Reviewerly/exHarmony huggingface dataset:
Financial Development and Economic Growth in Uganda: A Multivariate Causal Linkage

In this study, we have explored the dynamic causal relationship between financial development and economic growth in Uganda during the period from 1980 to 2015. Although the finance-growth nexus debate has been raging for decades, Uganda, similar to many other low-income sub-Saharan African countries, has not yet received adequate coverage on the subject. To eliminate the variable omission bias associated with some previous studies, two intermittent variables, namely savings and inflation, have been included alongside financial development and economic growth in a multivariate Granger-causality setting. In addition, five proxies of financial sector development have been used in the current study, namely money supply to GDP, deposit money bank assets as a percentage of bank assets, liquid liabilities to GDP, private credit by deposit money banks to GDP, and bank deposits to GDP. Using the ARDL approach, the findings of the study reveal that the direction of causality between financial development and economic growth in Uganda is not clear-cut. It varies from one model to the other, depending on the proxy used for financial development. When financial development is proxied by liquid liabilities to GDP and bank deposits to GDP, a unidirectional causal flow from financial development to economic growth is found to prevail. When deposit money bank assets to bank assets ratio is considered a proxy of financial development, astrong bi-directional causal relationship between financial development and economic growth is found to predominate. However, when money supply to GDP and private credit by deposit money banks to GDP are used as proxies, no causality is found to exist between financial development and economic growth in either direction. Based on these results, it is recommended that when drafting policies aimed at boosting economic growth, policymakers should target growth-led financial development proxies as policy implementation outcomes may vary depending on the targeted financial development proxy.
ARCE: Towards Code Pointer Integrity on Embedded Processors Using Architecture-Assisted Run-Time Metadata Management

Code Pointer Integrity (CPI) is an efficient control flow protection technique focusing on sensitive code pointers with a formal proof of security, but it relies on software lookup tables or Memory Management Unit (MMU) based address translation and instruction-level memory isolation which are impractical for resource-constrained embedded processors. This paper enables Architecture-assisted Run-time CPI on Embedded Processors (ARCE) with 2-level metadata to balance security, performance and resource overhead. The first level 2-bit property metadata colors data into different domains and the second level boundary metadata holds structure constraints for indirect code pointers only. With memory and instruction extensions, metadata shares the address space with program data and is propagated at runtime to maintain a precise set of sensitive code pointers. It lazily validates the content and boundary of sensitive pointers at dereference stage to eliminate false alarms. We implemented ARCE based on a shallow 3-stage pipeline processor Z-scale and validated its security effectiveness with code pointer attack vectors in RIPE. It introduces less than 1 percent performance overhead for benchmarks in C with 7.33 percent logic and 6.25 percent memory overhead. ARCE eliminates address space waste and dependency on advanced hardware which makes CPI practical even for systems with bare metal applications.
TinyCORP

Current trend of seamless connections between computing systems and their surrounding environments requires software to be more reactive and adaptable, and reactive programming (RP) and context-oriented programming (COP) have been studied to directly support reactive behavior and dynamic adaptation. Sometimes reactive behavior and dynamic adaptation interact with each other. One issue of such interactions is how to avoid a loop of reactive behavior and dynamic adaptation when there are mutually recursive dependencies between them. This paper proposes TinyCORP, a core calculus for context-oriented reactive programming that is designed in a main-stream, general-purpose language setting. This calculus is expressive enough to represent both features of signals (i.e., time-varying values in RP) and layer-based partial methods in COP, and their interactions including the ability to specify the mutually recursive dependencies between dynamic adaptation and reactive behavior. We also demonstrate that the computation in TinyCORP do not result in the loop of reactive behavior and dynamic adaptation.
Location Leakage from Network Access Patterns

We evaluate the power of simple network side-channels to encroach upon user location privacy on Android devices. Specifically, we show that an unprivileged application or third party may infer several elements of a user's location from a different location-privileged application (such as Google Maps) by inspecting black-box network meta-data alone (i.e., traffic statistics such as transmission time and size of packets). Moreover, we do this with relatively simple learning and classification methods and only basic network statistics. For many Android phones currently on the market, process-level network traffic statistics are accessible at fine detail without permission control, although, we demonstrate that even device-level statistics available on the newest devices are sufficient for some of our inferences. In effect, it may be possible for any application running on these phones or co-located on a local network to identify privacy-revealing elements of a user's location, such as places of worship, point-of-care medical establishments, or political activity.
Boosting Verification Scalability via Structural Grouping and Semantic Partitioning of Properties

