desires (Extended Abstract)

This paper focuses on the other-condemning anger emotion which is a social type of anger triggered by the behaviour of other agents. Other-condemning anger responds to frustration of committed goals by others, and motivates goal-congruent behavior towards the blameworthy agents. Understanding this type of anger is crucial for modelling human behavior in social settings as well as designing socially aware artificial systems. We summarize some existing psychological theories on other-condemning anger and advocate building logical frameworks to formally specify this emotion. We believe that a formalization should provide a precise conceptualization and characterization of other-condemning anger in terms of social and cognitive concepts such as beliefs, goals, intentions, controllability, accountability, and blameworthiness.
Imaging and time reversal in random media

Time reversal in random media can result in refocusing on the source with a resolution that is better than the diffraction limit. This is because of the multipathing that occurs in the random medium, making the time reversal array appear to be wider than it is. What is not clear, however, is why this phenomenon is statistically stable. That is, why the refocused spot size does not fluctuate in the random medium. The reason for this statistical stability is that it is a time domain phenomenon and does not occur with narrow band signals. I will explain this effect analytically and will also show how it can be used for imaging in random media.
Practical prefetching via data compression

An important issue that affects response time performance in current OODB and hypertext systems is the I/O involved in moving objects from slow memory to cache. A promising way to tackle this problem is to use prefetching, in which we predict the user's next page requests and get those pages into cache in the background. Current databases perform limited prefetching using techniques derived from older virtual memory systems. A novel idea of using data compression techniques for prefetching was recently advocated in [KrV, ViK], in which prefetchers based on the Lempel-Ziv data compressor (the UNIX compress command) were shown theoretically to be optimal in the limit. In this paper we analyze the practical aspects of using data compression techniques for prefetching. We adapt three well-known data compressors to get three simple, deterministic, and universal prefetchers. We simulate our prefetchers on sequences of page accesses derived from the OO1 and OO7 benchmarks and from CAD applications, and demonstrate significant reductions in fault-rate. We examine the important issues of cache replacement, size of the data structure used by the prefetcher, and problems arising from bursts of “fast” page requests (that leave virtually no time between adjacent requests for prefetching and book keeping). We conclude that prediction for prefetching based on data compression techniques holds great promise.
Scleractinian Corals of Loha Barrack Crocodile Sanctuary, Andaman and Nicobar Islands

Loha Barrack Crocodile Sanctuary is the only protected area out of 96 for conserving crocodiles in its natural habitat at the western side of South Andaman region of Andaman and Nicobar Islands which is surrounded by Bay of Bengal. More than 91% of the sanctuary is covered by marine ecosystem. A total of 146 species of scleractinian corals under 49 genera and 14 families were reported during the present study. The maximum number of 34 species was found under the family Fungiidae which is the nucleus of scleractinian corals reef ecosystem. On the basis of then present study, more conservatory measures can be drawn on scleractinian corals in this sanctuary along with the target animal i.e. crocodile.
Multilingual Neural Machine Translation in Low Resource Settings

Neural Machine Translations (NMT) models are capable of translating a single bilingual pair and require a new model for each new language pair. Multilingual Neural Machine Translation (M-NMT) models are capable of translating multiple language pairs, even pairs which it hasn’t seen before in training. Availability of parallel sentences is a known problem in machine translation. M-NMT model leverages information from all the languages to improve itself and performs better. We propose a data augmentation technique that further improves this model profoundly. The technique helps achieve a jump of more than 15 points in BLEU score from the Multilingual NMT Model. A BLEU score of 36.2 was achieved for Sindhi–English translation, which is higher than any score on the leaderboard of the LoResMT SharedTask at MT Summit 2019, which provided the data for the experiments.
Decision-Based Speci fi cation and Comparison of Table Recognition Algorithms

We wished to compare the Handley [2] and Hu et al. [4]. table structure recognition algorithms and the complete set of table cell hypotheses they each generated, including any rejected in the final result. Rebuilding the systems using procedural code that transformed data structures for interpretations in-place would not have achieved this goal. Initially we translated the strategies to a formal model-based (specifically grammarbased) framework. A well designed model-driven system (such as DMOS by Coüasnon [1]) makes it easier to observe and record decision making, and can be programmed succinctly by a model specification. However, we found mapping the sequence of operations in the strategies to a model based description was difficult, and the formal system required frequent and substantial reconfiguration in order to incorporate unanticipated requirements.
University of Birmingham Blending human and artificial intelligence to support Autistic children’s social communication skills

