Reviewerly/exHarmony huggingface dataset:
Software Reusability Estimation based on Dynamic Metrics using Soft Computing Techniques

Dynamic metrics capture the run time features of object-oriented languages, i.e., runtime polymorphism, dynamic binding, etc., that are not covered by static metrics. Therefore, in this paper, we derived a new approach to measuring the software reusability of a design pattern based on dynamic metrics. To achieve this, the authors proposed a model based on five parameters, i.e., polymorphism, inheritance, number of children, coupling, and complexity, to measure the reusability factor by using various soft computing techniques, i.e., Fuzzy, Neural Network, and Neuro-Fuzzy. Further, we also compared the proposed model with four existing machine learning algorithms. Lastly, we found that the proposed model using the neuro-fuzzy technique is trained well and predicts well with MAE (Mean absolute error) 0.003 and RMSE (Root mean square error) 0.009 based on dynamic metrics. Hence, it is concluded that dynamic metrics are a better predictor of the reusability factor than static metrics.
An Overview on Digital Content Watermarking

With the rapid development of multimedia technology, balancing multimedia visual enjoyment and information security has become one of the hot spots of current research. Due to the complexity and the cost of video content production, the corresponding video services face many security challenges, and higher requirements for the protection of video content copyright are also been put forward. Among them, digital watermarking technology is a typical information hiding method, which covers text, image and video. In the case of copyright disputes, watermark has become an important way to confirm the copyright and identity information. Digital watermarking technology uses the redundancy and randomness of media content data to embed the cipher text of copyright identification into the protected data. Except for data processing, the rest is invisible to ensure high security. This paper summarizes the current research progress of digital watermarking technology and digital watermarking algorithms, and then puts forward possible research directions in the future.
Adaptive Image-to-Video Scene Graph Generation via Knowledge Reasoning and Adversarial Learning

Scene graph in a video conveys a wealth of information about objects and their relationships in the scene, thus benefiting many downstream tasks such as video captioning and visual question answering. Existing methods of scene graph generation require large-scale training videos annotated with objects and relationships in each frame to learn a powerful model. However, such comprehensive annotation is time-consuming and labor-intensive. On the other hand, it is much easier and less cost to annotate images with scene graphs, so we investigate leveraging annotated images to facilitate training a scene graph generation model for unannotated videos, namely image-to-video scene graph generation. This task presents two challenges: 1) infer unseen dynamic relationships in videos from static relationships in images due to the absence of motion information in images; 2) adapt objects and static relationships from images to video frames due to the domain shift between them. To address the first challenge, we exploit external commonsense knowledge to infer the unseen dynamic relationship from the temporal evolution of static relationships. We tackle the second challenge by hierarchical adversarial learning to reduce the data distribution discrepancy between images and video frames. Extensive experiment results on two benchmark video datasets demonstrate the effectiveness of our method.
Information extraction in photon-counting experiments

We demonstrate a comparison of different multiplexing architectures based on quantum detector tomography. Using the purity of their measurement outcomes, we gain insight into the photon-number-resolving ability of the devices. Further, we calculate the information each measurement outcome can extract from a Hilbert space with given dimension. Our work confirms that more multiplexing outcomes enable higher photon-number-resolving ability; however, the splitting between those outcomes must be optimized as well.
Problems Encountered by Indonesian EFL Learners in Mastering Speaking Skills

The aims of this research are to analyze and find the linguistic and non-linguistic faced by Indonesian EFL learners in mastering speaking skill. This research uses a qualitative approach with descriptive analysis method. The researchers involved as observers and informants who directly engaged in this research. The research data were obtained by involving as the subjects of study. This research focuses on the problems experienced by students in mastering English speaking skill as the object of research. The data collection technique of this research was conducted by observation, interview, and documentation with main informants and additional informants. The researchers used the main data source from the informants’ interview to find out the problems faced by Indonesian EFL learners in mastering speaking skill. Result of the data indicated that Indonesian EFL learners experienced the problems both in linguistic and non-linguistic aspects. The linguistic aspects comprise of: (1) comprehension, (2) pronunciation, (3) vocabulary, (4) grammar and (5) fluency. Whereas the aspects of non-linguistic included; (1) nervous to speak, (2) no motivation, (3) afraid of making errors, and (4) low participation in class. The result of this research showed that the highest percentage that caused students' speaking mastery problems was the linguistic aspect, which reached 77% while the non-linguistic aspect only reached 23%.
Short term complex hydro thermal scheduling using integrated PSO-IBF algorithm

