Reviewerly/exHarmony huggingface dataset:
The Knowledge Map Analysis about the Theme and Trend of Classroom Study in Higher Vocational Education in China

Abstract The article searched the periodical literature of classroom research for Higher Vocational Education in CNKI. And used knowledge atlas analysis and other research methods to draw knowledge maps of high-frequency keywords relationship maps and keyword co-occurrence maps based on data analysis software of Bicomb2、 Ucinet and Citespace, and got seven research themes of the classroom of Higher Vocational Education in China. Finally the paper proposed the research trend of classroom research in higher vocational colleges using centrality analysis and multi-dimensional scale analysis Atlas of SPSS.
Comprehensive and Comparative Study of Efficient Location Tracking Based on Apriori and Dijkstra Algorithms

With the advent of Google maps, pointing the precise location and tracking the movement of objects, people and materials have become an integral part of SCM System. The ERP systems include Logistics and Management as core functionalities along with Artificial Intelligent Systems to facilitate positioning and tracking of materials. Similar to material tracking, it is also important to track the location and movement of the person which is a huge demand in present era. However, the designers of such larger ERP systems use patented protocols for material location identification, predicting the shortest distance and tracking the precise location of cargo when they are on the move. The similar techniques neither fully adopted nor applied with humans. This research paper is aimed at finding out the most efficient route by comparing the most popular Apriori and the Dijkstra algorithms. Apriori algorithm involves supervised mining using association rules which can be used for finding right paths whereas the same can be estimated by the Dijkstra’s algorithm, taking the connecting nodes in the graph.
Improving Core Topics Discovery in Semantic Markup Literature

This research configures a corpus of articles related to the aspects being investigated in Semantic Markup, knowledge domain that has evolved and expanded over the last decade and conduct a manual process to identify the Topics being addressed. Then, it is used LDA, an unsupervised probabilistic topic model, and other tools, for automatically recognize the topics of interest within this corpus; this aims to interpret, validate and complement the results manually obtained. The results let us argue that using combined techniques contribute to improving the human expert analysis, and it is helpfully for the discovery of core topics in Semantic Markup Literature.
Optimasi Pendistribusian Barang Menggunakan Algoritma Artificial Bee Colony

Distribution of goods is one of the important problems for a company. The spread of the customer location and the number of route choices and the absence of an information system application to determine the closest route, have an impact and influence on the distribution process. Delivery of goods carried out on time will increase customer satisfaction. The choice of route in distribution has an effect on the cost and time of travel, where the farther the distance traveled in the delivery of goods will lead to long delivery times resulting in delays in distribution and operational costs will be higher. Based on the settlement, optimization of the distribution process of goods is carried out. This study uses the Artificial Bee Colony algorithm to assist in finding the shortest route. The Artificial Bee Colony algorithm is an optimization algorithm that is considered quite good in the process of finding the shortest route.
Handwritten Numerals' Recognition in Kurdish Language Using Double Feature Selection

In the contemporary rapidly developing world, machine learning and pattern recognition has achieved a special level of attention by the academicians and international researchers. Such methods have economized time and increased the speed of processing tons of data in a single click. Pattern recognition is being used in a broad context, especially in industry, finance and banking, forensic issues, etc. Even though pattern selection programs have economized our time in an eye-catching way, yet choosing the best and most accurate algorithm is still a challenge for many researchers. In this paper, a Double Feature Selection (DFS) method as a new trend, using Correlation and ReliefF, is being elaborated in order to appraise the ratio of digit recognition accuracy, instead of using them individually. For this purpose, a comparison is implied to illustrate the accuracy of the four algorithms; Decision Tree (J48), KNN, Multi-Layer Perceptron (MLP), and Naïve Bayes (NB) as far as they are concerned with the digits and numerals used in Kurdish Language. The result of the study illustrates that DT has the best accuracy with the ratio of 95.6%.
An Analog of Optical Rabi Oscillation in Superconducting Josephson Circuits

