Reviewerly/exHarmony huggingface dataset:
Intelligent Fall Detection with Wearable IoT

Falls of older adults is a significant concern for themselves and caregivers as most of the times a fall leads to serious physical injuries. In the age of the Internet of things (IoT), connected smart homes and monitoring services have opened up opportunities for quality of life for the older adults. Detecting falls with wearable IoT devices can provide peace of mind for older adults and caregivers. Accelerometer based fall detection is investigated in this paper. Feed Forward Neural Network (FFNN) and Long Short Term Memory (LSTM) based Deep Learning network is applied to detect fall. LSTM network provides good accuracy based on the experiment. This experiment provides a promising indication that IoT-based fall monitoring can assure post-fall procedures to older adults and caregivers and this can increase the safety level and well-being of the older adults.
Free Choice Impossibility Results

Free Choice is the principle that possibly p or q implies and is implied by possibly p and possibly q. A variety of recent attempts to validate Free Choice rely on a nonclassical semantics for disjunction, where the meaning of p or q is not a set of possible worlds. This paper begins with a battery of impossibility results, showing that some kind of nonclassical semantics for disjunction is required in order to validate Free Choice. The paper then provides a positive account of Free Choice, by identifying a family of dynamic semantics for disjunction that can validate the inference. On all such theories, the meaning of p or q has two parts. First, p or q requires that our information is consistent with each of p and q. Second, p or q narrows down our information by eliminating some worlds. It turns out that this second component of or is well behaved: there is a strongest such meaning that p or q can express, consistent with validating Free Choice. The strongest such meaning is the classical one, on which p or q eliminates any world where both p and q are false. In this way, the classical meaning of disjunction turns out to be intimately related to the validity of Free Choice.
Morphological Verb-Aware Tibetan Language Model

The Tibetan language model (TLM) is the key to Tibetan natural language processing.In this paper, we first observe that, different from widely used languages, Tibetan contains many morphological verbs that rarely appear in natural sentences but play a key role in accurate text prediction.This property is usually ignored by existing methods and makes traditional training strategies less effective in constructing accurate and robust TLMs.Hence, we propose a morphological verb-aware TLM by offline learning via a character frequency reweighting strategy and online tuning of discriminative weights conditioned on morphological verbs.However, because of the influence of morphological verbs on the tense and semantics of sentences, it is necessary to consider the morphological verbs in Tibetan.As a result, compared with stateof-the-art methods, our method not only reduces the perplexity but also improves the character error on tasks of the text prediction and automatic speech recognition (ASR).
CAKA as Physics Learning Media Based on Android Apps on Smartphones

Abstract In this research, we aimed to design physics learning media based on Android apps on smartphones. The media were tested to ensure their validity, practicality and efficiency for learning activities. We used the Analysis, Design, Development, Implementation and Evaluation (ADDIE) model as the model of development. The media were tested through three steps: peer, expert and small group test. Data were collected via questionnaires, interviews and observations. This research produced physics learning media based on an application named CAKA (Becak Fisika). CAKA contains some senior high school physics topics such as work and energy, Newton’s laws and circular motion. The results showed that the CAKA learning media had good compatibility and there were no significant problems when they were used. The CAKA application was tested, and the result was found to be valid, practical and efficient for learning activities.
Suffix Array Construction on Multi-GPU Systems

Suffix arrays are prevalent data structures being fundamental to a wide range of applications including bioinformatics, data compression, and information retrieval. Therefore, various algorithms for (parallel) suffix array construction both on CPUs and GPUs have been proposed over the years. Although providing significant speedup over their CPU-based counterparts, existing GPU implementations share a common disadvantage: input text sizes are limited by the scarce memory of a single GPU. In this paper, we overcome aforementioned memory limitations by exploiting multi-GPU nodes featuring fast NVLink interconnects. In order to achieve high performance for this communication-intensive task, we design a parallel inter-GPU (re-)merging scheme. To handle segments spanning multiple GPUs, we propose an efficient strategy for the merging phase facilitated by a fast partitioning search. On 8 GPUs our implementation achieves speedups between 133 and 354 over sequential CPU-based libdivsufsort, between 30 and 68 over its multi-threaded shared memory version using 80 threads on 40 CPU cores for large datasets ranging from 697M to 3159M characters in size. For medium-sized datasets ranging between 104M and 236M, our approach yields maximum (minimum) speedups of 11.7 (4.5) and 6.45 (4.5) over existing single-GPU implementations (CUDPP, NVBIO). We are able to construct the suffix array of a full human genome on a single DGX-1 server within a runtime of 3.44~seconds which is faster than the 4.8 seconds that were previously reported employing 1600 cores on 100 nodes on a CPU-based HPC cluster. Our implementation is publicly available at https://gitlab.rlp.net/pararch/multi-gpu-suffix-array/.
Object Recognition and Classification Based on Improved Bag of Features using SURF AND MSER Local Feature Extraction

