Reviewerly/exHarmony huggingface dataset:
Maximin Active Learning with Data-Dependent Norms

Overparameterized machine learning models are often fit perfectly to training data, yet remarkably generalize well to new data. However, learning good models can require an enormous number of labeled training data. This challenge motivates the study of active learning algorithms that sequentially and adaptively request labels for “informative” examples for a large pool of unlabeled data. A maximin criterion was recently proposed for active learning specifically in the overparameterized and interpolating regime. Roughly speaking, the maximin criterion selects the example that is most difficult to interpolate, as measured by an appropriate norm on the interpolating function. Data-dependent norms perform best empirically, exhibiting intriguing adaptivity to cluster structure within the data. The main contribution of this paper is to mathematically characterize this behavior. Our main results show that the maximin criterion based on data-dependent norms provably discovers clusters and also automatically generates labeled coverings of the dataset.
Using Smart Insoles and RGB Camera for Identifying Stationary Human Targets

Identity recognition is an important component for creating a personalized service in loT applications. Current prevailing technologies have to pre-train with large datasets or need the privacy-sensitive information from users such as facial features, voice features, and fingerprint. In this work, we address the problem of identifying stationary humans (less movements) targets, which cannot be solved by other motion-based fusion mechanisms. In the future IoT world, many wearable sensors on human beings are foreseeable. We exploit RGB camera and smart insoles to design a system for dealing with the stationary identity recognition. We utilize machine learning algorithms to explore the correlation of lower body postures from the viewpoints of heterogeneous sensors. Then we can make the identity matching according to the trained models. Evaluation results show that our mechanism can achieve good performance if users' postures are differentiable.
Forensic Analysis of Cloud Virtual Environments

This paper outlined a process via which forensic analysis can be done with the Microsoft Azure cloud environment. Cloud benefits from a number of characteristics such as agility and virtual environment which do not benefit the forensic process once an incident takes place. The forensic process, which includes Identification, Preservation, Acquisition, Examination and Reporting is analyzed in this paper vis-à-vis Azure's capability to investigate an incident once an alert of incident is generated. Azure has tooling and capability in place that aims to reduce the need for an investigative process, i.e. prevent an incident from taking place, but the same tools are then used to aide the investigative process as those tools present the line defense against a rouge attacker, and once subverted, traces left in those tools can be used for forensics analysis.
Implementation of Manifold Learning Algorithm Isometric Mapping

In dealing with high-dimensional data, such as the global climate model, facial data analysis, human gene distribution and so on, the problem of dimensionality reduction is often encountered, that is, to find the low dimensional structure hidden in high-dimensional data.Nonlinear dimensionality reduction facilitates the discovery of the intrinsic structure and relevance of the data and can make the high-dimensional data visible in the low dimension.The isometric mapping algorithm (Isomap) is an important algorithm for nonlinear dimensionality reduction, which originates from the traditional dimensionality reduction algorithm MDS.The MDS algorithm is based on maintaining the distance between the samples in the original space and the distance between the samples in the lower dimensional space; the distance used here is Euclidean distance, and the Isomap algorithm discards the Euclidean distance, and calculates the shortest path between samples by Floyd algorithm to approximate the geodesic distance along the manifold surface.Compared with the previous nonlinear dimensionality reduction algorithm, the Isomap algorithm can effectively compute a global optimal solution, and it can ensure that the data manifold converges to the real structure asymptotically.
From Chaos to Order: The Role of Content and Context of Daily Activities in Rearranging Lifelogs Data

Lifelogs is known as a set of heterogeneous data recorded daily from various types of wearable sensors from the first-person perspective. Nevertheless, getting insights from lifelogs still a journal to a mystical land where many obstacles need to be overcome. One of the exciting obstacles is to augment human memory when the chaos of memory snapshots must be rearranged chronologically to give a whole picture of a user's life moment. In this paper, a new method to tackle this challenge by leveraging the content and context of daily activities is introduced. The backbone idea of this method is, under the same context (e.g., a particular activity), one image should have at least one associative image (e.g., same content, same concepts) taken from different moments. Thus, a given set of images can be rearranged chronologically by ordering their associative images whose orders are known precisely. The proposed method is evaluated on the imageCLEFlifelog 2019 dataset and compared to participants joined this event. The experimental results confirm the high productivity of the proposed method in both stable and accuracy aspects.
Research on the Design and Effect of Computer Hardware Courses in the Smart Learning Environment