This paper examines the educational efficacy of a learning environment in which children diagnosed with Autism Spectrum Conditions (ASC) engage in social interactions with an artificially intelligent (AI) virtual agent and where a human practitioner acts in support of the interactions. A multi-site intervention study in schools across the UK was conducted with 29 children with ASC and learning difficulties, aged 4-14 years old. For reasons related to data completeness and amount of exposure to the AI environment, data for 15 children was included in the analysis. The analysis revealed a significant increase in the proportion of social responses made by ASC children to human practitioners. The number of initiations made to human practitioners and to the virtual agent by the ASC children also increased numerically over the course of the sessions. However, due to large individual differences within the ASC group, this did not reach significance. Although no evidence of transfer to the real-world post-test was shown, anecdotal evidence of classroom transfer was reported. The work presented in this paper offers an important contribution to the growing body of research in the context of AI technology design and use for autism intervention in real school contexts. Specifically, the work highlights key methodological challenges and opportunities in this area by leveraging interdisciplinary insights in a way that (i) bridges between educational interventions and intelligent technology design practices, (ii) considers the design of technology as well as the design of its use (context and procedures) on par with one another, and (iii) includes design contributions from different stakeholders, including children with and without ASC diagnosis, educational practitioners and researchers. trials that are focused on ascertaining effectiveness), smaller scale efficacy studies should be done to demonstrate their potential. We argue that our evaluation resides at a ’pre-efficacy’ stage, in which we believe that ECHOES shows promise in that children with both autism and intellectual difficulties engage with the environment, interact with the AI agent and with a human partner, progress through different learning activities and seem to change their behaviour whilst in the environment. However, our results are tempered with caution, given the lack of a direct comparison group (e.g. an ASC wait-list control). Our findings suggest, though, that the type of technology-based intervention adopted in the ECHOES project may result in real behaviour change, even if it is limited to the ECHOES environment. More recent research based on ECHOES by Alcorn 2016 provides supporting evidence for these conclusions.
Blending Human and Artificial Intelligence to Support Autistic Children

KAŚKA PORAYSKA-POMSTA, University College London, UCL Institute of Education, United Kingdom ALYSSA M. ALCORN, University College London, UCL Institute of Education, United Kingdom KATERINA AVRAMIDES, University College London, UCL Institute of Education, United Kingdom SANDRA BEALE, Brickbeck College, Department of Psychological Sciences, United Kingdom SARA BERNARDINI, Department of Computer Science, Royal Holloway, United Kingdom MARY-ELLEN FOSTER, School of Computing Science, University of Glasgow, United Kingdom CHRISTOPHER FRAUENBERGER, HCI Group, TU Wien, Austria JUDITH GOOD, Department of Informatics, University of Sussex, United Kingdom KAREN GULDBERG, School of Education, University of Birmingham, United Kingdom WENDY KEAY-BRIGHT, Cardiff School of Art and Design, Cardiff Metropolitan University, United Kingdom LILA KOSSYVAKI, School of Education, University of Birmingham, United Kingdom OLIVER LEMON, School of Mathematical and Computer Sciences Heriot-Watt University, United Kingdom MARILENA MADEMTZI, Yale University, Child Study Center, USA RACHEL MENZIES, University of Dundee School of Science and Engineering, United Kindgom HELEN PAIN, School of Informatics, University of Edinburgh , United Kingdom GNANATHUSHARAN RAJENDRAN, Department of Psychology, Heriot-Watt University, United Kingdom ANNALU WALLER, University of Dundee, School of Science and Engineering, United Kingdom SAMWASS, University of East London, United Kingdom TIM J. SMITH, Department of Psychological Sciences, Birkbeck, University of London, United Kingdom
Epidemic Algorithms for Reliable Multicasting in Ad Hoc Networks

Characteristics of ad hoc networks and even their existence depend on the nodes forming them. Thus, services and applications designed for ad hoc networks should adapt to this dynamic and distributed environment. In particular, multicast algorithms having reliability and scalability requirements should abstain from centralized approaches. We aspire to define a reliable and scalable multicast protocol for ad hoc networks. Our target is to utilize epidemic techniques for this purpose. In this paper, we present a brief survey of epidemic algorithms for reliable multicasting in ad hoc networks, and describe formulations and analytical results for simple epidemics. Then, P2P anti-entropy algorithm for content distribution and our prototype simulation model are described together with our initial results demonstrating the behavior of the algorithm. Keywords— Ad hoc networks, epidemic, peer-to-peer, reliable multicast.
First record of the “small cave bear” in Bulgaria and the taxonomic status of bears of the Ursus savini Andrews – Ursus rossicus Borissiak group

