Reviewerly/exHarmony huggingface dataset:
Lifelong Learning in Sensor-Based Human Activity Recognition

Sensor-based human activity recognition is to recognise users' current activities from a collection of sensor data in real time. This ability presents an unprecedented opportunity to many applications, and ambient assisted living (AAL) for elderly care is one of the most exciting examples. For example, from the meal preparation activities, we can derive the user's diet routine and detect any anomaly or decline in physical or cognitive condition, leading to immediate, appropriate change in their care plan. With the rapidly increasing ageing population and overstretched strains on our healthcare system, there is a rapidly growing need for industry in AAL. However, the complexity in real-world deployment is significantly challenging current sensor-based human activity recognition, including the inherent imperfect nature of sensing technologies, constant change in activity routines, and unpredictability of situations or events occurring in an environment. Such complexity can result in decreased accuracies in recognising activities over time and further a degradation of the performance of an AAL system. The state-of-the-art methodology in studying human activity recognition is cultivated from short-term lab or testbed experimentation, i.e., relying on well-annotated sensor data and assuming no change in activity models, which is no longer suitable for long-term, large-scale, real-world deployment. This creates a need for an activity recognition system capable of embedding the means of automatic adaptation to changes, i.e., lifelong learning. This talk will discuss new challenges and opportunities in lifelong learning in human activity recognition, with particular focus on transfer learning on activity labels across heterogeneous datasets.
A Multi-Stage Encryption Technique to Enhance the Secrecy of Image

This paper proposes a multi-stage encryption technique to enhance the level of secrecy of image to facilitate its secured transmission through the public network.A great number of researches have been done on image secrecy.The existing image encryption techniques like visual cryptography (VC), steganography, watermarking etc. while are applied individually, usually they cannot provide unbreakable secrecy.In this paper, through combining several separate techniques, a hybrid multi-stage encryption technique is proposed which provides nearly unbreakable image secrecy, while the encryption/decryption time remains almost the same of the exiting techniques.The technique consecutively exploits VC, steganography and one time pad (OTP).At first it encrypts the input image using VC, i.e., splits the pixels of the input image into multiple shares to make it unpredictable.Then after the pixel to binary conversion within each share, the exploitation of steganography detects the least significant bits (LSBs) from each chunk within each share.At last, OTP encryption technique is applied on LSBs along with randomly generated OTP secret key to generate the ultimate cipher image.Besides, prior to sending the OTP key to the receiver, first it is converted from binary to integer and then an asymmetric cryptosystem is applied to encrypt it and thereby the key is delivered securely.Finally, the outcome, the time requirement of encryption and decryption, the security and statistical analyses of the proposed technique are evaluated and compared with existing techniques.
Anonymous Deniable Identification in Ephemeral Setup and Leakage Scenarios (Brief Announcement)

We present anonymous identification schemes, where a verifier can check that the user belongs to an ad-hoc group of users (just like in case of ring signatures), however a transcript of a session executed between a user and a verifier is deniable: neither the verifier nor the prover can convice a third party that a given user has been involved in a session but also he cannot prove that any user has been interacting with the verifier. Our realization of this idea is based on Schnorr identification scheme and ring signatures. We present two constructions, a simple 1-of-n case and a more advanced k-of-n, where the prover must use at least k private keys. They are immune to leakage of ephemeral keys and with minor modifications this property can be sacrificed for a simpler construction.
LINGOBLM: using LINGO kernel in Bipartite Local Model

Predicting potential drug-target interactions from heterogeneous biological data could benefit novel drugs discovery and improve human medicine. Computational prediction is a suitable alternative for the traditional time-consuming and expensive experimental process of drug-target interactions prediction. New computational drug-target interactions prediction approaches are divided into two categories: machine learning-based and network-based. In this paper, we extended the Bipartite Local Model (BLM), one of the most well-known approaches for predicting drug-target interactions. BLM has a high computational complexity due to the use of a two-dimensional kernel for the drug side. Instead, we used LINGO, a one-dimensional kernel, to calculate the similarity between drugs. In order to compare our work with previously published results, we performed experiments using publicly available real-world drug-target interactions datasets. The results suggested that our approach is competitive and outperformed BLM.
Combining Channel Theory and Semantic Web Technology to build up a Production Capability Matching Framework

One of the main theories behind the worldwide ‘Factories of the Future’ movement is the idea that machines and systems, and thus the production system, should reach a certain level of self-organisation. We support this self-organisation of the production system by introducing a production capability matching framework that is based on an extended application of Channel Theory combined with the extensive use of Semantic Web technologies like OWL ontologies and rules based on SWRL. This approach shows that the combination of Channel Theory and Semantic Web technology is able to lay the groundwork for an automated equipment assignment process in global and local value chains.
Migrating two legacy systems to SOA: a new approach for service selection based on data flow diagram

