Reviewerly/exHarmony huggingface dataset:
Morphosyntactic complexity in Japanese advertising slogans

This study applies mathematical linguistics to explore the morphological richness and syntactic freedom of Japanese advertising slogans and identify associations between the morphosyntactic features and the popularity of slogans. The data were drawn from the Tokyo Copywriters Yearbook, which includes 12 genres of advertising slogans. Mean dependency distance (MDD) and entropy (ENTR) were employed to measure syntactic diversity, and moving-average morphological richness (MAMR), moving-average mean size of paradigm (MAMSP), and mean word length (MWL) to measure lexical diversity. The findings indicate that in terms of syntactic complexity, slogans of the imperative form were the simplest and slogans of the volitional form were the most complex. In information amount, exclamatory slogans had the highest and volitional slogans the lowest. Spearman’s rank correlation test for MMR, MAMSP, MWL, MDD, and ENTR showed that MAMR and MDD reflect the proposed ‘complexity trade-off hypotheses’. Furthermore, the analysis of the Tokyo Copywriters Club (TCC)-Prize-winning slogans from 2018 to 2021 reveals a tendency towards popularity among the Japanese in the past four years, such that the simpler the slogans, the better. We hope that this research will aid copywriters in producing effective advertising slogans.
The impact of a constructed device in the rehabilitation of a partial rupture of the anterior cruciate ligament of the knee

rupture The impact of a constructed device in the rehabilitation of a partial rupture of the anterior cruciate ligament of the knee of the anterior cruciate ligament of the knee
Buttonhole and Edge Sewing Machine Technology Training to Increase the Quality Production of Small Convection Creative Industry in Probolinggo

Based on the culture of new clothes on the New Year, Eid, and other special days, the demand for convection products will increase drastically. This condition is a big obstacle for small businesses in the creative industry in Indonesia, especially in Probolinggo. Increasing the amount of convection production in Probolinggo is by applying new technologies to accelerate the production. If convection production, both custom and general order, can be made quickly, the amount of production will increase. A buttonhole sewing machine is a machine that has the ability to embroider and buttonhole automatically. Edge sewing machine, a machine that works as a tool that is able to sew and smooth the edges of the fabric automatically. With these two machines, convection products will be faster. Both machine training service activities were carried out well. The results made using a buttonhole sewing machine show a good and short level of neatness. Edge sewing machines also provide satisfactory results. To sew and tidy up the fabric, it can be done quickly and neatly. All forms of service activities are carried out to be able to solve the problems experienced by small business partners in the creative convection industry in Probolingo.
Integration of IOT and Blockchain

Abstract: As of now, we are in the realm of computerized upset. Utilization of cell phones and web advanced quickly because of the effect of the clever Coronavirus infection. The entire world began to turn computerized. The industrialization of internet of Things (IoT) empowers more gadgets to associate and impart which prompts numerous information move exchanges. The engineering of IoT is incorporated. The dispersed and decentralized engineering of Blockchain can be utilized to give secure and versatile exchanges of IoT gadgets. Blockchain is a dispersed record innovation, which gives secure information exchanges that can't be altered and adjusted. In this paper, we give benefits and difficulties of coordinating IoT and Blockchain. We likewise give unique structures and calculations proposed by analysts to give secure information exchanges. We will likewise reveal insight into the future exploration headings of incorporating Blockchain and IoT
Handwritten Mathematical Expression Solver using CNN

Abstract: Mathematics is the universal language. It is the core of all the scientific discoveries and is used extensively in all possible fields. Hence, there arises the need for fast computation and instant results. This has led to the development of various technologies and machines which would ease the strain on humans and also reduce the error and delays caused by them. Our project caters to these needs by reducing the effort on user side. We want to make the experience of experimenting with equations as simple as possible. The application is capable of performing fast calculations on human handwritten mathematical expressions. Basic calculations like arithmetic and trigonometric functions can be done along with simultaneous equations. The expression is extracted from the image and each character, symbol and number is recognised and then the entire expression is further being solved displaying all possible solutions. The application developed is simple and user-friendly tool that can leverage the power of existing powerful math packages.
Image Encryption using AES Algorithm: Study and Evaluation