With the continuous optimization and development of teaching methods in the information age, smart education has gradually entered people's field of vision. This paper is based on the advantageous characteristics of the smart educational environment, or designing the teaching mode of intelligent learning. Twenty freshmen in education technology profession were used as teaching research objects, and the information-based intelligent device (tablet) was used as the learning tool. The ARA software developed by the team based on the learning tool was used to assist the teaching, and then the teacher used the electronic interactive whiteboard for feedback to the students (incomprehensible and error-prone knowledge). After class, the computer hardware is virtually assembled, which further strengthens and consolidates the basic concepts and features of each part of the hardware. The research results showed that the independent inquiry learning, cooperation exchange, and interactive feedback in the context of smart learning were conducive to the optimization of the learning process. Most students could master the key knowledge of common computer hardware in time. Students could be successfully installed on the virtual machine motherboard, and they also interacted effectively with students and teachers. Under this teaching mode, students' learning enthusiasm, participation and interest level have improved significantly.
Improving Awareness of Emotional Meaning of Emoticon by Representing as Numerical Vectors

In recent years, the internet has spread exponentially. The message exchange using texts prevails in SNS such as Twitter or LINE using a smartphone. In SNS, we can not see the other person's face or gesture. We have to read all the information from the text only such as the other person's emotions and the meaning hidden in sentences. Japanese language and culture is highly context sensitive with many abstract expressions. If we use only text, it may transmit a different meaning from the true intention. In SNS, emoticons are frequently used as means to express emotions that can not be transmitted by text alone. Emotions of sentences can be better understood by analyzing emotions of emoticons. Types of emoticons have increased with the spread of SNS. Therefore, it is difficult to list and grasp the meaning of all currently confirmed emoticons. In this research, emoticon vectors are acquired by learning SNS contents using word2vec. The purpose is to analyze the emotions of unknown emoticons using emoticon vectors. Word2vec can learn the relationship between words from a text corpus, and convert the meaning of a word into a vector. In this reseach, classification experiments are performed using the semantic vectors of emoticons calculated by word2vec. The effectiveness of clustering is described from the result of experiments.
Chapter 4. C’era i fascisti e i tedeschi

The aim of this paper is the discussion of preliminary results of the ongoing study of an Italian substandard variety (italiano popolare) with the ParVa spoken corpus (Guerini 2016). The focus is on the interplay of two structural factors: contact with Italo-Romance dialects and linguistic simplification. The corpus shows previously overlooked syntactic features, which may improve the view of this subvariety and also allows a quantitative analysis of agreement in existential constructions.
A Vision-Based Method of Reverse Engineering for 2D CNC Machining

An automated vision-based method for profile reconstruction is presented in this paper in order to generate NC-code script file for 2D CNC machining from sample object. A profile reconstruction system capturing images of sample objects was developed and located on the flat table. Based on the sample image, the boundary in the image is detected by several image processing algorithms. After that the profile data is acquired and converted into NC-code commands. Experimental results show that the proposed method is effective in reverse engineering and the developed profile-reconstruction system has acquired high accuracy and flexibility.
Adaptive Control of Nonaffine Nonlinear Systems by Neural state Feedback

In this paper, a new control method for a class of single input single output nonaffine nonlinear systems is considered using radial basis function (RBF) neural networks (NNs). Firstly, the existence of an ideal implicit feedback linearization control is established based on implicit function theory. An online RBF system is introduced to approximate this ideal implicit feedback linearization law. The proposed neural fuzzy adaptive controller ensures that the system output tracks a given bounded reference signal, while the closed loop stability results are provided and guaranteed using Lyapunov theory. The effectiveness of the proposed controller is illustrated through a simulation to a nonaffine nonlinear system.
Towards a global knowledge for environmentally sustainable development agenda in 21 st century southern Africa

