Reviewerly/exHarmony huggingface dataset:
The Time Efficiency Gain in Sharing and Reuse of Research Data

Among the frequently stated benefits of sharing research data are time efficiency or increased productivity. The assumption is that reuse or secondary use of research data saves researchers time in not having to produce data for a publication themselves. This can make science more efficient and productive. However, if there is no reuse, time costs in making data available for reuse will have been made with no return on this investment. In this paper a mathematical model is used to calculate the break-even point for time spent sharing in a scientific community, versus time gain by reuse. This is done for several scenarios; from simple to complex datasets to share and reuse, and at different sharing rates. The results indicate that sharing research data can indeed cause an efficiency revenue for the scientific community. However, this is not a given in all modeled scenarios. The scientific community with the lowest reuse needed to reach a break-even point is one that has few sharing researchers and low time investments for sharing and reuse. This suggests it would be beneficial to have a critical selection of datasets that are worth the effort to prepare for reuse in other scientific studies. In addition, stimulating reuse of datasets in itself would be beneficial to increase efficiency in scientific communities.
Multi-Criteria Selection of Capability-Based Cybersecurity Solutions

Given the increasing frequency and severity of cyber attacks on information systems of all kinds, there is interest in rationalized approaches for selecting the "best" set of cybersecurity mitigations.However, what is best for one target environment is not necessarily best for another.This paper examines an approach to selection that uses a set of weighted criteria, where the security engineer sets the weights based on organizational priorities and constraints.The approach is based on a capability-based representation for cybersecurity mitigations.The paper discusses a group of artifacts that compose the approach through the lens of Design Science research and reports performance results of an instantiation artifact.
On Estimating Model in Feature Selection With Cross-Validation

Both wrapper and hybrid methods in feature selection need the intervention of learning algorithm to train parameters.The preset parameters and dataset are used to construct several sub-optimal models, from which the final model is selected.The question is how to evaluate the performance of these sub-optimal models?What are the effects of different evaluation methods of sub-optimal model on the result of feature selection?Aiming at the evaluation problem of predictive models in feature selection, we chose a hybrid feature selection algorithm, FDHSFFS, and conducted comparative experiments on four UCI datasets with large differences in feature dimension and sample size by using five different cross-validation (CV) methods.The experimental results show that in the process of feature selection, twofold CV and leaveone-out-CV are more suitable for the model evaluation of low-dimensional and small sample datasets, tenfold nested CV and tenfold CV are more suitable for the model evaluation of high-dimensional datasets; tenfold nested CV is close to the unbiased estimation, and different optimal models may choose the same approximate optimal feature subset.
SmartEye

Instant photo taking and sharing has become one of the most popular forms of social networking. However, taking high-quality photos is difficult as it requires knowledge and skill in photography that most non-expert users lack. In this paper we present SmartEye, a novel mobile system to help users take photos with good compositions in-situ. The back-end of SmartEye integrates the View Proposal Network (VPN), a deep learning based model that outputs composition suggestions in real time, and a novel, interactively updated module (P-Module) that adjusts the VPN outputs to account for personalized composition preferences. We also design a novel interface with functions at the front-end to enable real-time and informative interactions for photo taking. We conduct two user studies to investigate SmartEye qualitatively and quantitatively. Results show that SmartEye effectively models and predicts personalized composition preferences, provides instant high-quality compositions in-situ, and outperforms the non-personalized systems significantly.
Statistical modeling of keystroke dynamics samples for the generation of synthetic datasets

Biometrics is an emerging technology more and more present in our daily life. However, building biometric systems requires a large amount of data that may be difficult to collect. Collecting such sensitive data is also very time consuming and constrained, s.a. GDPR legislation in Europe. In the case of keystroke dynamics, most existing databases have less than 200 users. For these reasons, it is crucial for this biometric modality to be able to generate a significant and realistic synthetic dataset of keystroke dynamics samples. We propose in this paper an original approach for the generation of synthetic keystroke data given samples from known users as a first step towards the generation of synthetic datasets. Experimental results show the capability of the proposed statistical model to generate realistic samples from existing datasets in the literature.
GPU Accelerated Sub-Sampled Newton's Method for Convex Classification Problems

