Reviewerly/exHarmony huggingface dataset:
Evaluating the Reliability of Digital Forensics Tools for Cyber-Physical Systems

The wide use of Cyber-Physical System (CPS) currently carry some risks and means for cybercriminals to use in their attacks against organizations, governments or individuals. Cybercriminals in their pursuit to bring down these systems may cause disruption of services either for revenge or fame, political motive, cyber terrorism, economic war and cyberwar. Digital forensics is one of the sciences that has been used to investigate and apprehend cyber criminals. To investigate and apprehend cybercriminals, digital forensic investigators (DFIs) make use of digital forensic tools. These tools play a major role in cybercrime investigations and without them, DFIs cannot conduct investigations. They use them to collect and recover digital evidence that can be used in the court of law. However, most tools used by DFIs have not been evaluated because tool evaluation is time-consuming and expensive. As a result, DFIs tend to rely on vendor results, which are not well documented or proven publicly. The danger of using unevaluated tools is that, even if they are unreliable, that would not be known. Using an unevaluated tool may result in faulty criminal sentences, improper civil judgments, lost opportunity, time wasting and loss of money. For example, if a DFI spends time working on a case wherein the evidence would later be dismissed in court because of doubt in the accuracy of the tool used to produce evidence, that DFI would have wasted time and money working on the case. In an attempt to assist DFIs with tool evaluation, we developed a model for evaluating the reliability of digital forensic tools for Cyber-Physical Systems using Bayesian Networks (BN). Our model uses historical data from the Computer Forensic Tool Testing (CFTT) project in conjunction with the feedback provided by DFIs to recommend a suitable tool to use for investigations based on the task they want to perform, the category of the tool and its cost. For this reason, BN was used due to its ability to model complex situations and to show good prediction accuracy even with rather small sample sizes. The proposed model attained a utility performance of 91.7%. Given the outstanding results, our model can help DFIs with tool evaluation and tool makers to improve their tools.
Solution Path Clustering with Robust Loss and Concave Penalty

Author(s): Schuberg, Edward | Advisor(s): Yao, Weixin; Ma, Shujie | Abstract: The main purpose of this dissertation is to demonstrate that using a robust loss function (instead of the usual least squares loss) improves the clustering quality in the solution path clustering scheme. Cluster analysis simultaneously attempts to determine the number of clusters and estimate cluster location and membership. Convex clustering, distinguishing itself from other popular clustering methods, casts the clustering objective as a convex optimization problem and thus admits a global solution. It is a useful exploratory technique which outputs a solution path, evoking the name, ``solution path clustering. The solution path is a tree-like structure with cluster results ranging from n clusters down to a single cluster. Now, the benefits of convex clustering come at a cost since the use of a convex penalty can seriously bias the results and ruin the search for good cluster results. To lessen the bias, Ma and Huang (2017) proposed concave penalties to form the cluster centers. While the clustering objective is no longer convex, the quality of the solutions is improved. We extend the solution path clustering scheme by implementing robust loss functions instead of the usual least squares loss. Following Ma and Huang (2017), we also use a concave penalty to form clusters. The robust loss and concave penalty work together to mitigate the influence of outliers and minimize bias in the estimation of cluster locations, especially when the true distance between clusters is large. We introduce the IRLS-ADMM algorithm to minimize our proposed objective function and prove its convergence to a local minimum. Any loss function that admits an IRLS formulation or a majorizing surrogate can be used. We also study asymptotic and oracle properties of the estimator. Finally, we demonstrate the performance of our proposed method through simulation experiments and on real data sets, as well as provide some preliminary results on choosing the number of clusters via the modified BIC (Wang, Li, and Leng, 2009).­­
Information Transmission and Nonspecificity in Feature Selection

In this paper we propose a novel feature selection method which is based on fuzzy measures. More specifically, we apply a similarity measure to form similarity matrices from the data and apply nonspecificity on similarity degrees in order to conduct feature selection. To measure how relevant a particular feature is, we apply an information transmission measure. We exemplify our method on a simple artificial case to demonstrate its ability to select informative features. Moreover, we test our method on two real world data sets, the chronic kidney disease and the diabetic retinopathy Debrecen dataset. The nonspecificity-based feature selection method leads for both datasets to improvements in the mean classification performance. In comparison with the popular ReliefF algorithm and the Fisher Score, the new method reaches competitive results and also accomplishes the highest mean accuracy for both datasets.
Integrating EBSCO's OpenAthens with Alma and Primo

