Reviewerly/exHarmony huggingface dataset:
Airborne radiometric data for identifying lateritic surfaces in southwestern Amazonia, Brazil

Airborne gamma-ray spectrometry and altimetric data have been used to recognize lateritic surfaces in southwestern Amazonia, Brazil. Three altimetric surfaces, marked by three main types of lateritic duricrust, comprise the landscape of the studied area. Surface 1 (SP1 ≤ 134 m a.s.l.) is dominated by a deep dissecting U-shaped valley, lowlands, steep slopes, and hills gathering Mn to Mn-Al-Fe duricrusts. Surface 2 (SP2–134 to 186 m a.s.l.) has rounded hills supported by Fe and FeAl duricrusts. Surface 3 (SP3–186 to 290 m a.s.l.) consists of strongly dissected plateaus to rounded hills sustained by bauxites, massive Fe duricrust, rare Mn-Al-Fe duricrusts, and Mn colluviums. Specifically, all lateritic duricrusts are associated with high values of eTh, and these data were employed in conjunction with SRTM data for generating favorability maps for lateritic surfaces using Boolean and fuzzy techniques. Altimetric data is an important input data in the boolean and fuzzy models since the boolean model overestimated the lateritic surfaces in surface 1, while not considering regional stratigraphic stacking when gathering ironstones as lateritic duricrust (lateritic duricrusts are older than ironstones). Both boolean and fuzzy models successfully recognized the lateritic duricrusts in surfaces 2 and 3 (134–290 m a.s.l.), even though the fuzzy product overestimated the lateritic surfaces above 134 m a.s.l. A lateritic index (LI = (eTh/eU) x K + (eTh x eU)) was also applied as complementary method in relation to the fuzzy and boolean models. LI divided the studied area into three main domains according to weathering intensity (lowest LI, low to intermediate LI, and high to extremely high LI associated with duricrust). Although the LI does not consider the altitude, the obtained model describes areas favorable for lateritic surface occurrences, being a useful and rapid method for preliminary large-scale mapping of lateritic surfaces. Mn duricrusts with K-bearing minerals were also identified by the intersection between F-factor (FK x (eU/eTh)) and eTh fuzzy membership maps. Nevertheless, geological control is important, since protolith influences the F-factor responses. The combination among the eTh fuzzy membership map, ternary map of K, eTh and eU on the RGB channels, the geological and soil map database and some chemical analysis allowed improving future research on supergene deposits as bauxite and manganese duricrusts, phosphorous and gold+sulphides.
LSTM Based Approach for Diabetic Symptomatic Activity Recognition Using Smartphone Sensors

Being concerned about the rising rate of the usage level of smartphone for last few years, researchers are striving to let out the disguised assets of smartphones. Regarding this phrase, the embedding of a variety of sensors such as accelerometer sensor, gyroscope sensor, humidity sensor etc. has attained the considerable attention of researchers for facilitating the human activity recognition task employing the sensor's applications. In this paper, we practiced the Long Short-Term Memory a.k.a. LSTM deep learning model with an aim to recognize thirteen human activities stated as walking, walking upstairs, walking downstairs, sitting, standing, jogging, squatting in the toilet, fallen down, lying, cycling, drinking, eating and genital itching. We opted for these activities with an intention of early diagnosing of diabetes in near future. We amassed data from four sensors stated accelerometer sensor, gyroscope sensor, humidity sensor and temperature sensor subjecting ten volunteers implying a frequency of 10Hz. The data was attained using an android application which was developed by us for the purpose of accumulation of sensor data from the smartphone.
Attention-based face alignment: A solution to speed/accuracy trade-off

Accuracy and speed are two important aspects of face alignment. One of the main concerns is the trade-off between speed and accuracy. To this end, we propose a novel attention-based deep learning framework consisting of a feature extraction module and an attention module. The feature extraction module is based on a two-branch architecture with a trunk branch and a mask branch. The trunk branch extracts shallow features, while mask branch is intermediately supervised to generate coarse masks with rough geometric information. An attention module is designed to select relevant features from trunk features under the guidance of geometric information from the coarse masks. The above attention module helps increase robustness of our framework to unconstrained faces while reducing complexity of our deep network. Extensive experiments are conducted on two public benchmark datasets, 300-W and AFLW. Ablation studies indicate its superior performance on challenging faces. Comparing with the state of the art, our method provides a competent trade-off between speed and accuracy. Our approach achieves 3.35% mean error with 0.29% failure rate on 300-W fullset, and 1.44% mean error with 0.40% failure rate on AFLW-Full datasets. Meanwhile, our method is capable of processing images at real-time speed (13ms) on 300-W with 68 landmarks.
One Size Doesn't Fit All: Quantifying Performance Portability of Graph Applications on GPUs

