Reviewerly/exHarmony huggingface dataset:
Literature Review of Service-Oriented Architecture (SOA) Adoption Researches and the Related Significant Factors

In this chapter based on the problem of the study the academic literature will be presented. In this chapter, author focuses more on the Service Oriented Architecture (SOA) adoption in an organization and extracts significant factors influence by SOA adoption based on previous studies. Then Technology–Organization–Environment (TOE) framework for adoption will be described. This chapter presents the literature review of SOA adoption, issues, and challenges related to this matter. Then based on papers reviewed potential factors are extracted from previous studies from 2009 till 2013. At the end of this section, TOE framework is explained briefly and three SOA adoption models based on TOE framework are described. All the information and definitions are conducted according to previous work on SOA adoption through surfing the net, reading prior journals and papers. The main purpose of this chapter is to focus significant points about adoption of SOA in organizations.
Educational Data Mining Techniques Approach to Predict Student’s Performance

Predicting student's performance is one way that can be conducted by university to monitor their student to prevent student failed.Student final GPA is one parameter that must be full fill by student to graduate from university and it can be used to measure student's performance.Educational Data Mining is popular techniques to predict student's performance.This study tried to implement two popular data mining clustering and classification analysis to predict student's performance.K-means algorithm is used since it is very popular and easy to be implemented clustering algorithm.Linear Regression and Support Vector Machine (SVM) then used to predict the final GPA since the attributes used in this study is numerical data.The clustered data and non-clustered data were evaluated in the classification analysis and the MSE was compared.The result showed that clustered data had smaller RMSE and Linear Regression was better than SVM.
Symbol Denoising in High Order M-QAM using Residual learning of Deep CNN

This paper presents an integrating concept of de-noising convolutional neural networks (DnCNN) with quadrature amplitude modulation (QAM) for symbol denoising. DnCNN is used to estimate and denoise the Gaussian noise from the received constellation symbols of QAM with unknown noise level. Proposed system shows a significant gain in terms of peak signal-to-noise ratio, system throughput and bit-error rate; in comparison with conventional QAM systems. The basic concept, system level integration, and simulated performance gains are presented to elucidate the concept.
Nonparametric Blind Super-Resolution Using Adaptive Heavy-Tailed Priors

Single-image nonparametric blind super-resolution is a fundamental image restoration problem yet largely ignored in the past decades among the computational photography and computer vision communities. An interesting phenomenon is observed that learning-based single-image super-resolution (SR) has been experiencing a rapid development since the boom of the sparse representation in 2005s and especially the representation learning in 2010s, wherein the high-res image is generally blurred by a supposed bicubic or Gaussian blur kernel. However, the parametric assumption on the form of blur kernels does not hold in most practical applications because in real low-res imaging a high-res image can undergo complex blur processes, e.g., Gaussian-shaped kernels of varying sizes, ellipse-shaped kernels of varying orientations, curvilinear kernels of varying trajectories. The paper is mainly motivated by one of our previous works: Shao and Elad (in: Zhang (ed) ICIG 2015, Part III, Lecture notes in computer science, Springer, Cham, 2015). Specifically, we take one step further in this paper and present a type of adaptive heavy-tailed image priors, which result in a new regularized formulation for nonparametric blind super-resolution. The new image priors can be expressed and understood as a generalized integration of the normalized sparsity measure and relative total variation. Although it seems that the proposed priors are simple, the core merit of the priors is their practical capability for the challenging task of nonparametric blur kernel estimation for both super-resolution and deblurring. Harnessing the priors, a higher-quality intermediate high-res image becomes possible and therefore more accurate blur kernel estimation can be accomplished. A great many experiments are performed on both synthetic and real-world blurred low-res images, demonstrating the comparative or even superior performance of the proposed algorithm convincingly. Meanwhile, the proposed priors are demonstrated quite applicable to blind image deblurring which is a degenerated problem of nonparametric blind SR.
Using Morphological Data in Language Modeling for Serbian Large Vocabulary Speech Recognition

Serbian is in a group of highly inflective and morphologically rich languages that use a lot of different word suffixes to express different grammatical, syntactic, or semantic features. This kind of behaviour usually produces a lot of recognition errors, especially in large vocabulary systems-even when, due to good acoustical matching, the correct lemma is predicted by the automatic speech recognition system, often a wrong word ending occurs, which is nevertheless counted as an error. This effect is larger for contexts not present in the language model training corpus. In this manuscript, an approach which takes into account different morphological categories of words for language modeling is examined, and the benefits in terms of word error rates and perplexities are presented. These categories include word type, word case, grammatical number, and gender, and they were all assigned to words in the system vocabulary, where applicable. These additional word features helped to produce significant improvements in relation to the baseline system, both for n-gram-based and neural network-based language models. The proposed system can help overcome a lot of tedious errors in a large vocabulary system, for example, for dictation, both for Serbian and for other languages with similar characteristics.
Proof-Carrying Smart Contracts

