Reviewerly/exHarmony huggingface dataset:
MICKEY 2.0.85: A Secure and Lighter MICKEY 2.0 Cipher Variant with Improved Power Consumption for Smaller Devices in the IoT

Lightweight stream ciphers have attracted significant attention in the last two decades due to their security implementations in small devices with limited hardware. With low-power computation abilities, these devices consume less power, thus reducing costs. New directions in ultra-lightweight cryptosystem design include optimizing lightweight cryptosystems to work with a low number of gate equivalents (GEs); without affecting security, these designs consume less power via scaled-down versions of the Mutual Irregular Clocking KEYstream generator—version 2-(MICKEY 2.0) cipher. This study aims to obtain a scaled-down version of the MICKEY 2.0 cipher by modifying its internal state design via reducing shift registers and modifying the controlling bit positions to assure the ciphers’ pseudo-randomness. We measured these changes using the National Institutes of Standards and Testing (NIST) test suites, investigating the speed and power consumption of the proposed scaled-down version named MICKEY 2.0.85. The (85) refers to the new modified bit-lengths of each MICKEY 2.0 register. The results show that it is faster, requires less power, and needs fewer GEs. The proposed variant will enhance the security of applications, such asRadio-frequency identification (RFID) technology, sensor networks, and in Internet of things (IoT) in general. It also will enhance research on the optimization of existing lightweight cryptosystems.
Type-1 and Type-2 Fuzzy Logic Controllers for Autonomous Robotic Motion

This paper proposes type-1 and type-2 Takagi-Sugeno (TS) fuzzy logic controllers for autonomous mobile robot motion. Two basic behaviors have been studied and simulated: goal-seeking and obstacle-avoidance. Fuzzy logic controllers (FLC) are the robust control systems for the navigation task of mobile robot because it is encountered by imprecision and uncertainties. This paper deals with the design of autonomous robot behaviors using type-1 and type-2 fuzzy logic systems. The proposed systems are used to control a wheeled mobile robot. To show the effectiveness of the proposed fuzzy controllers, simulation results will be discussed and compared.
Multi-view Reconstruction of 3D Human Pose with Procrustes Analysis

Recovery of 3D human pose from cameras has been the subject of intensive research in the last decade. Algorithms that can estimate the 3D pose from a single image have been developed. At the same time, many camera environments have an array of cameras. In this paper, after aligning the poses obtained from single images using Procrustes Analysis, median filtering is utilized to eliminate outliers to find final reconstructed 3D body joint coordinates. Experiments performed on the CMU Panoptic, and Human3.6M databases demonstrate that the proposed system achieves accurate 3D body joint reconstructions. Additionally, we observe that camera selection is useful to decrease the system complexity while attaining the same level of reconstruction performance.
MimickNet, Matching Clinical Post-Processing Under Realistic Black-Box Constraints

Image post-processing is used in clinical-grade ultrasound scanners to improve image quality (e.g., reduce speckle noise and enhance contrast). These post-processing techniques vary across manufacturers and are generally kept proprietary, which presents a challenge for researchers looking to match current clinical-grade workflows. We introduce a deep learning framework, MimickNet, that transforms conventional delay-and-summed (DAS) beamformed images into the approximate post-processed images found on clinical-grade scanners. Training MimickNet only requires post-processed image samples from a scanner of interest without the need for explicit pairing to DAS data. Unpaired image flexibility allows MimckNet to hypothetically approximate any manufacturer's post-processing without hacking into commercial machines for pre-processed data. MimickNet generates images with an average similarity index measurement (SSIM) of 0.930±0.0892 on a 300 cineloop test set, and it generalizes to cardiac cineloops achieving an SSIM of 0.967±0.002 despite using no cardiac data in the training process. To our knowledge, this is the first work to approximate current clinical-grade ultrasound post-processing under realistic black-box constraints where before and after post-processing data is unavailable. MimickNet can be used out of the box or retrained to serve as a clinical post-processing baseline to compare against for future works in ultrasound image formation. To this end, we have made the MimickNet software open source at https://github.com/ouwen/mimicknet.
A multi-controlled semi-autonomous wheelchair for old and physically challenged people

