Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of Climate Change on Crops’ Productivity across Selected Agro-ecological Zones in Pakistan

This study estimates the impact of major climate variables (temperature and rainfall) on crops’ productivity across four agro-ecological zones of Pakistan. The crops selected were rice, wheat, maize, cotton and sugarcane. The study used panel data from 1991 to 2010 and applied panel least square techniques. The results revealed that the effect of climatic variables on crops yield varied across agro climatic zone due to differences in their climate conditions. Temperature and rainfall were the important determinants affecting crops productivity across agro climatic zones of Pakistan. Wheat productivity has been impacted more in Northern Irrigated Plain-a by average temperature and in Northern Dry Mountains by rainfall than the other zones. Rice productivity has been impacted more in Dry Mountains by average temperature and in the Indus Delta by rainfall than other zones. Sugarcane productivity has been impacted more by average temperature and rainfall in Indus Delta than zone IV. Maize productivity has been impacted more by average temperature and rainfall in Northern Dry Mountains than other zones. Finally the study recommends proper mitigative and adaptative strategies to enhance the positive and lessen the adverse impact of climate change on crops productivity across agro climatic zones of Pakistan.
A Binary Decision Diagram to discover low threshold support frequent itemsets

Discovering association rules that identify relationships among sets of items is an important problem in data mining. Finding frequent itemsets is computationally the most expensive step in association rule discovery and therefore it has grasped significant research focus [1]. Discovery of frequently occurring subsets of items, called itemsets, is the core of many data mining methods. Most of the previous studies adopt Apriori- like algorithms, whom iteratively generate candidate itemsets and check their occurrence frequencies in the database. These approaches suffer from serious costs of repeated passes over the analyzed database. In this paper, we propose a new BDD-based (Binary Decision Diagram) data structure called TreeSupBDD. The TREESUPBDD extends the idea claimed by the authors of FP-TREE [9] and ITL-Tree [5] structures, aiming to improve storage compression and to allow frequent pattern mining without an "explicit" candidate itemset generation step. To address this problem, we propose a novel method, called TreeSupBDD- MlNE, for reducing database activity of frequent itemset discovery algorithms. The idea of TREESUPBDD-MlNE consists in using a Binary Decision Diagram and a tree for representing both database and frequent itemsets. The proposed method requires one scan over the source database : to create the associated tree and BDD and check discovered itemset supports. The originality of our work stands on the fact that the proposed algorithm extracts the frequent itemsets directly from the TreeSupBDD. Carried out experiments showed very encouraged results. Its performance improvements have been shown in a series of our experiments. We extend the binary decision diagram structure to store transaction groups and propose a new method to discover frequents itemsets. To study the trade-offs in the new representation of transactions in binary decision diagram, we compare the performance of our algorithm with the fastest Apriori [2] implementation algorithm and the latest extension of FP-Growth [15]. We have tested all the algorithms using different benchmark datasets. The performance study shows that the new algorithm significantly reduces the processing time for mining frequent itemsets from dense datasets that contain relatively long patterns and for low threshold. We discuss the performance results in detail and also the strengths and limitations of our algorithm.
Multifocus image fusion based on nonsubsampled contourlet transform

In recent years, image fusion has become the focus of image processing. For the fusion problems of the multifocus images with the same scence, this paper proposed a new fusion method based on nonsampled contourlet transform(NSCT). Firstly, source images are decomposed in different scales and directions by NSCT, thus the low frequency subband coefficients and the high frequency subband coefficients are obtained. Secondly, for the low frequency subband coefficients, we present a fusion rule based on the local entropy; while for the high frequency subband coefficients, a fusion rule based on the directional contrast combined with the local area energy is applied. Finally, the fused image is obtained through the inverse NSCT. Compared with the fusion method based wavelet and other fusion methods, the experiments show that this approach can achieve better results than them.
Comparison of Three Data Expansion Algorithms for Air Pollution Data in Irregularly Placed Measuring Stations

In some major cities there are stations for measuring atmospheric pollutants. These stations are often distributed in an irregular pattern. In order to predict pollutant's behavior, it is necessary to order data in a regular, uniform grid. For this, we employ expansion data algorithms. Our work centers on the software implementation and evaluation of three of these algorithms: Cressman, Voronoi and Kriging. For evaluation, we use real data of atmospheric pollutants, including the actual position of stations that measure air pollutants in Mexico City. We use actual values taken from different pollutants.
Development of high-strength alloyed rail steels suitable for heavy duty applications

