Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluating Machine Learning Algorithms for Prediction of the Adverse Valence Index Based on the Photographic Affect Meter

In recent years, numerous studies have explored the use of machine learning algorithms for supporting applications in social and clinical psychology. In particular, there is an increasing prevalence of smartphone-based techniques for collecting data through embedded sensors and efficient in-situ questionnaires. Models are then built to explore the patterns between these data types. In this paper, we study the application of machine learning for the task of predicting mental states of adverse valence, based on the Photographic Affect Meter data. We present a technique for daily aggregation, which is designed to detect significant negative events. A variety of features is used as input, including GPS-based metrics and features assessing social interactions, sleep and phone usage. Experimental evidence is presented, which suggests that machine learning algorithms could successfully be employed for such a prediction task.
AI innovation for advancing public service: The case of China's first Administrative Approval Bureau

The adoption of artificial intelligence (AI) is becoming increasingly popular in the public sector, but there is a severe lack of relevant theoretical research. The government of China also has high expectations for AI innovation. This paper proposes a four-stage model for AI development in public sectors to help public administrators think about the impact of AI on their organizations. We empirically investigate a case of AI adoption for delivering public services in local government in China. The findings improve our understanding of not only the status of AI innovation but also the factors motivating and challenging public sectors that are intending to adopt AI. Given that AI application in public sectors is still in its infancy, this study provides us with an opportunity to conduct longitudinal tracking of AI innovation in local government in China. This could help public administrators to think more comprehensively about the changes and transformations that AI may bring to the public sector.
Identifying Security Challenges in Renewable Energy Systems: A Wind Turbine Case Study

Distributed renewable energy systems (DRESs) and their interconnection network, typically using Internet-based protocols, are susceptible to a wide range of cybersecurity and resilience challenges. These challenges have been shown to cause problems for the overall grid optimization process. In order to detect such events, we argue that an adequate correlation between network and energy generation data is required. Therefore, in this study, we provide a work-in-progress insight related to the profiling of real network data and energy generation measurements gathered by a local windturbine at Lancaster University. We argue that such an analysis is very important to profile various attack vectors in the modern energy networks that consider DRESs and take necessary actions to prevent any data breaches in the future.
Taxes and location decisions of firms

In traditional tax literature it is argued that (further) integration among countries will inevitably lead to a race to the bottom of corporate tax rates. In order to attract firms, countries are assumed to lower their tax rates below the ones of their ’competitors’. According to the New Economic Geography literature this however does not necessarily need to be the case. It is argued that more agglomerated regions benefit from what is called agglomeration rents and that these rents can be taxed. As integration moves on, one might expect the tax difference between more and less agglomerated regions to increase instead of fading out. The purpose of this paper is to test this theory for Belgian firms.
Artification and the Drawing of Distinctions: an Analysis of Categories and Their Uses

The aim of the article is to examine how we distinguish between art, decoration, and furnishing within a research interview. The interview specimens here are examined by adapting the ethnomethodologically oriented method of Membership Categorization Analysis. The results indicate that the speakers rely, for example, on the context of the interview situation and also use flexible logical means, such as conditioning and comparison, to make the discussed issues more comprehensive. The results of the analysis are interpreted in the context of artification, emphasizing in particular the notion of the situated process of categorical resiliency.
Cryptanalysis of Plantlet

Plantlet is a lightweight stream cipher designed by Mikhalev, Armknecht and Müller in IACR ToSC 2017. It has a Grain-like structure with two state registers of size 40 and 61 bits. In spite of this, the cipher does not seem to lose in security against generic Time-Memory-Data Tradeoff attacks due to the novelty of its design. The cipher uses a 80-bit secret key and a 90-bit IV. In this paper, we first present a key recovery attack on Plantlet that requires around 276.26 Plantlet encryptions. The attack leverages the fact that two internal states of Plantlet that differ in the 43rd LFSR location are guaranteed to produce keystream that are either equal or unequal in 45 locations with probability 1. Thus an attacker can with some probability guess that when 2 segments of keystream blocks possess the 45 bit difference just mentioned, they have been produced by two internal states that differ only in the 43rd LFSR location. Thereafter by solving a system of polynomial equations representing the keystream bits, the attacker can find the secret key if his guess was indeed correct, or reach some kind of contradiction if his guess was incorrect. In the latter event, he would repeat the procedure for other keystream blocks with the given difference. We show that the process when repeated a finite number of times, does indeed yield the value of the secret key. In the second part of the paper, we observe that the previous attack was limited to internal state differences that occurred at time instances that were congruent to 0 mod 80. We further observe that by generalizing the attack to include internal state differences that are congruent to all equivalence classed modulo 80, we lower the total number of keystream bits required to perform the attack and in the process reduce the attack complexity to 269.98 Plantlet encryptions.
Predicting Road Traffic Accident Severity using Accident Report Data in South Africa