Object recognition and classification is a challenging task in computer vision because of the large variation in shape, size and other attributes within the same object class. Also we need to consider other challenges such as the presence of noise and haze, occlusion, low illumination conditions, blur and the cluttered backgrounds. Due to these facts, object recognition and classification gained attention in recent years. Many researchers have proposed different methods to address the problem of recognition. This paper proposes a method for object recognition and classification based improved bag of features using SURF(Speeded Up Robust Features) and MSER(Maximally Stable External Regions) local feature extraction. Combination of SURF and MSER feature extraction algorithm can improve the recognition efficiency and the classification accuracy can be improved by spatial pyramid matching. SURF and MSER extracts the local features of an image and generate a image histogram codebook. Spatial pyramid matching is applied to this histogram, which improves the accuracy of classification. The experiment is conducted on Caltech 101 and Caltech 256 dataset.
Heuristic analysis, cognitive walkthroughs &amp; expert reviews

Heuristic Analysis, Cognitive Walkthroughs and Expert Reviews are all types of inspection methods using analytical techniques that do not require users. They generate results in a fraction of the time and costs as empirical techniques. This chapter defines heuristic evaluations and cognitive walkthroughs, describes their differences and communicates the processes of each technique for the purposes of medical device design. Each of these methods are valuable for the determination of usability issues and are complementary to formal usability testing with users. They are often performed by experts in usability/human factors and result in: a list of usability issues and strengths, a severity or priority ranking, and recommendations for design improvement. They can and should be used throughout the design process, often with multiple iterations as the design of the user interface increases in fidelity.
A Fuzzy Adaptive Binary Global Learning Colonization-MLP model for Body Fat Prediction

Body fat prediction is a step toward addressing obesity issues. In this paper, we propose a machine learning-based prediction model incorporating a novel fuzzy adaptive global learning binary colonization method for feature selection. Two fuzzy inference systems are used to select input features more purposefully. The proposed model is validated against several well-known feature selection-based models. Experimental results show that it is able to outperform the other models in comparison on most of the performance metrics considered.
Keep on rating – on the systematic rating and comparison of authentication schemes

Purpose Six years ago, Bonneau et al. (2012) proposed a framework to compare authentication schemes to the ubiquitous text password. Even though their work did not reveal an alternative outperforming the text password on every criterion, the framework can support decision makers in finding suitable solutions for specific authentication contexts. The purpose of this paper is to extend and update the database, thereby discussing benefits, limitations and suggestions for continuing the development of the framework. Design/methodology/approach This paper revisits the rating process and describes the application of an extended version of the original framework to an additional 40 authentication schemes identified in a literature review. All schemes were rated in terms of 25 objective features assigned to the three main criteria: usability, deployability and security. Findings The rating process and results are presented along with a discussion of the benefits and pitfalls of the rating process. Research limitations/implications While the extended framework, in general, proves suitable for rating and comparing authentication schemes, ambiguities in the rating could be solved by providing clearer definitions and cut-off values. Further, the extension of the framework with subjective user perceptions that sometimes differ from objective ratings could be beneficial. Originality/value The results of the rating are made publicly available in an authentication choice support system named ACCESS to support decision makers and researchers and to foster the further extension of the knowledge base and future development of the extended rating framework.
The k-subset sum problem over finite fields of characteristic 2

We study the k-subset sum problem over finite fields of characteristic 2. We obtain some sufficient conditions for the solvability of the k-subset sum problem over binary finite fields when k≥3 and apply this condition to two well-known examples.
Lifelong Learning on the Go: English Language Mobile Learning in China

Abstract As China is playing an active role in the World's politics, economy, and cultural circles, being able to communicate in English has become one of the basic life and work skills and a key for global access and integration. High mobile technology penetration, together with the proliferation of mobile learning applications, have made it possible for Chinese adult learners to learn English on the go. Mobile technology's core features including portability, instant power‐on, on‐demand, and the ability to accommodate multimodal learning styles and preferences are accounted for to describe mobile learning apps’ affordances for lifelong adult learners’ English learning. Four popular English language learning apps, are compared to illustrate how Chinese lifelong adult learners are learning English on the go.
New approach for reducing energy consumption and load balancing in data centers of cloud computing