Abstract The discovery of rich, well preserved skull material in the latest Middle Pleistocene deposits of Mishin Kamik cave (N-W Bulgaria) presents an opportunity for a new analysis of the taxonomy and the phylogeny of the so called “small cave bears”. Not all the small cave bears known would have necessarily had a common origin, the size decrease could be related in a number of cases to a parallelism. The bear from Mishin Kamik is identical with “Ursus rossicus” Borissiak from Krasnodar (S. Russia). Both these samples must be referred to U. savini Andrews from Bacton near Cromer (England). The specimens from Krasnodar and Mishin Kamik could be classified as U. sa. rossicus, a late form of the species. This species may have affinities with some Middle Pleistocene Siberian forms. U. savini is a small but very robust spelaeoid bear which is more advanced in a number of features than U. deningeri. In several aspects it attained the evolutional adaptations of the cave bears of the U. spelaeus-U. ingressus group and represents an independent lineage of spelaeoid bears. The Mishin Kamik population was adapted to a mosaic landscape of forests and open areas in hilly terrain. This bear had well developed grazing adaptations but had weak motor abilities.
International Journal of Soft Computing and Engineering

116 Abstract—Beside all Engineering Systems Elect. Power System are merely subjected to uncertain failures. The electrical parameters of power system are also responsible for the losses, thermal breakdown and failure rates. The effect of 'R' 'L' and 'C' in failure rates of power system components can be simulated using fuzzy linguistic variables. 'RLC' power system may be lower low medium, higher and high, these. Fuzzy linguistic variables may be used to evaluate the failure rates of lower, low medium, higher and high values. The reciprocal of failure rates of different components will give the MTBF's of those components, along with its membership function or fuzzy grade of truths. The cardinality and relative cardinality of components may be evaluated. Assuming relative cardinality as an average reliability of the system its reliability may be investigate. Fuzzy relations may be used for analyzing the reliability attributes of power system. The optimized reliable condition may be evaluated.
Cross-lingual transfer of semantic role labeling models

Semantic role labeling is an important step in natural language understanding, offering a formal representation of events and their participants as described in natural language, without requiring the event or the participants to be grounded. Extensive annotation efforts have enabled statistical models capable of accurately analyzing new text in several major languages. Unfortunately, manual annotation for this task is complex and requires training and calibration even for professional linguists, which makes the creation of manually annotated resources for new languages very costly. The process can be facilitated by leveraging existing resources for other languages using techniques such as cross-lingual transfer and annotation projection. This work addresses the problem of improving semantic role labeling models or creating new ones using cross-lingual transfer methods. We investigate different approaches to adapt to the availability and nature of the existing target-language resources. Specifically, cross-lingual bootstrapping is considered for the case where some annotated data is available for the target language, but using an annotation scheme different from that of the source language. In the more common setup, where no annotated data is available for the target language, we investigate the use of direct model transfer, which requires no sentence-level parallel resources. Finally, for cases where the parallel resources are of limited size or poor quality, we propose a novel method, referred to as feature representation projection, combining the strengths of direct transfer and annotation projection.
EDUCATIONAL DATA MINING: A COMPREHENSIVE STUDY

: Research in the field of academics is developing expeditiously. Exploration and Experimentation in the domain of Educational Data Mining (EDM) is increasing on account of the various benefits achieved from the knowledge gained from the analysis of educational data with the help of Data mining (DM) processes implemented. The academic institutions can utilize EDM not only to examine the performance of students but also in improving teaching – learning process, resource utilization etc. EDM helps in creation of new methods for discovering knowledge from educational database and this helps to enhance decision making processes in educational systems at all levels . This study is an effort towards understanding the educational datamining concept and its various aspects. At the same time, the study tries to uncover number of issues that drives the need of the research community to furnish comprehensive reporting of methods and results. There is a need to increase efforts to validate the work and gives a direction for future work to be carried out in the field.
ASHP statement on the pharmacist's role in antimicrobial stewardship and infection prevention and control.

The American Society of Health-System Pharmacists (ASHP) believes that pharmacists have a responsibility to take prominent roles in antimicrobial stewardship programs and participate in the infection prevention and control programs of health systems. This responsibility arises, in part, from pharmacists’ understanding of and influence over antimicrobial use within the health system. Further, ASHP believes that the pharmacist’s ability to effectively participate in antimicrobial stewardship and infection prevention and control efforts can be realized through clinical endeavors focused on proper antimicrobial utilization and membership on multidisciplinary work groups and committees within the health system. These efforts should contribute to the appropriate use of antimicrobials, ultimately resulting in successful therapeutic outcomes for patients with infectious diseases, and reduce the risk of infections for other patients and health care workers.
Improving Classification Accuracy based on Random Forest Model with Uncorrelated High Performing Trees

Random forest can achieve high classification performance through a classification ensemble with a set of decision trees that grow using randomly selected subspaces of data. The performance of an ensemble learner is highly dependent on the accuracy of each component learner and the diversity among these components. In random forest, randomization would cause occurrence of bad trees and may include correlated trees. This leads to inappropriate and poor ensemble classification decision. In this paper an attempt has been made to improve the performance of the model by including only uncorrelated high performing trees in a random forest. Experimental results have shown that, the random forest can be further enhanced in terms of the classification accuracy.
Sparse Bayesian Regression for Head Pose Estimation

