Reviewerly/exHarmony huggingface dataset:
Quality Assessment of On-site Solar Radiation Data and Estimating Global Tilted Irradiation in Middle East Technical University Northern Cyprus Campus

Financial institutions require bankable datasets to guarantee investments in large-scale solar energy projects, reducing the overall uncertainty of energy yield estimates. Long-term satellite-based data is mostly available globally; however, their systematic errors and bias should be removed by integrating quality-checked ground measurements if available. In this study, a quality assessment was done to detect erroneous/ missing data points using several quality-control tests. In METU NCC, global horizontal irradiation (GHI), direct normal irradiation (DNI), and global tilted irradiation (GTI) were recorded since 2010, 2013 and 2016, respectively. Physical threshold and quality envelope tests revealed that lower-than- expected GHI values were measured in some periods. Thus, erroneous GHI data was estimated using the Erbs model and measured DNI data. While up to 2 hours of missing GHI data were filled by linear interpolation, more extended missing data were filled by estimated GHI. Both measured and constructed GHI data were compared with satellite-based GHI data downloaded from the Photovoltaic Geographical Information System (PVGIS) for the time period 2010-2016. The results indicated that the average relative root-mean-square error (rRMSE) of daily total GHI reduced from 34.63% to 17.77% after the data filing process. The rRMSE decreased to 8.78% for the annual mean daily total GHI data. Additionally, GTI was estimated using the isotropic sky-diffuse model, and it was compared with measured GTI. RMSE of daily total estimated GTI was 22.41%, whereas satellite-based GTI had an RMSE of 20.81%. Finally, energy yield was estimated for 1 MWp solar photovoltaic (PV) plant using satellite-based and estimated GTI.
NFC-enabled packaging to detect tampering and prevent counterfeiting: Enabling a complete supply chain using blockchain and CPS

Medicines play a vital role in human lives and that’s why the market size of medicines is quite large. Amid the pandemic of COVID-19 in the 21st century, the governments of many countries as well as healthcare industries, hospitals, doctors, and patients are facing a crucial threat of tampering and counterfeiting of vaccines and drugs. This is not an issue that can be ignored easily, as it troubles all of society in a significant way. New technology such as near field communication (NFC), combined with the latest technologies such as Hyperledger, blockchain, and the Internet of Things have given efficient results. The methodology used in this research manuscript (the authors named it “AKSHAT”) is that an NFC chip will be installed in the caps of medicines. When a consumer scans the label on a product with a phone or any NFC-enabled device, the device authenticates the product and informs as to whether it is a genuine product. When a consumer finds that the NFC seal is broken or if it looks intact but NFC data cannot be read, then apparently the product has been tampered with. In NFC, all data and information can be stored and cannot be changed again. The research results presented here establish that NFC is time-efficient, cost-effective, secure, and accurate. With the use of NFC tags, registered transactions on a private network and the tracing of the vaccine are very easy. The authors’ team has demonstrated this as a product that can currently store every transaction as immutable and transparent. The proposed anticounterfeiting solution “AKSHAT” uses NTAG-213 based tags, which are cost-effective as well as have a mutual understanding with all NFC-enabled smartphones. This provides the option for all product manufacturers and brand owners to integrate NFC technology at a fraction of the money.
A Novel Traceback Technology for E-mail Sender Verification : Type of submission: Regular Research Paper / Most relevant symposium: CSCI-ISCW

Given the rapid growth and the large-scale investment in information technology, many organizations conduct business through email. With the active use of email, malicious cyberattacks have also increased. In particular, the damage caused by impersonation, forgery, and alteration of email has been drastically increasing. While the existing email security solutions scan and examine large mails, advertising mails, and junk mails as major targets, they are not able to identify or distinguish the sender of impersonated or forged email. This study developed a novel email processing system based on SMTP (Simple Mail Transfer Protocol) reply codes. In the proposed system, the SMTP reply codes related to the loss of email are used to perform traceback and verify the sender of emails. When an email is received, the system generates verification request session and conducts communication to the sender&#x2019;s email server and DNS. According to information that the sender&#x2019;s email server offers back, the proposed system discriminates the authenticity of email senders. The proposed system was introduced to actual institutions and performance was validated. As a result, the proposed system has significantly improved the rate of email sender verification. We hope that the results of this study can serve as a useful guideline for the development of email security.
A Neural Architecture Search for Automated Multimodal Learning