In December 2018, the Iowa State University Library signed an agreement with EBSCO to integrate the OpenAthens solution as our secured gateway to the electronic resources we subscribe to. We will be implementing the OpenAthens solution over the course of the spring semester, with plans to go-live with OpenAthens in the summer of 2019. This presentation will discuss the reasons the ISU library decided to implement OpenAthen, and talk about our early experiences in implementing and testing the product alongside Alma and Primo.
Exponential Word Embeddings: Models and Approximate Learning

The digital era floods us with an excessive amount of text data. To make sense of such data automatically, there is an increasing demand for accurate numerical word representations. The complexity of natural languages motivates to represent words with high dimensional vectors. However, learning in a high dimensional space is challenging when the amount of training data is noisy and scarce. Additionally, lingual dependencies complicate learning, mostly because computational resources are limited and typically insufficient to account for all possible dependencies. This thesis addresses both challenges by following a probabilistic machine learning approach to find vectors, word embeddings, performing well under aforementioned limitations. An important finding of this thesis is that by bounding the length of the vector that represents a word as well as penalizing the discrepancy between vectors representing different words make a word embedding robust, which is especially beneficial when noisy and little training data is available. This finding is important because it shows how current word embedding methods are sensitive to small variations in the training data. Although, one might conclude from this finding that more data is not necessary anymore, this thesis does show that training on multiple sources, such as dictionaries and thesaurus, improves performance. But, each data source should be treated carefully, and it is important to weigh informative parts of each data source appropriately. To deal with lingual dependencies, this thesis introduces statistical negative sampling with which the learning objective of a word embedding can be approximated. There are many degrees of freedom in the approximated learning objective, and this thesis argues that current embedding strategies are based on weak heuristics to constrain these freedoms. Novel and more theoretical founded constraints are being proposed to constrain the approximations that are based on global statistics and maximum entropy. Finally, many words in a natural language have multiple meanings, and current word embeddings do not address this because they are built on a common assumption that one vector per word representation can capture all word meanings. This thesis shows that a representation based on multiple vectors per word easily overcomes this limitation by having different vectors representing the different meanings of a word. Taken together, this thesis proposes new insights and a more theoretical foundation for word embeddings which are important to create more powerful models able to deal with the complexity of natural languages.
Comprehensive performance measurement system and work performance: the moderating role of organisational culture

Prior studies have reported relationships between both performance measurement system (PMS) and organisational culture (OC) with work performance (WP), but with separate investigations at individual levels. Drawing upon Ouchi's Z and goal-setting theories, this study examined the interlinks between these three variables in a single unified model. The findings, following a Partial Least Square (PLS) analysis on a survey of 166 lecturers working in Iraqi public universities, demonstrate that PMS and OC individually have a significant and positive linkage with WP. However, the moderating effect of OC on PMS-WP relationship is significantly negative. This research offers extra empirical evidence to the literature on PMS and OC by extending existing conceptualisation in this area.
Developing vision substitution products using computer vision

The purpose of this thesis is two-fold. The first is to investigate if cutting edge technology can be used to enhance the life for people with visual impairments. The second is to provide a simple example of how the design process can look from the beginning to a finished Hi-Fi prototype. This was achieved by gathering requirements for a product and creating a prototype, This was carried out in three phases. First relevant literature was researched, and the discoveries were distilled into a set of design methods which was used. The second phase is the requirements gathering where the methods chosen in the first phase is used to elicit requirements for the prototype. The third phase is the implementation of said prototype with evaluation. The overarching method used in the requirements gathering is the umbrella approach. In the beginning a very broad perspective is kept which iteratively shrunk until the requirements for the prototype is set. This follows the user centered approach to product design. The functionality which was decided to be prototype was a hand gesture in front of a camera which could select certain areas of the video feed. This part of the image can then be analyzed in different ways, for example, with OCR. The functionality was eventually implemented by tracking different colored stickers placed on the tip of the index finger and thumb. The prototype was unstable which made the results from the user evaluations inconclusive. The results of the thesis were semi-successful. Many different types of functionality were investigated but only superficially. The hand gesture functionality may be feasible from an implementation perspective, some implementation improvements possibilities is provided under discussion. It may also be feasible from a usability perspective; more evaluation needs to be done with a more stable prototype. (Less)
Motorized Circular Rail with RGB-D Sensor on Cart for Physical Rehabilitation

