Reviewerly/exHarmony huggingface dataset:
The Application for Reading Comprehension and Reading Speed Test

Vision is considered one of the most important human senses. People acquire a large amount of data by reading, so the faster someone reads, the more information he/she should learn. However, this is not always the case. Electrooculography (EOG) plays a significant role in research on reading. We recorded the EOG signals with the JINS MEME ES_R smart glasses from eight subjects who read a text and then completed a reading comprehension test implemented as a multiple-choice test. Reading speed was calculated as the time of reading the text with a constant number of words. The visualization of reading was based on detecting the saccades in the horizontal component of the EOG signal. The algorithm consists of signal preprocessing and FCM (fuzzy c-means) thresholding. Three types of saccades and their duration were detected and associated with blinking, eye movement to the left, and eye movement to the right. The developed application shows the reading process with the use of an animation of eye-walking on the text. The application tests carried out show that it works properly.
Content-Based Telegram Group Recommender System

Finding groups is very useful for marketers to grow their business. Also, that can solve the problem of confusing people in finding groups that are relevant to the user’s interests. Telegram currently has about 500 million monthly active users. It is also known as a social network in many countries such as Iran but does not have the important capabilities of social networks like item recommendation. A content-based recommendation is one of the most successful recommendation techniques based on content correlation. In this paper, we present a new combination model of content based recommendation and clustering method, which mainly consists of three parts. In the first step, the groups and user profiles are extracted from the data using TF-IDF. Second, by clustering groups’ profiles, this model speeds up the identification of groups associated with a user’s profile and assigns a cluster number to them. Finally, to find the most relevant groups, the similarity between the user profile model and the profile of groups with the same cluster is calculated. The role of the proposed algorithm is to improve the performance of the content-based recommendation using the clustering optimization method. The actual dataset of this study includes more than 700,000 supergroups and 70 million users on Telegram. To evaluate the accuracy and performance of the proposed method, we will use precision, recall, and f-measure. Experiments on the proposed method show the effectiveness of recommendations, high accuracy, and speed improvements.
On Boolean threshold functions with minimum specification number

A set S of Boolean points is a specifying set for a threshold function f if the only threshold function consistent with f on S is f itself. The minimal cardinality of a specifying set for f is the specification number of f and it is never smaller than n+1 for a function with n relevant variables. In the present paper, we develop an inductive approach to describing the set of Boolean threshold functions with minimum specification number by means of operations that allow us to extend functions of n variables in this set to functions of n+1 variables.
Generalized Nonparametric Composite Tests for High-Dimensional Data

In this paper, composite high-dimensional nonparametric tests for two samples are proposed, by using component-wise Wilcoxon–Mann–Whitney-type statistics. No distributional assumption, moment condition, or parametric model is required for the development of the tests and the theoretical results. Two approaches are employed, for estimating the asymptotic variance of the composite statistic, leading to two tests. In both cases, banding of the covariance matrix to estimate variance of the test statistic is involved. An adaptive algorithm, for selecting the banding window width, is proposed. Numerical studies are provided, to show the favorable performance of the new tests in finite samples and under varying degrees of dependence.
Method of railway shunting operation sheet information extraction guided by table header

The railway shunting operation sheet is the written plan of the shunting operation. In the current shunting operation preparation process, signalmen must input the plans manually for subsequent execution, which is inefficient and at risk of misoperation. Here, an information extraction method is proposed to convert shunting operation sheet images to readable instructions for Continuous Train Control (CTC) systems. First, the shunting operation sheet images are preprocessed to facilitate subsequent text recognition based on the Paddle OCR model. Second, the table is located and reconstructed with the results of recognized contents and coordinates of texts. Then, the results of the table reconstruction were optimized based on the tabulation rules of shunting operation. Finally, tabular texts containing structural and attribute information are transcribed into eXtensible Markup Language (XML) format, which can be read automatically by CTC systems based on the C programming language. The proposed approach was evaluated on real-world samples, and the results showed that the comprehensive accuracy of information extraction was 0.85. Furthermore, a pre-processing combination strategy was established to improve the accuracy and generalizability of the proposed method.
H-Map-Based Technique for Mining High Average Utility Itemset