Abstract: Image security is important these days as data is increasing a lot. These data can be images, videos, text, audio, etc. so to protect these images from attackers who can destroy the image quality or modify the images, some technologies like AES, DES, RSA, etc. have been invented. With the generation, data security has also become an essential issue. Considering these issues, the proposed technique ensures confidentiality, integrity, and authentication. Using these techniques, the host can encrypt and decrypt the image and can keep the digital images safe. When AES was chosen 16 years ago, digital technology was completely different from today and the scale of challenges was smaller, so with recent advanced technology and the emergence of new applications such as Big Data applications, in addition to applications running with 64-bit and many other applications have become necessary to design a new current algorithm for current requirements. Advanced Encryption Algorithm (AES) is a symmetric algorithm, which we will further discuss in detail in our research, and in addition to new recommendations for future work, a list of shortcomings and vulnerabilities of the internal structure of the AES algorithm will be diagnosed.
Distance similarity measure based profiling attacks against masked chips

The attack price of higher order side channel attacks (HOSCA) grows exponentially with the mask order. When the mask order is larger than 3, HOSCA can be inapplicable. In light of this, the distance similarity measure based profiling attack (DSMPA) is proposed. In DSMPA, the leakage property of masks can be characterised in a profiling scenario and the so called templates can be obtained. Then, DSM can be used to recover mask values with templates. With mask values recovered, the effect of masking disappears and one can use first-order side-channel attacks to recover the secret key used by a masked chip. The key-recovery efficiency of DSMPA is evaluated in real scenarios, and the advantage of DSMPA over HOSCA in terms of the key-recovery efficiency can be obvious under the success rate metric. Overall, the contribution of this work may provide evaluators a powerful tool to evaluate the security of masked chips.
Post-quantum secure fully-dynamic logarithmic-size deniable group signature in code-based setting

&lt;p style='text-indent:20px;'&gt;Since its introduction by Chaum and Heyst, &lt;i&gt;group signature&lt;/i&gt; has been one of the most active areas of cryptographic research with numerous applications to computer security and privacy. Group signature permits the members of a group to sign a document on behalf of the entire group keeping signer's identity secret and enabling disclosure of the signer's identity if required. In this work, we present the first code-based fully-dynamic group signature scheme which allows group members to join or leave the group at any point of time. We employ a code-based updatable &lt;i&gt;Merkle-tree accumulator&lt;/i&gt; in our design to achieve logarithmic-size signature and utilize randomized &lt;i&gt;Niederreiter&lt;/i&gt; encryption to trace the identity of the signer. More positively, we equipped our scheme with &lt;i&gt;deniability&lt;/i&gt; characteristic whereby the tracing authority can furnish evidence showing that a given member is not the signer of a particular signature. Our scheme satisfies the security requirements of &lt;i&gt;anonymity&lt;/i&gt;, &lt;i&gt;non-frameability&lt;/i&gt;, &lt;i&gt;traceability&lt;/i&gt; and &lt;i&gt;tracing-soundness&lt;/i&gt; in the random oracle model under the hardness of generic decoding problem. We emphasize that our scheme provides full-dynamicity, features deniability in contrast to the existing code-based group signature schemes and works favourably in terms of signature size, group public key size and secret key size.&lt;/p&gt;
A Delayed-Choice Quantum Eraser with Photon-Counting MKIDs (Experimental Design)

Abstract An extension to the delayed-choice quantum eraser (DCQE) photonic experiment is described in this paper. It is proposed to employ an array of superconducting detectors on the measurement plane, and use their high sensitivity, low-noise, and fast response time to provide significantly better control of the system in a real-time, photon-counting format. We present an experimental design that would allow a pulse-triggered feedback loop to be added to be system, in an attempt to test an apparent information paradox. We show how new light might be shed on the interpretations and conclusions made from the somewhat puzzling results of the DCQE. While we focus on using an array of microwave kinetic induction detectors for this experiment, other, potentially better-suited superconducting devices are also considered.
Modern popular culture and literature in the digital age: The phenomenon of multivariance, serialization, transmedia

The article is a review of the collective monograph presented by the team of authors within the framework of the international transdisciplinary project Cult-goods: the phenomenon of mass literature/culture in modern Russia. Published at Yanka Kupala State University of Grodno, the book has gathered many scientists from different countries whose scientific interests are related to the phenomenon of modern mass literature and culture in general. The authors of the articles are philosophers, historians, psychologists, but the vast majority are philologists. Among the studied problems are the social and aesthetic aspects of the phenomenon of seriality; transmedia as a noticeable strategy; genre experiments in modern culture and literature and their diversity; the impact of digitalization on the culture of the 21st century and the like. It is noted that the subject and object of the analysis are diverse phenomena of mass culture in modern society, which makes the peer-reviewed publication useful for representatives of all humanities.
Assessment of Covariance Selection Methods in High-Dimensional Gaussian Graphical Models