There are many service identification methods (SIMs) to simplify service identification in SOA lifecycle. These SIMs vary in terms of their features (e.g., input artefact, technique). Due to this diversity, few evaluation frameworks have been proposed to guide organisations in selecting a suitable SIM based on their available input artefacts (e.g., source code, business process). This research concerns with SIMs that consider data flow diagram (DFD) as an input artefact, in order to migrate two legacy systems, modelled with DFD, to SOA. Only two SIMs are found in the literature to identify services based on DFD. However, these SIMs do not provide a way to select among the services identified to be implemented as web services. Therefore, this paper aims to bridge this gap by proposing a new approach for service selection based on DFD to assist organisations in speeding up the process of migrating their legacy systems to SOA.
Multiplicative Models for Recurrent Language Modeling

Recently, there has been interest in multiplicative recurrent neural networks for language modeling. Indeed, simple Recurrent Neural Networks (RNNs) encounter difficulties recovering from past mistakes when generating sequences due to high correlation between hidden states. These challenges can be mitigated by integrating second-order terms in the hidden-state update. One such model, multiplicative Long Short-Term Memory (mLSTM) is particularly interesting in its original formulation because of the sharing of its second-order term, referred to as the intermediate state. We explore these architectural improvements by introducing new models and testing them on character-level language modeling tasks. This allows us to establish the relevance of shared parametrization in recurrent language modeling.
Big variates — visualising and identifying key variables in a multivariate world

Big Data involves both a large number of events but also many variables. This paper will concentrate on the challenge presented by the large number of variables in a Big Dataset. It will start with a brief review of exploratory data visualisation for large dimensional datasets and the use of parallel coordinates. This motivates the use of information theoretic ideas to understand multivariate data. Two key information-theoretic statistics (Similarity Index and Class Distance Indicator) will be described which are used to identify the key variables and then guide the user in a subsequent machine learning analysis. Key to the approach is a novel algorithm to histogram data which quantifies the information content of the data. The Class Distance Indicator also sets a limit on the classification performance of machine learning algorithms for the specific dataset.
An Improved Algorithm for Computing All the Best Swap Edges of a Tree Spanner

A tree $$\sigma $$ -spanner of a positively real-weighted n-vertex and m-edge undirected graph G is a spanning tree T of G which approximately preserves (i.e., up to a multiplicative stretch factor $$\sigma $$ ) distances in G. Tree spanners with provably good stretch factors find applications in communication networks, distributed systems, and network design. However, finding an optimal or even a good tree spanner is a very hard computational task. Thus, if one has to face a transient edge failure in T, the overall effort that has to be afforded to rebuild a new tree spanner (i.e., computational costs, set-up of new links, updating of the routing tables, etc.) can be rather prohibitive. To circumvent this drawback, an effective alternative is that of associating with each tree edge a best possible (in terms of resulting stretch) swap edge—a well-established approach in the literature for several other tree topologies. Correspondingly, the problem of computing all the best swap edges of a tree spanner is a challenging algorithmic problem, since solving it efficiently means to exploit the structure of shortest paths not only in G, but also in all the scenarios in which an edge of T has failed. For this problem we provide a very efficient solution, running in $$O(n^2 \log ^4 n)$$ time, which drastically improves (almost by a quadratic factor in n in dense graphs) on the previous known best result.
AccurIT: a prototype of a machine translation engine for English to Arabic translation

Current machine translators have reached an unprecedented level of sophistication in dealing with not only isolated words, but also longer sentences and paragraphs. Despite the advances achieved in this field, several challenges remain to be resolved for machine translation (MT) to be on par with professional human translation, including the quality of grammar and context accuracy, pragmatics, relevance, choice of vocabulary and ability to translate large files effectively based on this list's criteria. Another extremely problematic area that we have observed is incorrect literal translation of English phrases, proverbs, idioms, figurative speech and cliches, which proves to be an issue with most current translation programs, even ones built using a phrase-based approach. Therefore, this study's objective was to develop a prototype of an English-Arabic MT engine, AccurIT, to address MTs' English-to-Arabic translation-accuracy issues in general. We compared the results of our tool against Google and Azure Translation Hub based on some excerpts from the legal realm to demonstrate AccurIT's efficacy, and the results are promising.
A Language Generation Toolkit for Expressing Agreement in Small Talk

Developing rapport is a fundamental component of social interaction. Mentioning shared beliefs or experiences is a common technique for rapport building. Social avatars, therefore, often need to agree with the user during their small talks. Repeatedly using phrases like "I agree" or "I also think so" will not only lead to dull conversations but also make the agreements seem not sincere and hence risking hurting the believability of the character. In this work, we propose a toolkit for creating responses that show agreement in small talk. The toolkit emphasizes two essential factors in rapport building: shared common ground and playfulness. Five techniques are included, ranging from simply echoing the user's opinion to contributing relevant information and using wordplay to add a sense of playfulness.
Towards Fine Whole-Slide Skeletal Muscle Image Segmentation through Deep Hierarchically Connected Networks