The boom of artificial intelligence in the past decade is owed to the research and development of deep learning and moreover, that of accessible deep learning. But the goal of Artificial General Intelligence (AGI) cannot be achieved by having application-specific, parameter sensitive neural networks that need to be defined and tuned for every use case. General intelligence also involves understanding different types of data, rather than having dedicated models for each functionality. Thus both automating machine learning while also giving importance to generalizing over multiple modalities has great potential to help move AGI research forward.
Distributed subgraph query for RDF graph data based on MapReduce

Nowadays, Resource Description Framework (RDF) query has been widely used in social networks, biomedicine and other fields. With the explosion of RDF data due to the Internet of Things and Semantic Web, people's demand for intelligent computing and intelligent search is increasing, effectively querying RDF has become a major challenge. The current query methods often introduce a large number of join operations, and repeatedly traverse in some subgraphs during the query process, which makes the query efficiency and query performance poor. To address the above problems, this paper proposes a subgraph query algorithm for RDF graph data in a distributed environment. The graph structure is used to decompose the stars of the RDF graph, and the optimal query sequence of the stars is calculated. Fewer intermediate results can be produced based on the query sequence to reduce repeated calculations. Besides, adjacency lists are used to store RDF graphs, which are distributed across multiple tables. Multiple table operations can reduce the scope of subject node traversal, and further improve the query efficiency of RDF subgraph by matching one star per iteration. Experimental results show that our work can improve the query efficiency of RDF subgraphs.
From Publics to Counterpublics

In this paper we examine the conceptualization of publics by participatory design scholars and consider how the emphasis on communities as publics supports or hinders the agenda to repoliticize participatory design research. Our paper begins by setting out the alternative concept of counter-publics, which we argue is an overlooked yet salient critical lens that can offer productive insights for the field. Focusing on “new economy” counter-publics, we consider how we might unsettle institutionalized norms and assemble new ones, rebalance towards autonomy at the outset of any participatory design project, and approach designing counter-institutions in ways that aim to sustain oppositional practices in the face of co-optation. Based on this argumentation, we reflect on counter-designing as a tactical practice to employ against pervasive issues of norming and co-optation and towards collective autonomy.
Optimal tilt angle of a solar panel for a wide range of latitudes. Comparison between tilted and horizontal configurations

The intensity of solar radiation incident on a flat surface is closely related to its angle of inclination. Therefore, optimal tilt angle (OTA) is needed to maximise this incident energy. However, the OTA depends on the latitude of the site and the period of year. This study proposes correlations that evaluate the optimum tilt angle (OTA) of a solar panel facing the equator. Four configurations were studied: monthly tilt angle (MTA), seasonal tilt angle (STA), annual tilt angle (ATA) and two-axis tracking system. Correlations are then proposed to evaluate these OTAs according to the latitude. Moreover, we show that the incident energy on a surface first increases by using ATA and then further improves by STA, then by MTA and finally by the two-axis tracking system. Furthermore, we compare two photovoltaic (PV) field situations whose modules are oriented towards the equator with a tilt angle of ATA and horizontal. The rows of tilted PV panels are spaced to avoid shading between them. Thus, it appears that the horizontal PV panels, which cover the entire ground surface, intercept more solar energy than the tilted one. The improvement reaches 8% at 10° of latitude and about 413% at 60° of latitude.
Involution-based network with contrastive learning for efficient image super-resolution

Single-image super-resolution (SISR) studies have achieved superior improvement with the development of convolution neural networks. However, most methods sink into the high computation cost. To tackle this issue, we propose an involution-based lightweight method with contrastive learning for efficient SISR. Unlike the original involution, we set the group number of involution operations to the input feature channels. This setting guarantees the spatial- and channel-specific peculiarity. Moreover, our implemented involution not only learns the weight but also the bias for convolution. Simultaneously, we rethink the kernel generation functions of involution. Instead, we utilize Sigmoid with reparameterized convolution. We additionally apply residual path to involution operation. Furthermore, contrastive learning is adopted during training to learn universal features. Compared with state-of-the-art efficient SISR methods, our proposed methods achieve the best performance with similar or fewer parameters.
COMPARISON OF THE SUCCESS OF META-HEURISTIC ALGORITHMS IN TOOL PATH PLANNING OF COMPUTER NUMERICAL CONTROL MACHINE