This paper introduces a motorized circular rail managing the movement of two carts equipped with an RGB-D sensor each. The proposal is aimed at continuously tracking a person who is undergoing a series of physical rehabilitation exercises from two different viewpoints to monitor if the exercises are being correctly conducted. More concretely, this work offers all details of the trajectory calculation for safe movement of both carts on the motorized circular rail. Then, two study cases are presented to show the efficiency of the control algorithms implemented.
Automated migration of large-scale build systems

Upgrading or migrating a build system can be a daunting task. Complete build system migration requires significant effort. To make the process more effective, we automated the first steps of migrat ...
Blockchain i försörjningskedjan för läkemedel : Ökad transparens och spårbarhet i kampen mot förfalskade och undermåliga läkemedel

Falsified and substandard drugs are classified as a global problem and continue to increase as manufacturing becomes globalized and the complexity of distribution systems expands. The falsified and ...
ROBOTICS PROJECTS IN SCHOOL INFORMATICS COURSE

The article describes the prerequisites for the use of educational robotics in the school course of informatics, the history of the development of its directions and the normative basis for its use in modern school education. A typical model of an educational robotic project for the organization of research and project activities of students has been proposed. The technological chart of the lesson as an example of the implementation of a robotic project in the framework of the research activities on informatics is considered. Approaches to the organization of educational activities, teaching tools and ways of evaluation in informatics class on the theme “Information processes. Information transmission” when using the project approach are described.
Twitter bot detection & categorization - a comparative study of machine learning methods

Automated Twitter accounts, or Twitter bots, have gained increased attention lately. In particular, a novel type of bot, so called social bots, are piquing peoples’ interest, as these bots have recently been involved in a number of political events. Most of the previous work on how to detect bots has not distinguished between such novel bot types, and other, less sophisticated ones. Instead, they have all been lumped together, producing models to detect automated behaviour in general. Although indeed useful, the above approach might cause issues when the task at hand concerns one particular type of bot account. This thesis therefore attempts at making the classification of bots more fine grained, viewing social bots, traditional spambots, and fake followers as three separate bot categories, to be classified together with the category for actual human users (called ”genuine users”). Four machine learning methods are trained and com- pared for this purpose, showing that the random forest performs slightly better than the rest in all performance measures used. However, all mod- els yield an overall accuracy above 90%, which is relatively high compared to similar studies in the field. The analysis also indicates that data sam- pling has been biased, skewing the data to yield some unexpected results. For instance, genuine users show much more activity than would be expected of the average human-controlled Twitter account. Additionally, traditional bots, which are supposed to be the easiest to classify, instead appear to be the opposite. If the data sampling has indeed been biased, the validity of the models trained on this skewed data is called into question. Hence, more research into sampling techniques is suggested, and it is concluded that the models produced should be tested on more diverse datasets. Without these kinds of repeated studies, the impact of the sup- posed sampling bias, and consequently the usefulness of the models in real world situations, cannot be properly assessed. (Less)
NHCPL Guides: Computer and Internet Basics: Internet Safety & Identity Theft

Looking to improve your basic computer skills, learn coding, graphic design, or more? Start here! Be a safe searcher! Become internent saavy and learn tips to use while surfing the net.
Vulnerability Management Policy

This IT policy, and all policies referenced herein, shall apply to all members of the University community including faculty, students, administrative officials, staff, alumni, authorized guests, delegates, and independent contractors (the “User(s)” or “you”) who use, access, or otherwise employ, locally or remotely, the University’s IT Resources, whether individually controlled, shared, stand-alone, or networked.
Investigation of the Hydraulic Unit Operation Features Based on Vibration System Data Mining