In the first section of the paper, a summary will be given of the various defects which can develop in rails subjected to high axle loads, such as corrugations, gross plastic deformation and abrasive wear of the railhead,checking and spalling, shelling and transverse defects. The second section of the paper will cover in detail the developmental work on high-strength alloyed rail steels conducted at Broken Hill Proprietary Co. Ltd. Melbourne Research Laboratories. An essential part of the work has been the development of techniques for laboratory simulation of industrial rail production and on-site welding procedures. Various other material properties have also been used in assessing material types. The major emphasis of the work has been directed to optimizing the mechanical properties of three rail steel types, namely: chromium-columbium-vanadium steels, chromium-vanadium steels, and chromium-molybdenum steels. Fully pearlitic steels exhibiting yield strengths greater than 900 MPa have been developed without adversely influencing other mechanical properties. The steels can be flash butt welded using modified procedures.
Knowledge Media Institute myPlanet : An Ontology-Driven Web-Based Personalized News Service

In this paperwe presentmyPlanet , an ontologydriven personalisedWeb-basedservice. We extended the existing infrastructureof the PlanetOnto newspublishingsystem.Our concernswere mainly to provide lightweight meansfor ontology maintenanceandeasetheaccessto repositoriesof newsitems,arich resourcefor informationsharing. We reasonaboutthe informationbeingsharedby providing anontology-driveninterest-profilingtool which enableusersto specify their interests. We also developedontology-driven heuristicsto find news itemsrelatedto users’interests. This paper arguesfor therole of ontology-drivenpersonalised Web-basedservicesin informationsharing.
Resilience of riparian vegetation after restoration measures on River Inn

River restoration is widely applied, although its effects are poorly understood, and degraded habitats might be difficult to improve. Moreover, there is a lack of monitoring as well as few systematic comparisons of restoration methods. This study presents results of a 4‐year monitoring on River Inn (southern Germany) investigating restoration by gravel or sand addition or embankment removal. The results were compared with reference sites that represent the pre‐restoration conditions. At the landscape scale, we analysed vegetation types based on aerial photographs, whereas at a smaller scale, we undertook vegetation surveys and evaluated species composition, growth, and life form, as well as the proportion of the target vegetation. After 4 years, the data indicated a “negative resilience” of the vegetation back to the state prior to restoration. The structural analysis revealed an extensive spread of reed at expense of bare soil. Thus, the species composition largely regressed to the pre‐restoration conditions, and neither annuals nor other pioneer species showed a long‐term benefit of river restoration. There were differences among the three restoration treatments after 2 years, but no longer after 4 years. However, the river restoration had three positive outcomes: (a) There was a temporary benefit for pioneer vegetation that most likely replenished the seed bank of the respective species, (b) the valuable reed communities showed resilience, and (c) the measures allowed some practical learning as expected for adaptive restoration.
Shading Atlas Streaming Demonstration

Streaming high quality rendering for virtual reality applications requires minimizing perceived latency. We introduce Shading Atlas Streaming (SAS) [1], a novel object-space rendering framework suitable for streaming virtual reality content. SAS decouples server-side shading from client-side rendering, allowing the client to perform framerate upsampling and latency compensation autonomously for short periods of time. The shading information created by the server in object space is temporally coherent and can be efficiently compressed using standard MPEG encoding. Our results show that SAS compares favorably to previous methods for remote image-based rendering in terms of image quality and network bandwidth efficiency. SAS allows highly efficient parallel allocation in a virtualized-texture-like memory hierarchy, solving a common efficiency problem of object-space shading. With SAS, untethered virtual reality headsets can benefit from high quality rendering without paying in increased latency. Visitors will be able to try SAS by roaming the exhibit area wearing a Snapdragon 845 based headset.
A 2.4-GHz low-IF receiver for wideband WLAN in 6-μm CMOS-architecture and front-end

This paper presents the 2.4-GHz front-end and the first downconversion section of a fully integrated low-IF receiver. The dual-conversion receiver rejects the image repeatably by 60 dB using integrated polyphase filters without calibration or tuning. The gain of the RF mixer and IF amplifier is switchable to slide the available dynamic range of the following stages based on the conditions of the input signal. The front-end and downconversion sections drain 35 mA on average from a 3.3-V supply. Minimum cascade noise figure is 7.2 dB, and maximum cascade IIP3 is -3.4 dBm.
A differential approach to undefined behavior detection