Carrying out an engineering process with the least cost and within the shortest time is the basic purpose in many fields of industry. In Computer Numerical Control (CNC) machining, performing a process by following a certain order reduces cost and time of the process. In the literature, there are research works involving varying methods that aim to minimize the length of the CNC machine tool path. In this study, the trajectory that the CNC machine tool follows while drilling holes on a plate was discussed within the Travelling Salesman Problem (TSP). Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Grey Wolf Optimizer (GWO) methods were used to solve TSP. The case that the shortest tool path was obtained was determined by changing population size parameter in GA, PSO, and GWO methods. The results were presented in tables.
Track-to-track association methodology for operational surveillance scenarios with radar observations

Abstract This paper proposes a novel track-to-track association methodology able to detect and catalogue resident space objects (RSOs) from associations of uncorrelated tracks (UCTs) obtained by radar survey sensors. It is a multi-target multi-sensor algorithm approach able to associate data from surveillance sensors to detect and catalogue objects. The association methodology contains a series of steps, each of which reduces the complexity of the combinational problem. The main focus are real operational environments, in which brute-force approaches are computationally unaffordable. The hypotheses are scored in the measurement space by evaluating a figure of merit based on the residuals of the observations. This allows us to filter out most of the false hypotheses that would be present in brute-force approaches, as well as to distinguish between true and false hypotheses. The suitability of the proposed track-to-track association has been assessed with a simulated scenario representative of a real operational environment, corresponding to 2 weeks of radar survey data obtained by a single survey radar. The distribution and evolution of the hypotheses along the association process is analysed and typical association performance metrics are included. Most of the RSOs are detected and catalogued and only one false positive is obtained. Besides, the rate of false positives is kept low, most of them corresponding to particular cases or objects with high eccentricity or limited observability.
Authenticated Multiparty Quantum Key Agreement for Optical-Ring Quantum Communication Networks

Quantum communication networks are connected by various devices to achieve communication or distributed computing for users in remote locations. In order to solve the problem of generating temporary session key for secure communication in optical-ring quantum networks, a quantum key agreement protocol is proposed. In the key agreement protocols, an attacker can impersonate a legal user to participate in the negotiation process and eavesdrop the agreement key easily. This is often overlooked in most quantum key agreement protocols, which makes them insecure in practical implementation. Considering this problem, the function of authenticating the user's identity is added in the proposed protocol. Combining classical hash function with identity information, we design the authentication operation conforming to the characteristics of quantum search algorithm. In the security analysis of the proposed protocol, quantum state discrimination is utilized to show that the protocol is secure against common attacks and impersonation attack. In addition, only single photons need to be prepared and measured, which makes our protocol feasible with existing technology.
Transformation Invariant Pashto Handwritten Text Classification and Prediction

The use of handwritten recognition tools has increased yearly in various commercialized fields. Due to this, handwritten classification, recognition, and detection have become an exciting research subject for many scholars. Different techniques have been provided to improve character recognition accuracy while reducing time for languages like English, Arabic, Chinese and European languages. The local or regional languages need to consider for research to increase the scope of handwritten recognition tools to the global level. This paper presents a machine learning-based technique that provides an accurate, robust, and fast solution for handwritten Pashto text classification and recognition. Pashto belongs to cursive script division, which has numerous challenges to classify and recognize. The first challenge during this research is developing efficient and full-fledged datasets. The efficient recognition or prediction of Pashto handwritten text is impossible by using ordinary feature extraction due to natural transformations and handwriting variations. We propose some useful invariant features extracting techniques for handwritten Pashto text, i.e., radial, orthographic grid, perspective projection grid, retina, the slope of word trajectories, and cosine angles of tangent lines. During the dataset creation, salt and pepper noise was generated, which was removed using the statistical filter. Another challenge to face was the invalid disconnected handwritten stroke trajectory of words. We also proposed a technique to minimize the problem of disconnection of word trajectory. The proposed approach uses a linear support vector machine (SVM) and RBF-based SVM for classification and recognition.
DeepLook: a deep learning computed diagnosis support for breast tomosynthesis

The aim of the DeepLook project, funded by INFN (Italy), is to implement a deep learning architecture for Computed Aided Detection (CAD), based on neural networks developed with deep learning methods, for the automatic detection and classification of breast lesions in DBT images. A preliminary step (started 2 years ago and still ongoing) was the creation of a dataset of annotated images. This dataset includes images acquired with different clinical DBT units and different acquisition geometries, on several hundred patients, containing a variety of possible breast lesions and normal cases of absence of lesions. This will make the diagnostic capacity of the CAD system particularly extensive in various clinical situations and on a significant sample of patients, so allowing the network to diagnose various types of lesions (at the level of the single tomosynthesis slices) and capable of operate on commercial DBT systems, also available from different vendors, as found in breast diagnosis departments. The developed CAD and first result of the indication of the slice containing the suspected mass will be presented.
Privacy Shield: A System for Edge Computing using Asynchronous Federated Learning