Physical disability has been incessant thinking for the past several eras. Wheelchairs are the sift partner for most of the disabled people in their day-to-day life. In order to collaborate with physically disabled people, a multi-controlled semiautonomous wheelchair is developed in this paper which can carry them prudently without the assistance of any other human caretaker. The disabled people can navigate and control the speed of the wheelchair by joystick, voice command or gesture command. The system can detect the obstacle in the front side and back side of the wheelchair and can stop the movement automatically to avoid collation. It can also detect any hole when it is moving in the font direction and stops the movement to protect the disabled person from an accident. In case of an accident if wheelchair tilted extensively from its normal orientation the system automatically stops the motors, sends SMS to the relative with GPS location and sounds the buzzer to draw the attention from others. The evaluation of the performance of the system shows that it can be a very helpful friend in the daily life of physically challenged people.
Baseball Pitch Type Recognition Based on Broadcast Videos

In this paper, we report our work on baseball pitch type recognition based on broadcast videos using two-stream inflated 3D convolutional neural network (I3D). To improve the state-of-the-art of research, we developed our own high-quality dataset, trained and tuned the I3D model extensively, primarily combating the problem of overfitting while still trying to improve final validation accuracy. In the end, we are able to achieve an accuracy of 53.43% ± 3.04% when oversampling and 57.10% ± 2.99% when not oversampling, which is a significant improvement over the published best result of an accuracy of 36.4% on the same six pitch type classes.
Generic Construction of ElGamal-Type Attribute-Based Encryption Schemes with Revocability and Dual-Policy

Cloud is a computing paradigm for allowing data owners to outsource their data to enjoy on-demand services and mitigate the burden of local data storage. However, secure sharing of data via cloud remains an essential issue since the cloud service provider is untrusted. Fortunately, asymmetric-key encryption, such as identity-based encryption (IBE) and attribute-based encryption (ABE), provides a promising tool to offer data confidentiality and has been widely applied in cloud-based applications. In this paper, we summarize the common properties of most of IBE and ABE and introduce a cryptographic primitive called ElGamal type cryptosystem. This primitive can be used to derive a variety of ABE schemes. To illustrate the feasibility, we present generic constructions of revocable attribute-based encryption and dual-policy attribute-based encryption with formal definitions and security proofs. By applying our proposed generic constructions, we also present instantiations of these schemes. Furthermore, we demonstrate the high performance of the proposed schemes via experiments.
A high-throughput fully digit-serial polynomial basis finite field $\text{GF}(2^{m})$ multiplier for IoT applications

The performance of many data security and reliability applications depends on computations in finite fields $\text{GF} (2^{m})$ . In finite field arithmetic, field multiplication is a complex operation and is also used in other operations such as inversion and exponentiation. By considering the application domain needs, a variety of efficient algorithms and architectures are proposed in the literature for field $\text{GF} (2^{m})$ multiplier. With the rapid emergence of Internet of Things (IoT) and Wireless Sensor Networks (WSN), many resource-constrained devices such as IoT edge devices and WSN end nodes came into existence. The data bus width of these constrained devices is typically smaller. Digit-level architectures which can make use of the full data bus are suitable for these devices. In this paper, we propose a new fully digit-serial polynomial basis finite field $\text{GF} (2^{m})$ multiplier where both the operands enter the architecture concurrently at digit-level. Though there are many digit-level multipliers available for polynomial basis multiplication in the literature, it is for the first time to propose a fully digit-serial polynomial basis multiplier. The proposed multiplication scheme is based on the multiplication scheme presented in the literature for a redundant basis multiplication. The proposed polynomial basis multiplication results in a high-throughput architecture. This multiplier is applicable for a class of trinomials, and this class of irreducible polynomials is highly desirable for IoT edge devices since it allows the least area and time complexities. The proposed multiplier achieves better throughput when compared with previous digit-level architectures.
ECNN: Activity Recognition Using Ensembled Convolutional Neural Networks

Human Activity Recognition (HAR) has been a compelling problem in the field of computer vision since a long time. Our focus is to address the problem of trimmed activity recognition which is to identify the class of human activity in a video which is temporally trimmed to contain only those periods where human activity is present. In the past few years there has been a transition from handcrafted features for classification to deep convolutional neural networks which work on raw video data to extract features and classify human activities. 3D convolutional neural networks learn features from both the temporal as well as spatial dimensions and prove to be very powerful in finding correlations in signals containing spatiotemporal information. 3D-CNNs have been extremely successful in activity recognition. We explore the shortcomings of a 3D-CNN architecture and propose ensembling with a 2D-CNN to overcome these for a significantly better performance in activity recognition.
Privacy-Preserving Pedestrian Detection for Smart City with Edge Computing

