Reviewerly/exHarmony huggingface dataset:
Implementation of Digital Technologies in Pharmaceutical Products Lifecycle

Pharmaceutical industry is a very complicated field and in the modern world it requires solutions based on digital technologies to operate effectively. The key factor to provide effectiveness is the data exchange unification on the basis of common information space organization. Terminological dictionaries and Unified Reference Classifier of Drugs. In this work, major pharmaceutical products lifecycle stages have been discussed in accordance with regulatory standards implemented. Also, digital solutions have been described that are used for process automation. As well as data requirements for systems integration and pharmaceutical institutions integration with one another. For research and development stages digital solutions may be implemented in the form of laboratory automation systems. In clinical trials, electronic data capture systems and documentation management software are in place. On the regulatory stage, electronic systems for data acquisition from pharmaceutical companies are in place. On stages after market authorization, systems for storage automation and labeling and tracking solutions may be used. Another type of systems is different kinds of analytical systems for medicines procurement monitoring. On medical use stage, digital data exchange may help for electronic prescriptions and decision-making support. Further opportunities may arrive from Big Data analysis technology, Blockchain technology, artificial neural networks implementation, and others.
Information Retrieval Model for Open Educational Resources

In the field of higher education, students and teachers need information that allows them to facilitate and improve their education. For this reason, the web is one of the most useful information sources nowadays. Unfortunately, when a student requires information on a specific topic, traditional search engines do not search for what the student really needs. Due to this problem, many organizations have implemented repositories of open educational resources, but the main disadvantage that these have, is that they are scattered, and even unknown. So, this article presents a proposal to identify open educational resources in the repositories through an extraction process that includes the obtaining of metadata and description of each of the resources in its natural language, and later with a recommendation strategy analyze each one of the resources.
Reducing Annotation Effort in Automatic Essay Evaluation Using Locality Sensitive Hashing

Automated essay evaluation systems use machine learning models to predict the score for an essay. For such, a training essay set is required which is usually created by human requiring time-consuming effort. Popular choice for scoring is a nearest neighbor model which requires on-line computation of nearest neighbors to a given essay. This is, however, a time-consuming task. In this work, we propose to use locality sensitive hashing that helps to select a small subset of a large set of essays such that it will likely contain the nearest neighbors for a given essay. We provided experiments on real-world data sets provided by Kaggle. According to the experimental results, it is possible to achieve good performance on scoring by using the proposed approach. The proposed approach is efficient with regard to time complexity. Also, it works well in case of a small number of training essays labeled by human and gives comparable results to the case when a large essay sets are used.
Communication with Students in Smart e-Learning System Using LMS Moodle

Despite the advantages of smart e-learning systems, their active use brings new problems to the educational process. One of these problems is the reduction in the effectiveness of students’ perception of information due to the deterioration in the quality of communication associated with a reduction in personal communication.
Students' Voices Towards the Integration of MALL to Promote Autonomous Language Learning

Utilizing technology in autonomous language learning may bring some implications along the line, particularly in EFL context where the learners are mostly accustomed to the traditional way of learning. However, in this current mass development of technology, technology-integrated learning is almost inevitable, not to mention in EFL educational context. Therefore, this study attempts to investigate how the learners perceive the new technology integration in the language learning called Mobile-assisted Language Learning (MALL) in account to promote autonomous language learning. Applying qualitative case study design, the data were collected through questionnaires and interview by involving 30 graduate students of a state university in Bandung, Indonesia. This study found that the participants have a positive attitude towards the integration of MALL to foster autonomous language learning. However, it was also found that teachers need to be aware of several constraints before applying it into practice. One of them is the culture of Indonesian learners that mostly still depend on teachers as they are not familiar with the practice of autonomous learning since their early education, and their access into the technology. Therefore, there is a need for further study towards the teacher's practice in integrating MALL into autonomous language learning.
Performance Modeling of MPI-based Applications on Cloud Multicore Servers