The covariance selection in Gaussian graphical models consists in selecting, based on a sample of a multivariate normal vector, all those pairs of variables that are conditionally dependent given the remaining variables. This problem is equivalent to estimate the graph identifying the nonzero elements on the off-diagonal entries of the precision matrix. There are different proposals to carry out covariance selection in high-dimensional Gaussian graphical models, such as neighborhood selection and Glasso, among others. In this paper we introduce a methodology for evaluating the performance of graph estimators, defining the notion of non-informative estimator. Through a simulation study, the empirical behavior of Glasso in different structures of the precision matrix is investigated and its performance is analyzed according to different degrees of density of the graph. Our proposal can be used for other covariance selection methods.
An Experimental Assessment on Effects of Solid Design Principles on the quality of Software using CKJM Metric Analysis

Abstract: Design is one of the imperative segments of the software development life cycle, which directly influences the project's entire life cycle. If the design is proficient, all the other stages of the Software development life cycle like coding, support, and maintenance will be hassle-free. The design has a substantial role that precisely affects the performance and quality of software. Today, the realm of Agile Methodology has steered the developer to contend with the newest tech stacks and features in the marketplace. Though, if there are no standard guidelines available it is intricate for the raw consumers to uphold the design quality. Software Design with appropriate patterns and principles can augment the software maintainability, reusability, and scalability. To maintain standards of software design, some principles are introduced with consideration of concepts like Cohesion and Coupling. This research paper emphasizes experimental scrutiny to attest to the SOLID design principles instructions by applying the design principles to a working prototype and then assessing the prototype using CKJM (Chidamber &amp; Kemerer Java Metrics).
Research on School-based Training System for Novice Teachers in Colleges

Improving the quality of teachers is the key to the development of schools and the quality of teaching. There is widespread agreement among researchers and practitioners that teachers are the most significant component in the quality of education in schools, and it is therefore obvious that supporting the professional development of teachers makes a key contribution toward improving educational systems and the learning outcomes of students. "School-based training" refers to a type of in-school vocational training that is initiated, organized and planned by schools, with the goal of improving teachers' educational teaching and research capabilities, as well as to promote school development through educational teaching and educational research activities. As one of the forms of on-the-job training for teachers, school-based training has experienced a long period of development and has gradually become an important form of on-the-job training for teachers. However, the quality of development achieved will be heavily influenced by the help they get at each stage of development. To achieve this, we should not only focus on the lifelong development for all teachers, but have to investigate how to successfully help the novice teachers to develop themselves. In this study, the writer tries to analysis the current problems of school-based training program and tries to improve the operation system of school-based training for novice teachers from the practical level of colleges.
Proposal of Decision-Making Method Under Multi-Task Based on Q-Value Weighted by Task Priority

Robots make decisions in a variety of situations requiring multitasking. Therefore, in this work, a method is studied to address multiple tasks based on reinforcement learning. Our previous method selects an action when the q-values of the action for each task correspond to a priority value in the q-table. However, the decision-making would select an ineffective action in particular situations. In this study, an action value weighted by priority is defined (termed as action priority) to indicate that the selected action is effective in accomplishing the task. Subsequently a method is proposed for selecting actions using action priorities. It is demonstrated that the proposed method can accomplish tasks faster with fewer errors.
DRN-SEAM: A deep residual network based on squeeze-and-excitation attention mechanism for motion recognition in education

In order to solve the shortcomings of the traditional motion recognition methods and obtain better motion recognition effect in education, this paper proposes a residual network based on Squeeze-and-Excitation attention mechanism. Deep residual network is widely used in various fields due to the high recognition accuracy. In this paper, the convolution layer, adjustment batch normalization layer and activation function layer in the deep residual network model are modified. Squeeze-and-Excitation (SE) attention mechanism is introduced to adjust the structure of network convolution kernel. This operation enhances the feature extraction ability of the new network model. Finally, the expansibility experiments are conducted on WISDM(Wireless Sensor Data Mining), and UCI(UC Irvine) data sets. In terms of F1, the value exceeds 90%. The results show that the proposed model is more accurate than other state-of-the-art posture recognition models. The proposed method can obtain the ideal motion recognition results.
A novel motion recognition method based on improved two-stream convolutional neural network and sparse feature fusion

Motion recognition is a hot topic in the field of computer vision. It is a challenging task. Motion recognition analysis is closely related to the network input, network structure and feature fusion. Due to the noise in the video, traditional methods cannot better obtain the feature information resulting in the problem of inaccurate motion recognition. Feature selection directly affects the efficiency of recognition, and there are still many problems to be solved in the multi-level feature fusion process. In this paper, we propose a novel motion recognition method based on an improved two-stream convolutional neural network and sparse feature fusion. In the low-rank space, because sparse features can effectively capture the information of motion objects in the video, meanwhile, we supplement the network input data, in view of the lack of information interaction in the network, we fuse the high-level semantic information and low-level detail information to recognize the motions by introducing attention mechanism, which makes the performance of the two-stream convolutional neural network have more advantages. Experimental results on UCF101 and HMDB51 data sets show that the proposed method can effectively improve the performance of motion recognition.
Human action recognition using a depth sequence key-frames based on discriminative collaborative representation classifier for healthcare analytics

