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The genus Amphidinium Clap. & J. Lachm. comprises a high diversity of planktonic and benthic (epiphytic and sand-dwelling) dinoflagellates from marine and freshwater ecosystems. High morphological plasticity and vaguely defined genus characteristics (e.g., a small epicone size) have complicated the clear delineation of species boundaries. Although six Amphidinium morphospecies have been reported from Mexican coastal waters, species identifications are uncertain and not generally supported by molecular phylogenetic data. In this study, seven isolates of Amphidinium from diverse benthic coastal locations on the NE Pacific, Gulf of California, and southern Gulf of Mexico were subjected to critical morphological analysis using photonic and scanning electron microscopy. The phylogenetic reconstruction was based on nuclear-encoded, partial large-subunit (LSU) rDNA and internal transcribed spacer I and II (ITS1 and ITS2) sequences. The revised phylogenetic analysis was consistent with the traditional subdivision of the genus Amphidinium into two sister groups: Herdmanii and Operculatum clades. This study provided the first confirmed records of A. theodorei and A. massartii from coastal waters of Mexico. The molecular phylogenetic evidence indicated that the morphologically described A. cf. carterae from Baja California was in fact more closely allied with A. eilatiensis sequences. A few Amphidinium species are known to form toxigenic (i.e., fish-killing) harmful algal blooms worldwide, and therefore knowledge on species diversity and biogeography is critical in developing effective strategies for evaluating the potential emerging threat in Mexican coastal waters.

Over the course of a playing career that spanned roughly fifty years, John Mahon (pronounced M’hone) introduced and popularized the clarinet as a solo and chamber instrument throughout Britain. As well as performing extensively in London, Mahon also travelled to many provincial towns and cities, bringing both the clarinet and his musical talents to the attention of audiences. This article presents biographies of John and William Mahon, and information on the Mahon family, many of whom were outstanding musicians. \ud \ud Like several musicians of the late eighteenth century, John Mahon was active as a military musician, likely beginning as a youth and extending to about 1781. By this time, he was actively performing in London orchestras during their regular seasons and continued to augment his income by travelling to various provincial towns to play in concerts and music festivals, over 800 of which are documented in newspaper advertisements and other published sources. His playing was of a high standard and he achieved wide recognition as a clarinettist. The article also discusses John Mahon as a military band musician; as an orchestral musician; as a soloist, accompanist and chamber musician with reference to contemporary reviews; Mahon’s published music and clarinet tutor; illustrations of clarinets and a basset horn that he\ud would have played during his career; Mahon as a teacher; later life; and influence and legacy. This is followed by a section on William Mahon’s concerts and reviews, and a conclusion.

Drawing from Michel Foucault’s reading of Immanuel Kant’s essay “What is Enlightenment?,” and specifically his definition ofascesis, we associate maturity with a capacity for, and interest in, forming the self. On the basis of an empirical study of making vinyl records following the successful commercialization of digital media, we identify micro-disciplinary techniques of self-forming that emerge as enthusiasts steadily learn the craft of vinyl record manufacturing. It is, we argue, through technology, rather than against it, that organizational immaturity can be resisted. Craftwork involves testing and transforming, rather than just acquiring, traditional skills. Maturity involves an ongoing struggle of selectively and reflectively engaging with technologies via attempts to be the subject of one’s own subjection. The former contributes to the latter.

openaire: EC/H2020/101016509/EU//CHARITY | openaire: EC/H2020/101069732/EU//aerOS Artificial Intelligence and Machine Learning (AI/ML) can transform mobile communications, enable new applications and services, and pave the way beyond 5G. The adoption of AI/ML may also advance network optimizations and service life-cycle management. This article provides an overview of AI/ML analytics exploring the main concepts in relation to network automation and service-based architectures. Furthermore, it sheds light on AI/ML analytics service enablers by considering a system-level approach elaborating on the key technologies related to service discovery, request, control, and reporting of AI/ML analytics. Finally, it provides an analysis for maintaining the services of AI/ML analytics up-to-date, by considering modifications of the AI/ML model in order to detect, interpret, and compensate for potential performance drifts.

The scope of this research is that of revealing the interconnected nature of medieval Romance lyric by looking at musical imitations. In the Middle Ages, melodic imitation was an essential part of artistic creation as old melodies were constantly borrowed by new authors, a practice known as contrafaction. In this study, we propose to analyse the complex relations between medieval lyricists resulting from this practice using temporal networks. We construct networks by representing each author’s body of work as a single node and connecting a lyricist’ corpus to that of another lyricist via a directed link when the latter author borrowed a melody from the former. To each directed link, we associate the temporal information of when the imitation was composed. Such networks provide a convenient visualization tool to explore the dataset and its connections in an intuitive fashion and are available online at: https://medmus.warwick.ac.uk/networks. They also provide an analytical tool: we use the networks to show how ideas might have spread among lyricists along time-respecting paths, and obtain measures of the authors’ centrality and influence on the overall literary corpus. We compare the results obtained with the temporal networks with those obtained via more traditional centrality measures computed for corresponding static networks, and explain why the temporally informed measures may provide a more accurate depiction of authors’ influence.

