Reviewerly/exHarmony huggingface dataset:
An Efficient Blockchain-based Hierarchical Authentication Mechanism for Energy Trading in V2G Environment

Vehicle-to-grid (V2G) networks have emerged as a new technology in modern electric power transmission networks. It allows bi-directional flow of communication and electricity between electric vehicles (EVs) and the Smart Grid (SG), in order to provide more sophisticated energy trading. However, due to the involvement of a huge amount of trading data and the presence of untrusted entities in the visiting networks, the underlying V2G infrastructure suffers from various security and privacy challenges. Although, several solutions have been proposed in the literature to address these problems, issues like lack of mutual authentication and anonymity, incapability to protect against several attack vectors, generation of huge overhead, and dependency on centralized infrastructures make security and privacy issues even more challenging. To address the above mentioned problems, in this paper, we propose a blockchain oriented hierarchical authentication mechanism for rewarding EVs. The overall process is broadly classified into the following phases: 1) System Initialization, 2) Registration, 3) Hierarchical Mutual Authentication, and 4) Consensus; wherein blockchain's distributed ledger has been employed for transaction execution in distributed V2G environments while Elliptic curve cryptography (ECC) has been used for hierarchical authentication. The designed hierarchical authentication mechanism has been employed to preserve the anonymity of EVs and support mutual authentication between EVs, charging stations (CSs) and the central aggregator (CAG). Additionally, it also supports minimal communicational and computational overheads on resource constrained EVs. Further, formal security verification of the proposed scheme on widely accepted Automated Validation of Internet Security Protocols and Applications (AVISPA) tool validates its safeness against different security attacks.
THE WRITING SKILL STIMULATION OF CHILDREN AGED 5-6 YEARS

The purpose of this research was examined the implementation of writing skill stimulation at TK Negeri Pembina Manahan Surakarta. The research used a qualitative approach with ethnomethodology study. The subject of research were teachers and children aged 5-6 years. The data collecting techniques used interview, observation, and documentation. The techniques for testing the data validity by triangulation of technique and source. The data analysis technique used the interactive data model by Milles and Huberman.The research result showed that TK Negeri Pembina Manahan did the writing skill stimulation through the learning activity based on the Early Childhood Education Curriculum of 2013. The stimulation did through writing activity and writing skill reinforcement activity (drawing, coloring, and cutting). The thematic learning with scientific approach supports the varied writing skill stimulation. These activities are able to support the children’development of writing skill, including: (1) the children are able to grip the pencil; (2) writing the self-name; and (3) writing word and number.
Micro-expression detection in long videos using optical flow and recurrent neural networks

Facial micro-expressions are subtle and involuntary expressions that can reveal concealed emotions. Micro-expressions are an invaluable source of information in application domains such as lie detection, mental health, sentiment analysis and more. One of the biggest challenges in this field of research is the small amount of available spontaneous micro-expression data. However, spontaneous data collection is burdened by time-consuming and expensive annotation. Hence, methods are needed which can reduce the amount of data that annotators have to review. This paper presents a novel micro-expression spotting method using a recurrent neural network (RNN) on optical flow features. We extract Histogram of Oriented Optical Flow (HOOF) features to encode the temporal changes in selected face regions. Finally, the RNN spots short intervals which are likely to contain occurrences of relevant facial micro-movements. The proposed method is evaluated on the SAMM database. Any chance of subject bias is eliminated by training the RNN using Leave-One-Subject-Out cross-validation. Comparing the spotted intervals with the labeled data shows that the method produced 1569 false positives while obtaining a recall of 0.4654. The initial results show that the proposed method would reduce the video length by a factor of 3.5, while still retaining almost half of the relevant micro-movements. Lastly, as the model gets more data, it becomes better at detecting intervals, which makes the proposed method suitable for supporting the annotation process.
INFER: INtermediate representations for FuturE pRediction

In urban driving scenarios, forecasting future trajectories of surrounding vehicles is of paramount importance. While several approaches for the problem have been proposed, the best-performing ones tend to require extremely detailed input representations (eg. image sequences). But, such methods do not generalize to datasets they have not been trained on. We propose intermediate representations that are particularly well-suited for future prediction. As opposed to using texture (color) information, we rely on semantics and train an autoregressive model to accurately predict future trajectories of traffic participants (vehicles) (see fig. above). We demonstrate that using semantics provides a significant boost over techniques that operate over raw pixel intensities/disparities. Uncharacteristic of state-of-the-art approaches, our representations and models generalize to completely different datasets, collected across several cities, and also across countries where people drive on opposite sides of the road (left-handed vs right-handed driving). Additionally, we demonstrate an application of our approach in multi-object tracking (data association). To foster further research in transferrable representations and ensure reproducibility, we release all our code and data.
Health information systems security: Risks, prospects and frameworks

