Reviewerly/exHarmony huggingface dataset:
Face Completion Using Generative Adversarial Network

In this paper, we present a novel idea of hybrid algorithm a two-stage generative adversarial networks (GANs) each comprising of convolutional neural network (CNN) tackling the problem of face inpainting. In order to address various peculiarities of face, we aid the user by accepting textual inputs for the same. We intend to achieve the results by applying pixel generation using GAN (Stage 1) combined with text-to-image conversion using GAN (Stage 2) benchmarking on a custom-made dataset for this task. The details of the implementation and the intermediate experiments along with the recorded observation, data curation and data preprocessing step are also presented.
Open, High and Low: Getting Deep in the Sound Source Code

This text presents a research progress about the relationship between artistic practice and technological development in the field of sound and computer music made with code.In this context, the materials of sound artists and computer musicians are the textual code and the sound generated or controlled by the code.The discussion is related to an apparent dissociation between artistic practice and technological development.To understand this relationship I start from the computer science's concept of high and low programming levels.Based on this, I observe that artistic practice is usually centred in the high level while technological development points out to both levels.To study this relationship I designed a graph in which artistic practice and technological development have the categories of layers and are connected to the high and low levels by the source code.The access to lower levels makes it possible to understand the control structures that define modes of artistic production of the software, which implies somehow different discourses and questions.This is possible when the source code of software is open, but in order to understand the technological structures in lower levels, from an artistic perspective, a specialized knowledge is required.
Human Activity Recognition

With advancement in the field of health and medicine, new and better sensors are being developed which help monitor various aspects of an individual’s health. To provide the users with effective analysis related to their health using these sensors, an optimized and accurate solution is required. Due to the increase in health awareness and user-friendliness, growth in the fitness tracker market is expected to grow in the coming years. The main objective of this study is to analyze various machine learning algorithms which will use the data provided by these sensors and extrapolate information from this data which will be useful in tracking the physical activities of a user. Different research papers already published on this have been studied in order to understand their methodology and obtained results are improved. A comparative study of different algorithms to find the most accurate one has been implemented and performed.
Arduino Based Iot Device for Septuagenarian Fall Detection

Arduino based IOT device for septuagenarian fall detection has developed especially for the elder peoples who are staying alone in home. If the old age peoples felt down in most of the cases, it will lead to serious injuries and they can’t raise voice due to shock. This work possess to make the detector on arduino based device for the old age people who fall down. The device is used to detect the falling of a person with the help of accelerometer and this is specially designed as to contact an entitled person as soon. The implementation is based on the combination of both hardware and software that detects and triggers the entitled person through SMS alert. The special feature of this device is portable and it can fix in any space like wheel chair, bed, and the old age person can also fix it with their watch they wear.
Single-Copy Certification of Two-Qubit Gates Without Entanglement

A quantum state transformation can be generally approximated by single- and two-qubit gates. This, however, does not hold with noisy intermediate-scale quantum technologies due to the errors appearing in the gate operations, where errors of two-qubit gates such as controlled-not and swap operations are dominated. In this work we present a cost efficient single-copy certification for a realization of a two-qubit gate in the presence of depolarization noise, where we aim to identify if the realization is noise free, or not. We show that entangled resources such as entangled states and a joint measurement are not necessary for the purpose, i.e., a noise-free two-qubit gate is not needed to certify an implementation of a two-qubit gate. We present a proof-of-principle demonstration with photonic qubits.
ScAmPER: Generating Test Suites to Maximise Code Coverage in Interactive Fiction Games

We present ScAmPER, a tool that generates test suites that maximise coverage for a class of interactive fiction computer games from the early 1980s. These games customise a base game engine with scripts written in a simple language. The tool uses a heuristic-guided search to evaluate whether these lines of code can in fact be executed during gameplay and, if so, outputs a sequence of game inputs that achieves this. Equivalently, the tool can be seen as attempting to generate a set of test cases that maximises coverage of the scripted code. The tool also generates a visualisation of the search process.
Research on IPv6 network construction and application in Higher Vocational Colleges

The main network of colleges and universities is also generally based on IPv4, but due to the limitations of IPv4 protocol, the Internet has been gradually transitioning to IPv6. The promotion of IPv6 carried by education network in China has also lasted for more than ten years. Due to technology, application, market and other reasons, IPv6's replacement for IPv4 is not smooth. It is slow to promote at the operator level, and the resources of colleges and universities are not rich, especially in higher vocational colleges. This paper introduces the practical experience of IPv6 in the construction and promotion of higher vocational colleges, and solves the problems of compatibility, application service and nterconnection in the process of IPv6 network construction. It involves pure IPv6 test network, tunnel docking, NAT-PT and other technical schemes, aiming to ensure the stable operation of the existing IPv4 network, and gradually transition to pure IPv6 network.
Belief Changes and Cognitive Development: Doxastic Logic $${\mathsf {LCB}}$$