Automatic skeletal muscle image segmentation (MIS) is crucial in the diagnosis of muscle-related diseases. However, accurate methods often suffer from expensive computations, which are not scalable to large-scale, whole-slide muscle images. In this paper, we present a fast and accurate method to enable the more clinically meaningful whole-slide MIS. Leveraging on recently popular convolutional neural network (CNN), we train our network in an end-to-end manner so as to directly perform pixelwise classification. Our deep network is comprised of the encoder and decoder modules. The encoder module captures rich and hierarchical representations through a series of convolutional and max-pooling layers. Then, the multiple decoders utilize multilevel representations to perform multiscale predictions. The multiscale predictions are then combined together to generate a more robust dense segmentation as the network output. The decoder modules have independent loss function, which are jointly trained with a weighted loss function to address fine-grained pixelwise prediction. We also propose a two-stage transfer learning strategy to effectively train such deep network. Sufficient experiments on a challenging muscle image dataset demonstrate the significantly improved efficiency and accuracy of our method compared with recent state of the arts.
Influence of the User Experience in Switching Speed of the Digital Reading Interface

Objective: To measure the impact of the speed of interface switching on the digital reading user experience on mobile terminals. Methods: The subjective scale scoring method and semi-structured interviews are used to test the influence of the speed of the digital reading interface switching on the user experience, to explore the feedback of the perceived speed of the subject, and collect the subjective feelings of the user after the user-product interaction process ends. And use the method of summarization to summarize the user feedback. Results: The most comfortable interface switching speed when humans read digitally, and the effect of interface switching speed on the psychological perception of users with different operational purposes. Conclusion: The interface switching speed affects the user’s reading experience. The different operational purposes of the user have different requirements for the interface switching speed.
Multi-Value Sequence Generated over Sub Extension Field and Its Properties

Pseudo-random sequences with long period, low correlation, high linear complexity, and uniform distribution of bit patterns are widely used in the field of information security and cryptography.This paper proposes an approach for generating a pseudo-random multi-value sequence (including a binary sequence) by utilizing a primitive polynomial, trace function, and k-th power residue symbol over the sub extension field.All our previous sequences are defined over the prime field, whereas, proposed sequence in this paper is defined over the sub extension field.Thus, it's a new and innovative perception to consider the sub extension field during the sequence generation procedure.By considering the sub extension field, two notable outcomes are: proposed sequence holds higher linear complexity and more uniform distribution of bit patterns compared to our previous work which defined over the prime field.Additionally, other important properties of the proposed multi-value sequence such as period, autocorrelation, and cross-correlation are theoretically shown along with some experimental results.
Ten Artificial Human Optimization Algorithms

The term “Artificial Human Optimization” was first coined by the corresponding author of this work in December 2016 when he published a paper titled “Entrepreneur : Artificial Human Optimization” at Transactions on Machine Learning and Artificial Intelligence (TMLAI) Volume 4, No 6 (December 2016). According to that paper published in 2016, Artificial Human Optimization Field is defined as the collection of all those optimization algorithms which were proposed based on Artificial Humans. In real world we (Humans) solve the problems. In the same way Artificial Humans imitate real Humans in the search space and solve the optimization problems. In Particle Swarm Optimization (PSO) the basic entities in the solution space are Artificial Birds where as in Artificial Human Optimization the basic entities in search space are Artificial Humans. Each Artificial Human corresponds to a point in the solution space. Ten Artificial Human Optimization methods titled “Human Bhagavad Gita Particle Swarm Optimization (HBGPSO)”, “Human Poverty Particle Swarm Optimization (HPPSO)”, “Human Dedication Particle Swarm Optimization (HuDePSO)”, “Human Selection Particle Swarm Optimization (HuSePSO)”, “Human Safety Particle Swarm Optimization (HuSaPSO)”, “Human Kindness Particle Swarm Optimization (HKPSO)”, “Human Relaxation Particle Swarm Optimization (HRPSO)”, “Multiple Strategy Human Particle Swarm Optimization (MSHPSO)”, “Human Thinking Particle Swarm Optimization (HTPSO)”, “Human Disease Particle Swarm Optimization (HDPSO)” are applied on various benchmark functions and results obtained are shown in this work. &nbsp;&nbsp;&nbsp
Separate-file indexing

Indexes are often created in files that are separate from the source document. Walter Greulich suggests a new term to describe this method of indexing. He differentiates it from embedded indexing and provides an overview of software programs and methods for separate-file indexing.
Video Stabilization of the NAO Robot Using IMU Data