Road traffic accidents (RTAs) have a significant impact on individuals, their families and the nation. Without knowledgeable action, road traffic injuries are said to be the seventh leading cause of death by the year 2030. Generally, classification models have been created to predict injury severity in RTAs while taking into account vehicle, light & weather conditions, and road characteristics. This study looked at how to best create a classification model that predicts injury severity of drivers by adding distance features of nearest places of interest namely, malls, bars, schools, buildings, and points of interest. The reason for this was to determine if distances to closest places of interest has an impact on injury severity of drivers in RTAs. The classification models were created with the Multivariate logistic regression (MLR) classification method and the Extreme gradient boosting tree (XGBoost) classification method, where XGBoost out-performed MLR. The findings also showed that data that is collected on South African Accident Report (AR) forms and spatial data have the ability to significantly improve the overall performance of a injury severity model. Also, truck licenses code C1, light motor duty license code EB, vehicle type (motor car or station wagon), single vehicle: overturned accident type, vehicle manoeuvre, and the distance to the closest building were among the top predictors that contributed to injury severity of drivers in RTAs.
Towards Privacy-preserving Mobile Applications with Federated Learning: The Case of Matrix Factorization (poster)

Recommender systems have gained prominence in bringing users tailor-made content from the web aiding their decision making process. However, this personalization comes at a cost of privacy of sharing personal information with the recommendation provider. Moreover, with growing sizes of datasets and models, centralized processing of such data has become challenging. To this end, we propose a federated matrix factorization algorithm to enable personal data to be stored and used on-device for training while sending updates to train a centralized model. We illustrate preliminary results from our algorithm applied to recommendation of articles to users of a mobile application based on their reading history. We compare our performance with centralized matrix factorization applied on the same dataset.
DaRTree: Deadline-Aware Multicast Transfers in Reconfigurable Wide-Area Networks

The increasing amount of data replication across datacenters introduces a need for efficient bulk data transfer protocols which meet QoS guarantees, notably timely completion. We present DaRTree which leverages emerging optical reconfiguration technologies, to jointly optimize topology and multicast transfers, and thereby maximize throughput and acceptance ratio of transfer requests subject to deadlines. DaRTree is based on a novel integer linear program relaxation and deterministic rounding scheme. To this end, DaRTree uses multicast Steiner trees and adaptive routing based on the current network load. DaRTree provides its guarantees without need for rescheduling or preemption. Our evaluations show that DaRTree increases the network throughput and the number of accepted requests by up to 70%, especially for larger Wide-Area Networks (WANs). In fact, we also find that DaRTree even outperforms state-of-the-art solutions when the network scheduler is only capable of routing unicast transfers or when the WAN topology is bound to be non-reconfigurable.
Breaking ACORN with a Single Fault

Assuring security of the Internet of Things (IoT) is much more challenging than assuring security of centralized environments, like the cloud. A reason for this is that IoT devices are often deployed in domains that are remotely managed and monitored. Thus, their physical security cannot be guaranteed as reliably as physical security of data centers. Some believe that physical security becomes less important if all data processed and stored within a device is encrypted. However, an attacker with a physical access to a device implementing an encryption algorithm may be able to extract the encryption key and decrypt data. As a demonstration, in this paper we attack ACORN stream cipher, a finalist of CESAR competition for authenticated encryption. By injecting a single stuck-at-0 fault into ACORN’s implementation, we reduce its non-linear feedback function to a linear one. Since this obviously makes ACORN weaker, many known attacks can be applied to break it. We apply an algebraic attack which recovers the key from 2 keystream bits using 2 operations.
Trim Loss Optimization by an Improved Differential Evolution

The “trim loss problem” (TLP) is one of the most challenging problems in context of optimization research. It aims at determining the optimal cutting pattern of a number of items of various lengths from a stock of standard size material to meet the customers’ demands that the wastage due to trim loss is minimized. The resulting mathematical model is highly nonconvex in nature accompanied with several constraints with added restrictions of binary variables. This prevents the application of conventional optimization methods. In this paper we use synergetic differential evolution (SDE) for the solution of this type of problems. Four hypothetical but relevant cases of trim loss problem arising in paper industry are taken for the experiment. The experimental results compared with those of the other techniques show the competence of the SDE to solve the problem.
Modelling the Requirements Process: where are the people?

