Reviewerly/exHarmony huggingface dataset:
A Study on Hyperparameter Configuration for Human Activity Recognition

Human Activity Recognition is a machine learning task for the classification of human physical activities. Applications for that task have been extensively researched in recent literature, specially due to the benefits of improving quality of life. Since wearable technologies and smartphones have become more ubiquitous, a large amount of information about a person’s life has become available. However, since each person has a unique way of performing physical activities, a Human Activity Recognition system needs to be adapted to the characteristics of a person in order to maintain or improve accuracy. Additionally, when smartphones devices are used to collect data, it is necessary to manage its limited resources, so the system can efficiently work for long periods of time. In this paper, we present a semi-supervised ensemble algorithm and an extensive study of the influence of hyperparameter configuration in classification accuracy. We also investigate how the classification accuracy is affected by the person and the activities performed. Experimental results show that it is possible to maintain classification accuracy by adjusting hyperparameters, like window size and window overlap, depending on the person and activity performed. These results motivate the development of a system able to automatically adapt hyperparameter settings for the activity performed by each person.
An External Data Integrity Tracking and Verification System for Universal Stream Computing System Framework

With the development of the cloud computing and big data, massive data based computing and storage have emerged as core technologies. Moreover, a key problem to ensure data availability and analysis accuracy is the data integrity which has received extensive attention. Compared to the mature solution in the batch computing, the data integrity in the stream computing did not receive due attention. However, the characters of timeliness, volatility, suddenness and disorder of big data make the data integrity in the stream computing a very difficult problem. Based on the framework of current general stream computing system, in this paper, we construct an externally data integrity tracking and verification system for the real-time tracking and analysing of the data content and processing path of each message, and the discovering of the data integrity problem. We use the common cryptographic algorithm to construct a universal data integrity tracking and verification scheme outside the stream computing system to ensure the integrity and consistency of the data. Then we give the formal security analysis in standard model, and perform simulations in cloud environment. Results show that the proposed system can verify data integrity efficiently without compromising the efficiency.
Software Engineering Dashboards: Types, Risks, and Future

The large number of artifacts created or modified in a software project and the flood of information exchanged in the process of creating a software product call for tools that aggregate this data to communicate higher-level insights to all stakeholders involved. In many projects -- in software engineering as well as in other domains -- dashboards are used to communicate information that may bring insights on the productivity of project activities and other insights. The goal of dashboards is to transform the raw data contained in an organization’s repositories into consumable information. In software engineering, dashboards are used to provide information related to questions such as “is this project on schedule?”, “what are the current bottlenecks?”, and “what is the progress of other teams?”. In this chapter, we review the different types of dashboards that are commonly used in software engineering and the risks that are associated with their use.
Preprocessing Argumentation Frameworks via Replacement Patterns

A fast-growing research direction in the study of formal argumentation is the development of practical systems for central reasoning problems underlying argumentation. In particular, numerous systems for abstract argumentation frameworks (AF solvers) are available today, covering several argumentation semantics and reasoning tasks. Instead of proposing another algorithmic approach for AF solving, we introduce in this paper distinct AF preprocessing techniques as a solver-independent approach to obtaining performance improvements of AF solvers. We establish a formal framework of replacement patterns to perform local simplifications that are faithful with respect to standard semantics for AFs. Moreover, we provide a collection of concrete replacement patterns. Towards potential applicability, we employ the patterns in a preliminary empirical evaluation of their influence on AF solver performance.
Engelli çocuklar için yenilikçi psikolojik destek teknolojileri

The relevance of this study is conditioned by the problem of developing and implementing innovative methods, technologies of working with disabled children. The purpose of this article research is to consider and to form a presentation of psychological aid innovative methods in order to support children with special needs by the methods of canis therapy and puppet theatre psycho-technology. The leading method of investigation this problem is the method of theoretical analysis, the method of ascertaining and educational experiment in the form of individual and group work. The proposed microtechniques of these innovative technologies make it possible to create a safe psychotherapeutic environment, reduce resistance, anxiety, and remove the used psychological protection of children and adolescents with special needs. The materials of the article can be useful for all subjects of the educational environment, who are taking part in inclusive education and upbringing: psychologists, teachers, speech therapists, speech pathologist, social workers, medical workers.
Expressive Meanings and Expressive Commitments. A Case of Meaning as Use

