Miracle-dz/UniFAR-FTUs huggingface dataset:
Computational Visual Media Computational Visual

Analyzing and understanding Internet news are important for many applications, such as market sentiment investigation and crisis management. However, it is challenging for users to interpret a massive amount of unstructured text, to dig out its accurate meaning, and to spot noteworthy news events. To overcome these challenges, we propose a novel visualization-driven approach for analyzing news text. We ﬁrst collect Internet news from diﬀerent sources and encode sentences into a vector representation suitable for input to a neural network, which calculates a sentiment score, to help detect news event patterns. A subsequent interactive visualization framework allows the user to explore the development of and relationships between Internet news topics. In addition, a method for detecting news events enables users and domain experts to interactively explore the correlations between market sentiment, topic distribution, and event patterns. We use this framework to provide a web-based interactive visualization system. We demonstrate the applicability and eﬀectiveness of our proposed system using case studies involving blockchain news.
Effectiveness of an educational mobile-app intervention in improving the knowledge of COVID-19 preventive measures Informatics in Medicine Unlocked

Patient education plays an essential role in the modern digital era when the spread of COVID-19 can be prevented using smartphone apps as an alternative learning medium. Although previous studies have shown that the app provides more benefits than any conventional media, its implementation in learning has not been evaluated in hospitalized patients in Indonesia. Therefore, this study aimed to determine the effectiveness of an educational mobile app intervention in improving the knowledge of COVID-19 preventive measures. This quasi-experiment included a pretest-posttest control group design involving 140 participants from various backgrounds. The comparison and treatment cate-gories were randomly selected before and after each educational intervention. Based on the mode of data collection, an EduCovid-19 ® app and a knowledge-based questionnaire were utilized, with the total pre-test and post-test scores being statistically analyzed descriptively and hypothetically (Mann-Whitney and Wilcoxon). The comparison between pre-test and post-test scores of control and treatment groups were to determine the impact levels of the mobile app-assisted educational intervention for subsequent development. The results confirmed that this intervention significantly improved the knowledge of COVID-19 preventive measures. The mean value of the pre-test and post-test of the treatment group was found to increase as many as 3.46, and the difference of the test scores was significant at a 0.001 significance level. For both hypothetical tests (Mann-Whitney and Wilcoxon), p-values significantly improved ’ COVID-19
BMC Systems Biology

Background: Large scale screening for synthetic lethality serves as a common tool in yeast genetics to systematically search for genes that play a role in specific biological processes. Often the amounts of data resulting from a single large scale screen far exceed the capacities of experimental characterization of every identified target. Thus, there is need for computational tools that select promising candidate genes in order to reduce the number of follow-up experiments to a
Late Holocene environmental changes and anthropogenic impact in Dee Why Lagoon, New South Wales

Abstract Late Holocene environmental changes were examined in Dee Why Lagoon, New South Wales, based on the sedimentological, geochemical and geochronological analysis of six cores collected from the fluvial delta and lagoon basin. The lagoon formed about 7300–7200 cal yr BP, following the post-glacial marine transgression and establishment of a sand barrier. Infilling of the lagoon occurred at a rate of 0.09–0.15 mm/yr until about 3300–3200 cal yr BP, when the barrier closed, resulting in mostly freshwater conditions, as evidenced by a change from pyrite-rich units to sediment containing little sulfur and a lack of foraminifera. Sedimentation rates increased to 0.23 then 0.43 mm/yr until about 100 years ago. Post-European land clearance led to an increase in sedimentation rates to 1.0–1.3 mm/yr on the fluvial delta, which are lower than those from other wave-dominated estuaries in New South Wales as well as those estimated by previous studies in Dee Why Lagoon. Our study shows that the fluvial delta started forming much earlier than originally thought, based on results of radiometric dating, and confirmed using sedimentological and geochemical data, as well as a critical examination of historic aerial photographs. Human impact has resulted in an increase in heavy metal (Cu, Pb, Zn) and metalloid (As) concentrations in the recent sediment, most likely attributed to stormwater discharge. Pb and As concentrations are above the ANZECC high sediment quality guideline values at the site closest to the stormwater outlet, with As-based pesticides one of the possible sources. Using the mean enrichment quotient, which is based on normalised Cu, Pb and Zn concentrations over their normalised background concentrations, we show that the surface sediments in Dee Why Lagoon are severely enriched, reflecting the recent anthropogenic impact that has also led to an increase in sedimentation rates.
Personality Factors in Human Comparing Human to Mac