This paper reports the outcomes of a three year study into the requirements process, an important but poorly understood part of systems development. Humans play an essential role in determining requirements yet few models of the requirements process include human actors. This paper presents an understanding of the requirements process expressed in a descriptive model that integrates the social and technical aspects of the requirements process. The model comprises five key areas: the problem domain, analysts’ domain, requirements sub-processes, problem space and management of the requirements process. The model may be used to support, manage and improve the requirements process in practice.
ACN-Sim: An Open-Source Simulator for Data-Driven Electric Vehicle Charging Research

Smart electric vehicle charging has recently garnered significant attention in the research community due to need to charge vast numbers of electric vehicles (EVs) economically, as well as the potential of providing grid services using EVs. However, research into practical online charging algorithms has been hampered by the lack of a widely available, realistic simulation environment in which to evaluate algorithms and test assumptions. To meet this need, we have developed ACN-Sim, a data-driven, open-source simulator based on our experience building and operating real-world charging systems. ACN-Sim provides a modular, extensible architecture which models the complexity of real charging systems, including battery charging behavior and unbalanced three-phase infrastructure. In addition, ACN-Sim integrates with a broader ecosystem of research tools for EV charging, including ACN-Data, an open dataset of EV charging sessions to provide realistic simulation scenarios, and ACN-Live, a framework for field-testing charging algorithms.
This is Your President Speaking: Spoofing Alerts in 4G LTE Networks

Modern cell phones are required to receive and display alerts via the Wireless Emergency Alert (WEA) program, under the mandate of the Warning, Alert, and Response Act of 2006. These alerts include AMBER alerts, severe weather alerts, and (unblockable) Presidential Alerts, intended to inform the public of imminent threats. Recently, a test Presidential Alert was sent to all capable phones in the United States, prompting concerns about how the underlying WEA protocol could be misused or attacked. In this paper, we investigate the details of this system, and develop and demonstrate the first practical spoofing attack on Presidential Alerts, using both commercially available hardware as well as modified open source software. Our attack can be performed using a commercially-available software defined radio, and our modifications to the open source NextEPC and srsLTE software libraries. We find that with only four malicious portable base stations of a single Watt of transmit power each, almost all of a 50,000-seat stadium can be attacked with a 90% success rate. The true impact of such an attack would of course depend on the density of cell phones in range; fake alerts in crowded cities or stadiums could potentially result in cascades of panic. Fixing this problem will require a large collaborative effort between carriers, government stakeholders, and cell phone manufacturers. To seed this effort, we also discuss several defenses to address this threat in both the short and long term.
MixGeo: Efficient Secure Range Queries on Encrypted Dense Spatial Data in the Cloud

As the location-based applications are flourishing, we will witness soon a prodigious amount of spatial data will be stored in the public cloud with the geometric range query as one of the most fundamental search functions. The rising demand of outsourced data is moving larger-scale datasets and wider-scope query size. To protect the confidentiality of the geographic information of individuals, the outsourced data stored at the cloud server should be preserved especially when they are queried. While the problem of secure range query on outsourced encrypted data has been extensively studied, the current schemes are far from the practice in terms of efficiency and scalability. In this paper, we propose a novel solution based on Geohash and predicate symmetric searchable encryption for secure range queries named as MixGeo. We present a multi-level indexes structure tailored for efficient and large-scale spatial data lookup in the cloud server while preserving data privacy. Our experiment on a real-world spatial dataset in the cloud environment shows that it only takes less than 0.1s for once update operation over 65, 128 encrypted dense data and is over 100 times faster than the existing solutions.
Competition Kinetics of the Free-Radical Addition, Inhibition of Nonbranched-Chain Processes by Low-Reactive Radicals

Five reaction schemes are suggested for the initiated nonbranched-chain addition of free radicals to the multiple bonds of the unsaturated compounds. The proposed schemes include reactions competing with chain propagation reactions through a reactive free radical. The chain evolution stage in these schemes involves three or four types of free radicals. One of them is relatively low-reactive and inhibits the chain process by shortening of the kinetic chain length. Based on the suggested schemes, nine rate equations (containing one to three parameters to be determined directly) are deduced using quasi-steady-state treatment. These equations provide good fits for the nonmonotonic (peaking) dependences of the formation rates of the molecular products (1:1 adducts) on the concentration of the unsaturated component in binary systems consisting of a saturated component (hydrocarbon, alcohol, etc.) and an unsaturated component (olefin, allyl alcohol, formaldehyde, or dioxygen). The unsaturated compound in these systems is both a reactant and an autoinhibitor generating low-reactive free radicals. A similar kinetic description is applicable to the nonbranched-chain process of the free-radical hydrogen oxidation, in which the oxygen with the increase of its concentration begins to act as an oxidation autoinhibitor (or an antioxidant). The energetics of the key radical-molecule reactions is considered.
Association and Linkage of Definite Multiple Sclerosis with HLA and the Potential Protective Role of Helicobacter Pylori Infection Among Egyptians