Due to increase in IoT devices, the data produced every day are also increasing rapidly. The growth in data means more processing and more computations are required without delay. This introduced us to a new horizon of the computing infrastructure, i.e., edge computing. Edge computing gained prominence as a solution to the problem of delayed transmission, processing, and response by the cloud architecture. It was further augmented with the field of artificial intelligence. It has become a topic in research with preservation of data privacy as the focal point. This paper provides Privacy Shield, a system for edge computing using asynchronous federated learning, where multiple edge nodes perform federated learning while keeping their private data hidden from one another. Contrary to the pre-existing distributed learning, the suggested system reduces the calls between the edge nodes and the main server during the training process ensuring that there is no negative impact on the accuracy of the mode. We used different values of Ω that affect the accuracy and the compression ratio. In the interval of Ω = [0.2, 0.4], the C-ratio increases and the value of Ω and the compression ratio value are directly proportional regardless of the fluctuations. We analyzed the accuracy of the model which increases along with the increase in compression ratio. Compressing the gradient communications reduces the likelihood of the data of being attacked. As the nodes train asynchronously on limited and different kinds of samples, the binary weights adjustment method was used to handle the resulting “unbalanced” learning. The MNIST and Cifar10 dataset were used for testing in both tasks, respectively.
Toward a Comprehensive Domestic Dirt Dataset Curation for Cleaning Auditing Applications

Cleaning is an important task that is practiced in every domain and has prime importance. The significance of cleaning has led to several newfangled technologies in the domestic and professional cleaning domain. However, strategies for auditing the cleanliness delivered by the various cleaning methods remain manual and often ignored. This work presents a novel domestic dirt image dataset for cleaning auditing application including AI-based dirt analysis and robot-assisted cleaning inspection. One of the significant challenges in an AI-based robot-aided cleaning auditing is the absence of a comprehensive dataset for dirt analysis. We bridge this gap by identifying nine classes of commonly occurring domestic dirt and a labeled dataset consisting of 3000 microscope dirt images curated from a semi-indoor environment. The dirt dataset gathered using the adhesive dirt lifting method can enhance the current dirt sensing and dirt composition estimation for cleaning auditing. The dataset's quality is analyzed by AI-based dirt analysis and a robot-aided cleaning auditing task using six standard classification models. The models trained with the dirt dataset were capable of yielding a classification accuracy above 90% in the offline dirt analysis experiment and 82% in real-time test results.
Pho(SC)-CTC—a hybrid approach towards zero-shot word image recognition

Annotating words in a historical document image archive for word image recognition purpose demands time and skilled human resource (like historians, paleographers). In a real-life scenario, obtaining sample images for all possible words is also not feasible. However, zero-shot learning methods could aptly be used to recognize unseen/out-of-lexicon words in such historical document images. Based on previous state-of-the-art method for zero-shot word recognition "Pho(SC)Net", we propose a hybrid model based on the CTC framework (Pho(SC)-CTC) that takes advantage of the rich features learned by Pho(SC)Net followed by a "connectionist temporal classification" (CTC) framework to perform the final classification. Encouraging results were obtained on two publicly available historical document datasets and one synthetic handwritten dataset, which justifies the efficacy of Pho(SC)-CTC and Pho(SC)Net.
Active Learning for Contextual Search with Binary Feedback

In this paper, we study the learning problem in contextual search, which is motivated by applications such as crowdsourcing and personalized medicine experiments. In particular, for a sequence of arriving context vectors, with each context associated with an underlying value, the decision maker either makes a query at a certain point or skips the context. The decision maker will only observe the binary feedback on the relationship between the query point and the value associated with the context. We study a probably approximately correct learning setting, where the goal is to learn the underlying mean value function in context with a minimum number of queries. To address this challenge, we propose a trisection search approach combined with a margin-based active learning method. We show that the algorithm only needs to make [Formula: see text] queries to achieve an ε-estimation accuracy. This sample complexity significantly reduces the required sample complexity in the passive setting where neither sample skipping nor query selection is allowed, which is at least [Formula: see text]. This paper was accepted by J. George Shanthikumar, data science. Funding: X. Chen and Q. Liu were supported by the National Science Foundation [Grant IIS-1845444].