Expressives, i.e. words such as ‘damn’ or ‘bastard’, seem to convey a specific kind of content, different from, or on top of, “regular” descriptive meaning. Following the seminal work of Chris Potts (2005), the meaning of expressives is often conceptualized within a two-dimensional semantic framework, in which descriptive and expressive contents are separated as a result of special rules of semantic composition (cf. Gutzmann 2015). This approach is successful in accounting for some interesting semantic properties of expressives, e.g. their projective behavior, and has also been extended to other classes of expressions, such as racial slurs or honorifics. However, it does not offer any actual insight into the nature of expressive meaning (the two-dimensional formalism operates on dummy values, independently of what they may stand in for). The present paper offers an alternative, pragmatic account of expressives based on the observations that expressive meanings seem to involve directly the speaker (her states, emotions or attitudes) rather than just abstract (e.g. truth-conditional) contents, and that the utterer of an expressive is responsible for the choice of loaded, often taboo vocabulary. The account is developed in a commitment-based scorekeeping model of discourse (inspired by Lewis 1979 and Brandom 1994), in which hearers interpret speakers’ utterances by attributing commitments to them. Besides assertoric commitments (and potentially other kinds), expressive commitments can be distinguished. These are commitments to the appropriateness or applicability of a given expression, which also can be attributed to speakers based on their utterances (separately from assertoric commitments). What characterizes expressives as a lexical class is that they always raise the issue of speaker’s expressive commitment. In short, expressive meanings are commitments to the appropriateness of strongly charged (often vulgar or taboo) vocabulary - which, in turn, can signal a speaker’s heightened emotional state, negative attitude etc.
Improving Accuracy of Evolving GMM Under GPGPU-Friendly Block-Evolutionary Pattern

As a classical clustering model, Gaussian Mixture Model (GMM) can be the footstone of dominant machine learning methods like transfer learning. Evolving GMM is an approximation to the classical GMM under time-critical or memory-critical application scenarios. Such applications often have constraints on time-to-answer or high data volume, and raise high computation demand. A prominent approach to address the demand is GPGPU-powered computing. However, the existing evolving GMM algorithms are confronted with a dilemma between clustering accuracy and parallelism. Point-wise algorithms achieve high accuracy but exhibit limited parallelism due to point-evolutionary pattern. Block-wise algorithms tend to exhibit higher parallelism. Whereas, it is challenging to achieve high accuracy under a block-evolutionary pattern due to the fact that it is difficult to track evolving process of the mixture model in fine granularity. Consequently, the existing block-wise algorithm suffers from significant accuracy degradation, compared to its batch-mode counterpart: the standard EM algorithm. To cope with this dilemma, we focus on the accuracy issue and develop an improved block-evolutionary GMM algorithm for GPGPU-powered computing systems. Our algorithm leverages evolving history of the model to estimate the latest model order in each incremental clustering step. With this model order as a constraint, we can perform similarity test in an elastic manner. Finally, we analyze the evolving history of both mixture components and the data points, and propose our method to merge similar components. Experiments on real images show that our algorithm significantly improves accuracy of the original general purpose bock-wise algorithm. The accuracy of our algorithm is at least comparable to that of the standard EM algorithm and even outperforms the latter under certain scenarios.
CORRELATIONAL STUDY OF STUDENTS’ SPEAKING COMPETENCE AND WRITING COMPETENCE AND THE IMPACT IN STUDENTS’ PERFORMANCE IN ENGLISH

Purpose of Study: This research is intended to answer the question of whether there is a correlation between students speaking competence and writing competence and the impact on students’ performance in writing and speaking. This research was preceded with the early assumption that students with high competence in speaking will surely have high competence in writing since both are productive skills which demand students to produce language orally or written. But in reality, the researcher found several pieces of evidence that someone with high competence in writing usually does not have good speaking performance. Similarly, most students who are active in speaking cannot write very well.&#x0D; Methodology: This research is done to English Department students at Muhammadiyah University. The data of the research is students’ final scores in Speaking IV and Writing III. Both of the subjects are chosen because they are reflected to have an equal measurement on students’ competence in productive skills. In speaking IV (Public Speaking), students are assessed their ability to speak in front of the public with different contexts. While Writing III (Essay Writing) students are trained to write several topics of the essay.&#x0D; Results: The result of the correlation test between speaking and writing used the statistic test Pearson Correlation, with a sample of 36 students. The researcher does not take all of the students who have writing and speaking lesson. The researcher only examines 36 students as the sample.&#x0D; Implications/Applications: From the 36 students there are 7 students who have a significantly better score of speaking than writing. Meanwhile, there are 6 students who got a significantly better score on writing than speaking. The rest, 23 students have the same average score between speaking and writing. From this raw measurement supported by the statistic correlation result that shows there is a very low correlation between speaking and writing. It can be concluded that students who have competence in speaking don’t always have the same level of competence in writing, although both skills are the same productive skills that require students to produce language performance. Oral performance and written performance require slightly different competence.
Deep Ordinal Classification Based on the Proportional Odds Model

