Reviewerly/exHarmony huggingface dataset:
Combined DTCWT-SVD-Based Video Watermarking Algorithm Using Finite State Machine

The video carrying the watermark will undergo various attacks such as transcoding, noise, and temporal synchronization during network propagation (upload/ download), and the existing algorithm doesn't involve analyzing and testing the combined attacks encountered by the video during network propagation. In order to correctly extract the water-mark in the video after network propagation to protect the rights of copyright holders, this paper proposes a video watermarking algorithm based on the finite state machine in DTCWT-SVD domain. Firstly, the carrier video is divided into several groups of the frame and the watermark image is scanned into several segments by Zig-Zag. Then, the watermark sequence collection is generated by the finite state machine, and each watermark sequence is embedded into the corresponding video frame by modifying the singular value of the DTCWT low-frequency coefficient matrix. Finally, the watermark is extracted according to the predefined relationship of singular values. The experimental results show that the proposed algorithm can effectively resist transcoding, noise, temporal synchronization or other attacks. It is also can accurately extract watermark information in the video after network propagation, which can effectively resist the combined attacks encountered by video in the process of network propagation.
A Case Study for an Accelerated DCNN on FPGA-Based Embedded Distributed System

Face Detection (FD) recently became the base of multiple applications requiring low latency but also with limited resources and energy budgets. Deep Convolutional Neural Networks (DCNNs) are especially accurate in FD, but latency requirements and energy budgets call for Field Programmable Gate Arrays (FPGAs)-based solutions, trading flexibility and efficiency. Nonetheless, the offer of FPGAs solutions is limited and different chips often require expensive re-design phases, while developers desire solutions whose resources can scale proportionally to the demands. Therefore, this work presents an FD solution based on a DCNN on a distributed, embedded system with FPGAs, proposing a general approach to reduce the DCNN size and to design its FPGA cores and investigating its accuracy, performance, and energy efficiency.
Local Regression Based Box-Cox Transformations for Resource Management in Cloud Networks

Understanding and implementing approaches to efficiently manage the infrastructure resources of cloud data centers has become essential. Energy consumption and disorganized resource usage can expensively produce an impressive increase in the operational cost of cloud services. This increase turns to a remarkable rise in the cloud customers' invoices. Providing an exceptional quality of service running on well-organized resources with efficient energy is a critical issue that needs to be carefully considered by both industrial and academics. Although, the cloud providers are trying to deliver sufficient quality of services to their customers with a comparatively proper cost. One of the effective techniques to address these issues in cloud data centers is a dynamic virtual machine consolidation. This technique intends to improve energy efficiency and resource utilization by reallocating multiple virtual machines including various workload among available hosts and turning the unutilized hosts to an ideal state. However, consolidating the virtual machines due to fluctuating workload in cloud application can cause a violation in service level agreement. In this paper, we propose a host overload detection algorithm based on the Box-Cox transformations and the local regression model to predict overloaded hosts. This algorithm transforms the historical data of the host workload by using the Box-Cox transformations technique, and it also applies the local regression to predict the future state of the selected host. The experiments and simulation results based on dynamic workloads show the proposed algorithm outperforms the other competitive host overload detection algorithms.
Manipulation of superconducting qubit with direct digital synthesis*

We investigate the XY control and manipulation of the superconducting qubit state using direct digital synthesis (DDS) for the microwave pulse signal generation. The decoherence time, gate fidelity, and other qubit properties are measured and carefully characterized, and compared with the results obtained by using the traditional mixing technique for the microwave pulse generation. In particular, the qubit performance in the state manipulation with respect to the sampling rate of DDS is studied. Our results demonstrate that the present technique provides a simple and effective method for the XY control and manipulation of the superconducting qubit state. Realistic applications of the technique for the possible future scalable superconducting quantum computation are discussed.
Multimodal Image Captioning Through Combining Reinforced Cross Entropy Loss and Stochastic Deprecation

Recently, Cross Entropy Loss (CEL) has been proved to be useful in encoder-decoder based multimodal image captioning; however, it still faces the difficulty of inconsistency between optimizing function and evaluation metrics. In this paper, we propose a new approach for multimodal image captioning. It consists of 1) Reinforced Cross Entropy Loss (RCEL) to maximize the probability of ground truth captions and optimize evaluation metrics directly, and 2) Stochastic Deprecation (SD) to automatically select high-quality ground truth sentences without losing the diversity of corpus. The proposed RCEL and SD are generic and can improve the existing natural language generation models while combining them (RCEL-SD) can achieve the best result. Experimental results on the benchmark MSCOCO dataset show that the proposed RCEL-SD respectively outperforms CEL in terms of all the 7 evaluation metrics on three recent image captioning models.
Assessment of an Immersive Virtual Supermarket to Train Post-stroke Patients: A Pilot Study on Healthy People