Previous studies of human performance in deception detection have found that humans generally are quite poor at this task, comparing unfavorably even to the performance of automated procedures. However, different scenarios and speakers may be harder or easier to judge. In this paper we compare human to machine performance detecting deception on a single corpus, the ColumbiaSRI-Colorado Corpus of deceptive speech. On average, our human judges scored worse than chance — and worse than current best machine learning performance on this corpus. However, not all judges scored poorly. Based on personality tests given before the task, we find that several personality factors appear to correlate with the ability of a judge to detect deception in speech.
Simulation of drying phenomena associated with vegetation change caused by invasion of alder (Alnus japonica) in Kushiro Mire

A new process‐based distributed model, the National Institute for Environmental Studies (NIES) integrated catchment‐based ecohydrology (NICE) model, was developed. The model includes surface‐unsaturated‐saturated water processes and assimilates land‐surface processes describing the variation in phenology with Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data. The model was applied to the Kushiro River catchment (northern Japan, area of 2204.7 km2) with a resolution of 500 m and 8 days averaged vegetation changes. Excellent agreement between simulated and measured values was obtained for soil temperature, soil moisture, groundwater level, and river flow discharge during the 6 month snow‐free period, achieved by taking into account vegetation phenology, soil properties, and geological structure. The model explains water cycle change and drying phenomena in the Kushiro Mire associated with vegetation change caused by the increased sediment load due to river channelization and consequently the invasion of alder (Alnus japonica) into the mire.
Investigating the climatic impacts of stratospheric aerosol injection

Page | 2 Abstract In this thesis, we assess various climatic impacts of stratospheric aerosol injection (SAI) – a geoengineering proposal that aims to cool Earth by enhancing the sunlightreflecting aerosol layer in the lower stratosphere. To this end, we employ simple radiative transfer models, a detailed radiative transfer code (SOCRATES), and two Hadley Centre general circulation models (HadGEM2-CCS and HadGEM2-ES). We find that the use of a light-absorbing aerosol (black carbon) for SAI would result in significant stratospheric warming and an unprecedented weakening of the hydrological cycle. Conversely, we find that SAI with sulphate or titania aerosol could counteract many of the extreme climate changes exhibited by a business-as-usual scenario (RCP8.5) by the end of this century. In a separate investigation, we show that volcanic aerosol dispersion following low-altitude volcanic eruptions can exhibit high sensitivity to the ambient weather state. ↑olcanic aerosol may get ‘trapped’ in a single hemisphere or transported to the opposite hemisphere depending simply on the meteorological conditions on the day of the eruption. In a final study, we investigate the impacts of SAI on North Atlantic tropical storm frequency. We find that SAI exclusively promoted in the southern hemisphere would increase North Atlantic storm frequency, and vice versa for northern hemisphere SAI. The results of this thesis should promote further research into SAI, which could conceivably be deployed to maintain global-mean temperature below the COP21 target of +1.5 K above pre-industrial levels, whilst society transitions onto a sustainable energy pathway. Conversely, the possibility of SAI being weaponised, for instance, to specifically increase North Atlantic tropical storm frequency, should motivate policymakers to implement effective regulation and governance to deter unilateral SAI deployments.
Reconfiguration of Shipboard Power Systems

: The electrical system on a ship is well contained. Among the challenges proffered by naval systems is real time monitoring for the purpose of fault analysis and reconfiguration. The power system can be considered as a grid of interconnected trunk lines each with its own equivalent parallel load impedance and series transmission impedance. These equivalent impedances provide a natural means of reducing a rather large, complex system to a small compact system. The reduced system can be optimized to maximize power flow through the equivalent parallel impedances and minimize the loss in the series components. Binary integer optimization techniques offer the most promise in solving these problems quickly.
2 Perceived Benefits of Legacy Systems 3 . 2 . 1 Business Critical

Existing research in legacy system modernization has traditionally focused on technical challenges, and takes the standpoint that legacy systems are obsolete, yet crucial for an organization’s operation. Nonetheless, it remains unclear whether practitioners in the industry also share this perception. This paper describes the outcome of an exploratory study in which 26 industrial practitioners were interviewed on what makes a software system a legacy system, what the main drivers are that lead to the modernization of such systems, and what challenges are faced during the modernization process. The findings of the interviews have been validated by means of a survey with 198 respondents. The results show that practitioners value their legacy systems highly, the challenges they face are not just technical, but also include business and organizational aspects.
Z ERO -S HOT R EWARD S PECIFICATION VIA G ROUNDED N ATURAL L ANGUAGE