First order optimization methods, which rely only on gradient information, are commonly used in diverse machine learning (ML) applications, owing to their simplicity of implementations and low per-iteration computational/storage costs. However, they suffer from significant disadvantages; most notably, their performance degrades with increasing problem ill-conditioning. Furthermore, they often involve a large number of hyperparameters, and are notoriously sensitive to parameters such as the step-size. By incorporating additional information from the Hessian, second-order methods, have been shown to be resilient to many such adversarial effects. However, these advantages come at the expense of higher per-iteration costs, which in “big data” regimes, can be computationally prohibitive.In this paper, we show that, contrary to conventional belief, second-order methods, when designed suitably, can be much more efficient than first-order alternatives for large-scale ML applications. In convex settings, we show that variants of classical Newton's method in which the Hessian and/or gradient are randomly subsampled, coupled with efficient GPU implementations, far outperform state of the art implementations of existing techniques in popular ML software packages such as TensorFlow. We show that our proposed methods (i) achieve better generalization errors in significantly lower wall-clock time – orders of magnitude faster, compared to first-order alternatives (in TensorFlow) and, (ii) offers significantly smaller (and easily parameterized) hyperparameter space making our methods highly robust.
Chinese NER Using Dynamic Meta-Embeddings

Named entity recognition (NER) is one of the basic techniques in natural language processing tasks.Chinese NER is complicated and difficult which remains a major challenge.One of the main reasons is that the boundaries of entities in Chinese are blurred and closely related to word segmentation results.Previous studies for this task have broadly divided into two categories, word-based, and character-based methods.However, the former class suffers from the word segmentation errors, and the latter cannot make full use of the information on multiple granularities.To address these problems, we investigate a new dynamic meta-embeddings method and apply it to Chinese NER task, which utilizes attention mechanism to combine features of both character and word granularity in embedding layer.The meta-embeddings created by our method are dynamic, data-specific, and task-specific, since the meta-embeddings for same characters in different sentence sequences are distinct.The experiments on MSRA and LiteratureNER datasets validate the effectiveness of our model, and this method achieves the state-of-the-art results on LiteratureNER.
Practical Method of Improving the Teamwork of Engineering Students Using Team Contracts to Minimize Conflict Situations

Educational models are incorporating methodologies to train students in teamwork skills in response to companies' information technology (IT) requirements. Conflict management is the key to effective teamwork. This paper proposes a method to improve students' organization, teamwork, and conflict management skills. This method consists of brief training on leadership and management styles, with minimal extra work, followed by the signing of team contracts that outline group rules, and promoting the assumption of responsibilities. The experimental results showed that this method reduced conflict rates, improved group communication and indirectly improved satisfaction, and responsibility and motivation in work groups. Consequently, students' overall academic performance improved, both as a group and as individuals.
A Coupled Operational Semantics for Goals and Commitments

Commitments capture how an agent relates to another agent, whereas goals describe states of the world that an agent is motivated to bring about. Commitments are elements of the social state of a set of agents whereas goals are elements of the private states of individual agents. It makes intuitive sense that goals and commitments are understood as being complementary to each other. More importantly, an agent's goals and commitments ought to be coherent, in the sense that an agent's goals would lead it to adopt or modify relevant commitments and an agent's commitments would lead it to adopt or modify relevant goals. However, despite the intuitive naturalness of the above connections, they have not been adequately studied in a formal framework. This article provides a combined operational semantics for goals and commitments by relating their respective life cycles as a basis for how these concepts (1) cohere for an individual agent and (2) engender cooperation among agents. Our semantics yields important desirable properties of convergence of the configurations of cooperating agents, thereby delineating some theoretically well-founded yet practical modes of cooperation in a multiagent system.
IoT Semantic Interoperability: A Systematic Mapping Study

The Internet of Things (IoT) is a paradigm in which the Internet connects people and the environment using devices and services that are spread in the user daily routine. In this scenario, different agents, devices and services are able to exchange data and knowledge using a common vocabulary or mappings that represent and integrate heterogeneous sources. This semantic interoperability is facilitated by the Semantic Web that provides consolidated technologies, languages and standards, offering data and platforms interoperability. In this context, this work reviews and analyzes the state-of-the-art of IoT semantic interoperability, investigating and presenting not only which Semantic Web technologies are employed but also the challenges that support the studies in this area of research.
Permutation rules and genetic algorithm to solve the traveling salesman problem

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Smart Cupboard for Assessing Memory in Home Environment