The ability to send humans to Mars is impossible with the current state of technology (Redfern, 2021). Simply attempting to land on Mars has created a graveyard of probes and rovers due to the thinness of Mars’s atmosphere and the forces on the spacecraft as a result (Katz, 2021). A mission to Mars has physical limitations of mass, weight, and volume. With the demands of this odyssey, current communication systems are insufficient. Radio frequency systems are slow to transmit signals, take up precious payload space, and use too much of the spacecraft’s energy supply (Edwards, Israel, Wilson, Moores, & Fletcher, 2012). Being able to communicate quickly with astronauts is vital when traveling into deep space where disaster can strike at any moment. At the same time, the more payload space and resources taken up by communication systems, the less space there is for other vital supplies like food, water, and building materials for shelter. Furthermore, any inefficient drains on the power supply will limit the distance a spacecraft can travel (Williams, 2021). Ensuring that astronauts can reach their destination on Mars safely is a top priority (Tran, 2019). Astronauts cannot be burdened with inadequate technology or a broken safety culture that could jeopardize their well-being. Astronaut safety can not only be disrupted by malfunctions of spacecraft technology but also by the failings of a safety culture that values schedules and budgets over safety and lives. The Columbia space shuttle completed its 16-day adventure through space and the seven astronauts inside the vessel were killed as it re-entered the Earth’s atmosphere. An investigation of the disaster found structural issues with the safety culture at NASA played a large role in the explosion (Smith, 2003). Astronauts must be aware and informed of the accepted risk and the safety procedures for their own personal well-being. Since ensuring technology can physically transport astronauts to Mars is foundational to future missions, improving the communication systems between astronauts and ground control stations and investigating the safety culture at NASA is vital to protecting the safety of astronauts on board. The current state of laser optical communications requires greater certainty about cloud formation and prediction in order to discover optimal ground station placements. The atmospheric effects team comprised of meteorological experts at Northrop Grumman had decided to tackle this technical challenge by using machine learning to increase the availability of the optical network. The team addressed the problem using a convolutional neural net and image recognition to create cloud mask predictions of the sky dome. They used python-based libraries like TensorFlow and Keras to create the neural net, as well as data science libraries like Numpy and Pandas to process the data. The research conducted during the internship resulted in an improved cloud mask model that over-predicted clouds less often. The research could be applied to optical communication systems, solar panel placement, and missile launch sites. Future work on the project could include gathering more/better quality data, testing more predictors, and using improved thresholding on the weather-measuring device. The research to investigate the safety culture at NASA centered around a case study of the Columbia explosion. By looking at reports and analyses of the tragedy, key structural issues like schedule pressures were compiled and discussed. Responses and proposed solutions from sociologists and ethics researchers were analyzed through the lens of technological determinism. The research and analysis found that the proposed solutions did attempt to address the structural faults with NASA’s safety culture. However, many of the previous recommendations did not consider the normalized deviance of accepted risk or the harms and fails associated with technological determinism and malfunction. The findings of the research recommended that the safety culture at NASA conduct periodic investigations, case studies, and recommendations for changes and improvements with the safety procedures. Each safety concern should be encouraged and investigated with recommendations for greater safety rather than dismissal. Normalized deviance and accepted risk should be more closely compiled and logged to ensure a more informed level of risk, even if not all risk may be known. By working on both projects simultaneously, I was able to see how the ethical frameworks researched and applied in the STS research paper could be applied to the technical research paper. I found that assuming there to be a technical solution to the deep-space communication problem was present in my technical research. I learned to consider systems, ethics, and non-technical solutions rather than purely relying on technical solutions and code. When I was studying the harms of technical technologies on society, the reflection made me consider what the effects of my machine learning models could have on social groups I had not considered before. I gained a new understanding of engineering and the ethical principles and dilemmas baked into the field as well as how to better deal with these dilemmas. References Edwards, B., Israel, D., Wilson, K., Moores, J., & Fletcher, A. (2012). Overview of the Laser Communications Relay Demonstration Project. SpaceOps. doi: 10.2514/6.2012-1261897 Katz, O. (2021, May 21). Landing on Mars is difficult, often fails, and will never be risk-free. Phys. Retrieved from https://phys.org/news/2021-05-mars-difficult-risk-free.html Redfern, M. (2021). Will we ever set foot on Mars?. BBC Earth. Retrieved from https://www.bbcearth.com/news/will-we-ever-set-foot-on-mars Smith, M. S. (2003). NASA's Space Shuttle Columbia: Synopsis of the Report of the Columbia Accident Investigation Board. CRS Report for Congress. Retrieved from https://ntrs.nasa.gov/api/citations/20040040191/downloads/20040040191.pdf Tran, L. (2019, August 7). How NASA Will Protect Astronauts From Space Radiation at the Moon. NASA. Retrieved from https://www.nasa.gov/feature/goddard/2019/how-nasa-protects-astronauts-from-space-radiation-at-moon-mars-solar-cosmic-rays Williams, M. (2021, February 5). Every challenge astronauts will face on a flight to Mars. Phys. Retrieved from https://phys.org/news/2021-02-astronauts-flight-mars.html

Human-based object detection in synthetic aperture RADAR (SAR) imagery is complex and technical, laboriously slow but time critical—the perfect application for machine learning (ML). Training an ML network for object detection requires very large image datasets with imbedded objects that are accurately and precisely labeled. Unfortunately, no such SAR datasets exist. Therefore, this paper proposes a method to synthesize wide field of view (FOV) SAR images by combining two existing datasets: SAMPLE, which is composed of both real and synthetic single-object chips, and MSTAR Clutter, which is composed of real wide-FOV SAR images. Synthetic objects are extracted from SAMPLE using threshold-based segmentation before being alpha-blended onto patches from MSTAR Clutter. To validate the novel synthesis method, individual object chips are created and classified using a simple convolutional neural network (CNN); testing is performed against the measured SAMPLE subset. A novel technique is also developed to investigate training activity in deep layers. The proposed data augmentation technique produces a 17% increase in the accuracy of measured SAR image classification. This improvement shows that any residual artifacts from segmentation and blending do not negatively affect ML, which is promising for future use in wide-area SAR synthesis. Outstanding Thesis Major, United States Air Force Approved for public release. Distribution is unlimited.

The novel coronavirus (nCOV) is a new strain that needs to be hindered from spreading by taking effective preventive measures as swiftly as possible. Timely forecasting of COVID-19 cases can ultimately support in making significant decisions and planning for implementing preventive measures. In this study, three common machine learning (ML) approaches via linear regression (LR), sequential minimal optimization (SMO) regression, and M5P techniques have been discussed and implemented for forecasting novel coronavirus disease-2019 (COVID-19) pandemic scenarios. To demonstrate the forecast accuracy of the aforementioned ML approaches, a preliminary sample-study has been conducted on the first wave of the COVID-19 pandemic scenario for three different countries including the United States of America (USA), Italy, and Australia. Furthermore, the contributions of this study are extended by conducting an in-depth forecast study on COVID-19 pandemic scenarios for the first, second, and third waves in India. An accurate forecasting model has been proposed, which has been constructed on the basis of the results of the aforementioned forecasting models of COVID-19 pandemic scenarios. The findings of the research highlight that LR is a potential approach that outperforms all other forecasting models tested herein in the present COVID-19 pandemic scenario. Finally, the LR approach has been used to forecast the likely onset of the fourth wave of COVID-19 in India.

Severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2) induced cytokine storm is the major cause of COVID-19 related deaths. Patients have been treated with drugs that work by inhibiting a specific protein partly responsible for the cytokines production. This approach provided very limited success, since there are multiple proteins involved in the complex cell signaling disease mechanisms. We targeted five proteins: Angiotensin II receptor type 1 (AT1R), A disintegrin and metalloprotease 17 (ADAM17), Nuclear Factor‑Kappa B (NF‑κB), Janus kinase 1 (JAK1) and Signal Transducer and Activator of Transcription 3 (STAT3), which are involved in the SARS‑CoV‑2 induced cytokine storm pathway. We developed machine-learning (ML) models for these five proteins, using known active inhibitors. After developing the model for each of these proteins, FDA-approved drugs were screened to find novel therapeutics for COVID‑19. We identified twenty drugs that are active for four proteins with predicted scores greater than 0.8 and eight drugs active for all five proteins with predicted scores over 0.85. Mitomycin C is the most active drug across all five proteins with an average prediction score of 0.886. For further validation of these results, we used the PyRx software to conduct protein-ligand docking experiments and calculated the binding affinity. The docking results support findings by the ML model. This research study predicted that several drugs can target multiple proteins simultaneously in cytokine storm-related pathway. These may be useful drugs to treat patients because these therapies can fight cytokine storm caused by the virus at multiple points of inhibition, leading to synergistically effective treatments.

Data set description

The central purpose of this research is to clarify the relationship between depression and self-attention. The value in answering this question is that it has significant implications on how specific symptoms of depression could be treated psychologically and where in the brain there may be disordered functioning. The neurocognitive literature exploring ‘self in depression’ has produced contradictory and inconclusive findings in the exploration of the relationship between depression and self-attention. This may be due to research classifying participants as ‘depressed’ with very little attention to the specific depressive symptoms and therefore disorder subtypes. Research on non-clinical populations consistently shows that people have an attentional bias to information about themselves. This is the case even if this information is linked to them tenuously; for example, an arbitrary geometric shape associated with them. Studies have found results indicating that people with depression have higher than average attention to self, lower than average attention to self and no difference from controls. By using detailed psychometric questionnaires in tandem with an experimental design, this project can provide novel and useful insights into how depressive symptoms are related to self-attention bias. One of the ways to evaluate the specific symptomatic expression of depression is to use an index called Beck’s depression inventory (BDI-II). The scores on the BDI-II form two distinct clusters: cognitive and somatic affective. These two clusters have been robustly validated as accurately representing these types of symptoms by several meta-analyses. Gaining insight into the relationship between self-related attention and symptom clusters can help indicate if, for example, aberrant self-attention is predominantly a maladaptive internal self-representation (somatic) or, ‘self’ maladaptively construe in the emotional domain (cognitive). In addition to clarifying this relationship, this research will also provide insight into what aspect of the ‘self’ people with depression focus on (good aspects/bad aspects). The relationship between psychological characteristics and self-referential processing will be analysed using statistical analysis. This project will, however, further implement the use of Machine Learning (ML) to explore the utility of the experimental data in predicting symptoms as a potential diagnostic tool.

本研究では、誰もが直感的に理解できるものとして「音楽の複雑性」を取り上げ、これを用いた条件付け音楽生成モデルを検討する。算出された音楽の複雑性が、人が音楽を聴取した際の感覚と一致しているかを確かめるため、アンケートを用いて実験参加者の音楽の複雑さの感じ方について調査を行う。

Question answering systems can facilitate the practice of evidence-based medicine (EBM) by providing guidance based on the latest and most relevant evidence. The review aims to (a) characterize medical question answering systems that have been developed and evaluated and (b) assess their suitability for use in healthcare.

Sescom NILM Energy Dataset (SESNED ) description

Artificial Intelligence (AI) and machine learning (ML) have gained significant interest in the last few years. Furthermore, the exponential growth of the Internet of Things (IoT) and the new paradigm of Artificial Intelligence of Things (AIoT) in recent years have made everything move towards digitization, increasing the demand for high-performance energy-efficient computing for ML inferences at the edge of IoT and motivating a great number of research efforts to develop AI accelerators. While most of the AI accelerators have been developed and evaluated in isolation with little support for full SoC integration, this thesis presents LiteAIRISC to bridge these research gaps where we present an instructional manual for integrating a RISC-V processor with tightly integrated AI accelerators in an FPGA System of Chip (SoC) framework to enable system-level evaluations in real-world platforms. The LiteAIRISC system was successfully built on the Xilinx VC707 board, Xilinx Alveo U280 board, and Xilinx Kintex 705 board. At the same time, with the wide adoption of inferences on tiny ML or Edge AI platforms, it becomes increasingly demanding to develop optimal models that fit the limited hardware budget. Layer-wise mixed-precision quantization (MPQ) has become prevailing for edge inference since it strikes a better balance between accuracy and efficiency than the uniform quantization scheme. This thesis proposes QRISC to support the efficient post-training MPQ on edge chips. We tightly integrate the quantized inference units as part of the processor pipeline through Instruction Set Architecture (ISA) and micro-architecture co-design.

Mobile networks are adopting disaggregation and modularisation to support flexibility. However, large modular networks with a wide range of heterogeneous components have many degrees of freedom, making its Management and Orchestration complex. The use of Machine Learning techniques is expected to improve the efficiency of the operation of 6G networks, by introducing data-driven approaches into their Management and Orchestration. In this paper, we review the current best practices of ML usage to support Management and Orchestration, and we present the H2020 European project Hexa-X Management and Orchestration architecture. We then identify the main challenges ahead to fully embrace a Machine Learning driven operation.

Diabetes therapy 13(1), 57-73 (2022). doi:10.1007/s13300-021-01179-8, Published by Springer Healthcare Communications, New York, NY [u.a.]

This review article provides a deep insight into the Brain–Computer Interface (BCI) and the application of Machine Learning (ML) technology in BCIs. It investigates the various types of research undertaken in this realm and discusses the role played by ML in performing different BCI tasks. It also reviews the ML methods used for mental state detection, mental task categorization, emotion classification, electroencephalogram (EEG) signal classification, event-related potential (ERP) signal classification, motor imagery categorization, and limb movement classification. This work explores the various methods employed in BCI mechanisms for feature extraction, selection, and classification and provides a comparative study of reviewed methods. This paper assists the readers to gain information regarding the developments made in BCI and ML domains and future improvements needed for improving and designing better BCI applications.