Abstract We present the logic $${\mathsf {LCB}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>LCB</mml:mi> </mml:math> which is expressed in a propositional language constantly enriched by new atomic expressions. Our formal framework is the propositional doxastic logic $${\mathsf {KD45}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>KD</mml:mi> <mml:mn>45</mml:mn> </mml:mrow> </mml:math> with the belief operator $${\mathcal {B}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>B</mml:mi> </mml:math> , extended by the $${\mathcal {C}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>C</mml:mi> </mml:math> operator, to be read it changes that ... . We describe the changing beliefs of an agent who uses progressively expanding language. The approach presented here allows us to weaken pragmatic objections to the so-called principle of negative retrospection accepted in $${\mathsf {KD45}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>KD</mml:mi> <mml:mn>45</mml:mn> </mml:mrow> </mml:math> and the problem of logical omniscience . In what follows, we present the expanding propositional language used in our formalism, interpreted using an epistemic version of Kripke semantics. Next, we give a syntactic characterization of logic $${\mathsf {LCB}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>LCB</mml:mi> </mml:math> and prove the soundness and completeness of $${\mathsf {LCB}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>LCB</mml:mi> </mml:math> in respect to our semantics. Finally, we compare the idea of expanding language with the notion of agent awareness and we relate our formalism to two epistemic temporal logics.
Security analysis of a certificateless signcryption mechanism without bilinear mapping

Certificateless signcryption mechanism can not only provide security services, such as message integrity, non-repudiation and confidentiality, but also solve the problems of public key certificate management and key escrow. Zhou et al. proposed a certificateless signcryption mechanism without bilinear mapping and gave its security proof under the discrete logarithm problem and the computational Diffie Hellman problem in the random oracle model. However, the analysis show that this scheme has security flaws. That is, attackers can forge legitimate signatures of any messages. Finally, we give the specific attack process.
A Study on Layers of Deep Neural Networks

Artificial intelligence is a field of technology that can reflect the same abilities as human intelligence through machines. In recent information systems, artificial intelligence is applied in various areas to solve problems effectively. Among the various artificial intelligence technologies, deep learning is used in prediction problems through trained models from a set of previously known training data. The deep learning uses neural networks designed with multiple layers to establish a model for training to find a solution for problems. In designing a neural network, it is important to decide the structures of layers that are the basic elements for maintaining the features of a given problem identified from learning procedures with a set of input training data. Thus, deciding the number of layers and nodes in the neural network for constructing layers is one of the most basic criteria for designing a neural network. In this study, we evaluate the effect of the numbers of layers and nodes in the neural network design with respect to the training accuracy of the neural network and study the composition of layers for designing an efficient neural network.
State encoding for low power in FSM using non-oscillating self-adaptive particle swarm optimization (NOS-SAPSO)

In this paper, states encoding for the low power design in the Finite State Machine has been presented. Power reduction is considered in the dynamic portion of power consumption where switching activity between states decides the majority factor of power dissipation. Switching activity can be minimized up to a great extent with the optimal assignment of state encoding. Hence, a non-oscillatory variant of discrete self-adaptive particle swarm optimization has proposed to obtain the optimal encoding of states. The proposed method provides the diversity-based self-adaptive parametrization of inertia weight, cognition and social constant along with the best solution evolved ever to direct the progress for the population. The proposed method has delivered the non-oscillatory faster convergence with a high rate of success in delivering a global solution. It is found that the proposed method has surpassed various variants of the existing form of particle swarm optimization over the benchmark problem of the finite state machine.
Learning to Calibrate and Rerank Multi-label Predictions

A multi-label classifier assigns a set of labels to each data object. A natural requirement in many end-use applications is that the classifier also provides a well-calibrated confidence (probability) to indicate the likelihood of the predicted set being correct; for example, an application may automate high-confidence predictions while manually verifying low-confidence predictions. The simplest multi-label classifier, called Binary Relevance (BR), applies one binary classifier to each label independently and takes the product of the individual label probabilities as the overall label-set probability (confidence). Despite its many known drawbacks, such as generating suboptimal predictions and poorly calibrated confidence scores, BR is widely used in practice due to its speed and simplicity. We seek in this work to improve both BR’s confidence estimation and prediction through a post calibration and reranking procedure. We take the BR predicted set of labels and its product score as features, extract more features from the prediction itself to capture label constraints, and apply Gradient Boosted Trees (GB) as a calibrator to map these features into a calibrated confidence score. GB not only produces well-calibrated scores (aligned with accuracy and sharp), but also models label interactions, correcting a critical flaw in BR. We further show that reranking label sets by the new calibrated confidence makes accurate set predictions on par with state-of-the-art multi-label classifiers—yet calibrated, simpler, and faster.
A laser light source control system for continuous variable quantum key distribution