High Utility Itemset Mining (HUIM) is the process of locating itemsets that are profitable and useful to users. One of the key flaws in HUIM is that as the length of the itemset increases, the utility also increases. The true utility/profit of the itemset is not revealed in HUIM. High Average Utility Itemset mining overcomes the limitations of HUIM by taking the length of the itemset into account when estimating the utility. Existing pruning methods used for eliminating weak candidates overestimate the average usefulness of itemsets, causing the mining process to slow down. To prevent processing unpromising candidate itemsets and efficiently reduce the search space and processing time, the proposed methodology employs Upper Bound using Remaining Items Utility, Maximum Itemset Utility, and Sum of Maximum Utility in a Transaction. It also uses the multithreaded parallel approach to reduce the processing time. The H-Map-based data structure (H-Map) used for storing the utility values reduces the lookup time and joins used for itemset extension compared to existing state-of-the-art High Average Utility Itemset mining algorithms. The performance of the proposed work is evaluated in terms of memory usage and the time taken for processing. The proposed work increases the overall efficiency of the system employing effective pruning algorithms for pruning poor candidate itemsets and an efficient data structure for storing utility values.
Network Latency and Application Performance Aware Cluster Scheduling in Data Centers

Data-center-based cloud computing has revolutionized the way businesses use computing infrastructure. Instead of building their own data centers, companies rent computing resources and deploy their applications on cloud hardware. Providing customers with well-defined application performance guarantees is of paramount importance. A user's application performance is subject to the constraints of the resources it has been allocated and to the impact of the network conditions in the data center. Given the network latency variability observed in data centers, applications' performance is also determined by their placement within the data center. We present NoMora, a cluster scheduling architecture whose core is represented by a latency-driven, application-performance-aware cluster scheduling policy. The policy places the tasks of an application taking into account the expected performance based on the measured network latency between pairs of hosts in the data center. If a tenant's application experiences increased network latency, and thus lower application performance, their application may be migrated to a better placement. Experiments on a testbed and in simulations show that our architecture improves the overall average application performance by up to 32.5 and 42 percent, respectively, demonstrating that application performance can be improved by exploiting the relationship between network latency and application performance.
Defending against fake VIP in scant-transparency information systems with QoS differentiation

In client–server information systems with quality of service (QoS) differentiation, Client may deplete Server’s resources by demanding unduly high QoS level. Such QoS abuse has eluded systematic treatment; known defenses using Client authorization, payments, or service request inspection prior to QoS assignment, are heuristic and environment-specific. We offer a game-theoretic approach on the premise that a service request is occasionally trusted to reduce the inspection cost. We call Fake VIP attack (FVA) a form of QoS abuse that consciously exploits Server’s trust. An FVA strategy instills trust to maximize Client’s utility gained from successful FVAs, whereas a trust strategy maximizes Server’s utility by trading her loss due to successful FVAs against the request inspection cost. We consider a realistic scant-transparency setting where only long-term utilities are observable. Against a probabilistic FVA strategy we design a trust strategy based on double-blind reputation. Assuming a memoryless service request stream we analyze the impact of the request inspection cost and information leakage on the utilities at the Stackelberg equilibrium of the arising game. Experimental comparison with a real-world internally correlated stream is also shown.
Locating Side Channel Leakage in Time through Matched Filters

Side channel attacks provide an effective way to extract secret information from the execution of cryptographic algorithms run on a variety of computing devices. One of the crucial steps for a side channel attack to succeed is the capability to locate the time instant in which the cryptographic primitive being attacked is effectively leaking information on the side channel itself, and synchronize the data obtained from the measurements on that instant. In this work, we propose an efficient and effective solution relying on the digital signal processing technique known as matched filters. We derive our matched filter with a small amount of profiling information which can be obtained from a device matching the one under attack. Our technique reliably identifies the cryptographic operation being computed, even when system interrupts or software multithreading are enabled on our target platform. We validate our approach through a successful attack against an unprotected AES implementation running on a Cortex-M4-based microcontroller.
A Practice of Human-Machine Collaboration for Persian Text Summarization

Nowadays, a large volume of daily produced texts is accessible to users. The amount of time spent in reading has a huge impact on the cultural developments of each society, and one of the most vital problems that countries, like Iran, encountered with is a significant drop in reading, specifically in young and future maker generation. Therefore, this study aims to facilitate reading for those people who spend little time on reading, for any reason. In this regard, human intelligence is used along with linguistic techniques and learning algorithms. Thus, people who are interested in reading books can help the proposed summarization system in producing an efficient summary for each book In this paper, a Persian extractive summarization system is proposed using an interactive approach, and its results are evaluated and compared with similar systems. It revealed that human interference in the summarization process can enhance the F-measure of the system in comparison with related research, while it is more simple and not requiring any complex processing methods used in the similar previous research.
Efficient Method for Timing-based Information Flow Verification in Hardware Designs