Edge computing is an ideal platform for pedestrian detection in smart city because of low latency and location awareness. In edge computing, data collected by IoT devices are processed on edge servers rather than being transported to cloud server. Compared with cloud computing, edge computing could avoid the possibility of pedestrians' privacy being leaked from cloud server or being stolen in the process of transmission. However, edge servers are not always safe. For instance, there are researches show that 89% of WiFi hotspots are unsecured. Hence, it is possible for attackers to know where you go at a given time of the day, which places you prefer to visit from images collected by IoT devices, such as camera, UAVs. Considering the data collected by IoT devices could include the sensitive information about users, we propose a scheme that applies differential privacy to protect the collected data. We experiment on the INRIA Person Dataset and use three deep learning networks. Results show that even though adding differential privacy makes images blurred, the deep learning network on edge servers can detect pedestrians in the images with accuracy as high as 97.3%.
Performance &amp; Energy Consumption Metrics Of A Data Center According To The Energy Consumption Models Cubic, Linear, Square And Square Root

Cloud computing provides computing resources in the form of services rather than products on a network. It controls the administration of huge data centers that run a very diverse sequence of events and requires techniques for optimizing the use of resources and power utilization. Since the use of real test structures to justify such optimization techniques requires large investments, simulation tools are generally the most practical way to manage experiments in this area. In this paper, we will explore the need of cloud simulators, we will mention the common architecture of cloud simulators, and we will present the CloudReports simulator. Then we will evaluate different energy aware power models to check the performance of each model of power and we will discuss the results obtain from the simulator.
Keystroke based User Authentication using Modified Differential Evolution

User Authentication is a difficult problem yet to be addressed accurately. Little or no work is reported in literature dealing with clustering-based anomaly detection techniques for user authentication for keystroke data. Therefore, in this paper, Modified Differential Evolution (MDE) based subspace anomaly detection technique is proposed for user authentication in the context of behavioral biometrics using keystroke dynamics features. Thus, user authentication is posed as an anomaly detection problem. Anomalies in CMU's keystroke dynamics dataset are identified using subspace-based and distance-based techniques. It is observed that, among the proposed techniques, MDE based subspace anomaly detection technique yielded the highest Area Under ROC Curve (AUC) for user authentication problem. We also performed a Wilcoxon Signed Rank statistical test to corroborate our results statistically.
Study on Machine Learning Techniques with Conventional Tools for Payment Fraud Detection

Payment fraud is intentional deception with the purpose of obtaining financial gain or causing loss by implicit or explicit trick, committed by many parties in order to gain significant financial benefits. That had been a major reason for personal financial losses that account over a billion losses a year. At the same time, fraud detection has been improved and currently is embraced by the cutting-edge information technology “Machine Learning”. However, majority of the available studies have been studying with the deep high-end techniques with various costly technologies, and focusing on accuracy and time of the model. They have also been limited to past fraud histories. This study is conducted with multiple machine learning techniques with the use of synthesized dataset, which is not limited to the history, and our study is performed by using the conventional open source data science tools. However, the results seem to be above the expectation.
Stochastic optimization

Stochastic optimization includes a wide range of applications and methods that modify the objective functions when we face with statistical problems. The stochastic diffusion search (SDS) is the most useful method. In this chapter, the SDS was explained completely. From theoretical considerations, it is concluded that the SDS is one of the best techniques that have ability of solving invariant pattern recognition problems within a search space easily. In addition, SDS is an appropriate optimization technique that has been used for various problems by creating connection among the agents. The agents can be categorized into two different classes in which the first class that is known as an active agent is capable of finding appropriate solution and the second one is called a passive (inactive) agent that seeks for rest of search space.
Fuzzy-Based Session Key as Restorative Power of Symmetric Key Encryption for Secured Wireless Communication

A symmetric key encryption with fuzzy-based session key has been proposed for wireless communication by satisfying the key issues like security and increase the strength of the symmetric key. In this paper, we generate “n” number of sub-keys from symmetric key using XOR operation between a random character matrix and symmetric key. Among these sub-keys, we generate a session key using fuzzy logic. Now the encryption is done by using this session key and a symmetric key. Here, we transmit the session key to the recipient end by amalgamating with the symmetric key. This amalgamated form is concatenated with ciphertext and sent to the recipient end for decryption with these keys. Here, a new authentication scheme is used with cipher text. Different types of randomness tests have been done to test the randomness of our session key. The brute-force attack analysis for this scheme and comparative study with the existing standard methods has been done with satisfactory results.
Challenges to Sustainability in the Graphic Design Practices of a Developing Nation

