Survey of Machine Learning Techniques for Malware Analysis

Coping with malware is getting more and more challenging, given their relentless growth in complexity and volume. One of the most common approaches in literature is using machine learning techniques, to automatically learn models and patterns behind such complexity, and to develop technologies for keeping pace with the speed of development of novel malware. This survey aims at providing an overview on the way machine learning has been used so far in the context of malware analysis. We systematize surveyed papers according to their objectives (i.e., the expected output, what the analysis aims to), what information about malware they specifically use (i.e., the features), and what machine learning techniques they employ (i.e., what algorithm is used to process the input and produce the output). We also outline a number of problems concerning the datasets used in considered works, and finally introduce the novel concept of malware analysis economics, regarding the study of existing tradeoffs among key metrics, such as analysis accuracy and economical costs

Early aspects: aspect-oriented requirements engineering and architecture design

This paper reports on the third Early Aspects: Aspect-Oriented Requirements Engineering and Architecture Design Workshop, which has been held in Lancaster, UK, on March 21, 2004. The workshop included a presentation session and working sessions in which the particular topics on early aspects were discussed. The primary goal of the workshop was to focus on challenges to defining methodical software development processes for aspects from early on in the software life cycle and explore the potential of proposed methods and techniques to scale up to industrial applications

Contextualized News in Corporate Disclosures: A Neural Language Approach

I quantify and explain value-relevant news in textual disclosures using word context. I improve upon current methods by applying a new textual analysis approach—a BERT-based neural language model—to characterize disclosures as sequentially connected and interacting elements (rather than stand-alone words). I denote this enhanced measurement as contextualized, and I apply it to predicting the magnitude and direction of disclosure news. The contextualized text in earnings announcements (1) explains three times more variation in short-window stock returns than text measured using traditional narrative attributes or recent machine learning techniques, and (2) offers large incremental explanatory power relative to reported earnings modeled using traditional or machine learning methods. Contextualized disclosures also strongly predict future earnings, with most news arising from (a) word order (i.e., context), (b) text describing numbers, and (c) text at the beginning of disclosures. This study highlights the importance of contextualized disclosures for researchers, regulators, and practitioners

Technology requirements for communication satellites in the 1980's

The key technology requirements are defined for meeting the forecasted demands for communication satellite services in the 1985 to 1995 time frame. Evaluation is made of needs for services and technical and functional requirements for providing services. The future growth capabilities of the terrestrial telephone network, cable television, and satellite networks are forecasted. The impact of spacecraft technology and booster performance and costs upon communication satellite costs are analyzed. Systems analysis techniques are used to determine functional requirements and the sensitivities of technology improvements for reducing the costs of meeting requirements. Recommended development plans and funding levels are presented, as well as the possible cost saving for communications satellites in the post 1985 era

Golden Opportunity or False Hope? Anglogold Ashanti's Proposed Gold Mine in the Democratic Republic of Congo

Deals with the exploitation of resourcesin DR

Circular bioeconomy potential and challenges within an African context: From theory to practice

A circular bioeconomy has the potential to minimize the environmental impacts of biowaste while simultaneously generating value-added bioproducts and bioenergy. Currently, most countries of the African Union lack well-defined policies, requisite infrastructure, and expertise for biowaste valorisation, thus limiting the potential development of the region. Against this background, it is necessary to deploy circular bioeconomy principles based on the awareness of the biocapacity of territories through the nexus of biowaste management and life cycle thinking. In the present study, a preliminary assessment of waste management practices in a tourist hotel in Victoria Falls in Zimbabwe is explored. The hotel produces about 3.26 tons per month of biowaste, which is often improperly disposed in non-engineered waste dumps. Furthermore, the disposal options for 1 tonne of biowaste are explored using City of Harare (CoH) as a case study. The preliminary results show composting as the most environmentally favourable option (9.6 kg CO2 eq), followed by anaerobic digestion (56.4 kg CO2 eq), and finally, biowaste incineration (140 kg CO2 eq). Anaerobic digestion and composting remain the most viable biowaste disposal alternatives in Africa, due to limited expenses and expertise for construction, operation, and maintenance. However, both technologies remain under-utilized, hence, a significant portion of the source-separated biowaste is still disposed of in waste dumps and this reflects the lack of supportive institutional, regulatory and policy frameworks. Overall, these early results point to the potential to develop a circular bioeconomy in Africa, while calling for shared responsibilities among the state, market, and civil society actors to develop and adopt appropriate institutional, regulatory, policy and funding models