We propose a way to reconcile the apparent contradiction between the immutability of idealized smart contracts and the real-world need to update contracts to fix bugs and oversights. Our proposal is to raise the contract's level of abstraction to guarantee a specification $$\varphi $$ instead of a particular implementation of that specification. A combination of proof-carrying code and proof-aware consensus allows contract implementations to be updated as needed, but so as to guarantee that $$\varphi $$ cannot be violated by any future upgrade. We propose proof-carrying smart contracts (PCSCs), aiming to put formal correctness proofs of smart contracts on the chain. Proofs of correctness for a contract can be checked by validators, who can enforce the restriction that no update can violate $$\varphi $$ . We discuss some architectural and formal challenges, and include an example of how our approach could address the well-known vulnerabilities in the ERC20 token standard.
Morphological, geochemical and mineralogical studies of two soil profiles developed on the itabirites of Ntem Complex, southern Cameroon.

Itabirite-related ferrallitic soils in the humid tropical zone of southern Cameroon have proven economic and scientific interests. However, they have received very little attention. The morphological, mineralogical and geochemical features of two soil profiles (TCN in Nkout and NT in Anyouzok) with significant morphological differences and developed on itabirite in the Ntem Complex of southern Cameroon were studied using macroscopic and microscopic observations, conventional X-ray diffraction (XRD), X-ray fluorescence (XRF) spectrometry and Inductively Coupled Plasma-Mass Spectrometry (ICP-MS) analyses. The two profiles have similar sets of horizons including a lower weathering unit, a ferruginous and a loose upper units. However, TCN has a saprolite horizon above the saprock and numerous platy ferruginous concretions. The two profiles have similar mineralogical composition with the predominance of hematite, goethite, quartz and magnetite and a low concentration of kaolinite and gibbsite. The geochemical composition is characterized by the dominance of Fe2O3t (58.90–90.76 wt %) and SiO2 (5.63–38.45 wt %) both showing opposite trends across the profiles. These observations suggest that oxidation and dissolution were the dominant geochemical processes during the formation of the profiles. The positive Ce anomalies (Ce/Ce*: 1.12 to 2.27) and negative Eu anomalies (Eu/Eu*: 0.59 to 0.99) are indicators of oxidations coupled with mobile elements leaching in the profiles. The morphological differences between the two profiles are probably due to differences in weathering conditions which led to the development of a thick oxidized iron ore deposit in Nkout compared to Anyouzok.
Toward secure trading of unlicensed spectrum in cyber-physical systems

Cyber-physical systems rely more and more on wireless technologies that operate in unmanaged, unlicensed frequency bands. Already today, this leads to security issues and a significant degradation of network performance due to interference and traffic congestion. Only collaboration between stakeholders leads to a sustainable solution. To facilitate collaboration, we designed a platform that heavily relies on the concept of Software-Defined Networking, introducing a central controller for programmable radio parameters. Part of this solution is a Brokering Platform that automates negotiation for spectral resources between access point (AP) operators. In this article we present a novel architecture for a Brokering Platform. We also propose secure distribution of Brokering Platform functionality over the APs using blockchain.
ZooJamming

This is a report of three ZooJams that have taken place at the annual Animal-Computer Interaction conference. The ZooJam is a type of workshop whose aim is to extend the reach of UX design beyond human experience in order to become inclusive of other species and their interactions with technology. As organisers, our attempts have knitted together colleagues from a range of disciplines, all focused on developing practical solutions to different environmental enrichment challenges.
Low Weight Discrete Logarithm and Subset Sum in $$2^{0.65n}$$ with Polynomial Memory

We propose two heuristic polynomial memory collision finding algorithms for the low Hamming weight discrete logarithm problem in any abelian group G. The first one is a direct adaptation of the Becker-Coron-Joux (BCJ) algorithm for subset sum to the discrete logarithm setting. The second one significantly improves on this adaptation for all possible weights using a more involved application of the representation technique together with some new Markov chain analysis. In contrast to other low weight discrete logarithm algorithms, our second algorithm’s time complexity interpolates to Pollard’s $$|G|^{\frac{1}{2}}$$ bound for general discrete logarithm instances. We also introduce a new heuristic subset sum algorithm with polynomial memory that improves on BCJ’s $$2^{0.72n}$$ time bound for random subset sum instances $$a_1, \ldots , a_n, t \in \mathbb {Z}_{2^n}$$. Technically, we introduce a novel nested collision finding for subset sum – inspired by the NestedRho algorithm from Crypto ’16 – that recursively produces collisions. We first show how to instantiate our algorithm with run time $$2^{0.649n}$$. Using further tricks, we are then able to improve its complexity down to $$2^{0.645n}$$.
Mobile Technology in Running Science and Medicine: Are We Ready?