The software and hardware level used for on-line monitoring of the hydropower equipment functioning parameters as well as the large amounts of stored data create the necessary conditions for application of innovative methods and technologies of data analysis in the tasks of analytical assessment of equipment condition. This paper presents the results of detecting the operational patterns of the hydraulic unit in various modes and functioning conditions based on the data mining techniques – principal component analysis and cluster analysis – applied to the monitoring data of the vibration control system. In multidimensional data space, two principal components have been selected and interpreted taking into account the contribution of the data attributes to the principal components. On the plane of the first two principal components, a five-cluster structure has been constructed to define the moments of time when the system demonstrates a characteristic behaviour. In addition, the monitored parameters have been analysed in terms of time series at characteristic moments of time. As a result, the comprehensive multidimensional analysis of monitoring data has allowed us to discover the hydraulic unit operation patterns and dependencies, determine the character of the influence induced by its constructive elements and work out the ratio between the ranges of key parameters in various modes of equipment operation.
PROGRAM OF FORMING CULTURE OF COMMUNICATION IN THE NETWORK OF THE INTERNET IN FUTURE FACULTY OF THE SOCIAL SPHERE

The article examines the conceptual theoretical approaches to the study of the problem of forming a culture of communication in the Internet on future specialists in the social sphere. The article reveals the importance of studying the problem of the culture of communication in the Internet of students of higher educational institutions. In the article a generalized analysis of the essence of the concepts "culture", "culture of communication", "Internet network", "culture of communication on the Internet" is carried out. The article determines that the culture of Internet communication can be defined as a component of the general professional culture of a specialist reflecting the level of knowledge gained within the global computer Internet environment, technologies and ethical norms of exchange of information, skills and abilities of interaction in computer networks and use them in practical activity according to their own value orientations and orientations. The article clarifies that the formation of the culture of communication of students in the Internet plays an important role in shaping the professional and communicative competence of future professionals in the social sphere. The author offers the main aspects of a practical program for creating a culture of communication on the Internet from future specialists in the social sphere. The author presents for familiarization the components of the culture of communication in the network, stages of the program, blocks, indicators, basic forms and methods of work
Energy Efficiency Evaluation of Distributed Systems

Rapid growth in Big Data and Cloud technologies has fueled rising energy demands in large server systems such as data centers, leading to a need for effective power management. In this paper, we investigate the energy consumption characteristics of data-intensive distributed applications in terms of the CPU and memory subsystem. To this end, we develop PowerSave as a lightweight software framework that enables dynamic reconfiguration of power limits. PowerSave uses Running Average Power Limit (RAPL) to impose power limits. Our evaluation study, conducted on three different real systems, demonstrates that for workloads typical of servers used in data centers, higher power caps correlate with higher overall CPU energy use.
Correction to: Dragon: Decision Tree Learning for Link Discovery

In the acknowledgements of the originally published version, the project number of OPAL was wrong. This has been corrected so that the new version now reads: “This work has been supported by the BMVI projects LIMBO (project no. 19F2029C) and OPAL (project no. 19F2028A).”
Traceability in the Food Industry

Throughout history to this day, the food industry has been facing numerous food contamination incidents. Globalization and an increasing complexity of the food supply chain creates the need for high trust in the information exchanged, therefore there is a lot of focus on food traceability. While there have been improvements to food traceability by the advancements in information systems, transparency and trustworthiness of data still remain key problems. Blockchain technology has been one of the latest technological developments that has the potential to greatly improve trust between the members in a food supply chain. However, blockchain technology is still in an early adoption phase. This thesis aims to evaluate blockchain technology in the context of food traceability. The authors present a framework that compares blockchain technology characteristics against the driving forces of food traceability and evaluates challenges faced in the adoption of blockchain technology. The findings of the study indicate that blockchain is in a good position to positively impact trust in the food industry by increasing transparency and integrity of data. However, blockchain technology may be very difficult to implement in long chains due high need of collaboration between organizations and poor scalability.
Hybrid Constrained Evolutionary Algorithm for Numerical Optimization Problems

Constrained optimization are naturally arises in many real-life applications, and is therefore gaining a constantly growing attention of the researchers.Evolutionary algorithms are not directly applied on constrained optimization problems. However, different constraint-handling techniques are incorporated in their framework to adopt it for dealing with constrained environments. This paper suggests an hybrid constrained evolutionary algorithm (HCEA) that employs two penalty functions simultaneously. The suggested HCEA has two versions namely HCEA-static and HCEA-adaptive. The performance of the HCEA-static and HCEA-adaptive algorithms are examined upon the constrained benchmark functions that are recently designed for the special session of the $2006$ IEEE Conference of Evolutionary Computation (IEEE-CEC'06). The experimental results of the suggested algorithms are much promising as compared to one of the recent constrained version of the JADE. The converging behaviour of the both suggested algorithms on each benchmark function is encouraging and promising in most cases.