In this article, an integrated evolutionary technique such as particle swarm optimization (PSO) algorithm and improved bacterial foraging algorithm (IBFA) have been developed to provide an optimum solution to the scheduling problem with complex thermal and hydro generating stations. PSO algorithm is framed based on the intelligent behavior of the fish school and a flock of birds and the optimal solution in the multidimensional search region is achieved by assigning a random velocity to each potential solution (called the particle). BFA is designed by following the prey-seeking (chemotactic) nature of E. coli bacteria. This technique is followed in an improved manner to get the convergence rate in dynamic for a hyperspace problem by implementing a chemotactic step in a linearly decreased way instead of the static one. The effectiveness of this integrated algorithm is evaluated by using it in a complex thermal and hydro generating system. In this testing system, multiple numbers of cascaded reservoirs in hydro plants have a time coupling effect and thermal power units have a valve point loading effect. The simulation results indicate its merits by comparing it with other meta-heuristic techniques related to the fuel cost required to generate the thermal power .
On Structuring Functional Programs with Monoidal Profunctors

We study monoidal profunctors as a tool to reason and structure pure functional programs both from a categorical perspective and as a Haskell implementation. From the categorical point of view we approach them as monoids in a certain monoidal category of profunctors. We study properties of this monoidal category and construct and implement the free monoidal profunctor. We study the relationship of the monoidal construction to optics, and introduce a promising generalization of the implementation which we illustrate by introducing effectful monoidal profunctors.
Secure Data Access using Steganography and Image Based Password

Technology has developed to a very great extent, and the use of smart systems has introduced an increasing threat to data security and privacy. Most of the applications are built-in unsecured operating systems, and so there is a growing threat to information cloning, forging tampering counterfeiting, etc. This will lead to an un-compensatory loss for end-users particularly in banking applications and personal data in social media. The shoulder surfing attack in an assault that can be achieved with the aid of the adversary to accumulate the individual’s password by way of searching over the client’s shoulder as he enters his password. As conventional password schemes are vulnerable to Shoulder Surfing, Steganography and Cryptography proposed three shoulder surfing resistant graphical password schemes. However, most of the current graphical password schemes is liable to shoulder-browsing a recounted hazard wherein an attacker can seize a password by means of way of direct statement or by recording the authentication consultation. Because of the visual interface, shoulder-browsing becomes an exacerbated problem in graphical passwords. A graphical password is easier than a textual content-based password for the majority to undergo in thoughts. Suppose an eight-man or woman password is critical to benefit access into a specific computer network. Sturdy passwords may be produced which might be proof against guessing, dictionary assault. Key-loggers, shoulder surfing and social engineering. Graphical passwords have been utilized in authentication for mobile phones, ATM machines, E-transactions.
Long Document Re-ranking with Modular Re-ranker

Long document re-ranking has been a challenging problem for neural re-rankers based on deep language models like BERT. Early work breaks the documents into short passage-like chunks. These chunks are independently mapped to scalar scores or latent vectors, which are then pooled into a final relevance score. These encode-and-pool methods however inevitably introduce an information bottleneck: the low dimension representations. In this paper, we propose instead to model full query-to-document interaction, leveraging the attention operation and modular Transformer re-ranker framework. First, document chunks are encoded independently with an encoder module. An interaction module then encodes the query and performs joint attention from the query to all document chunk representations. We demonstrate that the model can use this new degree of freedom to aggregate important information from the entire document. Our experiments show that this design produces effective re-ranking on two classical IR collections Robust04 and ClueWeb09, and a large-scale supervised collection MS-MARCO document ranking.
Natural Involvement to Video Conference Through ARM-COMS

The authors have proposed an idea of augmented tele-presence system called ARM-COMS (ARm-supported eMbodied COmmunication Monitor System), which detects the orientation of a subject face by face-tracking based on image processing technique, and mimics the head motion of a remote person to behave as if its avatar makes interaction during video communication. In addition to that, ARM-COMS makes appropriate reactions by audio signals during talk when a remote person speaks even without any significant motion in video communication. Based on the basic idea of ARM-COMS, this study implemented an advanced prototype system using ROS platform for natural video conference involvement. This paper describes the prototype system developed in this study, shows how the basic procedure was implemented, and discusses the natural involvement targeted by this approach.
Using Language Models for Classifying the Party Affiliation of Political Texts