Information is the most precious asset of any organization and assessing risk to information is a core mandate of any institutional management to ensure availability of effective controls to protect information assets. The increasing digitization of health information and the ever-changing cyber security threat environment, has led to some public health data breaches and as information security become increasingly important to the continued success for businesses, majority of organizations are searching for an appropriate security framework. Security risk assessment framework enables identification of threats and vulnerabilities. Although numerous frameworks available in the market, selection of the right framework to meet the organization’s need is a challenge due to lack of prescriptiveness, standard, inconsistencies, complexity, compliance, cost, and certifications. To address the gap, this study assessed the security of health information system and privacy risks in addition to existing frameworks and developed an enhanced framework. The study adopted a descriptive cross – sectional design and was conducted in Siaya County, in Kenya. A questionnaire was used to collect data which was analyzed and presented inform of tables, and charts. The results indicated that confidentiality of information is good (use of identifiers and passwords at 96.8% approval rate), availability of physical controls to protect authorized access at 95.2%, availability of policies stating staff responsible for protection of information confidentiality at 91.9%, availability of written policy on patient confidentiality and privacy at 74.2% and use of access privileges at 68.8%. The findings on integrity of information was poor with availability of systems to review data accuracy having 71.9% approval rate, frequency of data review at 81.2%, availability of written description of information security manager’s responsibility at 39.5%, monitoring of electronic systems to detect potential breaches at 40%, creation of audit logs to track system transactions at 54% and frequency of reviewing audit logs at 51.5%. The findings on availability of information was good (availability of inventory of computers at 69.9%), regular updates of inventory at 61.3%, updates of patient data on laptops and desktops at 68.2%, sharing of data confidentiality and security policy at 36%, and regular backups of audited logs at 51% approval rates. Regarding the assessment of existing security frameworks, it was noted that HIPAA has the following shortcomings: lacks complete valid risk analysis, not certifiable, security rule is safeguarding electronic protected health information only, the security does not regulate emails and does not require encryption, and commitment on security is verbal. On the other hand, ISO/IEC 27001 is expensive, requires specific IT budget, special expertise, and more time to apply in public hospitals. Finally, NIPP framework is expensive, and uses consequence’s assessment which is outside the scope of this study.
Utilizing machine learning techniques to classify network traffic

With in community of internet, it may be essential to recognize whatever programs are flowing via the networks in order to execute particular activities. Network traffic categorization is primarily used by Internet service providers (ISPs) to determine the qualities needed to construct a connection, which in turn influences the cable network current effectiveness. Stream, bandwidth, and machine-learning methods were all used to categorise internet protocol, and each has its own benefits and drawbacks. Because of its widespread use across disciplines as well as the increasing awareness between many investigators of its [5] methodology when especially in comparison to everyone else, the Machine Learning method is popular these times. Nave Bayes as well as K-nearest algorithm results are then compared in this research whenever applied to a networking given dataset taken through live stream feeds using Ethernet program. Python's sklearn module and the pandas and numpy arrays modules are utilised as assist modules to create a machine learning algorithm. Our findings show that K closest approach is more efficient than Nave Bayes, Decision Tree, and Support Vector Machine algorithms.
Sistem Pakar Penyakit Kulit Pada Manusia Menggunakan Metode Case Base Reasoning (CBR) Dengan Algoritma Sorensen Coefficient

The problem that often occurs community in health world is availability experts who have certain knowledge, such as skin specialists, who are not easily obtained have limited time, are constrained lack funds for people who want seek treatment or meet with experts. Therefore necessary build system that can help patients and doctors able ease their work. System is expert system, expert system is system that is able imitate the reasoning of expert into computer so that can solve problems is usually done by experts. This study uses the case base reasoning method for reasoning method and uses the Sorensen coefficient algorithm find the value of the proximity of new cases old cases of skin diseases caused by fungi, bacteria, viruses, parasites, allergies and burns with 130 cases which divided into 104 cases training data and 26 cases test data. Based on the test results, this system has the highest accuracy rate of 100% and the lowest is 83.33%.
Penerapan Algoritma Fuzzy C-Means (FCM) dalam Pengelompokan Provinsi di Indonesia berdasarkan Indikator Penyakit Menular Manusia