While cloud computing is widely adopted in many application domains, it is not yet the case for the high performance computing (HPC) domain. HPC traditionally runs on homogeneous, high-cost servers with fast networking providing for predictable performance; while bare-metal cloud offerings is promising, the underlying hardware is heterogeneous, with slower network connection, making it difficult to predict performance and hence tune applications. In this paper we consider performance modelling message passing interface (MPI)-based applications, being a major class of HPC applications. In particular, we present a queueing network performance model to account for computation and communication contentions on the underlying heterogeneous, relatively slow-interconnect architecture of the cloud bare-metal servers. The proposed model uses a non-linear problem solver to enhance the parameters acquired by profiling. We utilise our model to conduct an initial study of the performance of two benchmarks from SPECMPI-2007 suite and two NASA Parallel kernels, executing on a small cluster with varying number of multicore servers ranging from 2 to 8. Comparing the predicted and actual execution times of workloads with different number of processes shows 86% average accuracy for the benchmarks used.
Scalable Anomaly Detection for Large-Scale Heterogeneous Data in Cloud Using Optimal Elliptic Curve Cryptography and Gaussian Kernel Fuzzy C-Means Clustering

In most systems, a smart functionality is enabled through an essential vital service such as detecting anomalies from complex, large-scale and dynamic data. However, ensuring the privacy and security for the cloud data is the most crucial and challenging task in the present world. Moreover, it is important to safeguard the security of sensitive data and its privacy from unauthorized parties who are trying to access the data. Therefore, to accomplish this task, several encryption, decryption and key generation mechanisms were introduced in the existing works for privacy preserving in cloud platform. But, there still remain open issues such as increased communication overhead, reduced security and increased time consumption. Also, these existing works followed the symmetric key cryptographic mechanism for privacy preservation of data; hence, a single secret key is shared by several users for accessing the original data. Due to this fact, a high security risk arises and it allows unauthorized parties to access the data. Thus, this work introduces a cloud-based privacy preserving model for offering a scalable and reliable anomaly detection service for sensor data through holding the benefits of cloud resources. Also, this paper aims to impose a newly developed Elliptic Curve Cryptography-based Collective Decision Optimization (ECDO) approach over the proposed framework for improving the privacy and security of the data. Furthermore, to perform the data clustering computation we used the Gaussian kernel fuzzy [Formula: see text]-means clustering (GKFCM) algorithm within the cloud platform, especially for data partitioning and to classify the anomalies. Thus, the computational difficulties are limited by adopting this suitable privacy preserving model which collaborates a private server and a set of public servers through a cloud data center. Moreover, on encrypted data the granular anomaly detection operations are performed by the virtual nodes executed over public servers. Experimental validation was performed on four datasets resulting from Intel Labs publicly available sensor data. The experimental outcomes demonstrate the ability of the proposed framework in providing high anomaly detection accuracy without any degradation in data privacy.
The “Bachelor Project”: Project Based Computer Science Education

One of the challenges of educating the next generation of computer scientists is to teach them to become team players, that are able to communicate and interact not only with different IT systems, but also with coworkers and customers with a non-it background. The “bachelor project” is a project based on team work and a close collaboration with selected industry partners. The authors hosted some of the teams since spring term 2014/15. In the paper at hand we explain and discuss this concept and evaluate its success based on students' evaluation and reports. Furthermore, the technology-stack that has been used by the teams is evaluated to understand how self-organized students in IT-related projects work. We will show that and why the bachelor is the most successful educational format in the perception of the students and how this positive results can be improved by the mentors.
Perceptual Judgments to Detect Computer Generated Forged Faces in Social Media

There has been an increasing interest in developing methods for image representation learning, focused in particular on training deep neural networks to synthesize images. Generative adversarial networks (GANs) are used to apply face aging, to generate new viewpoints, or to alter face attributes like skin color. For forensics specifically on faces, some methods have been proposed to distinguish computer generated faces from natural ones and to detect face retouching. We propose to investigate techniques based on perceptual judgments to detect image/video manipulation produced by deep learning architectures. The main objectives of this study are: (1) To develop technique to make a distinction between Computer Generated and photographic faces based on Facial Expressions Analysis; (2) To develop entropy-based technique for forgery detection in Computer Generated (CG) human faces. The results show differences between emotions in both original and altered videos. These computed results were large and statistically significant. The results show that the entropy value for the altered videos is reduced comparing with the value of the original videos. Histograms of original frames have heavy tailed distribution, while in case of altered frames; the histograms are sharper due to the tiny values of images vertical and horizontal edges.
Living in a Sensor Limited World