Technological improvements have contributed to the development of new innovative approaches to rehabilitation. The use of virtual reality (VR) in physical and cognitive rehabilitation brings several advantages, such as greater motivation and patient enjoyment, allowing for the performance of everyday life tasks in a safe and controlled environment. This work presents a virtual supermarket to be experienced with the HTC Vive Head Mounted Display, aiming at the training of post-stroke patients. The immersive virtual environment was validated on a sample of healthy subjects; a comparison was made with the previous software version of the virtual supermarket developed to train cognitive capabilities in elderly users. System usability, cyber-sickness and sense of presence have been investigated to highlight desirable modifications before a final assessment on clinical patients.
Study on Star-Galaxy Image Generation Method Based on GAN

GAN technology has been widely used in image generation field. Generating images of stars and galaxy is of great significance for the prediction of unknown stars and galaxy. GAN has been used to generate star-galaxy images in this paper; the GAN model structure was built; the training strategy for GAN was designed; in order to stabilize the training procedure, we proposed a gird search method for the optimization of several hyper-parameters and an improved neuron discard method, EM-distance was used to modify the loss function in original GAN model. Taking the star-galaxy images in the Sloan digital sky survey (SDSS) as the training dataset, the improved method proposed in this paper and the original GAN were respectively used to generate two kinds of stars and galaxy images with different resolutions, and the comparison has been made to verify the effectiveness of the improved method.
Software Architecture for Health Care Supportive Home Systems to Assist Patients with Diabetes Mellitus

Diabetes Mellitus is a life-long health condition that causes a high concentration of glucose in the blood. The foundation of the treatment and prevention of diabetes lays on reeducation and medication to prevent complications caused by high levels of glucose in the blood. New methods, products, and services, like those provided by the Ambient Assisted Living (AAL) domain, for supporting activities of the daily life of people with chronic diseases are effective to assist the management of diabetes disease. Particularly, Health Care Supportive Home (HSH) systems can provide an autonomous life in their residence to patients and supply them with autonomy through the use of different types of technologies. We present the software architecture of DiaManT@Home, a service-oriented HSH system that assists patients diagnosed with diabetes mellitus, and that enhances the self-management of their condition at home. DiaManT@Home was constructing following a well-established process based on the instantiation of a reference architecture for HSH systems. The software architecture of DiaManT@Home was assessed regarding the completeness of its software elements to achieve functional and non-functional requirements using prototypes. Results showed that DiaManT@Home can be considered as a complete solution to an HSH system for assisting patients at home. As future works, DiaManT@Home will be coded following the architecture presented in this work.
Exemplar-based compounds: The case of Chinese

The aim of this paper is to investigate a specific naming strategy, which is based on compounding and exemplification, examining data from Chinese. We will focus on what we will label ‘exemplar-based compounds’, i.e. compounds consisting of at least one lexeme denoting an exemplar of the category referred to by the whole compound. We propose that ‘exemplar-based’ compounds in Chinese be divided into two macro-types: (1) [exemplar1-exemplar2]category, in which the exemplars may or may not exhaustively list the members of the category denoted by the compound (e.g. dāoqiāng ‘sword-spear, sword and spear > ‘swords, spears and similar things = weapons’); (2) [exemplar-class]category, in which the first constituent exemplifies the class denoted by the second one; this type includes compounds in which the second constituent is a classifier (e.g. niǎozhī ‘bird’, chuánzhī ‘ship’, with zhī ‘clf’). After a detailed discussion of exemplar-driven category naming and of compounding and classifiers in Chinese, we will present the results of a corpus-based study, based on data of Premodern and Modern Chinese. We will show how the exemplar-driven abstraction characterising these constructions evolved into systematic reference to a category and to its individual items, revealing a change from a procedural category construction to a naming concept label.
Beyond the Card Reader: Anti-election Rigging Technology and National Security in Nigeria

With the increasing spread of information technology in Africa, digitalisation of elections is gradually becoming a popular phenomenon in the continent. However, there has not been sufficient awareness on the potential dangers in the process of digitalising elections. In this regard, this article provides analysis of the connection between the use of technological model in elections and security using the Nigerian example. The article acknowledges the potency of the technological model to solve the problem of electoral malpractices and ultimately address issues concerning electoral violence over claims of fraud. Notwithstanding its usefulness, the article analyses the challenges associated with the technological model and how this represents a major security threat. In this regard, the key question is: does Nigeria have the technical capacity to manage effectively electronic devices that have the potential of being used to unleash massive electoral fraud that could completely undermine peaceful coexistence within its fragile and sensitive electoral context? Following this, the article advances a number of policy recommendations on the measures to put the technological model into an effective use in order to promote peaceful elections in Nigeria.
Sniffing Android Code Smells: An Association Rules Mining-Based Approach