We analyze the use of language models for political text classification. Political texts become increasingly available and language models have succeeded in various natural language processing tasks. We apply two baselines and different language models to data from the UK, Germany, and Norway. Observed accuracy shows language models improving on the performance of the baselines by up to 10.35% (Norwegian), 12.95% (German), and 6.39% (English).
Propagating single photons from an open cavity: Description from universal quantization

Over the last decades, quantum optics has evolved from high quality factor cavities in the early experiments toward new cavity designs involving leaky modes. Despite very reliable models, in the concepts of cavity quantum electrodynamics, photon leakage is most of the time treated phenomenologically. Here, we take a different approach, and starting from first principles, we define an inside-outside representation which is derived from the original true-mode representation, in which one can determine effective Hamiltonian and Poynting vector. Contrary to the phenomenological model, they allow a full description of a leaking single photon produced in the cavity and propagating in free space. This is applied for a laser-driven atom-cavity system. In addition, we propose an atom-cavity non-resonant scheme for single photon generation, and we rigorously analyze the outgoing single photon in time and frequency domains for different coupling regimes. Finally, we introduce a particular coupling regime ensuring adiabatic elimination for which the pulse shape of the outgoing single photon is tailored using a specifically designed driving field envelope.
Hand Gesture Recognition Based on Continuous Wave (CW) Radar Using Principal Component Analysis (PCA) and K-Nearest Neighbor (KNN) Methods

Human-computer interaction (HCI) is a field of study studying how people and computers interact. One of the most critical branches of HCI is hand gesture recognition, with most research concentrating on a single direction. A slight change in the angle of hand gestures might cause the motion to be misclassified, thereby degrading the performance of hand gesture detection. Therefore, to improve the accuracy of hand gesture detection, this paper focuses on analyzing hand gestures based on the reflected signals from two directions, which are front and side views. The radar system employed in this paper is equipped with two sets of 24 GHz continuous wave (CW) monostatic radar sensors with a sampling rate of 44.1 kHz. Four different hand gestures, namely close hand, open hand, OK sign, and pointing down, are collected using SignalViewer software. The data is stored as a waveform audio file format (WAV) where one data consists of 20 segments, and the data is then examined by using MATLAB software to be segmented. To evaluate the effectiveness of the classification system, principal component analysis (PCA) and k-nearest neighbor (KNN) are integrated. The PCA findings are depicted in Pareto and 2-D scatter plot for both radar directions. The Leave-One-Out (LOO) method is then used in this analysis to verify the accuracy of the classification method, which is represented in the confusion matrix. At the end of the analysis, the classification results indicated that both angles achieved near-perfect accuracy for most hand gestures.
Investigating the Factors Impacting Adversarial Attack and Defense Performances in Federated Learning

Despite the promising success of federated learning in various application areas, its inherent vulnerability to adversarial attacks hinders its applicability in security-critical areas. This calls for developing viable defense measures against such attacks. A prerequisite for this development, however, is the understanding of what creates, promotes, and aggravates this vulnerability. To date, developing this understanding remains an outstanding gap in the literature. Accordingly, this paper presents an attempt at developing such an understanding. Primarily, this is achieved from two main perspectives. The first perspective concerns addressing the factors, elements, and parameters contributing to the vulnerability of federated learning models to adversarial attacks, their degrees of severity, and combined effects. This includes addressing diverse operating conditions, attack types and scenarios, and collaborations between attacking agents. The second perspective regards analyzing the appearance of the adversarial property of a model in how it updates its coefficients and exploiting this for defense purposes. These analyses are conducted through extensive experiments on image and text classification tasks. Simulation results reveal the importance of specific parameters and factors on the severity of this vulnerability. Besides, the proposed defense strategy is shown able to provide promising performances.
Accurate 3-D Shape Measurement for Large Objects Using Speckle-Assisted Fringe Projection and Global Markers Localization

Digital fringe projection (DFP) three-dimensional (3D) shape measurement technique has been extensively researched and applied due to its advantages of high accuracy and high efficiency. To retain the measurement accuracy for large objects, results of multiple measurements are required to be aligned based on the combination of DFP and auxiliary positioning instruments, which decreases the measurement efficiency and limits the practical application. This paper presents an accurate and efficient scheme for large-scale 3D shape measurement based on speckle-assisted fringe projection and global markers localization. Taking advantage of the triple-view geometric constraints and stereo structured-light model, arbitrary 3D surface can be reconstructed with high quality by projecting only four patterns. To align multiple local shape data of large objects without using auxiliary positioning instruments, an accurate data registration method based on global markers reconstruction and localization is presented, which firstly calculates 3D coordinates of all non-coded markers pasted on the object surface by sequential matching, incremental markers reconstruction and global optimization based on bundle adjustment followed by multiple local data registration through local-to-global ICP matching. The experimental results indicate that the proposed method can substantially decrease the accumulated measurement error and achieve commensurate accuracy with the auxiliary positioning methods.
The Four Phases of Pervasive Computing: From Vision-Inspired to Societal-Challenged