A digital twin for a multifunctional technology for flexible manufacturing on an assembly, disassembly, and repair mechatronics line (A/D/RML), assisted by a complex autonomous system (CAS), is presented in the paper. The hardware architecture consists of the A/D/RML and a six-workstation (WS) mechatronics line (ML) connected to a flexible cell (FC) and equipped with a six-degree of freedom (DOF) industrial robotic manipulator (IRM). The CAS has in its structure two driving wheels and one free wheel (2DW/1FW)-wheeled mobile robot (WMR) equipped with a 7-DOF robotic manipulator (RM). On the end effector of the RM, a mobile visual servoing system (eye-in-hand MVSS) is mounted. The multifunctionality is provided by the three actions, assembly, disassembly, and repair, while the flexibility is due to the assembly of different products. After disassembly or repair, CAS picks up the disassembled components and transports them to the appropriate storage depots for reuse. Disassembling or repairing starts after assembling, and the final assembled product fails the quality test. The virtual world that serves as the digital counterpart consists of tasks assignment, planning and synchronization of A/D/RML with integrated robotic systems, IRM, and CAS. Additionally, the virtual world includes hybrid modeling with synchronized hybrid Petri nets (SHPN), simulation of the SHPN models, modeling of the MVSS, and simulation of the trajectory-tracking sliding-mode control (TTSMC) of the CAS. The real world, as counterpart of the digital twin, consists of communication, synchronization, and control of A/D/RML and CAS. In addition, the real world includes control of the MVSS, the inverse kinematic control (IKC) of the RM and graphic user interface (GUI) for monitoring and real-time control of the whole system. The “Digital twin” approach has been designed to meet all the requirements and attributes of Industry 4.0 and beyond towards Industry 5.0, the target being a closer collaboration between the human operator and the production line.

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Tarutung mencari perjaka balige cari om om. Mesuji cari selingkuh gedong tataan cari simpanan. Prabumulih cowok bo pangkalan balai cari sewaan pria, tebing tinggi mencari telpon laki lahat cari telepon cowok. Pulau morotai cowok seksi pulau taliabu suara cowok mendesah, ternate desahan cowok tidore kepulauan cowok kuat main ml. Salak cari chating sex pria pangururan mencari pendamping hidup laki. Kraksaan cari duda sampang mencari perjaka, sidoarjo cari om om situbondo mencari sugar daddy. Pulang pisau cowok seksi seruyan suara cowok mendesah, sukamara desahan cowok palangka raya cowok kuat main ml. Manado cari teman phone sex pria tomohon cari lawan phone sex pria, lolak mencari pasangan phonesex cowok bolaang uki cari phone sex pria. Jambi mencari sugar daddy palembang cowok hot, bengkulu cowok sexy bandar lampung cowok seksi. Nduga mencari chat sex cowok paniai cari chating sex pria, pegunungan bintang mencari pendamping hidup laki puncak cari sleep call pria. Kalimantan barat cari telepon cowok kalimantan tengah mencari teman vc pria, kalimantan selatan cari teman vcs laki kalimantan timur cari teman vcs cowok. Kapuas cari duda katingan mencari perjaka, kotawaringin barat cari om om kotawaringin timur mencari sugar daddy. Deiyai cari pelakor dogiyai mencari open bo cowok. Probolinggo cowok sange surabaya ganteng. Rasiei cari phone sex pria asmat mencari ml laki, biak numfor cari gebetan cowok boven digoel mencari selingkuhan pria. Malaka mencari ttm manggarai cari selingkuh, manggarai barat cari simpanan manggarai timur mencari chat sex cowok. Pahuwato tampan gorontalo cowok gede, tilamuta cari pasangan cowok suwawa mencari pacar pria. Ogan komering ulu selatan cari phone sex pria ogan komering ulu timur mencari ml laki, penukal abab lematang ilir cari gebetan cowok lubuk linggau mencari selingkuhan pria. Muara teweh foto kuala kurun cowok masturbasi, kuala kapuas video kasongan cowok coli. Bima tampan dompu cowok gede, lombok barat cari pasangan cowok lombok tengah mencari pacar pria. Halmahera barat cari duda halmahera selatan mencari perjaka, halmahera tengah cari om om halmahera timur mencari sugar daddy. Unaaha cowok hot langara cowok sexy. Aceh utara mencari ml laki bener meriah cari gebetan cowok. Lamandau cowok hot murung raya cowok sexy. Malang foto mojokerto cowok masturbasi, kertosono video pasuruan cowok coli. Tahuna mencari selingkuhan pria melonguane cari pelakor, tondano mencari open bo cowok amurang cowok bo. Aceh barat tampan aceh barat daya cowok gede, aceh besar cari pasangan cowok aceh jaya mencari pacar pria. Nanga pinoh cowok gede sungai pinyuh cari pasangan cowok. Kutai barat cari teman phone sex pria kutai kartanegara cari lawan phone sex pria, kutai timur mencari pasangan phonesex cowok mahakam ulu cari phone sex pria. Betun cowok masturbasi ruteng video. Tanah datar cari sleep call pria bukittinggi mencari sleep chat laki, padang cari cowok pemuas wanita padangpanjang cari duda. Takalar mencari ml laki tana toraja cari gebetan cowok. Nganjuk mencari chat sex cowok. Jasa pemuas memuaskan wanita cowok hot seksi phonesex vcs chat sex dewasa gede orgasme hypersex selingkuh pasangan jodoh pacar ttm simpanan bronndong ngawi. cari chating sex pria. Jasa pemuas memuaskan wanita cowok hot seksi phonesex vcs chat sex dewasa gede orgasme hypersex selingkuh pasangan jodoh pacar ttm simpanan bronndong pacitan mencari pendamping hidup laki pamekasan cari sleep call pria. Mempawah suara cowok mendesah sambas desahan cowok. Kampar cari pelakor kepulauan meranti mencari open bo cowok. Tangerang cari pelakor tangerang selatan mencari open bo cowok. Barru foto bone cowok masturbasi, bulukumba video enrekang cowok coli. Mukomuko cari selingkuh rejang lebong cari simpanan. Dairi mencari sleep chat laki deli serdang cari cowok pemuas wanita. 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Kuningan cari pelakor majalengka mencari open bo cowok. Pinrang cari teman phone sex pria sidenreng rappang cari lawan phone sex pria, sinjai mencari pasangan phonesex cowok soppeng cari phone sex pria. Seluma mencari chat sex cowok bengkulu cari chating sex pria, manna mencari pendamping hidup laki karang tinggi cari sleep call pria. Maluku cari chating sex pria maluku utara mencari pendamping hidup laki. Kefamenanu cari phone sex pria