Compared with discrete variable quantum key distribution, continuous variable quantum key distribution (CVQKD) has the advantages of low cost, good stability, and high channel capacity, in addition to unconditional security and detectability for wiretapping. The basis of CVQKD system operation is the realization of loading information into photon quantum states. In this paper, a laser light source control system has realized the control and adjustment of the photon quantum states and completed the encoding of quantum states. In order to generate synchronous optical signals, FPGA, multi-channel DAC and micro power ADC are used to control the source of the synchronous optical laser. The control and modulation of multiple optical components are realized based on FPGA, multiple 16-bit 1.0 GSPS DACs and high-performance operational amplifiers. The electronic test results show that the system implements the control and adjustment functions of lasers, amplitude modulator, phase modulators, variable optical attenuators, polarization controllers, as well as detection of synchronized light. The performance of the laser light source control system fully meets the requirements of the CVQKD system.
Computational Intelligence for Temporal Expression Retrieval in Code-Mixed Text

Text on social media contains code-mixed contents which can be used to extract temporal information. These temporal information is hard to be retrieved in transliterated domain. The extraction of temporal expression used by the people to express their opinions on web isa challenging task in code mixed environment.The work presented in this paper tries to identify temporal information in code mixed social media text in transliterated domain. Thehand crafted rulesfor identifying temporal content in text is proposed, that takes codemixed text as input and on the basis of rules proposed, the system produces a tagged temporal output by identifying the temporal expression in the sentence.The hypothesis is evaluated on the basis of experiments undertaken in terms of the hand-crafted rule approach and to the statistical approaches of Conditional Random Field and Support Vector Machines. On the obtained results from mentioned approaches, a voting technique is applied which selects the best temporal tag for a word based on majority. This voting tag is also useful when the applied approaches give dissimilar tag for a word. The voting approach helps in considering the best temporal tag. The used votingtechniqueprovides the best results for tagging as compared to other different approaches used in the experiment with high accuracy.
Optimized IoT Cryptoprocessor Based on QC-MPDC Key Encapsulation Mechanism

The key encapsulation mechanism (KEM) is an important cryptographic tool to protect communication in the Internet of Things (IoT). In the near future, classical algorithms used to construct KEMs, such as RSA and elliptic curve cryptography, will be vulnerable to attacks from quantum computers. Recently, Yamada et al. proposed the quasicyclic medium density parity check (QC-MDPC) KEM, which is considered one of the most advanced code-based cryptosystems to resist quantum attacks. In this article, an optimized implementation of QC-MDPC KEM for IoT applications is presented. Our main contributions are threefold: 1) the fastest QC-MDPC McEliece decryption in field-programmable gate array (FPGA); 2) the first QC-MDPC KEM implementation in FPGA; and 3) the first iteration count attack-resistant QC-MDPC decoder in FPGA. To improve the decryption speed, we introduce a novel customized rotation engine (CRE) and incorporated several recent techniques reported in the literature, including adaptive threshold and Hamming weight estimation. The best-achieved throughput in our implementation on Xilinx Virtex 7 FPGA is 12.7% faster than the state-of-the-art result reported by Heyse et al. The proposed CRE was then integrated with QC-MDPC KEM to produce a fast and secure KEM. Furthermore, to prevent timing attacks demonstrated recently, a constant-time implementation of the QC-MDPC McEliece decoder was presented.
ynamic Adaptation of Approximate Bit-width for CNNs based on Quantitative Error Resilience

As an emerging paradigm for energy-efficiency design, approximate computing can reduce power consumption through simplification of logic circuits. Although calculation errors are caused by approximate computing, their impacts on the final results can be negligible in some error resilient applications, such as Convolutional Neural Networks (CNNs). Therefore, approximate computing has been applied to CNNs to reduce the high demand for computing resources and energy. Compared with the traditional method such as reducing data precision, this paper investigates the effect of approximate computing on the accuracy and power consumption of CNNs. To optimize the approximate computing technology applied to CNNs, we propose a method for quantifying the error resilience of each neuron by theoretical analysis and observe that error resilience varies widely across different neurons. On the basic of quantitative error resilience, dynamic adaptation of approximate bit-width and the corresponding configurable adder are proposed to fully exploit the error resilience of CNNs. Experimental results show that the proposed method further improves the performance of power consumption while maintaining high accuracy. By adopting the optimal approximate bit-width for each layer found by our proposed algorithm, dynamic adaptation of approximate bit-width reduces power consumption by more than 30% and causes less than 1% loss of the accuracy for LeNet-5.
Hybrid Chaining Security Approach in Cloud Computing