Timing side channels are a serious threat to the security of hardware designs. By analyzing the execution times of a design, the attacker can expose the secret information. This paper proposes an approach to verify and monitor timing-based information flow properties. In addition, the method can highlight the path that is vulnerable to leakage, making it easier to trace the leaking channel. The method can be used during formal verification, dynamic verification during simulation, post-fabrication validation, and run-time monitoring if one is necessary. The method reduces the overhead of the security model, which helps speed up the verification process and create an efficient run-time hardware monitor. Various timing-based information flow properties from five different hardware designs were verified. The results show that our approach can accurately detect hardware timing channels with lower overhead.
When experimentation starts as a solution to raise ROI

Experimentation in organizations often starts with the marketing department hiring an agency to raise the ROI of their marketing efforts. Scaling experimentation in the whole organization leads to a larger quantity of experiments, preventing changes without testing, thus making better decisions, and reducing risk. However, we have learned that broadly scaling experimentation has negative effects on the marketing departments KPI of more revenue, which is preventing experimentation from scaling up. The main objective of this paper is to make organizations aware of this Catch-22 when starting experimentation with the scope of raising ROI. We want to convince marketing departments, their agencies, and their vendors that if experimentation starts at marketing, then they need to create more awareness for the full power of experimentation by presenting all the results including the more common inconclusive and negative outcomes of experiments. Our learnings suggest that this awareness must lead to budget and resources for a stand-alone experimentation Center of Excellence (CoE) outside of the marketing department, that different (not ROI focused) KPIs are needed when scaling up experimentation, and that specific organizational conditions must be met to maintain a future-proof set-up.
Ethical Engagement and Relational Materialism: A Dialogue

This article is derived from a webinar series conversation titled, “Post Philosophies and the Doing of Inquiry.” During the webinar panelists were asked to respond to four questions: 1. How does your philosophical approach influence your ways of doing inquiry? 2. What does this philosophical approach make thinkable or possible for inquiry? 3. What are your perspectives on methodology(ies) and/or methods? How do you envision that in your approaches to doing inquiry? 4. What mechanisms could be put in place at universities to help supervisors and/or committees support students doing post philosophy inspired ways of inquiring?
Automated Machine Learning-Algorithm Selection with Fine-Tuned Parameters

In recent years, the impact and usage of Machine Learning have increased multi-fold. To get the best outcome, it is imperative to choose the right algorithm. Hence, Algorithm Selection is one of the most important stages in the Machine Learning Lifecycle. Selecting the optimal algorithm out of many possible algorithms for a given problem statement is always a tedious and time-consuming process. This project assists the researchers and Machine Learning enthusiasts in selecting the appropriate algorithm by providing a comparative selection among the different machine learning algorithms available for a pre-processed dataset. Besides the best Algorithm Selection, this project also aims to provide the best hyper-parameters for the opted algorithm using a novel technique. The Algorithm Selection functionality is furnished by comparing the various accuracy metrics like Accuracy, Recall, Precision, F1 Score, R2 Score, Mean Squared Error, etc. The objective of this project is to compare numerous algorithms and suggest the best algorithm. Thus, the user can instead concentrate on Data Collection and Data Pre-processing to achieve success in building the best model.
Single Image Dehazing Using Global Illumination Compensation

The existing dehazing algorithms hardly consider background interference in the process of estimating the atmospheric illumination value and transmittance, resulting in an unsatisfactory dehazing effect. In order to solve the problem, this paper proposes a novel global illumination compensation-based image-dehazing algorithm (GIC). The GIC method compensates for the intensity of light scattered when light passes through atmospheric particles such as fog. Firstly, the illumination compensation was accomplished in the CIELab color space using the shading partition enhancement mechanism. Secondly, the atmospheric illumination values and transmittance parameters of these enhanced images were computed to improve the performance of atmospheric-scattering models, in order to reduce the interference of background signals. Eventually, the dehazing result maps with reduced background interference were obtained with the computed atmospheric-scattering model. The dehazing experiments were carried out on the public data set, and the dehazing results of the foggy image were compared with cutting-edge dehazing algorithms. The experimental results illustrate that the proposed GIC algorithm shows enhanced consistency with the real-imaging situation in estimating atmospheric illumination and transmittance. Compared with established image-dehazing methods, the peak signal-to-noise ratio (PSNR) and the structural similarity (SSIM) metrics of the proposed GIC method increased by 3.25 and 0.084, respectively.
Crystallization of Anti-Immigration Discourse in Times of Crises : Rising Toxic Narratives About Syrians in Turkey