Background: Multiple sclerosis (MS) is the most common chronic inflammatory demyelinating disease of the central nervous system. The linkage of multiple sclerosis to the Human Leukocyte Antigen (HLA) has been known a few decades ago. Some investigators have shown a relationship between H. pylori infection and Multiple sclerosis development. However, this relationship is still controversial. Objectives: This study was designed to detect the linkage between HLA-DRB1 types and Multiple Sclerosis development, clinical presentation and course, and to try solving the controversy of the role of H. pylori infection in Multiple Sclerosis. Methods: This study was carried out on 130 Egyptian individuals at the Neurology department of Mansoura University Hospital in 20122015. Group I included 80 definite relapsing remittent MS patients and Group II included 50 healthy control people. Both patients and control groups were subjected to HLA-DRB1 typing using the INNOLiPA HLA-DRB1 Plus kit. Correlation of HLA-DRB1 type to clinical presentation and course of the disease was tested. Both study groups were subjected to H. pylori CagA and VacA IgG detection in their sera by qualitative immunoassay. All statistical analyses were performed using SPSS version 16.0. Results: Data pooled from this study showed that number of MS cases with HLA-DRB1 * 1, 4 &7 is statistically significantly higher than it was in the control group (P=0.01, P=0.049 and P=0.02 respectively). The number of control individuals with HLA-DRB1 * 15g &11 types was statistically significantly higher than it was in MS patients (P=0.003 and P 0.05). Conclusion: Carriers of HLA-DRB1 * 1, 4 &7 are at higher risk to develop MS, HLA-DRB1 * 15g &11 exhibit a protective role against the development of Multiple Sclerosis. H. pylori infection may have a controversial causative relation to Multiple Sclerosis development.
Effect of seed priming on growth and physiological traits of five Jordanian wheat (Triticum aestivum L.) landraces under salt stress

This study was conducted to evaluate the effects of salt stress and seed priming on germination performance, growth and physiology of five wheat (Triticum aestivum L.) landraces from Jordan namely; Ramtha, Ajloun, Madaba, Tafila and Karak. Results indicated that salinity decreased seed germination, relative water content, membranes stability index, shoot dry weight, leaf chlorophyll content and nitrate reductase activity. These effects were more pronounced in Ramtha, Ajloun and Madaba landraces as compared to Karak and Tafila. Significant increase in the accumulation of proline, total soluble sugars, soluble proteins and H2O2 was also observed in response to salinity, although variations existed among ladraces. Based on tolerance indices collected from parameters tested, the five landraces were clustered in two groups; Tafila and Karak landraces formed the first cluster while the second cluster consisted of Ajloun, Madaba and Ramtha landraces. The landraces of cluster 1 were classified, based on similarity and distance indices, as salt tolerant while those of cluster 2 were considered as salt sensitive Priming seeds with 50mM CaCl2 stimulated salt tolerance in all stressed wheat landraces via improving growth, stabilizing cell membranes, enhancing chlorophyll content, promoting the nitrate reductase activity and ROS scavenging activities in addition to up-regulating the accumulation of proline and soluble sugars. Results suggested that seed priming with CaCl2can be utilized for enhancing the salt tolerance potential of wheat and could contribute to promoting its cultivation in salt affected soils.
Estimation of economic values in three breeding perspectives for longevity and milk production traits in Holstein dairy cattle in Iran

Abstract The objectives of this study were to estimate economic values (EVs) for three production traits (milk, fat and protein yields) and longevity and to develop a national selection index. The proposed Iranian selection index was compared with selection indices of three other countries in the world. A simple and appropriate model was used to describe the Holstein dairy cattle industry under an Iranian production system. Production parameters and economic data were collected from two Holstein dairy farms in Tehran province. The EVs were estimated at farm level for three breeding perspectives (maximized profit, minimized costs, and economic efficiency) and two restrictions in production system (fixed herd size and fixed total input). The average absolute EVs on profit perspective and herd size restriction for milk, fat, and protein yields (based on $/kg) and longevity ($/month) were 0.11, 0.89, -0.20, and 6.20, respectively. The average absolute EVs under minimized costs per unit of product interest for milk, fat, protein yields and longevity were -0.30, -3.43, 0.88 and -20.40, respectively. The average absolute EVs under maximized economic efficiency for milk, fat and protein yields and longevity were 0.34, 2.73, -0.99 and 36.33, respectively. Relative emphasis for three production traits and longevity were 59.7, 14.3, -3.0 and 23.1, respectively. The comparison of the proposed Iranian index with those countries where most of the semen and embryos are imported points out that developing a national selection index to improve cow profitability and optimum generic trends is necessary. Sensitivity analysis indicated that the influence of milk payment changes on EVs was the greatest as its influence on fat and protein EVs is substantial. EVs for milk and fat yields, with respect to price changes (milk, feed and non-feed), were the least sensitive and most sensitive, respectively.