Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Learning Based Sentiment Classification in Social Network Services Datasets

Social network services (SNS) are more and more popular. People are increasingly accustomed to express their opinions on SNS in two ways: (1) product reviews in online shopping sites and (2) posts, comments, tweets, and chats in social network sites. SNS text classification is challenging due to various natural language phenomena, such as spelling mistakes and variations, polysemy, contextual ambiguity and semantic variations. In this paper, we propose a novel deep learning architecture called Hybrid two Convolutional Neural Networks and Bidirectional LSTM (H2CBi) which combines the strength of both Convolutional Neural Networks (CNNs) and Bidirectional LSTM (BLSTM). We use two CNNs for extracting different positive/negative or bullied/no bullied features from SNS data. We use BLSTM to produce a sentence-level representation by maintaining the order of words and get the ability to learn sequential correlations for the sentence which is the negative sentence without having any negative word. We used two kinds of SNS data in this paper: product review data (Amazon, Movie Review and Yelp) and (2) social network sites data (Twitter I, Twitter II, Facebook and FormSpring.me). Some of our H2CBi models, namely 2WH2CBi, WFH2CBi and WGH2CBi have better performance than baseline models in six out of the seven SNS datasets in terms of accuracy and F measure.
Typical Versus Atypical Antipsychotics for Acute Mania.

BACKGROUND Mania is challenging to treat. Typical antipsychotics may be more efficient compared with atypical antipsychotics, however, with unfavorable side effects. STUDY QUESTION To investigate the courses of acute manic episodes and correlations between changes of severity during manic episodes and type of antipsychotic treatment. STUDY DESIGN This case record study included patients admitted with mania (International Classification of Diseases 10th revision code F30, F31.0, F31.1, F31.2 or F31.6) at the Department of Affective Disorders, Aarhus University Hospital from June 1, 2013 to April 1, 2016. MEASURES AND OUTCOMES The doses of typical and atypical antipsychotics were standardized as defined daily dose according to the World Health Organization's guidelines. The severity of mania was measured up to 3 times daily with the Modified Bech-Rafaelsen Mania Scale (MAS-M), a nurse administered scale. We applied a linear regression in a mixed model approach to compare MAS-M score over time under the influence of typical plus atypical antipsychotics and atypical antipsychotics only. We further analyzed by mania with and without psychosis and by concomitant use of lithium and/or antiseizure medication. RESULTS We included 56 admissions on 46 patients. The courses of the manic episodes measured by MAS-M varied between patients-both daily variations and changes over time. Patients receiving typical antipsychotics had higher baseline MAS-M, more recent admissions, and were mechanically constrained more often compared with patients receiving atypical antipsychotics only. Adjusted for age, gender, mechanical constraint, and dosage of antipsychotics, the difference in reduction of mania was -0.02 MAS-M points/d (95% confidence interval, -0.05 to 0.01) higher in the group receiving atypical antipsychotics only; however, it is not statistically or clinically significant. CONCLUSIONS The rate of improvement of mania was similar in the two groups which supports that atypical antipsychotics can be recommended over typical antipsychotics to reduce risk of severe side effects.
A Novel Approach to Solve Set-Theory Word Problems Automatically

Mathematical Word Problems (MWPs) are verbal formulations of real-world scenarios representing an abstract mathematical idea. They aid us in demonstrating the relevance of mathematics in extrapolating everyday tasks. Set Theory is a field of mathematics that is used to discern the nature of sets and the relations between them. Set-theory operations are binary in nature. They are used to depict the type of association between two sets. In the current scenario, the difference in the levels of abstraction of deriving regular expressions and interpreting natural language poses a challenging task. In this paper, we present a novel approach to solve set-theory based word problems automatically using the semantics of the language. Our system analyzes set-theory questions obtained from various sources of informal text that are crowd sourced such as online forums, social media and competitive examination portals and computes the result by discerning the language of the problem. Our algorithm mimics the paradigm through which humans attempt to solve set-theory MWPs. We discretized our approach into two sub-tasks, information extraction and problem solving, implementing separate evaluation for each stage and subsequently ensuring that the error propagation from one phase to the next is curbed and efficiency for each stage can be individually determined. Our system uses language semantics to identify the set entities in a word problem, and subsequently maps these entities in an expression that embodies the problem. In the problem solving phase, we obtain the final result of the word problem by inferring the relation that is to be determined and implementing the corresponding set-theory function to compute the solution. We corroborated our result for the two phases individually using supervised learning. In the information extraction phase, our system exhibited an accuracy of 83.5% and its performance in the problem solving phase is 60%, which though needs improvement but is a good beginning.
Machine Learning Optimization of Parameters for Noise Estimation