Rewards in reinforcement learning are difficult to design for most tasks and often require access to underlying state information. The alternatives to reward signals are usually demonstrations or goal images which can be labor-intensive to collect. Describing the goal as a text description is a low effort way of communicating the desired task to an agent. However, goal-text conditioned policies so far have been trained in scenarios either with rewards that assume access to states or with labeled expert demonstrations. In this work, we devise a model that leverages CLIP to ground objects in a scene described by the goal-text paired with spatial relationship rules to provide an off-the-shelf reward signal using only raw pixels as input. We distill the policies learned with this reward signal on several tasks to obtain a single text conditioned policy that can be applied to solve new tasks at deployment. We show the effectiveness of this framework on a set of robotic manipulation tasks.
A Two-stage Classifier for Protein β-turn Prediction Using Support Vector Machines

not only because of their sheer abundance, which is estimated to be approximately 25% of all protein residues, but also because of their significance in high-order structures of proteins. In this study, we introduce a new method of β-turn prediction that uses a two-stage classification scheme and an integrated framework for input features. Tenfold cross validation based on a benchmark dataset of 426 non-homologue protein chains is used to evaluate our method's performance. The experimental results demonstrate that it achieves substantial improvements over BetaTurn, the current best method. The prediction accuracy, Q total , and the Matthews correlation coefficient (MCC) of our approach are 79% and 0.47 respectively, compared to 77% and 0.45 respectively for BetaTurn.
Comprehensive and cell‐type‐based characterization of the dorsal midbrain during development

The layer structure has been intensively characterized in the developing neocortex and cerebellum based on the various molecular markers. However, as to the developing dorsal midbrain, comprehensive analyses have not been intensely carried out, and thus, the name as well as the definition of each layer is not commonly shared. Here, we redefined the three layers, such as the ventricular zone, intermediate zone and marginal zone, based on various markers for proliferation and differentiation in embryonic dorsal midbrain. Biphasic Ki67 expression defines the classical VZ, in which there is clear separation of the mitotic and interphase zones. Next, we mapped the distribution of immature neurons to the defined layers, based on markers for glutamatergic and GABAergic lineage. Interestingly, Tbr2 and Neurog2 were expressed in the postmitotic neurons. We also report that active (phosphorylated) JNK is a useful marker to demarcate layers during the embryonic stage. Finally, we validated the final arrival layers of the migratory glutamatergic and GABAergic neurons. These results form a foundation for analyses of brain development, especially in the proliferation and migration of excitatory and inhibitory neurons in the dorsal midbrain.
Power Minimization in Wireless Network Virtualization with Massive MIMO by Mohammadmoein Soltanizadeh A thesis submitted in conformity with the requirements

Power Minimization in Wireless Network Virtualization with Massive MIMO Mohammadmoein Soltanizadeh Master of Applied Science Graduate Department of Electrical and Computer Engineering University of Toronto 2017 This thesis aims to study wireless network virtualization and particularly its challenges when it is applied to massive MIMO technology. Virtualization has been well studied in the wired networks and computational resources. However, when it comes to wireless networks new challenges arise. We consider virtualization in the context of massive MIMO technology and develop a novel precoding schemes to abstract and share a massive MIMO base station with the objective of minimizing power. This problem is investigated when perfect channel information is available and when is not. Furthermore, we show that our formulation is general enough to be easily extended to multi-cell and address some concerns of the service provider. We obtain optimal precoding when perfect channel information is available and a nearly optimal solution when the channel information is corrupted.
Document Image Retrieval: An Overview

The economic feasibility of creating a large database of document image has left a tremendous need for robust ways to access the information. Printed documents are scanned for archiving or in an attempt to move towards a paperless office and are stored as images. In this paper, we provide a survey of methods developed by researchers to access document images. The survey includes papers covering the current state of art on the research in document image retrieval based on images such as signature, logo, machine-print, different fonts etc.
Low-cost Hardware Fault Detection and Diagnosis for Multicore Systems

Continued technology scaling is resulting in systems with b llions of devices. Consequently, these devices are are prone to fai lures from various sources resulting in a growing reliability thr eat. As this reliability problem is expected to affect the broad com puting market, traditional solutions involving high redundancy, or piecemeal solutions targeting specific failure modes will no long er be viable. Recently, researchers have proposed SWAT, a low-co st solution that handles hardware faults by treating the resulti ng software anomalies. SWAT uses near-zero cost always-on symptom monitors for error detection and incurs higher diagnosis co t in the relatively rare event of a fault. The SWAT approach has been s hown to be highly effective for single-threaded applications. I t is, however, unclear whether such an approach would remain effecti ve for the widely used multithreaded software on multicore system . This paper presents mSWAT – SWAT for multicore architecture s running multithreaded software, focusing on detection and diagnosis. For detection, we use the symptom-based detectors in SW AT and show that symptom detection results in a very low Silent D ata Corruption (SDC) rates for both permanent and transient har dw e faults. The challenge in this new setting is in fault diagnos is, owing to the fault propagating out of the faulty core, and causing s ymptoms from fault-free cores. To this end, we propose a novel pe rmanent fault diagnosis mechanism that identifies the faulty co re even under such circumstances. Our diagnosis procedure (1) capt ures the trace of the in-situ execution that activates the underlyin g fault, (2) deterministically replays the execution on different core s, and (3) compares the execution traces to isolate the faulty core. Ou r res lts show that this diagnosis technique, successfully diagnose s 95% of the detected faults while incurring minimal hardware overh eads. This work is supported in part by the Gigascale Systems Research Center (funded under FCRP, an SRC program), the National Science Foundation under Grants NSF CCF 05-41383, CNS 07-20743, and NGS 04-06351, an OpenSPARC Center of Excellence at the University of Illinois at Urbana-Champaign supported by Sun Microsystems, and an equipment donation from AMD. Permission to make digital or hard copies of all or part of thi s work for personal or classroom use is granted without fee provided th at copies are not made or distributed for profit or commercial advantage an d that copies bear this notice and the full citation on the first page. To cop y therwise, to republish, to post on servers or to redistribute to lists, re quires prior specific permission and/or a fee. MICRO ’09, December 12–16, 2009, New York, NY, USA. Copyright 2009 ACM 978-1-60558-798-1/09/12 . $10.00.
CPU CPU GPU GPU GPU GPU Static Resource Allocator Dynamic Resource Allocator Mem Mem Mem Mem Mem Mem Runtime Query Optimization CPU backend GPU backend ⨝ σ Γ ⨝