In cavity quantum electrodynamics (QED), the key physical systems include Fabry-Pérot cavity resonators and multilevel cold atoms. Since ultra-strong electromagnetic interaction between photons and quantized cavity modes occurs in cavity QED, many novel quantum optical effects such as optical Rabi oscillation, quantum collapse/revival of atomic-level population and atom-field quantum entanglement can be realized in such quantum microcavities. As is known in quantum optics, these schemes can be generalized to circuit QED, which is a circuit analog of cavity QED. Fundamental devices in circuit QED are superconducting coplanar waveguide microwave resonators and mesoscopic Cooper-pair boxes. Here, the coplanar waveguide resonator with photons stored can be identified as a resonant cavity and the Cooper-pair boxes with large electric dipoles play the role of multilevel cold atoms. Thus, all the quantum optical effects in conventional cavity QED can also be exhibited in the present superconducting circuit QED system. We consider the theoretical working mechanism of superconducting charge qubits based on tunable Josephson junction coupling. It can be found that an analog of optical Rabi oscillation in Cooper-pair boxes can be achieved by tuning an applied external magnetic field. Time evolution of energy levels in a Cooper-pair box, which is manipulated by a time-dependent magnetic flux through its Josephson-junction ring, is studied within the framework of time-dependent quantum mechanics.
Cost-Sensitive Feature Selection using Particle Swarm Optimization: A Post-Processing Approach

Feature selection is one of the important tasks in machine learning. Feature selection task deals with selecting a subset of feature from an original feature set. An important consideration in feature selection is the usefulness of a feature i.e. a set of feature which is selected is neither irrelevant nor redundant. Most of the existing algorithms in the domain of feature selection are designed to optimize the aforementioned objective. In our research we have addressed a third dimension of usefulness i.e. cost of the feature. Cost-effectives of a solution is most apt in cases where there is an asymmetric cost of data acquisition such as medical diagnosis applications. In this regard, our research deals with enhancing the existing feature selection techniques with a post-processing stage in which cost consideration is also accounted for. The resultant solution is optimized for both important as well as cost-effective features. We have used particle swarm optimization with post processing over chronic kidney disease dataset for generating a feature subset set which is both salient and cost-effective.
Structure-Based Modeling of Professional Physical Education for Students of Bridge Construction Majors

Abstract This paper dwells on the issue of curricula and teaching and learning materials efficiency in shaping professional physical education for students majoring in bridge and transport tunnel construction. Aiming at modeling students’ professional physical education predetermines profiling the mandatory PE instruction so that future professionals will gain maturity in necessary social and personality skills through physical learning. The experiment conducted and its results reveal the level of gain theoretical knowledge, vocationally relevant physical and personality traits that are vital for bridge construction occupations. The paper presents the conclusions that are drawn from the research and proves that in-depth profiling of professional physical education course at the university level has positive effect upon shaping certain aspects of one’s personality traits and skills which are important for both social and vocational maturity.
An Improved YOLOV3 for Pedestrian Clothing Detection

Pedestrian clothing detection, which is dedicated to the detection of pedestrian clothing, is of great significance in pose estimation, pedestrian classification, security and so on. We propose an algorithm named YOLOV3-PCD (An Improved YOLOV3 for Pedestrian Clothing Detection) for pedestrian clothing detection, which finds out the types and positions of clothes on pedestrians. Since there is no available dataset for pedestrian clothing detection task, we build our own dataset in which most objects are large and re-cluster the anchor boxes as well. In potential application fields of pedestrian clothing detection such as pose estimation and security scenarios, targets that need to be detected are usually large. So, we remove the scale used to detect small objects in the original YOLOV3. In addition, we simultaneously consider the detection of the rest medium and maximum scales, and introduce the down-sampling parallel to the original YOLOV3's up-sampling. Based on the above two improvements, we effectively increase the propagation and reuse of features, and improve network performance in the big object detection. In the end, for the application of embedded device in different scenarios, we prune the network to make it fast and small. Experiments show that the mAP of our proposed model reaches 91.99% which is 2% higher than the original YOLOV3 model and the number of parameters reduces to 28.74% of the original YOLOV3 model after pruning.
Evaluating Energy and Thermal Efficiency of Anomaly Detection Algorithms in Edge Devices

Although IoT delivers several benefits, it also raises concerns regarding privacy and security, from revenue disruption in industrial facilities to life-threatening situations caused by smart houses hacking. As a consequence, anomaly detection algorithms stand out to improve data reliability. However, little has been said about the implications of running these com putationally expensive programs in hardware-constrained edge devices. Therefore, in this paper, we present an evaluation of six anomaly detection algorithms running in an edge device regarding performance, accuracy, temperature, and power consumption. The results showed that time complexity, resources demand, and detection approach directly impact on the feasibility of running anomaly detection algorithms in edge devices. Based on these results, we present a recommendation on which algorithms would best satisfy the requirements of several IoT environments.
American Sign Language Recognition