Sensor systems for the Internet of Things (IoT) make it possible to continuously monitor people, gathering information without any extra effort from them. Thus, the IoT can be very helpful in the context of early disease detection, which can improve peoples’ quality of life by applying the right treatment and measures at an early stage. This paper presents a new use of IoT sensor systems—we present a novel three-door smart cupboard that can measure the memory of a user, aiming at detecting potential memory losses. The smart cupboard has three sensors connected to a Raspberry Pi, whose aim is to detect which doors are opened. Inside of the Raspberry Pi, a Python script detects the openings of the doors, and classifies the events between attempts of finding something without success and the events of actually finding it, in order to measure the user’s memory concerning the objects’ locations (among the three compartments of the smart cupboard). The smart cupboard was assessed with 23 different users in a controlled environment. This smart cupboard was powered by an external battery. The memory assessments of the smart cupboard were compared with a validated test of memory assessment about face–name associations and a self-reported test about self-perceived memory. We found a significant correlation between the smart cupboard results and both memory measurement methods. Thus, we conclude that the proposed novel smart cupboard successfully measured memory.
Internet of things based multi‐sensor patient fall detection system

Accidental falls of patients cannot be completely prevented. However, timely fall detection can help prevent further complications such as blood loss and unconsciousness. In this study, the authors present a cost-effective integrated system designed to remotely detect patient falls in hospitals in addition to classifying non-fall motions into activities of daily living. The proposed system is a wearable device that consists of a camera, gyroscope, and accelerometer that is interfaced with a credit card-sized single board microcomputer. The information received from the camera is used in a visual-based classifier and the sensor data is analysed using the k-Nearest Neighbour and Naïve Bayes' classifiers. Once a fall is detected, an attendant at the hospital is informed. Experimental results showed that the accuracy of the device in classifying fall versus non-fall activity is 95%. Other requirements and specifications are discussed in greater detail.
GIS BASED SOLAR POWER PLANTS SITE SELECTION USING ANALYTIC HIERARCHY PROCESS (AHP) IN ISTANBUL, TURKEY

Abstract. Today, countries are shifting their energy policies towards to renewable energy sources. The main reasons for this can be summarized as the reduction of fossil fuel resources, resulting in cost increase and their harmful effect on the ecological balance. Since renewable energy sources are both economical and eco-friendly, for countries which have high solar energy potential such as Turkey, it is reasonable to direct their energy policies to solar energy which is a renewable energy source.In this study, the development of renewable energy legislation in Turkey, from past to present has been examined and implementation steps for the licenced and unlicensed generation of electricity from solar energy have been introduced. Ten impact factors have been identified as the first step for the implementation of the solar power plant site selection in Istanbul, which was determined as the pilot region. Impact factors weighted using Analytical Hierarchy Process (AHP) method. Concurrently, the weights of these determined impact factors were compared with the weights obtained by evaluating the results of the “Evaluation of Solar Energy Power Plant Site Selection Factors” survey conducted during the study. After obtaining the weights, the relevant data were collected and the necessary analyses were performed with the help of the GIS software and the most suitable places were provided for the solar power plant for Istanbul.
Hubless Nearest Neighbor Search for Bilingual Lexicon Induction

Bilingual Lexicon Induction (BLI) is the task of translating words from corpora in two languages.Recent advances in BLI work by aligning the two word embedding spaces.Following that, a key step is to retrieve the nearest neighbor (NN) in the target space given the source word.However, a phenomenon called hubness often degrades the accuracy of NN.Hubness appears as some data points, called hubs, being extra-ordinarily close to many of the other data points.Reducing hubness is necessary for retrieval tasks.One successful example is Inverted SoFtmax (ISF), recently proposed to improve NN.This work proposes a new method, Hubless Nearest Neighbor (HNN), to mitigate hubness.HNN differs from NN by imposing an additional equal preference assumption.Moreover, the HNN formulation explains why ISF works as well as it does.Empirical results demonstrate that HNN outperforms NN, ISF and other state-ofthe-art.For reproducibility and follow-ups, we have published all code 1 .
LSTMEmbed: Learning Word and Sense Representations from a Large Semantically Annotated Corpus with Long Short-Term Memories

While word embeddings are now a de facto standard representation of words in most NLP tasks, recently the attention has been shifting towards vector representations which capture the different meanings, i.e., senses, of words.In this paper we explore the capabilities of a bidirectional LSTM model to learn representations of word senses from semantically annotated corpora.We show that the utilization of an architecture that is aware of word order, like an LSTM, enables us to create better representations.We assess our proposed model on various standard benchmarks for evaluating semantic representations, reaching state-of-the-art performance on the SemEval-2014 word-to-sense similarity task.
Paraphrases as Foreign Languages in Multilingual Neural Machine Translation