Over the last years, modern servers have been adopting hardware accelerators, such as GPUs, in order to improve their power efficiency and computational capacity. Modern analytical query processing engines have been highly optimized for multi-core multiCPU query execution, but lack the necessary abstractions to support concurrent hardware-conscious query execution over multiple heterogeneous devices and exploit the available accelerators. This work presents a Heterogeneity-conscious Analytical query Processing Engine (HAPE), a blueprint for hardware-conscious analytical engines for efficient and concurrent multi-CPU multi-GPU query execution. HAPE decomposes query execution on heterogeneous hardware into: 1) efficient single-device and 2) concurrent multi-device query execution. It uses hardware-conscious algorithms designed for single-device execution and combines them into efficient intra-device hardware-conscious execution modules, via code generation. HAPE combines these modules to achieve multi-device execution by handling data and control transfers. We validate our design by building a prototype and evaluate its performance using radix-join co-processing and the TPC-H benchmark. We show that it achieves up to 10x and 3.5x speed-up on the radix-join against CPU and GPU alternatives and 1.6x-8x against state-of-the-art CPUand GPU-based commercial DBMSs on the selected TPC-H queries.
Field and Service Robotics: Results of the 8th International Conference

FSR, the International Conference on Field and Service Robotics, is the leading single track conference of robotics for field and service applications. This book presents the results of FSR2012, the eighth conference of Field and Service Robotics, which was originally planned for 2011 with the venue of Matsushima in Tohoku region of Japan. However, on March 11, 2011, a magnitude M9.0 earthquake occurred off the Pacific coast of Tohoku, and a large-scale disaster was caused by the Tsunami which resulted, therefore the conference was postponed by one year to July, 2012. In fact, this earthquake raised issues concerning the contribution of field and service robotics technology to emergency scenarios. A number of precious lessons were learned from operation of robots in the resulting, very real and challenging, disaster environments. Up-to-date study on disaster response, relief and recovery was then featured in the conference. This book offers 43 papers on a broad range of topics including: Disaster Response, Service/Entertainment Robots, Inspection/Maintenance Robots, Mobile Robot Navigation, Agricultural Robots, Robots for Excavation, Planetary Exploration, Large Area Mapping, SLAM for Outdoor Robots, and Elemental Technology for Mobile Robots.
TRANSFORM-BASED FEATURE V FOR DISTRIBUTED SPEEC

The technique of distributed speech recognition (DSR) has recently become an interesting area of research. One of the main issues with DSR is the need to compress the feature vector stream, produced on the terminal device, into a sufficiently low bit-rate such that it can be sent across low bandwidth channels. This work proposes a compression technique based upon first transforming a block of feature vectors into a more compact matrix representation. Columns of the resulting matrix that correspond to faster temporal variation can be removed without loss in recognition performance. The number of bits allocated to the remaining coefficients in the matrix is determined automatically, based on a measure of the information present. Experiments show that the transform-based compression gives good recognition accuracy at bit rates of 4800, 2400 and 1200bps. For example at 1200bps the recognition performance is 98.03% compared to 98.57% with uncompressed speech.
SemEval-2010 Task 3: Cross-Lingual Word Sense Disambiguation

The goal of this task is to evaluate the feasibility of multilingual WSD on a newly developed multilingual lexical sample data set. Participants were asked to automatically determine the contextually appropriate translation of a given English noun in five languages, viz. Dutch, German, Italian, Spanish and French. This paper reports on the sixteen submissions from the five different participating teams.