Infectious diseases are diseases that can be spread from sick people to healthy people. Infectious diseases in Indonesia include HIV, AIDS, tuberculosis and dengue fever. The purpose of this study is that the grouping of provinces in Indonesia based on indicators of infectious diseases and population density is needed to determine the level of spread of the disease in an area and also as an anticipation of the government in distributing health facilities. The clustering method used in this research is Fuzzy C-Means (FCM). From the research that has been done, the results obtained in 2019 and 2020, the grouping of provinces in Indonesia is divided into 3 clusters. The highest Shillouette Coefficient value in 2019 was 0.9151. Meanwhile, in 2020, the Shillouette Coefficient value is 0.91899.
REUSING WASTE BOTTLES AS WATER FILTERS

Clean water is essential for everyone in both household needs and the business world. Health management or world health organization is needed to get clear water. The objective of this technique application is to provide a valuable lesson that can be used in the community to address clean water issues. From the practice of using used bottles as clean water filters, the water that was initially cloudy turned into clear, odorless water. It is because the materials used are coral, stone, gravel, sand, palm fiber, and charcoal.
Forming Local Intersections of Projections for Classifying and Searching Histopathology Images

In this paper, we propose a novel image descriptor called Forming Local Intersections of Projections (FLIP) and its multi-resolution version (mFLIP) for representing histopathology images. The descriptor is based on the Radon transform wherein we apply parallel projections in small local neighborhoods of gray-level images. Using equidistant projection directions in each window, we extract unique and invariant characteristics of the neighborhood by taking the intersection of adjacent projections. Thereafter, we construct a histogram for each image, which we call the FLIP histogram. Various resolutions provide different FLIP histograms which are then concatenated to form the mFLIP descriptor. Our experiments included training common networks from scratch and fine-tuning pre-trained networks to benchmark our proposed descriptor. Experiments are conducted on the publicly available dataset KIMIA Path24 and KIMIA Path960. For both of these datasets, FLIP and mFLIP descriptors show promising results in all experiments.Using KIMIA Path24 data, FLIP outperformed non-fine-tuned Inception-v3 and fine-tuned VGG16 and mFLIP outperformed fine-tuned Inception-v3 in feature extracting.
Faster SVM Training via Conjugate SMO

We propose an improved version of the SMO algorithm for training classification and regression SVMs, based on a Conjugate Descent procedure. This new approach only involves a modest increase on the computational cost of each iteration but, in turn, usually results in a substantial decrease in the number of iterations required to converge to a given precision. Besides, we prove convergence of the iterates of this new Conjugate SMO as well as a linear rate when the kernel matrix is positive definite. We have implemented Conjugate SMO within the LIBSVM library and show experimentally that it is faster for many hyper-parameter configurations, being often a better option than second order SMO when performing a grid-search for SVM tuning.
Closed sets of finitary functions between finite fields of coprime order

We investigate the finitary functions from a finite field $\mathbb{F}_q$ to the finite field $\mathbb{F}_p$, where $p$ and $q$ are powers of different primes. An $(\mathbb{F}_p,\mathbb{F}_q)$-linearly closed clonoid is a subset of these functions which is closed under composition from the right and from the left with linear mappings. We give a characterization of these subsets of functions through the invariant subspaces of the vector space $\mathbb{F}_p^{\mathbb{F}_q\backslash\{0\}}$ with respect to a certain linear transformation with minimal polynomial $x^{q-1} - 1$. Furthermore we prove that each of these subsets of functions is generated by one unary function.
Y-Autoencoders: disentangling latent representations via sequential-encoding

In the last few years there have been important advancements in generative models with the two dominant approaches being Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). However, standard Autoencoders (AEs) and closely related structures have remained popular because they are easy to train and adapt to different tasks. An interesting question is if we can achieve state-of-the-art performance with AEs while retaining their good properties. We propose an answer to this question by introducing a new model called Y-Autoencoder (Y-AE). The structure and training procedure of a Y-AE enclose a representation into an implicit and an explicit part. The implicit part is similar to the output of an autoencoder and the explicit part is strongly correlated with labels in the training set. The two parts are separated in the latent space by splitting the output of the encoder into two paths (forming a Y shape) before decoding and re-encoding. We then impose a number of losses, such as reconstruction loss, and a loss on dependence between the implicit and explicit parts. Additionally, the projection in the explicit manifold is monitored by a predictor, that is embedded in the encoder and trained end-to-end with no adversarial losses. We provide significant experimental results on various domains, such as separation of style and content, image-to-image translation, and inverse graphics.
Quantum Algorithm for Distribution-Free Junta Testing