After three decades of research and theorizing on the information andcommunication approach to development, there is no universal consensusamong researchers, academics, communication practitioners anddevelopment experts on the age-Old question: how does communicationand information causally relate to development? What has, instead,emerged is an alphabet soup of theories, concepts, approaches,paradigms and models on how information and communication mightimpact on development. Whether one looks at the various models ofinformation and communication for development; or whatever one calls it,the theories advanced under the various acronyms still do not conclusivelyaddress the basic question raised above. Is there a missing link, variableor factor in all of these attempts to causally relate information andcommunication to development? What assumptions are embedded in thecurrent theories of information and communication for development?The, article will, by way of addressing these questions, propose aninformation, communication and knowledge networking strategy that willaccommodate in a systematic way the varied and ever-changinginformation and communication needs of grassroots communities. Thepaper will map out a strategic vision for the information agenda forsouthern Africa, with a focus on the development of a new cadre ofinformation agents drawn from a cross section of society, and suggestpractical down-ta-earth strategies that can be implemented to successfully.and causally relate information, communication and knowledge todevelopment on a sustained basiS.
Analytical Calculation of Hidden Layer Biases in Extreme Learning Machines with Sinusoidal Activation Function

ELM (Extreme Learning Machine) networks have been gaining attention due to high speed, ease of implementation and minimal human intervention compared to classical feedforward neural networks with iterative training algorithms. Part of the studies that seek improvements in ELM involves the specification of input weights and hidden layer biases, traditionally randomized. In this paper is proposed a variant of ELM network in which the hidden layer biases are analytically calculated simultaneously to the output weights in the solution of the least squares problem. The method is valid for SLFNs (single-hidden layer feedforward neural networks) with sinusoidal activation functions. Experiments have shown that calculating biases with the proposed method may reduce the amount of hidden nodes required to solve a given problem. The proposed variant received the designation CBTELM-Calculated (Hidden Layer) Biases Trigonometric ELM. In addition to standard applications of neural networks, CBTELM may be successfully used for amplitude and phase detection of noisy signals composed by sinusoids with known frequencies, as was verified in experiments.
Finding Hidden Shrines using AR and Clustering Techniques

This paper describes the use of augmented reality technology in an offline mobile application to recognize and find shrines. This application uses clustering algorithms to cluster shrines and then a convex hull is used to combine the geographical coordinates of multiple shrines. The photographs and videos of shrines can be downloaded by tapping on any clusters. Images downloaded will have their features extracted and stored in an augmented database which then will be used by ARCore, which is an augmented reality API, to identify and recognize all shrines in that cluster.
Technological features of educating specialists for digital factories of the Industry 4.0

Abstract The task is to improve the technology of specialist’s preparation to work in digital companies of the economy industrial sector. The main principles to form competences being studied in the education companies while the specialist’s preparation of the Industry 4.0 being conducted. It is clear that the primary disciplines and its scientific content must be determined after the technologies being used in the digital companies. They propose to implement net forms to realize educative programs in close cooperation of educative companies and industrial companies equipped with cyber and physical systems. Oriented on practice approach to organize educative process may provide a high level of specialist preparation quality for the Industry 4.0. There is a scheme how to form a set of profile disciplines and its resource base which are oriented to prepare highly qualified cadres for the Industry 4.0.
Evaluative Study Of Integration Of Clinic Management Information System With Pcare BPJS of health Using COBIT 4.1 Approach

The aim purposed of this research is to evaluate clinical management information system called (E-CLINIC) which is integrated with the Primary Care (Pcare) application provided by Indonesia sosial healt insurance organization called BPJS Kesehatan to first level of health cares facility called as FKTP. This evaluation is intended to measure the maturity level on used of two integrated applications using COBIT 4.1 framework. The integration of the Clinic's management information system with PCare is considered to be the main resource that has strategic value to be able to manage information effectively and efficiently for the achievement of organizational goals. The results achieved have been running in accordance with the organization's business objectives, this can be seen from the level of maturity that reaches level 3 (Defined), which is a condition in which the organization has formal and written standard procedures that have been socialized to all levels of management and employees to be obeyed and run in daily activities.
Hierarchical Attention CNN and Entity-Aware for Relation Extraction

Convolution neural network is a widely used model in the relation extraction (RE) task. Previous work simply uses max pooling to select features, which cannot preserve the position information and deal with the long sentences. In addition, the critical information for relation classification tends to present in a certain segment. A better method to extract feature in segment level is needed. In this paper, we propose a novel model with hierarchical attention, which can capture both local syntactic features and global structural features. A position-aware attention pooling is designed to calculate the importance of convolution features and capture the fine-grained information. A segment-level self-attention is used to capture the most important segment in the sentence. We also use the skills of entity-mask and entity-aware to make our model focus on different aspects of information at different stages. Experiments show that the proposed method can accurately capture the key information in sentences and greatly improve the performance of relation classification comparing to state-of-the-art methods.
Connect Real World: Design of Educational Resources Platform for Integrated Curriculum