Cloud computing is a new framework, which is facing a numerous type of challenges including resource management and energy consumption of data centers. One of the most important duties of cloud service providers is to manage resources and schedule tasks for reducing energy consumption in data cente rs. In this paper, fuzzy logic is used for finding most adequate DC, improved DVFS algorithm is used for selecting ideal host and developed version of EDF-VD algorithm is utilized for Task scheduling and load balance in cloud computing. Our approach improvement to the current methods including EEVS, DVFS, Homogeneous, MBFD and EEVS-N.
Handling of Voluminous Tweets and Analyzing the Sentiment of Tweets

Nowadays, Social Media is used as a medium to express the views of the people about the recent trends, or issues. This results in generating large amount of days everyday. These data's need to be handled in more efficient manner to analyse the ambiguous. These real time streaming of data are to be stored and processed efficiently for further analysis and decision by using big data tools. Twitter is used as data source and sentimental analysis is performed over the tweets. In addition, big data tools such as Flume, Kafka, Hdfs, Hbase and Solr were used for effective handling of tweets.
Pscenegan: Multi-Domain Particular Scenes Generation Based on Conditional Generative Adversarial Networks

Generative adversarial networks (GANs) have made remarkable success in image generations. However, how to deal with the multi-domain particular scenes generation, which converts specific object images to different reasonable scene domians, is still an open problem. In this paper, we propose a multi-domain particular scene generation model named PSceneGAN (Particular Scene Generative Adversarial Nets) that is a novel dual-condition GAN. PSceneGAN is the first model to achieve one-to-many specific scene generation under the guidance of semantics using only one model. In addititon, we collect and label a novel high-quality clothing data set named DRESS and use it to verify our PSceneGAN through a challenging task. The results show that PSceneGAN not only accurately generates corresponding reasonable scene images according to input scene and semantic descriptions, but also achieves desired results in quantitative and qualitative evaluation, among which frechet inception distance (FID) and inception score (IS) are 25.40 and 36.24, respectively.
TinyKeys: A New Approach to Efficient Multi-Party Computation

We present a new approach to designing concretely efficient MPC protocols with semi-honest security in the dishonest majority setting. Motivated by the fact that within the dishonest majority setting the efficiency of most practical protocols does not depend on the number of honest parties, we investigate how to construct protocols which improve in efficiency as the number of honest parties increases. Our central idea is to take a protocol which is secure for $$n-1$$ corruptions and modify it to use short symmetric keys, with the aim of basing security on the concatenation of all honest parties’ keys. This results in a more efficient protocol tolerating fewer corruptions, whilst also introducing an LPN-style syndrome decoding assumption. We first apply this technique to a modified version of the semi-honest GMW protocol, using OT extension with short keys, to improve the efficiency of standard GMW with fewer corruptions. We also obtain more efficient constant-round MPC, using BMR-style garbled circuits with short keys, and present an implementation of the online phase of this protocol. Our techniques start to improve upon existing protocols when there are around $$n=10$$ parties with $$h=4$$ honest parties, and as these increase we obtain up to a 13 times reduction (for $$n=400,h=120$$ ) in communication complexity for our GMW variant, compared with the best-known GMW-based protocol modified to use the same threshold.
Comparative Study on Artificial Intelligence Techniques in Crime Forecasting

An application of efficient crime analysis is beneficial and helpful to understand the behavior of trend and pattern of crimes. Crime forecasting is an area of research that assists authorities in enforcing early crime prevention measures. Statistical technique has been widely applied in the past to develop crime forecasting models. However, it has been observed that researchers have begun to shift their research interests from statistical model to artificial intelligence model in crime forecasting. Thus, this study is conducted to observe the capabilities of artificial intelligence technique in improving crime forecasting. The main objective of this study is to conduct a comparative analysis on forecasting performance capabilities of four artificial intelligence techniques, namely, artificial neural network (ANN), support vector regression (SVR), random forest (RF), and gradient tree boosting (GTB) in forecasting crime rate. Forecasting capability of each technique was assessed in terms of measurement of errors. From the result obtained, GTB showed the highest performance capability where it scored the lowest measurement of errors compared to SVR, RF, and ANN.
Automatic Text Categorization Using Fuzzy Semantic Network

Knowledge Representation (KR) is an art of representing knowledge in such a form that later it can be retrieved, searched, queried and inferred with a precision and along with all possible associations. That is why it can readily be said that KR is one of the key areas of research in Semantic web, Artificial Intelligence Data mining and big data analytics. In this regard, several techniques have been investigated in the literature. In this paper, the fuzzy semantic network (FSN) has been investigated for automatic text categorization in religious domain. The network is consisted of religious entities and their relationships obtained from various religious book and scriptures. This state of the art network is built and investigated for various search criteria. The results show that the scheme is promising in terms of better categorization.
A Layered Approach to Automatic Essay Evaluation Using Word-Embedding