In this paper, a fast and effective method of parameter optimization for noise estimation is proposed for various types of noise. The proposed method is based on gradient descent, which is one of the optimization methods used in machine learning. The learning rate of gradient descent was set to a negative value for optimizing parameters for a speech quality improvement problem. The speech quality was evaluated using a suite of measures. After parameter optimization by gradient descent, the values were re-checked using a wider range to prevent convergence to a local minimum. To optimize the problem’s five parameters, the overall number of operations using the proposed method was 99.99958% smaller than that using the conventional method. The extracted optimal values increased the speech quality by 1.1307%, 3.097%, 3.742%, and 3.861% on average for signal-to-noise ratios of 0, 5, 10, and 15 dB, respectively.
Using MISR as Countermeasure Against Scan-Based Side-Channel Attacks

Scan-based Design-for-Test (DfT) feature aims to fulfil the need for better testability and diagnosability of a modern-day VLSI chip. However, an unprotected scan architecture can be exploited by an unauthorized user to steal sensitive data such as a secret encryption key which is embedded on a cryptographic chip. In this work, a new technique is proposed to secure the scan architecture through test authorization mechanism. The proposed technique locks down the scan infrastructure whenever the circuit enters into the test mode of operation. The user needs to pass a test authorization step in order to unlock the scan feature and exercise the scan test. The test authorization step is a one time process which must be passed at the start of the test session. The proposed secure scan test technique has no overhead in terms of test time and test data volume. Furthermore, the proposed secure scan design has marginal area overhead and has similar debug capabilities as the conventional scan design.
Two new polyketides isolated from a diethyl sulphate mutant of marine-derived Penicillium purpurogenum G59

Abstract Two new polyketides, purpurofuranone (1) and purpuropyranone (2), were isolated along with the known polyketides, cillifuranone (3) and taiwapyrone (4), from a mutant BD-3n-1 derived from the diethyl sulfate (DES) mutagenesis of a marine-derived Penicillium purpurogenum G59. The structures of 1 and 2 were elucidated by spectroscopic methods especially on the basis of X-ray diffraction and calculated optical rotations data. The plausible biosynthesis of 1 − 4 was also proposed and discussed. In preliminary MTT assay, 1 − 4 showed no notable inhibitory effects on the tested four human cancer cell lines. Graphical Abstract
Design and Evaluation of a Remote Actuated Finger Exoskeleton Using Motion-Copying System for Tendon Rehabilitation

Hand recovery process is a major issue in the rehabilitation field as hands are vital to perform most daily activities. This paper presents the design and evaluation of a one actuated degree-of-freedom exoskeleton finger using flexible actuator to assist patients in rehabilitation process and more specifically in tendon recovery protocols. The control strategy based on the motion-copying system is used to be able to take advantages of the sensation of the patient. The wearability and adaptability of the device are improved, compared to other specific devices, mainly by the use of remote actuation and light-weight materials. Evaluation of the device in terms of wearability, adaptability, repeatability and accuracy of the position estimation are conducted. All these criteria are confirmed. The next step is to test the device in actual recovery protocols to evaluate its efficiency.
A Comparative Study of Full-Bridge Inverter Based DVR and Semi-Z-Source Inverter Based DVR

There is always a necessity to protect the voltage sensitive industrial systems from Power Quality (PQ) issues to prevent the loss of product quality and revenue. Generally, custom power devices take the responsibility to mitigate these PQ problems, restore the voltage and thus maintain the PQ standards. Dynamic Voltage Restorer (DVR) is one among the custom power devices which offers an economical compensation of load voltage under abnormal supply voltage conditions. The voltage injected in series by the DVR compensates the load profile during PQ events; appropriate voltage injected by the DVR in series with supply protects the load and restores the voltage to pre-defined values. The required injected voltage is generated by the inverter circuit in the DVR system. The most commonly used DVR inverter is the full-bridge inverter which consists of four switches. The Semi-Z-Source (SZS) inverter based DVR which offers same injection capability with only two-switches is compared with the full-bridge inverter. The in-phase compensation technique is employed in both the DVRs for injecting required voltage into the grid. The efficiency, quality of the injected voltage and load voltage are studied. This paper presents simulation results in MATLAB/Simulink environment to validate thecomparison.
Quad-Sec: Preserving Privacy of Data in Cloud