Using deep map sequence to recognize human action is an important research field in computer vision. The traditional deep map-based methods have a lot of redundant information. Therefore, this paper proposes a new deep map sequence feature expression method based on discriminative collaborative representation classifier, which highlights the time sequence of human action features. In this paper, the energy field is established according to the shape and action characteristics of human body to obtain the energy information of human body. Then the energy information is projected onto three orthogonal axes to obtain deep spatialtemporal energy map. Meanwhile, in order to solve the problem of high misclassification probability of similar samples by collaborative representation classifier (CRC), a discriminative CRC (DCRC) is proposed. The classifier takes into account the influence of all training samples and each kind of samples on the collaborative representation coefficient, it obtains the highly discriminative collaborative representation coefficient, and improves the discriminability of similar samples. Experimental results on MSR Action3D data set show that the redundancy of key-frame algorithm is reduced, and the operation efficiency of each algorithm is improved by 20%-30%. The proposed algorithm in this paper reduces the redundant information in deep map sequence and improves the extraction rate of feature map. It not only preserves the spatial information of human action through the energy field, but also records the temporal information of human action in a complete way. What?s more, it still maintains a high recognition accuracy in the action data with temporal information.
The Influence of organizational ethics on employee work performance by intervening organizational commitment and Job satisfaction

This research has been conducted on the respondents of private banking sector of Pakistan, in context of implications of organizational ethics on work performance of bank employees by intervening role of organizational commitment and job satisfaction. The analysis has been done through Smart PLs software by using Algorithm and Bootstrapping analysis techniques of Structural equation Modeling (SEM). Survey operationalized the findings of 143 respondents which were employee of the banks. The findings of the research has explored that there is positive linkages among organizational wok ethics on employee performance directly and indirectly via mediation role of job satisfaction and commitment. Furthermore, research has contributed by accumulation in literature and demonstrating the administrative insinuations
Impact of Motivation on Employee Performance working in Private Banks in Hyderabad City

A quantitative research is conducted to determine how motivation affects employee performance and how to it can be practiced, as well as the relationship between motivation and promotion and reward programs in Pakistani private banks. Questionnaires were administered to three levels of staff including high-level, middle-level and frontline managers. A total of 280 closed questionnaires were published among participants to examine motivation and employee performance, and Smart PLS SEM was used to examine the relationship between influences and variables. The results of structural equation modeling show that there is a positive and significant relationship between motivation and employee performance. The results of the current study suggest that Pakistan’s private banking industry should pay attention to rewards and promotions. Employees perform better, but receive less rewards. Prizes and rewards are not based on the number of tasks or workloads. In addition, current research and analysis have increased the importance of motivation and other human resource practices for employee performance in Pakistan’s private banking sector.
A REVIEW PAPER ON FACE RECOGNITION USING DEEP LEARNING

A person's greatest distinguishing feature is their face. Everyone, including twins, has a unique face. As a result, facial recognition and ID are likely to be distinct. A framework for facial recognition is utilized as validation when a biometric technique is employed to track down a person's identify. Face recognition has became a widely used approach nowadays in a variety of applications, as well as criminal identifying evidence, the telephone open framework, and, shockingly, home security framework. This system is safer because it only needs a facial image instead of other requirements like a key or card. Face discovery and face ID are the two main stages of the human acknowledgment framework. Deep learning is a way to deal with play out the face acknowledgment and is by all accounts a satisfactory technique to do confront acknowledgment because of its high precision. Its applications are wide, going from policing purchaser applications, and industry proficiency and observing arrangements.
AN INNOVATIVE IMPLEMENTATION OF ENCRYPTION ALGORITHM ON FPGA IN MANY CORE ARCHITECTURES.

In this paper, we grant the remodeling with the aid of “categorizes” of amenities—a category being explained as a group of amenities that share innovative operations and are mapped to the same key value based on Encryption schemes of sub-bytes transformation method. The sub-bytes transformation method is the high-speed standard Encryption module. AES Algorithm is synthesized by utilizing Verilog code and encountered into FPGA. We implement a new design of AES Algorithm in FPGA in many core Architectures which is an in-built one.