Hand-optimising graph algorithm code for different GPUs is particularly labour-intensive and error-prone, involving complex and ill-understood interactions between GPU chips, applications, and inputs. Although the generation of optimised variants has been automated through graph algorithm DSL compilers, these do not yet use an optimisation policy. Instead they defer to techniques like autotuning, which can produce good results, but at the expense of portability. In this work, we propose a methodology to automatically identify portable optimisation policies that can be tailored (“semi-specialised”) as needed over a combination of chips, applications and inputs. Using a graph algorithm DSL compiler that targets the OpenCL programming model, we demonstrate optimising graph algorithms to run in a portable fashion across a wide range of GPU devices for the first time. We use this compiler and its optimisation space as the basis for a large empirical study across 17 graph applications, 3 diverse graph inputs and 6 GPUs spanning multiple vendors. We show that existing automatic approaches for building a portable optimisation policy fall short on our dataset, providing trivial or biased results. Thus, we present a new statistical analysis which can characterise optimisations and quantify performance trade-offs at various degrees of specialisation. We use this analysis to quantify the performance tradeoffs as portability is sacrificed for specialisation across three natural dimensions: chip, application, and input. Compared to not optimising programs at all, a fully portable approach provides a 1.15× improvement in geometric mean performance, rising to 1.29 × when specialised to application and inputs (but not hardware). Furthermore, these semi-specialised optimisations provide insights into performance-critical features of specialisation. For example, optimisations specialised by chip reveal subtle, yet performance-critical, characteristics of various GPUs.
User Profiling Based Recommendation System for E-Learning

The current e-Learning management systems contain a huge pool of data gathered from multiple sources but main challenge that these systems are facing is the provision of relevant and quality content to the users and reduce the time wasted by the users to search this content. Also, with differences in the learning capabilities not all users can undertake the same learning track for understanding a particular content. In this paper, different user profiling methods, personalization methods and filtering techniques have been discussed and an approach has been presented to develop a web portal for adaptive e-learning. Furthermore, the paper discusses keyword focused web crawler to extract relevant information and use it further as a part of recommendation systems.
Dynamic Composition for Conversational Domain Exploration

We study conversational domain exploration (CODEX), where the user's goal is to enrich her knowledge of a given domain by conversing with an informative bot. Such conversations should be well grounded in high-quality domain knowledge as well as engaging and open-ended. A CODEX bot should be proactive and introduce relevant information even if not directly asked for by the user. The bot should also appropriately pivot the conversation to undiscovered regions of the domain. To address these dialogue characteristics, we introduce a novel approach termed dynamic composition that decouples candidate content generation from the flexible composition of bot responses. This allows the bot to control the source, correctness and quality of the offered content, while achieving flexibility via a dialogue manager that selects the most appropriate contents in a compositional manner. We implemented a CODEX bot based on dynamic composition and integrated it into the Google Assistant . As an example domain, the bot conversed about the NBA basketball league in a seamless experience, such that users were not aware whether they were conversing with the vanilla system or the one augmented with our CODEX bot. Results are positive and offer insights into what makes for a good conversation. To the best of our knowledge, this is the first real user experiment of open-ended dialogues as part of a commercial assistant system.
Why Is My Bug Wontfix?

Developers often use bug reports to triage and fix bugs. However, not every bug can be fixed eventually. To understand the underlying reasons why bugs are wontfix, we conduct an empirical study on three open source projects (i.e., Mozilla, Eclipse and Apache OpenOffice) in Bugzilla. First, we manually analyzed 600 wontfix bug reports. Second, we used the open card sorting approach to label these bug reports why they were wontfix, and we summarized 12 categories of reasons. Next, we further studied the frequency distribution of the categories across projects. We found that Not Support bug reports are the majority of the wontfix bug reports. Moreover, the frequency distribution of wontfix bug reports across the 12 categories is basically similar for the three open source projects.
Limited lookahead in imperfect-information games

Limited lookahead has been studied for decades in perfect-information games. We initiate a new direction via two simultaneous deviation points: generalization to imperfect-information games and a game-theoretic approach. We study how one should act when facing an opponent whose lookahead is limited. We study this for opponents that differ based on their lookahead depth, based on whether they, too, have imperfect information, and based on how they break ties. We characterize the hardness of finding a Nash equilibrium or an optimal commitment strategy for either player, showing that in some of these variations the problem can be solved in polynomial time while in others it is PPAD-hard, NP-hard, or inapproximable. We proceed to design algorithms for computing optimal commitment strategies—for when the opponent breaks ties favorably, according to a fixed rule, or adversarially. We then experimentally investigate the impact of limited lookahead. The limited-lookahead player often obtains the value of the game if she knows the expected values of nodes in the game tree for some equilibrium—but we prove this is not sufficient in general. Finally, we study the impact of noise in those estimates and different lookahead depths.
Improving Unsupervised Acoustic Word Embeddings using Speaker and Gender Information

