Forestry, A Valuable Crop for the Pastoral Farmer

The integration of forestry into a pastoral system (sheep & beef cattle) will be discussed with respect to the economic sustainability of the system. The cash flow implications for a case study farm of forest development, and methods used through forestry right agreements to facilitate these by reducing risk, will be developed

A Critique of the Use of the Balanced Scorecard in Multi-Enterprise Family Farm Businesses

Business strategy is very important to small and medium family businesses as many are both fragile and vulnerable; strategy provides a solid foundation for survival. Various studies have identified that businesses that engage in strategic management outperform those that do not. Despite this knowledge the uptake of many aspects of strategic management by farm businesses has been slow. Although the development of business plans is now common there is often a disconnect between monitoring and strategy. The Balanced Scorecard (BSC) was applied to case study farms during both the planning process and as they implemented and controlled their strategic choices to determine areas of difference that restrict or enhance it as a management tool for both family and farming businesses. The BSC was immediately applicable in the strategic management process for those businesses with current business plans. It could be used to test the degree of balance between the goals already identified in their plans. It was able to be used to critique the control measures they had in place and to determine how well they could be used to derive the causal chain from the operational level to family goals. In some instances either outcome or driver measures were recognized as being missing, in others the wiring within the balanced scorecard revealed some strategic measures without linkages.Farm Management,

The Need for Sensemaking in Networked Privacy and Algorithmic Responsibility

This paper proposes that two significant and emerging problems facing our connected, data-driven society may be more effectively solved by being framed as sensemaking challenges. The first is in empowering individuals to take control of their privacy, in device-rich information environments where personal information is fed transparently to complex networks of information brokers. Although sensemaking is often framed as an analytical activity undertaken by experts, due to the fact that non-specialist end-users are now being forced to make expert-like decisions in complex information environments, we argue that it is both appropriate and important to consider sensemaking challenges in this context. The second is in supporting human-in-the-loop algorithmic decision-making, in which important decisions bringing direct consequences for individuals, or indirect consequences for groups, are made with the support of data-driven algorithmic systems. In both privacy and algorithmic decision-making, framing the problems as sensemaking challenges acknowledges complex and illdefined problem structures, and affords the opportunity to view these activities as both building up relevant expertise schemas over time, and being driven potentially by recognition-primed decision making

'It's Reducing a Human Being to a Percentage'; Perceptions of Procedural Justice in Algorithmic Decisions

Data-driven decision-making consequential to individuals raises important questions of accountability and justice. Indeed, European law provides individuals limited rights to ‘meaningful information about the logic’ behind significant, autonomous decisions such as loan approvals, insurance quotes, and CV filtering. We undertake three studies examining people's perceptions of justice in algorithmic decision-making under different scenarios and explanation styles. Dimensions of justice previously observed in response to human decision-making appear similarly engaged in response to algorithmic decisions. Qualitative analysis identified several concerns and heuristics involved in justice perceptions including arbitrariness, generalisation, and (in)dignity. Quantitative analysis indicates that explanation styles primarily matter to justice perceptions only when subjects are exposed to multiple different styles—under repeated exposure of one style, scenario effects obscure any explanation effects. Our results suggests there may be no 'best’ approach to explaining algorithmic decisions, and that reflection on their automated nature both implicates and mitigates justice dimensions

Population, sexual and reproductive health, rights and sustainable development: forging a common agenda.

This article suggests that sexual and reproductive health and rights activists seeking to influence the post-2015 international development paradigm must work with sustainable development advocates concerned with a range of issues, including climate change, environmental issues, and food and water security, and that a way of building bridges with these communities is to demonstrate how sexual and reproductive health and rights are relevant for these issues. An understanding of population dynamics, including urbanization and migration, as well as population growth, can help to clarify these links. This article therefore suggests that whether or not sexual and reproductive health and rights activists can overcome resistance to discussing "population", become more knowledgeable about other sustainable development issues, and work with others in those fields to advance the global sustainable development agenda are crucial questions for the coming months. The article also contends that it is possible to care about population dynamics (including ageing and problems faced by countries with a high proportion of young people) and care about human rights at the same time. It expresses concern that, if sexual and reproductive health and rights advocates do not participate in the population dynamics discourse, the field will be left free for those for whom respecting and protecting rights may be less of a priority