This dataset provides tracking data from a virtual reality experiment with 18 participants. During the 15 minutes of each session participatns were doing multiple tasks including natural walking on straight and curved paths, searching for a target and avoiding obstacles. During these tasks data was gathered from multiple sensors including eye tracking, head position, trunk orientation, controller position and orientation. Related publications: N. Stein, G. Bremer and M. Lappe, "Eye Tracking-based LSTM for Locomotion Prediction in VR," 2022 IEEE Conference on Virtual Reality and 3D User Interfaces (VR), 2022, pp. 493-503, doi: 10.1109/VR51125.2022.00069. Bremer, G., Stein, N., Lappe, M. (2021) Predicting Future Position From Natural Walking and Eye Movements with Machine Learning. Proceedings of the IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR) 2021, 19-28, doi:10.1109/AIVR52153.2021.00013 Stein, N., Analyzing Visual Perception and Predicting Locomotion using Virtual Reality and Eye Tracking (2021) IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), 2021, 727-728, doi: 10.1109/VRW52623.2021.00246

Contemporary thought is moving away from the notion that the human is a clear-cut concept. In particular, non-anthropocentric views are proliferating within the interdisciplinary area of critical posthumanism, with emphasis on non-dualistic views on relations between human and technology. In this article, the author shows how such a view can inform electroacoustic and computer music practice, and sees improvisation linked with composition as a fruitful avenue in this. Following a philosophical preparation and a discussion of relevant music discourse, two computer music works created by the author are discussed to demonstrate a model of music-making which merges composition and improvisation, based on the concepts of cognitive assemblages and intra-action, following the writings of N. Katherine Hayles and Karen Barad respectively. The works employ techniques related to artificial intelligence and cybernetics, such as machine learning algorithms, agent-based organisation and feedback systems. It is argued that the acousmatic sound domain is an important aspect of sound in this practice. The research is thus situated not only in the frames of improvisation practice and music technology, but also within spatial acousmatic composition and performance.

Aspects towards the area of array signal processing are majorly confined to two techniques, Direction of arrival (DOA) estimation and adaptive beamforming (ABF). There exist different traditional techniques for estimating the direction of incoming signals such as spectral and Eigen structure-based methods that find the direction of incoming signals. The major drawback of these techniques are that they fail to find the direction of the incoming signal in environments of low signal to noise (SNR). The maximum likelihood (ML) method has an upper hand in terms of statistical performance as compared to conventional methods and finds the direction of signal in low SNR conditions. In this article, the chicken swarm optimization (CSO) algorithm is explored for the optimization of ML function to find the direction of signals in uniform linear arrays (ULA). The algorithm is inspected with respect to the root mean square error (RMSE) and the probability of resolution (PR). Simulation results of the proposed technique prove that the ML-CSO algorithm outperforms other heuristic approaches such as the flower pollination algorithm (FPA) and other conventional techniques such as Capon, multiple signal classification (MUSIC), estimation of signal parameters via rotational invariance technique (ESPRIT) algorithm in lower SNR environment.

Diabetes therapy 12(4), 1073-1097 (2021). doi:10.1007/s13300-021-01030-0, Published by Springer Healthcare Communications, New York, NY [u.a.]

A current bottleneck that prevents Machine Learning (ML) from being successful outside of a few restricted fields such as chess playing and highway driving is its impairment in ap- propriately using context to infer the meaning of what it is observing. This paper describes techniques to allow ML systems to derive meaning from context, derived from how the human cortex works. In particular, this paper shows how the multiplication a horizontal vector representing a Sparse Distributed Representation (SDR) of patterns in sensory data by a vertical vector representing a SDR of patterns in context data followed by a pattern recognition operation on the result- ing matrix results in the integration of relevant context and in the output of data containing meaning.

In this paper, the author examines the differences in handling complexity between the human brain and artificial intelligences. It will be shown how the current modularity and tendency to reduce complexity of most current AIs is a limit to their potential to reach artificial general intelligence (AGI). Finally, techniques to address these limitations and to properly address complexity are ad- dressed.

This study is a continuation of our previous registered Study 1 (https://osf.io/rht43). This study aims to investigate how humans use explanations about how an experimental artificial intelligence system reached a particular decision to detect whether the system has a systematic gender bias in the data. For the study, two machine learning-based algorithms were trained on a publicly available loan decision dataset. The first algorithm was trained on the original dataset. For the second algorithm, data was manipulated to systematically disadvantage women based on their gender. Participants are asked to evaluate this algorithm and indicate whether they identify any bias in the decisions. This study extends the findings for Study 1 by showing the effect of stigma consciousness priming on the interaction with the loan forecasting system.

Research data for the article: "Machine Learning Regression Techniques for Test Case Prioritization in Continuous Integration Environment"

Reducing the congestion of busy parking lots by giving people in the vicinity an accurate idea of how many spots are open is a feature that smart parking systems can provide. so far, these systems have been deployed mostly for indoor locations using expensive sensor-based technology. As research and development of image-based detection techniques has increased, it follows that many commercial products are using smart parking technology, and thus, the need for the systems is growing. This research uses a binary Support Vector Machine (SVM) classifier with an image classifier trained using a Convolutional Neural Network (CNN) to identify the presence of vehicles in parking spots. Classifier training and testing used deep CNN features drawn from public datasets with varying light and weather conditions. So, we check how well the technique does with regard to transfer learning using a dataset designed for our study. We've concluded that our approach is good for solving issues in outdoor settings, as shown by our 99.7 percent detection accuracy and 96.7 percent accuracy for the public dataset and our dataset, respectively.