For many languages, there is little or no labelled speech data available for training speech processing models. In zero-resource settings where unlabelled speech audio is the only available resource, speech applications for search, discovery and indexing often need to compare speech segments of different durations. Acoustic word embeddings are fixed dimensional representations of variable length speech sequences, allowing for efficient comparisons. Unsupervised acoustic word embedding models often still retain nuisance factors such as a speaker's identity and gender. Here we investigate how to improve the invariance of unsupervised acoustic embeddings to speaker and gender characteristics. We assume that speaker and gender labels are available for the untranscribed training data. We then consider two different methods for normalising out these factors: speaker and gender conditioning, and adversarial training. We apply both methods to two unsupervised embedding models: a recurrent neural network (RNN) autoencoder and a RNN correspondence autoencoder. In a word discrimination task, we find little benefit by explicitly normalising the embeddings to speaker and gender on English data. But on Xitsonga, substantial improvements are achieved. We speculate that this is due to the higher number of speakers present in the unlabelled Xitsonga training data.
Intercomprehension in Retrieval

Most of the Web is published in languages not accessible to many potential users who are only able to read and understand their local languages. Prior research has focused on using translation to provide users with information written in other languages, yet there are still many languages with little or no such resources. In this paper, we propose the use of intercomprehension -- a form of communication in which speakers of two different languages communicate using their own languages, mainly due to similarities between the languages. Accordingly, we conducted a user study on six Sub-Saharan languages to explore: user interaction behaviour in a retrieval environment where intercomprehension is expected; to investigate the usefulness of search results that assume intelligibility and relevance; and investigate affective episodes associated with intercomprehension in retrieval through retrospection. Although intercomprehension may come with a cost to understand unfamiliar languages, our results show that user preference of ranking of search results in related languages incorporates intelligibility. Our findings also suggest that intercomprehension is useful in retrieval for related languages -- users are able to identify relevant documents as well as complete search tasks by applying intercomprehension. However, the negative emotions or frustration associated with intercomprehension suggest that this type of interaction should be used in extreme cases where there are no or very few relevant documents in the language of the query.
LCQS: an efficient lossless compression tool of quality scores with random access functionality

Advanced sequencing machines dramatically speed up the generation of genomic data, which makes the demand of efficient compression of sequencing data extremely urgent and significant. As the most difficult part of the standard sequencing data format FASTQ, compression of the quality score has become a conundrum in the development of FASTQ compression. Existing lossless compressors of quality scores mainly utilize specific patterns generated by specific sequencer and complex context modeling techniques to solve the problem of low compression ratio. However, the main drawbacks of these compressors are the problem of weak robustness which means unstable or even unavailable results of sequencing files and the problem of slow compression speed. Meanwhile, some compressors attempt to construct a fine-grained index structure to solve the problem of slow random access decompression speed. However, they solve the problem at the sacrifice of compression speed and at the expense of large index files, which makes them inefficient and impractical. Therefore, an efficient lossless compressor of quality scores with strong robustness, high compression ratio, fast compression and random access decompression speed is urgently needed and of great significance.In this paper, based on the idea of maximizing the use of hardware resources, LCQS, a lossless compression tool specialized for quality scores, was proposed. It consists of four sequential processing steps: partitioning, indexing, packing and parallelizing. Experimental results reveal that LCQS outperforms all the other state-of-the-art compressors on all criteria except for the compression speed on the dataset SRR1284073. Furthermore, LCQS presents strong robustness on all the test datasets, with its acceleration ratios of compression speed increasing by up to 29.1x, its file size reducing by up to 28.78%, and its random access decompression speed increasing by up to 2.1x. Additionally, LCQS also exhibits strong scalability. That is, the compression speed increases almost linearly as the size of input dataset increases.The ability to handle all different kinds of quality scores and superiority in compression ratio and compression speed make LCQS a high-efficient and advanced lossless quality score compressor, along with its strength of fast random access decompression. Our tool LCQS can be downloaded from https://github.com/SCUT-CCNL/LCQSand freely available for non-commercial usage.
Temporal logic planning in uncertain environments with probabilistic roadmaps and belief spaces

Navigation problems expressed via temporal logics show promise for autonomous robot applications due to their versatility. In this paper, we introduce a method for planning with these specifications in uncertain environments that yields guaranteed satisfaction probabilities. We show that point-based value iteration can be combined with probabilistic roadmaps to solve this planning problem over the belief space of the uncertain environment.
An End-to-End Real-Time Face Identification and Attendance System using Convolutional Neural Networks