This paper proposes a deep neural network model for ordinal regression problems based on the use of a probabilistic ordinal link function in the output layer. This link function reproduces the Proportional Odds Model (POM), a statistical linear model which projects each pattern into a 1-dimensional space. In our case, the projection is estimated by a non-linear deep neural network. After that, patterns are classified using a set of ordered thresholds. In order to further improve the results, we combine this link function with a loss cost that takes the distance between classes into account, based on the weighted Kappa index. The experiments are based on two ordinal classification problems, and the statistical tests confirm that our ordinal network outperforms the nominal version and other proposals considered in the literature.
Counting Strategies for the Probabilistic Description Logic $$\mathcal {ALC}^\mathsf {ME}$$ Under the Principle of Maximum Entropy

We present $$\mathcal {ALC}^\mathsf {ME}$$ , a probabilistic variant of the Description Logic $$\mathcal {ALC}$$ that allows for representing and processing conditional statements of the form “if E holds, then F follows with probability p” under the principle of maximum entropy. Probabilities are understood as degrees of belief and formally interpreted by the aggregating semantics. We prove that both checking consistency and drawing inferences based on approximations of the maximum entropy distribution is possible in $$\mathcal {ALC}^\mathsf {ME}$$ in time polynomial in the domain size. A major problem for probabilistic reasoning from such conditional knowledge bases is to count models and individuals. To achieve our complexity results, we develop sophisticated counting strategies on interpretations aggregated with respect to the so-called conditional impacts of types, which refine their conditional structure.
How Quantum Computing Can Help with (Continuous) Optimization

It is known that the use of quantum computing can reduce the time needed for a search in an unsorted array: from the original non-quantum time T to a much smaller quantum computation time $$T_q\sim \sqrt{T}$$. In this paper, we show that for a continuous optimization problem, with quantum computing, we can reach almost the same speed-up: namely, we can reduce the non-quantum time T to a much shorter quantum computation time $$\sqrt{T}\cdot \ln (T)$$.
A Flexible Platform Architecture for the Dynamic Composition of Third-Party-Services

The increasing availability of IP-based services and the rise of the platform economy in several industrial branches allow vendors to conquer new sales markets and develop new business models. Technical interoperability among services available on platforms is a desirable feature, but also a major challenge. Typically, service providers need to implement APIs of other services directly - an expensive, cumbersome, and often error-prone task. We present a platform architecture that allows to define, publish, and implement third-pary APIs to ease dynamic service composition.
Deep Learning and Change Detection for Fall Recognition

Early fall detection is a crucial research challenge since the time delay from fall to first aid is a key factor that determines the consequences of a fall. Wearable sensors allow a reliable way for daily-life activities tracking, able to detect immediately a high-risk fall via a machine learning framework. Towards this direction, accelerometer devices are used widely for the assessment of fall risk. Although there is a plethora of studies under this perspective with promising results, several challenges still remain such as the extremely demanding data and power management as well as the discovery of false positive falls. In this work we propose a complete methodology based on the combination of the computationally demanding convolutional neural networks along with a lightweight change detection method. Our basic assumption is that it is possible to control computational resources for the operation of a classifier, suffice to be activated when a strong change in user’s movements is identified. The proposed methodology was applied to real experimental data providing reliable results that justify the original hypothesis.
Using a Robotic Arm for Measuring BRDFs

The measurement of a bidirectional reflectance distribution function (BRDF) requires a purpose-built instrument. We ease this requirement by presenting a relative method for measuring a BRDF using a multipurpose robotic arm. Our focus is on the alignment of the system to perform accurate camera positioning and orientation. We use a six degrees of freedom robotic arm to move a camera on a hemisphere surrounding a flat material sample. Point-like light sources, fixed on a quarter circle arc, sequentially illuminate the sample from different directions. The resulting images are used to reconstruct the material BRDF. We limit ourselves to tristimulus (RGB) isotropic BRDF acquisition.
Cyclic Queuing and Forwarding for Large Scale Deterministic Networks: A Survey

This paper provides a comprehensive survey of queueing and scheduling mechanisms for supporting large scale deterministic networks (LDNs). The survey finds that extensive mechanism design research and standards development for LDNs has been conducted over the past few years. However, these mechanism design studies have not been followed up with a comprehensive rigorous evaluation. The main outcome of this survey is a clear organization of the various research and standardization efforts towards queueing and scheduling mechanisms for LDNs as well as the identification of the main strands of mechanism development and their interdependencies. Based on this survey, it appears urgent to conduct a comprehensive rigorous simulation study of the main strands of mechanisms.
The usage of deep learning algorithm in medical diagnostic of breast cancer