This paper studies undefined behavior arising in systems programming languages such as C/C++. Undefined behavior bugs lead to unpredictable and subtle systems behavior, and their effects can be further amplified by compiler optimizations. Undefined behavior bugs are present in many systems, including the Linux kernel and the Postgres data-base. The consequences range from incorrect functionality to missing security checks. This paper proposes a formal and practical approach, which finds undefined behavior bugs by finding “unstable code” in terms of optimizations that leverage undefined behavior. Using this approach, we introduce a new static checker called Stack that precisely identifies undefined behavior bugs. Applying Stack to widely used systems has uncovered 161 new bugs that have been confirmed and fixed by developers.
PARAMETRIC MULTI-CHANNEL SEPARATION AND RE-PANNING OF HARMONIC SOURCES

In this paper, a method for separating stereophonic mixtures into their harmonic constituents is proposed. The method is based on a harmonic signal model. An observed mixture is decomposed by first estimating the panning parameters of the sources, and then estimating the fundamental frequencies and the amplitudes of the harmonic components. The number of sources and their panning parameters are estimated using an approach based on clustering of narrowband interaural level and time differences. The panning parameter distribution is modelled as a Gaussian mixture and the generalized variance is used for selecting the number of sources. The fundamental frequencies of the sources are estimated using an iterative approach. To enforce spectral smoothness when estimating the fundamental frequencies, a codebook of magnitude amplitudes is used to limit the amount of energy assigned to each harmonic. The source models are used to form Wiener filters which are used to reconstruct the sources. The proposed method can be used for source re-panning (demonstration given), remixing, and multi-channel upmixing, e.g. for hi-fi systems with multiple loudspeakers.
Building adaptive data mining models on streaming data in real-time:

Advances in hardware and software, over the past two decades have enabled the capturing, recording and processing of potentially large and infinite streaming data. The field of research in Data Stream Mining (DSM) has emerged to respond to the challenges and opportunities of developing the required analytics to unlock valuable knowledge. Thus DSM is focused on building Data Mining models, workflows and algorithms enabling the efficient and effective analysis of such streaming data at a large scale- the so-called “Big Data”. Examples of application areas of Data Stream Mining techniques include real-time telecommunication data, telemetric data from large industrial plants, credit card transactions, cyber security threat modelling, social media data, etc. For some applications it is acceptable to provide data processing, modelling and analysis in batch mode using the traditional Data Mining approaches. However, for other application, particularly where continuous monitoring and contingent response are required, the model building and analytics have to take place in real-time as soon as new data becomes available i.e. to accommodate infinite streams and fast changing concepts in the data. This article highlights some of the key concepts and emergent techniques in DSM as presented in the authors’ recent publications as also outlined in a talk given at the UK Symposium on Knowledge Discovery from Data in London on May 24th, 2019 discussing the challenges, opportunities and innovative solutions in Data Stream Mining.
From Principles to Practice: An Accountability Metrics Catalogue for Managing AI Risks

Artificial Intelligence (AI), particularly through the advent of large-scale generative AI (GenAI) models such as Large Language Models (LLMs), has become a transformative element in contemporary technology. While these models have unlocked new possibilities, they simultaneously present significant challenges, such as concerns over data privacy and the propensity to generate misleading or fabricated content. Current frameworks for Responsible AI (RAI) often fall short in providing the granular guidance necessary for tangible application, especially for Accountability —a principle that is pivotal for ensuring transparent and auditable decision-making, bolstering public trust, and meeting increasing regulatory expectations. This study bridges the Accountability gap by introducing a comprehensive metrics catalogue, formulated through a systematic multivocal literature review (MLR) that integrates findings from both academic and grey literature. Our catalogue delineates process metrics that underpin procedural integrity, resource metrics that provide necessary tools and frameworks, and product metrics that reflect the outputs of AI systems. This tripartite framework is designed to operationalize Accountability in AI, with a special emphasis on addressing the intricacies of GenAI. The proposed metrics catalogue provides a robust framework for instilling Accountability in AI systems. It offers practical, actionable guidance for organizations, thereby shaping responsible practices in the field.
Distributed loop‐scheduling schemes for heterogeneous computer systems

Distributed computing systems are a viable and less expensive alternative to parallel computers. However, a serious difficulty in concurrent programming of a distributed system is how to deal with scheduling and load balancing of such a system which may consist of heterogeneous computers. Some distributed scheduling schemes suitable for parallel loops with independent iterations on heterogeneous computer clusters have been designed in the past. In this work we study self‐scheduling schemes for parallel loops with independent iterations which have been applied to multiprocessor systems in the past. We extend one important scheme of this type to a distributed version suitable for heterogeneous distributed systems. We implement our new scheme on a network of computers and make performance comparisons with other existing schemes. Copyright © 2005 John Wiley & Sons, Ltd.
Workload Class Importance Policy in Autonomic Database Management Systems