Interest in mobile applications (mobile apps) has grown significantly in recent years and has become an important part of the software development market. Indeed, mobile apps become more and more complex and evolve constantly, while their development time decreases. This complexity and time pressure might lead developers to adopt bad design and implementation choices, which are known as code smells. Code smells in mobile apps could lead to performance issues such as overconsumption of hardware resources (CPU, RAM, battery) or even downtime and crashes. Some tools have been proposed for the detection of code smells in Android apps, such as PAPRIKA or ADOCTOR tools. These tools rely on metrics-based detection rules, which are defined manually according to code smell definitions. However, manually defined rules might be inaccurate and subjective because they are based on user interpretations. In this paper, we present a tool-based approach, called FAKIE, which allows the automatic inference of detection rules by analysing code smells data using an association rules algorithm: FP-GROWTH. We validated FAKIE by applying it on a manually analysed validation dataset of 48 opensource mobile apps. We were able to generate detection rules for a dozen code smells, with an average F-measure of 0.95. After all of that, we performed an empirical study by applying FAKIE on 2,993 apps downloaded from ANDROZOO, a repository of mobile apps.
Combining Image Targets and SLAM for AR-Based Cultural Heritage Fruition

Augmented Reality (AR) is one of the prominent technologies in Cultural Heritage (CH) exploitation. Taking advantage of commonly used tools as smartphones and tablets, digital contents have the potential to improve visitors' understanding and enjoyment of historical buildings and museums. In this regard, the early stage research described in this paper aims to develop an AR app combining image target-based AR and Simultaneous Localization And Mapping (SLAM). Leading visitors' attention, the app will enhance CH fruition turning it in an interactive learning experience. The presented case study, the "Studiolo" of the Duke in the "Palazzo Ducale" of Urbino, with its high concentration of depicted elements, is ideal to explain the advantages of combining image target and SLAM to achieve a stable and reliable AR. In addition to information superimposed to Points of Interest (POIs), SLAM can be used to anchor suggestions about different POIs into specific positions inside the "Studiolo", guiding the users' orientation during the visit.
Understanding in-context interaction: An investigation into on-the-go mobile search

Recent years have seen a profound change in how most users interact with search engines: the majority of search requests now come from mobile devices, which are used in a number of distracting contexts. This use of mobile devices in various situational contexts away from a desk presents a range of novel challenges for users and, consequently, possibilities for interface improvements. However, there is at present a lack of work that evaluates interaction in such contexts to understand what effects context and mobility have on behaviour and errors and, ultimately, users’ search performance. Through a controlled study, in which we simulate walking conditions on a treadmill and obstacle course, we use a combination of interaction logs and multiple video streams to capture interaction behaviour as participants (n = 24) complete simple search tasks. Using a bespoke tagging tool to analyse these recordings, we investigate how situational context and distractions impact user behaviour and performance, contrasting this with users in a baseline, seated condition. Our findings provide insights into the issues these common contexts cause, how users adapt and how such interfaces could be improved.
Query-Free Embedding Attack Against Deep Learning

Deep neural networks are vulnerable to adversarial examples, subtly perturbed images which can fool networks to output incorrect classification results. To deceive deep learning models, in this paper, instead of utilizing the weakness of networks themselves, we present Embedding Attack, which is to attack the common image resizing operation in the deep learning preprocessing pipeline. By this attack, adversaries can embed a small target image into a benign image to produce adversarial examples without querying the target network. When the adversarial example is resized to the required shape, the embedded target image will be recovered. We design embedding attacks for three common image resizing methods and prove that our algorithms are optimal when the target image can be fully recovered. Furthermore, we design a universal embedding attack that enables adversarial examples to work under different resizing methods.
Making a faster Curry with extensional types

