‘Sikate Tamaa ni jina la diwani ya kwanza ya mashairi ya Said Ahmed Mohammed. Niligundua diwani hii siku moja maktabani nilipokuwa kidato cha tatu. Kilichonivutia kwanza ilikuwa ni muundo wa mashairi ya diwani hii – mengi yalikuwa mafupi na, tofauti na nilivyozoea, ni machache tu yaliyokuwa tarbia. Nilianza kusoma shairi la kwanza huku nikijitayarisha kukumbana na misamiati.

Shairi lenyewe lilitirirka – maneno pia na mawazo. Hili lilikuwa shairi lililopendeza kusoma, lenye maudhui dhabiti na lenye lugha isiyoficha maana. Halikuvunja urari wa vina au kanuni zingine zilizotawala ushairi – idadi ya vina katika kila mshororo. Hii ilikuwa ndio sanaa iliyokamilika.

Ilikuwa ndio mara yangu ya kwanza kupata shairi la kiswahili lililopendeza hivi. Baada ya kusoma mashairi mawili au matatu yaliyofuatia nilijua kwamba lazima ningenua kitabu hiki. Malenga huu alipata mfuasi.

Shairi hilo la kwanza, ‘Sikate Tamaa, limenipa moyo mara nyingi pale nilipokaribia kufa tamaa.

Umeanguka, inuka, simama kama mnazi
Umechunika, inuka, tia dawa kwa ujuzi
Sasa inuka, inuka, kijana ianze kazi
Sikate tamaa

Usife tamma, nyanyuka, ni muweza wa kutenda
Kuna hadaa, nyanyuka, anza tena kujipinda
Dunia baa, nyanyuka, anza tena kujiunda
Sikate tamaa

Sivunjwe moyo, dunia, hivyo itakunyanyasa
Futa kiliyo, dunia, hiyo idhibiti sasa
Ipe kamiyo, dunia, kamwe, siache kufusa
Sikate tamaa

Una nguvu, simama, wewe upambane nao
Una werevu, simama, uzepuke njama zao
Usiche kovu, simama, ujifunze vumilio
Sikate tamaa

Shairi hili ni aina ya tarbia lakini si tarbia ya kawaida. Ingawa kila ubeti una mishororo minne, tunapata kwamba kila mshororo umegawa kwa vipande vitatu badala ya kawaida ya vipande viwili. Vipande vyenyewe bado vinadhihirisha urari wa vina na kipande cha kati kinarudiwa kwa kila ubeti. Kurudia huku kunachangia utamu/ladha ya shairi kwani kuna usawa fulani. Pia kurudia huku kunahimiza ujumbe wa shairi hili.

In senior year of college, I took an class that blended information theory, algorithms and networking. It was called “Algorithms at the end of the wire”. My project for that class was an application that finds links for articles in Wikipedia.

Working off the ideas presented in class on search results ranking and vector space models, we proposed that given a query article (an article to add links in), we can find some k articles already in Wikipedia that are most similar to it. We can then we can use the links in those articles to infer the links to create in the query article. In particular, each of the neighboring articles could suggest links for text that they had in common with the query and the set of neighbors would vote on the link with weighing applied based on how close the particular voting article was to the query article. As the mechanism for determining the k nearest neighbors, we would fetch articles from the Wikipedia corpus that had text occurring in the query article then rank the results and pick the top k. Ranking was done separately using PageRank and using Latent Semantic Indexing then the rankings were aggregated.

You can download a prototype of an editor implementing our algorithm here . The editor depends on a web service so you need to be connected to the internet to use it. This is a C# application so you can run it in windows or in Linux using Mono.

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