A key advantage of Autonomic Computing Systems will be their ability to manage according to business policies. A key challenge to realizing this ability is the problem of automatically translating high-level business policies into low-level system tuning policies, which is the result of the different semantics used at the two levels. Economic models, which are expressed using business level concepts, have been used successfully in computer resource allocation problems. In this paper, we utilize an economic model to map business policies to resource allocation decisions in a database management system (DBMS). We focus on business policies that describe the relative importance of competing workloads on a DBMS. We present experiments with a simulation of the model that investigate a number of meanings of importance and identify how this additional information can be used to effectively allocate main memory resources in a commercial DBMS.
Predicting lineage-specific differences in open chromatin across dozens of mammalian genomes 2 3

17 Many phenotypes have evolved through gene expression, meaning that differences between species are 18 caused in part by differences in enhancers. Here, we demonstrate that we can accurately predict 19 differences between species in open chromatin status at putative enhancers using machine learning 20 models trained on genome sequence across species. We present a new set of criteria that we designed 21 to explicitly demonstrate if models are useful for studying open chromatin regions whose orthologs are 22 not open in every species. Our approach and evaluation metrics can be applied to any tissue or cell type 23 with open chromatin data available from multiple species. 24
Machine Learning by Function Decomposition

We present a new machine learning method that, given a set of training examples, induces a deenition of the target concept in terms of a hierarchy of intermediate concepts and their deenitions. This eeectively decomposes the problem into smaller, less complex problems. The method is inspired by the Boolean function decomposition approach to the design of digital circuits. To cope with high time complexity of nding an optimal decomposition , we propose a suboptimal heuristic algorithm. The method, implemented in program HINT (HIerarchy Induction Tool), is experimentally evaluated using a set of artii-cial and real-world learning problems. It is shown that the method performs well both in terms of classiication accuracy and discovery of meaningful concept hierarchies.
SQUAD : A Spectrum-based Quality Adaptation for Dynamic Adaptive Streaming over

The dynamics of the application-layer based control loop of dynamic adaptive streaming over HTTP (DASH) make video bitrate selection for DASH a difficult problem. In this work we provide a DASH quality adaptation algorithm, named SQUAD, that is specifically tailored to provide high quality of experience (QoE). First, we review and provide new insights into why DASH rate estimation is hard. In a nutshell, in addition to the On-OFF behavior of DASH clients, there exists a discrepancy in the time scales that form the basis of the rate estimates across (i) different video segments, (ii) the rate control loops of DASH and TCP. With these observations in mind, we design SQUAD using with the objective of maximizing the average quality bitrate while minimizing the quality variations. We test our implementation of SQUAD together with a number of different quality adaptation algorithms of different methodologies under various conditions in the GENI testbed, as well as, in a cross-Atlantic measurement campaign. The measurement results show, that by sacrificing little to none in average quality bitrate SQUAD provides significantly better QoE in terms of quality switching and magnitude.
Improvement of irregular DTM for SPH modelling of flow-like landslides

Irregular topography of real slopes largely affects the propagation stage of flowlike landslides and accurate digital terrain models (DTMs) are absolutely necessary for realistic simulations and assessments. In this paper a simple yet effective method is proposed to improve the accuracy of existing DTMs which is applied to the topographical models used in well equipped laboratory experiments. Aimed at evaluating the effects of different DTMs in the results of the propagation modelling, a depth-integrated SPH model is used to simulate two series of laboratory tests referring to a frictional rheological model while using either the available DTM or the DTM improved through the proposed procedure. The obtained results show that the proposed method provides a more accurate topographical model for all the analyzed cases. Particularly, the new topographical model allows better reproducing the laboratory evidences in terms of run-out distances, inundated areas and geometrical characteristics of the final deposits. Furthermore, SPH analyses with progressively finer topographical inputs outline the role of DTM’s precision towards the accuracy of the numerical simulations.
A Disclosure Framework for Service Accountability in SOA

Accountability has become a key concern for management in recent years since the collapse of several large corporations in energy, communications and financial consulting industries. Traditionally, accountability has not been a significant concern in the IT literature. Recently, some protocols, models and frameworks that address certain aspects of accountability have emerged. However, an architecture framework that addresses the overall accountability requirements is nonetheless missing in a service-oriented computing environment. In this paper, we review the accountability literature in an IT context and argue that disclosure, as a crucial requirement of accountability, has been largely ignored in the current literature. Based on this proposition, we propose a new architectural framework to address the important disclosure requirement that extends and integrates the existing architecture models. The proposed framework can be used to build SOA solutions that support the key accountability requirements.