Nowadays, the use of multimedia data gets in-creased day by day. Thus we are in need of larger and larger stor-age for these data, but the client devices often fail. So, we depend on cloud storage, but new security challenges arises in which the privacy of user's data is compromised. In this paper, we propose a secure framework for outsourced privacy-preserving storage by introducing Quad-Sec; which enables secure data transfer, sharing and storage in the cloud while preserving privacy against cloud administrators and external hackers. The Quad-Sec is a file encryption scheme which performs double encryption on each file that is to be outsourced. 3-DES and AES are the two encryption techniques used. Low complexity and easy implementation make the proposed method widely applicable safeguard in the cloud computing.
An E-Band Gate-Pump SSB Mixer for Vital Signs Doppler Radar

In this paper, we propose a CMOS gate-pump single side band (SSB) mixer at E-band. To improve conversion gain and efficiency, the transistors M1-M4 are biased at near class B region with the LO signal. We also carefully select LO power to get optimum conversion gain. Compared with traditional gate-pump mixer, the LO of this mixer is applied to the gate and the IF is applied to the source. This approach can assure that transistors are biased in class B region and also improve linearity. The peak conversion gain is −11.98 at 72 GHz. The measured LO-to-RF isolation is better than 35 dB and IRR is better than 29 dBc from 68 - 80 GHz. To our best knowledge, this SSB mixer has the highest IRR and good conversion gain at E-band among passive SSB mixers.
Iterative Closest Point Based Volume Analysis on UAV Made Time-Series Large-Scale Point Clouds

With new methods of 3D model creation, the examination of real objects and natural or artificial formations in virtual space become possible. A precondition is that the models be created along the principles of photogrammetry. The bases of the method introduced in the paper are the precise flight ability of small-sized machines piloted by a robot and a mounted camera with distortion-free optics. Surveys conducted by such devices can be faster and more economical than the usage of conventional aircraft requiring human personnel. As long as all the parameters of the devices are known, it is possible to create a kind of 3D model on which volume calculation can be conducted referring to the real object. In this present paper, we introduce the algorithmic and automatized version of the previous analysis method demanding manual engineering work. The solution can be appropriate for the complete substitution of geodetic field work; it produces faster and more accurate results.
Making Robotic Sense of Incomplete Human Instructions in High-Level Programming for Industrial Robotic Assembly

In this paper we describe our NLP supported programming-by-demonstration approach to high-level robot programming that allows users to generate skills and robot program primitives for later refinement and re-use. Our ideas incorporate the identification of common user strategies (interaction patterns) in the programming process, which can be exploited to support a human user in establishing common ground with a robotic system. We have evaluated a prototype implementation of this approach in a user study and use observations from this study to define further research efforts, which we discuss in this short paper.
Reactive task and motion planning for robust whole-body dynamic locomotion in constrained environments

Contact-based decision and planning methods are becoming increasingly important to endow higher levels of autonomy for legged robots. Formal synthesis methods derived from symbolic systems have great potential for reasoning about high-level locomotion decisions and achieving complex maneuvering behaviors with correctness guarantees. This study takes a first step toward formally devising an architecture composed of task planning and control of whole-body dynamic locomotion behaviors in constrained and dynamically changing environments. At the high level, we formulate a two-player temporal logic game between the multi-limb locomotion planner and its dynamic environment to synthesize a winning strategy that delivers symbolic locomotion actions. These locomotion actions satisfy the desired high-level task specifications expressed in a fragment of temporal logic. Those actions are sent to a robust finite transition system that synthesizes a locomotion controller that fulfills state reachability constraints. This controller is further executed via a low-level motion planner that generates feasible locomotion trajectories. We construct a set of dynamic locomotion models for legged robots to serve as a template library for handling diverse environmental events. We devise a replanning strategy that takes into consideration sudden environmental changes or large state disturbances to increase the robustness of the resulting locomotion behaviors. We formally prove the correctness of the layered locomotion framework guaranteeing a robust implementation by the motion planning layer. Simulations of reactive locomotion behaviors in diverse environments indicate that our framework has the potential to serve as a theoretical foundation for intelligent locomotion behaviors.
Engineering Trustworthy Self-Adaptive Software with Dynamic Assurance Cases

Building on concepts drawn from control theory, self-adaptive software handles environmental and internal uncertainties by dynamically adjusting its architecture and parameters in response to events such as workload changes and component failures. Self-adaptive software is increasingly expected to meet strict functional and non-functional requirements in applications from areas as diverse as manufacturing, healthcare and finance. To address this need, we introduce a methodology for the systematic ENgineering of TRUstworthy Self-adaptive sofTware (ENTRUST). ENTRUST uses a combination of (1) design-time and runtime modelling and verification, and (2) industry-adopted assurance processes to develop trustworthy self-adaptive software and assurance cases arguing the suitability of the software for its intended application. To evaluate the effectiveness of our methodology, we present a tool-supported instance of ENTRUST and its use to develop proof-of-concept self-adaptive software for embedded and service-based systems from the oceanic monitoring and e-finance domains, respectively. The experimental results show that ENTRUST can be used to engineer self-adaptive software systems in different application domains and to generate dynamic assurance cases for these systems.
Preserving Functional Correctness of Cyber-Physical System Controllers: From Model to Code