This paper is about one way to address a troubling aspect of our use of software-intensive systems in complex environments: information flow across the system boundary. Everything we (and any other organism or embedded system) can know about the world around us is limited and largely determined by our sensor data, and what we can deduce or otherwise learn about the regularities they can be expected to exhibit. As system designers, we can program some of this knowledge into our systems, but we are almost always wrong in important and unforeseen ways. We need to help the systems we design mitigate these problems by actively participating in creating this knowledge. We want our systems to observe their environments, make critical inferences about their present and future behaviors, and use the resulting models to inform their own decisions. Moreover, we expect these systems to do the same analyses on their own internal structure and behavior, and on their interactions with the environment, to help them react more effectively to unexpected partial system failures and environmental surprises. In this paper, we show how the Wrappings integration infrastructure applies to this class of problems, by describing the architecture of self-modeling systems, which have models of their own behavior that are used to generate and manage that behavior.
A Feature Fusion Method for Effective Face Recognition Under Variant Illumination and Noisy Conditions

Faces extracted in bad light are affected in terms of unequal contrast, noise, and variant illumination. These kinds of disruptions decrease the accuracy of facial authentication real and complex environments. In this paper, a feature fusion method is provided to achieve illumination-robust face recognition. In this model, the Gaussian filter and Gabor filters are applied on facial image to generate the illumination-variant features. Each of the Gaussian and Gabor face is processed by LBP filter to generate the effective visual description for face. A block-level feature fusion is applied on Gaussian-LBP and Gabor-LBP faces to generate the composite feature pattern. This most relevant and adaptive feature patterns are processed on SVM classifier to recognize the face accurately. The proposed feature fusion model is applied on illumination, noise, and contrast-variant sample sets of extended Yale databases. The comparative results against SVM, KNN, and ANN methods verified the significant gain in accuracy of facial identification in complex environmental conditions.
Pair Programming as a Didactical Approach in Higher Education and its Evaluation

To handle heterogeneity within students, to foster needed generic competencies, to motivate them, and to increase their employability, Pair Programming seems to be a suitable didactical method to teach and learn programming. In this context, Pair Programming was used in programming lessons and its effectiveness was evaluated. This paper presents the results of the evaluations in order to derive whether Pair Programming is a suitable method to meet the mentioned challenges in a didactically meaningful way.
Cross-pose face recognition by integrating regression iteration and interactive subspace

At present, the pose change of the face test sample is the main reason that affects the accuracy of face recognition, and the design of cross-pose recognition algorithm is a technical problem to be solved. In this paper, a cross-pose face recognition algorithm which integrates the regression iterative method and the interactive subspace method was proposed, and through the regression iteration, the target function converges rapidly and important characteristics of the sample were extracted. Then, the posture of cross-pose face image was estimated, and finally, the interactive subspace method was applied to judge the similarity of the human face. The experimental results of FERET face database (± 45°and ± 90° posture) and MIT-CBCL face database (N-fold cross-validation) showed that the proposed RIM-ISM algorithm had a higher recognition rate and robustness, and it could effectively solve the difficulty of cross-pose face recognition.
Conceptualization of IMS that Estimates Learners’ Mental States from Learners’ Physiological Information Using Deep Neural Network Algorithm

To improve the efficiency of teaching and learning, it is substantially important to know learners' mental states during their learning processes. In this study, we tried to extract the relationships between the learner's mental states and the learner's physiological information complemented by the teacher's speech acts using machine learning. The results of the system simulation showed that the system could estimate the learner's mental states in high accuracy. Based on the construction of the system, we further discussed the concept of IMS and the necessary future work for IMS development.
Keyphrase Enhanced Diverse Beam Search: A Content-Introducing Approach to Neural Text Generation

Neural text generation has been a challenging task, among which the text representation and the beam search are crucial techniques. By improving these techniques, we propose a novel model to generate texts of higher quality in this paper. First, we leverage the global and local contextual features by combining the structure of both the recurrent neural network (RNN) and convolutional neural network (CNN) to learn a joint representation for the source text. Next, we introduce a modified diverse beam search to foster the diversity in the generated sentences during decoding, and then we rank these sentences according to its saliency score which measures the co-occurrence of keyphrases with the source text. Such a ranking mechanism promotes the semantical relevance between the source text and the generated sentence. To evaluate our model, we conduct extensive experiments on two neural generation tasks, including document summarization and headline generation. The results on both tasks show that our proposed model contributes to promising improvement in performance compared with the state-of-the-art baselines.
Enhancing Game Agent Pathfinding Through Dynamic Graph Reweighting

