The ubiquitous presence of software in the products we use, together with Artificial Intelligence in these products, has led to an increasing need for consumer trust. Consumers often lose faith in products, and the lack of Trust propagates to the companies behind them. This is even more so in mission-critical systems such as autonomous vehicles and clinical support systems. This paper follows grounded theory principles to elicit knowledge related to Trust, Ethics, and Transparency. We approach these qualities as Non-Functional Requirements (NFRs), aiming to build catalogs to subsidize the construction of Socially Responsible Software. The corpus we have used was built on a selected collection of literature on Corporate Social Responsibility, with an emphasis on Business Ethics. Our challenge is how to encode the social perspective knowledge, mainly through the view of Corporate Social Responsibility, on how organizations or institutions achieve trustworthiness. Since our ground perspective is that of NFRs, results are presented by a catalogue of Trust as a Non-Functional Requirement, represented as a Softgoal Interdependency Graph (SIG). The SIG language helps software engineers in understanding alternatives they have to improve Trust in software products.

This paper shares the five-year development and deployment of MyCS, a CS curriculum for "middle-years students," roughly in US grades 4-10. Consistent with many middle-years curricula, MyCS promotes positive individual identification with its field, CS, especially as it intersects with other facets of students' maturing identities. A detailed assessment of students' MyCS experiences reveal significant positive outcomes relative to a control group, as well as many neutral (no-distinction) results relative to the control. Feedback from teacher and administrators have refined MyCS and, perhaps more importantly, built curricular bridges to both elementary- and high-school CS. By tracing MyCS's assessment and evolution, this work highlights how two districts used a middle-years CS foothold from which to establish deeper, district-wide changes in identity.

A variant of the best-path (BP) algorithm that can be used for deducing a posteriori symbol probabilities for input sequences of unlimited length is proposed. Decoders using the proposed algorithm have fixed memory requirements and fixed decoding delays regardless of the length of the transmitted sequence. This is made possible by utilizing the Viterbi algorithm's ability to self-initialize itself and by segmenting the decoding process.

A soft-output Viterbi algorithm (SOVA) that can be used on trellis-coded modulation (TCM), rate-k/n convolutional codes, and intersymbol interference (ISI) channels is proposed. The algorithm utilizes the postdetector architecture proposed by Berrou et al. (1993) to achieve low computational complexity. By starting with Battail's (1987) generalized revision algorithm and rereferencing the "relative values" to the surviving path to each state, substantial simplifications are made possible. By comparing the revision operations dictated by the simplified revision equation for a rate-1/n convolutional code to the operations mandated by the rate-1/n postdetector algorithm presented by Berrou et al., it is possible to deduce the additional modifications necessary to produce a rate-k/n postdetector algorithm. Computer simulations suggest that the derived rate-k/n algorithm produces reasonably good a posteriori input probability estimates for rate-k/n convolutional codes and trellis codes. The algorithm may also be used for soft-output Viterbi equalization (SOVE) provided that the channel impairments are not too severe.