The implementation of a video stabilization system of the NAO robot is presented through the data of the IMU, this stabilized image is used to detect and track QR codes. Once the QR code is located, the NAO robot tracks and monitors it. The system was developed under the ROS platform, with modules implemented in C++ and Python languages. The system provides data for subsequent processes, which need to use video data for object recognition, task tracking, among others. Can get sequences of stable images and with the least amount of vibrations or sudden movements. One of the main benefits of this work is the visual tracking of objects through stable images during walking of the NAO robot, which introduces an erratic motion of the head camera, the effect that is mitigated with the digital visual gyrostabilized method presented in this work.
CHALLENGES FOR THE DESIGN OF NEW PEDAGOGIES BASED ON MOBILE TECHNOLOGIES

Abstract Despite the fact that investment in technology has increased in Latin America, little is yet known about the integration of mobile devices in educational practices. The objective of this article is to analyse the access, perception of usefulness and frequency of use of the laptops provided free of charge by Plan Ceibal from Uruguay in the pre-professional practices of student teachers. A mixed method was used for the investigation, which includes a survey to 385 students and 15 in-depth interviews. Results show a limited pedagogical use of mobile devices as well as important challenges for emergent pedagogies. Most of the participants incorporate the mobile devices in traditional teaching approaches.
A New Basic Probability Assignment Method for Multi-Source and High Conflict Information and its Application

In the field of target detection, material processing, such as nondestructive testing, welding and joining, many sensors are used to acquire information from different sources. In order to solve the problem of low precision of fusion results caused by conflicts between multi-source information, a new multi-source high-conflict information basic probability assignment method without any prior knowledge is proposed. Three most basic classifiers and the random selection of multiple sets of data in the UCI international public database are selected for simulation experiments to verify the feasibility and effectiveness of the proposed new method.
Beyond Posters

Objective: Although stair climbing could contribute to daily physical activity goals, and there is potential for broad population reach, most stair climbing interventions are single-component and have limited effect. Methods: In a longitudinal quasiexperimental study, a 1-month multicomponent intervention, followed by 6 months with point-of-choice prompts, was evaluated in a six-story university building. All floors climbed and lifts ascended were measured by infrared people counters. Regular building occupants were invited to participate in the Stairtember Challenge intervention component. Results: During the intervention, stair climbing increased by 15%. Stair to lift ratios increased from 0.46 to 0.56 (odds ratio 1.22; 95% confidence interval 1.18 to 1.27). Conclusions: The multicomponent stair climbing intervention achieved a moderate increase in average daily stair climbing and stair to lift ratio. As these effects are larger than previously found for traditional signage-based interventions, future interventions should go beyond posters for larger effects on population health.
Conversion from RLBWT to LZ77.

Converting a compressed format of a string into another compressed format without an explicit decompression is one of the central research topics in string processing. We discuss the problem of converting the run-length Burrows-Wheeler Transform (RLBWT) of a string to Lempel-Ziv 77 (LZ77) phrases of the reversed string. The first results with Policriti and Prezza's conversion algorithm [Algorithmica 2018] were $O(n \log r)$ time and $O(r)$ working space for length of the string $n$, number of runs $r$ in the RLBWT, and number of LZ77 phrases $z$. Recent results with Kempa's conversion algorithm [SODA 2019] are $O(n / \log n + r \log^{9} n + z \log^{9} n)$ time and $O(n / \log_{\sigma} n + r \log^{8} n)$ working space for the alphabet size $\sigma$ of the RLBWT. In this paper, we present a new conversion algorithm by improving Policriti and Prezza's conversion algorithm where dynamic data structures for general purpose are used. We argue that these dynamic data structures can be replaced and present new data structures for faster conversion. The time and working space of our conversion algorithm with new data structures are $O(n \min \{ \log \log n, \sqrt{\frac{\log r}{\log\log r}} \})$ and $O(r)$, respectively.
Distinguishing Features of the Activity of Extreme Right Groups under Conditions of State Counteraction to Online Extremism in Russia

The conservative shift taken by Russian authorities forced members of the Russian extreme right to seek shelter online. Nevertheless, they fell under censorship restrictions. The objective of this study is to reveal the distinguishing features of extreme right online groups and their participants' activity under conditions of censorship. The groups studied were identified by means of linguistic markers of extreme right sentiments and attitudes. The metrics of social network analysis were used to analyze interconnections between the groups and the internal migrations of closed communities. The study revealed that (1) extreme right online communities use the tactic of creating mirror Internet sites in case the main group is blocked; (2) blocking of the most extreme oppositional extreme right online groups induces the remaining ones to imitate obedience to law, using "softer forms" of extremist rhetoric; (3) the audience of the blocked groups continues spreading extremist ideas through channels related to other subjects. The study's authors conclude that prohibiting extreme right discourse promotes the proliferation of extreme right ideas and sentiments.