In the innovative world, the advance progression of information in cloud computing has become the concern since information is the most significant asset which needs to be secure. Several encryption techniques are broadly available and used in information security across all platforms. Encryption algorithms are arranged into two major groups: symmetric key and asymmetric key. Asymmetric-key encryption is utilized close by symmetric-key encryption to develop the best execution of information exchange. Numerous analysts have proposed different encryption calculations, for example, AES, DES, Blowfish, and so forth. However, as security level is expanding, the time and multifaceted nature of calculation are additionally expanding. This is the significant reason for diminishing the speed and proficiency of the encryption framework, and cloud computing is growing expertise that is vague to several security issues. The proposed scheme is based on hybridization of RSA and AES, encryption algorithm, i.e., “Hybrid Chaining Algorithm (HCA)” which upgrades the security execution. The HCA is a blend of open private (AES and RSA) key encryption algorithm that utilizes 128–1024 bits key size and also uses MD5 hashing procedure to expand the security. This calculation utilizes MD5 hashing procedure to expand the security. The fundamental focal point of this framework is to hinder data access from cloud information stockpiling centers by unapproved clients. This superbly put away data at cloud information stockpiling centers and recovers information from it once it is required. This gives the highly secured state of information security for online virtual framework and spares the misrepresentation clients to utilize your insight.
On Finding Similar Verses from the Holy Quran using Word Embeddings

Finding semantic text similarity (STS) between two pieces of text is a well-known problem in Natural Language Processing. Its applications are nearly in every field such as plagiarism detection, finding related user queries in customer services or finding similar questions in search engines or forums like Stack Overflow, Quora and Stack exchange. If applied to any religious text, it can help to relate how similar pieces of knowledge are described in different places. This paper uses Word2Vec and Sent2Vec models to facilitate the process of knowledge extraction from a given corpus. The paper makes use of several English translations of the Holy Quran which is the most sacred book for Muslims. Sent2vec models have been trained from several translations of the book and the trained models are then subsequently utilized to study the semantic relationship between different words and sentences. The performance of the custom-built word embeddings is compared against the pre-trained embeddings provided by the Spacy library.
Security camera design using smart phones

Abstract The purpose of this study is to identify the importance of using information technology for security, especially for supervision. The method used in this research is a descriptive method, describing the function of using smartphones as a security tool. The results of this study that smartphones can be used as a security tool by using a cellphone camera as a surveillance camera and wireless computer network. This research was carried out with the results of analysis and experiments using previous references and supporting equipment. From the results of this study, it can be found that smartphones can be utilized as security systems. Therefore, the results of this study can be utilized by various parties or can be used as a reference in the use of technology and information.
Ensemble Method Combination: Bagging and Boosting

Boosting and bagging are ensemble techniques that create decision committees using the base learning algorithms. Both have their own merits and weakness in learning from different types of datasets. Boosting has a better efficiency than bagging on noiseless data, whereas for noisy data bagging is more efficient. Combining the two can overcome the demerits and reduce the error in prediction. Here in the proposed method, the concept of variance reduction of bagging is applied to boosting. For this, the dataset is divided based on attributes into different size of combinations. Individual boosting is performed on these smaller subsets of data and then for final prediction, the results are added. The results of the proposed method are compared with boosting, bagging and the combined method over a range of standard UCI datasets in terms of the accuracies.
Persistent Surveillance of Events with Unknown Rate Statistics

We present a novel algorithm for persistent monitoring of stochastic events that occur at discrete locations in the environment with unknown event rates. Prior research on persistent monitoring assumes knowledge of event rates, which is often not the case in robotics applications. We consider the multi-objective optimization of maximizing the total number of events observed in a balanced manner subject to real-world autonomous system constraints. We formulate an algorithm that quantifies and leverages uncertainty over events’ statistics to greedily generate adaptive policies that simultaneously consider learning and monitoring objectives. We analyze the favorable properties of our algorithm as a function of monitoring cycles and provide simulation results demonstrating our method’s effectiveness in real-world inspired monitoring applications.