Automating industrial and commercial activities through the use of manipulative robots capable of movement and carrying a variety of products is gaining popularity. When it comes to mobile robots, their operating systems are more complex, which means they are more in demand due to their use in research, which increases their price. These devices run on a variety of operating systems. Obstacle avoidance, line tracking, and mobile explorers are just a few examples. As a result of our findings, we propose a vision system that utilizes a feature extraction and classification algorithm DNN for obstacles in RGB-D images in order to detect and avoid obstacles in known environments.

Solar PV-DG (PV-DG) in the power system is an extremely intriguing alternative due to the advantages of clean energy consumption and distributed generation. Nonetheless, it is necessary to investigate the best placement and size of a PV-DG in order to meet the power grid's needs while also analyzing solar energy's potential. Recent research on PV-DG allocation is reviewed from two perspectives: (1) the DG itself, including optimization methodologies and objectives, and (2) the methodology for estimating PV potential. As a result of this review, holes in previous research were discovered, and a novel strategy was developed to address them. The authors are certain that their effort will contribute to resolving the power system's difficulties. Additionally, it adds to the expansion of solar energy use in poor nations.

The steady growth in the number of devices connected to the Internet has attracted cyber criminals looking for vulnerabilities in computer networks and systems. The objective of this paper is to develop a model to identify DDoS, Infiltration, Web and Brute force attacks on computer networks, using Machine Learning (ML) techniques, increasing the accuracy, sensitivity, precision and measurement values. -F in relation to existing work.

The emergence of the Internet of Things (IoT) concept as a new direction of technological development raises new problems such as valid and timely identification of such devices, security vulnerabilities that can be exploited for malicious activities, and management of such devices. The communication of IoT devices generates traffic that has specific features and differences with respect to conventional devices. This research seeks to analyze the possibilities of applying such features for classifying devices, regardless of their functionality or purpose. This kind of classification is necessary for a dynamic and heterogeneous environment, such as a smart home where the number and types of devices grow daily. This research uses a total of 41 IoT devices. The logistic regression method enhanced by the concept of supervised machine learning (logitboost) was used for developing a classification model. Multiclass classification model was developed using 13 network traffic features generated by IoT devices. Research has shown that it is possible to classify devices into four previously defined classes with high performances and accuracy (99.79%) based on the traffic flow features of such devices. Model performance measures such as precision, F-measure, True Positive Ratio, False Positive Ratio and Kappa coefficient all show high results (0.997–0.999, 0.997–0.999, 0.997–0.999, 0–0.001 and 0.9973, respectively). Such a developed model can have its application as a foundation for monitoring and managing solutions of large and heterogeneous IoT environments such as Industrial IoT, smart home, and similar.

The research on chatbots has gained momentum over the past few years. Academics and practitioners investigate how these tools for communication with customers or internal team can be improved in terms of their performance, acceptance, and deployment. Although there is a plethora of recent studies available, not all of them deal with the digital business transformation implications of chatbots. The main aim of the research presented in this paper was to conduct a systematic literature review of high-quality journal research papers in order to summarise the current state of research on chatbots, identify their role in digital business transformation and suggest the areas warranting further attention. 74 papers were included in the research. Topical (focus and applications), methodological (methods used, sample size, sample type,and countries studied) and bibliometric (publication outlet, citations, and Altmetric Attention Score) aspects are evaluated and described. Scholars and practitioners can use the results to identify topics, areas, and applications that are intensely discussed in the literature and require further attention, select a methodology for their research that is well established in the field or is emerging, identify the most influential publications not to be missed in their research or identify publication outlets for publishing their research on chatbots. Refereed/Peer-reviewed

Prior work has shown Convolutional Neural Networks (CNNs) trained on surrogate Computer Aided Design (CAD) models are able to detect and classify real-world artefacts from photographs. The applications of which support twinning of digital and physical assets in design, including rapid extraction of part geometry from model repositories, information search \& retrieval and identifying components in the field for maintenance, repair, and recording. The performance of CNNs in classification tasks have been shown dependent on training data set size and number of classes. Where prior works have used relatively small surrogate model data sets ($, 6 Pages, 5 Figures, 2 Tables, Conference, Design Engineering, CNN, Digital Twin

Massive open online courses (MOOCs) provide hundreds of students with teaching materials, assessment tools, and collaborative instruments. The assessment activity, in particular, is demanding in terms of both time and effort; thus, the use of artificial intelligence can be useful to address and reduce the time and effort required. This paper reports on a system and related experiments finalised to improve both the performance and quality of formative and summative assessments in specific data science courses. The system is developed to automatically grade assignments composed of R commands commented with short sentences written in natural language. In our opinion, the use of the system can (i) shorten the correction times and reduce the possibility of errors and (ii) support the students while solving the exercises assigned during the course through automated feedback. To investigate these aims, an ad-hoc experiment was conducted in three courses containing the specific topic of statistical analysis of health data. Our evaluation demonstrated that automated grading has an acceptable correlation with human grading. Furthermore, the students who used the tool did not report usability issues, and those that used it for more than half of the exercises obtained (on average) higher grades in the exam. Finally, the use of the system reduced the correction time and assisted the professor in identifying correction errors.

Few pianists have been as acclaimed or as abominated as Vladimir Horowitz. Long ago the subject of a notorious hatchet-job by Michael Steinberg in the New Grove Dictionary of Music, and of an equally trenchant, if undeniably wittier, defense by Richard Taruskin in the New York Times, Horowitz’s playing polarized critical opinion in a manner clearly unattainable by polished mediocrity. But now, nearly thirty years after his death, the time is finally ripe for a reassessment of his artistry and public persona, not least because of the recent release of dozens of live recordings from the later decades of his career. These recordings not only allow a hitherto unattainable overview of Horowitz’s pianism, but prompt a reevaluation of his earlier discography. Moreover, they provide evidence for an analysis of the myths surrounding the art of interpretation in general. Horowitz was routinely castigated by critics for alleged crimes against acceptable practice, while idolized by audiences for many of the same reasons. A reappraisal of Horowitz’s legacy serves to evaluate its place in the history of pianism and seeks to illuminate competing esthetic perspectives on the art of interpretation itself, arguing that for nearly two centuries many pianists and critics have been missing the point: some supposedly purely musical issues in performance practice have as much to do with concepts of ethics and ownership, authority and morality, as with art.

openaire: EC/H2020/871808/EU//INSPIRE-5Gplus The ETSI's Zero touch network and Service Management (ZSM) framework is a prominent initiative to tame the envisioned complexity in operating and managing 5G and beyond networks. To this end, the ZSM framework promotes the shift toward full Automation of Network and Service Management and Operation (ANSMO) by leveraging the flexibility of SDN/NFV technologies along with Artificial Intelligence, combined with the portability and reusability of model-driven, open interfaces. Besides its benefits, each leveraged enabler will bring its own security threats, which should be carefully tackled to make the ANSMO vision a reality. This paper introduces the ZSM's potential attack surface and recommends possible mitigation measures along with some research directions to safeguard ZSM system security.