Since the running revolution of the 1970s, one of the major challenges has been the burden of running-related injuries (RRIs). Researchers, sports medicine practitioners, and strength and conditioning coaches are striving to develop an understanding of which factors may increase an individuals risk of developing RRIs, which strategies can be used to ensure optimal rehabilitation and recovery from an injury, and how to best optimize athletic performance. This Viewpoint explores these factors to demonstrate how recent advances in mobile technology may allow us to uncover novel insights related to the science and medicine of running. J Orthop Sports Phys Ther 2019;49(3):122-125. doi:10.2519/jospt.2019.0604.
Bangkok CCTV Image through a Road Environment Extraction System Using Multi-Label Convolutional Neural Network Classification

Information regarding the conditions of roads is a safety concern when driving. In Bangkok, public weather sensors such as weather stations and rain sensors are insufficiently available to provide such information. On the other hand, a number of existing CCTV cameras have been deployed recently in various places for surveillance and traffic monitoring. Instead of deploying new sensors designed specifically for monitoring road conditions, images and location information from existing cameras can be used to obtain precise environmental information. Therefore, we propose a road environment extraction framework that covers different situations, such as raining and non-raining scenes, daylight and night-time scenes, crowded and non-crowded traffic, and wet and dry roads. The framework is based on CCTV images from a Bangkok metropolitan dataset, provided by the Bangkok Metropolitan Administration. To obtain information from CCTV image sequences, multi-label classification was considered by applying a convolutional neural network. We also compared various models, including transfer learning techniques, and developed new models in order to obtain optimum results in terms of performance and efficiency. By adding dropout and batch normalization techniques, our model could acceptably perform classification with only a few convolutional layers. Our evaluation showed a Hamming loss and exact match ratio of 0.039 and 0.84, respectively. Finally, a road environment monitoring system was implemented to test the proposed framework.
Blockchain in construction logistics: state-of-art, constructability, and the advent of a new digital business model in Sweden

Research on blockchain technology implementation within civil engineering is quickly emerging.The use of blockchain for the integration of the material and economic flows within construction supply chain -thus creating a new digital business model for the related actors -to enhance a construction project's production, logistics management, and constructability, has been only scarcely investigated.In this paper, this shortage is documented through a comprehensive literature review.Then, the potential of Swedish independent third-party logistics consultants as a fertile ground for such a digital business model, is examined.Finally, conclusions about the pursuance of this potential paradigm shift are drawn.
Dynamic Window with Virtual Goal (DW-VG): a New Reactive Obstacle Avoidance Approach Based on Motion Prediction

Summary This paper proposes a dynamic window with virtual goal (DW-VG) method for local collision avoidance in dynamic environments. Firstly, the debounce filter and polynomial curve-fitting algorithm are combined to predict the trajectory of the obstacles with timestamps. Based on the motion prediction of the obstacles, the virtual goal is proposed to replace the real goal, so that the robot can escape from the concave trap and avoid the dynamic obstacles. According to the timestamps and virtual goal, the optimal linear and angular velocities are selected from the dynamic window, which drive the robot toward its real goal. The simulation and experimental results show that the DW-VG method can not only escape the local minima and avoid dynamic obstacles but also is applicable to the dense environment. Furthermore, the simulation results also verify that the DW-VG method drives the robot to reach its goal faster and safer than other reactive obstacle avoidance methods.
Efficiently Inferring Top-k Elephant Flows based on Discrete Tensor Completion

Finding top- k elephant flows is a critical task in network measurement, with applications such as congestion control, anomaly detection, and traffic engineering. Traditional top- k flow detection problem focuses on using a small amount of memory to measure the total number of packets or bytes of each flow. Instead, we study a challenging problem of inferring the top- k elephant flows in a practical system with incomplete measurement data as a result of sub-sampling for scalability or data missing. The recent study shows it is promising to more accurately interpolate the missing data with a 3-D tensor compared to that based on a 2-D matrix. Taking full advantage of the multilinear structures, we apply tensor completion to first recover the missing data and then find the top- k elephant flows. To reduce the computational overhead, we propose a novel discrete tensor completion model which uses binary codes to represent the factor matrices. Based on the model, we further propose three novel techniques to speed up the whole top- k flow inference process: a discrete optimization algorithm to train the binary factor matrices, bit operations to facilitate quick missing data inference, and simplifying the finding of top- k elephant flows with binary code partition. In our discrete tensor completion model, only one bit is needed to represent the entry in the factor matrices instead of a real value (32 bits) needed in traditional tensor completion model, thus the storage cost is reduced significantly. Extensive experiments using two real traces demonstrate that compared with the state of art tensor completion algorithms, our discrete tensor completion algorithm can achieve similar data inference accuracy using significantly smaller time and storage space.
CASCADENET: An LSTM Based Deep Learning Model for Automated ICD-10 Coding