Integrated curriculum are essential to cultivate students' innovative spirit, social responsibility, core competencies, and global vision. However, its significant challenges are educational resource shortages and teacher inexperience. The rapid development of current technology has brought new solutions but existing educational platforms or communities are mainly test-oriented, and teaching resources are outdated and isolated. There is no specific solution to the problem of integrated curriculum mentioned above. This paper proposes design principles of the platform and resource sharing mechanism of the discovery-design-share cycle through a user behavior analysis of an informal WeChat community. Based on the resource sharing mechanism, this paper designs the platform architecture and specific functional modules, including the resource library, curriculum design tools, cloud workspace, and teacher community, and provides mobile access. Therefore, teachers can find sustainable, suitable and real-world resources on the platform, and improve their capabilities through designing and sharing integrated curriculum on the platform.
The Application Of Cooperative Learning Methods In The Developing And Analyzing The Quality Of An Educational Game

Abstract The purposes of this study were: (1) to develop an educational game application on the basic programming material, and (2) to reveal the quality of the developed educational game application. This research methodology was research and development. The results of this research were as follows. (1) The educational game application was developed using the Codeigniter framework and consisted of the main feature to evaluate students’ learning cooperatively by dividing students into teams, playing games, following tournaments among teams, and recognition of teams. (2) The test results showed that the developed educational game application met the ISO/IEC 25010 standards on (a) functional suitability aspect, which worked 100% and had X = 1; (b) performance efficiency, which could load web pages within 2.76 seconds, PageSpeed’s performance of 91% (grade A) and YSlow of 93% (grade A); (c) usability, with the percentage of SMKN 1 Limboto being 89.12% and SMKN 1 Gorontalo 88.18%; (d) security, with the vulnerability to attack level being at level 1 (low); (e) reliability, with the result of successful session criteria, hits and pages by 100%; (f) maintainability, which had the maintainability index value of 92.26; (g) portability, which successfully ran on four different desktop and mobile browsers.
Research on Improvement of Entrepreneurship Project Management Ability

Abstract Project management is a kind of power of action and a way of thinking, which is not only beneficial to the work of project managers, but also to the future development of enterprises and individuals. Entrepreneurial projects are synchronized with enterprise projects, and the introduction of project management concepts is conducive to the success of entrepreneurship. Taking the opportunity of the reform of contemporary higher education, this paper, from the perspective of theoretical elaboration, market research and project management system analysis, recognizes the problems existing in the entrepreneurship project management ability, and dialectically demonstrates the strategy of improving entrepreneurship management ability, so as to provide reference for education of entrepreneurship and employment.
Using Hyperplanes to Create Neutral Networks in Pseudo-Boolean Optimization

The premature convergence is a serious problem for Evolutionary Algorithms (EAs). We propose a new approach for escaping from local optima in highly multimodal optimization problems. In the proposed strategies, hyperplanes are used to create neutral networks in the fitness landscape of an instance of an optimization problem. When population converges, the fitness landscape is changed by randomly adding or removing hyperplanes. The proposed strategies were compared to other strategies currently used by EAs to escape from local optima in three pseudo-Boolean problems. Experiments with instances of two problems indicated that the induced neutral networks allowed populations to escape faster from local optima, resulting in better performance for the EA. However, for a third problem with fitness landscapes with abrupt changes, the proposed strategies resulted in worse performance.
Block-Based Neural Network High Speed Optimization

Block-Based Neural Network (BBNN) consists of a 2-D array of memory-based modular component NNs with flexible structures and internal configuration that can be implemented in reconfigurable hardware such as a field programmable gate array (FPGA). The network structure and the weights are encoded in bit strings and globally optimized using the genetic operators. An asynchronous BBNN (ABBNN), which is a new model of BBNN, enables higher performance for BBNN by utilizing the parallel computation and the pipeline architecture. An ABBNN’s operating frequency is kept stable for all scales of the network, while conventional BBNN’s decreases accordingly. The architecture of ABBNN provides the capabilities to process and analyze high sample rate data at the same time. However, optimization by the genetic algorithm is a high-cost task, and the memory access is one of the causes which degrade the training performance. In this paper, we introduce a new algorithm to reduce the memory access in BBNN optimization. ABBNN, optimized with the proposed evolutionary algorithm, is applied to general classifiers to verify the effectiveness with regards to the reduction of memory access.