'I make up a silly name': Understanding Children's Perception of Privacy Risks Online

Children under 11 are often regarded as too young to comprehend the implications of online privacy. Perhaps as a result, little research has focused on younger kids' risk recognition and coping. Such knowledge is, however, critical for designing efficient safeguarding mechanisms for this age group. Through 12 focus group studies with 29 children aged 6-10 from UK schools, we examined how children described privacy risks related to their use of tablet computers and what information was used by them to identify threats. We found that children could identify and articulate certain privacy risks well, such as information oversharing or revealing real identities online; however, they had less awareness with respect to other risks, such as online tracking or game promotions. Our findings offer promising directions for supporting children's awareness of cyber risks and the ability to protect themselves online

Approximate Semantic Matching Over Linked Data Streams

In the Internet of Things (IoT),data can be generated by all kinds of smart things. In such context, enabling machines to process and understand such data is critical. Semantic Web technologies, such as Linked Data, provide an effective and machine-understandable way to represent IoT data for further processing. It is a challenging issue to match Linked Data streams semantically based on text similarity as text similarity computation is time consuming. In this paper, we present a hashing-based approximate approach to efficiently match Linked Data streams with users’ needs. We use the Resource Description Framework (RDF) to represent IoT data and adopt triple patterns as user queries to describe users’ data needs. We then apply locality-sensitive hashing techniques to transform semantic data into numerical values to support efficient matching between data and user queries. We design a modified k nearest neighbors (kNN) algorithm to speedup the matching process. The experimental results show that our approach is up to five times faster than the traditional methods and can achieve high precisions and recalls

Witnessing eigenstates for quantum simulation of Hamiltonian spectra

The efficient calculation of Hamiltonian spectra, a problem often intractable on classical machines, can find application in many fields, from physics to chemistry. Here, we introduce the concept of an "eigenstate witness" and through it provide a new quantum approach which combines variational methods and phase estimation to approximate eigenvalues for both ground and excited states. This protocol is experimentally verified on a programmable silicon quantum photonic chip, a mass-manufacturable platform, which embeds entangled state generation, arbitrary controlled-unitary operations, and projective measurements. Both ground and excited states are experimentally found with fidelities >99%, and their eigenvalues are estimated with 32-bits of precision. We also investigate and discuss the scalability of the approach and study its performance through numerical simulations of more complex Hamiltonians. This result shows promising progress towards quantum chemistry on quantum computers.Comment: 9 pages, 4 figures, plus Supplementary Material [New version with minor typos corrected.

On the experimental verification of quantum complexity in linear optics

The first quantum technologies to solve computational problems that are beyond the capabilities of classical computers are likely to be devices that exploit characteristics inherent to a particular physical system, to tackle a bespoke problem suited to those characteristics. Evidence implies that the detection of ensembles of photons, which have propagated through a linear optical circuit, is equivalent to sampling from a probability distribution that is intractable to classical simulation. However, it is probable that the complexity of this type of sampling problem means that its solution is classically unverifiable within a feasible number of trials, and the task of establishing correct operation becomes one of gathering sufficiently convincing circumstantial evidence. Here, we develop scalable methods to experimentally establish correct operation for this class of sampling algorithm, which we implement with two different types of optical circuits for 3, 4, and 5 photons, on Hilbert spaces of up to 50,000 dimensions. With only a small number of trials, we establish a confidence >99% that we are not sampling from a uniform distribution or a classical distribution, and we demonstrate a unitary specific witness that functions robustly for small amounts of data. Like the algorithmic operations they endorse, our methods exploit the characteristics native to the quantum system in question. Here we observe and make an application of a "bosonic clouding" phenomenon, interesting in its own right, where photons are found in local groups of modes superposed across two locations. Our broad approach is likely to be practical for all architectures for quantum technologies where formal verification methods for quantum algorithms are either intractable or unknown.Comment: Comments welcom