The impact of such current state-of-the-art technology as machine learning (ML) on organizational knowledge integration is indisputable. This paper synergizes investigations of knowledge integration and ML in technologically advanced and innovative companies, in order to elucidate the value of these approaches to organizational performance. The analyses are based on the premise that, to fully benefit from the latest technological advances, entity interpretation is essential to fully define what has been learned. Findings yielded by a single case study involving one technological firm indicate that tacit and explicit knowledge integration can occur simultaneously using ML, when a data analysis method is applied to transcribe spoken words. Although the main contribution of this study stems from the greater understanding of the applicability of machine learning in organizational contexts, general recommendations for use of this analytical method to facilitate integration of tacit and explicit knowledge are also provided.

Artificial intelligence (AI) and machine learning (ML) have emerged as the fastest growing workloads ranging from applications like object detection, natural language processing and facial recognition to self-driving cars. The proliferation of these compute-intensive workloads resulted in numerous hardware accelerators to fill the gap between the performance and energy-efficiency requirements of AI applications and the capabilities of current architectures like CPU and GPU. In most cases, these accelerators are specialized for a particular task, are costly to produce, require special programming tools, and can become obsolete as new ML algorithms are introduced. Hence, there is a requirement for a system-level solution to streamline the integration of different AI accelerators into standard computing and programming stacks. Furthermore, majority of the AI and ML workloads are currently run in cloud data centers due to the lack of efficient hardware devices to process these workloads at the edge of Internet of Things (IoT). This dissertation presents AI-RISC as a solution to bridge these research gaps. AI-RISC is a scalable processor developed using hardware, ISA (Instruction Set Architecture) and software co-design methodology to extend the open-source RISC-V architecture for accelerating edge AI applications. AI-RISC tightly integrates hardware accelerators as AI functional units (AFU) inside the RISC-V processor pipeline to allow seamless processing of both AI and non-AI tasks on the same hardware. This allows AI accelerators to be integrated in the RISC-V processor pipeline at a fine-granularity and treated as regular functional units during the execution of instructions. This tight integration is especially beneficial for edge devices which perform inference on a small batch size (usually batch size 1) and smaller neural network models compared to cloud AI workloads. AI-RISC also extends the RISC-V ISA with custom instructions which directly target the added AFUs and expose the hardware functionality to software programmers. Additionally, AI-RISC generates a complete software stack including compiler, assembler, linker, simulator and profiler while preserving the high-level programming abstraction offered by popular AI domain-specific languages and frameworks like TensorFlow, PyTorch, MXNet, Keras etc. AI-RISC allows designers to quickly adapt to any hardware, ISA or software changes and enables comprehensive design-space exploration of various available hardware, instruction and software framework options. Thus, AI-RISC addresses current AI/ML workloads, gives the flexibility to hot-swap AFUs when better hardware is available and scales with new instructions as AI algorithms evolve in the future along with defining a standard hardware/software co-design methodology to specialize processor architectures for AI applications. Detailed evaluation results presented in this dissertation prove that AI-RISC accelerates the processing of vector-matrix multiply (VMM) kernel by 17.63x and of ResNet-8 neural network model from industry standard MLPerf Tiny benchmark by 4.41x compared to RISC-V processor baseline. AI-RISC also outperforms the state-of-the-art Arm Cortex-A72 IoT edge processor by 2.45x on average over the complete MLPerf Tiny inference benchmark. Additionally, AI-RISC provides 3.93x improvement in energy-efficiency over the baseline RISC-V processor and 11.49x energy-efficiency improvement over state-of-the-art Gemmini systolic array accelerator.

The increasing capabilities of Artificial Intelligence enable the support of users in a continuously growing number of applications. Current systems typically dictate that interaction between user input and AI output unfolds in discrete steps, as is the case with, for example, conversational agents. Novel scenarios require AI systems to adapt and respond to continuous user input, e.g., image-guided surgery and AI-supported text entry. In and across these applications, AI systems need to support more varied and dynamic interactions in which users and AI interact continuously and in parallel. Current methods and guidelines are often inadequate and sometimes even detrimental to user needs when considering continuous usage scenarios. Realizing a continuous interaction between users and AI requires a substantial change in perspective when designing Human-AI systems. In this SIG, we support the exchange of cutting-edge research contributing to a better understanding and improved methods and tools to design continuous Human-AI interaction.

The introduction of machine learning to small molecule research– an inherently multidisciplinary field in which chemists and data scientists combine their expertise and collaborate - has been vital to making screening processes more efficient. In recent years, numerous models that predict pharmacokinetic properties or bioactivity have been published, and these are used on a daily basis by chemists to make decisions and prioritize ideas. The emerging field of explainable artificial intelligence is opening up new possibilities for understanding the reasoning that underlies a model. In small molecule research, this means relating contributions of substructures of compounds to their predicted properties, which in turn also allows the areas of the compounds that have the greatest influence on the outcome to be identified. However, there is no interactive visualization tool that facilitates such interdisciplinary collaborations towards interpretability of machine learning models for small molecules. To fill this gap, we present CIME (ChemInformatics Model Explorer), an interactive web-based system that allows users to inspect chemical data sets, visualize model explanations, compare interpretability techniques, and explore subgroups of compounds. The tool is model-agnostic and can be run on a server or a workstation.

In response to socioeconomic development, the number of machine learning applications has increased, along with the calls for algorithmic transparency and further sustainability in terms of energy efficient technologies. Modern computer algorithms that process large amounts of information, particularly artificial intelligence methods and their workhorse machine learning, can be used to promote and support sustainability; however, they consume a lot of energy themselves. This work focuses and interconnects two key aspects of artificial intelligence regarding the transparency and sustainability of model development. We identify frameworks for measuring carbon emissions from Python algorithms and evaluate energy consumption during model development. Additionally, we test the impact of explainability on algorithmic energy consumption during model optimization, particularly for applications in health and, to expand the scope and achieve a widespread use, civil engineering and computer vision. Specifically, we present three different models of classification, regression and object-based detection for the scenarios of cancer classification, building energy, and image detection, each integrated with explainable artificial intelligence (XAI) or feature reduction. This work can serve as a guide for selecting a tool to measure and scrutinize algorithmic energy consumption and raise awareness of emission-based model optimization by highlighting the sustainability of XAI.