This paper presents a high performance head pose estimation system based on the newly-proposed sparse Bayesian regression technique (relevance vector machine, RVM) and sparse representation of facial patterns. In our system, after localizing 20 key facial points, sparse features of these points are extracted to represent facial property, and then RVM is utilized to learn the relation between the sparse representation and yaw and pitch angle. Because RVM requires only a very few kernel functions, it can guarantee better generalization, faster speed and less memory in a practical implementation. To thoroughly evaluate the performance of our system, we compare it with conventional methods such as CCA, kernel CCA, SVR on a large database; In experiments, we also investigate the influence of the facial points localization error on pose estimation by using manually labelled results and automatically localized results separately, and the influence of different features on pose estimation such as geometrical features and texture features. These experimental results demonstrate that our system can estimate face pose more accurately, robustly and fast than those based on conventional methods
Efficient Hyper-parameter Search for Knowledge Graph Embedding

While hyper-parameters (HPs) are important 001 for knowledge graph (KG) embedding, exist- 002 ing methods fail to search them efﬁciently. To 003 solve this problem, we ﬁrst analyze the proper- 004 ties of different HPs and quantize the transfer- 005 ability from small subgraph to the large graph. 006 Based on the analysis, we propose an efﬁcient 007 two-stage search algorithm, which efﬁciently 008 explores HP conﬁgurations on small subgraph 009 at the ﬁrst stage and transfers the top conﬁgura- 010 tions for ﬁne-tuning on the large whole graph 011 at the second stage. Experiments show that our 012 method can consistently ﬁnd better HPs than 013 the baseline algorithms with the same time 014 budget. We achieve 10.8% average relative im- 015 provement for four embedding models on the 016 large-scale KGs in open graph benchmark. 017
the Eighteenth Symposium on Usable Privacy and Security (SOUPS

Sensors embedded in wearable and smart home devices collect data that can be used to infer sensitive, private details about people’s lives. Privacy norms have been proposed as a foundation upon which people might coordinate to set and enforce preferences for acceptable or unacceptable data practices. Through a qualitative study, this research explored whether normative beliefs inﬂuenced participants’ reactions to plausible but unexpected inferences that could be made from sensor data collected by everyday wearable and smart home devices. Some reactions were grounded in normative beliefs involving existing disclosure taboos, while others stigmatized the choice to limit one’s use of technologies to preserve one’s privacy. The visible nature of others’ technology use contra-dicts individual concern about sensor data privacy, which may lead to an incorrect assumption that privacy is not important to other people. Findings suggest that this is a barrier to collective privacy management, and that awareness interventions focused on information about the beliefs of other users may be helpful for collective action related to data privacy.
( Not ) Bounding the True Error

Wepresentanew approachto boundingthetrueerrorrateof acontinuous valuedclassifierbaseduponPAC-Bayesbounds.Themethodfirst constructsa distribution over classifiersby determininghow sensiti ve each parameterin the modelis to noise. The trueerror rateof the stochastic classifierfoundwith thesensiti vity analysiscanthenbetightly bounded usinga PAC-Bayesbound.In this paperwe demonstratethemethodon artificial neuralnetworkswith resultsof a orderof magnitudeimprovementvs. thebestdeterministicneuralnetbounds.
Hybrid Job-Driven Scheduling for Heterogeneous MapReduce Clusters

It is cost-efficient for a tenant with a limited budget to establish a heterogeneous virtual MapReduce clusters by renting various virtual private servers (VPSs) from a VPS provider. To provide an appropriate scheduling scheme for this type of computing environment, and MapReduce still performs poorly on heterogeneous clusters, we propose in this paper a hybrid job-driven scheduling scheme (JoSS for short) from a tenant perspective. JoSS provide not only job level scheduling, but also Map-task level scheduling and Reduce-task level scheduling; The deployment of MapReduce in data canters and clouds present several challenges, improve data locality for both map-level task and reduce-level task, avoid job starvation and improve job execution performance. Two variations of JoSS-Task and JoSS-Job are further introduced to separately achieve a better map-data locality and a faster task assignment. We conduct extensive experiments to evaluate and compare the two variations (JoSS-T and JoSS-J) with current scheduling algorithms supported by Hadoop. The result shows that the two variations crush the opposite tested algorithms in terms of map and reduce data locality , and network overhead while not acquisition significant overhead. Additionally, the two variations area unit severally appropriate for various MapReduce-workload eventualities and supply the most effective job performance among all tested algorithms.