American Sign Language (ASL) is the primary language used by many deaf individuals in North America, and it is also used by hard-of-hearing and hearing individuals. The language is as rich as spoken languages and employs signs made with the hand, along with facial gestures and bodily postures.American Sign Language (ASL) substantially facilitates communication in the deaf community. However, there are only ~250,000-500,000 speakers which significantly limits the number of people that they can easily communicate with. The alternative of written communication is cumbersome, impersonal and even impractical when an emergency occurs. The automatic sign language recognition leads to understand the meaning of different signs without the help from expert persons.
Research on Path Planning of Ship Cleaning Robot

With the development of the shipping industry, a large number of long-term ships on the sea have serious problems in the accumulation of hull attachments. It is difficult and inefficient to clean up the attachment manually. It is one of the main ways to use intelligent cleaning robot instead of artificial cleaning. It can to solve the problem of low coverage, high repetition rate and low efficiency of path optimization algorithm etc. In this paper, a path planning of cluster robot algorithm is proposed based on a biological excitation neural network. MATLAB is used to simulate the algorithm. The results show that the algorithm has the characteristics of full coverage of hull path points and low repetition rate, which verifies the effectiveness and feasibility of the algorithm.
Model-Based Interview Method Selection Approach in Participatory Design

Participatory design is a technique which is being used by system designers to involve the end users and product owners throughout the design process. Even though utilizing this approach brings customers to the design process, implementing it requires a budget, a place, time, and other resources. This chapter demonstrates a model-based approach to facilitate the selection of interviews for each design phase such as listing elements for the interface, choosing location for components, making decision for the general look of the component, finally making the component interactable. Interface designers can use the model to choose different type of interview method for different design phases such as interface components, sketching, lo-fi prototyping and hi-fi prototyping, according to their resources. The research focus is on four different participatory design interview method, which are GUI-ii face-to-face, GUI-ii screen-sharing, GUI-ii Ozlab, and traditional face-to-face interview.
Syntactic Generation of Memorable Passwords

Most tools for generating alphanumeric passwords often combine string tokens without considering semantics, as semantics contribute to the remembrance of a password string. We propose a formal method for the generation of passwords with semantic annotations using context-free grammars. A context-free grammar is a system with which one can make an infinite set of strings or objects. Users are allowed to specify their interests (such as music, movie, etc.) and phrases from these interests are used, in combination with at most three digits and one special character, to generate memorable passwords. This idea is implemented in a software tool called PassGen. PassGen takes, as input user interests, and generate n passwords for the user to choose from. 20,000 samples of generated passwords can be found at https://tinyurl.com/SyntacticPassword2018. A Survey of twenty seven responses shows that eighteen respondents agree that a tool that generates memorable secure passwords suggestion for users to pick from will be useful, while fourteen respondents agree that some passwords we generated using context-free grammars are memorable.
Intelligent Image Collection: Building the Optimal Dataset

Key recognition tasks such as fine-grained visual categorization (FGVC) have benefited from increasing attention among computer vision researchers. The development and evaluation of new approaches relies heavily on benchmark datasets; such datasets are generally built primarily with categories that have images readily available, omitting categories with insufficient data. This paper takes a step back and rethinks dataset construction, focusing on intelligent image collection driven by: (i) the inclusion of all desired categories, and, (ii) the recognition performance on those categories. Based on a small, author-provided initial dataset, the proposed system recommends which categories the authors should prioritize collecting additional images for, with the intent of optimizing overall categorization accuracy. We show that mock datasets built using this method outperform datasets built without such a guiding framework. Additional experiments give prospective dataset creators intuition into how, based on their circumstances and goals, a dataset should be constructed.
Tamper Resistance Evaluation of TWINE Implemented on 8-bit Microcontroller

Lightweight ciphers, which can be used in limited resources of internet of things devices, have been attracted attention in recent years. In particular, TWINE has good performances in software implementation of a small embedded device. Even though encryption algorithm is computationally secured, the threat of power analysis which can easily estimate a secret key stored into a cryptographic circuit is pointed out. This study proposes a power analysis method for a lightweight cipher TWINE of software implementation to evaluate the tamper resistance (security evaluation). The proposed method introduces two attack points which are obtained by an analysis of assembly code of TWINE round function. Evaluation experiments use an AVR 8-bit microcontroller Atmega328P mounted on Arduino-UNO. These experiments revealed the vulnerability of TWINE software implementation against the proposed power analysis method.
Side-Channel Leakage Amount Estimation Based on Communication Theory