Automated Essay Evaluation (AEE) use a set of features to evaluate and score students essay solutions. Most of the features like lexical similarity, syntax, vocabulary and shallow content were addressed but evaluating students essays using the semantics and context of the essay are not addressed well. To address the issue which are related to the semantics and context, we propose a layered approach to AEE which uses neural word embedding in order to evaluate student answers semantically and the similarity will be computed by using Word Mover’s Distance. We also implemented a plagiarism detection algorithms to protect the students from submitting someone else solution as their own using k-shingles and local sensitive hashing. We also implemented an algorithm that penalize students who are trying to fool the system by submitting only content bearing works. The performance of the proposed AEE was evaluated and compared to other state-of-the-art methods qualitatively and quantitatively. The experimental results show that the proposed AEE approach using neural word embedding achieve higher level of accuracy as compared to others baselines and are promising in evaluating students essay solutions semantically.
Large Scale Linguistic Processing of Tweets to Understand Social Interactions among Speakers of Less Resourced Languages: The Basque Case

Social networks like Twitter are increasingly important in the creation of new ways of communication. They have also become useful tools for social and linguistic research due to the massive amounts of public textual data available. This is particularly important for less resourced languages, as it allows to apply current natural language processing techniques to large amounts of unstructured data. In this work, we study the linguistic and social aspects of young and adult people’s behaviour based on their tweets’ contents and the social relations that arise from them. With this objective in mind, we have gathered over 10 million tweets from more than 8000 users. First, we classified each user in terms of its life stage (young/adult) according to the writing style of their tweets. Second, we applied topic modelling techniques to the personal tweets to find the most popular topics according to life stages. Third, we established the relations and communities that emerge based on the retweets. We conclude that using large amounts of unstructured data provided by Twitter facilitates social research using computational techniques such as natural language processing, giving the opportunity both to segment communities based on demographic characteristics and to discover how they interact or relate to them.
Learners’ Gaze Behaviors and Metacognitive Judgments with an Agent-Based Multimedia Environment

65 undergraduate students from a North American university interacted with MetaTutorIVH, an agent-based multimedia learning environment that fosters self-regulated learning (SRL) strategy use (e.g., metacognition) while presenting information on several human body systems. Participants completed a self-paced task to study the influence of relevant content and an agents' expressed emotions on metacognitive judgments, gaze behaviors, and science learning. The goal of this study was to examine eye gaze behavior and metacognitive process use relative to their perceived relevancy of content based on discrepancies between multimedia materials and artificial agent's facial expressions. Results indicate an increase in overall fixation duration on multimedia content (e.g., text, diagram) when the text was perceived as less relevant, specifically revealing longer time spent fixating on text when the text was perceived as less relevant compared text and diagrams that were perceived as relevant. Further analyses reveal an increase in diagram fixation duration when the text was judged as being less relevant, but when compared to instances where the text and diagrams were both seen as only somewhat relevant. Across trials, there was no indication of the actual relevancy of content influencing the gaze behaviors of participants.
A Multidirectional-Difference-Hash-Based Image Clutter Metric for Targeting Performance

Quantification of image clutter plays an important role in predicting target acquisition performances of a photoelectric imaging system due to the strong effect of optoelectronic image clutter.Accuracy in predicting the targeting performance of previous reported clutter metrics was relatively low because of disadvantages, such as lack of ability to accurately quantify the image with complex clutters and threshold selection problem.To address this problem, a novel multidirectional-difference-Hash-based (MDHash-based) image clutter metric is proposed in this paper.Initially, an image similarity measure method based on multidirectional difference hash is established.Then, this method is applied to the quantification of image clutter, and an MDHash-based image clutter metric is obtained.A comparative experiment is conducted using Search_2 dataset.Results show that the proposed clutter metric correlates effectively with probability of detection, false alarm rate, and search time of observers.
A Heuristic Virtual Machine Scheduling Algorithm in Cloud Data Center

As a new model of data storage and computing, cloud data center attracts much attention. Task scheduling is always a hot topic in the research of cloud data center. At the same time, As the cost of cloud data center increases, the cost for users to use cloud data center becomes higher and higher. Less execution time means lower cost. Based on the above reasons, a short average execution time task scheduling algorithm is proposed in this paper, which can reduce the average execution time of the cloud data center according to the difference of the execution time of cloud tasks in the data center, and the effect of this method is more obvious with the increase of cluster size and the increase of the number of tasks. Finally, the algorithm is simulated in cloudsim to verify the effectiveness of the algorithm.