Curried functions apparently take one argument at a time, which is slow. So optimizing compilers for higher-order languages invariably have some mechanism for working around currying by passing several arguments at once, as many as the function can handle, which is known as its arity. But such mechanisms are often ad-hoc, and do not work at all in higher-order functions. We show how extensional, call-by-name functions have the correct behavior for directly expressing the arity of curried functions. And these extensional functions can stand side-by-side with functions native to practical programming languages, which do not use call-by-name evaluation. Integrating call-by-name with other evaluation strategies in the same intermediate language expresses the arity of a function in its type and gives a principled and compositional account of multi-argument curried functions. An unexpected, but significant, bonus is that our approach is equally suitable for a call-by-value language and a call-by-need language, and it can be readily integrated into an existing compilation framework.
Deep Convolutional Neural Network for Robust Facial Emotion Recognition

Emotion and the ability to understand them are considered a channel of non-verbal communication. It is an important factor to achieve a smooth and yet robust interaction between machines and humans. In this paper, we review CNN-based methods for facial emotion recognition and we propose a new cutting edge deep learning approach to classify facial expressions from pictures. To guarantee the efficacy of the method, we used multiple datasets: FER2013, AffectNet, RaFD, and KDEF. We obtained results respectively 82.3%, 76.79%, 78.58 %, and 77.08 %. Those results surpassed the current state of the art. We also compared our achieved measurements to available APIs for facial emotion recognition.
The density of the terms in an elliptic divisibility sequence having a fixed G.C.D. with their indices

Let D=(Dn)n≥1 be an elliptic divisibility sequence associated to the pair (E,P). For a fixed integer k, we define AE,k={n≥1:gcd⁡(n,Dn)=k}. We give an explicit structural description of AE,k. Also, we explain when AE,k has positive asymptotic density using bounds related to the distribution of trace of Frobenius of E. Furthermore, we obtain an explicit density for AE,k using the Möbius function.
QoS Based Efficient Resource Allocation and Scheduling in Cloud Computing

The Cloud environment is a large pool of virtually available resources that perform thousands of computational operations in real time for resource provisioning. Allocation and scheduling are two major pillars of said provisioning with quality of service (QoS). This involves complex modules such as: identification of task requirement, availability of resource, allocation decision, and scheduling operation. In the present scenario, it is intricate to manage cloud resources, as Service provider aims to provide resources to users on productive cost and time. In proposed research article, an optimized technique for efficient resource allocation and scheduling is presented. The proposed policy used heuristic based, ant colony optimization (ACO) for well-ordered allocation. The suggested algorithm implementation done using simulation, shows better results in terms of cost, time and utilization as compared to other algorithms.
An Efficient Hybrid DE-WOA Algorithm for Numerical Function Optimization

A newly hybrid algorithm called DEWOA is proposed with the combination of Whale Optimization Algorithm (WOA) and Difference Evolution (DE). The main idea of this algorithm is to integrate the ability of exploitation in WOA with the ability of exploration in DE to synthesize both algorithms’ strength. To evaluate the solution quality and the performance of the proposed algorithm, some unimodal, multimodal, and fixed-dimension multimodal benchmark functions are tested, whose results show that the proposed algorithm is feasible and effective.
Blind Calibration for Focused Plenoptic Cameras

Because of the subtle structure of focused plenoptic camera, the exact geometry parameters cannot be retrieved, which leads to inaccurate f-number matching or refocusing errors in light field processing. In this paper, a novel blind calibration method is proposed to calculate the geometry parameters for the focused plenoptic cameras. The blind calibration model is derived based on the geometry projection analysis to establish the relationship between the patch-size of each micro-image used in subaperture image rendering and geometry parameters. A triple-level calibration board is designed to realize calibration via single shot. A gradient-SSIM-based fractional-pixel matching is proposed to retrieve the precise rendering patch-size for the calibration model. Experimental results demonstrate that the proposed method is robust to different focused plenoptic cameras and can get the geometry parameters with high accuracy.
Creating a Map of User Data in NTFS to Improve File Carving

Digital forensics and, especially, file carving are burdened by the large amounts of data that need to be processed. Attempts to solve this problem include efficient carving algorithms, parallel processing in the cloud and data reduction by filtering uninteresting files. This research addresses the problem by searching for data where it is more likely to be found. This is accomplished by creating a probability map for finding unique data at various logical block addressing positions in storage media. SHA-1 hashes of 512 B sectors are used to represent the data. The results, which are based on a collection of 30 NTFS partitions from computers running Microsoft Windows 7 and later versions, reveal that the mean probability of finding unique hash values at different logical block addressing positions vary between 12% to 41% in an NTFS partition. The probability map can be used by a forensic analyst to prioritize relevant areas in storage media without the need for a working filesystem. It can also be used to increase the efficiency of hash-based carving by dynamically changing the random sampling frequency. The approach contributes to digital forensic processes by enabling them to focus on interesting regions in storage media, increasing the probability of obtaining relevant results faster.