From equivalence checking to functional verification to design-space exploration, industrial verification tasks entail checking a large number of properties on the same design. State-of-the-art tools typically solve all properties concurrently, or one-at-a-time. They do not optimally exploit subproblem sharing between properties, leaving an opportunity to save considerable verification resource via concurrent verification of properties with nearly identical cone of influence (COI). These high-affinity properties can be concurrently solved; the verification effort expended for one can be directly reused to accelerate the verification of the others, without hurting per-property verification resources through bloating COI size. We present a near-linear runtime algorithm for partitioning properties into provably high-affinity groups for concurrent solution. We also present an effective method to partition high-structural-affinity groups using semantic feedback, to yield an optimal multi-property localization abstraction solution. Experiments demonstrate substantial end-to-end verification speedups through these techniques, leveraging parallel solution of individual groups.
Semantic three-stream network for social relation recognition

In this paper, we propose a semantic three-stream network (STN) for social relation recognition, which learns discriminative features from facial images directly by exploiting semantic information effectively. Specifically, we employ a semantic augmentation structure to extract enriched semantic features from original face images, where a Siamese network is used to extract features from a pair of face images. We concatenate features of three streams for social relation recognition. Unlike most existing relation recognition methods, our method explicitly uses semantic information to discover the social relation. The proposed semantic augmentation structure can be easily embedded into the off-the-shell deep neural network, which leads to a powerful and flexible semantic augmentation network. Experimental results show that our proposed method outperforms the state-of-the-arts.
Supervised Text Style Transfer Using Neural Machine Translation: Converting between Old and Modern Turkish as an Example

Languages evolve and change over time. Accordingly, texts that were written a hundred years ago may become incomprehensible, such as hundred-year-old Turkish texts. Additionally, making old written work accessible to today's generation requires qualified writers, who are responsible for the process of conversion. Unfortunately, that is costly in both time and resources. To work out this problem, we develop an automatic style conversion system. We formulate our problem as a machine translation problem and use the recently popularized Neural Machine Translation techniques. Furthermore, we introduce a data-driven approach to align source and target word vectors. Although we do not introduce new model components over the standard RNN encoder-decoder, the way we utilize monolingual data to pre-train our word vectors lead to significant improvements. Despite the simplicity of our approach, we outperform complex approaches. We achieve a BLEU score of 33.8 points, improving our baseline by 12 points.
A conceptual model-based approach to explore community livelihood adaptation under uncertainty for adaptive delta management

Delta communities worldwide are facing a multitude of challenges in their life and livelihood. In many developing countries, improving the quality of life and livelihood is a key challenge. While development is a central goal of delta planning in such countries, the effectiveness of planning is challenged by uncertain changes in climate and socio-economy. Bangladesh (one of the countries) is moving towards the adaptive delta management approach to deal with such uncertainties. Historical examples illustrate that Community Livelihood Adaptation (CLA) can critically influence the effectiveness of a policy strategy. Therefore, there is a clear need to explore CLA under uncertainty. For that purpose, this paper develops and applies a conceptual model-based approach combining the mental model and scenarios techniques. Our approach starts by using a participatory process to elicit mental models a farmers’ community uses when considering adaptation decisions; we capture these in the form of a cognitive map, and this map can serve as a conceptual model for analyzing livelihood adaptation decision-making in a future-oriented scenario analysis. To illustrate the approach, a case study of cropping decision-making of farmers community at a polder location under the saline condition in the southwest of Bangladesh has been elaborated. Results show that the approach is useful in structuring the cognitive and qualitative nature of complex decision-making process, and helps in understanding the dynamic interactions of farmers’ adaptation decisions with other actors, their environmental attributes, and market traits. It can help policymakers anticipate the adaptation direction of policy strategies.
Improving the Collection and Understanding the Quality of Datasets for the Aim of Human Activity Recognition

In the last few decades, life expectancy has been increasing. This has resulted in a higher proportion of older adults and increased prevalence of chronic conditions, posing challenges facing care needs. A possible solution is to foster both the prevention and health-related re-education, supporting healthier lifestyle and facilitating independent living. To facilitate this, it is crucial to measure individual’s key health metrics. For instance, human activity recognition through sensors provides valuable information about an individual’s lifestyle. Some crucial decisions, among which the quality of data collection, strengthen the methodological approach. This chapter addresses how the quality of data may affect the recognition performance. Two datasets of daily activities were collected through a triaxial accelerometer placed on the subject’s dominant wrist. The first dataset was collected by 141 users, whereas the second one comprised semi-realistic activities executed by three individuals. Specifically, outcomes were based on a comparison of activity recognition performance of six machine learning classifiers. Results show that, firstly, a higher number of features may not improve the recognition rate. Secondly, one approach may be robust in a laboratory setting but not generalizable to real-world applications. Finally, a great variability may increase the generalization of classifiers for successful activity recognition.
Frobenius linear translators giving rise to new infinite classes of permutations and bent functions