In this paper, we examine graphic design practices to highlight challenges to sustainability, how they manifest, and their associated outcomes in Ghana. It was discovered that the key challenges to sustainable practices were a lack of sustainability education, sustainability practice guidelines, and the improper disposal of waste chemicals. Other significant problems were air pollution, a lack of eco-papers and inks, poor gate-keeping of design content, and weak collaboration among designers. In addition, poor printing and weak design caused economic deficits, while some products designed for use were harmful to society. To minimize these challenges, we propose sustainability education for graphic designers, their production team, and clients. We further recommend that graphic designers should observe professional ethics in their graphic design practices to promote sustainability.
Specification of Requirements and Software Architecture for the Customisation of Enterprise Software: A Multi-case Study Based on the RE4SA Model

Many failed software projects can be traced to bad requirements management. Additionally, there is a big gap be-tween state of the art and practice in software architecture. For enterprise software customisation, not only do these issues apply, but additional challenges exist too. Instead of one standard soft-ware product, vendors often have to deal with customised ver-sions with additional maintenance challenges. In this research, we apply the Requirements Engineering for Software Architecture (RE4SA) model via a multi-case study to show how the require-ments engineering and software architecture disciplines can be linked, and in doing so provide improvements to both areas. Our multi-case study regards enterprise software customisation and shows improvements in requirements management and higher alignment between the software architecture and requirements.
Blockchain Guided Trustworthy Interactions for Distributed Disaster Management

Given the devastating outcomes of the recent hurricanes, earthquakes, and floods across the globe, governments all over the world started to pay serious attention to their disaster management protocols. The current reliance on centralized, infrastructure-dependent solutions is a serious threat to the whole management process. The entire process is mainly about processing help requests based on real-time information about the disaster, and efficiently responding to such requests by dispensing a set of limited resources available at the management's disposal. Relying on independent anonymous help givers can expand the resource availability and facilitate help provisioning. Further, Crowdsourcing can be a valuable source of information that can better guide help provisioning process. However, establishing dynamic trust relationship between help requesters, information providers, and help givers is the key enabler to such enhancement. Help requests should be probably authenticated not to waste the valuable resources, help provisioning should be rewarded to motivate help givers, and Data sharing should be verified not to act on false information. In this paper, we present a decentralized Blockchain based trust management framework to facilitate cooperative anonymous help provisioning at disaster time considering infrastructure-supported scenario.
Adheetee: A Comprehensive Bangla Virtual Assistant

A Virtual Assistant is an application program that can understand natural language (audio and text) commands and perform tasks for the user. The importance and usage of the virtual assistant are growing exponentially worldwide. In this paper, we have demonstrated a comprehensive design and development methodology of a Bangla virtual AI assistant `Adheetee (Erudite)' for smart devices such as smartphones and personal computers. Bangla is the native language of Bangladesh and some states of India with millions of native speakers worldwide. There are some popular virtual assistants available in English and other languages but not in Bangla. Therefore, using a virtual assistant become very difficult for Bangla speaking people around the world. Moreover, a large portion of Bengali speaking people is illiterate. This makes using a virtual assistant more difficult. This gives us the motivation to build a comprehensive virtual assistant for Bangla language. We have covered vast varieties of most frequent commands in our system with the accuracy of 94.065%.
An accurate phase-height mapping algorithm by using a virtual reference plane

In the phase measurement profilometry, the phase-height mapping is to calibrate the system by the height and phase of several known planes. The four planes method, which only needs the four references, is widely used due to its least amount of data. But for its height reconstruction, a large reconstructed height error will be introduced when the phase difference is close to 0. An improved phase-height mapping method, which is to create a virtual reference plane with a known height along the original reference plane, is proposed. Then the coefficient tables a, b, c, which are capable to suit for the whole calibration space, will be calculated and can improve the accuracy of 3D object reconstruction as well as the efficiency. The experimental results show that the mapping height errors can be significantly reduced. The RMS error of the reconstructed object is less than 4.8μm with the proposed method, while that of the traditional method is 350μm.
A critical look at modular adders using residue number system

Presently, computer scientists and researchers show greater interest in one of the ancient techniques, namely, Residue Number System (RNS) to use in different fields. In this paper, an introduction to RNS and Modular adders, their overview and detailed explanation are presented. In the RNS system, conventional data is encoded to RNS data in the first stage referred to as forward conversion. Later the reverse conversion is needed which decodes RNS data to conventional data. Among the conversions, the reverse conversion is more complex over the forward conversion. The decoding process can however be performed by availing the Chinese Remainder Theorem (CRT) or Mixed Radix Conversion (MRC) technique. In RNS applications such as modular multipliers, digital signal processing (DSP) applications, residue to binary converters, etc. The crucial component was modular adders, so that some of the modular adders are presented at the end of this paper.