This article reflects on the visions and motivations underlying Pervasive Computing and advances made ending with considering future directions for the field. It describes these in terms of four phases: 1) vision-inspired, 2) the design of engaging experiences, 3) innovation-based, and 4) addressing societal challenges. It is proposed that in the future we will need to embrace a paradigm shift that will be far more challenging than previously. While we can continue to harness pervasive computing advances to augment ever more aspects of ourselves and the environment, we will need in the current climate to be more mindful and responsible of our aspirations. This may mean, paradoxically, contemplating how the field scales down its technology innovation in order to scale up its impact. This article sets out how to achieve this.
A Randomized Link Transformer for Diverse Open-Domain Dialogue Generation

A major issue in open-domain dialogue generation is the agent's tendency to generate repetitive and generic responses.The lack in response diversity has been addressed in recent years via the use of latent variable models, such as the Conditional Variational Auto-Encoder (CVAE), which typically involve learning a latent Gaussian distribution over potential response intents.However, due to latent variable collapse, training latent variable dialogue models are notoriously complex, requiring substantial modification to the standard training process and loss function.Other approaches proposed to improve response diversity also largely entail a significant increase in training complexity.Hence, this paper proposes a Randomized Link (RL) Transformer as an alternative to the latent variable models.The RL Transformer does not require any additional enhancements to the training process or loss function.Empirical results show that, when it comes to response diversity, the RL Transformer achieved comparable performance compared to latent variable models.
SURVEY ON CRIME ANALYSIS AND PREDICTION USING MACHINE LEARNING TECHNIQUES

As of late, wrongdoing has turned into a noticeable type of mischief to individuals and society. The ascent in wrongdoing is making imbalance in the society. To examine and answer to the idea of a wrongdoing, it is critical to see how the wrongdoing was perpetrated. It is a wrongdoing used to recognize wrongdoing in each side of the globe. The principal aim of this venture is to decrease wrongdoing in the most impacted regions. In this task, we anticipate the assortment of wrongdoings, the most apparent month, the most noticeable time, and the date of event. Some AI calculations, like Naive Bayes, were portrayed in this work to carry out various sorts of wrongdoing, and the current uprightness was higher than arranged.
A Method of Data Distribution and Traceability Based on Blockchain

In view of the privacy and particularity of power data, this paper proposes a traceability encryption data distribution method based on encryption technology in power system, including data distribution based on encryption technology in different storage modes and traceability mechanism suitable for different leak scenarios, so as to improve the traditional data distribution centralized degree, unclear rights allocation, low distribution efficiency and complex traceability conditions. This method uses league chain to construct block chain service, formulates different encrypted data forwarding schemes according to different storage modes of data files in the sender, adapts different data leakage traceability strategies according to different scenarios of data leakage in various forwarding schemes. A traceability, tamper-free, open and transparent shared traceability model is established. It ensures the safe transmission of power data and the traceability of data, and realizes the effective supervision of data sender and data receiver, so as to ensure the integrity of data distribution.
Analysis classification of households who received "raskin" in Semarang City using Fuzzy K-Nearest Neighbor (FKNN) and Support Vector Machine (SVM)

Data mining or Big Data is a very important part of going to the industrial revolution era 4. Data mining is inseparable from statistical analysis for classification methods. Data mining is data with a very large size. Two of the methods in data mining is classification using Fuzzy K Nearest Neighbor (FKNN) and Support Vector Machine (SVM). The concept of FKNN is based on fuzzy members, while the SVM method is based on a hyperplane. In this study, the classification of poor rice receipt in the city of Semarang used the FKNN and SVM methods. These methods applied to classification the household wom receipt “raskin” in Semarang city, Indonesia. “raskin” is one of Indonesia government program to assist the households who categorized in the poor households. We used some variables independent such as the characteristic of house and the criteria of head households. The data collected from SUSENAS-social economic survey 2016 in Semarang city with 930 households. From the results of the analysis, it was found that the characteristics of residential houses more influenced the factors of “raskin” revenue compared to the characteristics of the head of the household. The SVM method produces better accuracy than FKNN. The best accuracy value reaches 90% with the radial base function kernel function.