We show the existence of many infinite classes of permutations over finite fields and bent functions by extending the notion of linear translators, introduced by Kyureghyan (J. Combin. Theory Ser. A 118(3), 1052–1061, 2011). We call these translators Frobenius translators since the derivatives of $f:{\mathbb F}_{p^{n}} \rightarrow {\mathbb F}_{p^{k}}$, where n = rk, are of the form $f(x+u\gamma )-f(x)=u^{p^{i}}b$, for a fixed $b \in {\mathbb F}_{p^{k}}$ and all $u \in {\mathbb F}_{p^{k}}$, rather than considering the standard case corresponding to i = 0. It turns out that Frobenius translators correspond to standard linear translators of an exponentiated version of f, namely to $f^{p^{k-i}}$ with respect to $b^{p^{k-i}}$. Nevertheless, this concept turns out to be useful for providing further explicit specification of a rather rare family {f} of quadratic polynomials (especially sparse ones) admitting linear translators. In this direction, we solve a few open problems in the recent article (Cepak et al., Finite Fields Appl. 45, 19–42, 2017) concerning the existence and an exact specification of f admitting classical linear translators. In addition, an open problem introduced in Hodžić et al. (2018), of finding a triple of bent functions f1,f2,f3 such that their sum f4 is bent and that the sum of their duals satisfies $f_{1}^{*}+f_{2}^{*}+f_{3}^{*}+f_{4}^{*}=1$, is also resolved. We also specify two huge families of permutations over ${\mathbb F}_{p^{n}}$ related to the condition that $G(y)=-L(y)+(y+\delta )^{s}-(y+\delta )^{p^{k}s}$ permutes the set ${\mathcal S}=\{\beta \in {\mathbb F}_{p^{n}}: T{r_{k}^{n}}(\beta )=0\}$, where n = 2k and p > 2. Finally, we give some generalizations of constructions of bent functions in Mesnager et al. (2017) and describe some new bent families using the permutations found in Cepak et al. (Finite Fields Appl. 45, 19–42, 2017).
Towards the Composition of Services by End-Users

Nowadays, we live surrounded by heterogeneous and distributed services that are available to people anytime and anywhere. Even though these services can be used individually, it is through their synchronized and combined usage that end-users are provided with added value. However, existing solutions to service composition are not targeted at ordinary end-users. In fact, these solutions require technical knowledge to deal with the technological heterogeneity in which they are offered to the market. To this end, the paper presents a tool-supported platform that is aided by: (1) EUCalipTool, an end-user mobile tool that implements a Domain Specific Visual Language, which has been specifically designed to compose services on mobile devices; (2) a Faceted Service Registry, which plays the role of gateway between service implementations and end-users, hiding technological issues from the latter when including services in a composition; and (3) a Generation Module, which transforms end-user descriptions into BPMN specification that are interpreted by an execution infrastructure developed for that purpose.
Crypto-currency: Is the future dark or bright?

AbstractAbstractPurpose: This study reviews the research work that has been carried out in the field of cryptocurrency (cc) and financial risk during past five years ( 2013-2018 ).This paper tracks and reviews the research work publish in leading academic journal in above mentioned journal. It then reviews from pragmatic and methodological approach.Design methodology and approach: this study analyses the selected contribution from the perspective of "Financial risk in cryptocurrency." And further compares various studies on the article type and methodologies and research designs used in them. Using relational analysis approach recurrent themes are identified and research clusters are formed.Findings: The analysis leads to the identification of the major parameters namely: Financial risk, market risk, liquidity risk and operational risk and these rea the parameters that are impacting the values of cryptocurrency. We have also analysed the issues and challenges in the cryptocurrency market of India.Implications: We have tried to propose a model and collected the information from various reports and papers. We have also find out that various risk which are associated with CC market are controllable in nature on the other hand some of them are uncontrollable in nature.Originality value: Most reviews rely on bibliometric analysis or simple thematic analysis for literature review. This study provides greater insights by using a research paradigm framework to categorize and summarize the literature. This approach provides a comprehensive yet concise picture of the literature to future researchers.Paper type: Research typeSubject Classification: 94A60Keywords: CryptocurrencyCryptocurrency legislationDigital moneyVirtual currencyCrypto assets
A Dictionary-Based Convolutional Recurrent Neural Network Model for Sentiment Analysis