This study explores topics of anti-immigration discourse against Syrians on Twitter, as well as the accounts and hashtags and mentions in which negative sentiments is most frequently processed. We provide quantitative evidence on some of the key points that contain various wordings that would directly relate to Syrians including "Suriyeli, Suriye etc." and some others indirectly related as in "mülteci, mülteciler, etc." Data included various intersections of the derivative words used together or separately, posted from March 1, 2019 to March 31, 2020 on Twitter. The search revealed five key points representing an agenda of high profile events related to Syrians. Next, these threads were compared with the same timelines on the Twitter Turkey agenda. Social Media data analysis was made by compiling Twitter data about the interactions related to the Twitter Syrian agenda. The tweets sent in line with the keywords and date limits were collected and placed in the excel sheet, and these data were loaded into the SPSS program and quantitative content analysis were generated. The word phrases in the cluster were developed in accordance with the algorithms of Brand Watch, which is a software that can analyze content using artificial intelligence. Even on the busiest days, data revealed Twitter's Syrian agenda accounted for only 2.02 percent of the Turkish agenda. Significant events were found indicative of the key points of the Syrian agenda and negative sentiments against Syrians. Finally, evidence shows high-activity hashtags and accounts hold the power to dominate the Twitter Syrian agenda. The current study reveals how negative sentiments on social media represents a very small part of the agenda in volume, yet to our awe, a certain segment is effective enough to set the agenda using significant events.
Security Attacks Prediction Model for Cloud Computing

Cloud computing has become an indispensable computing platform of today's Information Communication Technology (ICT) world. The virtual environment provided by the cloud computing applications is neither under control of Cloud Service Providers (CSP) or users, completely. Consequently, it elicits the challenge of preventing security breaches considering that the data shared or stored in cloud computing environment can be crucial in nature. It is required to develop a system with capabilities to anticipate future security attacks. In this paper, we propose a security attacks prediction model which is based on Bayesian Networks, a probabilistic graphical model. The proposed Security Attacks Prediction Model (SAPM) takes vulnerable root causes of security loopholes, processes them through the Bayesian network and predicts the probability of security attacks.
A functional platform for remote use of electron microscopes using web conferencing systems

We propose a useful system for remote sharing of transmission electron microscope (TEM) images by connecting three computers: a computer connected to a TEM, a computer distributing images and a computer receiving images. Then, we confirmed the performance of three web conferencing systems, Microsoft Teams, Zoom and Google Meet, to evaluate the usefulness of their remote use based on the clarity of images, smoothness of movement and time lag in images on each computer in the system. Display image of operation computer for TEM can be captured using the following two methods: a virtual camera of a video distribution software that provided a good reaction speed to transfer images and the screen sharing by conference system software that could share high-quality images.
Optimization of Big Data Mining Algorithm Based on Spark Framework

Abstract: Frequent itemsets mining is the core of association rule mining data. However, with the continuous increase of data, the traditional Apriori algorithm cannot meet people's daily needs, and the algorithm efficiency is low. This paper proposes the Eclat algorithm based on the Spark framework. In view of the shortcomings of serial algorithm in processing big data, it is modified. Using the vertical structure to avoid repetitive traversal of large amounts of data, while computing based on memory can greatly reduce I/O load and reduce computing time. Combined with the pruning strategy, the calculation of irrelevant itemsets is reduced, and the parallel computing capability of the algorithm is improved. The experimental results show that the efficiency of the Eclat algorithm based on the Spark framework is far better than that of the Eclat algorithm, and it has high efficiency and good scalability when processing massive data.
Comparison of sun-tracking and fixed solar panel data with Python's PVLib classes

The advancements in technology have helped us utilize sustainable and renewable sources of energy in a more efficient manner. The popularity of solar panels has increased in the past years across the globe. This paper presents the power and energy analysis of sun-tracking panel and fixed solar panel. Python’s PVLib classes are used for obtaining the angle of elevation and irradiance for the same location as the hardware setup. We have graphically compared the hardware results with it to understand its functioning. From this report, we have drawn conclusions of power efficiency, energy and overall solar-panel performance.