Carrying out the attendance process in any academic organization is a very significant task. However, the manual attendance process is very tedious and time- consuming. Hence, an automatic face attendance system using CCTV camera may be helpful by reducing the manpower and it also makes the attendance process faultless. There are some automated systems available commercially, but most of them deploy near frontal faces and processes them one by one, which is again a prolonged task. Some deep learning-based face attendance approaches have been proposed in the literature and improving the efficiency of the face attendance is still under research. In this paper, we propose an end-to-end face identification and attendance approach using Convolutional Neural Networks (CNN), which processes the CCTV footage or a video of the class and mark the attendance of the entire class in a single shot. One of the main advantages of the proposed solution is its robustness against usual challenges like occlusion (partially visible/covered faces), orientation, alignment and luminescence of the classroom. The proposed method obtained a real-time accuracy of 96.02% which is better than that of the existing end-to-end face attendance systems.
Tomography of binary quantum detectors

A tomography method for binary detectors is developed. In this method, different input states are employed and the measurement data are then collected. First a primary estimation of the detector is obtained through least squares estimation, without considering the restriction on the eigenvalues of the detector. Then this possibly nonphysical estimation is projected onto the physical subspace to obtain a final estimation. We analyze the computational complexity of this algorithm, and present a theoretical error upper bound. Numerical simulation on a two-qubit example validates the effectiveness of the algorithm.
Anticipatory QoE Mechanisms for 5G Data Analytics

3GPP which is working on the 5G specification is also working on the NWDAF (Network Data Analytics Function) which is used for data collection and data analytics in a centralized manner in the 5G Core. The 5G Network elements and/or the OAM (Operations and Network Management) can decide how to use the data analytics provided by NWDAF to improve the overall system performance. The way NWDAF is used is left to the imagination of the operators, but some of the use cases belong to one or several domains, e.g. QoS, traffic steering, network dimensioning. In this paper, we show how to use the NWDAF to address QoE issues. QoS is evaluated by monitoring KPIs, but this alone is insufficient for addressing QoE *issues as KPIs does not necessarily address QoE. We show that the anticipatory AI functionality will help address QoE in a mobile video streaming use case.
Energy Efficient Genetic Algorithm for Container Consolidation in Cloud System

A lot of work has been done and extensive research is going on for reducing the energy consumption of large-scale cloud data centers along with the maximization of host level utilization. Because of on-demand service provisioning, the number of users' requests increases which leads to an increase in the number of physical machines and data center size. This results in increased energy consumption in the data center as energy consumption is directly proportional to the number of the active physical machine. The container is a lightweight approach to operating system virtualization, offers an opportunity to improve the efficiency in cloud data centers. Containerized cloud gaining widespread popularity in recent years, so an efficient container consolidation is an open research challenge. Container consolidation is used to optimize energy consumption, as the resource utilization by the containers is directly relates to energy consumption. Container consolidation is an NP-Hard problem which can be solved by heuristic and metaheuristic algorithm. Therefore, in this paper, we implemented the heuristic and metaheuristic algorithm for energy reduction in the cloud. We implemented the next fit, best fit, best fit decreasing, first fit, first fit decreasing and compare their result. The experimental result shows that the best fit decreasing and first fit decreasing gave the almost same result. Further, this result is compared with the proposed Energy Efficient Genetic Algorithm(EEGA). We got the best result with the proposed EEGA.
Wordy: Interactive Word Cloud to Summarize and Browse Online Videos to Enhance eLearning

In this paper, we describe our Wordy system, which creates a word cloud based on video content and allows users to summarize and search for video content using the word cloud. We focus on lecture videos: converting speech information from the videos into text, extracting keywords from videos through morphological analysis, and creating a word cloud comprised of those keywords. In addition, the keywords in the word cloud are selectable. When a user selects a keyword, the system will find several points of the video in which a teacher talks about the keyword; the system then generates a relevant list of thumbnails. In order to save time for the user, the system offers a digest playback mode that consecutively browses the scenes concerned with the keywords. Herein, we describe our concept and the implementation of the prototype system.
Secure Video Transmission Framework for Battery-Powered Video Devices

Selective video encryption schemes for battery-powered video devices have been proposed to prevent illegal acquisition of recorded video during transmission. However, the transmission security of conventional schemes is lower than the fully encrypted transmission schemes because a substantial part of the recorded video remains unencrypted including significant metadata for decoding recorded video. In order to address this limitation, we propose a secure video transmission framework that selectively encrypts video metadata and frames on a per packet basis with a consideration of currently available computing power and transmission distance. Evaluation on a testbed demonstrates that the proposed framework provides enhanced video transmission security with less than half of the battery power consumption required by the fully encrypted transmission scheme.