In this paper, a cascading hierarchical architecture using LSTM is proposed for automatic mapping of ICD-10 codes from clinical documents. The fact that it becomes increasingly difficult to train a robust classifier as the number of classes (over 93k ICD-10 codes) grows, coupled with other challenges such as the variance in length, structure and context of the text data, and the lack of training data, puts this task among some of the hardest tasks of Machine Learning (ML) and Natural Language Processing (NLP). This work evaluates the performance of various methods on this task, which include basic techniques such as TF-IDF, inverted indexing using concept aggregation based on exhaustive Unified Medical Language System (UMLS) knowledge sources, as well as advanced methods such as SVM trained on a bag-of-words model, CNN and LSTM trained on distributed word embeddings. The effect of breaking down the problem into a hierarchy is also explored. Data used is an aggregate of ICD-10 long descriptions along with anonymised annotated training data provided by few of the private hospitals from India. A study of the above-mentioned techniques leads to the observation that hierarchical LSTM network outperforms other methods in terms of accuracy as well as micro and macro-averaged precision and recall scores on the held out data (or test data).
Significance of Psychological and Pedagogical Training in Developing Professional Competence of Engineers

The paper considers the peculiarities of studying psychological and pedagogical disciplines in today’s university of engineering, defines the role of these disciplines in developing the professional competence of would-be engineers. The paper focuses on the significance of using contemporary humanitarian technologies, active and interactive forms and methods of learning and cooperative learning. The authors present a model of organizing psychological and pedagogical training for engineers, providing the results of experimental work carried out at the St. Petersburg Polytechnic University over a period of three years.
Collusion Defender: Preserving Subscribers’ Privacy in Publish and Subscribe Systems

Social cues, such as eye gaze and pointing fingers, can increase the prioritisation of specific locations for cognitive processing.A previous study using a manual reaching task showed that, although both gaze and pointing cues altered target prioritisation (reaction times [RTs]), only pointing cues affected action execution (trajectory deviations).These differential effects of gaze and pointing cues on action execution could be because the gaze cue was conveyed through a disembodied head; hence, the model lacked the potential for a body part (i.e., hands) to interact with the target.In the present study, the image of a male gaze model, whose gaze direction coincided with two potential target locations, was centrally presented.The model either had his arms and hands extended underneath the potential target locations, indicating the potential to act on the targets (Experiment 1), or had his arms crossed in front of his chest, indicating the absence of potential to act (Experiment 2).Participants reached to a target that followed a nonpredictive gaze cue at one of three stimulus onset asynchronies.RTs and reach trajectories of the movements to cued and uncued targets were analysed.RTs showed a facilitation effect for both experiments, whereas trajectory analysis revealed facilitatory and inhibitory effects, but only in Experiment 1 when the model could potentially act on the targets.The results of this study suggested that when the gaze model had the potential to interact with the cued target location, the model's gaze affected not only target prioritisation but also movement execution.
Joint Spatial and Discrete Cosine Transform Domain-Based Counter Forensics for Adaptive Contrast Enhancement

Contrast enhancement (CE) is a common post-processing step in image forgery to create visually convincing tampered images.However, the artifacts embedded during this process can be captured to determine the presence of CE.To overcome these artifacts, we propose a novel counter-forensic technique using adaptive CE as an enhancement operation, whereas previous works only deal with global CE.We derive an optimization formulation, which enhances the attacked image using the L2 distance in both the spatial and DCT domains.The proposed algorithm suppresses the detectable artifacts, thereby reducing the CE detection performance.Furthermore, the formulation also preserves natural spatial statistics using Huber Markov random field.A major advantage of working jointly in both the domains is that the complementary information can be leveraged while suppressing the artifacts in both the domains.We evaluate the proposed method using various visual quality metrics and against the state-of-the-art CE detectors.In our experiments, we observe a reduction of more than 17% in accuracy for a false positive rate of 1% for deep learning as well as steganalysis-DCT feature-based detectors.We also show that the proposed model generates high visual quality images.
Twitter Data for Predicting Election Results: Insights from Emotion Classification

The advent of social media and microblogging sites has paved the way for individuals and communities to freely express their opinions, feelings, and thoughts on a variety of topics in the form of short and limited size texts such as tweets. These tweets can hold a wealth of information on how individuals communicate their thoughts, emotions (happiness, anxiety, depression, etc.) and feelings within their social network [1].