Natural Language Processing is an important direction in the field of computer science and artificial intelligence. It studies various theories and methods that can realize effective communication between human and computer in natural language. Sentiment analysis is a common and important task in natural language processing. It is often used to analyze and judge the sentiment types of text description. Movie reviews can provide valuable reference information for people to choose high quality movies. In order to effectively interpret movie reviews and understand the sentiment factors in movie reviews, the text proposes a deep learning method based on sentiment dictionary to realize the sentiment analysis of film reviews and to restore the real feelings of users as far as possible. The method first preprocesses the collected movie reviews data, uses the skip-gram model in the word2vec network to automatically generate word vectors, and transforms the sentiment vocabulary marked by Dalian University of Technology into word vectors. Then, the words vectors generated by text and dictionary are input into the convolutional neural network to learn sentence representation, extract local features. Finally, the Long Short-Term Memory network in recurrent neural network captures the semantic information and long-term dependencies between sentences, and inputs them into the logistic regression classifier to realize the sentiment analysis of movie reviews. The proposed method is compared with the Naive Bayesian and Support Vector Machine in the traditional machine learning method, the convolutional neural network and the recurrent neural network in the deep learning method. The experimental results show that the proposed model can extract more abundant features and achieve state-of-the-art classification effect than the baseline models.
An Optimized Deep Convolutional Neural Network Architecture for Concept Drifted Image Classification

Machine Learning (ML) is a branch of Artificial Intelligence, which is continuously evolving to overcome current technological challenges faced by industries. These technological changes are due to modernization in industries for Business Intelligence (BI) i.e., 4th Industrial Revolution. Among the other ML approaches, Image Classification plays a significant role for Business Intelligence and upfront several new challenges in online and non-stationary environment, such as Concept Drift. To overcome the CD issue, one of the fundamental requirements is optimization of classifier. Whereas, Convolutional Neural Network (CNN) is considered best classifier/model for Image Classification. Therefore, the aim of this study is to investigate the optimize architecture for CNN in Concept Drifted environment. This study examines the variety of CNN architectures (CNN1 to CNN4) through different configuration of CNN layers and tuning parameters under certain Concept Drift scenarios. Furthermore, a comparative analysis is performed among these CNN models by monitoring their classification accuracy, loss and computational complexity to validate the optimized CNN model experimentally. In future, proposed Optimize Deep Neural Network architecture will be further investigated for high dimensional Imagery data-streams, for example color and multispectral imagery.
Using a Fine-Tuning Method for a Deep Authentication in Mobile Cloud Computing Based on Tensorflow Lite Framework

Remarkable progress in the areas of deep learning and the mobile cloud environment. The security has become most challenging part on mobile cloud., this is due to the growth of persons who use their mobiles for access to the services provides by cloud such as health care., storage., games and so on. This paper aims to suggest using deep learning framework for mobile “Tensorflow lite” to make the recognition on mobile without the need of the cloud or computational resources and to avoid that users sends their picture for each time. Improving the accuracy of the work that have been done with 99.50 % of accuracy to 100 % by applying the fine-tuning method using Keras library in Python.
Semantic Evolution Analysis of Feature Models

During the development process, feature models change continuously. Analyzing the semantic differences between consecutive feature model versions is important throughout the entire development process to detect unintended changes of the modeled product line. Previous work introduced a semantic differencing technique for feature models based on a closed-world assumption, which reveals the differences between two feature models when allowing products to only contain features used in the models. However, this does not reflect the stepwise refinement of feature models in early development stages. Therefore, we introduce an open-world semantics, an automatic method for semantic differencing of feature models with respect to the novel semantics, and formally relate the open- and closed-world semantics. We formally prove our results, including the relation between the different semantics as well as the soundness and completeness of the semantic differencing procedure. In conjunction with previous work, the results enable effective semantic feature model evolution analyses throughout the entire development process.
Tracking in Urban Traffic Scenes from Background Subtraction and Object Detection

In this paper, we propose to combine detections from background subtraction and from a multiclass object detector for multiple object tracking (MOT) in urban traffic scenes. These objects are associated across frames using spatial, colour and class label information, and trajectory prediction is evaluated to yield the final MOT outputs. The proposed method was tested on the Urban tracker dataset and shows competitive performances compared to state-of-the-art approaches. Results show that the integration of different detection inputs remains a challenging task that greatly affects the MOT performance.
Visual Analytics to Make Sense of Large-Scale Administrative and Normative Data

The paper presents ongoing research aiming to ease the interaction with large amounts of public sector data. The project focuses on the development of a modular online platform for both desktop and mobile devices exploiting Visual Analytics to offer citizens, researchers and policymakers crosscutting reading of heterogeneous information. We experiment new ways to integrate, analyze, and visualize administrative, legal, and economic data as they appear to be a powerful ally not only to increase the transparency of government activities but also to enhance evidence-based policy making and agenda setting.