Side-channel attacks (SCAs) have been a serious threat to crypto devices. It is necessary to evaluate the resistance of a crypto device to SCAs. The evaluation criteria include information theoretic metrics and security metrics. The former measure the leakage amount of a crypto device, e.g. mutual information (MI). MI is one of the most commonly used metrics because of its clear information theoretic meaning. However, due to the fact that the real leakage distribution of a crypto device is hard to know, the estimation of MI is difficult. In previous work, there are two ways to estimate the leakage distribution: the nonparametric one and the parametric one. The former is non-profiling, but may bring a significant error because the leakage model is empirically selected by an evaluator. By comparison, the latter is more precise, but needs to profile the leakage model, which may be unfeasible in practice. To combine the merits of two kinds of methods, we bypass the estimation of the leakage distribution, and propose a parametric estimation method without profiling from the view of the noise distribution estimation. The side-channel is viewed as a communication channel in this paper, and naturally the side-channel MI can be deemed to be the average MI of the communication channel. Moreover, the channel capacity can be regarded as a new information theoretic metric which furnishes an estimation of the leakage amount in the worst case scenario. As compared to the previous research, the paper provides a novel black box method to estimate the side-channel leakage amount of a crypto device. The evaluation procedure can be viewed as a preliminary before advanced security evaluation.
NoSQL in Undergrad Courses is NoProblem

Relational databases have dominated both industry usage and academic database courses for decades. More recently, there has been a dramatic increase in the use of NoSQL database systems, especially in data science. Bringing NoSQL databases to the classroom is important not only to prepare our students for the technology that they will face, but also because the underlying paradigm introduces new ideas that are not typically emphasized in a relational-only database course alone. However, NoSQL systems seem to receive dramatically less coverage in undergraduate database curricula than relational systems do. This panel discusses examples of how NoSQL can be introduced into undergraduate education, and the possible challenges faced in doing so. Questions from the audience will be invited.
Cybercrimes: Threats, Challenges, Awareness, and Solutions in Sierra Leone

The internet hosts all online activities either for public or private usage. It is a powerful online podium where people socialize, make new friends, conduct academic research, perform online business transactions, share sensitive data, communication over the internet, surveillance purpose by security agencies, monitor climatic condition, used in e-healthcare system, online banking, online pay, e-commerce, defense system, and host of others critical infrastructures are the new order of the day. This has attracted online criminals to diverse numerous malicious techniques to invade people’s privacy and also exploit those data. A new game has resulted in these online malicious activities are known as “cybercrime or internet crime” which is a rewarding business as of today. Therefore, it has become difficult to protect those online activities as cybercrimes are growing daily, which suggests that effective and appropriate countermeasures are needed to combat those threats and make online activities more secure. The research paper presents the various types of cybercrime activities, countermeasures, and suggestions for online users.
WordBlender

Combining text and images is a powerful strategy in graphic design because images convey meaning faster, but text conveys more precise meaning. Word blends are a technique to combine both elements in a succinct yet expressive image. In a word blend, a letter is replaced by a symbol relevant to the message. This is difficult because the replacement must look blended enough to be readable, yet different enough to recognize the symbol. Currently, there are no known design principles to find aesthetically pleasing word blends. To establish these principles, we run two experiments and find that to be readable, the object should have a similar shape as the letter. However, to be aesthetically pleasing, the font should match some of the secondary features of the image: color, style, and thickness. We present WordBlender, an AI-powered design tool to quickly and easily create word blends based on these visual design principles. WordBlender automatically generates shape-based matches and allows users to explore combinations of color, style, and font that improve the design of blends.
Person Identification System in a Platform for Enabling Interaction With Individuals Affected by Profound and Multiple Learning Disabilities

Individuals with profound and multiple learning disabilities have restricted mobility together with sensory and intellectual impairments. They are unable to produce conventional behaviors to communicate particular needs. Within the INSENSION project, an intelligent platform for enabling the interaction of this kind of people with others, is developed. Its goal is to increase their ability of self-communication through digital services enhancing their well-being. The system will recognize facial expressions, body gestures, vocalizations, and physiological parameters using the information captured by cameras and sensors, and it will associate them with their meaning in an individualized way. Hence, person identification is required in order to personalize the understanding. In this work, a new facial recognition method is developed and configured to be included in the INSENSION platform. The proposed system identifies six individuals as well as discards the other people that could appear in the videos, assuring the monitoring of the right person.