Inspired by a recent classical distribution-free junta tester by Chen, Liu, Serverdio, Sheng, and Xie (STOC'18), we construct a quantum tester for the same problem with complexity $O(k/\varepsilon)$, which constitutes a quadratic improvement. We also prove that there is no efficient quantum algorithm for this problem using quantum examples as opposed to quantum membership queries. This result was obtained independently from the $\tilde O(k/\varepsilon)$ algorithm for this problem by Bshouty.
System Administration and Database Administration in Cloud Computing Era

The ever-changing nature of IT demands a changing role for both system administrators and database administrators. Being system admin personnel will require more knowledge, broader skillset, and more tools on hand to be able to adapt to the cloud computing culture. The cloud computing has changed the way for IT Operations. The future system administrator will thus have to help configure, implement and manage the applications that run on cloud platforms. The same thing happens to database administrators as well. The role of DBAs will shift to cloud DBAs, and adding to their responsibilities will manage the cloud based database and NoSQL databases in serverless settings. Cloud DBAs will have to deal with data protection and play a key role in providing data compatibility across the cloud platforms and maybe migrating data storage from one cloud service provider to another. In short DBAs role moved from tactical infrastructure caretakers towards a more strategic role of being an advisor for other developers. In this poster, we describe changes of roles of system administrators and database administrators in the era of cloud computing.
Biologically Inspired Optimization Solutions for Homeostatic Maintenance

Anthony Ratledge, Weidong Liao (Faculty Advisor), Department of Computer Science, Mathematics and Engineering, School of Natural Sciences and Mathematics, Shepherd University, Shepherdstown, WV 25443. Biologically Inspired Optimization Solutions for Homeostatic Maintenance Optimization when applied in computer science refers to implementation of methods to solve problems more efficiently: to realize the most cost effective or highest achievable performance from a computing system. Traditional computation is done primarily through serial processing; one process after another is done, and so on. It is highly inefficient when dealing with complex factors, such as nonlinear computation, multi-objective functions, or nondeterministic variables. Researchers and scientists have found inspiration for such optimization problems in nature and developed an entire design philosophy called biologically inspired computing. This poster discusses optimization with an emphasis on homeostatic maintenance, which involves adapting internal systems to regulate a consistent state in response to the external stimuli of the environment. In nature, this is an automatic, ongoing process accomplished by the peripheral nervous system through reflexes. This presentation will explore several different biologically inspired optimization software solutions in two rather broad categories: non-swarm intelligence-based algorithms and swarm intelligence-based algorithms. It will then delve into the construction of artificial neural networks as a proposed optimization solution, which features both hardware and software considerations, ultimately to argue in favor of one swarm intelligence implementation for homeostatic maintenance, with experimental support. Finally, this poster presentation will propose a swarm optimization implementation into an artificial neural network to achieve homeostatic maintenance.
An Optimal Task Allocation Strategy for Heterogeneous Multi-Robot Systems

For a team of heterogeneous robots executing multiple tasks, we propose a novel algorithm to optimally allocate tasks to robots while accounting for their different capabilities. Motivated by the need that robot teams have in many real-world applications of remaining operational for long periods of time, we allow each robot to choose tasks taking into account the energy consumed by executing them, besides the global specifications on the task allocation. The tasks are encoded as constraints in an energy minimization problem solved at each point in time by each robot. The prioritization of a task over others -- effectively signifying the allocation of the task to that particular robot -- occurs via the introduction of slack variables in the task constraints. Moreover, the suitabilities of certain robots towards certain tasks are also taken into account to generate a task allocation algorithm for a team of robots with heterogeneous capabilities. The efficacy of the developed approach is demonstrated both in simulation and on a team of real robots.
Statistical Privacy in Distributed Average Consensus on Bounded Real Inputs

This paper proposes a privacy protocol for distributed average consensus algorithms on bounded real-valued inputs that guarantees statistical privacy of honest agents' inputs against colluding (passive adversarial) agents, if the set of colluding agents is not a vertex cut in the underlying communication network. This implies that privacy of agents' inputs is preserved against $t$ number of arbitrary colluding agents if the connectivity of the communication network is at least $(t+1)$. A similar privacy protocol has been proposed for the case of bounded integral inputs in our previous paper~\cite{gupta2018information}. However, many applications of distributed consensus concerning distributed control or state estimation deal with real-valued inputs. Thus, in this paper we propose an extension of the privacy protocol in~\cite{gupta2018information}, for bounded real-valued agents' inputs, where bounds are known apriori to all the agents.