Every conception of criticism conceals a notion of time which informs the manner in which the critic conceives of history, representation and criticism itself. This thesis reveals the philosophies of time inherent in certain key modern critical concepts: allegory, irony and the sublime. Each concept opens a breach in time, a disruption of chronology. In each case this gap or aporia is emphatically closed, elided or denied. Taking the philosophy of time elaborated by Giorgio Agamben as an introductory proposition, my argument turns in Chapter One to the allegorical temporality which Walter Benjamin sees as the time of photography. The second chapter examines the aesthetics of the sublime as melancholic or mournful untimeliness. In Chapter Three, Paul de Man's conception of irony provides an exemplary instance of the denial of this troubling temporal predicament. In opposition to the foreclosure of the disturbing temporalities of criticism, history and representation, the thesis proposes a fundamental rethinking of the philosophy of time as it relates to these categories of reflection. In a reading of an inaugural meditation on the nature of time, and in examining certain key contemporary philosophical and critical texts, I argue for a critical attendance to that which eludes those modes of thought that attempt to map time as a recognizable and essentially spatial field. The Confessions of Augustine provide, in the fourth chapter, a model for thinking through the problems set up earlier: Augustine affords us, precisely, a means of conceiving of the gap or the interim. In the final chapter, this concept is developed with reference to the criticism of Arnold and Eliot, the fiction of Virginia Woolf and the philosophy of cinema derived from Deleuze and Lyotard. In conclusion, the philosophical implications of the thesis are placed in relation to a conception of the untimeliness of death.

International audience; Real-time constraints in image processing applications often force their optimization using hardware accelerators. This is the case for intraoperative medical images used during surgical procedures. In this context, the challenge consists in processing large volumes of data while employing high complexity algorithms in a limited period of time. Newly developed algorithms must meet both quality-accurate and hardware-efficient characteristics. In this work, we have evaluated the impact of using different data types in a processing chain to classify tissues from hyperspectral video in surgical environments. The software was run on two different embedded CPU+GPU platforms. The results show an improvement in performance by up to 9 times without increasing power consumption by reducing the bit depth from 64 to 16. The impact these reduction have on quality has been measured analytically, by calculating the RMSE, and subjectively, by surveying neurosurgeons. In both cases the results show a minimal impact on the overall quality.

NPS NRP Executive Summary Naval platforms are inherently multi-mission - they execute a variety of missions simultaneously. Ships, submarines, and aircraft support multiple missions across domains, such as integrated air and missile defense, ballistic missile defense, anti-submarine warfare, strike operations, naval fires in support of ground operations, and intelligence, surveillance, and reconnaissance. Scheduling and position of these multi-mission platforms is problematic since one warfare area commander desires one position and schedule, while another may have a completely different approach. Commanders struggle to decide and adjudicate these conflicts, because there is plenty of uncertainty about the enemy and the environment. This project will explore emerging innovative data analytic technologies to optimize naval resource allocation and replanning across mission domains. NPS proposes a study that will evaluate the following three solution concepts for this application: (1) game theory, (2) machine learning, and (3) wargaming. The study will first identify a set of operational scenarios that involve distributed and diverse naval platforms and resources and a threat situation that requires multiple concurrent missions in multiple domains. The NPS team will use these scenarios to evaluate the three solution concepts and their applicability to supporting resource allocation and replanning. This project will provide valuable insights into innovative data analytic solution concepts to tackle the Navy's challenge of conducing multiple missions with cross-domain resources. N2/N6 - Information Warfare This research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrp Chief of Naval Operations (CNO) Approved for public release. Distribution is unlimited.

This thesis is a comparative study of some of the English feminist novels by Michele Roberts and Sara Maitland on the one hand, and some of the Arab feminist novels by Hanan AlShaykh and Ahdaf Soueif on the other. It consists of a general introduction and six chapters. The "General Introduction" deals mainly with the theoretical and methodological issues which have both informed and manifested in the analyses. It also includes some background information about the intellectual strands within a feminist discourse and a purview of the major issues as tackled by specifically feminist critics. Some of the basic objectives that the study attends to are also pinpointed. \ud \ud Chapter one is entitled "Feminism: An Historical Context". It attempts to expose the complex relationship between an Arab feminist discourse as informed by historical relations with the West in colonial and postcolonial eras. A particular emphasis is placed on the emergence of feminism within an Arab-Islamic context. A socio-cultural and political interpretation of such relations is provided to promote a method of reading the novels as necessarily contextualised activities. Since genuine and constructive visions of transformation as pertaining to women's issues need be inherently embedded in certain political and historical realities, the second and third chapters are basically devoted to unfolding the entwining dynamics as informing the realities of both the Western and Arab "houses". How authentic both the Arab and British writers are in depicting their own distinctive realities in the different feminist novels arehighlighted and assessed. Also examined is the extent to which feminist political concerns could be seen as pursued at the expense of the aesthetic aspects of the novels. Chapter Four is devoted to analysing images of men in the feminist novels at hand. It also unravels how these images can be integrated into a comprehensive vision of change. The 111 relation between reductionist and stereotypical representations of men and the tenability of transformative visions are also discussed. Chapter Five, "Absences and Presences: Religious Icons and Ephemeral Images", is concerned with analysing the politics of certain exclusions and inclusions in the feminist novels especially in connection with some religious and other cultural forces pertaining to specific realities. Relating that politics to wider socio-cultural contexts helps in accounting for the convergences and divergences between the Arab feminist novel and its English counterpart and works to expose the nature of the intricate relationship between the two. Chapter Six, "Conclusion", underscores the most important findings of the study. The tenability of visions of change that would affect the lives of women and their societies at large as disseminated by the feminist novelists are assessed against various socio-cultural and political givens. I-low the profundity and comprehensibility of these visions are inherently linked to the novels' aesthetic value is also discussed.