Diagnosis is a crucial step to identify the disease that experienced by the patient. Diagnosis includes information gathering, integration, and interpretation. However, diagnosis process is not an easy task. Diagnostic accuracy is depending on the experience and cognitive ability of diagnosticians. The new algorithm called deep learning that is developed by simulating the human visual mechanism has been implemented in medical diagnostics. One of the diseases that can be diagnosed by using deep learning algorithm is the breast cancer. Several studies showed that deep learning algorithm can be used for detecting and classifying lesions, detecting mitosis, and predicting specific gene status. In this review article, 16 research journals were reviewed and discussed. The limitations of each algorithm are provided. All of the journals showed that deep learning algorithm has high diagnostics accuracy in assisting the professional diagnosticians to determine diagnosis outcome accordingly.
Is the LOD cloud at risk of becoming a museum for datasets? Looking ahead towards a fully collaborative and sustainable LOD cloud

The Linked Open Data (LOD) cloud has been around since 2007. Throughout the years, this prominent depiction served as the epitome for Linked Data and acted as a starting point for many. In this article we perform a number of experiments on the dataset metadata provided by the LOD cloud, in order to understand better whether the current visualised datasets are accessible and with an open license. Furthermore, we perform quality assessment of 17 metrics over accessible datasets that are part of the LOD cloud. These experiments were compared with previous experiments performed on older versions of the LOD cloud. The results showed that there was no improvement on previously identified problems. Based on our findings, we therefore propose a strategy and architecture for a potential collaborative and sustainable LOD cloud.
Evaluating the Four-Way Performance Trade-Off for Stream Classification

Machine Learning (ML) solutions need to deal efficiently with a huge amount of data available, addressing scalability concerns without sacrificing predictive performance. Moreover, this data comes in the form of a continuous and evolving stream imposing new constraints, e.g., limited memory and energy resources. In the same way, energy-aware ML algorithms are gaining relevance due to the power constraints of hardware platforms in several real-life applications, as the Internet of Things (IoT). Many algorithms have been proposed to cope with the mutable nature of data streams, with the Very Fast Decision Tree (VFDT) being one of the most widely used. An adaptation of the VFDT, called Strict VFDT (SVFDT), can significantly reduce memory usage without putting aside the predictive performance and time efficiency. However, the analysis of energy consumption regarding data stream processing of the VFDT and SVFDT is overlooked. In this work, we compare the four-way relationship between predictive performance, memory costs, time efficiency and energy consumption, tuning the hyperparameters of the algorithms to optimise the resources devoted to it. Experiments over 23 benchmark datasets revealed that the SVFDT-I is the most energy-friendly algorithm and greatly reduced memory consumption, being statistically superior to the VFDT.
The opportunity prior: a proof-based prior for criminal cases

One of the greatest challenges to the use of probabilistic reasoning in the assessment of criminal evidence is the ‘problem of the prior’, i.e. the difficulty in establishing an acceptable prior probability of guilt. Even strong supporters of a Bayesian approach have often preferred to ignore priors and focus on the likelihood ratio (LR) of the evidence. But to calculate if the probability of guilt, given the evidence reaches the probability required for conviction (the standard of proof), the LR has to be combined with a prior. In this article, we propose a solution to the ‘problem of the prior’: the defendant shall be treated as a member of the set of ‘possible perpetrators’ defined as the people who had the same or better opportunity as the defendant to commit the crime. For this purpose, we introduce the concept of an ‘extended crime scene’. The number of people who had the same or better opportunity as the defendant is the number of people who were just as close or closer to the crime scene, in time and space. We demonstrate how the opportunity prior is incorporated into a generic Bayesian network model that allows us to integrate other evidence about the case.
Fast PU Early Termination Algorithm Based on WMSE for ERP Video Intra Prediction

As virtual reality becomes more popular, 360-degree video coding becomes challenging. Projected videos of 360-degree videos and traditional videos are both planar videos, but the projected videos have distortion whose degree depends on the latitude. Traditional coding algorithms cannot effectively adapt to this feature, and 360-degree videos typically have high resolution and frame rate, which results in a high coding complexity. In this study, a fast prediction unit (PU) early termination algorithm based on weighted mean square error (WMSE) of 360-degree video is proposed. In the proposed algorithm, WMSE is used as a basis for the early termination of further PU partitioning. First, the full intra prediction process of the current CU is performed. After that, the similarity between the current CU and its four sub-CUs is calculated using WMSE for 2N×2N; and distortion between predicted and original blocks is calculated using WMSE for N×N. the similarity and distortion is used to terminate PU partitioning. The experimental results show that the algorithm achieves a 31% time reduction, and an average of only 0.3% of the luma Bjontegaard delta rate (BD rate) increases.