In this paper, we outline a methodology allowing to support the formal verification of functional properties for generated code. When relying on a code generator, a model is directly mapped into the target embedded code, in C for instance. At model level, a specification can be associated to the model and used to assess the validity of the model with respect to its requirements. At code level, other means such as deductive methods can be used to ensure similar goals. While the analysis of user-specified properties at model-level is developed and tractable, the automatic verification of these specification at code level remains an open issue. We present here a framework which builds a semantics layer connecting model specification to code specification, as well as associated proof evidences. This approach has been designed and developed in the context of dataflow languages such as Simulink, SCADE or Lustre, typically used in the design of cyber-physical system controllers, but it could also be revisited in other contexts. The model is analyzed by SMT-based model checking and convex optimization-based static analysis. At code level, deductive techniques, such as implemented in Frama-C, are used to prove the functional correctness. Our approach combines static analysis with refinement to drive the proof at code level, relying on analysis results obtained at model level. The refinement relates the initial model semantics with the one of the code. This papers only outlines the methodology combining analyses. It has been applied manually on some examples. A fully implementation remains a future work.
Cyber-Physical System for Industrial Control Automation Based on the Holonic Approach and the IEC 61499 Standard

In recent years, new control automation schemes resulted from the complex needs of the manufacturing systems. The dynamism of the current market is not covered by traditional hierarchical structures yet. Thus, innovative models facing the demands of industrial requirements are now based on intelligent entities. These modern paradigms are realized by evolved programming techniques to implement flexible and distributed control systems (non-hierarchical), e.g. a Cyber-Physical System (CPS). CPS is described as an innovative model that involves transdisciplinary engineering. In fact, CPS defines a modern term that integrates additional information (e.g. real-time data) and modern communication technology (ICT) into the physical world. CPS is often associated with the Industry 4.0 (I4.0) perspective and its specific implementation could be defined by holons, a software built by autonomous and intelligent entities. A holon present in a CPS may contain a physical resource that is linked to the software system by distributable and suitable communication protocols (e.g. based on the IEC 61499 standard). The results of this paper show that both paradigms-holonic and CPS-are a complementary way for manufacturing control automation. The goal of this research is sustained by an overview of UML models, and the application of industrial Ethernet-based protocol (Profinet), to satisfy a distributed CPS with real-time characteristics.
Transfer Learning for Wearable Long-Term Social Speech Evaluations

With an increase of stress in work and study environments, mental health issue has become a major subject in current social interaction research. Generally, researchers analyze psychological health states by using the social perception behavior. Speech signal processing is an important research direction, as it can objectively assess the mental health of a person from social sensing through the extraction and analysis of speech features. In this paper, a series of four-week long-term social monitoring experiment study using the proposed wearable device has been conducted. A set of well-being questionnaires among of a group of students is employed to objectively generate a relationship between physical and mental health with segmented speech-social features in completely natural daily situation. In particular, we have developed transfer learning for acoustic classification. By training the model on the TUT Acoustic Scenes 2017 data set, the model learns the basic scene features. Through transfer learning, the model is transferred to the audio segmentation process using only four wearable speech-social features (energy, entropy, brightness, and formant). The obtained results have shown promising results in classifying various acoustic scenes in unconstrained and natural situations using the wearable long-term speech-social data set.
HiCOO: Hierarchical Storage of Sparse Tensors

This paper proposes a new storage format for sparse tensors, called Hierarchical COOrdinate (HiCOO; pronounced: “haiku”). It derives from coordinate (COO) format, arguably the de facto standard for general sparse tensor storage. HiCOO improves upon COO by compressing the indices in units of sparse tensor blocks, with the goals of preserving the “mode-agnostic” simplicity of COO while reducing the bytes needed to represent the tensor and promoting data locality. We evaluate HiCOO by implementing a single-node, multicore-parallel version of the matricized tensor-times-Khatri-Rao product (MTTKRP) operation, which is the most expensive computational core in the widely used CANDECOMP/PARAFAC decomposition (CPD) algorithm. This MTTKRP implementation achieves up to 23.0× (6.8× on average) speedup over COO format and up to 15.6× (3.1× on average) speedup over another state-of-the-art format, compressed sparse fiber (CSF), by using less or comparable storage of them. When used within CPD, we also observe speedups against COO- and CSF-based implementations.