This paper proposes a computationally inexpensive algorithm that utilizes player data to optimize nonplayer character pathfinding in a competitive, multiplayer environment and focuses on imitating the player behavior. The algorithm’s input consists of player statistics gathered during the current and previous matches with additional time and space context, similar in design to influence maps. The input is then enriched with two additional, novel variables, allowing easy online fine-tuning of the output. The obtained result influences the final edge values of the map graph. Any known pathfinding algorithm that works with digraphs can then be utilized to control the agent. This paper contains exemplary results obtained when analyzing input on a map modeled after an existing map in the video game Unreal Tournament.
Method of Big-Graph Partitioning Using a Skeleton Graph

We propose a new method of graph partitioning for big graphs that include a conceptual schema. The conceptual schema of a graph database, called a schema graph, is defined implicitly as part of the graph database itself. A graph database is stored in a distributed triple-store, i.e., a distributed database system for managing graph edges represented by triples. We define the statistics of a graph database on the basis of the schema graph. The statistics are gathered for all schema triples, i.e., the types of graph edges. The space of the schema triples is partially ordered by the is-more-general relationship that is defined through the class and predicate taxonomies. The graph partitioning method has two phases. A skeleton graph of the triple-store is computed in the first phase. The skeleton graph is composed of the set of schema triples that have the extensions of an appropriate size to serve as the fragments of the distribution. The edges of the skeleton graph are selected in a top-down manner, i.e., from the most general schema triple to more specific schema triples. The edges of the skeleton graph are clustered into n partitions in the second phase of the algorithm. The function distance that is used in the clustering algorithm is based on the statistics of the schema triples. The graph partitioning function maps each schema triple from the skeleton graph to its partition, stored on a separate data server. The partitioning function is well defined in that it maps the types of the triple-patterns to k fragments such that k corresponds to the size of the portions of the triple-store addressed by the triple-patterns. In other words, it maps the types of triple-patterns that address a large number of triples to multiple distributed fragments, and the types of triple-patterns that address few triples to a single fragment.
A Phrase Topic Model for Large-scale Corpus

The topic model is an unsupervised learning model, one of the important tools for large-scale corpus analysis, widely used in information retrieval, natural language processing, and machine learning. Traditional topic models, such as Latent Dirichlet Allocation (LDA), ignore the order of words. However, in many text-mining tasks, word order and phrases are often crucial for capturing the meaning of texts efficiently. We propose a phrase topic model based on the LDA model, which integrates a regular expression constraint condition. Our model makes the topic more meaningful and interpretable based on a limited increase in the dimensions of the vocabulary. The experimental results show that our algorithm can find meaningful phrases and have generic applicability in our test data set.
Linear transformation on x‐vector for text‐independent speaker verification

Total variability model-based i-vector and deep neural network-based embedding x-vector are both widely used for text-independent speaker verification. In this Letter, a novel model is proposed, which can contain information of both i-vector and x-vector by using parallel factor analysis. The authors aim to obtain a linear transformation expression for x-vectors based on background i-vectors and x-vectors, and consider the linearly transformed x-vector as the novel model, thus they name it as -vector. The novel -vector can maximise intra and minimise inner speaker variability, in addition, it can improve the system performance without latency. Experiments were conducted on NIST 2010 dataset, and in terms of equal error rate, they observe up to 37.27 and 53.38% relative improvement of the authors proposed -vector model compared to the i-vector and x-vector models, respectively.
The Hydra Framework for Principled, Automated Bug Bounties

The Hydra Framework is a new, principled approach to modeling and detecting security-critical bugs. Fusing a variant of classical N-version (redundant) programming with automated bug bounty payouts, Hydra provides economically rigorous and cost-effective bounty protections for smart contracts.
Detecting Sarcasm in Text

Sarcasm is a nuanced form of speech extensively employed in various online platforms such as social networks, micro-blogs etc. and sarcasm detection refers to predicting whether the text is sarcastic or not. Detecting sarcasm in text is among the major issues facing sentiment analysis. In the last decade, researchers have been working rigorously on sarcasm detection so as to amend the performance of automatic sentiment analysis of data. In this paper, a supervised machine learning (ML) approach, which learns from different categories of features and their combinations, is presented. These feature sets are employed to classify instances as sarcastic and not-sarcastic using different classifiers. In particular, the impact of sarcastic patterns based on POS tags has been investigated and the results show that they are not useful as a feature set for detecting sarcasm as compared to content words and function words. Also, the Naïve Bayes classifier outperforms all other classifiers used.