Paraphrases, the rewordings of the same semantic meaning, are useful for improving generalization and translation.However, prior works only explore paraphrases at the word or phrase level , not at the sentence or corpus level.Unlike previous works that only explore paraphrases at the word or phrase level, we use different translations of the whole training data that are consistent in structure as paraphrases at the corpus level.We train on parallel paraphrases in multiple languages from various sources.We treat paraphrases as foreign languages, tag source sentences with paraphrase labels, and train on parallel paraphrases in the style of multilingual Neural Machine Translation (NMT).Our multi-paraphrase NMT that trains only on two languages outperforms the multilingual baselines.Adding paraphrases improves the rare word translation and increases entropy and diversity in lexical choice.Adding the source paraphrases boosts performance better than adding the target ones.Combining both the source and the target paraphrases lifts performance further; combining paraphrases with multilingual data helps but has mixed performance.We achieve a BLEU score of 57.2 for French-to-English translation using 24 corpus-level paraphrases of the Bible, which outperforms the multilingual baselines and is +34.7 above the single-source singletarget NMT baseline.
Algorithm for Surfaces Profiles and Thickness Variation Measurement of a Transparent Plate Using a Fizeau Interferometer with Wavelength Tuning

An interferogram obtained from a transparent plate contains information on the profiles of both surfaces and on the thickness variation. The present work is devoted to the processing of interferograms of this type. The processing technique is based on a 36-step algorithm developed by the authors for characterization of transparent plates having approximately equal reflections from both sides. The algorithm utilizes weighted multi-step phase shifting that enables one not only separately to extract the front and rear surface profiles together with the thickness variation of the tested plate but also to suppress the coupling errors between the higher harmonics and phase-shift deviation. The proposed measuring method was studied on a wavelength tunable Fizeau interferometer. The tested sample had an optical thickness and surface profile deviations equal to 0.51 µm, 1.38 µm and 0.89 µm, respectively. According to the results obtained using 10 repeated measurements, the root mean square (RMS) errors for determining both surface profiles did not exceed 1.5 nm. Experimental results show that the setup and presented 36-step algorithm are suitable for the measurement of a transparent plate of arbitrary thickness.
A single video super-resolution GAN for multiple downsampling operators based on pseudo-inverse image formation models

The popularity of high and ultra-high definition displays has led to the need for methods to improve the quality of videos already obtained at much lower resolutions. Current Video Super-Resolution methods are not robust to mismatch between training and testing degradation models since they are trained against a single degradation model (usually bicubic downsampling). This causes their performance to deteriorate in real-life applications. At the same time, the use of only the Mean Squared Error during learning causes the resulting images to be too smooth. In this work we propose a new Convolutional Neural Network for video super resolution which is robust to multiple degradation models. During training, which is performed on a large dataset of scenes with slow and fast motions, it uses the pseudo-inverse image formation model as part of the network architecture in conjunction with perceptual losses, in addition to a smoothness constraint that eliminates the artifacts originating from these perceptual losses. The experimental validation shows that our approach outperforms current state-of-the-art methods and is robust to multiple degradations.
Local-LDA: Open-Ended Learning of Latent Topics for 3D Object Recognition

Service robots are expected to be more autonomous and work effectively in human-centric environments. This implies that robots should have special capabilities, such as learning from past experiences and real-time object category recognition. This paper proposes an open-ended 3D object recognition system which concurrently learns both the object categories and the statistical features for encoding objects. In particular, we propose an extension of Latent Dirichlet Allocation to learn structural semantic features (i.e., visual topics), from low-level feature co-occurrences, for each category independently. Moreover, topics in each category are discovered in an unsupervised fashion and are updated incrementally using new object views. In this way, the advantages of both the (hand-crafted) local features and the (learned) structural semantic features have been considered and combined in an efficient way. An extensive set of experiments has been performed to assess the performance of the proposed Local-LDA in terms of descriptiveness, scalability, and computation time. Experimental results show that the overall classification performance obtained with Local-LDA is clearly better than the best performances obtained with the state-of-the-art approaches. Moreover, the best scalability, in terms of number of learned categories, was obtained with the proposed Local-LDA approach, closely followed by a Bag-of-Words (BoW) approach. Concerning computation time, the best result was obtained with BoW, immediately followed by the Local-LDA approach.